From 76f5bba76f06e3a00a0f4de61f42592082f27a85 Mon Sep 17 00:00:00 2001 From: Christopher Kenny Date: Fri, 5 Jul 2024 15:23:43 -0400 Subject: [PATCH] run all code through styler for consistency --- 03_limits.qmd | 41 +-- 04_calculus.qmd | 58 ++-- 05_optimization.qmd | 22 +- 06_probability.qmd | 7 +- 11_data-handling_counting.qmd | 32 ++- 12_matricies-manipulation.qmd | 13 +- 13_functions_obj_loops.qmd | 72 +++-- 14_visualization.qmd | 42 +-- 15_project-dempeace.qmd | 39 +-- 16_simulation.qmd | 8 - 18_text.qmd | 134 ++++----- 23_solution_programming.qmd | 49 ++-- DESCRIPTION | 2 +- R_exercises/01_data-handling_counting.R | 73 +++-- R_exercises/02_matrices-manipulation.R | 199 +++++++------- R_exercises/03_functions_obj_loops.R | 215 +++++++-------- R_exercises/04_visualization.R | 228 ++++++++-------- R_exercises/05_project-dempeace.R | 147 +++++----- R_exercises/06_simulation.R | 258 +++++++++--------- ...th-Prefresher-for-Political-Scientists.pdf | Bin 4394375 -> 4395586 bytes 20 files changed, 830 insertions(+), 809 deletions(-) diff --git a/03_limits.qmd b/03_limits.qmd index 3108661..8ac1a03 100644 --- a/03_limits.qmd +++ b/03_limits.qmd @@ -57,12 +57,14 @@ means <- cumsum(Xs) / 1:n ggplot(tibble(n = 1:n, estimate = means), aes(x = n, y = estimate)) + geom_line() + - scale_x_continuous(labels = comma, limit = c(0, n*1.1), breaks = seq(0, n, length.out = 5)) + + scale_x_continuous(labels = comma, limit = c(0, n * 1.1), breaks = seq(0, n, length.out = 5)) + scale_y_continuous(limits = c(0, 1)) + - annotate(geom = "text", x = 1.1*n, y = means[n], label = glue("Estimate at\nn = {comma(n)}", ":\n", means[n]), size = 2.8) + + annotate(geom = "text", x = 1.1 * n, y = means[n], label = glue("Estimate at\nn = {comma(n)}", ":\n", means[n]), size = 2.8) + annotate(geom = "point", x = n, y = means[n]) + - labs(x = "n, or the number of times of a coin-flip experiment", - y = "Estimate of the Probability of Heads after n trials") + labs( + x = "n, or the number of times of a coin-flip experiment", + y = "Estimate of the Probability of Heads after n trials" + ) ``` ## Sequences @@ -90,16 +92,16 @@ We find the sequence by simply "plugging in" the integers into each $n$. The imp #| echo: false #| fig-cap: Behavior of Some Sequences seq <- 1:20 -df <- tibble(n = seq, A = 2 - 1/(seq^2), B = (seq^2 + 1)/(seq), C = (-1)^seq * (1 - 1/seq)) +df <- tibble(n = seq, A = 2 - 1 / (seq^2), B = (seq^2 + 1) / (seq), C = (-1)^seq * (1 - 1 / seq)) -g0 <- ggplot(df, aes(x = n, y = A)) + geom_point() +g0 <- ggplot(df, aes(x = n, y = A)) + + geom_point() gA <- g0 + labs(y = expression(A[n])) gB <- g0 + aes(y = B) + labs(y = expression(B[n])) gC <- g0 + aes(y = C) + labs(y = expression(C[n])) gA + gB + gC + plot_layout(nrow = 1) - ``` ## The Limit of a Sequence @@ -230,13 +232,13 @@ range2 <- tibble::tibble(x = c(-2, 2)) fx1 <- ggplot(range1, aes(x = x)) + stat_function(fun = function(x) sqrt(x), size = 0.5) + - labs(x = expression(x), y = expression(f(x)), title = expression(f(x)==sqrt(x))) + + labs(x = expression(x), y = expression(f(x)), title = expression(f(x) == sqrt(x))) + expand_limits(y = max(range1$x)) -fx2 <- +fx2 <- ggplot(range2, aes(x = x)) + - stat_function(fun = function(x) ifelse(x == 0, NA, 1/x), size = 0.5) + - labs(x = expression(x), y = expression(f(x)), title = expression(f(x)==frac(1,x))) + stat_function(fun = function(x) ifelse(x == 0, NA, 1 / x), size = 0.5) + + labs(x = expression(x), y = expression(f(x)), title = expression(f(x) == frac(1, x))) fx1 + fx2 + plot_layout(nrow = 1) ``` @@ -282,21 +284,21 @@ range4 <- tibble(x = c(0, 5)) fx1 <- ggplot(range1, aes(x = x)) + stat_function(fun = function(x) sqrt(x), size = 0.5) + - labs(y = expression(f(x)), title = expression(f(x)==sqrt(x))) + labs(y = expression(f(x)), title = expression(f(x) == sqrt(x))) fx2 <- ggplot(range2, aes(x = x)) + stat_function(fun = function(x) exp(x), size = 0.5) + - labs(y = expression(f(x)), title = expression(f(x)==e^x)) + labs(y = expression(f(x)), title = expression(f(x) == e^x)) fx3 <- ggplot(range3, aes(x = x)) + - stat_function(fun = function(x) ifelse(x == 0, NA, 1 + 1/(x^2)), size = 0.5) + - labs(y = expression(f(x)), title = expression(f(x)==1+frac(1,x^2))) + stat_function(fun = function(x) ifelse(x == 0, NA, 1 + 1 / (x^2)), size = 0.5) + + labs(y = expression(f(x)), title = expression(f(x) == 1 + frac(1, x^2))) fx4 <- ggplot(range4, aes(x = x)) + stat_function(fun = function(x) ifelse(1 - (x %% 1) < 9e-2, NA, floor(x)), size = 0.5) + geom_point(data = tibble(x = 0:5, y = 0:5), aes(y = y), pch = 19) + geom_point(data = tibble(x = 1:5, y = (1:5) - 1), aes(y = y), pch = 21, fill = "white") + - labs(y = expression(f(x)), title = expression(f(x)==plain("floor(x)"))) + labs(y = expression(f(x)), title = expression(f(x) == plain("floor(x)"))) fx1 + fx2 + fx3 + fx4 + plot_layout(ncol = 2) ``` @@ -329,7 +331,6 @@ Example @exm-seqbehav Exercise @exr-limseq2 ```{r} - ``` Example @exm-limfun1 @@ -370,7 +371,7 @@ Divide each part by $x$, and we get $x + \frac{2}{x}$ on the numerator, $1$ on t range0 <- tibble(x = c(-4, 2)) ggplot(range0, aes(x = x)) + - stat_function(fun = function(x) ifelse(x == 0, NA, (x^2 + 2*x)/(x)), size = 0.5) + - labs(y = expression(f(x)), title = expression(f(x)==frac(x^2+2*x, x^2))) + - geom_point(data = tibble(x = 0, y = 2), aes(y = y), pch = 21, fill = "white") + stat_function(fun = function(x) ifelse(x == 0, NA, (x^2 + 2 * x) / (x)), size = 0.5) + + labs(y = expression(f(x)), title = expression(f(x) == frac(x^2 + 2 * x, x^2))) + + geom_point(data = tibble(x = 0, y = 2), aes(y = y), pch = 21, fill = "white") ``` diff --git a/04_calculus.qmd b/04_calculus.qmd index 8d6c790..5561376 100644 --- a/04_calculus.qmd +++ b/04_calculus.qmd @@ -56,21 +56,21 @@ range <- tibble::tibble(x = c(-3, 3)) fx0 <- ggplot(range, aes(x = x)) + labs(x = expression(x), y = expression(f(x))) -fx <- fx0 + - stat_function(fun = function(x) 2*x, size = 0.5) + +fx <- fx0 + + stat_function(fun = function(x) 2 * x, size = 0.5) + labs(title = "f(x) = 2x") + expand_limits(y = 0) fprimex <- fx0 + stat_function(fun = function(x) 2, size = 0.5, linetype = "dashed") + - labs(x = expression(x), y = expression(f~plain("'")~(x))) + labs(x = expression(x), y = expression(f ~ plain("'") ~ (x))) -gx <- fx0 + +gx <- fx0 + stat_function(fun = function(x) x^3, size = 0.5) + - labs(y = expression(g(x)), title = expression(g(x)==x^3)) + + labs(y = expression(g(x)), title = expression(g(x) == x^3)) + expand_limits(y = 0) -gprimex <- fx0 + stat_function(fun = function(x) 3*(x^2), size = 0.5, linetype = "dashed") + - labs(x = expression(x), y = expression(g~plain("'")~(x))) +gprimex <- fx0 + stat_function(fun = function(x) 3 * (x^2), size = 0.5, linetype = "dashed") + + labs(x = expression(x), y = expression(g ~ plain("'") ~ (x))) fx + fprimex + gx + gprimex + plot_layout(ncol = 2) ``` @@ -246,15 +246,17 @@ To repeat the main rule in Theorem @thm-derivexplog, the intuition is that range <- tibble::tibble(x = c(-3, 3)) fx0 <- ggplot(range, aes(x = x)) + - labs(x = expression(x), y = expression(f(x)), - caption = expression(f(x)==e^x)) + labs( + x = expression(x), y = expression(f(x)), + caption = expression(f(x) == e^x) + ) -fx <- fx0 + +fx <- fx0 + stat_function(fun = function(x) exp(x), size = 0.5) + expand_limits(y = 0) fprimex <- fx0 + stat_function(fun = function(x) exp(x), size = 0.5) + - labs(x = expression(x), y = expression(f~plain("'")~(x))) + labs(x = expression(x), y = expression(f ~ plain("'") ~ (x))) fx + fprimex + plot_layout(nrow = 1) ``` @@ -286,15 +288,17 @@ The natural log is the mirror image of the natural exponent and has mirroring pr range <- tibble::tibble(x = c(-0.1, 3)) fx0 <- ggplot(range, aes(x = x)) + - labs(x = expression(x), y = expression(f(x)), - caption = expression(f(x)==log(x))) + labs( + x = expression(x), y = expression(f(x)), + caption = expression(f(x) == log(x)) + ) -fx <- fx0 + +fx <- fx0 + stat_function(fun = function(x) ifelse(x <= 0, NA, log(x)), size = 0.5) + expand_limits(y = 0) -fprimex <- fx0 + stat_function(fun = function(x) ifelse(x <= 0, NA, 1/x), size = 0.5) + - labs(x = expression(x), y = expression(f~plain("'")~(x))) +fprimex <- fx0 + stat_function(fun = function(x) ifelse(x <= 0, NA, 1 / x), size = 0.5) + + labs(x = expression(x), y = expression(f ~ plain("'") ~ (x))) fx + fprimex + plot_layout(nrow = 1) ``` @@ -473,10 +477,10 @@ fx <- ggplot(range1, aes(x = x)) + labs(y = expression(f(x))) Fx <- ggplot(range1, aes(x = x)) + - stat_function(fun = function(x) (x^3)/3 - 4*x, size = 0.5, linetype = "dashed") + - stat_function(fun = function(x) (x^3)/3 - 4*x + 1, size = 0.5, linetype = "dotted") + - stat_function(fun = function(x) (x^3)/3 - 4*x - 1, size = 0.5, linetype = "dotdash") + - labs(y = expression(integral(f(x)*dx))) + stat_function(fun = function(x) (x^3) / 3 - 4 * x, size = 0.5, linetype = "dashed") + + stat_function(fun = function(x) (x^3) / 3 - 4 * x + 1, size = 0.5, linetype = "dotted") + + stat_function(fun = function(x) (x^3) / 3 - 4 * x - 1, size = 0.5, linetype = "dotdash") + + labs(y = expression(integral(f(x) * dx))) fx + Fx + plot_layout(ncol = 1) ``` @@ -516,23 +520,23 @@ Suppose we want to determine the area $A(R)$ of a region $R$ defined by a curve #| label: fig-defintfig #| echo: false #| fig-cap: The Riemann Integral as a Sum of Evaluations -f3 <- function(x) -15*(x - 5) + (x - 5)^3 + 50 +f3 <- function(x) -15 * (x - 5) + (x - 5)^3 + 50 -d1 <- tibble(x = seq(0, 10, 1)) %>% mutate(f = f3(x)) -d2 <- tibble(x = seq(0, 10, 0.1)) %>% mutate(f = f3(x)) +d1 <- tibble(x = seq(0, 10, 1)) %>% mutate(f = f3(x)) +d2 <- tibble(x = seq(0, 10, 0.1)) %>% mutate(f = f3(x)) range <- tibble::tibble(x = c(0, 10)) fx0 <- ggplot(range, aes(x = x)) + labs(x = expression(x), y = expression(f(x))) -fx <- fx0 + - expand_limits(y = 0) + +fx <- fx0 + + expand_limits(y = 0) + scale_y_continuous(expand = c(0, 0)) + scale_x_continuous(expand = c(0, 0)) -g1 <- fx + geom_col(data = d1, aes(x, f), width = 1, fill = "gray", alpha = 0.5, color = "black") + stat_function(fun = f3, size = 1.5) + labs(title = "Evaluating f with width = 1 intervals") -g2 <- fx + geom_col(data = d2, aes(x, f), width = 0.1, fill = "gray", alpha = 0.5, color = "black") + stat_function(fun = f3, size = 1.5) +labs(title = "Evaluating f with width = 0.1 intervals") +g1 <- fx + geom_col(data = d1, aes(x, f), width = 1, fill = "gray", alpha = 0.5, color = "black") + stat_function(fun = f3, size = 1.5) + labs(title = "Evaluating f with width = 1 intervals") +g2 <- fx + geom_col(data = d2, aes(x, f), width = 0.1, fill = "gray", alpha = 0.5, color = "black") + stat_function(fun = f3, size = 1.5) + labs(title = "Evaluating f with width = 0.1 intervals") g1 + g2 + plot_layout(nrow = 1) ``` diff --git a/05_optimization.qmd b/05_optimization.qmd index cd464d7..46ac52a 100644 --- a/05_optimization.qmd +++ b/05_optimization.qmd @@ -91,14 +91,16 @@ So for example, $f(x) = x^2 + 2$ and $f^\prime(x) = 2x$ #| fig-cap: Maxima and Minima range <- tibble::tibble(x = c(-3, 3)) fx0 <- ggplot(range, aes(x = x)) + - labs(x = expression(x), y = expression(f(x)), - labs = expression(f(x)==x^2+2)) -fx <- fx0 + + labs( + x = expression(x), y = expression(f(x)), + labs = expression(f(x) == x^2 + 2) + ) +fx <- fx0 + stat_function(fun = function(x) x^2 + 2, linewidth = 0.5) + expand_limits(y = 0) -fprimex <- fx0 + stat_function(fun = function(x) 2*x, linewidth = 0.5, linetype = "dashed") + - labs(x = expression(x), y = expression(f~plain("'")~(x))) +fprimex <- fx0 + stat_function(fun = function(x) 2 * x, linewidth = 0.5, linetype = "dashed") + + labs(x = expression(x), y = expression(f ~ plain("'") ~ (x))) fx + fprimex + plot_layout(nrow = 1) ``` @@ -117,14 +119,14 @@ fx0 <- ggplot(range, aes(x = x)) + labs(x = expression(x), y = expression(f(x))) + theme_bw() -fx <- fx0 + +fx <- fx0 + stat_function(fun = function(x) x^3 + x^2 + 2, size = 0.5) + expand_limits(y = 0) -fprimex <- fx0 + stat_function(fun = function(x) 3*x^2, size = 0.5, linetype = "dashed") + - labs(x = expression(x), y = expression(f~plain("'")~(x))) +fprimex <- fx0 + stat_function(fun = function(x) 3 * x^2, size = 0.5, linetype = "dashed") + + labs(x = expression(x), y = expression(f ~ plain("'") ~ (x))) -fx + fprimex + plot_annotation(expression(f(x)==x^3+2)) +fx + fprimex + plot_annotation(expression(f(x) == x^3 + 2)) ``` The second derivative $f''(x)$ identifies whether the function $f(x)$ at the point $x$ is @@ -179,7 +181,7 @@ A function $f$ is strictly concave over the set S \underline{if} $\forall x_1,x_ range1 <- tibble(x = c(-4, 4)) fx1 <- ggplot(range1, aes(x = x)) + stat_function(fun = function(x) -x^2, size = 0.5) + - labs(y = expression(f(x)), title = "Concave") + labs(y = expression(f(x)), title = "Concave") fx2 <- ggplot(range1, aes(x = x)) + stat_function(fun = function(x) x^2, size = 0.5) + labs(y = expression(f(x)), title = "Convex") diff --git a/06_probability.qmd b/06_probability.qmd index e1c1ed1..b91fb44 100644 --- a/06_probability.qmd +++ b/06_probability.qmd @@ -126,7 +126,7 @@ Consider subsets A {2, 8} and B {2,3,7} of the sample space you found. What is #| echo: false #| fig-cap: Probablity as a Measure^[Images of Probability and Random Variables drawn #| by Shiro Kuriwaki and inspired by Blitzstein and Morris] -knitr::include_graphics('images/probability.png') +knitr::include_graphics("images/probability.png") ``` ### Probability Definitions: Formal and Informal {.unnumbered} @@ -324,7 +324,7 @@ Most questions in the social sciences involve events, rather than numbers per se #| label: fig-rv-image #| echo: false #| fig-cap: The Random Variable as a Real-Valued Function -knitr::include_graphics('images/rv.png') +knitr::include_graphics("images/rv.png") ``` ::: {#def-rv} @@ -757,14 +757,13 @@ range <- tibble::tibble(x = c(-5, 5)) fx0 <- ggplot(range, aes(x = x)) + labs(x = expression(x), y = expression(f(x))) -fx <- fx0 + +fx <- fx0 + stat_function(fun = function(x) dnorm(x, mean = 0, sd = 1), linewidth = 0.5) + stat_function(fun = function(x) dnorm(x, mean = 0, sd = sqrt(2)), linewidth = 1.5) + expand_limits(y = 0) + labs(caption = "Thick line: variance = 2, Normal line: variance = 1") fx - ``` ## Summarizing Observed Events (Data) diff --git a/11_data-handling_counting.qmd b/11_data-handling_counting.qmd index 651e96e..07c64d5 100644 --- a/11_data-handling_counting.qmd +++ b/11_data-handling_counting.qmd @@ -102,7 +102,7 @@ R is an object oriented programming language primarily used for statistical comp - Different objects have different allowable procedures: ```{r} -# Adding a string and a string does not work because the '+' operator +# Adding a string and a string does not work because the '+' operator # does not work for strings: # 'Harvard' + 'Gov' @@ -114,7 +114,7 @@ R is an object oriented programming language primarily used for statistical comp x <- 9 class(9) class(x) -class('Harvard') +class("Harvard") ``` Object oriented programming makes languages flexible and powerful: @@ -143,7 +143,6 @@ n_pb <- 3 n_jelly <- 9 # write instructions in R here - ``` ## Base-R vs. tidyverse @@ -271,7 +270,8 @@ dim(ober) From your tutorials, you also know how to do graphics! Graphics are useful for grasping your data, but we will cover them more deeply in Chapter @sec-dataviz. ```{r} -ggplot(ober, aes(x = Fame)) + geom_histogram() +ggplot(ober, aes(x = Fame)) + + geom_histogram() ``` What about the distribution of fame by regime? @@ -312,10 +312,12 @@ First get a map of the Greek world. #| message: false #| warning: false #| eval: false -greece <- get_map(location = c(lon = 22.6382849, lat = 39.543287), - zoom = 5, - source = "stamen", - maptype = "toner") +greece <- get_map( + location = c(lon = 22.6382849, lat = 39.543287), + zoom = 5, + source = "stamen", + maptype = "toner" +) ggmap(greece) ``` @@ -329,12 +331,14 @@ Ober's data has the latitude and longitude of each polis. Because the map of Gre #| warning: false #| eval: false gg_ober <- ggmap(greece) + - geom_point(data = ober, - aes(y = Latitude, x = Longitude), - size = 0.5, - color = "orange") -gg_ober + - scale_x_continuous(limits = c(10, 35)) + + geom_point( + data = ober, + aes(y = Latitude, x = Longitude), + size = 0.5, + color = "orange" + ) +gg_ober + + scale_x_continuous(limits = c(10, 35)) + scale_y_continuous(limits = c(32, 44)) + theme_void() ``` diff --git a/12_matricies-manipulation.qmd b/12_matricies-manipulation.qmd index f73eb39..69bfa3d 100644 --- a/12_matricies-manipulation.qmd +++ b/12_matricies-manipulation.qmd @@ -329,17 +329,20 @@ cen10 %>% slice(1:20) # Below two lines of code do the same thing cen10[1:20, c("race", "age")] -cen10 %>% slice(1:20) %>% select(race, age) +cen10 %>% + slice(1:20) %>% + select(race, age) ``` A vector is a special type of matrix with only one column or only one row ```{r} - # One column cen10[1:10, c("age")] -cen10 %>% slice(1:10) %>% select(c("age")) +cen10 %>% + slice(1:10) %>% + select(c("age")) # One row cen10[2, ] @@ -360,7 +363,9 @@ all_equal(ca_subset, ca_subset_tidy) # subset for CA rows and select age and race ca_subset_age_race <- cen10[cen10$state == "California", c("age", "race")] -ca_subset_age_race_tidy <- cen10 %>% filter(state == "California") %>% select(age, race) +ca_subset_age_race_tidy <- cen10 %>% + filter(state == "California") %>% + select(age, race) all_equal(ca_subset_age_race, ca_subset_age_race_tidy) ``` diff --git a/13_functions_obj_loops.qmd b/13_functions_obj_loops.qmd index df0caa3..1c07124 100644 --- a/13_functions_obj_loops.qmd +++ b/13_functions_obj_loops.qmd @@ -37,7 +37,7 @@ cen10 <- read_csv("data/input/usc2010_001percent.csv", col_types = cols()) Objects are abstract symbols in which you store data. Here we will create an object from `copy`, and assign `cen10` to it. ```{r} -copy <- cen10 +copy <- cen10 ``` This looks the same as the original dataset: @@ -127,10 +127,12 @@ To change or create the class of any object, you can *assign* it. To do this, as We can start from a simple list. For example, say we wanted to store data about pokemon. Because there is no pre-made package for this, we decide to make our own class. ```{r} -pikachu <- list(name = "Pikachu", - number = 25, - type = "Electric", - color = "Yellow") +pikachu <- list( + name = "Pikachu", + number = 25, + type = "Electric", + color = "Yellow" +) ``` and we can give it any class name we want. @@ -139,7 +141,6 @@ and we can give it any class name we want. class(pikachu) <- "Pokemon" str(pikachu) pikachu$type - ``` ### Seeing R through objects @@ -156,10 +157,10 @@ Anything can be an object! Even graphs (in `ggplot`) can be assigned, re-assigne ```{r} #| warning: false -grp_race <- group_by(cen10, race)%>% +grp_race <- group_by(cen10, race) %>% summarize(count = n()) -grp_race_ordered <- arrange(grp_race, count) %>% +grp_race_ordered <- arrange(grp_race, count) %>% mutate(race = forcats::as_factor(race)) gg_tab <- ggplot(data = grp_race_ordered) + @@ -173,7 +174,7 @@ gg_tab You can change the orientation ```{r} -gg_tab<- gg_tab + coord_flip() +gg_tab <- gg_tab + coord_flip() ``` ### Parsing an object by `str()s` @@ -251,7 +252,7 @@ class(my_name) or more characters. Notice here that there's a difference between a vector of individual characters and a length-one object of characters. ```{r} -my_name_letters <- c("M","e","g") +my_name_letters <- c("M", "e", "g") my_name_letters class(my_name_letters) ``` @@ -294,7 +295,6 @@ If we wanted to generate a function that computed the number of men in your data ```{r} count_men <- function(data) { - nmen <- sum(data$sex == "Male") return(nmen) @@ -310,15 +310,14 @@ count_men(cen10) The point of a function is that you can use it again and again without typing up the set of constituent manipulations. So, what if we wanted to figure out the number of men in California? ```{r} -count_men(cen10[cen10$state == "California",]) +count_men(cen10[cen10$state == "California", ]) ``` Let's go one step further. What if we want to know the proportion of non-whites in a state, just by entering the name of the state? There's multiple ways to do it, but it could look something like this ```{r} nw_in_state <- function(data, state) { - - s.subset <- data[data$state == state,] + s.subset <- data[data$state == state, ] total.s <- nrow(s.subset) nw.s <- sum(s.subset$race != "White") @@ -332,7 +331,6 @@ Try it on your favorite state! ```{r} nw_in_state(cen10, "Massachusetts") - ``` ## Checkpoint {.unnumbered} @@ -343,7 +341,6 @@ Try making your own function, `average_age_in_state`, that will give you the ave ```{r} # Enter on your own - ``` #### 2 {.unnumbered} @@ -396,10 +393,10 @@ For example, ```{r} x <- 5 -if (x >0) { +if (x > 0) { print("positive number") -} else if (x == 0) { - print ("zero") +} else if (x == 0) { + print("zero") } else { print("negative number") } @@ -410,10 +407,10 @@ You can wrap that whole things in a function ```{r} #| warning: false is_positive <- function(number) { - if (number >0) { + if (number > 0) { print("positive number") - } else if (number == 0) { - print ("zero") + } else if (number == 0) { + print("zero") } else { print("negative number") } @@ -452,16 +449,14 @@ for (i in 1:length(fruits)) { ```{r} states_of_interest <- c("California", "Massachusetts", "New Hampshire", "Washington") -for( state in states_of_interest){ - state_data <- cen10[cen10$state == state,] +for (state in states_of_interest) { + state_data <- cen10[cen10$state == state, ] nmen <- sum(state_data$sex == "Male") n <- nrow(state_data) - men_perc <- round(100*(nmen/n), digits=2) - print(paste("Percentage of men in",state, "is", men_perc)) - + men_perc <- round(100 * (nmen / n), digits = 2) + print(paste("Percentage of men in", state, "is", men_perc)) } - ``` Instead of printing, you can store the information in a vector @@ -469,13 +464,13 @@ Instead of printing, you can store the information in a vector ```{r} states_of_interest <- c("California", "Massachusetts", "New Hampshire", "Washington") male_percentages <- c() -iter <-1 +iter <- 1 -for( state in states_of_interest){ - state_data <- cen10[cen10$state == state,] +for (state in states_of_interest) { + state_data <- cen10[cen10$state == state, ] nmen <- sum(state_data$sex == "Male") n <- nrow(state_data) - men_perc <- round(100*(nmen/n), digits=2) + men_perc <- round(100 * (nmen / n), digits = 2) male_percentages <- c(male_percentages, men_perc) names(male_percentages)[iter] <- state @@ -483,7 +478,6 @@ for( state in states_of_interest){ } male_percentages - ``` ## Nested Loops @@ -491,14 +485,13 @@ male_percentages What if I want to calculate the population percentage of a race group for all race groups in states of interest? You could probably use tidyverse functions to do this, but let's try using loops! ```{r} - states_of_interest <- c("California", "Massachusetts", "New Hampshire", "Washington") for (state in states_of_interest) { for (race in unique(cen10$race)) { race_state_num <- nrow(cen10[cen10$race == race & cen10$state == state, ]) state_pop <- nrow(cen10[cen10$state == state, ]) - race_perc <- round(100*(race_state_num/(state_pop)), digits=2) - print(paste("Percentage of ", race , "in", state, "is", race_perc)) + race_perc <- round(100 * (race_state_num / (state_pop)), digits = 2) + print(paste("Percentage of ", race, "in", state, "is", race_perc)) } } ``` @@ -511,7 +504,6 @@ Write your own function that makes some task of data analysis simpler. Ideally, ```{r} # Enter yourself - ``` ### Exercise 2: Using Loops {.unnumbered} @@ -521,7 +513,6 @@ Using a loop, create a crosstab of sex and race for each state in the set "state ```{r} states_of_interest <- c("California", "Massachusetts", "New Hampshire", "Washington") # Enter yourself - ``` ### Exercise 3: Storing information derived within loops in a global dataframe {.unnumbered} @@ -534,8 +525,8 @@ for (state in states_of_interest) { for (race in unique(cen10$race)) { race_state_num <- nrow(cen10[cen10$race == race & cen10$state == state, ]) state_pop <- nrow(cen10[cen10$state == state, ]) - race_perc <- round(100*(race_state_num/(state_pop)), digits=2) - print(paste("Percentage of ", race , "in", state, "is", race_perc)) + race_perc <- round(100 * (race_state_num / (state_pop)), digits = 2) + print(paste("Percentage of ", race, "in", state, "is", race_perc)) } } ``` @@ -543,5 +534,4 @@ for (state in states_of_interest) { Instead of printing the percentage of each race in each state, create a dataframe, and store all that information in that dataframe. (Hint: look at how I stored information about male percentage in each state of interest in a vector.) ```{r} - ``` diff --git a/14_visualization.qmd b/14_visualization.qmd index 0f246f1..22e166b 100644 --- a/14_visualization.qmd +++ b/14_visualization.qmd @@ -115,8 +115,8 @@ count(cen10, race, sort = TRUE) `count` is a kind of shorthand for `group_by()` and `summarize`. This code would have done the same. ```{r} -cen10 %>% - group_by(race) %>% +cen10 %>% + group_by(race) %>% summarize(n = n()) ``` @@ -158,7 +158,8 @@ We will now plot this grouped set of numbers. Recall that the `ggplot()` functio What is the right geometry layer to make a barplot? Turns out: ```{r} -ggplot(data = grp_race, aes(x = race, y = n)) + geom_col() +ggplot(data = grp_race, aes(x = race, y = n)) + + geom_col() ``` ## Improving your graphics @@ -168,11 +169,12 @@ Adjusting your graphics to make the point clear is an important skill. Here is a ```{r} par(oma = c(1, 11, 1, 1)) barplot(sort(table(cen10$race)), # sort numbers - horiz = TRUE, # flip - border = NA, # border is extraneous - xlab = "Number in Race Category", - bty = "n", # no box - las = 1) # alignment of axis labels is horizontal + horiz = TRUE, # flip + border = NA, # border is extraneous + xlab = "Number in Race Category", + bty = "n", # no box + las = 1 +) # alignment of axis labels is horizontal ``` Notice that we applied the `sort()` function to order the bars in terms of their counts. The default ordering of a categorical variable / factor is alphabetical. Alphabetical ordering is uninformative and almost never the way you should order variables. @@ -182,15 +184,17 @@ In ggplot you might do this by: ```{r} library(forcats) -grp_race_ordered <- arrange(grp_race, n) %>% +grp_race_ordered <- arrange(grp_race, n) %>% mutate(race = as_factor(race)) ggplot(data = grp_race_ordered, aes(x = race, y = n)) + geom_col() + coord_flip() + - labs(y = "Number in Race Category", - x = "", - caption = "Source: 2010 U.S. Census sample") + labs( + y = "Number in Race Category", + x = "", + caption = "Source: 2010 U.S. Census sample" + ) ``` The data ink ratio was popularized by Ed Tufte (originally a political economy scholar who has recently become well known for his data visualization work). See Tufte (2001), *The Visual Display of Quantitative Information* and his website . For a R and ggplot focused example using social science examples, check out Healy (2018), *Data Visualization: A Practical Introduction* with a draft at [^14_visualization-6]. There are a growing number of excellent books on data visualization. @@ -230,7 +234,7 @@ Check out each of these table objects in your console and familiarize yourself w How would you make the same figure with `ggplot()`? First, we want a count for each state $\times$ race combination. So group by those two factors and count how many observations are in each two-way categorization. `group_by()` can take any number of variables, separated by commas. ```{r} -grp_race_state <- cen10 %>% +grp_race_state <- cen10 %>% count(race, state) ``` @@ -246,14 +250,16 @@ Now, we want to tell `ggplot2` something like the following: I want bars by stat ```{r} #| fig-height: 8 -ggplot(data = grp_race_state, aes(x = state, y = n, fill = race)) + +ggplot(data = grp_race_state, aes(x = state, y = n, fill = race)) + geom_col(position = "fill") + # the position is determined by the fill ae scale_fill_brewer(name = "Census Race", palette = "OrRd", direction = -1) + # choose palette coord_flip() + # flip axes scale_y_continuous(labels = percent) + # label numbers as percentage - labs(y = "Proportion of Racial Group within State", - x = "", - source = "Source: 2010 Census sample") + + labs( + y = "Proportion of Racial Group within State", + x = "", + source = "Source: 2010 Census sample" + ) + theme_minimal() ``` @@ -311,7 +317,6 @@ Make a well-labelled figure that plots the proportion of the state's population ```{r} # Enter yourself - ``` #### 2: The swing justice {.unnumbered} @@ -332,7 +337,6 @@ Using the `justices_court-median.csv` dataset and building off of the plot that ```{r} # Enter yourself - ``` #### 3: Don't sort by the alphabet {.unnumbered} diff --git a/15_project-dempeace.qmd b/15_project-dempeace.qmd index 352fd8c..de769de 100644 --- a/15_project-dempeace.qmd +++ b/15_project-dempeace.qmd @@ -68,7 +68,7 @@ What does `polity` look like? ```{r} unique(polity$country) ggplot(polity, aes(x = year, y = polity2)) + - facet_wrap(~ country) + + facet_wrap(~country) + geom_line() head(polity) @@ -87,14 +87,18 @@ Notice that in the `mid` data, we have a start of a dispute vs. an end of a disp There are many ways to do this, but one is a loop. We go through one row at a time, and then for each we make a new dataset. that has `year` as a sequence of each year. A lengthy loop like this is typically slow, and you'd want to recast the task so you can do things with functions. But, a loop is a good place to start. ```{r} -mid_year_by_year <- data_frame(ccode = numeric(), - year = numeric(), - dispute = numeric()) - -for(i in 1:nrow(mid)) { - x <- data_frame(ccode = mid$ccode[i], ## row i's country - year = mid$StYear[i]:mid$EndYear[i], ## sequence of years for dispute in row i - dispute = 1) +mid_year_by_year <- data_frame( + ccode = numeric(), + year = numeric(), + dispute = numeric() +) + +for (i in 1:nrow(mid)) { + x <- data_frame( + ccode = mid$ccode[i], ## row i's country + year = mid$StYear[i]:mid$EndYear[i], ## sequence of years for dispute in row i + dispute = 1 + ) mid_year_by_year <- rbind(mid_year_by_year, x) } @@ -111,8 +115,9 @@ Here we can do a `left_join` matching rows from `mid` to `polity`. We want to ke ```{r} p_m <- left_join(polity, - distinct(mid_year_by_year), - by = c("ccode", "year")) + distinct(mid_year_by_year), + by = c("ccode", "year") +) head(p_m) ``` @@ -126,10 +131,11 @@ p_m$dispute[is.na(p_m$dispute)] <- 0 Reshape the dataset long to wide ```{r} -p_m_wide <- pivot_wider(p_m, - id_cols = c(scode, ccode, country), - names_from = year, - values_from = polity2) +p_m_wide <- pivot_wider(p_m, + id_cols = c(scode, ccode, country), + names_from = year, + values_from = polity2 +) select(p_m_wide, 1:10) ``` @@ -145,7 +151,6 @@ Often, files we need are saved in the `.xls` or `xlsx` format. It is possible to `readxl`/`readr`/`haven` packages() is constantly expanding to capture more file types. In day 1, we used the package `readxl`, using the `read_excel()` function. ```{r} - ``` #### Task 2: Data Merging {.unnumbered} @@ -159,7 +164,6 @@ To start, let's download and merge some data. - An *advanced* version of this task would be to download the dyadic form of the data and try merging that with polity. ```{r} - ``` #### Task 3: Tabulations and Visualization {.unnumbered} @@ -170,5 +174,4 @@ To start, let's download and merge some data. 4. Arrive at a tentative conclusion for how well the Democratic Peace argument seems to hold up in this dataset. Visualize this conclusion. ```{r} - ``` diff --git a/16_simulation.qmd b/16_simulation.qmd index 5711512..3776537 100644 --- a/16_simulation.qmd +++ b/16_simulation.qmd @@ -215,13 +215,11 @@ What can we learn from surveys of populations, and how wrong do we get if our sa (a) First, load `usc2010_001percent.csv` into your R session. After loading the `library(tidyverse)`, browse it. Although this is only a 0.01 percent extract, treat this as your population for pedagogical purposes. What is the population proportion of non-White residents? ```{r} - ``` (b) Setting a seed to `1669482`, sample 100 respondents from this sample. What is the proportion of non-White residents in this *particular* sample? By how many percentage points are you off from (what we labelled as) the true proportion? ```{r} - ``` (c) Now imagine what you did above was one survey. What would we get if we did 20 surveys? @@ -231,7 +229,6 @@ To simulate this, write a loop that does the same exercise 20 times, each time c Try doing this with a `for` loop and storing your sample proportions in a new length-20 vector. (Suggestion: make an empty vector first as a container). After running the loop, show a histogram of the 20 values. Also what is the average of the 20 sample estimates? ```{r} - ``` (d) Now, to make things more real, let's introduce some response bias. The goal here is not to correct response bias but to induce it and see how it affects our estimates. Suppose that non-White residents are 10 percent less likely to respond to enter your survey than White respondents. This is plausible if you think that the Census is from 2010 but you are polling in 2018, and racial minorities are more geographically mobile than Whites. Repeat the same exercise in (c) by modeling this behavior. @@ -239,19 +236,16 @@ Try doing this with a `for` loop and storing your sample proportions in a new le You can do this by creating a variable, e.g. `propensity`, that is 0.9 for non-Whites and 1 otherwise. Then, you can refer to it in the propensity argument. ```{r} - ``` (e) Finally, we want to see if more data ("Big Data") will improve our estimates. Using the same unequal response rates framework as (d), repeat the same exercise but instead of each poll collecting 100 responses, we collect 10,000. ```{r} - ``` (f) Optional - visualize your 2 pairs of 20 estimates, with a bar showing the "correct" population average. ```{r} - ``` #### Conditional Proportions {.unnumbered} @@ -265,13 +259,11 @@ In addition to some standard demographic questions, we will focus on one called (a) Drop the the respondents who answered the November poll (i.e. those for which `poll == "November"`). We do this in order to ignore this November population in all subsequent parts of this question because they were not asked the Presidential vote question. ```{r} - ``` (b) Using the dataset after the procedure in (a), find the proportion of *poll respondents* (those who are in the sample) who support Donald Trump. ```{r} - ``` (c) Among those who supported Donald Trump, what proportion of them has a Bachelor's degree or higher (i.e. have a Bachelor's, Graduate, or other Professional Degree)? diff --git a/18_text.qmd b/18_text.qmd index 6e956be..8919839 100644 --- a/18_text.qmd +++ b/18_text.qmd @@ -40,23 +40,23 @@ note `c(1,2,3)` is inputting three numbers in the function `c` - Use `{` `}` when you are defining a function or writing a `for` loop: ```{r} -#function -MyFunction <- function(InputMatrix){ +# function +MyFunction <- function(InputMatrix) { TempMat <- InputMatrix - for(i in 1:5){ - TempMat <- t(TempMat) %*% TempMat / 10 - } - return( TempMat ) + for (i in 1:5) { + TempMat <- t(TempMat) %*% TempMat / 10 + } + return(TempMat) } -myMat <- matrix(rnorm(100*5), nrow = 100, ncol = 5) -print( MyFunction(myMat) ) +myMat <- matrix(rnorm(100 * 5), nrow = 100, ncol = 5) +print(MyFunction(myMat)) -# loop -x <- c() -for(i in 1:20){ - x[i] <- i +# loop +x <- c() +for (i in 1:20) { + x[i] <- i } -print(x) +print(x) ``` ## Goals for today @@ -89,11 +89,11 @@ paste and sprintf are useful commands in text processing, such as for automatica Paste concatenates vectors together. ```{R} -#use collapse for inputs of length > 1 +# use collapse for inputs of length > 1 my_string <- c("Not", "one", "could", "equal") paste(my_string, collapse = " ") -#use sep for inputs of length == 1 +# use sep for inputs of length == 1 paste("Not", "one", "could", "equal", sep = " ") ``` @@ -102,9 +102,9 @@ For more sophisticated concatenation, use sprintf. This is very useful for autom ```{R} sprintf("Coefficient for %s: %.3f (%.2f)", "Gender", 1.52324, 0.03143) -#%s is replaced by a character string -#%.3f is replaced by a floating point digit with 3 decimal places -#%.2f is replaced by a floating point digit with 2 decimal places +# %s is replaced by a character string +# %.3f is replaced by a floating point digit with 3 decimal places +# %.2f is replaced by a floating point digit with 2 decimal places ``` ## Regular expressions @@ -121,10 +121,10 @@ Example in `R`. Extract the tweet mentioning Indonesia. ```{r} s1 <- "If only Bradley's arm was longer. RT" -s2 <- "Share our love in Indonesia and in the World. RT if you agree." +s2 <- "Share our love in Indonesia and in the World. RT if you agree." my_string <- c(s1, s2) grepl(my_string, pattern = "Indonesia") -my_string[ grepl(my_string, pattern = "Indonesia")] +my_string[grepl(my_string, pattern = "Indonesia")] ``` Key point: Many R commands use regular expressions. See `?grepl`. Assume that `x` is a character vector and that `pattern` is the target pattern. In the earlier example, `x` could have been something like `my_string` and `pattern` would have been "`Indonesia`". Here are other key uses: @@ -134,15 +134,17 @@ Key point: Many R commands use regular expressions. See `?grepl`. Assume that `x 2. REPLACE PATTERNS. `gsub(pattern, x, replacement)` goes through all the entries of `x` replaces the `pattern` with `replacement`. ```{r} -gsub(x = my_string, - pattern = "o", - replacement = "AAAA") +gsub( + x = my_string, + pattern = "o", + replacement = "AAAA" +) ``` 3. LOCATE PATTERNS. `regexpr(pattern, text)` goes through each element of the character string. It returns a vector of the same length, with the entries of the vector corresponding to the location of the first pattern match, or a -1 if no match was obtained. ```{r} -regex_object <- regexpr(pattern = "was", text = my_string) +regex_object <- regexpr(pattern = "was", text = my_string) attr(regex_object, "match.length") attr(regex_object, "useBytes") regexpr(pattern = "was", text = my_string)[1] @@ -169,8 +171,8 @@ Example in `R`: ```{r} my_string <- "Do you think that 34% of apples are red?" -gsub(my_string, pattern = "[[:digit:]]", replace ="DIGIT") -gsub(my_string, pattern = "[[:alpha:]]", replace ="") +gsub(my_string, pattern = "[[:digit:]]", replace = "DIGIT") +gsub(my_string, pattern = "[[:alpha:]]", replace = "") ``` ### Special Characters. @@ -181,12 +183,12 @@ Example in `R`: ```{r} my_string <- "Do *really* think he will win?" -gsub(my_string, pattern = "\\*", replace ="") +gsub(my_string, pattern = "\\*", replace = "") ``` ```{r} my_string <- "Now be brave! \n Dread what comrades say of you here in combat! " -gsub(my_string, pattern = "\\\n", replace ="") +gsub(my_string, pattern = "\\\n", replace = "") ``` ### Conditional patterns @@ -207,18 +209,19 @@ gsub(my_string, pattern = "(?<=%)", replace = " ", perl = TRUE) ``` ```{r} -my_string <- c("legislative1_term1.png", - "legislative1_term1.pdf", - "legislative1_term2.png", - "legislative1_term2.pdf", - "term2_presidential1.png", - "presidential1.png", - "presidential1_term2.png", - "presidential1_term1.pdf", - "presidential1_term2.pdf") +my_string <- c( + "legislative1_term1.png", + "legislative1_term1.pdf", + "legislative1_term2.png", + "legislative1_term2.pdf", + "term2_presidential1.png", + "presidential1.png", + "presidential1_term2.png", + "presidential1_term1.pdf", + "presidential1_term2.pdf" +) grepl(my_string, pattern = "^(?!presidential1).*\\.png", perl = TRUE) - ``` - Indicates which file names don't start with `presidential1` but do end in `.png` @@ -246,7 +249,7 @@ doc1 <- "Rage---Goddess, sing the rage of Peleus’ son Achilles, hurling down to the House of Death so many sturdy souls, great fighters’ souls." doc2 <- "And fate? No one alive has ever escaped it, - neither brave man nor coward, I tell you, + neither brave man nor coward, I tell you, it's born with us the day that we are born." doc3 <- "Many cities of men he saw and learned their minds, many pains he suffered, heartsick on the open sea, @@ -262,8 +265,8 @@ Now we can use utility functions in the `tm` package: ```{R} #| eval: false library(tm) -DocCorpus <- Corpus(VectorSource(DocVec) ) -DTM1 <- inspect( DocumentTermMatrix(DocCorpus) ) +DocCorpus <- Corpus(VectorSource(DocVec)) +DTM1 <- inspect(DocumentTermMatrix(DocCorpus)) ``` Consider the effect of different "pre-processing" choices on the resulting DTM! @@ -271,11 +274,12 @@ Consider the effect of different "pre-processing" choices on the resulting DTM! ```{r} #| eval: false DocVec <- tolower(DocVec) -DocVec <- gsub(DocVec, pattern ="[[:punct:]]", replace = " ") -DocVec <- gsub(DocVec, pattern ="[[:cntrl:]]", replace = " ") -DocCorpus <- Corpus(VectorSource(DocVec) ) -DTM2 <- inspect(DocumentTermMatrix(DocCorpus, - control = list(stopwords = TRUE, stemming = TRUE))) +DocVec <- gsub(DocVec, pattern = "[[:punct:]]", replace = " ") +DocVec <- gsub(DocVec, pattern = "[[:cntrl:]]", replace = " ") +DocCorpus <- Corpus(VectorSource(DocVec)) +DTM2 <- inspect(DocumentTermMatrix(DocCorpus, + control = list(stopwords = TRUE, stemming = TRUE) +)) ``` Stemming is the process of reducing inflected/derived words to their word stem or base (e.g. stemming, stemmed, stemmer --\> stem\*) @@ -299,8 +303,10 @@ Figure out why this command does what it does: Why does this command not work? ```{r} -try(sprintf("%s of spontaneous events are %s in the mind. Really, %.2f?", - "15.03322123", "puzzles", "15.03322123" ), TRUE) +try(sprintf( + "%s of spontaneous events are %s in the mind. Really, %.2f?", + "15.03322123", "puzzles", "15.03322123" +), TRUE) ``` #### 3 {.unnumbered} @@ -308,8 +314,10 @@ try(sprintf("%s of spontaneous events are %s in the mind. Really, %.2f?", Using `grepl`, these materials, Google, and your friends, describe what the following command does. What changes when `value = FALSE`? ```{r} -grep('\'', - c("To dare is to lose one's footing momentarily.", "To not dare is to lose oneself."), value = TRUE) +grep("'", + c("To dare is to lose one's footing momentarily.", "To not dare is to lose oneself."), + value = TRUE +) ``` #### 4 {.unnumbered} @@ -317,15 +325,17 @@ grep('\'', Write code to automatically extract the file names that DO end start with presidential and DO end in .pdf ```{r} -my_string <- c("legislative1_term1.png", - "legislative1_term1.pdf", - "legislative1_term2.png", - "legislative1_term2.pdf", - "term2_presidential1.png", - "presidential1.png", - "presidential1_term2.png", - "presidential1_term1.pdf", - "presidential1_term2.pdf") +my_string <- c( + "legislative1_term1.png", + "legislative1_term1.pdf", + "legislative1_term2.png", + "legislative1_term2.pdf", + "term2_presidential1.png", + "presidential1.png", + "presidential1_term2.png", + "presidential1_term1.pdf", + "presidential1_term2.pdf" +) ``` #### 5 {.unnumbered} @@ -341,9 +351,9 @@ Using the same string as in the above, write code to automatically extract the f Combine these two strings into a single string separated by a "-". Desired output: "The carbonyl group in aldehydes and ketones is an oxygen analog of the carbon–carbon double bond." ```{r} -string1 <- "The carbonyl group in aldehydes and ketones - is an oxygen analog of the carbon" -string2 <- "–carbon double bond." +string1 <- "The carbonyl group in aldehydes and ketones + is an oxygen analog of the carbon" +string2 <- "–carbon double bond." ``` #### 7 {.unnumbered} diff --git a/23_solution_programming.qmd b/23_solution_programming.qmd index 7390fe2..aad08fc 100644 --- a/23_solution_programming.qmd +++ b/23_solution_programming.qmd @@ -147,27 +147,31 @@ for (state in states_of_interest) { #| eval: false mid_b <- read_csv("data/input/MIDB_4.2.csv") polity <- read_excel("data/input/p4v2017.xls") - ``` ### Task 2: Data Merging {.unnumbered} ```{r} #| eval: false -mid_y_by_y <- data_frame(ccode = numeric(), - year = numeric(), - dispute = numeric()) +mid_y_by_y <- data_frame( + ccode = numeric(), + year = numeric(), + dispute = numeric() +) colnames(mid_b) -for(i in 1:nrow(mid_b)) { - x <- data_frame(ccode = mid_b$ccode[i], ## row i's country - year = mid_b$styear[i]:mid_b$endyear[i], ## sequence of years for dispute in row i - dispute = 1)## there was a dispute - mid_y_by_y <- rbind(mid_y_by_y, x) +for (i in 1:nrow(mid_b)) { + x <- data_frame( + ccode = mid_b$ccode[i], ## row i's country + year = mid_b$styear[i]:mid_b$endyear[i], ## sequence of years for dispute in row i + dispute = 1 + ) ## there was a dispute + mid_y_by_y <- rbind(mid_y_by_y, x) } merged_mid_polity <- left_join(polity, - distinct(mid_y_by_y), - by = c("ccode", "year")) + distinct(mid_y_by_y), + by = c("ccode", "year") +) ``` ### Task 3: Tabulations and Visualization {.unnumbered} @@ -175,26 +179,31 @@ merged_mid_polity <- left_join(polity, ```{r} #| eval: false -#don't include the -88, -77, -66 values in calculating the mean of polity -mean_polity_by_year <- merged_mid_polity %>% group_by(year) %>% summarise(mean_polity = mean(polity[which(polity <11 & polity > -11)])) +# don't include the -88, -77, -66 values in calculating the mean of polity +mean_polity_by_year <- merged_mid_polity %>% + group_by(year) %>% + summarise(mean_polity = mean(polity[which(polity < 11 & polity > -11)])) -mean_polity_by_year_ordered <- arrange(mean_polity_by_year, year) +mean_polity_by_year_ordered <- arrange(mean_polity_by_year, year) -mean_polity_by_year_mid <- merged_mid_polity %>% group_by(year, dispute) %>% summarise(mean_polity_mid = mean(polity[which(polity <11 & polity > -11)])) +mean_polity_by_year_mid <- merged_mid_polity %>% + group_by(year, dispute) %>% + summarise(mean_polity_mid = mean(polity[which(polity < 11 & polity > -11)])) -mean_polity_by_year_mid_ordered <- arrange(mean_polity_by_year_mid, year) +mean_polity_by_year_mid_ordered <- arrange(mean_polity_by_year_mid, year) mean_polity_no_mid <- mean_polity_by_year_mid_ordered %>% filter(dispute == 0) mean_polity_yes_mid <- mean_polity_by_year_mid_ordered %>% filter(dispute == 1) answer <- ggplot(data = mean_polity_by_year_ordered, aes(x = year, y = mean_polity)) + geom_line() + - labs(y = "Mean Polity Score", - x = "") + + labs( + y = "Mean Polity Score", + x = "" + ) + geom_vline(xintercept = c(1914, 1929, 1939, 1989, 2008), linetype = "dashed") -answer + geom_line(data =mean_polity_no_mid, aes(x = year, y = mean_polity_mid), col = "indianred") + geom_line(data =mean_polity_yes_mid, aes(x = year, y = mean_polity_mid), col = "dodgerblue") - +answer + geom_line(data = mean_polity_no_mid, aes(x = year, y = mean_polity_mid), col = "indianred") + geom_line(data = mean_polity_yes_mid, aes(x = year, y = mean_polity_mid), col = "dodgerblue") ``` ## Chapter @sec-simulation: Simulation diff --git a/DESCRIPTION b/DESCRIPTION index 35c19c7..e245413 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -16,4 +16,4 @@ Imports: data.table, ggrepel, patchwork, - rmarkdown \ No newline at end of file + rmarkdown diff --git a/R_exercises/01_data-handling_counting.R b/R_exercises/01_data-handling_counting.R index 71bd055..65b511f 100644 --- a/R_exercises/01_data-handling_counting.R +++ b/R_exercises/01_data-handling_counting.R @@ -12,15 +12,15 @@ library(fs) ## The Computer and You: Giving Instructions # We'll do the Peanut Butter and Jelly Exercise in class as an introduction to programming for those who are new. -# -# Assignment: Take 5 minutes to write down on a piece of paper, how to make a peanut butter and jelly sandwich. -# Be as concise and unambiguous as possible so that a robot (who doesn't know what a PBJ is) would understand. -# You can assume that there will be loaf of sliced bread, a jar of jelly, a jar of peanut butter, and a knife. -# -# Simpler assignment: Say we just want a robot to be able to tell us if we have enough ingredients to make a -# peanut butter and jelly sandwich. Write down instructions so that if told how many slices of bread, servings +# +# Assignment: Take 5 minutes to write down on a piece of paper, how to make a peanut butter and jelly sandwich. +# Be as concise and unambiguous as possible so that a robot (who doesn't know what a PBJ is) would understand. +# You can assume that there will be loaf of sliced bread, a jar of jelly, a jar of peanut butter, and a knife. +# +# Simpler assignment: Say we just want a robot to be able to tell us if we have enough ingredients to make a +# peanut butter and jelly sandwich. Write down instructions so that if told how many slices of bread, servings # of peanut butter, and servings of jelly you have, the robot can tell you if you can make a PBJ. -# +# # Now, translate the simpler assignment into R code using the code below as a starting point: n_bread <- 8 @@ -36,56 +36,57 @@ n_jelly <- 9 ## A is for Athens -# For our first dataset, let's try reading in a dataset on the Ancient Greek world. Political Theorists and Political -# Historians study the domestic systems, international wars, cultures and writing of this era to understand the first -# instance of democracy, the rise and overturning of tyranny, and the legacies of political institutions. - -# This POLIS dataset was generously provided by Professor Josiah Ober of Stanford University. This dataset includes -# information on city states in the Ancient Greek world, parts of it collected by careful work by historians and +# For our first dataset, let's try reading in a dataset on the Ancient Greek world. Political Theorists and Political +# Historians study the domestic systems, international wars, cultures and writing of this era to understand the first +# instance of democracy, the rise and overturning of tyranny, and the legacies of political institutions. + +# This POLIS dataset was generously provided by Professor Josiah Ober of Stanford University. This dataset includes +# information on city states in the Ancient Greek world, parts of it collected by careful work by historians and # archaeologists. It is part of his recent books on Greece (Ober 2015), "The Rise and Fall of Classical Greece" -### Locating the Data +### Locating the Data # What files do we have in the `data/input` folder? dir_ls("data/input") ### Reading in Data -# In Rstudio, a good way to start is to use the GUI and the Import tool. Once you click a file, an option -# to "Import Dataset" comes up. RStudio picks the right function for you, and you can copy that code, but -# it's important to eventually be able to write that code yourself. +# In Rstudio, a good way to start is to use the GUI and the Import tool. Once you click a file, an option +# to "Import Dataset" comes up. RStudio picks the right function for you, and you can copy that code, but +# it's important to eventually be able to write that code yourself. -# For the first time using an outside package, you first need to install it. +# For the first time using an outside package, you first need to install it. install.packages("readxl") -# After that, you don't need to install it again. But you __do__ need to load it each time. +# After that, you don't need to install it again. But you __do__ need to load it each time. library(readxl) -# The package `readxl` has a website: https://readxl.tidyverse.org/. Other packages are not as user-friendly, -# but they have a help page with a table of contents of all their functions. +# The package `readxl` has a website: https://readxl.tidyverse.org/. Other packages are not as user-friendly, +# but they have a help page with a table of contents of all their functions. help(package = readxl) -# From the help page, we see that `read_excel()` is the function that we want to use. -# Let's try it. +# From the help page, we see that `read_excel()` is the function that we want to use. +# Let's try it. ober <- read_excel("data/input/ober_2018.xlsx") -# Review: what does the `/` mean? Why do we need the `data` term first? Does the argument need to be in quotes? +# Review: what does the `/` mean? Why do we need the `data` term first? Does the argument need to be in quotes? -### Inspecting +### Inspecting # For almost any dataset, you usually want to do a couple of standard checks first to understand what you loaded. ober dim(ober) -# From your tutorials, you also know how to do graphics! +# From your tutorials, you also know how to do graphics! # Graphics are useful for grasping your data, but we will cover them more deeply in Chapter @sec-dataviz. -ggplot(ober, aes(x = Fame)) + geom_histogram() +ggplot(ober, aes(x = Fame)) + + geom_histogram() # What about the distribution of fame by regime? @@ -101,11 +102,11 @@ ggplot(ober, aes(y = Fame, x = Regime, group = Regime)) + # What is the Fame value of Delphoi? -### 2 +### 2 # Find the polis with the top 10 Fame values. -### 3 +### 3 # Make a scatterplot with the number of colonies on the x-axis and Fame on the y-axis. @@ -114,9 +115,9 @@ ggplot(ober, aes(y = Fame, x = Regime, group = Regime)) + # * `data/input/acs2015_1percent.csv`: A one percent sample of the American Community Survey # * `data/input/gapminder_wide.tab`: Country-level wealth and health from Gapminder^[Formatted and taken from ] # * `data/input/gapminder_wide.Rds`: A Rds version of the Gapminder (What is a Rds file? What's the difference?) -# * `data/input/Nunn_Wantchekon_sample.dta`: A sample from the Afrobarometer survey (which we'll explore tomorrow). `.dta` is a Stata format. -# * `data/input/german_credit.sav`: A hypothetical dataset on consumer credit. `.sav` is a SPSS format. -# +# * `data/input/Nunn_Wantchekon_sample.dta`: A sample from the Afrobarometer survey (which we'll explore tomorrow). `.dta` is a Stata format. +# * `data/input/german_credit.sav`: A hypothetical dataset on consumer credit. `.sav` is a SPSS format. +# # Our Recommendations: Look at the packages `haven` and `readr` @@ -124,9 +125,5 @@ ggplot(ober, aes(y = Fame, x = Regime, group = Regime)) + ### 5 -# Read Ober's codebook and find a variable that you think is interesting. Check the distribution of that variable +# Read Ober's codebook and find a variable that you think is interesting. Check the distribution of that variable # in your data, get a couple of statistics, and summarize it in English. - - - - diff --git a/R_exercises/02_matrices-manipulation.R b/R_exercises/02_matrices-manipulation.R index c574505..ebae870 100644 --- a/R_exercises/02_matrices-manipulation.R +++ b/R_exercises/02_matrices-manipulation.R @@ -9,16 +9,16 @@ library(ggplot2) ### Where are we? Where are we headed? # Up till now, you should have covered: -# +# # * R basic programming # * Data Import # * Statistical Summaries. -# -# +# +# # Today we'll cover -# +# # * Matrices & Dataframes in R -# * Manipulating variables +# * Manipulating variables # * And other `R` tips @@ -29,7 +29,7 @@ cen10 <- read_csv("data/input/usc2010_001percent.csv") head(cen10) -# What is the dimension of this dataframe? What does the number of rows represent? +# What is the dimension of this dataframe? What does the number of rows represent? # What does the number of columns represent? dim(cen10) @@ -54,7 +54,7 @@ head(cen10$race) unique(cen10$state) -# How many different states are represented (this dataset includes DC as a state)? +# How many different states are represented (this dataset includes DC as a state)? length(unique(cen10$state)) @@ -70,7 +70,7 @@ dim(cross_tab) cross_tab[6, 2] -# But a subset of your data -- individual values-- can be considered a matrix too. +# But a subset of your data -- individual values-- can be considered a matrix too. # First 20 rows of the entire data @@ -83,15 +83,19 @@ cen10 %>% slice(1:20) # Below two lines of code do the same thing cen10[1:20, c("race", "age")] -cen10 %>% slice(1:20) %>% select(race, age) +cen10 %>% + slice(1:20) %>% + select(race, age) -# A vector is a special type of matrix with only one column or only one row +# A vector is a special type of matrix with only one column or only one row # One column cen10[1:10, c("age")] -cen10 %>% slice(1:10) %>% select(c("age")) +cen10 %>% + slice(1:10) %>% + select(c("age")) # One row cen10[2, ] @@ -99,8 +103,8 @@ cen10[2, ] cen10 %>% slice(2) -# What if we want a special subset of the data? For example, what if I only want the -# records of individuals in California? What if I just want the age and race of individuals +# What if we want a special subset of the data? For example, what if I only want the +# records of individuals in California? What if I just want the age and race of individuals # in California? # subset for CA rows @@ -113,14 +117,16 @@ all_equal(ca_subset, ca_subset_tidy) # subset for CA rows and select age and race ca_subset_age_race <- cen10[cen10$state == "California", c("age", "race")] -ca_subset_age_race_tidy <- cen10 %>% filter(state == "California") %>% select(age, race) +ca_subset_age_race_tidy <- cen10 %>% + filter(state == "California") %>% + select(age, race) all_equal(ca_subset_age_race, ca_subset_age_race_tidy) -# Some common operators that can be used to filter or to use as a condition. -# Remember, you can use the unique function to look at the set of all values a -# variable holds in the dataset. +# Some common operators that can be used to filter or to use as a condition. +# Remember, you can use the unique function to look at the set of all values a +# variable holds in the dataset. # all individuals older than 30 and younger than 70 s1 <- cen10[cen10$age > 30 & cen10$age < 70, ] @@ -147,14 +153,14 @@ all_equal(s7, s8) ## Checkpoint -### 1 -# Get the subset of cen10 for non-white individuals +### 1 +# Get the subset of cen10 for non-white individuals # (Hint: look at the set of values for the race variable by using the unique function) # Enter here -### 2 +### 2 # Get the subset of cen10 for females over the age of 40 # Enter here @@ -169,16 +175,16 @@ all_equal(s7, s8) ### data frames -# You can think of data frames maybe as matrices-plus, because a column -# can take on characters as well as numbers. As we just saw, this is often useful +# You can think of data frames maybe as matrices-plus, because a column +# can take on characters as well as numbers. As we just saw, this is often useful # for real data analyses. cen10 -# Another way to think about data frames is that it is a type of list. -# Try the `str()` code below and notice how it is organized in slots. +# Another way to think about data frames is that it is a type of list. +# Try the `str()` code below and notice how it is organized in slots. # Each slot is a vector. They can be vectors of numbers or characters. str(cen10) @@ -186,18 +192,18 @@ str(cen10) ## Motivation -# Nunn and Wantchekon (2011) “The Slave Trade and the Origins of Mistrust in Africa.” +# Nunn and Wantchekon (2011) “The Slave Trade and the Origins of Mistrust in Africa.” # American Economic Review 101(7): 3221–52. -# Argues that across African countries, the distrust of co-ethnics fueled by the slave -# trade has had long-lasting effects on modern day trust in these territories. They argued +# Argues that across African countries, the distrust of co-ethnics fueled by the slave +# trade has had long-lasting effects on modern day trust in these territories. They argued # that the slave trade created distrust in these bring them to the slave ships. -# Nunn and Wantchekon use a variety of statistical tools to make their case (adding controls, -# ordered logit, instrumental variables, falsification tests, causal mechanisms), many of which -# will be covered in future courses. In this module we will only touch on their first set of -# analysis that use Ordinary Least Squares (OLS). OLS is likely the most common application -# of linear algebra in the social sciences. We will cover some linear algebra, matrix manipulation, -# and vector manipulation from this data. +# Nunn and Wantchekon use a variety of statistical tools to make their case (adding controls, +# ordered logit, instrumental variables, falsification tests, causal mechanisms), many of which +# will be covered in future courses. In this module we will only touch on their first set of +# analysis that use Ordinary Least Squares (OLS). OLS is likely the most common application +# of linear algebra in the social sciences. We will cover some linear algebra, matrix manipulation, +# and vector manipulation from this data. @@ -206,28 +212,28 @@ str(cen10) library(haven) nunn_full <- read_dta("data/input/Nunn_Wantchekon_AER_2011.dta") - - -# Nunn and Wantchekon's main dataset has more than 20,000 observations. Each observation + + +# Nunn and Wantchekon's main dataset has more than 20,000 observations. Each observation # is a respondent from the Afrobarometer survey. head(nunn_full) colnames(nunn_full) -# First, let's consider a small subset of this dataset. +# First, let's consider a small subset of this dataset. set.seed(02138) sample <- sample_n(nunn_full, 10) sample <- select(sample, trust_neighbors, exports, ln_exports, export_area, ln_export_area) write_dta(sample, "data/input/Nunn_Wantchekon_sample.dta") - + nunn <- read_dta("data/input/Nunn_Wantchekon_sample.dta") nunn ## data.frame vs. matricies -# This is a `data.frame` object. +# This is a `data.frame` object. class(nunn) @@ -236,18 +242,18 @@ class(nunn) nrow(nunn) - -# `data.frame`s and matrices have much overlap in `R`, but to explicitly treat an object as a matrix, you'd need + +# `data.frame`s and matrices have much overlap in `R`, but to explicitly treat an object as a matrix, you'd need # to coerce its class. Let's call this matrix `X`. - + X <- as.matrix(nunn) - -# What is the difference between a `data.frame` and a matrix? A `data.frame` can have columns that are -# of different types, whereas --- in a matrix --- all columns must be of the same type (usually either + +# What is the difference between a `data.frame` and a matrix? A `data.frame` can have columns that are +# of different types, whereas --- in a matrix --- all columns must be of the same type (usually either # "numeric" or "character"). - -## Speed considerations + +## Speed considerations Nrow <- 100 Ncol <- 5 Xmat <- matrix(rnorm(Nrow * Ncol), nrow = Nrow, ncol = Ncol) @@ -258,7 +264,7 @@ head(Xdf) system.time(replicate(50000, colMeans(Xmat))) system.time(replicate(50000, colMeans(Xdf))) - + ## Handling matricies in `R` # You can easily transpose a matrix @@ -267,80 +273,80 @@ X t(X) - -# What are the values of all rows in the first column? + +# What are the values of all rows in the first column? X[, 1] - + # What are all the values of "exports"? (i.e. return the whole "exports" column) X[, "exports"] - + # What is the first observation (i.e. first row)? X[1, ] - + # What is the value of the first variable of the first observation? X[1, 1] - -# Pause and consider the following problem on your own. What is the following code doing? + +# Pause and consider the following problem on your own. What is the following code doing? X[X[, "trust_neighbors"] == 0, "export_area"] -# Why does it give the same output as the following? +# Why does it give the same output as the following? X[which(X[, "trust_neighbors"] == 0), "export_area"] - - - -# Some more manipulation + + + +# Some more manipulation X + X - + X - X - + t(X) %*% X - + cbind(X, 1:10) - + cbind(X, 1) colnames(X) - - + + ## Variable Transformations -# `exports` is the total number of slaves that were taken from the individual's ethnic group between -# Africa's four slave trades between 1400-1900. +# `exports` is the total number of slaves that were taken from the individual's ethnic group between +# Africa's four slave trades between 1400-1900. -# What is `ln_exports`? The article describes this as the natural log of one plus the `exports`. +# What is `ln_exports`? The article describes this as the natural log of one plus the `exports`. # This is a transformation of one column by a particular function - + log(1 + X[, "exports"]) -# Question for you: why add the 1? +# Question for you: why add the 1? # Verify that this is the same as `X[, "ln_exports"]` ## Linear Combinations - -# In Table 1 (in the prefresher document) we see "OLS Estimates". These are estimates of OLS coefficients and standard errors. -# You do not need to know what these are for now, but it doesn't hurt to getting used to seeing them. + +# In Table 1 (in the prefresher document) we see "OLS Estimates". These are estimates of OLS coefficients and standard errors. +# You do not need to know what these are for now, but it doesn't hurt to getting used to seeing them. -# A very crude way to describe regression is through linear combinations. The simplest linear +# A very crude way to describe regression is through linear combinations. The simplest linear # combination is a one-to-one transformation. # Take the first number in Table 1, which is -0.00068. Now, multiply this by `exports` @@ -381,29 +387,29 @@ summary(lm_1_1) -## Exercises +## Exercises -### 1 +### 1 # Let # A = [ # 0.6 & 0.2 # 0.4 & 0.8 # ] -# Use R to write code that will create the matrix A, and then consecutively multiply A to itself -# 4 times. What is the value of A^4? +# Use R to write code that will create the matrix A, and then consecutively multiply A to itself +# 4 times. What is the value of A^4? ## Enter yourself -# Note that R notation of matrices is different from the math notation. Simply trying `X^n` where `X` is a -# matrix will only take the power of each element to `n`. Instead, this problem asks you to perform matrix +# Note that R notation of matrices is different from the math notation. Simply trying `X^n` where `X` is a +# matrix will only take the power of each element to `n`. Instead, this problem asks you to perform matrix # multiplication. -### 2 -# Let's apply what we learned about subsetting or filtering/selecting. Use the `nunn_full` +### 2 +# Let's apply what we learned about subsetting or filtering/selecting. Use the `nunn_full` # dataset you have already loaded # a) First, show all observations (rows) that have a `"male"` variable higher than 0.5 @@ -415,7 +421,7 @@ summary(lm_1_1) ## Enter yourself -# c) Lastly, show all values of `"trust_neighbors"` and `"age"` for observations (rows) that have +# c) Lastly, show all values of `"trust_neighbors"` and `"age"` for observations (rows) that have # the "male" variable value that is higher than 0.5 ## Enter yourself @@ -423,18 +429,18 @@ summary(lm_1_1) ### 3 -# Find a way to generate a vector of "column averages" of the matrix `X` from the Nunn and Wantchekon -# data in one line of code. Each entry in the vector should contain the sample average of the values +# Find a way to generate a vector of "column averages" of the matrix `X` from the Nunn and Wantchekon +# data in one line of code. Each entry in the vector should contain the sample average of the values # in the column. So a 100 by 4 matrix should generate a length-4 matrix. ### 4 -# Similarly, generate a vector of "column medians". +# Similarly, generate a vector of "column medians". -### 5 +### 5 # Consider the regression that was run to generate Table 1: @@ -445,18 +451,18 @@ lm_1_1 <- lm(as.formula(form), nunn_full) coef(lm_1_1) -# First, get a small subset of the nunn_full dataset. This time, -# sample 20 rows and select for variables `exports`, `age`, `age2`, `male`, and `urban_dum`. -# To this small subset, add (`bind_cols()` in tidyverse or `cbind()` in base R) a column of 1's; -# this represents the intercept. If you need some guidance, look at how we sampled 10 rows selected -# for a different set of variables above in the lecture portion. +# First, get a small subset of the nunn_full dataset. This time, +# sample 20 rows and select for variables `exports`, `age`, `age2`, `male`, and `urban_dum`. +# To this small subset, add (`bind_cols()` in tidyverse or `cbind()` in base R) a column of 1's; +# this represents the intercept. If you need some guidance, look at how we sampled 10 rows selected +# for a different set of variables above in the lecture portion. # Enter here -# Next let's try calculating predicted values of levels of trust in neighbors by multiplying coefficients -# for the intercept, `exports`, `age`, `age2`, `male`, and `urban_dum` to the actual observed values -# for those variables in the small subset you've just created. +# Next let's try calculating predicted values of levels of trust in neighbors by multiplying coefficients +# for the intercept, `exports`, `age`, `age2`, `male`, and `urban_dum` to the actual observed values +# for those variables in the small subset you've just created. # Hint: You can get just selected elements from the vector returned by coef(lm_1_1) @@ -465,4 +471,3 @@ coef(lm_1_1)[1:3] # Also, the below code gives you the coefficient elements for intercept and male coef(lm_1_1)[c("(Intercept)", "male")] - diff --git a/R_exercises/03_functions_obj_loops.R b/R_exercises/03_functions_obj_loops.R index dff4a06..e2c7cae 100644 --- a/R_exercises/03_functions_obj_loops.R +++ b/R_exercises/03_functions_obj_loops.R @@ -4,15 +4,15 @@ ### Where are we? Where are we headed? # Up till now, you should have covered: -# +# # * R basic programming # * Data Import # * Statistical Summaries # * Visualization -# -# +# +# # Today we'll cover -# +# # * Objects # * Functions # * Loops @@ -20,9 +20,9 @@ ## What is an object? -# Now that we have covered some hands-on ways to use graphics, let's go into some fundamentals of the R language. +# Now that we have covered some hands-on ways to use graphics, let's go into some fundamentals of the R language. -# Let's first set up +# Let's first set up library(dplyr) library(readr) library(haven) @@ -34,9 +34,9 @@ cen10 <- read_csv("data/input/usc2010_001percent.csv", col_types = cols()) -# Objects are abstract symbols in which you store data. Here we will create an object from `copy`, and assign `cen10` to it. +# Objects are abstract symbols in which you store data. Here we will create an object from `copy`, and assign `cen10` to it. -copy <- cen10 +copy <- cen10 # This looks the same as the original dataset: copy @@ -68,14 +68,14 @@ my_list[["slot 2"]] <- "contents of slot named slot 2" my_list -# each slot can be anything. What are we doing here? We are defining the 1st slot of the list `my_list` +# each slot can be anything. What are we doing here? We are defining the 1st slot of the list `my_list` # to be a vector `c(1, 2, 3, 4, 5)` my_list[[1]] <- c(1, 2, 3, 4, 5) my_list -# You can even make nested lists. Let's say we want the 1st slot of the list to be another list of three elements. +# You can even make nested lists. Let's say we want the 1st slot of the list to be another list of three elements. my_list[[1]][[1]] <- "subitem 1 in slot 1 of my_list" my_list[[1]][[2]] <- "subitem 1 in slot 2 of my_list" @@ -86,45 +86,47 @@ my_list ## Making your own objects -# We've covered one type of object, which is a list. You saw it was quite flexible. How many types of objects are there? +# We've covered one type of object, which is a list. You saw it was quite flexible. How many types of objects are there? -# There are an infinite number of objects, because people make their own class of object. You can detect the type of +# There are an infinite number of objects, because people make their own class of object. You can detect the type of # the object (the class) by the function `class` -Object can be said to be an instance of a class. +# Objects can be said to be an instance of a class. -# Analogies: -# +# Analogies: +# # class - Pokemon, object - Pikachu -# +# # class - Book, object - To Kill a Mockingbird -# +# # class - DataFrame, object - 2010 census data -# +# # class - Character, object - "Programming is Fun" -# +# # What is type (class) of object is `cen10`? class(cen10) -# What about this text? +# What about this text? class("some random