Data Visualization Notes

This is a starter RMarkdown template to accompany Data Visualization (Princeton University Press, 2019). You can use it to take notes, write your code, and produce a good-looking, reproducible document that records the work you have done. At the very top of the file is a section of metadata, or information about what the file is and what it does. The metadata is delimited by three dashes at the start and another three at the end. You should change the title, author, and date to the values that suit you. Keep the output line as it is for now, however. Each line in the metadata has a structure. First the key (“title”, “author”, etc), then a colon, and then the value associated with the key.

This is an RMarkdown File

Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. A code chunk is a specially delimited section of the file. You can add one by moving the cursor to a blank line choosing Code > Insert Chunk from the RStudio menu. When you do, an empty chunk will appear:

Code chunks are delimited by three backticks (found to the left of the 1 key on US and UK keyboards) at the start and end. The opening backticks also have a pair of braces and the letter r, to indicate what language the chunk is written in. You write your code inside the code chunks. Write your notes and other material around them, as here.

Before you Begin

To install the tidyverse, make sure you have an Internet connection. Then manually run the code in the chunk below. If you knit the document if will be skipped. We do this because you only need to install these packages once, not every time you run this file. Either knit the chunk using the little green “play” arrow to the right of the chunk area, or copy and paste the text into the console window.

## This code will not be evaluated automatically.
## (Notice the eval = FALSE declaration in the options section of the
## code chunk)

my_packages <- c("tidyverse", "broom", "coefplot", "cowplot",
                 "gapminder", "GGally", "ggrepel", "ggridges", "gridExtra",
                 "here", "interplot", "margins", "maps", "mapproj",
                 "mapdata", "MASS", "quantreg", "rlang", "scales",
                 "survey", "srvyr", "viridis", "viridisLite", "devtools")

install.packages(my_packages, repos = "http://cran.rstudio.com")

Set Up Your Project and Load Libraries

To begin we must load some libraries we will be using. If we do not load them, R will not be able to find the functions contained in these libraries. The tidyverse includes ggplot and other tools. We also load the socviz and gapminder libraries.

Notice that here, the braces at the start of the code chunk have some additional options set in them. There is the language, r, as before. This is required. Then there is the word setup, which is a label for your code chunk. Labels are useful to briefly say what the chunk does. Label names must be unique (no two chunks in the same document can have the same label) and cannot contain spaces. Then, after the comma, an option is set: include=FALSE. This tells R to run this code but not to include the output in the final document.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

gapminder
## # A tibble: 1,704 × 6
##    country     continent  year lifeExp      pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Afghanistan Asia       1952    28.8  8425333      779.
##  2 Afghanistan Asia       1957    30.3  9240934      821.
##  3 Afghanistan Asia       1962    32.0 10267083      853.
##  4 Afghanistan Asia       1967    34.0 11537966      836.
##  5 Afghanistan Asia       1972    36.1 13079460      740.
##  6 Afghanistan Asia       1977    38.4 14880372      786.
##  7 Afghanistan Asia       1982    39.9 12881816      978.
##  8 Afghanistan Asia       1987    40.8 13867957      852.
##  9 Afghanistan Asia       1992    41.7 16317921      649.
## 10 Afghanistan Asia       1997    41.8 22227415      635.
## # … with 1,694 more rows

The remainder of this document contains the chapter headings for the book, and an empty code chunk in each section to get you started. Try knitting this document now by clicking the “Knit” button in the RStudio toolbar, or choosing File > Knit Document from the RStudio menu.

Get Started

url <- "https://cdn.rawgit.com/kjhealy/viz-organdata/master/organdonation.csv"

