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title description
Presentation of results from cohort analyses
Cohorts are important scientific sources of health and wellness information. Because of this, how results are presented needs to be carefully considered. The medium of presentation, be it plots or tables, can impact how the findings are seen and consumed. In this chapter we will cover some tips and ways of presenting cohort findings.

Presenting cohort findings is tricky, be careful

type: VideoExercise
key: b1dbfa466f
xp: 50

@projector_key 24be66708a350c97c6e8d86a7b2f7bf4

Adding more details to each model item in a list

type: BulletExercise
key: 921d9175f4
xp: 100

Imagine you've ran several models. You:

  • Scaled predictors to compare estimates.
  • Set confounders and other predictors as baseline age, sex, smoking, and education.
  • Have each predictor with unadjusted and adjusted models (time and subject ID were included in all).
  • Tidied models and exponentiated estimates.

You have 8 models in total, stored as a list. We should add more details to each model to differentiate them from each other. Use map() from purrr to wrangle each model simultaneously. The new code here, term[2], selects the main predictor, which is the second element in the term column.

@pre_exercise_code

load(url("https://assets.datacamp.com/production/repositories/2079/datasets/07241773b32bd45d4d1eb59cb3524e5519e117e7/unadjusted_models_list.rda"))
load(url("https://assets.datacamp.com/production/repositories/2079/datasets/dc0bc2887b3a9f02c65275512b851fd3d0a8ddee/adjusted_models_list.rda"))
library(dplyr)
library(purrr)

type: NormalExercise
key: 0dffe6f56f
xp: 50

@instructions

  • Using map(), add a model column to indicate the models are "Unadjusted".

@hint

  • Add the adjustment column using model = "Unadjusted".

@sample_code

# Add predictor and model type to each list item
unadjusted_models_list <- ___(
    unadjusted_models_list,
  	# .x is purrr for "model goes here"
    ~mutate(.x,
            # This selects predictor, not confounder
            predictor = term[2],
          	# Indicate model "adjustment"
            model = ___
           ))

@solution

# Add predictor and model type to each list item
unadjusted_models_list <- map(
    unadjusted_models_list,
  	# .x is purrr for "model goes here"
    ~mutate(.x,
            # This selects predictor, not confounder
            predictor = term[2],
            # Indicate model "adjustment"
            model = "Unadjusted"
           ))

@sct

success_msg("Nice!")

type: NormalExercise
key: b91f78ffbb
xp: 50

@instructions

  • Do the same thing for the "Adjusted" model list.

@hint

  • Refer to each list object in map with .x.
  • Include the ~ before mutate.

@sample_code

# Add predictor and model type to each list item
adjusted_models_list <- map(
    adjusted_models_list,
  	# .x is purrr for "model goes here"
    ~mutate(.x,
          	# This selects predictor, not confounder
            predictor = term[2],
          	# Indicate model "adjustment"
            model = ___
           ))

@solution

# Add predictor and model type to each list item
adjusted_models_list <- map(
    adjusted_models_list,
  	# .x is purrr for "model goes here"
    ~mutate(.x,
          	# This selects predictor, not confounder
            predictor = term[2],
          	# Indicate model "adjustment"
            model = "Adjusted"
           ))

@sct

success_msg("Excellent! You made use of map to add more details to each model object.")

Combining the list of models into one data frame

type: NormalExercise
key: f30b964cce
xp: 100

The most efficient approach to later plotting and creating tables is to have all models in a single data frame. You've already prepared them a bit, now it's time to combine them together so you can continue working with them.

@instructions

  • Use bind_rows() to combine unadjusted_models_list and adjusted_models_list.
  • Continuing the pipe, add an outcome column with the value "got_cvd".
  • Finally, use filter() to keep only conditions where the predictor equals term and effect equals "fixed".

@hint

  • Filter when predictor and term are the same (predictor == term).

@pre_exercise_code

load(url("https://assets.datacamp.com/production/repositories/2079/datasets/07241773b32bd45d4d1eb59cb3524e5519e117e7/unadjusted_models_list.rda"))
load(url("https://assets.datacamp.com/production/repositories/2079/datasets/dc0bc2887b3a9f02c65275512b851fd3d0a8ddee/adjusted_models_list.rda"))
library(dplyr)
library(purrr)
unadjusted_models_list <- map(
    unadjusted_models_list,
    ~mutate(.x, predictor = term[2], model = "Unadjusted")
)
adjusted_models_list <- map(
    adjusted_models_list,
    ~mutate(.x, predictor = term[2], model = "Adjusted")
)

@sample_code

all_models <- bind_rows(
  		# Combine the two lists of models 
  		___, 
  		___
	) %>% 
    mutate(outcome = ___) %>% 
	# Keep only predictor rows and fixed effects
    filter(___ == , ___ == ___)

all_models

@solution

all_models <- bind_rows(
  		# Combine the two lists of models 
  		unadjusted_models_list, 
  		adjusted_models_list
	) %>% 
    mutate(outcome = "got_cvd") %>% 
	# Keep only predictor rows and fixed effects
    filter(predictor == term, effect == "fixed")

all_models

@sct

success_msg("Well done! You've now bound all models together and continued wrangling them.")

