Advanced R Visualization¶

Feng Li

School of Statistics and Mathematics

Central University of Finance and Economics

feng.li@cufe.edu.cn

https://feng.li/statcomp

>>> Link to Python version 1 2 3

"The simple graph has brought more information to the data analyst’s mind than any other device."

— John Tukey

The mpg data frame¶

mpg contains observations collected by the US Environment Protection Agency on 38 models of car. You can see more details via ?mpg. Among the variables in mpg are:

  1. displ, a car’s engine size, in litres.
  2. hwy, a car's fuel efficiency on the highway, in miles per gallon (mpg). A car with a low fuel efficiency consumes more fuel than a car with a high fuel efficiency when they travel the same distance.
  3. ...

Basic plots¶

Practice and look at the help document of plot

In [1]:
library(ggplot2)
attach(mpg)
plot(displ, hwy) 
abline(lm(hwy~displ))
title("Regression of MPG on engine size")

Histograms and density plots¶

In [2]:
hist(mpg$hwy)
In [3]:
d <- density(mpg$hwy) # returns the density data 
plot(d)

Piechart¶

In [4]:
car.table <- table(mpg$manufacturer)
pie.cars <- car.table
names(pie.cars) <- names(car.table)
pie(pie.cars)

Boxplots¶

In [5]:
# Boxplot of MPG
boxplot(mpg$hwy, main = 'Boxplot of MPG')

# Boxplot of MPG by Car Cylinders 
boxplot(hwy~cyl,data = mpg, main = "Car Milage Data", 
  	xlab = "Number of Cylinders", ylab = "Miles Per Gallon")

Correlation plot¶

In [6]:
# install.packages("corrplot")
data(mtcars)
library(corrplot)
M <- cor(mtcars)
corrplot(M, addCoef.col = "grey")

corrplot 0.92 loaded

Time series¶

In [7]:
library(forecast)
library(fpp)
plot(ausbeer)

Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 

Loading required package: fma

Loading required package: expsmooth

Loading required package: lmtest

Loading required package: zoo


Attaching package: ‘zoo’


The following objects are masked from ‘package:base’:

    as.Date, as.Date.numeric


Loading required package: tseries

Creating a ggplot¶

In [8]:
library(ggplot2)
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

  • With ggplot2, you begin a plot with the function ggplot(). ggplot() creates a coordinate system that you can add layers to.

  • geom_point() adds a layer of points to your plot, which creates a scatterplot. You can specify the color, size and shape of these points. Each geom function in ggplot2 takes a mapping argument.

Your turn (1) 💓¶

  1. Run ggplot(data = mpg). What do you see?

  2. How many rows are in mpg? How many columns?

  3. What does the drv variable describe? Read the help for ?mpg to find out.

  4. Make a scatterplot of hwy vs cyl.

  5. What happens if you make a scatterplot of class vs drv? Why is the plot not useful?

  6. What happens for the outliers?

In [9]:
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

Change the color, size and shape of points¶

In [10]:
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = 2, size = 3)

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), shape = 18)

Your turn (2) 💓¶

  1. What’s gone wrong with this code? Why are the points not blue?

  2. Which variables in mpg are categorical? Which variables are continuous?

  3. Map a continuous variable to color. How does it behave differently for categorical vs. continuous variables?

  4. What happens if you use something other than a variable name as the color, like aes(colour = displ < 5)?

In [11]:
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))

Facets¶

  • Another way, particularly useful for categorical variables, is to split your plot into facets, subplots that each display one subset of the data.
  • To facet your plot by a single variable, use facet_wrap(). The first argument should be a formula, which you create with ~ followed by a variable name.
In [12]:
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

Pairwise plots¶

In [13]:
library(GGally)
ggpairs(subset(mtcars, select = c(1, 3, 4, 5, 6)))

Registered S3 method overwritten by 'GGally':
  method from   
  +.gg   ggplot2


Attaching package: ‘GGally’


The following object is masked from ‘package:fma’:

    pigs

Bar chart¶

The following chart displays the total number of cars in the mpg dataset, grouped by drv.