text") -# To change or create the class of any object, you can _assign_ it. To do this, assign the -# name of your class to character to an object's `class()`. +# To change or create the class of any object, you can _assign_ it. To do this, assign the +# name of your class to character to an object's `class()`. -# We can start from a simple list. For example, say we wanted to store data about pokemon. -# Because there is no pre-made package for this, we decide to make our own class. +# We can start from a simple list. For example, say we wanted to store data about pokemon. +# Because there is no pre-made package for this, we decide to make our own class. -pikachu <- list(name = "Pikachu", - number = 25, - type = "Electric", - color = "Yellow") +pikachu <- list( + name = "Pikachu", + number = 25, + type = "Electric", + color = "Yellow" +) -# and we can give it any class name we want. +# and we can give it any class name we want. class(pikachu) <- "Pokemon" str(pikachu) @@ -133,19 +135,19 @@ pikachu$type ### Seeing R through objects -# Most of the R objects that you will see as you advance are their own objects. For example, +# Most of the R objects that you will see as you advance are their own objects. For example, # here's a linear regression object (which you will learn more about in Gov 2000): ols <- lm(mpg ~ wt + vs + gear + carb, mtcars) class(ols) -# Anything can be an object! Even graphs (in `ggplot`) can be assigned, re-assigned, and edited. +# Anything can be an object! Even graphs (in `ggplot`) can be assigned, re-assigned, and edited. -grp_race <- group_by(cen10, race)%>% +grp_race <- group_by(cen10, race) %>% summarize(count = n()) -grp_race_ordered <- arrange(grp_race, count) %>% +grp_race_ordered <- arrange(grp_race, count) %>% mutate(race = forcats::as_factor(race)) gg_tab <- ggplot(data = grp_race_ordered) + @@ -158,14 +160,14 @@ gg_tab # You can change the orientation -gg_tab<- gg_tab + coord_flip() +gg_tab <- gg_tab + coord_flip() gg_tab ### Parsing an object by `str()s` -# It can be hard to understand an `R` object because it's contents are unknown. The function `str`, +# It can be hard to understand an `R` object because it's contents are unknown. The function `str`, # short for structure, is a quick way to look into the innards of an object str(my_list) @@ -185,10 +187,10 @@ str(gg_tab) ## Types of variables -# In the social science we often analyze variables. As you saw in the tutorial, different types of -# variables require different care. +# In the social science we often analyze variables. As you saw in the tutorial, different types of +# variables require different care. -# A key link with what we just learned is that variables are also types of R objects. +# A key link with what we just learned is that variables are also types of R objects. ### scalars # One number. How many people did we count in our Census sample? @@ -202,37 +204,37 @@ unique(cen10$race) mean(cen10$race == "American Indian or Alaska Native") -# Hint: you can use the function `mean()` to calcualte the sample mean. The sample proportion is the mean of a +# Hint: you can use the function `mean()` to calcualte the sample mean. The sample proportion is the mean of a # sequence of number, where your event of interest is a 1 (or `TRUE`) and others are 0 (or `FALSE`). ### numeric vectors -# A sequence of numbers. +# A sequence of numbers. grp_race_ordered$count class(grp_race_ordered$count) -# Or even, all the ages of the millions of people in our Census. Here are just the first few numbers of the list. +# Or even, all the ages of the millions of people in our Census. Here are just the first few numbers of the list. head(cen10$age) ### characters (aka strings) -# This can be just one stretch of characters +# This can be just one stretch of characters my_name <- "Meg" my_name class(my_name) -# or more characters. Notice here that there's a difference between a vector of individual characters +# or more characters. Notice here that there's a difference between a vector of individual characters # and a length-one object of characters. -my_name_letters <- c("M","e","g") +my_name_letters <- c("M", "e", "g") my_name_letters class(my_name_letters) @@ -247,23 +249,23 @@ my_name == my_name2 ## What is a function? -# Most of what we do in R is executing a function. `read_csv()`, `nrow()`, `ggplot()` .. pretty much anything +# Most of what we do in R is executing a function. `read_csv()`, `nrow()`, `ggplot()` .. pretty much anything # with a parentheses is a function. And even things like `<-` and `[` are functions as well. -# A function is a set of instructions with specified ingredients. It takes an input, then -# manipulates it -- changes it in some way -- and then returns the manipulated product. +# A function is a set of instructions with specified ingredients. It takes an input, then +# manipulates it -- changes it in some way -- and then returns the manipulated product. -# One way to see what a function actually does is to enter it without parentheses. +# One way to see what a function actually does is to enter it without parentheses. table -# You'll see below that the most basic functions are quite complicated internally. +# You'll see below that the most basic functions are quite complicated internally. -# You'll notice that functions contain other functions. wrapper functions are functions that -# "wrap around" existing functions. This sounds redundant, but it's an important feature of -# programming. If you find yourself repeating a command more than two times, you should make your -# own function, rather than writing the same type of code. +# You'll notice that functions contain other functions. wrapper functions are functions that +# "wrap around" existing functions. This sounds redundant, but it's an important feature of +# programming. If you find yourself repeating a command more than two times, you should make your +# own function, rather than writing the same type of code. ### Write your own function @@ -277,9 +279,8 @@ my_fun <- function() { # If we wanted to generate a function that computed the number of men in your data, what would that look like? count_men <- function(data) { - nmen <- sum(data$sex == "Male") - + return(nmen) } @@ -290,27 +291,26 @@ count_men <- function(data) { count_men(cen10) -# The point of a function is that you can use it again and again without typing up the set of +# The point of a function is that you can use it again and again without typing up the set of # constituent manipulations. So, what if we wanted to figure out the number of men in California? -count_men(cen10[cen10$state == "California",]) +count_men(cen10[cen10$state == "California", ]) -# Let's go one step further. What if we want to know the proportion of non-whites in a state, just +# Let's go one step further. What if we want to know the proportion of non-whites in a state, just # by entering the name of the state? There's multiple ways to do it, but it could look something like this nw_in_state <- function(data, state) { - - s.subset <- data[data$state == state,] + s.subset <- data[data$state == state, ] total.s <- nrow(s.subset) nw.s <- sum(s.subset$race != "White") - + nw.s / total.s } -# The last line is what gets generated from the function. To be more explicit you can wrap the last +# The last line is what gets generated from the function. To be more explicit you can wrap the last # line around `return()`. (as in `return(nw.s/total.s`). `return()` is used when you want to break out of a function in the middle of it and not wait till the last line. # Try it on your favorite state! @@ -332,26 +332,26 @@ nw_in_state(cen10, "Massachusetts") ### 2 -# Try making your own function, `asians_in_state`, that will give you the number of `Chinese`, `Japanese`, +# Try making your own function, `asians_in_state`, that will give you the number of `Chinese`, `Japanese`, # and `Other Asian or Pacific Islander` people in a given state. -### 3 +### 3 -# Try making your own function, 'top_10_oldest_cities', that will give you the names of cities whose -# population's average age is top 10 oldest. +# Try making your own function, 'top_10_oldest_cities', that will give you the names of cities whose +# population's average age is top 10 oldest. ## What is a package? -# You can think of a package as a suite of functions that other people have already built for -# you to make your life easier. +# You can think of a package as a suite of functions that other people have already built for +# you to make your life easier. help(package = "ggplot2") -# To use a package, you need to do two things: (1) install it, and then (2) load it. +# To use a package, you need to do two things: (1) install it, and then (2) load it. # Installing is a one-time thing @@ -364,33 +364,33 @@ library(ggplot2) -# In `rstudio.cloud`, we already installed a set of packages for you. But when you start your own R -# instance, you need to have installed the package at some point. +# In `rstudio.cloud`, we already installed a set of packages for you. But when you start your own R +# instance, you need to have installed the package at some point. ## Conditionals -# Sometimes, you want to execute a command only under certain conditions. This is done through the -# almost universal function, `if()`. Inside the `if` function we enter a logical statement. -# The line that is adjacent to, or follows, the `if()` statement only gets executed if the -# statement returns `TRUE`. +# Sometimes, you want to execute a command only under certain conditions. This is done through the +# almost universal function, `if()`. Inside the `if` function we enter a logical statement. +# The line that is adjacent to, or follows, the `if()` statement only gets executed if the +# statement returns `TRUE`. x <- 5 -if (x >0) { +if (x > 0) { print("positive number") -} else if (x == 0) { - print ("zero") +} else if (x == 0) { + print("zero") } else { print("negative number") } -# You can wrap that whole things in a function +# You can wrap that whole things in a function is_positive <- function(number) { if (number > 0) { print("positive number") - } else if (number == 0) { - print ("zero") + } else if (number == 0) { + print("zero") } else { print("negative number") } @@ -403,11 +403,11 @@ is_positive(-3) ## For-loops -# Loops repeat the same statement, although the statement can be "the same" only in an abstract sense. -# Use the `for(x in X)` syntax to repeat the subsequent command as many times as there are elements in the +# Loops repeat the same statement, although the statement can be "the same" only in an abstract sense. +# Use the `for(x in X)` syntax to repeat the subsequent command as many times as there are elements in the # right-hand object `X`. Each of these elements will be referred to the left-hand index `x` -# First, come up with a vector. +# First, come up with a vector. fruits <- c("apples", "oranges", "grapes") @@ -419,8 +419,8 @@ for (fruit in fruits) { } -# Here `for()` and `in` must be part of any for loop. The right hand side `fruits` must be a thing -# that exists. Finally the `left-hand` side object is "Pick your favor name." It is analogous to how we +# Here `for()` and `in` must be part of any for loop. The right hand side `fruits` must be a thing +# that exists. Finally the `left-hand` side object is "Pick your favor name." It is analogous to how we # can index a sum with any letter. $\sum_{i=1}^{10}i$ and `sum_{j = 1}^{10}j` are in fact the same thing. @@ -432,14 +432,13 @@ for (i in 1:length(fruits)) { states_of_interest <- c("California", "Massachusetts", "New Hampshire", "Washington") -for( state in states_of_interest){ - state_data <- cen10[cen10$state == state,] +for (state in states_of_interest) { + state_data <- cen10[cen10$state == state, ] nmen <- sum(state_data$sex == "Male") - + n <- nrow(state_data) - men_perc <- round(100*(nmen/n), digits=2) - print(paste("Percentage of men in",state, "is", men_perc)) - + men_perc <- round(100 * (nmen / n), digits = 2) + print(paste("Percentage of men in", state, "is", men_perc)) } @@ -448,14 +447,14 @@ for( state in states_of_interest){ states_of_interest <- c("California", "Massachusetts", "New Hampshire", "Washington") male_percentages <- c() -iter <-1 +iter <- 1 -for( state in states_of_interest){ - state_data <- cen10[cen10$state == state,] +for (state in states_of_interest) { + state_data <- cen10[cen10$state == state, ] nmen <- sum(state_data$sex == "Male") n <- nrow(state_data) - men_perc <- round(100*(nmen/n), digits=2) - + men_perc <- round(100 * (nmen / n), digits = 2) + male_percentages <- c(male_percentages, men_perc) names(male_percentages)[iter] <- state iter <- iter + 1 @@ -467,24 +466,24 @@ male_percentages ## Nested Loops # What if I want to calculate the population percentage of a race group for all race groups in states of interest? - # You could probably use tidyverse functions to do this, but let's try using loops! +# You could probably use tidyverse functions to do this, but let's try using loops! states_of_interest <- c("California", "Massachusetts", "New Hampshire", "Washington") for (state in states_of_interest) { for (race in unique(cen10$race)) { race_state_num <- nrow(cen10[cen10$race == race & cen10$state == state, ]) state_pop <- nrow(cen10[cen10$state == state, ]) - race_perc <- round(100*(race_state_num/(state_pop)), digits=2) - print(paste("Percentage of ", race , "in", state, "is", race_perc)) + race_perc <- round(100 * (race_state_num / (state_pop)), digits = 2) + print(paste("Percentage of ", race, "in", state, "is", race_perc)) } } ## Exercises - + ### Exercise 1: Write your own function -# Write your own function that makes some task of data analysis simpler. Ideally, it would be a -# function that helps you do either of the previous tasks in fewer lines of code. You can use +# Write your own function that makes some task of data analysis simpler. Ideally, it would be a +# function that helps you do either of the previous tasks in fewer lines of code. You can use # the three lines of code that was provided in exercise 1 to wrap that into another function too! @@ -508,14 +507,12 @@ for (state in states_of_interest) { for (race in unique(cen10$race)) { race_state_num <- nrow(cen10[cen10$race == race & cen10$state == state, ]) state_pop <- nrow(cen10[cen10$state == state, ]) - race_perc <- round(100*(race_state_num/(state_pop)), digits=2) - print(paste("Percentage of ", race , "in", state, "is", race_perc)) + race_perc <- round(100 * (race_state_num / (state_pop)), digits = 2) + print(paste("Percentage of ", race, "in", state, "is", race_perc)) } } -# Instead of printing the percentage of each race in each state, create a dataframe, and store all that -# information in that dataframe. (Hint: look at how I stored information about male percentage in each +# Instead of printing the percentage of each race in each state, create a dataframe, and store all that +# information in that dataframe. (Hint: look at how I stored information about male percentage in each # state of interest in a vector.) - - diff --git a/R_exercises/04_visualization.R b/R_exercises/04_visualization.R index 86c7b53..2c237d5 100644 --- a/R_exercises/04_visualization.R +++ b/R_exercises/04_visualization.R @@ -11,16 +11,16 @@ library(scales) ### Where are we? Where are we headed? {.unnumbered} # Up till now, you should have covered: -# +# # * The R Visualization and Programming primers at # * Reading and handling data # * Matrices and Vectors # * What does `:` mean in R? What about `==`? `,`?, `!