Communicating cohort findings through graphs

type: VideoExercise
key: b1fd983ad5
xp: 50

@projector_key d22c077f314d124f7ab5f28fa0423465

Plotting model estimate and uncertainty

type: TabExercise
key: 69007ab10b
xp: 100

Statistical analysis used on cohort data usually output some time of regression estimate along with a measure of uncertainty (e.g. 95% confidence interval). Sometimes it makes sense to present these results in a table, but often the better approach is to create a figure instead. Figures show magnitude, direction, uncertainty, and comparison of results very effectively.

Create a plot of the unadjusted model results that highlights the estimate and uncertainty of the estimate.

@pre_exercise_code

load(url("https://assets.datacamp.com/production/repositories/2079/datasets/5a385fa413f9f3cfbe95857e390b53ae9966a62d/all_models.rda"))
library(dplyr)
library(ggplot2)

type: NormalExercise
key: 19bfd714ac
xp: 30

@instructions

  • For this exercise, filter to keep model that is equal to "Unadjusted".

@hint

  • Filter should be model == "Unadjusted".

@sample_code

# Keep only unadjusted models
unadjusted_results <- all_models %>% 
    filter(___ == ___)

# Check filtered data
unadjusted_results

@solution

# Keep only unadjusted models
unadjusted_results <- all_models %>% 
    filter(model == "Unadjusted")

# Check filtered data
unadjusted_results

@sct

success_msg("Great!")

type: NormalExercise
key: 4ffd9e23ed
xp: 35

@instructions

  • Set y as predictor, x as estimate, xmin as conf.low, and xmax as conf.high.
  • Add geom_point() and geom_errorbarh() layers.

@hint

  • The errorbarh (horizontal) requires xmin = conf.low, xmax = conf.high.

@sample_code

unadjusted_results <- all_models %>% 
    filter(model == "Unadjusted")

# Create a dot and error bar plot
model_plot <- unadjusted_results %>% 
    ggplot(aes(y = ___, x = ___,
              xmin = ___, xmax = ___)) +
    ___() +
    ___()

# Check the plot
model_plot

@solution

unadjusted_results <- all_models %>% 
    filter(model == "Unadjusted")

# Create a dot and error bar plot
model_plot <- unadjusted_results %>% 
    ggplot(aes(y = predictor, x = estimate, 
               xmin = conf.low, xmax = conf.high)) +
    geom_point() +
    geom_errorbarh()

# Check the plot
model_plot

@sct

success_msg("Great!")

type: NormalExercise
key: f5fe48b671
xp: 35

@instructions

  • Use geom_vline() to add a vertical line, setting xintercept as 1 for the "center line".

@hint

  • Set xintercept = 1 for the center line.

@sample_code

unadjusted_results <- all_models %>% 
    filter(model == "Unadjusted")

# Create a dot and error bar plot
model_plot <- unadjusted_results %>% 
    ggplot(aes(y = predictor, x = estimate, 
               xmin = conf.low, xmax = conf.high)) +
    geom_point() +
    geom_errorbarh() +
    # Add vertical line
    ___(xintercept = ___)

# Check the plot
model_plot

@solution

unadjusted_results <- all_models %>% 
    filter(model == "Unadjusted")

# Create a dot and error bar plot
model_plot <- unadjusted_results %>% 
    ggplot(aes(y = predictor, x = estimate, 
               xmin = conf.low, xmax = conf.high)) +
    geom_point() +
    geom_errorbarh() +
    # Add vertical line
    geom_vline(xintercept = 1)

# Check the plot
model_plot

@sct

success_msg("Excellent! See how this graph shows the uncertainty around individual model estimates? This is an effective way of presenting results from models.")

Create a more polished plot

type: NormalExercise
key: f6857cd149
xp: 100

Now that we've created this plot, let's polish it up. We want it to be "publication quality", since we'll eventually present this figure to others.

