Examples

Load data and check data

The mtcars dataset is a built-in dataset in R. It comprises 11 features of 32 automobiles from the 1974 Motor Trend US magazine.

# Data exploration
# View(mtcars)
# class(mtcars)
# ?mtcars
str(mtcars)

## 'data.frame':    32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

Correlation Plot

library(GGally)
ggpairs(mtcars)

library(GGally)
 
# From the help page:
data(flea)
ggpairs(flea, columns = 2:4, ggplot2::aes(colour=species)) 

Other Basic Plots

Scatter plot and smooth

# load ggplot2
library(ggplot2)
#library(hrbrthemes)

# mtcars dataset is natively available in R
# head(mtcars)
 
# A basic scatterplot with color depending on Species
ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) + 
    geom_point(size=6)  

#    theme_ipsum()

Histogram

# library & data
library(ggplot2)
data <- data.frame(x=rnorm(100))
 
# Basic plot with title
ggplot( data=data, aes(x=x)) + 
  geom_histogram(fill="skyblue", alpha=0.5) +
  ggtitle("A blue Histogram") +
  theme_minimal()

Boxplot

# Load ggplot2
library(ggplot2)
 
# The mtcars dataset is natively available
# head(mtcars)
 
# A really basic boxplot.
ggplot(mtcars, aes(x=as.factor(cyl), y=mpg)) + 
    geom_boxplot(fill="slateblue", alpha=0.2) + 
    xlab("cyl")

Fancy boxplot(jitters) and Violin plot

library(tidyverse)
#library(hrbrthemes)
library(viridis)
# create a dataset
data <- data.frame(
  name=c( rep("A",500), rep("B",500), rep("B",500), rep("C",20), rep('D', 100)  ),
  value=c( rnorm(500, 10, 5), rnorm(500, 13, 1), rnorm(500, 18, 1), rnorm(20, 25, 4), rnorm(100, 12, 1) )
)

# Plot
data %>%
  ggplot( aes(x=name, y=value, fill=name)) +
    geom_boxplot() +
    scale_fill_viridis(discrete = TRUE, alpha=0.6) +
    geom_jitter(color="black", size=0.4, alpha=0.9) +
#    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)
    ) +
    ggtitle("A boxplot with jitter") +
    xlab("")

# Boxplot basic
data %>%
  ggplot( aes(x=name, y=value, fill=name)) +
    geom_boxplot() +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  #  theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)
    ) +
    ggtitle("Basic boxplot") +
    xlab("")

# Violin basic
data %>%
  ggplot( aes(x=name, y=value, fill=name)) +
    geom_violin() +
    scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
#    theme_ipsum() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)
    ) +
    ggtitle("Violin chart") +
    xlab("")

Faceting with ggplot2

# Libraries
library(tidyverse)
#library(hrbrthemes)
library(babynames)
library(viridis)

# Load dataset from github
data <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/3_TwoNumOrdered.csv", header=T)
data$date <- as.Date(data$date)

# Load dataset from github
don <- babynames %>% 
  filter(name %in% c("Ashley", "Amanda", "Mary", "Deborah",   "Dorothy", "Betty", "Helen", "Jennifer", "Shirley")) %>%
  filter(sex=="F")

# Plot
don %>%
  ggplot( aes(x=year, y=n, group=name, fill=name)) +
    geom_area() +
    scale_fill_viridis(discrete = TRUE) +
    theme(legend.position="none") +
    ggtitle("Popularity of American names in the previous 30 years") +
 #   theme_ipsum() +
    theme(
      legend.position="none",
      panel.spacing = unit(0, "lines"),
      strip.text.x = element_text(size = 8),
      plot.title = element_text(size=13)
    ) +
    facet_wrap(~name, scale="free_y")

Multiple Graph

library(ggplot2)
library(gridExtra)
# Make 3 simple graphics:
g1 <- ggplot(mtcars, aes(x=qsec)) + geom_density(fill="slateblue")
g2 <- ggplot(mtcars, aes(x=drat, y=qsec, color=cyl)) + geom_point(size=5) + theme(legend.position="none")
g3 <- ggplot(mtcars, aes(x=factor(cyl), y=qsec, fill=cyl)) + geom_boxplot() + theme(legend.position="none")
g4 <- ggplot(mtcars , aes(x=factor(cyl), fill=factor(cyl))) +  geom_bar()
 
# Plots
grid.arrange(g2, arrangeGrob(g3, g4, ncol=2), nrow = 2)