organs <- read_csv(file = url)
## Rows: 238 Columns: 21
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (7): country, world, opt, consent.law, consent.practice, consistent, ccode
## dbl (14): year, donors, pop, pop.dens, gdp, gdp.lag, health, health.lag, pub...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
library(gapminder)
gapminder
## # A tibble: 1,704 × 6
##    country     continent  year lifeExp      pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Afghanistan Asia       1952    28.8  8425333      779.
##  2 Afghanistan Asia       1957    30.3  9240934      821.
##  3 Afghanistan Asia       1962    32.0 10267083      853.
##  4 Afghanistan Asia       1967    34.0 11537966      836.
##  5 Afghanistan Asia       1972    36.1 13079460      740.
##  6 Afghanistan Asia       1977    38.4 14880372      786.
##  7 Afghanistan Asia       1982    39.9 12881816      978.
##  8 Afghanistan Asia       1987    40.8 13867957      852.
##  9 Afghanistan Asia       1992    41.7 16317921      649.
## 10 Afghanistan Asia       1997    41.8 22227415      635.
## # … with 1,694 more rows
p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap, y = lifeExp))
p + geom_point()

Make a Plot

3.4 Build your plots layer by layer

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y=lifeExp))
p + geom_smooth()
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

p + geom_point() + geom_smooth() 
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

p + geom_point() + geom_smooth(method = "lm") 
## `geom_smooth()` using formula 'y ~ x'

p + geom_point() +
    geom_smooth(method = "gam") +
    scale_x_log10()
## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'

p + geom_point() +
    geom_smooth(method = "gam") +
    scale_x_log10(labels = scales::dollar)
## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'

3.5 Mapping aesthetics vs setting them

p <-  ggplot(data = gapminder,
             mapping = aes(x = gdpPercap,
                            y = lifeExp,
                            color = continent))
p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y = lifeExp,
                          color = "purple"))
p + geom_point() +
    geom_smooth(method = "loess") +
    scale_x_log10()
## `geom_smooth()` using formula 'y ~ x'

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y = lifeExp))
p + geom_point(color = "purple") +
    geom_smooth(method = "loess") +
    scale_x_log10()
## `geom_smooth()` using formula 'y ~ x'

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y = lifeExp)) 
p + geom_point(alpha = 0.3) +
    geom_smooth(color = "orange", se = FALSE, size = 8, method = "lm") +
    scale_x_log10()
## `geom_smooth()` using formula 'y ~ x'

p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y=lifeExp))
p + geom_point(alpha = 0.3) +
    geom_smooth(method = "gam") +
    scale_x_log10(labels = scales::dollar) +
    labs(x = "GDP Per Capita", y = "Life Expectancy in Years",
         title = "Economic Growth and Life Expectancy",
         subtitle = "Data points are country-years",
         caption = "Source: Gapminder.")
## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y = lifeExp,
                          color = continent))
p + geom_point() +
    geom_smooth(method = "loess") +
    scale_x_log10()
## `geom_smooth()` using formula 'y ~ x'

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y = lifeExp,
                          color = continent,
                          fill = continent))
p + geom_point() +
    geom_smooth(method = "loess") +
    scale_x_log10()
## `geom_smooth()` using formula 'y ~ x'

3.6 Aesthetics can be mapped per geom

p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp))
p + geom_point(mapping = aes(color = continent)) +
    geom_smooth(method = "loess") +
    scale_x_log10()
## `geom_smooth()` using formula 'y ~ x'

p <- ggplot(data = gapminder,
            mapping = aes(x = gdpPercap,
                          y = lifeExp))
p + geom_point(mapping = aes(color = log(pop))) +
    scale_x_log10()

3.7 Save your work

knitr::opts_chunk$set(fig.width=8, fig.height=5) 
p + geom_point()

ggsave(filename = "my_figure.png")
## Saving 12 x 9 in image
ggsave(filename = "my_figure.pdf")
## Saving 8 x 5 in image
p_out <- p + geom_point() +
    geom_smooth(method = "loess") +
    scale_x_log10()
ggsave("my_figure.pdf", plot = p_out)
## Saving 8 x 5 in image
## `geom_smooth()` using formula 'y ~ x'
# here() starts at the directory where the project is created.
# Create the "figures" folder in the directory where the project is created. 
ggsave("lifexp_vs_gdp_gradient.pdf", plot = p_out)
## Saving 8 x 5 in image
## `geom_smooth()` using formula 'y ~ x'
ggsave("lifexp_vs_gdp_gradient.pdf",
       plot = p_out, height = 8, width = 10, units = "in")
## `geom_smooth()` using formula 'y ~ x'