PlottingI_practical.utf8

Overview

In this practical you’ll practice plotting data with the amazing ggplot2 package. By the end of this practical you will know how to:

Build a plot step-by-step
Use multiple geoms
Adjust colors and add labels

Tasks

A - Setup

Open your BernRBootcamp R project. It should already have the folders 1_Data and 2_Code. Make sure that the data files listed in the Datasets section above are in your 1_Data folder.

# Done!

Open a new R script. At the top of the script, using comments, write your name and the date. Save it as a new file called plotting_practicalI.R in the 2_Code folder.
Using library() load the set of packages for this practical listed in the Functions section above.

## NAME
## DATE
## Plotting Practical

library(XX)     
library(XX)
#...

library(tidyverse)

For this practical, we’ll use the mcondalds.csv data set, which contains nutrition information about items from McDonalds. Using read_csv(), load the data into R and store it as a new object called mcdonalds.

mcdonalds <- read_csv("1_Data/mcdonalds.csv")

Take a look at the first few rows of the dataset(s) by printing them to the console.

mcdonalds

# A tibble: 260 x 24
   Category Item  `Serving Size` Calories `Calories from … `Total Fat`
   <chr>    <chr> <chr>             <dbl>            <dbl>       <dbl>
 1 Breakfa… Egg … 4.8 oz (136 g)      300              120          13
 2 Breakfa… Egg … 4.8 oz (135 g)      250               70           8
 3 Breakfa… Saus… 3.9 oz (111 g)      370              200          23
 4 Breakfa… Saus… 5.7 oz (161 g)      450              250          28
 5 Breakfa… Saus… 5.7 oz (161 g)      400              210          23
 6 Breakfa… Stea… 6.5 oz (185 g)      430              210          23
 7 Breakfa… Baco… 5.3 oz (150 g)      460              230          26
 8 Breakfa… Baco… 5.8 oz (164 g)      520              270          30
 9 Breakfa… Baco… 5.4 oz (153 g)      410              180          20
10 Breakfa… Baco… 5.9 oz (167 g)      470              220          25
# … with 250 more rows, and 18 more variables: `Total Fat (% Daily
#   Value)` <dbl>, `Saturated Fat` <dbl>, `Saturated Fat (% Daily
#   Value)` <dbl>, `Trans Fat` <dbl>, Cholesterol <dbl>, `Cholesterol (% Daily
#   Value)` <dbl>, Sodium <dbl>, `Sodium (% Daily Value)` <dbl>,
#   Carbohydrates <dbl>, `Carbohydrates (% Daily Value)` <dbl>, `Dietary
#   Fiber` <dbl>, `Dietary Fiber (% Daily Value)` <dbl>, Sugars <dbl>,
#   Protein <dbl>, `Vitamin A (% Daily Value)` <dbl>, `Vitamin C (% Daily
#   Value)` <dbl>, `Calcium (% Daily Value)` <dbl>, `Iron (% Daily
#   Value)` <dbl>

You’ll notice that the mcdonalds data frame has many column names with spaces and ‘bad’ characters like parentheses. Run the following code to fix that!

# Clean up the names of mcdonalds
mcdonalds <- mcdonalds %>%
  select(-contains("% Daily Value")) %>%  # Remove all '% Daily Value' columns
  rename_all(.funs = ~ gsub(" ", "", .))  # no more spaces!

# Clean up the names of mcdonalds
mcdonalds <- mcdonalds %>%
  select(-contains("% Daily Value")) %>%  # Remove all '% Daily Value' columns
  rename_all(.funs = ~ gsub(" ", "", .))  # no more spaces!