=` , `&`, `|`, `%in% ` # * What does `%>%` do? -# -# +# +# # Today we'll cover: -# +# # * Visualization # * A bit of data wrangling @@ -28,16 +28,16 @@ library(scales) # * How do you make a barplot, in base-R and in ggplot? # * How do you add layers to a ggplot? -# * How do you change the axes of a ggplot? -# * How do you make a histogram? +# * How do you change the axes of a ggplot? +# * How do you make a histogram? # * How do you make a graph that looks like this? -# +# ## Motivation: The Law of the Census -# In this module, let's visualize some cross-sectional stats with an actual Census. Then, we'll -# do an example on time trends with Supreme Court ideal points. +# In this module, let's visualize some cross-sectional stats with an actual Census. Then, we'll +# do an example on time trends with Supreme Court ideal points. ## Read data @@ -54,16 +54,16 @@ nrow(cen10) # This and all subsequent tasks involve manipulating and summarizing data, sometimes called "wrangling". -# As per last time, there are both "base-R" and "tidyverse" approaches. +# As per last time, there are both "base-R" and "tidyverse" approaches. # We have already seen several functions from the tidyverse: -# +# # * `select` selects columns # * `filter` selects rows based on a logical (boolean) statement # * `slice` selects rows based on the row number # * `arrange` reordered the rows in descending order. -# In this visualization section, we'll make use of the pair of functions `group_by()` and `summarize()`. +# In this visualization section, we'll make use of the pair of functions `group_by()` and `summarize()`. ## Tabulating @@ -73,7 +73,7 @@ nrow(cen10) # Here are two ways to count by group, or to tabulate. -# In base-R Use the `table` function, that provides how many rows exist for an unique value of the vector +# In base-R Use the `table` function, that provides how many rows exist for an unique value of the vector # (remember `unique`?) table(cen10$race) @@ -90,15 +90,15 @@ count(cen10, race) count(cen10, race, sort = TRUE) -# `count` is a kind of shorthand for `group_by()` and `summarize`. This code would have done the same. +# `count` is a kind of shorthand for `group_by()` and `summarize`. This code would have done the same. -cen10 %>% - group_by(race) %>% +cen10 %>% + group_by(race) %>% summarize(n = n()) -# If you are new to tidyverse, what would you _think_ each row did? Reading the function help page, -# verify if your intuition was correct. +# If you are new to tidyverse, what would you _think_ each row did? Reading the function help page, +# verify if your intuition was correct. ## base R graphics and ggplot @@ -107,18 +107,18 @@ cen10 %>% ### base R -# "Base-R" graphics are graphics that are made with R's default graphics commands. +# "Base-R" graphics are graphics that are made with R's default graphics commands. # First, let's assign our tabulation to an object, then put it in the `barplot()` function. barplot(table(cen10$race)) ### ggplot -# A popular alternative a `ggplot` graphics, that you were introduced to in the tutorial. `gg` stands -# for grammar of graphics by Hadley Wickham, and it has a new semantics of explaining graphics in R. -# Again, first let's set up the data. +# A popular alternative a `ggplot` graphics, that you were introduced to in the tutorial. `gg` stands +# for grammar of graphics by Hadley Wickham, and it has a new semantics of explaining graphics in R. +# Again, first let's set up the data. -# Although the tutorial covered making scatter plots as the first cut, often data requires summaries +# Although the tutorial covered making scatter plots as the first cut, often data requires summaries # before they made into graphs. # For this example, let's group and count first like we just did. But assign it to a new object. @@ -126,65 +126,69 @@ barplot(table(cen10$race)) grp_race <- count(cen10, race) -# We will now plot this grouped set of numbers. Recall that the `ggplot()` function takes two -# main arguments, `data` and `aes`. +# We will now plot this grouped set of numbers. Recall that the `ggplot()` function takes two +# main arguments, `data` and `aes`. # 1. First enter a single dataframe from which you will draw a plot. -# 2. Then enter the `aes`, or aesthetics. This defines which variable in the data the plotting functions -# should take for pre-set dimensions in graphics. The dimensions `x` and `y` are the most important. +# 2. Then enter the `aes`, or aesthetics. This defines which variable in the data the plotting functions +# should take for pre-set dimensions in graphics. The dimensions `x` and `y` are the most important. # We will assign `race` and `count` to them, respectively, -# 3. After you close `ggplot()` .. add layers by the plus sign. A `geom` is a layer of graphical -# representation, for example `geom_histogram` renders a histogram, `geom_point` renders a scatter plot. +# 3. After you close `ggplot()` .. add layers by the plus sign. A `geom` is a layer of graphical +# representation, for example `geom_histogram` renders a histogram, `geom_point` renders a scatter plot. # For a barplot, we can use `geom_col()` -# What is the right geometry layer to make a barplot? Turns out: - -ggplot(data = grp_race, aes(x = race, y = n)) + geom_col() +# What is the right geometry layer to make a barplot? Turns out: + +ggplot(data = grp_race, aes(x = race, y = n)) + + geom_col() ## Improving your graphics -# Adjusting your graphics to make the point clear is an important skill. Here is a base-R -# example of showing the same numbers but with a different design, in a way that aims to +# Adjusting your graphics to make the point clear is an important skill. Here is a base-R +# example of showing the same numbers but with a different design, in a way that aims to # maximize the "data-to-ink ratio". par(oma = c(1, 11, 1, 1)) barplot(sort(table(cen10$race)), # sort numbers - horiz = TRUE, # flip - border = NA, # border is extraneous - xlab = "Number in Race Category", - bty = "n", # no box - las = 1) # alignment of axis labels is horizontal + horiz = TRUE, # flip + border = NA, # border is extraneous + xlab = "Number in Race Category", + bty = "n", # no box + las = 1 +) # alignment of axis labels is horizontal -# Notice that we applied the `sort()` function to order the bars in terms of their counts. -# The default ordering of a categorical variable / factor is alphabetical. Alphabetical ordering +# Notice that we applied the `sort()` function to order the bars in terms of their counts. +# The default ordering of a categorical variable / factor is alphabetical. Alphabetical ordering # is uninformative and almost never the way you should order variables. # In ggplot you might do this by: library(forcats) -grp_race_ordered <- arrange(grp_race, n) %>% +grp_race_ordered <- arrange(grp_race, n) %>% mutate(race = as_factor(race)) ggplot(data = grp_race_ordered, aes(x = race, y = n)) + geom_col() + coord_flip() + - labs(y = "Number in Race Category", - x = "", - caption = "Source: 2010 U.S. Census sample") + labs( + y = "Number in Race Category", + x = "", + caption = "Source: 2010 U.S. Census sample" + ) ## Cross-tabs -# Visualizations and Tables each have their strengths. A rule of thumb is that more than a dozen -# numbers on a table is too much to digest, but less than a dozen is too few for a figure to be worth it. -# Let's look at a table first. +# Visualizations and Tables each have their strengths. A rule of thumb is that more than a dozen +# numbers on a table is too much to digest, but less than a dozen is too few for a figure to be worth it. +# Let's look at a table first. -# A cross-tab is counting with two types of variables, and is a simple and powerful tool to show the +# A cross-tab is counting with two types of variables, and is a simple and powerful tool to show the # relationship between multiple variables. xtab_race_state <- table(cen10$state, cen10$race) @@ -196,14 +200,14 @@ xtabs(~ state + race, cen10) -# What if we care about proportions within states, rather than counts? -# Say we'd like to compare the racial composition of a small state (like Delaware) -# and a large state (like California). In fact, most tasks of inference is about the -# unobserved population, not the observed data --- and proportions are estimates of a +# What if we care about proportions within states, rather than counts? +# Say we'd like to compare the racial composition of a small state (like Delaware) +# and a large state (like California). In fact, most tasks of inference is about the +# unobserved population, not the observed data --- and proportions are estimates of a # quantity in the population. -# One way to transform a table of counts to a table of proportions is the function `prop.table`. -# Be careful what you want to take proportions of -- this is set by the `margin` argument. +# One way to transform a table of counts to a table of proportions is the function `prop.table`. +# Be careful what you want to take proportions of -- this is set by the `margin` argument. # In R, the first margin (`margin = 1`) is _rows_ and the second (`margin = 2`) is _columns_. ptab_race_state <- prop.table(xtab_race_state, margin = 2) @@ -215,58 +219,60 @@ ptab_race_state ## Composition Plots -# How would you make the same figure with `ggplot()`? First, we want a count for each state * race combination. -# So group by those two factors and count how many observations are in each two-way categorization. -# `group_by()` can take any number of variables, separated by commas. +# How would you make the same figure with `ggplot()`? First, we want a count for each state * race combination. +# So group by those two factors and count how many observations are in each two-way categorization. +# `group_by()` can take any number of variables, separated by commas. -grp_race_state <- cen10 %>% +grp_race_state <- cen10 %>% count(race, state) # Can you tell from the code what `grp_race_state` will look like? - + grp_race_state -# Now, we want to tell `ggplot2` something like the following: I want bars by state, -# where heights indicate racial groups. Each bar should be colored by the race. With +# Now, we want to tell `ggplot2` something like the following: I want bars by state, +# where heights indicate racial groups. Each bar should be colored by the race. With # some googling, you will get something like this: - -ggplot(data = grp_race_state, aes(x = state, y = n, fill = race)) + + +ggplot(data = grp_race_state, aes(x = state, y = n, fill = race)) + geom_col(position = "fill") + # the position is determined by the fill ae scale_fill_brewer(name = "Census Race", palette = "Spectral", direction = -1) + # choose palette coord_flip() + # flip axes scale_y_continuous(labels = percent) + # label numbers as percentage - labs(y = "Proportion of Racial Group within State", - x = "", - source = "Source: 2010 Census sample") + + labs( + y = "Proportion of Racial Group within State", + x = "", + source = "Source: 2010 Census sample" + ) + theme_minimal() ## Line graphs -# Line graphs are useful for plotting time trends. +# Line graphs are useful for plotting time trends. -# The Census does not track individuals over time. So let's take up another example: +# The Census does not track individuals over time. So let's take up another example: # The U.S. Supreme Court. Take the dataset `justices_court-median.csv`. -# This data is adapted from the estimates of Martin and Quinn on their website -# .^[This exercise inspired from Princeton's R Camp Assignment.] +# This data is adapted from the estimates of Martin and Quinn on their website +# .^[This exercise inspired from Princeton's R Camp Assignment.] justice <- read_csv("data/input/justices_court-median.csv") # What does the data look like? How do you think it is organized? What does each row represent? - + justice # As you might have guessed, these data can be shown in a time trend from the range of the `term` -# variable. As there are only nine justices at any given time and justices have life tenure, there -# times on the court are staggered. With a common measure of "preference", we can plot time trends -# of these justices ideal points on the same y-axis scale. +# variable. As there are only nine justices at any given time and justices have life tenure, there +# times on the court are staggered. With a common measure of "preference", we can plot time trends +# of these justices ideal points on the same y-axis scale. ggplot(justice, aes(x = term, y = idealpt)) + geom_line() @@ -275,71 +281,67 @@ ggplot(justice, aes(x = term, y = idealpt)) + # enter a correction that draws separate lines by group. -# If you got the right aesthetic, this seems to "work" off the shelf. But take a moment to see why the code was -# written as it is and how that maps on to the graphics. What is the `group` aesthetic doing for you? - -# Now, this graphic already indicates a lot, but let's improve the graphics so people can actually read it. -# This is left for a Exercise. +# If you got the right aesthetic, this seems to "work" off the shelf. But take a moment to see why the code was +# written as it is and how that maps on to the graphics. What is the `group` aesthetic doing for you? + +# Now, this graphic already indicates a lot, but let's improve the graphics so people can actually read it. +# This is left for a Exercise. -# As social scientists, we should also not forget to ask ourselves whether these numerical measures are fit -# for what we care about, or actually succeeds in measuring what we'd like to measure. The estimation of -# these "ideal points" is a subfield of political methodology beyond this prefresher. For more reading, +# As social scientists, we should also not forget to ask ourselves whether these numerical measures are fit +# for what we care about, or actually succeeds in measuring what we'd like to measure. The estimation of +# these "ideal points" is a subfield of political methodology beyond this prefresher. For more reading, # see citations in the prefresher booklet. ## Exercises -# In the time remaining, try the following exercises. Order doesn't matter. +# In the time remaining, try the following exercises. Order doesn't matter. ### 1: Rural states -# Make a well-labelled figure that plots the proportion of the state's population (as per the census) -# that is 65 years or older. Each state should be visualized as a point, rather than a bar, and there -# should be 51 points, ordered by their value. All labels should be readable. +# Make a well-labelled figure that plots the proportion of the state's population (as per the census) +# that is 65 years or older. Each state should be visualized as a point, rather than a bar, and there +# should be 51 points, ordered by their value. All labels should be readable. -### 2: The swing justice +### 2: The swing justice -# Using the `justices_court-median.csv` dataset and building off of the plot that was given, -# make an improved plot by implementing as many of the following changes (which hopefully improves the graph): - -# * Label axes +# Using the `justices_court-median.csv` dataset and building off of the plot that was given, +# make an improved plot by implementing as many of the following changes (which hopefully improves the graph): + +# * Label axes # * Use a black-white background. # * Change the breaks of the x-axis to print numbers for every decade, not just every two decades. -# * Plots each line in translucent gray, so the overlapping lines can be visualized clearly. +# * Plots each line in translucent gray, so the overlapping lines can be visualized clearly. # (Hint: in ggplot the `alpha` argument controls the degree of transparency) -# * Limit the scale of the y-axis to [-5, 5] so that the outlier justice in the 60s is trimmed +# * Limit the scale of the y-axis to [-5, 5] so that the outlier justice in the 60s is trimmed # and the rest of the data can be seen more easily (also, who is that justice?) -# * Plot the ideal point of the justice who holds the "median" ideal point in a given term. To distinguish +# * Plot the ideal point of the justice who holds the "median" ideal point in a given term. To distinguish # this with the others, plot this line separately in a very light red _below_ the individual justice's lines. -# * Highlight the trend-line of only the nine justices who are _currently_ sitting on SCOTUS. Make sure this is -# clearer than the other past justices. -# * Add the current nine justice's names to the right of the endpoint of the 2016 figure, alongside their ideal point. -# * Make sure the text labels do not overlap with each other for readability using the `ggrepel` package. +# * Highlight the trend-line of only the nine justices who are _currently_ sitting on SCOTUS. Make sure this is +# clearer than the other past justices. +# * Add the current nine justice's names to the right of the endpoint of the 2016 figure, alongside their ideal point. +# * Make sure the text labels do not overlap with each other for readability using the `ggrepel` package. # * Extend the x-axis label to about 2020 so the text labels of justices are to the right of the trend-lines. -# * Add a caption to your text describing the data briefly, as well as any features relevant for the reader +# * Add a caption to your text describing the data briefly, as well as any features relevant for the reader # (such as the median line and the trimming of the y-axis) -### 3: Don't sort by the alphabet - -# The Figure we made that shows racial composition by state has one notable shortcoming: it orders the states -# alphabetically, which is not particularly useful if you want see an overall pattern, without having particular -# states in mind. - -# Find a way to modify the figures so that the states are ordered by the proportion of White residents in the sample. - +### 3: Don't sort by the alphabet +# The Figure we made that shows racial composition by state has one notable shortcoming: it orders the states +# alphabetically, which is not particularly useful if you want see an overall pattern, without having particular +# states in mind. -### 4 What to show and how to show it +# Find a way to modify the figures so that the states are ordered by the proportion of White residents in the sample. -# As a student of politics our goal is not necessarily to make pretty pictures, but rather make pictures that -# tell us something about politics, government, or society. If you could augment either the census dataset or -# the justices dataset in some way, what would be an substantively significant thing to show as a graphic? +### 4 What to show and how to show it - +# As a student of politics our goal is not necessarily to make pretty pictures, but rather make pictures that +# tell us something about politics, government, or society. If you could augment either the census dataset or +# the justices dataset in some way, what would be an substantively significant thing to show as a graphic? diff --git a/R_exercises/05_project-dempeace.R b/R_exercises/05_project-dempeace.R index fdec6db..80160e5 100644 --- a/R_exercises/05_project-dempeace.R +++ b/R_exercises/05_project-dempeace.R @@ -4,30 +4,30 @@ ## Where are we? Where are we headed? # Up till now, you should have covered: -# +# # * R basic programming -# * Counting. -# * Visualization. -# * Objects and Classes. +# * Counting. +# * Visualization. +# * Objects and Classes. # * Matrix algebra in R # * Functions. -# Today you will work on your own, but feel free to ask a fellow classmate nearby or the instructor. -# The objective for this session is to get more experience using R, but in the process (a) test a -# prominent theory in the political science literature and (b) explore related ideas of interest to you. +# Today you will work on your own, but feel free to ask a fellow classmate nearby or the instructor. +# The objective for this session is to get more experience using R, but in the process (a) test a +# prominent theory in the political science literature and (b) explore related ideas of interest to you. ## Motivation -# The "Democratic Peace" is one of the most widely discussed propositions in political science, -# covering the fields of International Relations and Comparative Politics, with insights to domestic -# politics of democracies (e.g. American Politics). The one-sentence idea is that democracies do not -# fight with each other. +# The "Democratic Peace" is one of the most widely discussed propositions in political science, +# covering the fields of International Relations and Comparative Politics, with insights to domestic +# politics of democracies (e.g. American Politics). The one-sentence idea is that democracies do not +# fight with each other. -# An empirical demonstration of the democratic peace is also a good example of a Time Series Cross -# Sectional (or panel) dataset, where the same units (in this case countries) are observed repeatedly -# for multiple time periods. Experience in assembling and analyzing a TSCS dataset will prepare you for +# An empirical demonstration of the democratic peace is also a good example of a Time Series Cross +# Sectional (or panel) dataset, where the same units (in this case countries) are observed repeatedly +# for multiple time periods. Experience in assembling and analyzing a TSCS dataset will prepare you for # any future research in this area. ## Setting up @@ -40,35 +40,35 @@ library(ggplot2) ## Create a project directory -# First start a directory for this project. This can be done manually or through +# First start a directory for this project. This can be done manually or through # RStudio's Project feature(`File > New Project...