→ Titles

Q1.1 The code below builds a basic histogram for Rbnb apartment prices on the French Riviera. It shows only value under 300 euros. Add code to:

• add a title with ggtitle()
• change axis labels xlab() and ylab()
• change axis limits with xlim() and ylim()

# Load dataset from github
data <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/1_OneNum.csv", header=TRUE)

# Make the histogram
data %>%
  filter( price<300 ) %>%
  ggplot( aes(x=price)) +
    geom_histogram() +
    ...

→ Chart components

All ggplot2 chart components can be changed using the theme() function. You can see a complete list of components in the official documentation.

Note: components are changed using different functions: element_text(), element_line() for lines and so on..

Q1.2 Reproduce the previous histogram and change:

• plot title size and color with plot.title
• X axis title size and color with axis.title.x
• Grid appearance with panel.grid.major

# Make the histogram
data %>%
  filter( price<300 ) %>%
  ggplot( aes(x=price)) +
    ... +
    theme(
      plot.title = element_text(size=..., color=...),
      ...,
      ...
    )

→ Themes

Q1.3 ggplot2 offers a set of pre-built themes. Try the followings to see which one you like the most:

• theme_bw()
• theme_dark()
• theme_minimal()
• theme_classic()

See a complete list here.

# Load dataset from github
data <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/1_OneNum.csv", header=TRUE)

# Make the histogram
data %>%
  ... +
    theme_classic()

Q1.4 The hrbrthemes package provides my favourite style. Install the package, load it, and apply the theme_ipsum(). Documentation is here.

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

# Load dataset from github
data <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/1_OneNum.csv", header=TRUE)

# Make the histogram
data %>%
  filter( price<300 ) %>%
  ggplot( aes(x=price)) +
    stat_bin(breaks=seq(0,300,10), fill="#69b3a2", color="#e9ecef", alpha=0.9) +
    ggtitle("Night price distribution of Airbnb appartements") +
    theme_ipsum()

→ Text

The most common type of annotation is text. Let’s say you have a spike in a line plot. It totally makes sense to highlight it, and explain more in details what it is about.

Q1.1 Build a line plot showing the bitcoin price evolution between 2013 and 2018. Dataset is located here and can be read directly with read.table(). What part of the chart would you highlight?

# 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)

# plot
...

Q1.2 Use the annotate() function to add text. Annotate requires several arguments:

• geom: type of annotation, use text
• x: position on the X axis
• y: position on the Y axis
• label: what you want to write
• Optional: color, size, angle and more.

# plot
data %>%
  ggplot( aes(x=date, y=value)) +
    geom_line(color="#69b3a2") +
    annotate( x=as.Date("2017-01-01"), ...)

→ Shape

Q1.3 Find the exact spike date and its value. Use this information to add a circle around the spike. This is done with the annotate() function once more:

• geom: use point
• x: position on the X axis
• y: position on the Y axis
• shape: use 21, to be able to change the fill and the color arguments. (fill=inside, color=stroke)
• size

# Find spike date and value:
# data %>% arrange(...) ...

# plot
data %>% 
  ggplot( aes(x=date, y=value)) +
    geom_line(color="#69b3a2") +
    annotate(geom="text", ...) +
    annotate(geom="point", ...)

→ Abline

Q1.4 Add a horizontal abline to show what part of the curve is over 5000 $. This is possible thanks to the geom_hline() function that requires its yintercept argument.

# Find spike date and value:
# data %>% arrange(desc(value)) %>% head(1)

# plot
data %>% 
  ...
    annotate(...) +
    annotate(...) +
    geom_hline(..., color=..., size=...)

→ Color

Q1.5 Build a scatterplot based on the gapminder dataset. Use gdpPercap for the X axis, lifeExp for the Y axis, and pop for bubble size. Keep only the year 2007.

Q1.6 Highlight South Africa in the chart: draw it in red, with all other circles in grey. Follow those steps:

• create a new column with mutate: this new column has the value yes if country=="South Africa", no otherwise. This is possible thanks to the ifelse function.
• in the aesthetics part of the ggplot call, use this new column to control dot colors
• use scale_color_manual() to control the color of both group. Use a bright color for the country to highlight, and grey for the others.

# Basic scatterplot
data %>%
  mutate(isSouthAfrica = ... ) %>%
  ggplot( .., color = isSouthAfrica)) +
    geom... +
    scale_color_manual(values=c("grey", "red")) +
    theme(legend.position="none")

→ Multiple text

Q1.7 Highlight every country with gdpPercap > 5000 & lifeExp < 60 in red. Write their names using the geom_text_repel of theggrepel` package to avoid text overlapping.

# ggrepel
library(ggrepel)

# prepare data
tmp <- data %>%
  mutate( annotation = ifelse(...))

# plot
tmp %>%
  ggplot( ...) +
    geom... +
    theme(...) +
    geom_text_repel(data=tmp %>% filter(annotation=="yes"), aes(label=country), size=4 )

→ facet_wrap()

Q3.1 Build a spaghetti chart showing the evolution of 9 baby names in the US. (See code here). What’s wrong with this chart?

# Libraries
library(babynames)

# Load dataset from github
data <- babynames %>% 
  filter(name %in% c("Ashley", "Amanda", "Jessica",    "Patricia", "Linda", "Deborah",   "Dorothy", "Betty", "Helen")) %>%
  filter(sex=="F")

...

Q3.2 Use the facet_wrap() function to build one area chart for each name. Basically, you have to provide a categorical variable to the function. It will build a chart for each of its level.

Have a look to the Y axis. What do you observe? Is it a good option?