As with the previous exercise, use the unadjusted_results data frame you created to plot the findings. This time, make the plot more polished and presentable.

@instructions

  • Set the point size to 3, the error bar height to 0.1, and the linetype to "dotted".
  • Include appropriate axis labels ("Predictors" on the y and "Odds Ratio (95% CI)" on the x). Recall that CI means confidence interval.
  • Set the theme to theme_classic().

@hint

  • The labels should be of the form x = "Axis Label" (e.g. for the x-axis).

@pre_exercise_code

load(url("https://assets.datacamp.com/production/repositories/2079/datasets/5a385fa413f9f3cfbe95857e390b53ae9966a62d/all_models.rda"))
library(dplyr)
library(ggplot2)
unadjusted_results <- all_models %>% 
    filter(model == "Unadjusted")

@sample_code

# Make the plot more polished
model_plot <- unadjusted_results %>% 
    ggplot(aes(y = predictor, x = estimate, xmin = conf.low, xmax = conf.high)) +
    geom_point(size = ___) +
    geom_errorbarh(height = ___) +
    geom_vline(xintercept = 1, linetype = ___) +
    labs(y = ___, x = ___) +
	# Set the theme
    ___()
model_plot

@solution

# Make the plot more polished
model_plot <- unadjusted_results %>% 
    ggplot(aes(y = predictor, x = estimate, xmin = conf.low, xmax = conf.high)) +
    geom_point(size = 3) +
    geom_errorbarh(height = 0.1) +
    geom_vline(xintercept = 1, linetype = "dotted") +
    labs(y = "Predictors", x = "Odds ratio (95% CI)") +
	# Set the theme
    theme_classic()
model_plot

@sct

success_msg("Amazing! You have a very nice figure now that is ready to be presented to others! There are other themes to use if you don't like this one. Check out the package ggthemes for more.")

Visualize unadjusted and adjusted model results

type: NormalExercise
key: 163d46569b
xp: 100

The STROBE best practices indicate to show both "crude" (unadjusted) and adjusted model results. Showing both can be informative and insightful into the research question. Create a plot of your results showing both unadjusted and adjusted models. Do the same steps as in the previous exercise for creating the plot.

@instructions

  • Use the all_models data frame this time.
  • Using facet_grid() to split the plot by rows with the model variable (called inside vars()).

@hint

  • The faceting variable should be called as vars(model).

@pre_exercise_code

load(url("https://assets.datacamp.com/production/repositories/2079/datasets/5a385fa413f9f3cfbe95857e390b53ae9966a62d/all_models.rda"))
library(ggplot2)
library(magrittr)

@sample_code

# Show results of both adjusted and unadjusted
plot_all_models <- ___ %>% 
    ggplot(aes(y = predictor, x = estimate, xmin = conf.low, xmax = conf.high)) +
    geom_point(size = 3) +
    geom_errorbarh(height = 0.1) +
    geom_vline(xintercept = 1, linetype = "dotted") +
    # Facet plot by model
    ___(rows = vars(___)) +
    labs(y = "Predictors", x = "Odds ratio (95% CI)") +
    theme_classic()
plot_all_models

@solution

# Show results of both adjusted and unadjusted
plot_all_models <- all_models %>% 
    ggplot(aes(y = predictor, x = estimate, xmin = conf.low, xmax = conf.high)) +
    geom_point(size = 3) +
    geom_errorbarh(height = 0.1) +
    geom_vline(xintercept = 1, linetype = "dotted") +
    # Split plot by model
    facet_grid(rows = vars(model)) +
    labs(y = "Predictors", x = "Odds ratio (95% CI)") +
    theme_classic()
plot_all_models

@sct

success_msg("Great job! You can see that there are large differences in some of the results between unadjusted and adjusted models.")

Use tables effectively to show your results

type: VideoExercise
key: fceefd27c4
xp: 50

@projector_key 8ecd82ad25933a2add144c7e10605b7d

Present the basic characteristics of the cohort

type: TabExercise
key: cb7b2cfe06
xp: 100

A classic use for tables is showing the basic characteristics of a cohort dataset, as there are diverse data types and summary statistics that need to be shown. Including a basic participant characteristics table is part of the STROBE best practices. This table can be quite informative for others when they interpret your analysis results.

Using the carpenter package, create a table showing summary statistics for each data collection visit.

@pre_exercise_code

load(url("https://assets.datacamp.com/production/repositories/2079/datasets/25722a0770c3779d3290fd5628362c56a9d7d21b/tidied_framingham.rda"))
library(carpenter)
library(dplyr)

type: NormalExercise
key: c653bbf2fa
xp: 25

@instructions

  • Set "followup_visit_number" for the header argument of outline_table().

