Get ready

→ I strongly suggest to use R Studio, the best way to develop R code.

→ Start a new script with File → new file → R Script. Save this file somewhere in your computer. You will store all your command lines in it.

→ This practical requires several R packages.

Q0.1 Install the ggplot2 package if needed and load it. ggplot2 is a very powerful package for data visualization with R. It is the main topic of this practical.

# Install the package if needed
#install.packages("ggplot2")

# Load it
library(ggplot2)

Q0.2 Install and load dplyr. dplyr is part of the tidyverse and is very useful for data manipulation. It also provides the %>% operators that allows to ‘pipe’ commands.

# Install the package if needed
#install.packages("dplyr")

# Load it
library(dplyr)

1- Correlation

The first part of this practical will teach you how to build scatterplots and bubble charts: the 2 most common chart types to visualize correlations.

Q1.1 Load the gapminder dataset stored in the gapminder package. Have a look to the 6 first rows using the head() function. Describe briefly what you see as comments in your script. Check how many rows are available with nrow()

# Install the package if needed
#install.packages("gapminder")

# Load it
library(gapminder)

# Have a look to the first rows
head(gapminder)

# How many rows?
nrow(gapminder)

[1] 1704

Q1.2 How many years are available in this dataset? How many data-points for each year? Full code is provided for this question.

# Number of different year?
gapminder %>%
  select(year) %>%
  unique() %>%
  nrow()
# or
length(unique(gapminder$year))

# Number of country available per year?
gapminder %>%
  group_by(year) %>%
  summarize( n=n() )

Q1.3 Build a scatterplot showing the relationship between gdpPercap and lifeExp in 1952. Use geom_point(). What do you observe?

# basic scatterplot
gapminder %>%
  filter(year=="1952") %>%
  ggplot( aes(x=gdpPercap, y=lifeExp)) +
    geom_point()

Q1.4 On the previous chart, one country is very different. Which one is it?

You should get something like:

# Number of different year?
gapminder %>%
  filter(year=="1952" & gdpPercap>90000) 

Q1.5 Build the same chart, but get rid of this country. What trend do you observe? Does it make sense? What’s missing? What could be better?

You should get a chart like this:

# basic scatterplot
gapminder %>%
  filter(year=="1952" & country!="Kuwait") %>%
  ggplot( aes(x=gdpPercap, y=lifeExp)) +
    geom_point()

Q1.6 Color dots according to their continent. In the aes() part of the code, use the color argument.

gapminder %>%
  filter(year=="1952" & country!="Kuwait") %>%
  ggplot( aes(x=gdpPercap, y=lifeExp, color=continent)) +
    geom_point()

Q1.7 Let’s observe an additional variable: make the circle size proportionnal to the population (pop). This is done with the size argument of aes(). How do you call this kind of chart?

gapminder %>%
  filter(year=="1952" & country!="Kuwait") %>%
  ggplot( aes(x=gdpPercap, y=lifeExp, color=continent, size=pop)) +
    geom_point()

Bonus You’re in advance? Try to do the following:

• custom the general appearance using the theme_ipsum of the hrbrthemes library.
• add transparency to circles to limit overlapping impact, with the alpha argument of aes
• sort your data by pop size to put the small circle on top of the chart, not hidden by big bubbles
• use the ggplotly() function of the plotly package to make this chart interactive

You should get something like this chart:

# Additionnal packages:
library(hrbrthemes) # for general style
library(plotly)     # to make the chart interactive

# Chart
p <- gapminder %>%
  filter(year=="1952" & country!="Kuwait") %>%
  arrange(desc(pop)) %>%
  ggplot( aes(x=gdpPercap, y=lifeExp, fill=continent, size=pop)) +
    geom_point(alpha=0.7, stroke="white", shape=21) +
    theme_ipsum()

# Interactive more
ggplotly(p)

2- Distribution

This second part is dedicated to the visualization of distribution. It is split in 2 parts:

• Visualizing one distribution
• Comparing distribution for several groups or variables

# Load dataset from github
data <- read.table("DATA/1_OneNum.csv", header=TRUE)

 nrow(data)

[1] 9995

 summary(data)

     price        
 Min.   :   11.0  
 1st Qu.:   69.0  
 Median :  103.0  
 Mean   :  179.4  
 3rd Qu.:  172.0  
 Max.   :17242.0  

data %>%
  ggplot( aes(x=price)) +
    geom_histogram()

data %>%
  filter(price<1500) %>%
  ggplot( aes(x=price)) +
    geom_histogram()

data %>%
  filter(price<400) %>%
  ggplot( aes(x=price)) +
    geom_histogram(binwidth = 2)

data %>%
  filter(price<1000) %>%
  ggplot( aes(x=price)) +
    geom_density(color="transparent", fill="#69b3a2", bw=5)

# Load dataset from github
data <- read.table("DATA/probability.csv", header=TRUE, sep=",")

# Data size?
nrow(data)

[1] 368

# occurence of each word:
table(data$text)

      About Even Almost Certainly Almost No Chance       Improbable 
              46               46               46               46 
          Likely     Probably Not         Unlikely Very Good Chance 
              46               46               46               46 

ggplot(data, aes(x=text, y=value, fill=text)) +
    geom_boxplot() 

# Library forcats to reorder data
library(forcats)

# Reorder data
data %>%
  mutate(text = fct_reorder(text, value, .fun = median)) %>%
  ggplot(aes(x=text, y=value, fill=text)) +
    geom_boxplot() +
    theme(
      legend.position = "none"
    ) +
    coord_flip()

