title	description
The Magic of ggplot2	Learn how ggplot2 turns variables into statistical graphics

--- type:NormalExercise lang:r xp:100 skills:1 key:8323fcbca1

Quick Data Frame Introduction

Before we start: please join the RBootcamp OHSU Group!

A data.frame is basically a table-like format which has the following properties:

Columns can each have a different type (numeric, character, boolean, factor)
Columns are called "variables"
Rows correspond to a single observation (ideally)
Can be subset or filtered based on criteria

Individual variables within a data.frame can be accessed with the $ operator (such as gap1992$pop). We won't use this very often, as the tidyverse lets us access the variables without it, as you'll see.

*** =instructions Run colnames() and head() on the gap1992 data to see what's in each column. Then see how many rows there are in the dataset using nrow(). Run these in console before you submit your answer.

*** =pre_exercise_code

library(dplyr)
library(gapminder)
library(ggplot2)
gap1992 <- gapminder %>% filter(year == 1992)

*** =sample_code

##run head on gap1992
head(----)
##run colnames here on gap1992
colnames(----)
##run nrow() on gap1992
nrow(-----)

*** =solution

##run head on gap1992
head(gap1992)
##run colnames here on gap1992
colnames(gap1992)
##run nrow() on gap1992
nrow(gap1992)

*** =sct

success_msg("Great! You learned some basics about `data.frame`s! Let's move on.")
test_function("colnames", incorrect_msg = "did you use colnames(gap1992)?")
test_function("nrow", incorrect_msg = "did you use nrow(gap1992)")

--- type:MultipleChoiceExercise lang:r xp:100 skills:1 key:d599f92ec8

Thinking about aesthetics

Now that we've learned a little about the data.frame, we can get to the fun part: making graphs.

The first thing we are going to is think about how we represent variables in a plot.

How do we visually represent a variable in our dataset? Take a look at this graph. What variable is mapped to y, and what is mapped to x, and what is mapped to color?

***=pre_exercise_code

library(dplyr)
library(gapminder)
library(ggplot2)
gap1992 <- gapminder %>% filter(year == 1992)

ggplot(gap1992, aes(x = log(gdpPercap), y = lifeExp, size=pop, color=continent)) +
  geom_point() + ggtitle("Gapminder for 1992")

*** =instructions

x = gdpPercap, y = log(lifeExp), color = continent
x = continent, y = year, color = pop
y = lifeExp, x = log(gdpPercap), color = continent

*** =hint Look at the y-axis.

*** =sct

msg1 = "You have things reversed, and you're taking the log of the wrong variable"
msg2 = "Wrong variables. Go back and look at what's being mapped"
msg3 = "Correct! We are displaying lifeExp as our y variable and log(gdpPercap) as our x variable"

test_mc(correct = 3, feedback_msgs=c(msg1, msg2, msg3))

--- type:NormalExercise lang:r xp:100 skills:1 key:bfe1375688

Mapping variables to produce geometric plots

A statistical graphic consists of:

A mapping of variables in data to
aes()thetic attributes of
geom_etric objects.

In code, this is translated as:

ggplot(data = gap1992, mapping = aes(x = log(gdpPercap), y=log(pop))) +
  geom_point()

Let's take the above example code apart. A ggplot2 call always starts with the ggplot() function. In this function, we need two things:

data - in this case, gap1992.
mapping - An aesthetic mapping, using the aes() function.

In order to map our variables to aesthetic properties, we will need to use aes(), which is our aes()thetic mapping function. In our example, we map x to log(gdpPercap) and y to log(pop).

Finally, we can superimpose our geometry on the plot using geom_point().

*** =instructions Based on the graph, map the appropriate variables to the x, and y aesthetics. Run your plot. Remember, you can try plots out in the console before you submit your answer.

*** =hint Look at the graph. If you need the variable names, you can always use head() or colnames() on the gap1992 dataset.

*** =pre_exercise_code

library(dplyr)
library(gapminder)
library(ggplot2)
gap1992 <- gapminder %>% filter(year == 1992)

ggplot(gap1992, aes(x = log(gdpPercap), y = lifeExp, size=pop, color=continent)) +
  geom_point() + ggtitle("Gapminder for 1992")

*** =sample_code

ggplot(data = gap1992, 
    mapping = aes(
      x = , 
      y =  
      )) + 
geom_point()

*** =solution

ggplot(data=gap1992, 
    mapping = aes(
      x = log(gdpPercap), 
      y = lifeExp 
      )) + 
geom_point()

*** =sct

success_msg("Wunderbar! Now you're on your way to recreating that gapminder plot")
test_ggplot(check_aes = TRUE, aes_fail_msg = "Not quite. Make sure you're mapping the right variables to the right aesthetics.")

--- type:MultipleChoiceExercise lang:r xp:50 skills:1 key:e507076f4e

More about aes

For geom_point(), there are lots of other aesthetics. The important thing to know is that aesthetics are properties of the geom. If you need to know the aesthetics that you can map to a geom, you can always use help() (such as help(geom_point)).

I'd ask you to look at help(geom_point), but the documentation is not correct on Datacamp. Instead, look here: http://ggplot.yhathq.com/docs/geom_point.html and look at all the aesthetic mappings.

Which of the following is not a mappable aesthetic to geom_point()?

