Intro2_RMarkdown.Rmd

---
title: "CodeChunks and more"
author: "Thomas Koentges"
date: "22/11/2021"
output: html_document
params:
  x_axis: "price"
  y_axis: "carat"
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, collapse = T)
library(tidyverse)
comma <- function(x) format(x, digits = 2, big.mark = ",")

smaller <- diamonds %>% 
  filter(carat <= 2.5)
```

## Code Chunks

You can insert new code chunks in 3 ways:

1. *CTRL/CMD + Alt/Option + I*
2. Click +C
3. Type opening and closing ```

## How a code chunk looks

````r
```{r chunk_name, echo=F}`r ''`
# if you read this code in the source file, this is not a code chunk!
library(dplyr)
mtcars %>% 
  group_by(cyl) %>% 
  summarize(n = n(), mean = mean(mpg))
```
````

```{r chunk_name, echo=F, eval=F}
# this would be a code chunk! But the code is neither printed (echo = F) nor executed (eval = F)
library(dplyr)
mtcars %>% 
  group_by(cyl) %>% 
  summarize(n = n(), mean = mean(mpg))
```

### How it works

```{r}
mtcars %>% 
  group_by(cyl) %>% 
  summarize(n = n(), mean = mean(mpg))
```


### Important chunk options

- `eval = FALSE`

- `include = FALSE`

- `echo = FALSE`

- `message = FALSE` or `warning = FALSE`

- `results = 'hide'` and `fig.show = 'hide'`

- `error = TRUE` 

- `cache = TRUE`

## Cache example

```{r iris_data, cache=TRUE}
my_data <- iris 
#  filter(Species != "virginica")
```

```{r iris_plot, cache = T}
my_plot <- my_data %>%
  ggplot(aes(Sepal.Width, col = Species)) +
  geom_boxplot()
```

```{r iris_vis}
my_plot
```

## Inline R

You can write R code inline to calculate variables. For instance: We have data about `r nrow(diamonds)` diamonds. Only `r nrow(diamonds) - nrow(smaller)` are larger than 2.5 carats. 

## Exercises

1. Make the numbers above nicer using the `comma()` function.

2. Add a section that explores how diamond sizes vary by cut, colour, and clarity. Assume you’re writing a report for someone who doesn’t know R, and instead of setting echo = FALSE on each chunk, set a global option.

3. Download diamond-sizes.Rmd from https://github.com/hadley/r4ds/tree/master/rmarkdown. Add a section that describes the largest 20 diamonds, including a table that displays their most important attributes.

4. Set up a network of chunks where d depends on c and b, and both b and c depend on a. Have each chunk print lubridate::now(), set cache = TRUE, then verify your understanding of caching.

## Other interesting things

:::: {.columns}
::: {.column width="40%"}

#### Info in 2-columns

On the right side is a very informative figure!

:::
::: {.column width="60%"}

```{r diamond-plot, fig.cap="Something useful about diamonds", fig.width=4, fig.height=3, echo=F}
ggplot(diamonds, aes_string(params$x_axis, params$y_axis)) + geom_point()
```


:::
::::