Regression Lab 3: Data Visualization in R

Level 3: Data Visualization

1. Intro to the layers of ggplot (data, aes, geom)
2. Changing the aesthetics (shape, color, size, fill, alpha)
3. Scatterplots
4. Histograms
5. Boxplots
6. Bar Graphs
7. Line Graphs
BONUS: Animated Graphs

Data Visualization

Exam and festival datasets are from: https://studysites.sagepub.com/dsur/study/articles.htm

library(ggplot2)
library(reshape)
library(plyr)

There are three essential grammatical elements: data, aesthetics, and geometries.

1. The data is obviously the data which we want to plot.

2. The aesthetics layer refers to the scales onto which we will map our data.

3. The geom layer refers to the actual shape the data will take in the plot.

mtcars$cyl <- as.factor(mtcars$cyl)

# Data, aethetics (x, y), point
ggplot(mtcars, aes(cyl, mpg)) +
  geom_point()

# Change the color aesthetic to a size aesthetic
ggplot(mtcars, aes(wt, mpg, color = disp, size = disp)) +
  geom_point()

Aesthetics using aes()

As a general rule, if you want to set an aesthetic to a specific value, you would specify that outside of aes(). For example, if you specify (color = “blue”), you would not place it in aes(). However, if you want to specify how the aesthetics should be used, you would place it inside aes(). For example, if you want gender to be represented as separate colors, you would use (aes(color = gender)).

Scatterplots

Aesthetic Options: geom_point ()

Shape, color, size, fill, alpha

examData <- read.delim("Exam Anxiety.dat",  header = TRUE)

#Simple scatter
scatter <- ggplot(examData, aes(Anxiety, Exam))
scatter + geom_point() + labs(x = "Exam Anxiety", y = "Exam Performance %") 

Aesthetic Options: geom_smooth ()

Color, size, fill, linetype, weight, alpha

#Simple scatter with smooth with CI
scatter <- ggplot(examData, aes(Anxiety, Exam))
scatter + geom_point() + geom_smooth() + labs(x = "Exam Anxiety", y = "Exam Performance %") 

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

#Simple scatter with regression line
scatter <- ggplot(examData, aes(Anxiety, Exam))
scatter + geom_point() + geom_smooth(method = "lm", colour = "Red", se = F) + labs(x = "Exam Anxiety", y = "Exam Performance %") 

#Simple scatter with regression line + CI
scatter <- ggplot(examData, aes(Anxiety, Exam))
scatter + geom_point() + geom_smooth(method = "lm", colour = "Red") + labs(x = "Exam Anxiety", y = "Exam Performance %") 

#Simple scatter with regression line + coloured CI
scatter <- ggplot(examData, aes(Anxiety, Exam))
scatter + geom_point() + geom_smooth(method = "lm", colour = "Red", alpha = 0.1, fill = "Red") + labs(x = "Exam Anxiety", y = "Exam Performance %") 

#Grouped scatter with regression line + CI
scatter <- ggplot(examData, aes(Anxiety, Exam, colour = Gender))

scatter + geom_point() + geom_smooth(method = "lm", aes(fill = Gender), alpha = 0.1) + labs(x = "Exam Anxiety", y = "Exam Performance %", colour = "Gender")

Change colors

scatter2 <- scatter + geom_point() + geom_smooth(method = "lm", aes(fill = Gender), alpha = 0.1) + labs(x = "Exam Anxiety", y = "Exam Performance %", colour = "Gender")

# Scatter plot
scatter2 + scale_color_manual(values=c("blue", "green"))

# Change line color
# http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf
scatter2 + scale_color_manual(values=c("deepskyblue1", "darkseagreen1"))

# Change panel color
scatter2 + theme(panel.background = element_rect(fill = 'white'))

# Change plot color
scatter2 + theme(plot.background = element_rect(fill = 'black', color = 'black'),
                 panel.background = element_rect(fill = 'black'),
                 axis.title = element_text(color = 'white'))

Histograms

Aesthetic Options: geom_histogram ()

Color, size, fill, linetype, weight, alpha

festivalData <- read.delim("DownloadFestival(No Outlier).dat",  header = TRUE)

festivalHistogram <- ggplot(festivalData, aes(day1))

festivalHistogram + geom_histogram(binwidth = 0.4) + labs(x = "Hygiene (Day 1 of Festival)", y = "Frequency")