# Library forcats to reorder data
library(forcats)

# Reorder data
data %>%
  mutate(text = fct_reorder(text, value, .fun = median)) %>%
  ggplot(aes(x=text, y=value, fill=text)) +
    geom_boxplot() +
    geom_jitter(color="grey", width=.4, size=.5, alpha=.8) +
    theme(
      legend.position = "none"
    ) +
    coord_flip()

3- Ranking

Let’s talk about the quantity of weapons exported by the top 50 largest exporters in 2017 (source). The dataset is available on github. Load it in R:

# Load dataset from github
data <- read.table("DATA/7_OneCatOneNum.csv", header=TRUE, sep=",")

Q3.1 Have a quick look to the dataset. Describe it. What kind of chart can you build with this dataset? Which one would be the best in your opinion? What are the countries on top of the ranking?

head(data)

nrow(data)

[1] 51

data %>% 
arrange(desc(Value)) %>%
head(5)

Q3.2 Start with a basic barplot using geom_bar().

Note: by default geom_bar() takes only one categorical variable as input, used for the x axis. It counts the number of cases at each x position and display it on the Y axis. In our case, we want to provide a y value for each group. This is why we need to specify stat="identity".

data %>%
  ggplot( aes(x=Country, y=Value) ) +
    geom_bar(stat="identity")

Q3.3 Color all bars with the same color: #69b3a2. Don’t like the color? Pick another one. Do you have to use fill or color? Why?

data %>%
  ggplot( aes(x=Country, y=Value) ) +
    geom_bar(stat="identity", fill="#69b3a2")

Q3.4 Set a different color for each bar. Do you like the output? Is it useful? Do you understand the difference between adding an option inside or outside aes() ?

data %>%
  ggplot( aes(x=Country, y=Value, fill=Country) ) +
    geom_bar(stat="identity")

Q3.5 Previous barplots are a bit disappointing aren’t they? What can you improve?

Q3.6 Try the following:

• use coord_flip() to get a horizontal version
• check the code below to reorder countries.

You should get that kind of output:

data %>%
  filter(!is.na(Value)) %>%
  arrange(Value) %>%
  mutate(Country=factor(Country, Country)) %>%
  ggplot( aes(x=Country, y=Value) ) +
    geom_bar(stat="identity", fill="#69b3a2") +
    coord_flip() +
    xlab("")

Q3.7 A lollipop plot is used in the same conditions as a barplot. Build it with:

• geom_segment() for the stems. Arguments needed are x, xend, y and yend.
• geom_point() for the circles. Needs x and y only.

You should get:

data %>%
  filter(!is.na(Value)) %>%
  arrange(Value) %>%
  mutate(Country=factor(Country, Country)) %>%
  ggplot( aes(x=Country, y=Value) ) +
    geom_segment( aes(x=Country ,xend=Country, y=0, yend=Value), color="grey") +
    geom_point(size=3, color="#69b3a2") +
    coord_flip() +
    xlab("")

BONUSTry the following:

• Improve the previous lollipop chart with the theme_ipsum. Be creative to make it even better.
• Visit the treemap section of the R graph gallery. Apply this kind of chart to the weapon dataset. You should get the chart below.
• Ask google what a circular barchart is. What are the pros and cons of this chart compared to the classic barplot? Try to implement it with R and ggplot2.

# Package
library(treemap)
data <- na.omit(data) #just to plot here simpler
# Plot
treemap(data,
            
            # data
            index="Country",
            vSize="Value",
            type="index",
            
            # Main
            title="",
            palette="Dark2",

            # Borders:
            border.col=c("black"),             
            border.lwds=1,                         
        
            # Labels
            # fontsize.labels=0.5,
            fontcolor.labels="white",
            fontface.labels=1,            
            bg.labels=c("transparent"),              
            align.labels=c("left", "top"),                                  
            overlap.labels=0.5,
            inflate.labels=T                        # If true, labels are bigger when rectangle is bigger.

            
            )

4- Evolution

Let’s consider the evolution of the bitcoin price between April 2013 and April 2018. Data are stored on github. Load the dataset using the following code:

# Load dataset from github
data <- read.table("DATA/3_TwoNumOrdered.csv", header=T)
data$date <- as.Date(data$date)
#Here is one change

Q4.1 Build a basic line chart showing the bitcoin price evolution using geom_line().

data %>%
  ggplot( aes(x=date, y=value)) +
    geom_line(color="#69b3a2")

Q4.2 Switch to an area chart using geom_area(). Use the color and fill argument to customize chart colors.

data %>%
  ggplot( aes(x=date, y=value)) +
    geom_area(color="#69b3a2", fill="#69b3a2")

Q4.3 Select the last 10 values using tail(). Build a connected scatterplot using geom_point(), geom_line() and geom_area().

data %>%
  tail(10) %>%
  ggplot( aes(x=date, y=value)) +
    geom_area(fill="#69b3a2", alpha=0.5) +
    geom_line(color="#69b3a2") +
    geom_point()

Bonus Visit the time series section of the R graph gallery. Try to use the HTML widget called dygraph to build an interactive version of this lineplot.

You should get something like this.

# Library
library(dygraphs)
library(xts)          # To make the convertion data-frame / xts format
library(lubridate)

# Then you can create the xts format
don <- xts(x = data$value, order.by = data$date)

# graph
dygraph(don) %>%
  dyOptions(labelsUTC = TRUE, fillGraph=TRUE, fillAlpha=0.1, drawGrid = FALSE, colors="#D8AE5A") %>%
  dyRangeSelector() %>%
  dyCrosshair(direction = "vertical") %>%
  dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = FALSE)  %>%
  dyRoller(rollPeriod = 1)