*** =instructions

x
shape
linetype

*** =pre_exercise_code

library(ggplot2)

*** =sct

success_msg("Great! Now you know where to look for mappable aesthetics.")
msg1 = "Nope. This is a mappable aesthetic to `geom_point().`"
msg3 = "Correct. `linetype` is not mappable to `geom_point()`. Points don't have a `linetype`, do they?"
test_mc(correct = 3, feedback_msgs=c(msg1, msg1, msg3))

--- type:NormalExercise lang:r xp:100 skills:1 key:f0a09d682e

Points versus lines

The great thing about ggplot2 is that it's easy to swap representations. Instead of x-y points, we can plot the data as a line graph by swapping geom_line() for geom_point().

*** =instructions First run the code to see the plot with points. Change the geom_point() in the following graph to geom_line(). What happened? How did the visual presentation of the data change?

*** =pre_exercise_code

library(dplyr)
library(gapminder)
library(ggplot2)
gap1992 <- gapminder %>% filter(year == 1992)

*** =sample_code

ggplot(gap1992, aes(x = log(gdpPercap), y = lifeExp, color=continent)) +
  geom_point()

*** =solution

ggplot(gap1992, aes(x = log(gdpPercap), y = lifeExp, color=continent)) +
  geom_line()

*** =sct

success_msg("Great! Now you know how to swap representations in ggplot2. Let's move on.")
test_function("geom_line", incorrect_msg="You need to change the geom.")

--- type:NormalExercise lang:r xp:100 skills:1 key:13ea4fbf3d

Geoms are layers on a ggplot

We are not restricted to a single geom on a graph! You can think of geoms as layers on a graph. Thus, we can use the + symbol to add geoms to our base ggplot() statement.

*** =instructions Add both geom_line() and geom_point() to the following ggplot. Are the results what you expected?

*** =pre_exercise_code

library(dplyr)
library(gapminder)
library(ggplot2)
gap1992 <- gapminder %>% filter(year == 1992)

*** =sample_code

ggplot(gap1992, aes(x = log(gdpPercap), y = lifeExp, color=continent)) +
## add code here

*** =solution

ggplot(gap1992, aes(x = log(gdpPercap), y = lifeExp, color=continent)) +
## add code here
  geom_line() + geom_point()

*** =sct

test_function("geom_line", incorrect_msg="you need to add geom_line()")
test_function("geom_point", incorrect_msg="you need to add geom_point()")

--- type:MultipleChoiceExercise lang:r xp:100 skills:1 key:349a622cb7

Quick review about ggplot2

What does the + in a ggplot statement do?

For example:

ggplot(gap1992, aes(x = log(gdpPercap), y = lifeExp, color=continent)) +
  geom_line() + geom_point()

*** =instructions

adds one data.frame to another data.frame
allows you to chain data and geoms together into a single statistical graphic
allows you to add variables together in a data.frame

*** =hint + combines things, but doesn't add them together

*** =sct

msg1 = "This is not the case. Go back and look at the ggplot code."
msg2 = "Correct! This is how we can add data and layer geoms together"
msg3 = "Look at the ggplot code and see if we are manipulating data or not. Are we?"
test_mc(correct = 2, feedback_msgs=c(msg1, msg2, msg3))

--- type:NormalExercise lang:r xp:300 skills:1 key:01ef5c54c5

Final Challenge: Recreate this Gapminder Plot

Your final challenge is to completely recreate this graph using the gap1992 data.

***=pre_exercise_code

library(dplyr)
library(gapminder)
library(ggplot2)
gap1992 <- gapminder %>% filter(year == 1992)

ggplot(gap1992, aes(x = log(gdpPercap), y = lifeExp, size=pop, color=continent)) +
  geom_point() + ggtitle("Gapminder for 1992")

*** =instructions

If you need to remember variable names, you can always call head(gap1992) or colnames(gap1992) in the console.
Recreate the above graphic by mapping the right variables to the right aesthetic elements. Remember, you can try plots out in the console before you submit your answer.

*** =sample_code

ggplot(gap1992, aes(x = , 
    y = , 
    color = ,
    size =
    )) + ggtitle("Gapminder for 1992") +

*** =solution

ggplot(gap1992, aes(x = log(gdpPercap), 
    y = lifeExp, 
    color = continent,
    size = pop
    )) + ggtitle("Gapminder for 1992") + 
    geom_point()

*** =sct

success_msg("Now you know the basics of ggplot and aesthetics. Congrats!")
test_ggplot(check_aes=TRUE, aes_fail_msg = "Not quite. Go back and map the variables to the correct aesthetics.")

--- type:NormalExercise lang:r xp:0 skills:1 key:fe7e851b1f

What you learned in this chapter

Basic ggplot2 syntax.
Plotting x-y data using ggplot2 using both geom_point() and geom_bar().
Mapping variables in a dataset to visual properties using aes()
geoms correspond to layers in a graph.
That ggplot2 can make some pretty cool graphs
That you can do this!

*** =instructions Just move on to the next chapter! (CTRL+K)

*** =hint

*** =pre_exercise_code

*** =sample_code

*** =solution

*** =sct

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chapter1.md

chapter1.md

Quick Data Frame Introduction

Thinking about aesthetics

Mapping variables to produce geometric plots

More about aes

Points versus lines

Geoms are layers on a ggplot

Quick review about ggplot2

Final Challenge: Recreate this Gapminder Plot

What you learned in this chapter

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chapter1.md

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Quick Data Frame Introduction

Thinking about aesthetics

Mapping variables to produce geometric plots

More about aes

Points versus lines

Geoms are layers on a ggplot

Quick review about ggplot2

Final Challenge: Recreate this Gapminder Plot

What you learned in this chapter