`) -# Directories is the computer science / programming name for folders. While advice about -# how to structure your working directories might strike you as petty, we believe that -# starting from some well-tested guides will go a long way in improving the quality and efficiency of your work. +# Directories is the computer science / programming name for folders. While advice about +# how to structure your working directories might strike you as petty, we believe that +# starting from some well-tested guides will go a long way in improving the quality and efficiency of your work. # Chapter 4 of Gentzkow and Shapiro's memo, Code and Data for the Social Scientist -# (https://web.stanford.edu/~gentzkow/research/CodeAndData.pdf)] provides a good template. +# (https://web.stanford.edu/~gentzkow/research/CodeAndData.pdf)] provides a good template. ## Data Sources -# Most projects you do will start with downloading data from elsewhere. For this task, you'll -# probably want to track down and download the following: +# Most projects you do will start with downloading data from elsewhere. For this task, you'll +# probably want to track down and download the following: -# * Correlates of war dataset (COW): Find and download the Militarized Interstate Disputes (MIDs) -# data from the Correlates of War website: . Or a +# * Correlates of war dataset (COW): Find and download the Militarized Interstate Disputes (MIDs) +# data from the Correlates of War website: . Or a # dyad-version on dataverse: -# * PRIO Data on Armed Conflict: Find and download the Uppsala Conflict Data Program (UCDP) and -# PRIO dyad-year data on armed conflict() or this link to to the flat -# csv file (). -# * Polity: The Polity data can be downloaded from their website (). +# * PRIO Data on Armed Conflict: Find and download the Uppsala Conflict Data Program (UCDP) and +# PRIO dyad-year data on armed conflict() or this link to to the flat +# csv file (). +# * Polity: The Polity data can be downloaded from their website (). # Look for the newest version of the time series that has the widest coverage. ## Example with 2 Datasets -# Let's read in a sample dataset. +# Let's read in a sample dataset. polity <- read_csv("data/input/sample_polity.csv") mid <- read_csv("data/input/sample_mid.csv") @@ -80,7 +80,7 @@ mid <- read_csv("data/input/sample_mid.csv") unique(polity$country) ggplot(polity, aes(x = year, y = polity2)) + - facet_wrap(~ country) + + facet_wrap(~country) + geom_line() head(polity) @@ -94,23 +94,27 @@ mid ## Loops -# Notice that in the `mid` data, we have a start of a dispute vs. an end of a -# dispute.In order to combine this into the `polity` data, we want a way to -# give each of the interval years a row. +# Notice that in the `mid` data, we have a start of a dispute vs. an end of a +# dispute.In order to combine this into the `polity` data, we want a way to +# give each of the interval years a row. -# There are many ways to do this, but one is a loop. We go through one row at a -# time, and then for each we make a new dataset. that has `year` as a sequence +# There are many ways to do this, but one is a loop. We go through one row at a +# time, and then for each we make a new dataset. that has `year` as a sequence # of each year. -mid_year_by_year <- tibble(ccode = numeric(), - year = numeric(), - dispute = numeric()) +mid_year_by_year <- tibble( + ccode = numeric(), + year = numeric(), + dispute = numeric() +) -for(i in 1:nrow(mid)) { - x <- data_frame(ccode = mid$ccode[i], ## row i's country - year = mid$StYear[i]:mid$EndYear[i], ## sequence of years for dispute in row i - dispute = 1) +for (i in 1:nrow(mid)) { + x <- data_frame( + ccode = mid$ccode[i], ## row i's country + year = mid$StYear[i]:mid$EndYear[i], ## sequence of years for dispute in row i + dispute = 1 + ) mid_year_by_year <- rbind(mid_year_by_year, x) } @@ -123,12 +127,13 @@ head(mid_year_by_year) # We want to combine these two datasets by merging. Base-R has a function called `merge`. # `dplyr` has several types of `joins` (the same thing). Those names are based on SQL syntax. -# Here we can do a `left_join` matching rows from `mid` to `polity`. We want to keep the rows +# Here we can do a `left_join` matching rows from `mid` to `polity`. We want to keep the rows # in `polity` that do not match in `mid`, and label them as non-disputes. p_m <- left_join(polity, - distinct(mid_year_by_year), - by = c("ccode", "year")) + distinct(mid_year_by_year), + by = c("ccode", "year") +) head(p_m) @@ -141,42 +146,43 @@ p_m$dispute[is.na(p_m$dispute)] <- 0 # Reshape the dataset long to wide -p_m_wide <- pivot_wider(p_m, - id_cols = c(scode, ccode, country), - names_from = year, - values_from = polity2) +p_m_wide <- pivot_wider(p_m, + id_cols = c(scode, ccode, country), + names_from = year, + values_from = polity2 +) ## Main Project -# Try building a panel that would be useful in answering the Democratic Peace Question, +# Try building a panel that would be useful in answering the Democratic Peace Question, # perhaps in these steps. ### Task 1: Data Input and Standardization -# Often, files we need are saved in the `.xls` or `xlsx` format. It is possible to read +# Often, files we need are saved in the `.xls` or `xlsx` format. It is possible to read # these files directly into `R`, but experience suggests that this process is slower than -# converting them first to `.csv` format and reading them in as `.csv` files. +# converting them first to `.csv` format and reading them in as `.csv` files. -# `readxl`/`readr`/`haven` packages() is constantly +# `readxl`/`readr`/`haven` packages() is constantly # expanding to capture more file types. In day 1, we used the package `readxl`, using the `read_excel()` function. # Input and standardize data here: ### Task 2: Data Merging -# We will use data to test a version of the Democratic Peace Thesis (DPS). Democracies -# are said to go to war less because the leaders who wage wars are accountable to voters -# who have to bear the costs of war. Are democracies less likely to engage in militarized +# We will use data to test a version of the Democratic Peace Thesis (DPS). Democracies +# are said to go to war less because the leaders who wage wars are accountable to voters +# who have to bear the costs of war. Are democracies less likely to engage in militarized # interstate disputes? - -# To start, let's download and merge some data. - -# * Load in the Militarized Interstate Dispute (MID) files. Militarized interstate disputes -# are hostile action between two formally recognized states. Examples of this would be threats -# to use force, threats to declare war, beginning war, fortifying a border with troops, and so on. -# * Find a way to merge the Polity IV dataset and the MID data. This process can be a bit tricky. -# * An advanced version of this task would be to download the dyadic form of the data and try + +# To start, let's download and merge some data. + +# * Load in the Militarized Interstate Dispute (MID) files. Militarized interstate disputes +# are hostile action between two formally recognized states. Examples of this would be threats +# to use force, threats to declare war, beginning war, fortifying a border with troops, and so on. +# * Find a way to merge the Polity IV dataset and the MID data. This process can be a bit tricky. +# * An advanced version of this task would be to download the dyadic form of the data and try # merging that with polity. # Merge data here: @@ -184,16 +190,11 @@ p_m_wide <- pivot_wider(p_m, ### Task 3: Tabulations and Visualization {.unnumbered} -# 1.Calculate the mean Polity2 score by year. Plot the result. Use graphical indicators of -# your choosing to show where key events fall in this timeline (such as 1914, 1929, 1939, 1989, 2008). +# 1.Calculate the mean Polity2 score by year. Plot the result. Use graphical indicators of +# your choosing to show where key events fall in this timeline (such as 1914, 1929, 1939, 1989, 2008). # Speculate on why the behavior from 1800 to 1920 seems to be qualitatively different than behavior afterwards. -# 2.Do the same but only among state-years that were invovled in a MID. Plot this line together with your -# results from 1. +# 2.Do the same but only among state-years that were invovled in a MID. Plot this line together with your +# results from 1. # 3.Do the same but only among state years that were _not_ involved in a MID. # 4.Arrive at a tentative conclusion for how well the Democratic Peace argument seems to hold up in -# this dataset. Visualize this conclusion. - - - - - +# this dataset. Visualize this conclusion. diff --git a/R_exercises/06_simulation.R b/R_exercises/06_simulation.R index e6667fc..2c417fe 100644 --- a/R_exercises/06_simulation.R +++ b/R_exercises/06_simulation.R @@ -6,15 +6,15 @@ library(dplyr) ### Where are we? Where are we headed? # Up till now, you should have covered: -# +# # * `R` basics # * Visualization # * Matrices and vectors # * Functions, objects, loops # * Joining real data -# -# -# In this module, we will start to work with generating data within R, from thin air, +# +# +# In this module, we will start to work with generating data within R, from thin air, # as it were. Doing simulation also strengthens your understanding of Probability. @@ -24,10 +24,10 @@ library(dplyr) # * What does `runif()` stand for? # * What is a `seed`? # * What is a Monte Carlo? -# -# +# +# # Check if you have an idea of how you might code the following tasks: -# +# # * Simulate 100 rolls of a die # * Simulate one random ordering of 25 numbers # * Simulate 100 values of white noise (uniform random variables) @@ -40,23 +40,23 @@ library(dplyr) # An increasing amount of political science contributions now include a simulation. Statistical Software, 45(7), 1-47.](http://www.jstatsoft.org/v45/i07/)]) -# Statistical methods also incorporate simulation: - -# * The bootstrap: a statistical method for estimating uncertainty around some parameter by re-sampling observations. -# * Bagging: a method for improving machine learning predictions by re-sampling observations, storing the estimate -# across many re-samples, and averaging these estimates to form the final estimate. A variance reduction technique. -# * Statistical reasoning: if you are trying to understand a quantitative problem, a wonderful first-step to -# understand the problem better is to simulate it! The analytical solution is often very hard (or impossible), but the simulation is often much easier :-) +# Statistical methods also incorporate simulation: + +# * The bootstrap: a statistical method for estimating uncertainty around some parameter by re-sampling observations. +# * Bagging: a method for improving machine learning predictions by re-sampling observations, storing the estimate +# across many re-samples, and averaging these estimates to form the final estimate. A variance reduction technique. +# * Statistical reasoning: if you are trying to understand a quantitative problem, a wonderful first-step to +# understand the problem better is to simulate it! The analytical solution is often very hard (or impossible), but the simulation is often much easier :-) ## Pick a sample, any sample ## The `sample()` function -# The core functions for coding up stochastic data revolves around several key functions, -# so we will simply review them here. +# The core functions for coding up stochastic data revolves around several key functions, +# so we will simply review them here. -# Suppose you have a vector of values `x` and from it you want to randomly sample a sample of +# Suppose you have a vector of values `x` and from it you want to randomly sample a sample of # length `size`. For this, use the `sample` function sample(x = 1:10, size = 5) @@ -64,14 +64,14 @@ sample(x = 1:10, size = 5) # There are two subtypes of sampling -- with and without replacement. -# 1. Sampling without replacement (`replace = FALSE`) means once an element of `x` is chosen, +# 1. Sampling without replacement (`replace = FALSE`) means once an element of `x` is chosen, # it will not be considered again: sample(x = 1:10, size = 10, replace = FALSE) ## no number appears more than once -# 2. Sampling with replacement (`replace = TRUE`) means that even if an element of `x` is -# chosen, it is put back in the pool and may be chosen again. +# 2. Sampling with replacement (`replace = TRUE`) means that even if an element of `x` is +# chosen, it is put back in the pool and may be chosen again. sample(x = 1:10, size = 10, replace = TRUE) ## any number can appear more than once @@ -82,8 +82,8 @@ sample(x = 1:10, size = 100, replace = FALSE) -# So far, every element in `x` has had an equal probability of being chosen. In some application, -# we want a sampling scheme where some elements are more likely to be chosen than others. +# So far, every element in `x` has had an equal probability of being chosen. In some application, +# we want a sampling scheme where some elements are more likely to be chosen than others. # The argument `prob` handles this. # For example, this simulates 20 fair coin tosses (each outcome is equally likely to happen) @@ -91,7 +91,7 @@ sample(x = 1:10, size = 100, replace = FALSE) sample(c("Head", "Tail"), size = 20, prob = c(0.5, 0.5), replace = TRUE) -# But this simulates 20 biased coin tosses, where say the probability of Tails is 4 times +# But this simulates 20 biased coin tosses, where say the probability of Tails is 4 times # more likely than the number of Heads sample(c("Head", "Tail"), size = 20, prob = c(0.2, 0.8), replace = TRUE) @@ -99,7 +99,7 @@ sample(c("Head", "Tail"), size = 20, prob = c(0.2, 0.8), replace = TRUE) ### Sampling rows from a dataframe -# In tidyverse, there is a convenience function to sample rows randomly: `sample_n()` and `sample_frac()`. +# In tidyverse, there is a convenience function to sample rows randomly: `sample_n()` and `sample_frac()`. # For example, load the dataset on cars, `mtcars`, which has 32 observations. @@ -116,40 +116,40 @@ sample_frac(mtcars, 0.10) # As a side-note, these functions have very practical uses for any type of data analysis: - -# * Inspecting your dataset: using `head()` all the same time and looking over the first few rows might lead you to + +# * Inspecting your dataset: using `head()` all the same time and looking over the first few rows might lead you to # ignore any issues that end up in the bottom for whatever reason. -# * Testing your analysis with a small sample: If running analyses on a dataset takes more than a handful of seconds, -# change your dataset upstream to a fraction of the size so the rest of the code runs in less than a second. -# Once verifying your analysis code runs, then re-do it with your full dataset (by simply removing the -# `sample_n` / `sample_frac` line of code in the beginning). While three seconds may not sound like much, +# * Testing your analysis with a small sample: If running analyses on a dataset takes more than a handful of seconds, +# change your dataset upstream to a fraction of the size so the rest of the code runs in less than a second. +# Once verifying your analysis code runs, then re-do it with your full dataset (by simply removing the +# `sample_n` / `sample_frac` line of code in the beginning). While three seconds may not sound like much, # they accumulate and eat up time. ## Random numbers from specific distributions -### `rbinom()` -# `rbinom` builds upon `sample` as a tool to help you answer the question -- what is the -# total number of successes I would get if I sampled a binary (Bernoulli) result from a -# test with `size` number of trials each, with a event-wise probability of `prob`. +### `rbinom()` +# `rbinom` builds upon `sample` as a tool to help you answer the question -- what is the +# total number of successes I would get if I sampled a binary (Bernoulli) result from a +# test with `size` number of trials each, with a event-wise probability of `prob`. # The first argument `n` asks me how many such numbers I want. -# For example, I want to know how many Heads I would get if I flipped a fair coin 100 times. +# For example, I want to know how many Heads I would get if I flipped a fair coin 100 times. rbinom(n = 1, size = 100, prob = 0.5) -# Now imagine this I wanted to do this experiment 10 times, which would require I flip the +# Now imagine this I wanted to do this experiment 10 times, which would require I flip the # coin 10 x 100 = 1000 times! Helpfully, we can do