In [14]:
ggplot(data = mpg) +
  geom_bar(mapping = aes(x = drv))
  • You can change the colors.
In [15]:
ggplot(data = mpg) + 
  geom_bar(mapping = aes(x = drv, fill = drv))

Histogram¶

In [16]:
ggplot(data = mpg) + 
  geom_histogram(mapping = aes(x = hwy))

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Boxplots¶

In [17]:
ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + 
  geom_boxplot()
ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + 
  geom_boxplot() +
  coord_flip()

Time series¶

In [18]:
library(forecast)
library(fpp)
library(ggplot2)
autoplot(ausbeer)

Interactive graphs with plotly¶

Scatter Plot¶

library(plotly)

set.seed(123)

x <- rnorm(1000)
y <- rchisq(1000, df = 1, ncp = 0)
group <- sample(LETTERS[1:5], size = 1000, replace = T)
size <- sample(1:5, size = 1000, replace = T)

ds <- data.frame(x, y, group, size)

p <- plot_ly(ds, x = x, y = y, mode = "markers", split = group, size = size) %>%
  layout(title = "Scatter Plot")
embed_notebook(p)

library(plotly)
plot_ly(mpg, type="scatter", mode = 'markers', x = ~displ, y = ~hwy, color = ~drv,
        size = ~cyl, text = ~paste("manufacturer: ", manufacturer))

Basic Histogram¶

plot_ly(mpg, x = ~hwy, type = "histogram")

Boxplots¶

plot_ly(mpg, y = ~hwy, color = ~as.factor(cyl), type = "box")

World maps¶

df <- read.csv('data/2014_world_gdp_with_codes.csv')

# light grey boundaries
l <- list(color = toRGB("grey"), width = 0.5)

# specify map projection/options
g <- list(
  showframe = FALSE,
  showcoastlines = FALSE,
  projection = list(type = 'Mercator')
)

plot_geo(df) %>%
  add_trace(
    z = ~GDP..BILLIONS., color = ~GDP..BILLIONS., colors = 'Blues',
    text = ~COUNTRY, locations = ~CODE, marker = list(line = l)
  ) %>%
  colorbar(title = 'GDP Billions US$', tickprefix = '$') %>%
  layout(
    title = '2014 Global GDP',
    geo = g
  )

Shiny App¶

Shiny apps have two components:

  1. A user-interface script. The user-interface (ui) script controls the layout and appearance of your app. It is defined in a source script named ui.R.
  2. A server script. The server.R script contains the instructions that your computer needs to build your app.

Put the two .R files in one folder and you can run the app in Rstudio.

Number of telephones in different regions¶

ui.R

# Rely on the 'WorldPhones' dataset in the datasets
# package (which generally comes preloaded).
library(datasets)

# Use a fluid Bootstrap layout
fluidPage(    

  # Give the page a title
  titlePanel("Telephones by region"),

  # Generate a row with a sidebar
  sidebarLayout(      

    # Define the sidebar with one input
    sidebarPanel(
      selectInput("region", "Region:", 
                  choices=colnames(WorldPhones)),
      hr(),
      helpText("Data from AT&T (1961) The World's Telephones.")
    ),

    # Create a spot for the barplot
    mainPanel(
      plotOutput("phonePlot")  
    )

  )
)

Number of telephones in different regions¶

server.R

# Rely on the 'WorldPhones' dataset in the datasets
# package (which generally comes preloaded).
library(datasets)

# Define a server for the Shiny app
function(input, output) {

  # Fill in the spot we created for a plot
  output$phonePlot <- renderPlot({

    # Render a barplot
    barplot(WorldPhones[,input$region]*1000, 
            main=input$region,
            ylab="Number of Telephones",
            xlab="Year")
  })
}

Density of a normal distribution¶

ui.R

library(shiny)

# Define UI 
fluidPage(

  # Application title
  titlePanel("Please choose a mean and sd"),

  # Sidebar with controls 
  sidebarLayout(
    sidebarPanel(
      numericInput("mean", 
                   label = h3("Mean"), 
                   value = 1),

      selectInput("sd", label = h3("Standard Deviation"), 
                  choices = list("1" = 1, "2" = 2,
                                 "3" = 3), selected = 1)

    ),


    mainPanel(
      h2("This is the density plot of normal distribution."),
      h3("Note how it changes with mean and sd."),
      plotOutput("normPlot")
    )
  )
)

Density of a normal distribution¶

server.R

library(shiny)
# Define server logic required to plot density of normal distribution
function(input, output) {
  output$normPlot <- renderPlot({
    plot(density(rnorm(1000, mean = as.numeric(input$mean), 
               sd = as.numeric(input$sd))), main = 'normal density',
         xlim = c(-10, 10))
  })
}

References¶

  1. Robert I. Kabacoff (2015). R in action (2ed). Manning.
  2. R for data science
  3. The plotly site
  4. The shiny site