@hint

  • Use quotes around followup_visit_number.

@sample_code

# Create a table of summary statistics
characteristics_table <- tidied_framingham %>% 
	# These discrete variables are numeric, but must be factors
	mutate_at(vars(followup_visit_number, got_cvd), as.factor) %>% 
	# Set followup visit number as table column
    outline_table(header = ___) 

# Check the table
characteristics_table

@solution

# Create a table of summary statistics
characteristics_table <- tidied_framingham %>% 
	# These discrete variables are numeric, but must be factors
    mutate_at(vars(followup_visit_number, got_cvd), as.factor) %>% 
	# Set followup visit number as table column
    outline_table(header = "followup_visit_number") 

# Check the table
characteristics_table

@sct

success_msg("Nice!")

type: NormalExercise
key: 3f50416623
xp: 25

@instructions

  • Add a row for the "got_cvd", "sex", and "education_combined" variables, using stat_nPct for the stat argument.

@hint

  • The variables should be quoted, e.g. "sex".

@sample_code

# Create a table of summary statistics
characteristics_table <- tidied_framingham %>% 
    mutate_at(vars(followup_visit_number, got_cvd), as.factor) %>% 
    outline_table(header = "followup_visit_number") %>% 
    # Show n (%) for discrete variables as rows
    add_rows(c(___, ___, ___), stat = ___)

# Check the table
characteristics_table

@solution

# Create a table of summary statistics
characteristics_table <- tidied_framingham %>% 
    mutate_at(vars(followup_visit_number, got_cvd), as.factor) %>% 
    outline_table(header = "followup_visit_number") %>% 
    # Show n (%) for discrete variables as rows
    add_rows(c("got_cvd", "sex", "education_combined"), stat = stat_nPct)

# Check the table
characteristics_table

@sct

success_msg("Great!")

type: NormalExercise
key: cf370dcbc3
xp: 25

@instructions

  • Add rows for "total_cholesterol", "body_mass_index", and "participant_age" using stat_medianIQR for the stat argument.

@hint

  • The variables need to be surrounded by quotes, just like the function above.

@sample_code

# Create a table of summary statistics
characteristics_table <- tidied_framingham %>% 
    mutate_at(vars(followup_visit_number, got_cvd), as.factor) %>% 
    outline_table(header = "followup_visit_number") %>% 
    add_rows(c("got_cvd", "sex", "education_combined"), stat = stat_nPct) %>% 
    # Show median (range) for continuous variables
    add_rows(c(___, ___, ___), stat = ___)

# Check the table
characteristics_table

@solution

# Create a table of summary statistics
characteristics_table <- tidied_framingham %>% 
    mutate_at(vars(followup_visit_number, got_cvd), as.factor) %>% 
    outline_table(header = "followup_visit_number") %>% 
    add_rows(c("got_cvd", "sex", "education_combined"), stat = stat_nPct) %>% 
    # Show median (range) for continuous variables
    add_rows(c("total_cholesterol", "body_mass_index", "participant_age"), stat = stat_medianIQR)

# Check the table
characteristics_table

@sct

success_msg("Great!")

type: NormalExercise
key: 81f9f129af
xp: 25

@instructions

  • Rename the table "header" to "Measures", "Baseline", "Second followup", and "Third followup", then build_table() to markdown format.

@hint

  • The new column headers should be given as a character vector.

@sample_code

characteristics_table <- tidied_framingham %>% 
    mutate_at(vars(followup_visit_number, got_cvd), as.factor) %>% 
    outline_table(header = "followup_visit_number") %>% 
    add_rows(c("got_cvd", "sex", "education_combined"), stat = stat_nPct) %>% 
    add_rows(c("participant_age", "body_mass_index", "total_cholesterol"), stat = stat_medianIQR) %>% 
    # Rename headers to better titles
    renaming("header", c(___, ___, ___, ___))

# Build the table and convert to markdown form
build_table(___)

@solution

characteristics_table <- tidied_framingham %>% 
    mutate_at(vars(followup_visit_number, got_cvd), as.factor) %>% 
    outline_table(header = "followup_visit_number") %>% 
    add_rows(c("got_cvd", "sex", "education_combined"), stat = stat_nPct) %>% 
    add_rows(c("participant_age", "body_mass_index", "total_cholesterol"), stat = stat_medianIQR) %>% 
    # Rename headers to better titles
    renaming("header", c("Measures", "Baseline", "Second followup", "Third followup"))

# Build the table and convert to markdown form
build_table(characteristics_table)

@sct

success_msg("Nice job! You've gotten the data formatted as a table for easy inclusion in a document or report and have provided basic participant characteristics from each cohort visit.")