Now print the dataset again, do the names look better?

mcdonalds

# A tibble: 260 x 14
   Category Item  ServingSize Calories CaloriesfromFat TotalFat SaturatedFat
   <chr>    <chr> <chr>          <dbl>           <dbl>    <dbl>        <dbl>
 1 Breakfa… Egg … 4.8 oz (13…      300             120       13            5
 2 Breakfa… Egg … 4.8 oz (13…      250              70        8            3
 3 Breakfa… Saus… 3.9 oz (11…      370             200       23            8
 4 Breakfa… Saus… 5.7 oz (16…      450             250       28           10
 5 Breakfa… Saus… 5.7 oz (16…      400             210       23            8
 6 Breakfa… Stea… 6.5 oz (18…      430             210       23            9
 7 Breakfa… Baco… 5.3 oz (15…      460             230       26           13
 8 Breakfa… Baco… 5.8 oz (16…      520             270       30           14
 9 Breakfa… Baco… 5.4 oz (15…      410             180       20           11
10 Breakfa… Baco… 5.9 oz (16…      470             220       25           12
# … with 250 more rows, and 7 more variables: TransFat <dbl>,
#   Cholesterol <dbl>, Sodium <dbl>, Carbohydrates <dbl>, DietaryFiber <dbl>,
#   Sugars <dbl>, Protein <dbl>

B - Building a plot step-by-step

In this section, you’ll build the following plot step by step.

ggplot(mcdonalds, aes(x = Calories, y = SaturatedFat, col = Category)) +
  geom_point() +
  geom_smooth(col = "black") +
  labs(title = "McDonalds Nutrition",
       subtitle = "Each point is a menu item",
       caption = "Source: Kaggle.com") +
  xlim(0, 1250) +
  theme_minimal()

Using ggplot(), create the following blank plot using the data and mapping arguments (but no geom). Use Calories for the x aesthetic and SaturatedFat for the y aesthetic

ggplot(data = mcdonalds, 
       mapping = aes(x = XX, y = XX))

ggplot(mcdonalds, aes(x = Calories, y = SaturatedFat))

Using geom_point(), add points to the plot

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

ggplot(mcdonalds, aes(x = Calories, y = SaturatedFat)) +
  geom_point()

Using the color aesthetic mapping, color the points by their Category.

ggplot(mcdonalds, aes(x = XX, y = XX, col = XX)) +
  geom_point()

ggplot(mcdonalds, aes(x = Calories, y = SaturatedFat, col = Category)) +
  geom_point()

Add a smoothed average line using geom_smooth().

ggplot(mcdonalds, aes(x = XX, y = XX, col = XX)) +
  geom_point() +
  geom_smooth()

ggplot(mcdonalds, aes(x = Calories, y = SaturatedFat, col = Category)) +
  geom_point() +
  geom_smooth()

Oops! Did you get several smoothed lines instead of just one? Fix this by specifying that the line should have one color: "black". When you do, you should then only see one line.

ggplot(mcdonalds, aes(x = XX, y = XX, col = XX)) +
  geom_point() +
  geom_smooth(col = "XX")

ggplot(mcdonalds, aes(x = Calories, y = SaturatedFat, col = Category)) +
  geom_point() +
  geom_smooth(col = "black")

Add appropriate labels using the labs() function.

ggplot(mcdonalds, aes(x = XX, y = XX, col = XX)) +
  geom_point() +
  geom_smooth(col = "XX") +
  labs(title = "XX",
       subtitle = "XX",
       caption = "XX")

ggplot(mcdonalds, aes(x = Calories, y = SaturatedFat, col = Category)) +
  geom_point() +
  geom_smooth(col = "black") +
  labs(title = "McDonalds Nutrition",
       subtitle = "Each point is a menu item",
       caption = "Source: Kaggle.com")

Set the limits of the x-axis to 0 and 1250 using xlim().

ggplot(mcdonalds, aes(x = XX, y = XX, col = XX)) +
  geom_point() +
  geom_smooth(col = "XX") +
  labs(title = "XX",
       subtitle = "XX",
       caption = "XX") +
  xlim(XX, XX)

ggplot(mcdonalds, aes(x = Calories, y = SaturatedFat, col = Category)) +
  geom_point() +
  geom_smooth(col = "black") +
  labs(title = "McDonalds Nutrition",
       subtitle = "Each point is a menu item",
       caption = "Source: Kaggle.com") +
  xlim(0, 1250)

