ggplotsStandardPlots

# load heights data
library(tidyverse)
library(dslabs)
data(heights)

# define p
p <- heights %>%
    filter(sex == "Male") %>%
    ggplot(aes(x = height))

# basic histograms
p + geom_histogram()
p + geom_histogram(binwidth = 1)

# histogram with blue fill, black outline, labels and title
p + geom_histogram(binwidth = 1, fill = "blue", col = "black") +
    xlab("Male heights in inches") +
    ggtitle("Histogram")

# Smooth density plots in ggplot2

p + geom_density()
p + geom_density(fill = "blue")

# Quantile-quantile plots in ggplot2

# basic QQ-plot
p <- heights %>% filter(sex == "Male") %>%
    ggplot(aes(sample = height))
p + geom_qq()

# QQ-plot against a normal distribution with same mean/sd as data
params <- heights %>%
    filter(sex == "Male") %>%
    summarize(mean = mean(height), sd = sd(height))
p + geom_qq(dparams = params) +
    geom_abline()

# QQ-plot of scaled data against the standard normal distribution
heights %>%
    ggplot(aes(sample = scale(height)) +
    geom_qq() +
    geom_abline()

# Grids of plots with the grid.extra package

# define plots p1, p2, p3
p <- heights %>% filter(sex == "Male") %>% ggplot(aes(x = height))
p1 <- p + geom_histogram(binwidth = 1, fill = "blue", col = "black")
p2 <- p + geom_histogram(binwidth = 2, fill = "blue", col = "black")
p3 <- p + geom_histogram(binwidth = 3, fill = "blue", col = "black")

# arrange plots next to each other in 1 row, 3 columns
library(gridExtra)
grid.arrange(p1, p2, p3, ncol = 3)