this in one line rbinom(n = 10, size = 100, prob = 0.5) -### `runif()` -# `runif` also simulates a stochastic scheme where each event has equal probability of getting chosen like +### `runif()` +# `runif` also simulates a stochastic scheme where each event has equal probability of getting chosen like # `sample`, but is a continuous rather than discrete system. We will cover this more in the next math module. -# The intuition to emphasize here is that one can generate potentially infinite amounts (size `n`) of noise +# The intuition to emphasize here is that one can generate potentially infinite amounts (size `n`) of noise # that is a essentially random runif(n = 5) @@ -157,13 +157,13 @@ runif(n = 5) ### `rnorm()` -# `rnorm` is also a continuous distribution, but draws from a Normal distribution -- +# `rnorm` is also a continuous distribution, but draws from a Normal distribution -- # perhaps the most important distribution in statistics. It runs the same way as `runif` rnorm(n = 5) -# To better visualize the difference between the output of `runif` and `rnorm`, let's +# To better visualize the difference between the output of `runif` and `rnorm`, let's # generate lots of each and plot a histogram. from_runif <- runif(n = 1000) @@ -178,29 +178,29 @@ hist(from_rnorm) ## r, p, and d # Each distribution can do more than generate random numbers (the prefix `r`). -# We can compute the cumulative probability by the function `pbinom()`, `punif()`, and -# `pnorm()`. Also the density -- the value of the PDF -- by `dbinom()`, `dunif()` and `dnorm()`. +# We can compute the cumulative probability by the function `pbinom()`, `punif()`, and +# `pnorm()`. Also the density -- the value of the PDF -- by `dbinom()`, `dunif()` and `dnorm()`. ## `set.seed()` -# `R` doesn't have the ability to generate truly random numbers! Random numbers are actually -# very hard to generate. (Think: flipping a coin --> can be perfectly predicted if I know wind -# speed, the angle the coin is flipped, etc.). Some people use random noise in the atmosphere -# or random behavior in quantum systems to generate "truly" (?) random numbers. Conversely, -# R uses deterministic algorithms which take as an input a "seed" and which then perform a -# series of operations to generate a sequence of random-seeming numbers (that is, numbers +# `R` doesn't have the ability to generate truly random numbers! Random numbers are actually +# very hard to generate. (Think: flipping a coin --> can be perfectly predicted if I know wind +# speed, the angle the coin is flipped, etc.). Some people use random noise in the atmosphere +# or random behavior in quantum systems to generate "truly" (?) random numbers. Conversely, +# R uses deterministic algorithms which take as an input a "seed" and which then perform a +# series of operations to generate a sequence of random-seeming numbers (that is, numbers # whose sequence is sufficiently hard to predict). -# Let's think about this another way. Sampling is a stochastic process, so every time you run -# `sample()` or `runif()` you are bound to get a different output (because different random -# seeds are used). This is intentional in some cases but you might want to avoid it in others. -# For example, you might want to diagnose a coding discrepancy by setting the random number +# Let's think about this another way. Sampling is a stochastic process, so every time you run +# `sample()` or `runif()` you are bound to get a different output (because different random +# seeds are used). This is intentional in some cases but you might want to avoid it in others. +# For example, you might want to diagnose a coding discrepancy by setting the random number # generator to give the same number each time. To do this, use the function `set.seed()`. -# In the function goes any number. When you run a sample function in the same command as a -# preceding `set.seed()`, the sampling function will always give you the same sequence of -# numbers. In a sense, the sampler is no longer random (in the sense of unpredictable to use; +# In the function goes any number. When you run a sample function in the same command as a +# preceding `set.seed()`, the sampling function will always give you the same sequence of +# numbers. In a sense, the sampler is no longer random (in the sense of unpredictable to use; # remember: it never was "truly" random in the first place) @@ -208,8 +208,8 @@ set.seed(02138) runif(n = 10) -# The random number generator should give you the exact same sequence of numbers if you precede -# the function by the same seed, +# The random number generator should give you the exact same sequence of numbers if you precede +# the function by the same seed, set.seed(02138) runif(n = 10) @@ -220,112 +220,112 @@ runif(n = 10) ## Exercises -### Census Sampling +### Census Sampling # What can we learn from surveys of populations, and how wrong do we get if our sampling is biased? -# Suppose we want to estimate the proportion of U.S. residents who are non-white (`race != "White"`). -# In reality, we do not have any population dataset to utilize and so we _only see the sample survey_. -# Here, however, to understand how sampling works, let's conveniently use the Census extract in some +# Suppose we want to estimate the proportion of U.S. residents who are non-white (`race != "White"`). +# In reality, we do not have any population dataset to utilize and so we _only see the sample survey_. +# Here, however, to understand how sampling works, let's conveniently use the Census extract in some # cases and pretend we didn't in others. -# (a) First, load `usc2010_001percent.csv` into your R session. After loading the `library(tidyverse)`, -# browse it. Although this is only a 0.01 percent extract, treat this as your population for pedagogical +# (a) First, load `usc2010_001percent.csv` into your R session. After loading the `library(tidyverse)`, +# browse it. Although this is only a 0.01 percent extract, treat this as your population for pedagogical # purposes. What is the population proportion of non-White residents? -# (b) Setting a seed to `1669482`, sample 100 respondents from this sample. What is the proportion of -# non-White residents in this _particular_ sample? By how many percentage points are you off from +# (b) Setting a seed to `1669482`, sample 100 respondents from this sample. What is the proportion of +# non-White residents in this _particular_ sample? By how many percentage points are you off from # (what we labelled as) the true proportion? -# (c) Now imagine what you did above was one survey. What would we get if we did 20 surveys? +# (c) Now imagine what you did above was one survey. What would we get if we did 20 surveys? -# To simulate this, write a loop that does the same exercise 20 times, each time computing a sample proportion. -# Use the same seed at the top, but be careful to position the `set.seed` function such that it generates -# the same sequence of 20 samples, rather than 20 of the same sample. +# To simulate this, write a loop that does the same exercise 20 times, each time computing a sample proportion. +# Use the same seed at the top, but be careful to position the `set.seed` function such that it generates +# the same sequence of 20 samples, rather than 20 of the same sample. -# Try doing this with a `for` loop and storing your sample proportions in a new length-20 vector. -# (Suggestion: make an empty vector first as a container). After running the loop, show a histogram +# Try doing this with a `for` loop and storing your sample proportions in a new length-20 vector. +# (Suggestion: make an empty vector first as a container). After running the loop, show a histogram # of the 20 values. Also what is the average of the 20 sample estimates? -# (d) Now, to make things more real, let's introduce some response bias. The goal here is not to -# correct response bias but to induce it and see how it affects our estimates. Suppose that -# non-White residents are 10 percent less likely to respond to enter your survey than White respondents. -# This is plausible if you think that the Census is from 2010 but you are polling in 2018, and racial -# minorities are more geographically mobile than Whites. Repeat the same exercise in (c) by modeling -# this behavior. +# (d) Now, to make things more real, let's introduce some response bias. The goal here is not to +# correct response bias but to induce it and see how it affects our estimates. Suppose that +# non-White residents are 10 percent less likely to respond to enter your survey than White respondents. +# This is plausible if you think that the Census is from 2010 but you are polling in 2018, and racial +# minorities are more geographically mobile than Whites. Repeat the same exercise in (c) by modeling +# this behavior. -# You can do this by creating a variable, e.g. `propensity`, that is 0.9 for non-Whites and 1 otherwise. +# You can do this by creating a variable, e.g. `propensity`, that is 0.9 for non-Whites and 1 otherwise. # Then, you can refer to it in the propensity argument. -# (e) Finally, we want to see if more data ("Big Data") will improve our estimates. Using the -# same unequal response rates framework as (d), repeat the same exercise but instead of each poll -# collecting 100 responses, we collect 10,000. +# (e) Finally, we want to see if more data ("Big Data") will improve our estimates. Using the +# same unequal response rates framework as (d), repeat the same exercise but instead of each poll +# collecting 100 responses, we collect 10,000. -# (f) Optional - visualize your 2 pairs of 20 estimates, with a bar showing the "correct" -# population average. +# (f) Optional - visualize your 2 pairs of 20 estimates, with a bar showing the "correct" +# population average. -### Conditional Proportions +### Conditional Proportions -# This example is not on simulation, but is meant to reinforce some of the probability -# discussion from math lecture. +# This example is not on simulation, but is meant to reinforce some of the probability +# discussion from math lecture. # Read in the Upshot Siena poll from Fall 2016, `data/input/upshot-siena-polls.csv`. -# In addition to some standard demographic questions, we will focus on one called `vt_pres_2` -# in the csv. This is a two-way presidential vote question, asking respondents who they plan to -# vote for President if the election were held today -- Donald Trump, the Republican, or Hilary Clinton, -# the Democrat, with options for Other candidates as well. For this problem, use the two-way vote question -# rather than the 4-way vote question. +# In addition to some standard demographic questions, we will focus on one called `vt_pres_2` +# in the csv. This is a two-way presidential vote question, asking respondents who they plan to +# vote for President if the election were held today -- Donald Trump, the Republican, or Hilary Clinton, +# the Democrat, with options for Other candidates as well. For this problem, use the two-way vote question +# rather than the 4-way vote question. # (a) Drop the the respondents who answered the November poll (i.e. those for which `poll == "November"`). -# We do this in order to ignore this November population in all subsequent parts of this question -# because they were not asked the Presidential vote question. +# We do this in order to ignore this November population in all subsequent parts of this question +# because they were not asked the Presidential vote question. -# (b) Using the dataset after the procedure in (a), find the proportion of poll respondents -# (those who are in the sample) who support Donald Trump. +# (b) Using the dataset after the procedure in (a), find the proportion of poll respondents +# (those who are in the sample) who support Donald Trump. -# (c) Among those who supported Donald Trump, what proportion of them has a Bachelor's degree or +# (c) Among those who supported Donald Trump, what proportion of them has a Bachelor's degree or # higher (i.e. have a Bachelor's, Graduate, or other Professional Degree)? - - -# (d) Among those who did not support Donald Trump (i.e. including supporters of Hilary Clinton, -# another candidate, or those who refused to answer the question), what proportion of them has a -# Bachelor's degree or higher? -# (e) Express the numbers in the previous parts as probabilities of specified events. Define your -# own symbols: For example, we can let T be the event that a randomly selected respondent in the -# poll supports Donald Trump, then the proportion in part (b) is the probability P(T). +# (d) Among those who did not support Donald Trump (i.e. including supporters of Hilary Clinton, +# another candidate, or those who refused to answer the question), what proportion of them has a +# Bachelor's degree or higher? + +# (e) Express the numbers in the previous parts as probabilities of specified events. Define your +# own symbols: For example, we can let T be the event that a randomly selected respondent in the +# poll supports Donald Trump, then the proportion in part (b) is the probability P(T). -# (f) Suppose we randomly sampled a person who participated in the survey and found that he/she -# had a Bachelor's degree or higher. Given this evidence, what is the probability that the same -# person supports Donald Trump? Use Bayes Rule and show your work -- that is, do not use data or R + +# (f) Suppose we randomly sampled a person who participated in the survey and found that he/she +# had a Bachelor's degree or higher. Given this evidence, what is the probability that the same +# person supports Donald Trump? Use Bayes Rule and show your work -- that is, do not use data or R # to compute the quantity directly. Then, verify this is the case via R. @@ -337,58 +337,54 @@ runif(n = 10) # Write code that will answer the well-known birthday problem via simulation -# The problem is fairly simple: Suppose $k$ people gather together in a room. +# The problem is fairly simple: Suppose $k$ people gather together in a room. # What is the probability at least two people share the same birthday? - -# To simplify reality a bit, assume that (1) there are no leap years, and so there are always -# 365 days in a year, and (2) a given individual's birthday is randomly assigned and independent -# from each other. + +# To simplify reality a bit, assume that (1) there are no leap years, and so there are always +# 365 days in a year, and (2) a given individual's birthday is randomly assigned and independent +# from each other. -# Step 1: Set `k` to a concrete number. Pick a number from 1 to 365 randomly, `k` times +# Step 1: Set `k` to a concrete number. Pick a number from 1 to 365 randomly, `k` times # to simulate birthdays (would this be with replacement or without?). # Your code: -# Step 2: Write a line (or two) of code that gives a `TRUE` or `FALSE` statement of whether +# Step 2: Write a line (or two) of code that gives a `TRUE` or `FALSE` statement of whether # or not at least two people share the same birth date. # Your code: -# Step 3: The above steps will generate a `TRUE` or `FALSE` answer for your event of interest, -# but only for one realization of an event in the sample space. In order to estimate the _probability_ -# of your event happening, we need a "stochastic", as opposed to "deterministic", method. To do this, -# write a loop that does Steps 1 and 2 repeatedly for many times, call that number of times `sims`. -# For each of `sims` iteration, your code should give you a `TRUE` or `FALSE` answer. Code up a way +# Step 3: The above steps will generate a `TRUE` or `FALSE` answer for your event of interest, +# but only for one realization of an event in the sample space. In order to estimate the _probability_ +# of your event happening, we need a "stochastic", as opposed to "deterministic", method. To do this, +# write a loop that does Steps 1 and 2 repeatedly for many times, call that number of times `sims`. +# For each of `sims` iteration, your code should give you a `TRUE` or `FALSE` answer. Code up a way # to store these estimates. # Your code: -# Step 4: Finally, generalize the function further by letting `k` be a user-defined number. -# You have now created a _Monte Carlo simulation_! +# Step 4: Finally, generalize the function further by letting `k` be a user-defined number. +# You have now created a _Monte Carlo simulation_! # Your code: -# Step 5: Generate a table or plot that shows how the probability of sharing a birthday changes -# by `k` (fixing `sims` at a large number like `1000`). Also generate a similar plot that shows -# how the probability of sharing a birthday changes by `sims` (fixing `k` at some arbitrary number -# like `10`). +# Step 5: Generate a table or plot that shows how the probability of sharing a birthday changes +# by `k` (fixing `sims` at a large number like `1000`). Also generate a similar plot that shows +# how the probability of sharing a birthday changes by `sims` (fixing `k` at some arbitrary number +# like `10`). # Your code -# Extra credit: Give an "analytical" answer to this problem, that is an answer through deriving the +# Extra credit: Give an "analytical" answer to this problem, that is an answer through deriving the # mathematical expressions of the probability. - - - - diff --git a/_book/Math-Prefresher-for-Political-Scientists.pdf b/_book/Math-Prefresher-for-Political-Scientists.pdf index 0c649365bf2d693f741de5faa7f3954fabe24239..c84e7e872336f323e2fc59aa66b5158e5a107d7a 100644 GIT binary patch delta 217603 zcmZ6RbzIYL_y5_(Mt66YbZ>Nrv~)`y|x!&9J5S1Md>@SH2*6%R_#8MjwDB#y1$ID=}Py%ok+MAXI z^QrK|iS1d%pLYaXE^7E}pI$n+HFhf4RzJ}Q#Fc%I8>L8ahySGD7XuCMkGnnh>*1cC z`_`+&@R)=jzph#=e*Fl#{O)x5O&KqqSe1?@&k8aWGwHdYu9f3CFmrC5&y~K)X_)bV z=|WNkhQpA1W3COa#OFuUUX2BFL}0E`-=TdvZo8%ByU}=I{sS(1D|2&UKrLFEr>?O{0jlB+24sH8e6jaoL%Swvf7eJYL9*{h+SpeN-O`99a_+&fKe 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