# install.packages("extrafont")
library(extrafont)

# Change color and font
festivalHistogram + geom_histogram(binwidth=0.2,color="black", fill="lightskyblue2") + theme(text = element_text(size = 12, family = "Comic Sans MS")) + facet_wrap("gender")

Density Plots

festivalDensity <- ggplot(festivalData, aes(day1))
festivalDensity + geom_density() + labs(x = "Hygiene (Day 1 of Festival)", y = "Density Estimate")

# Density by gender
festivalDensity + geom_density() + aes(fill = gender)

# Change opacity and labels
festivalDensity + geom_density(aes(fill = gender), alpha = 0.4) + labs(x = "Hygiene (Day 1 of Festival)", y = "Density Estimate")

Boxplots

Aesthetic Options: geom_boxplot ()

Color, size, fill, weight, alpha

festivalBoxplot2 <- ggplot(festivalData, aes(gender, day1))
festivalBoxplot2 + geom_boxplot() + labs(x = "Gender", y = "Hygiene (Day 1 of Festival)")

festivalBoxplot2 + geom_boxplot(aes(fill = gender)) + geom_point() + labs(x = "Gender", y = "Hygiene (Day 1 of Festival)")

festivalBoxplot2 + geom_boxplot(aes(fill = gender)) + geom_jitter(alpha = 0.2) + labs(x = "Gender", y = "Hygiene (Day 1 of Festival)")

Bar Charts

Stat_summary () comes from the Hmisc package and can be added a layer to your graphs.

library(haven)
#load dictator game data
df_dg <- read_sav("sample_dictatorgame.sav")

# inspect data
head(df_dg)

# change condition to factor with labels
df_dg$condition[df_dg$condition == -1] <- "Control"
df_dg$condition[df_dg$condition == 1] <- "Experimental"
df_dg$role[df_dg$role == -1] <- "Partner"
df_dg$role[df_dg$role == 1] <- "Actor"

bar <- ggplot(df_dg, aes(condition, share))

bar2 <- bar + 
  stat_summary(aes(condition, share, fill = role ), 
                           fun = mean, # display the means
                           geom = "bar", 
                           position="dodge") + 
  stat_summary(aes(condition, share, fill = role ), 
               fun.data = mean_cl_normal, # 95% CI assuming normality (other option would be _boot)
               geom = "errorbar", 
               position=position_dodge(width=0.90), 
               width = 0.2) + 
  labs(x = "Condition", y = "Money Shared", fill = "Role") + 
  scale_fill_manual(values=c("deepskyblue1", "slategray3"))

bar2

library(ggsignif)
bar <- ggplot(df_dg, aes(condition, share))

bar2 + 
  scale_y_continuous(breaks=seq(0, 15, 1)) + # specifies breaks (0-15 at every 1pt) 
  coord_cartesian(ylim =c(0, 15)) + # y-axis on 0-15 scale
  geom_signif(y_position = c(7.6, 8.5), xmin = c(0.8, 1.8), xmax = c(1.2, 2.2), #sig bars between roles
              annotation = c("NS", "**"), tip_length = 0, color= "#756F6F") + 
  geom_signif(comparisons = list(c("Control", "Experimental")), map_signif_level=TRUE, #sig bars between condition 
              annotations = "NS", y_position = 11, color= "#756F6F")  + theme_classic()

Line Graphs

depression_data <- read.csv("depression_example_data.csv")
# Simulate meaningful fake data
## Depression over time with/without treatment
fake_data3 <- sample(c(0,1), size = nrow(depression_data), replace = TRUE)
fake_data3 <- as.data.frame(ifelse(fake_data3==1, yes = rnorm(20, 20, 1), no = rnorm(20, 20, 1)))
dep_data <- cbind(depression_data, fake_data3)
names(dep_data)[4] <- "Baseline"

fake_data <- sample(c(0,1), size = nrow(depression_data), replace = TRUE)
fake_data <- as.data.frame(ifelse(fake_data==1, yes = rnorm(20, 20, 2), no = rnorm(20, 12, 2)))
names(fake_data)[1] <- "Six_Weeks"
fake_data <- fake_data %>% arrange(Six_Weeks)
dep_data <- dep_data %>% arrange(desc(intervention))
dep_data <- cbind(dep_data, fake_data)

dep_data$intervention[dep_data$intervention == 0] <- "No Intervention"
dep_data$intervention[dep_data$intervention == 1] <- "Intervention"

dep_data$ID <- seq_along(dep_data[,1])

Challenge!