...
    geom_area() +
    ... +
    facet_wrap(~name)

You should get something like this:

Q3.3 Find out how to use the scale option to have different Y axis limits for each subset. Does it make sense? In which conditions?

...
    geom_area() +
    ... +
    facet_wrap(~name, scale=)

You should get something like this:

→ facet_grid()

Bonus Find out what the facet_grid() function does. Why is it different to facet_wrap()?

BonusLoad this dataset in R. Build a histogram for every combination of day and sex using facet_wrap()

You should get something like:

→ One color

Q5.1 Several options exist to pick one color. Change the histogram color using the fill argument on the chart below using each of the following options:

• plain color name. Type colors() to see all the options.
• using rgb(). This function provides the quantity of red, green and blue to build the color. Plus an argument for the opacity. Example, try rgb(.7, .6, .3, .2)
• using HTML colors. Use this website to pick one you like.

# Load dataset from github
data <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/1_OneNum.csv", header=TRUE)

# Make the histogram
data %>%
  filter( price<300 ) %>%
  ggplot( aes(x=price)) +
    geom_histogram(fill="steelblue") +
    ggtitle("Night price distribution of Airbnb appartements") +
    theme_ipsum()

→ Discrete color palette

Q5.2 Build a scatterplot based on the iris dataset. Use Sepal.Length for the X axis, Petal.Length for the Y axis. Use color=Species to color groups.

Q5.3 It is possible to set the color scale manually using scale_color_manual(). Use the hint below to see how to use it and apply it to the previous scatterplot.

Note: it is a bad practice to pick colors randomly. Your palette will be ugly and will probably not be colorblind friendly.

ggplot(iris, aes(x=Sepal.Length, y=Petal.Length, color=Species)) +
  geom_point() +
  scale_color_manual( values=c("red","green","blue"))

Q5.4 Fortunately, people already tackeled this issue for us and created packages offering nice color palettes. The most famous one is RColorBrewer. Palettes are already available in ggplot2. See all of them here, and use one on your chart using scale_color_brewer().

Pick the one you like the most and apply it to to previous scatterplot. Use it to color the Species.

... +
  scale_color_brewer(palette = )

→ Continuous color palette

Q5.5 RColorBrewer also offers continuous color palette. However they must be called through the scale_color_distiller function. Use the palette you like the most to color circles depending on Sepal_length.

... +
  scale_color_distil...

→ Plotly

Q6.1 Build the gapminder bubble plot you’ve already done in the annotation part of this practical. Store it in an object called p

# load data
library(gapminder)
data <- gapminder %>% filter(year=="2007") %>% select(-year)

# Basic ggplot
p <- data %>%
  ggplot( ...
p

Q6.2 Install and load the plotly package. Build an interactive chart using the ggplotly() function. What are the new functionalities of this chart? Is it useful? What could be better?

# Interactive version
library(plotly)
ggplotly(p)

Q6.3 Let’s improve the tooltip of the chart:

• build a new column called myText. Fill it with whatever you want to show in the tooltip.
• add a new aesthetics: text=myText
• in the ggplotly() call, add tooltip="text"

# Basic ggplot
p <- data %>%
  mutate(myText=...) %>%
  ggplot( aes(...text=myText)) +
   ...

ggplotly(p, tooltip="text")

→ Leaflet

Q6.4 Use the HTML widget called leaflet to build an interactive map showing the earthquakes described in the dataset called quakes. Code is fully provided here, since cartography with R could deserve an entire practical. The idea is just to discover to potential offered in a few lines of code:

# Library
library(leaflet)
 
# load example data (Fiji Earthquakes) + keep only 100 first lines
data(quakes)
quakes =  head(quakes, 100)

# Create a color palette with handmade bins.
mybins=seq(4, 6.5, by=0.5)
mypalette = colorBin( palette="YlOrBr", domain=quakes$mag, na.color="transparent", bins=mybins)

# Final Map
leaflet(quakes) %>% 
  addTiles()  %>% 
  setView( lat=-27, lng=170 , zoom=4) %>%
  addProviderTiles("Esri.WorldImagery") %>%
  addCircleMarkers(~long, ~lat, 
    fillColor = ~mypalette(mag), fillOpacity = 0.7, color="white", radius=8, stroke=FALSE
  ) %>%
  addLegend( pal=mypalette, values=~mag, opacity=0.9, title = "Magnitude", position = "bottomright" )

→ Heatmap

The d3heatmap package allows to build interactive heatmaps in a few line of code. Let’s see how it works

Q6.5 Load this dataset in R. Have a look to the first rows. Describe it. (source)

# Load data
data <- read.csv(...)

Q6.6 R offers a heatmap() function to build… heatmaps! Apply it to the dataset. What do you observe? Are you happy with this heatmap? What’s wrong with it? How can we solve the issue?

Note: input dataset must be at the matrix format to be accepted by the function. Use as.matrix() to get this format.

Q6.7 Check the scale option of the heatmap() function. What is it for? Can it help us? How? Use it to improve the heatmap.

Q6.8 d3heatmap() uses exactly the same syntax than heatmap(). Use the function to get an interactive version of the previous heatmap!

# Load library
library(d3heatmap)

# Build heatmap
d3heatmap(...)

→ Time Seriesx

Q6.9 - Use the HTML widget called dygraphs to build an interactive line plot of the bitcoin price evolution. Try to reproduce the example below.

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

# 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)

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

# Use the dygraph HTML widget
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)

→ HTML widgets

BONUS - The packages showcased above are just a sample of the possibilities offered by the html widgets. Visit this website to have an overview of what kind of interactive chart you can do with R. Pick your favorite example and try to reproduce it.