Finally, set the plotting theme to theme_minimal(). You should now have the final plot!

ggplot(mcdonalds, aes(x = XX, y = XX, col = XX)) +
  geom_point() +
  geom_smooth(col = "XX") +
  labs(title = "XX",
       subtitle = "XX",
       caption = "XX")+
  xlim(XX, XX) +
  theme_minimal()

ggplot(mcdonalds, aes(x = Calories, y = SaturatedFat, col = Category)) +
  geom_point() +
  geom_smooth(col = "black") +
  labs(title = "McDonalds Nutrition",
       subtitle = "Each point is a menu item",
       caption = "Source: Kaggle.com") +
  xlim(0, 1250) +
  theme_minimal()

C - Adding multiple geoms

Create the following plot showing the relationship between menu category and calories

ggplot(data = mcdonalds, aes(x = XX, y = XX, fill = XX)) +
  geom_violin() +
  guides(fill = FALSE) +
  labs(title = "XX",
       subtitle = "XX")

ggplot(data = mcdonalds, aes(x = Category, y = Calories, fill = Category)) +
  geom_violin() +
  guides(fill = FALSE) +
  labs(title = "McDonalds",
       subtitle = "Calorie distribution by menu category")

Include the additional argument + stat_summary(fun.y = "mean", geom = "point", col = "white", size = 4) to include points showing the mean of each distribution

ggplot(data = mcdonalds, aes(x = Category, y = Calories, fill = Category)) +
  geom_violin() +
  guides(fill = FALSE) +
  stat_summary(fun.y = "mean", geom = "point", col = "white", size = 4) +
  labs(title = "McDonalds",
       subtitle = "Calorie distribution by menu category")

Now add + geom_jitter(width = .1, alpha = .5) to your plot, what do you see?

ggplot(data = mcdonalds, aes(x = Category, y = Calories, fill = Category)) +
  geom_violin() +
  geom_jitter(width = .1, alpha = .5) +
  guides(fill = FALSE) +
  stat_summary(fun.y = "mean", geom = "point", col = "white", size = 4) +
  labs(title = "McDonalds",
       subtitle = "Calorie distribution by menu category")

Play around with your plotting arguments to see how the results change! Each time you make a change, run the plot again to see your new output!

Change the summary function in stat_summary() from "mean" to "median".
Change the size of the points in stat_summary() to something much bigger (or smaller).
Change the width argument in geom_jitter() to width = 0.
Instead of using geom_violin(), try geom_boxplot().
Remove the fill = Category aesthetic entirely.

D - Summary statistics

Create the following plot showing the mean number of calories for each menu category using the following template:

ggplot(XX, aes(x = XX, y = X)) +
  stat_summary(geom = "bar", 
               fun.y = "mean") +
  labs(title = "XX",
       subtitle = "XX")

ggplot(mcdonalds, aes(x = Category, y = Calories)) +
  stat_summary(geom = "bar", 
               fun.y = "mean") +
  labs(title = "Calories by McDonalds menu category",
       subtitle = "Bars represent means")

Customize your plot!

Instead of showing the "mean", show the "median".
Give each bar a different color.
Add overlapping points showing the individual items using geom_point(), geom_count() or geom_jitter().

E - Adding labels

Let’s create the following plot with additional point labels using geom_text():

ggplot(mcdonalds, aes(x = Sugars, 
                      y = Calories, 
                      col = Category, 
                      label = Item)) +
  geom_point() +
  geom_text(data = mcdonalds %>% 
            filter(Calories > 1100),
            col = "black", 
            check_overlap = TRUE, 
            hjust = "left") +
  xlim(0, 150) +
  ylim(0, 2000) +
  theme_minimal() +
  labs(title = "Adding labels to a plot with geom_text()")

Start with the following template

ggplot(mcdonalds, aes(x = XX, 
                      y = XX, 
                      col = XX)) +
  geom_point() +
  xlim(XX, XX) +
  ylim(XX, XX) +
  theme_minimal() +
  labs(title = "XX")

