Plotting

Wikipedia

Overview

In this practical you’ll practice plotting data with the amazing ggplot2 package.

Datasets

library(tidyverse)
library(plotly)
library(ggthemes)
mcdonalds <- read_csv("https://raw.githubusercontent.com/therbootcamp/BaselRBootcamp_2018July/master/_sessions/_data/baselrbootcamp_data/mcdonalds.csv")
kc_house <- read_csv("https://raw.githubusercontent.com/therbootcamp/BaselRBootcamp_2018July/master/_sessions/_data/baselrbootcamp_data/kc_house.csv")

File	Rows	Columns
mcdonalds.csv	260	24

Packages

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

Cheatsheet

https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf

Examples

The following examples will take you through the steps of creating both simple and complex plots with ggplot2. Try to go through each line of code and see how it works!

# -----------------------------------------------
# Examples of using ggplot2 on the mpg data
# ------------------------------------------------

library(tidyverse)         # Load tidyverse (which 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")


# Facet by class
ggplot(data = mpg,
       mapping = aes(x = displ, 
                     y = hwy, 
                     color = factor(cyl))) + 
  geom_point() +
  facet_wrap(~ class) 


# 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")

References

• 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

Tasks

A - Setup

1. Open your baselrbootcamp 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!

2. 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_practical.R in the 2_Code folder.

3. Using library() load the set of packages for this practical listed in the packages section above.

## NAME
## DATE
## Plotting Practical

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

library(tidyverse)

4. 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")

5. 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>             <int>            <int>       <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)` <int>, `Saturated Fat` <dbl>, `Saturated Fat (% Daily
#   Value)` <int>, `Trans Fat` <dbl>, Cholesterol <int>, `Cholesterol (%
#   Daily Value)` <int>, Sodium <int>, `Sodium (% Daily Value)` <int>,
#   Carbohydrates <int>, `Carbohydrates (% Daily Value)` <int>, `Dietary
#   Fiber` <int>, `Dietary Fiber (% Daily Value)` <int>, Sugars <int>,
#   Protein <int>, `Vitamin A (% Daily Value)` <int>, `Vitamin C (% Daily
#   Value)` <int>, `Calcium (% Daily Value)` <int>, `Iron (% Daily
#   Value)` <int>

6. 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!

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

mcdonalds

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

B - Building a plot step-by-step

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

1. 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))

2. 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()

3. 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()

4. 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()

5. 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")

6. 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")

7. 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)

8. 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

1. 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")

2. 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")

3. 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")

4. 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 - Using facets

1. Create the following plot showing the relationship between Sodium and Calories.

ggplot(XX, aes(x = XX, y = XX)) +
  geom_point(alpha = .2) +
  facet_wrap(~ XX) +
  labs(title = "XX",
       subtitle = "XX") +
  theme_minimal()

ggplot(mcdonalds, aes(x = Sodium, y = Calories)) +
  geom_point(alpha = .2) +
  facet_wrap(~Category) +
  labs(title = "McDonales",
       subtitle = "Sodium vs. Calories") +
  theme_minimal()

2. Try the following ways to customise your plot:

• Color the points by Category.
• Add a smoothed line to each plot with geom_smooth().

E - Adjusting colors

1. Create a scatterplot showing the relationship between Cholesterol and Protein .

2. Color the points according to their Calories by specifying the col aesthetic.

3. Change the colors by including the additional argument + scale_colour_gradient(low = "blue", high = "red").

4. Use a gray color palette by using scale_color_grey() instead of scale_colour_gradient().

5. Customize! Look at all of the named colors in R by running colors(). Then, use two new colors in your plot.

F - Summary statistics

1. 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")

2. 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().

G - Saving plots

1. It’s time to save your favorite plot to an image file! Pick your favorite plot you’ve created so far. Then, assign the plot to a new object called mcdonalds_gg using mcdonalds_gg <- ggplot(...)

mcdonalds_gg <- ggplot(...) + ... # Include your plotting code here

2. Evaluate your mcdonalds_gg object to see that it does indeed contain your plot.

3. Save your plot to a .pdf-file called mcdonalds.pdf using ggsave(). When you finish, find your plot in 3_Figures and open it to see how it looks!

# Save mcdonalds_gg to a pdf file
ggsave(filename = "3_Figures/mcdonalds", 
       device = "pdf", 
       plot = mcdonalds_gg,
       width = 4, 
       height = 4, 
       units = "in")

4. Play around with the width and height arguments to change the dimensions of the plot.

5. Customize your code to create a jpeg image called mcdonalds.jpeg

H - Adding labels

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

1. 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")

2. Try adding labels to the plot indicating which item each point represents by adding + geom_text().

3. 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!

4. Customize your geom_text() by including the arguments: geom_text(col = "black", check_overlap = TRUE, hjust = "left").

5. 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)))

6. Play around!

• Specify that the size of the points should correspond to their Calories. Do this with the size aesthetic.
• Instead of mapping Category to the color aesthetic, try creating different facets for each Category with facet_wrap(~ Category).
• Try using a different plotting theme. For example, you can try theme_excel() included in the ggthemes package.

I - Make your plots interactive with plotly::ggplotly()

1. With the ggplotly()-function from the plotly package, you can turn any ggplot object into an interactive plot like the one below! Run the following code to see it in action.

# Create a standard ggplot object
MyPlot <- ggplot(data = mcdonalds,
                 aes(x = Calories, y = TotalFat, col = Category)) + 
  geom_point()


# Make it interactive with ggplotly()!
library(plotly)
ggplotly(MyPlot)

2. Play around with your plot! See what happens when you hover over the points with your mouse. You can even zoom in by dragging your mouse.

3. Try turning one of your favorite previous plots into an interactive plotly plot using the ggplotly() function!

X - Challenges

For these challenges, use the kc_house dataset. Load the data as kc_house

1. Make this plot

• Hint: use scale_color_gradient(low = "green", high = "red"))

2. Make this plot

• Hint: take the log of price with log(price)

3. Make this plot

• Hint: Start by creating an aggregated dataset with median home prices of the top 20 zipcodes. Then, use this dataset in ggplot!