Data Visualization Notes
Kieran Healy
2019-01-02
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 rowsLook at Data
Get Started
Make a Plot
p <- ggplot(data=gapminder)
# We've provided the data, but no mapping, geometry, coordinates, scales or labels
p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y= lifeExp))
# We've provided the data and configured a mapping, but again, no visualizing instructions
p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y= lifeExp)) +
geom_point()
p
We’ve provided enough to start visualizations- the data, a mapping for it, and a geometry. Our plot object is a nested list of instructions for drawing our plot.
But recall the layering. We can apply more geometries in the same graph - like transparencies on an overhead:
p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y= lifeExp)) +
geom_point() +
geom_smooth()
p## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Now we are seeing our data, but the data points are all bunched- GDP is not normally graphed against the age of a population. So we can adjust the scale on X logarithmicly:
p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y= lifeExp)) +
geom_point() +
geom_smooth() +
scale_x_log10()
p## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
And now we are seeing our data a little more clearly. Already we can see a trend where the age of a population increases where the GDP of its country increases. However, the X-axis labels are not very informative at this point. We need to modify the labels, which we can do in the scale argument.
The course took a shortcut to illustrate where you can use a function within a function- and call a function from a library you haven’t even loaded with the special syntax THE PACKAGE::THE FUNCTION. In this example, the function is the dollar function from the scales package:
p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y= lifeExp)) +
geom_point() +
geom_smooth() +
scale_x_log10(labels = scales::dollar)
p## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Mapping Aesthetics versus Setting Them: two very different pieces of code:
p1 <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y= lifeExp, color = continent))
p2 <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y= lifeExp) +
geom_point(color = "purple") + geom_smooth(method = "loess") + scale_x_log10())p1 instructs the aesthetic to express continent as a color in the available visual elements.
p2 assigns a named color to a specific element of the graph.