Try adding labels to the plot indicating which item each point represents by adding + geom_text().
Where are the labels? Ah, we didn’t tell ggplot which column in the data represents the item descriptions. Fix this by specifying the label aesthetic in your first call to the aes() function. That is, include label = Item underneath the line col = XX. Now you should see lots of labels!
Customize your geom_text() by including the arguments: geom_text(col = "black", check_overlap = TRUE, hjust = "left").
Using the data argument in geom_text(), specify that the labels should only apply to items over 1100 calories (hint: geom_text(data = mcdonalds %>% filter(XX > XX)))
Play around!

Specify that the size of the points should correspond to their Calories. Do this with the size aesthetic.
Try using a different plotting theme. For example, you can try theme_excel() included in the ggthemes package.

Examples

# ggplot2 -----------------------

library(tidyverse) # Load tidyverse (contains ggplot2!)

mpg # Look at the mpg data

# Just a blank space without any aesthetic mappings
ggplot(data = mpg)

# Now add a mapping where engine displacement (displ) and highway miles per gallon (hwy) are
# mapped to the x and y aesthetics
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy))   # Map displ to x-axis and hwy to y-axis

#  Add points with geom_point()
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy)) +
       geom_point()     

#  Add points with geom_count()
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy)) +
       geom_count()   

# Again, but with some additional arguments
# Also using a new theme temporarily

ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy)) +
       geom_point(col = "red",                  # Red points
                  size = 3,                     # Larger size
                  alpha = .5,                   # Transparent points
                  position = "jitter") +        # Jitter the points         
         scale_x_continuous(limits = c(1, 15)) +  # Axis limits
         scale_y_continuous(limits = c(0, 50)) +
  theme_minimal()


# Assign class to the color aesthetic and add labels with labs()

ggplot(data = mpg, 
  mapping = aes(x = displ, y = hwy, col = class)) +  # Change color based on class column
  geom_point(size = 3, position = 'jitter') +
  labs(x = "Engine Displacement in Liters",
       y = "Highway miles per gallon",
       title = "MPG data",
       subtitle = "Cars with higher engine displacement tend to have lower highway mpg",
       caption = "Source: mpg data in ggplot2")
  

# Add a regression line for each class

ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy, color = class)) +
  geom_point(size = 3, alpha = .9) + 
  geom_smooth(method = "lm")

# Add a regression line for all classes

ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy, color = class)) +
  geom_point(size = 3, alpha = .9) + 
  geom_smooth(col = "blue", method = "lm")


# Another fancier example

ggplot(data = mpg, 
       mapping = aes(x = cty, y = hwy)) + 
       geom_count(aes(color = manufacturer)) +     # Add count geom (see ?geom_count)
       geom_smooth() +                   # smoothed line without confidence interval
       geom_text(data = filter(mpg, cty > 25), 
                 aes(x = cty,y = hwy, 
                     label = rownames(filter(mpg, cty > 25))),
                     position = position_nudge(y = -1), 
                                check_overlap = TRUE, 
                     size = 5) + 
       labs(x = "City miles per gallon", 
            y = "Highway miles per gallon",
            title = "City and Highway miles per gallon", 
            subtitle = "Numbers indicate cars with highway mpg > 25",
            caption = "Source: mpg data in ggplot2",
            color = "Manufacturer", 
            size = "Counts")

Datasets

File	Rows	Columns
mcdonalds.csv	260	24

Functions

Packages

Package	Installation
`tidyverse`	`install.packages("tidyverse")`
`ggthemes`	`install.packages("ggthemes")`

Resources

Documentation

The main ggplot2 webpage at http://ggplot2.tidyverse.org/ has great tutorials and examples.
Check out Selva Prabhakaran’s website for a nice gallery of ggplot2 graphics http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html
ggplot2 is also great for making maps. For examples, check out Eric Anderson’s page at http://eriqande.github.io/rep-res-web/lectures/making-maps-with-R.html

Cheatsheets

from R Studio