This is a longitudinal dataset. To plot it as a line graph, we’ll need to convert it to a long dataset. Use pivot_longer() to reshape the data and call the new dataframe “dep_data1”

Plot the long data as a line graph

line <- ggplot(dep_data1, aes(Time, Depression_Level, color = intervention))
line + stat_summary(fun = mean, geom = "line", aes(group = intervention))

line + stat_summary(fun = mean, geom = "line", aes(group= intervention)) + stat_summary(fun.data = mean_cl_boot, geom = "errorbar", width = 0.2) + labs(x = "Time", y = "Depression", colour = "Intervention") + ylim(5, 30)

line + 
  stat_summary(fun = mean, geom = "point", aes(shape = intervention), size = 4) + # Shape of point by group
  stat_summary(fun = mean, geom = "line", aes(group= intervention, linetype = intervention)) + # Dashed or solid line by group
  stat_summary(fun.data = mean_cl_boot, geom = "errorbar", width = 0.2) + # 95% CI
  labs(x = "Time", y = "Mean Depression Score", colour = "Group", shape= "Group", linetype = "Group") + ylim(5, 30) # Labels and range of y-axis

Exploratory graphs

library(RColorBrewer)
library(gapminder)
library(dplyr)
gm2007.1 <- gapminder %>%
  filter(year == 2007) %>%
  slice_max(lifeExp, n = 10)

gm2007.2 <- gapminder %>%
  filter(year == 2007) %>%
  slice_min(lifeExp, n = 10)

gm2007 <- rbind(gm2007.1, gm2007.2)

# Add a geom_segment() layer
ggplot(gm2007, aes(x = lifeExp, y = country, color = lifeExp)) +
  geom_point(size = 4) +
  geom_segment(aes(xend = 30, yend = country), size = 2)

# Set the color scale
palette <- brewer.pal(5, "RdYlBu")[-(2:4)]

global_mean <- mean(gm2007$lifeExp)
x_start <- global_mean + 3
y_start <- 13
x_end <- global_mean
y_end <- 13.5

# Add a title and caption
plt_country_vs_lifeExp <- ggplot(gm2007, aes(x = lifeExp, y = country, color = lifeExp)) +
  geom_point(size = 4) +
  geom_segment(aes(xend = 30, yend = country), size = 2) +
  geom_text(aes(label = round(lifeExp,1)), color = "white", size = 1.5) +
  scale_x_continuous("", expand = c(0,0), limits = c(30,90), position = "top") +
  scale_color_gradientn(colors = palette) +
  labs(title = "Highest and lowest life expectancies, 2007", caption = "Source: gapminder")

plt_country_vs_lifeExp +
  theme_classic() +
  theme(axis.line.y = element_blank(),
        axis.ticks.y = element_blank(),
        axis.text = element_text(color = "black"),
        axis.title = element_blank(),
        legend.position = "none") +
  geom_vline(xintercept = global_mean, color = "grey40", linetype = 3) +
  annotate("text", x = x_start, y = y_start, label = "The\nglobal\naverage", vjust = 1.1, size = 3, family = "Times", color = "grey40") +
  annotate("curve", x = x_start, y = y_start, xend = x_end, yend = y_end, arrow = arrow(length = unit(0.1, "cm"), type = "closed"), color = "grey40"
  )  

 # theme(text = element_text(family = "Times"))

Animated Graphs

Code from: https://gganimate.com/

### Install animation packages
#devtools::install_github('thomasp85/gganimate')
library(gganimate)
#install.packages("gifski")
library(gifski)

library(gapminder)
gg <- gapminder

ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  facet_wrap(~continent) +
  # Here comes the gganimate specific bits
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  transition_time(year) +
  ease_aes('linear')