Supplemental tables of raw numbers for results

type: TabExercise
key: e1034d1d86
xp: 100

While the main messaging and presentation of results should emphasize figures over tables, often it is useful to other researchers (especially those doing meta-analyses or aggregating results) that the raw model results be given as well. Here we can use tables to give this data, as a supplement to the figure.

Provide the estimates and confidence intervals of the unadjusted and adjusted model results in a table format that you could include in a document or report. The packages glue, stringr, and knitr have been loaded.

@pre_exercise_code

load(url("https://assets.datacamp.com/production/repositories/2079/datasets/5a385fa413f9f3cfbe95857e390b53ae9966a62d/all_models.rda"))
library(knitr)
library(glue)
library(dplyr)
library(tidyr)
library(stringr)

type: NormalExercise
key: f26d103841
xp: 35

@instructions

  • Round the estimate, conf.low, and conf.high to 2 digits using the function round (don't use with ()).

@hint

  • mutate_at() takes variables (as the first argument) inside vars() and applies a function like round as the second argument.

@sample_code

# Prepare the results for the table
table_model_results <- all_models %>% 
    # Round values of variables to 3
    mutate_at(vars(___, ___, ___), ___, digits = ___)

# View wrangled data
table_model_results

@solution

# Prepare the results for the table
table_model_results <- all_models %>% 
    # Round values of variables to 3
    mutate_at(vars(estimate, conf.low, conf.high), round, digits = 2)

# View wrangled data
table_model_results

@sct

success_msg("Great!")

type: NormalExercise
key: 3934cab889
xp: 35

@instructions

  • Use glue() to create a new variable in the form estimate (conf.low, conf.high), then replace underscores with spaces in predictor with str_replace_all().

@hint

  • The estimate and CI variables should be placed inside the {} in glue().

@sample_code

# Prepare the results for the table
table_model_results <- all_models %>% 
    mutate_at(vars(estimate, conf.low, conf.high), round, digits = 2) %>% 
    # Use glue function to combine variables
    mutate(estimate_ci = glue("{___} ({___}, {___})"),
           # Underscores to spaces in predictor
           predictor = str_replace_all(___, "_", " "))

# View wrangled data
table_model_results

@solution

# Prepare the results for the table
table_model_results <- all_models %>% 
    mutate_at(vars(estimate, conf.low, conf.high), round, digits = 2) %>% 
    # Use glue function to combine variables
    mutate(estimate_ci = glue("{estimate} ({conf.low}, {conf.high})"),
           # Underscores to spaces in predictor
           predictor = str_replace_all(predictor, "_", " "))

# View wrangled data
table_model_results

@sct

success_msg("Amazing!")

type: NormalExercise
key: f015c03292
xp: 30

@instructions

  • Keeping model, predictor, and estimate_ci variables, use spread on model and estimate_ci.
  • Create the formatted table with kable().

@hint

  • spread takes two arguments: 1) the current discrete column (model) that will be the new columns names, and 2) the values (estimate_ci) that will be in the new columns.

@sample_code

# Prepare the results for the table
table_model_results <- all_models %>% 
    mutate_at(vars(estimate, conf.low, conf.high), round, digits = 2) %>% 
    mutate(estimate_ci = glue("{estimate} ({conf.low}, {conf.high})"),
           predictor = predictor %>% 
               str_remove("_scaled") %>% 
               str_replace_all("_", " ")) %>%
    # Keep then spread variables for final table
    select(___, ___, ___) %>% 
    spread(___, ___)

# Create a Markdown table
kable(___, caption = "Estimates and 95% CI from all models.")

@solution

# Prepare the results for the table
table_model_results <- all_models %>% 
    mutate_at(vars(estimate, conf.low, conf.high), round, digits = 2) %>% 
    mutate(estimate_ci = glue("{estimate} ({conf.low}, {conf.high})"),
           predictor = predictor %>% 
               str_remove("_scaled") %>% 
               str_replace_all("_", " ")) %>%
    # Keep then spread variables for final table
    select(model, predictor, estimate_ci) %>% 
    spread(model, estimate_ci)

# Create a Markdown table
kable(table_model_results, caption = "Estimates and 95% CI from all models.")

@sct

success_msg("Amazing! You've wrangled the data and prepared it to be presented as a table! You can now easily add this to your manuscript (especially easy if you use R Markdown).")