1. basic graphics with R
2. elegant graphics with ggplot2
3. interactive graphs with plotly
4. interactive web apps with shiny

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. …

Practice and look at the help document of plot

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

Histograms

hist(mpg$hwy)

Density plots

d <- density(mpg$hwy) ## returns the density data 
plot(d) 

car.table <- table(mpg$manufacturer)
pie.cars <- car.table
names(pie.cars) <- names(car.table)
pie(pie.cars)

## 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")

data(mtcars)
library(corrplot)
M <- cor(mtcars)
corrplot(M, addCoef.col = "grey")

plot(AirPassengers)

par(mfrow = c(1, 2))
plot(AirPassengers)
boxplot(ggplot2::mpg$hwy, data = ggplot2::mpg, main = 'Boxplot of MPG')

library(ggplot2)
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

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

2. You complete your graph by adding one or more layers to ggplot(). For example, 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.

ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

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?

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

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)

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

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))

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

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

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

• 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.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

• To facet your plot on the combination of two variables, add facet_grid() to your plot call.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ cyl)

library(GGally)
ggpairs(subset(mtcars, select = c(1, 3, 4, 5, 6)))

## left
p1 <- ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

## right
p2 <- ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy))

## put the two plots in one row
gridExtra::grid.arrange(p1, p2, ncol = 2)

• Both plots contain the same x and y, and both describe the same data. Each plot uses a different visual object to represent the data. In ggplot2 syntax, we say that they use different geoms.

• A geom is the geometrical object that a plot uses to represent data.

• Bar charts use bar geoms, line charts use line geoms, boxplots use boxplot geoms, and so on.

• You can also add multiple geom functions.

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

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

ggplot(data = mpg) +
  geom_bar(mapping = aes(x = drv))

You can change the colors.

ggplot(data = mpg) + 
  geom_bar(mapping = aes(x = drv, fill = drv))

ggplot(data = mpg) + 
  geom_histogram(mapping = aes(x = hwy))

ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + 
  geom_boxplot()

ggplot(data = mpg, mapping = aes(x = class, y = hwy)) + 
  geom_boxplot() +
  coord_flip()

library(ggplot2)
library(forecast)
autoplot(AirPassengers)

We will leave this to a case study in the next lecture (unsupervised methods).

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

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

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

df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/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<br>Source:<a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">CIA World Factbook</a>',
    geo = g
  )

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.

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")  
    )
    
  )
)

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")
  })
}

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")
    )
  )
)

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))
  })
}

I will leave you to think about how to design the wordcloud app or an app in your mind?

You may wonder how to put your app on the web. Please refer to this page.

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