session_3.Rmd

title: 'R.3: Transformations with ggplot2'
author: "Laurent Modolo [laurent.modolo@ens-lyon.fr](mailto:laurent.modolo@ens-lyon.fr), Hélène Polvèche [hpolveche@istem.fr](mailto:hpolveche@istem.fr)"
date: "2022"
output:
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library(fontawesome)

 r fa(name = "fas fa-house", fill = "grey", height = "1em")  https://can.gitbiopages.ens-lyon.fr/R_basis/

rm(list=ls())
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(comment = NA)

klippy::klippy(
  position = c('top', 'right'),
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  tooltip_message = 'Click to copy',
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Introduction

In the last session, we have seen how to use ggplot2 and The Grammar of Graphics. The goal of this practical is to practices more advanced features of ggplot2.

The objectives of this session will be to:

• learn about statistical transformations
• practices position adjustments
• change the coordinate systems

The first step is to load the tidyverse.

Solution

```{r packageloaded, include=TRUE, message=FALSE} library("tidyverse") ```

Like in the previous sessions, it's good practice to create a new .R file to write your code instead of using the R terminal directly.

ggplot2 statistical transformations

In the previous session, we have plotted the data as they are by using the variable values as x or y coordinates, color shade, size or transparency. When dealing with categorical variables, also called factors, it can be interesting to perform some simple statistical transformations. For example, we may want to have coordinates on an axis proportional to the number of records for a given category.

We are going to use the diamonds data set included in tidyverse.

• Use the help and View command to explore this data set.
• How much records does this dataset contain ?
• Try the str command, which information are displayed ?

str(diamonds)

Introduction to geom_bar

We saw scatterplot (geom_point()), smoothplot (geom_smooth()). Now barplot with geom_bar() :

ggplot(data = diamonds, mapping = aes(x = cut)) + 
  geom_bar()

More diamonds are available with high quality cuts.

On the x-axis, the chart displays cut, a variable from diamonds. On the y-axis, it displays count, but count is not a variable in diamonds!

geom and stat

The algorithm used to calculate new values for a graph is called a stat, short for statistical transformation. The figure below describes how this process works with geom_bar().

You can generally use geoms and stats interchangeably. For example, you can recreate the previous plot using stat_count() instead of geom_bar():

ggplot(data = diamonds, mapping = aes(x = cut)) + 
  stat_count()

Every geom has a default stat; and every stat has a default geom. This means that you can typically use geoms without worrying about the underlying statistical transformation. There are three reasons you might need to use a stat explicitly:

Why stat ?

You might want to override the default stat. For example, in the following demo dataset we already have a variable for the counts per cut.

demo <- tribble(
  ~cut,         ~freq,
  "Fair",       1610,
  "Good",       4906,
  "Very Good",  12082,
  "Premium",    13791,
  "Ideal",      21551
)

(Don't worry that you haven't seen tribble() before. You might be able to guess at their meaning from the context, and you will learn exactly what they do soon!)

So instead of using the default `geom_bar` parameter `stat = "count"` try to use `"identity"`

Solution

```{r 3_ab, include=TRUE, fig.width=8, fig.height=4.5} ggplot(data = demo, mapping = aes(x = cut, y = freq)) + geom_bar(stat = "identity") ```

You might want to override the default mapping from transformed variables to aesthetics ( e.g., proportion).

ggplot(data = diamonds, mapping = aes(x = cut, y = ..prop.., group = 1)) + 
  geom_bar()

In our proportion bar chart, we need to set `group = 1`. Why?

