library(MASS)     # Datasets
library(mice)     # Boys dataset
library(dplyr)    # Data manipulation
library(magrittr) # Pipes
library(ggplot2)  # Plotting suite

New functions:

• hist(): histogram
• plot(): R’s plotting device
• barplot(): bar plot function
• boxplot(): box plot function
• density(): function that calculates the density
• ggplot(): ggplot’s plotting device

• We can process a lot of information quickly with our eyes
• Plots give us information about
  • Distribution / shape
  • Irregularities
  • Assumptions
  • Intuitions
• Summary statistics, correlations, parameters, model tests, p-values do not tell the whole story

ALWAYS plot your data!

Source: Anscombe, F. J. (1973). “Graphs in Statistical Analysis”. American Statistician. 27 (1): 17–21.

Source: https://www.autodeskresearch.com/publications/samestats

• A few plots in base graphics in R
• Plotting with ggplot2 graphics

hist(boys$hgt, main = "Histogram", xlab = "Height")

dens <- density(boys$hgt, na.rm = TRUE)
plot(dens, main = "Density plot", xlab = "Height", bty = "L")

plot(x = boys$hgt, y = boys$wgt, main = "Scatter plot", 
     xlab = "Height", ylab = "Weight", bty = "L")

boxplot(boys$hgt ~ boys$reg, main = "Boxplot", 
        xlab = "Region", ylab = "Height")

boxplot(hgt ~ reg, boys,  main = "Boxplot", xlab = "Region", 
        ylab = "Height", lwd = 2, notch = TRUE, col = rainbow(5))

boys %>% md.pattern() # from mice

##     age reg wgt hgt bmi hc gen phb  tv     
## 223   1   1   1   1   1  1   1   1   1    0
## 19    1   1   1   1   1  1   1   1   0    1
## 1     1   1   1   1   1  1   1   0   1    1
## 1     1   1   1   1   1  1   0   1   0    2
## 437   1   1   1   1   1  1   0   0   0    3
## 43    1   1   1   1   1  0   0   0   0    4
## 16    1   1   1   0   0  1   0   0   0    5
## 1     1   1   1   0   0  0   0   0   0    6
## 1     1   1   0   1   0  1   0   0   0    5
## 1     1   1   0   0   0  1   1   1   1    3
## 1     1   1   0   0   0  0   1   1   1    4
## 1     1   1   0   0   0  0   0   0   0    7
## 3     1   0   1   1   1  1   0   0   0    4
##       0   3   4  20  21 46 503 503 522 1622

result <- lm(age~wgt, boys)
plot(result, which = 1)

Layered plotting based on the book The Grammer of Graphics by Leland Wilkinsons.

With ggplot2 you

1. provide the data
2. define how to map variables to aesthetics
3. state which geometric object to display
4. (optional) edit the overall theme of the plot

ggplot2 then takes care of the details

1: Provide the data

boys %>%
  ggplot()

2: map variable to aesthetics

boys %>%
  ggplot(aes(x = age, y = bmi))

3: state which geometric object to display

boys %>%
  ggplot(aes(x = age, y = bmi)) +
  geom_point()

Create the plot

gg <- 
  boys %>%
  ggplot(aes(x = age, y = bmi)) +
  geom_point(col = "dark green")

Add another layer (smooth fit line)

gg <- gg + 
  geom_smooth(col = "dark blue")

Give it some labels and a nice look

gg <- gg + 
  labs(x = "Age", y = "BMI", title = "BMI trend for boys") +
  theme_minimal()

plot(gg)

• x
• y
• size
• colour
• fill
• opacity (alpha)
• linetype
• …

gg <- 
  boys %>% 
  filter(!is.na(reg)) %>% 
  
  ggplot(aes(x      = age, 
             y      = bmi, 
             size   = hc, 
             colour = reg)) +
  
  geom_point(alpha = 0.5) +
  
  labs(title  = "BMI trend for boys",
       x      = "Age", 
       y      = "BMI", 
       size   = "Head circumference",
       colour = "Region") +
  theme_minimal()

plot(gg)

• geom_point

• geom_bar

• geom_line

• geom_smooth

• geom_histogram

• geom_boxplot

• geom_density

• Themes determine the overall appearance of your plot
• standard themes: e.g., theme_minimal(), theme_classic(), theme_bw(), …
• extra libraries with additional themes: e.g., ggthemes
• customize own theme using options of theme()