Solution

```{r diamonds_stats_challenge, include=TRUE, message=FALSE, fig.width=8, fig.height=4.5} ggplot(data = diamonds, mapping = aes(x = cut, y = ..prop..)) + geom_bar() ```

If group is not used, the proportion is calculated with respect to the data that contains that field and is ultimately going to be 100% in any case. For instance, the proportion of an ideal cut in the ideal cut specific data will be 1.

More details with stat_summary

You might want to draw greater attention to the statistical transformation in your code. you might use `stat_summary()`, which summarize the **y** values for each unique **x** value, to draw attention to the summary that you are computing

Solution

```{r 3_c, include=TRUE, fig.width=8, fig.height=4.5, message=FALSE}

ggplot(data = diamonds, mapping = aes(x = cut, y = depth)) + stat_summary()

</p>
</details>

<div class="pencadre">
Set the `fun.min`, `fun.max` and `fun` to the `min`, `max` and `median` function respectively
</div>

<details><summary>Solution</summary>
<p>
```{r 3_d, include=TRUE, fig.width=8, fig.height=4.5, message=FALSE}
ggplot(data = diamonds, mapping = aes(x = cut, y = depth)) + 
  stat_summary(
    fun.min = min,
    fun.max = max,
    fun = median
  )

Coloring area plots

You can color a bar chart using either the `color` aesthetic, or, more usefully `fill`: Try both solutions on a `cut`, histogram.

Solution

```{r diamonds_barplot_color, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE} ggplot(data = diamonds, mapping = aes(x = cut, color = cut)) + geom_bar() ```

ggplot(data = diamonds, mapping = aes(x = cut, fill = cut)) + 
  geom_bar()

You can also use `fill` with another variable: Try to color by `clarity`. Is `clarity` a continuous or categorial variable ?

Solution

```{r diamonds_barplot_fill_clarity, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE} ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + geom_bar() ```

Position adjustments

The stacking of the fill parameter is performed by the position adjustment position

Try the following `position` parameter for `geom_bar`: `"fill"`, `"dodge"` and `"jitter"`

Solution

```{r diamonds_barplot_pos_fill, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE} ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + geom_bar( position = "fill") ```

ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + 
  geom_bar( position = "dodge")

ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + 
  geom_bar( position = "jitter")

jitter is often used for plotting points when they are stacked on top of each other.

Compare `geom_point` to `geom_jitter` plot `cut` versus `depth` and color by `clarity`

Solution

```{r dia_jitter2, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE} ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) + geom_point() ```

ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) + 
  geom_jitter()

What parameters of `geom_jitter` control the amount of jittering ?

Solution

```{r dia_jitter4, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE} ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) + geom_jitter(width = .1, height = .1) ```

In the geom_jitter plot that we made, we cannot really see the limits of the different clarity groups. Instead we can use the geom_violin to see their density.

Solution

```{r dia_violon, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE} ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) + geom_violin() ```

Coordinate systems

Cartesian coordinate system where the x and y positions act independently to determine the location of each point. There are a number of other coordinate systems that are occasionally helpful.

ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) + 
  geom_boxplot()

Add the `coord_flip()` layer to the previous plot

Solution

```{r dia_boxplot_flip, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE} ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) + geom_boxplot() + coord_flip() ```

Add the `coord_polar()` layer to this plot:

ggplot(data = diamonds, mapping = aes(x = cut, fill = cut)) + 
  geom_bar( show.legend = FALSE,  width = 1 ) + 
  theme(aspect.ratio = 1) +
  labs(x = NULL, y = NULL)

Solution

```{r diamonds_bar2, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE} ggplot(data = diamonds, mapping = aes(x = cut, fill = cut)) + geom_bar( show.legend = FALSE, width = 1 ) + theme(aspect.ratio = 1) + labs(x = NULL, y = NULL) + coord_polar() ```

By combining the right geom, coordinates and faceting functions, you can build a large number of different plots to present your results.

See you in R.4: data transformation

To go further: animated plots from xls files

In order to be able to read information from a xls file, we will use the openxlsx packages. To generate animation we will use the ggannimate package. The additional gifski package will allow R to save your animation in the gif format (Graphics Interchange Format)

install.packages(c("openxlsx", "gganimate", "gifski"))

library(openxlsx)
library(gganimate)
library(gifski)

Use the `openxlsx` package to save the https://can.gitbiopages.ens-lyon.fr/R_basis/session_3/gapminder.xlsx file to the `gapminder` variable

Solution

2 solutions :

Use directly the url

gapminder <- read.xlsx("https://can.gitbiopages.ens-lyon.fr/R_basis/session_3/gapminder.xlsx")

Dowload the file, save it in the same directory as your script then use the local path

gapminder <- read.xlsx("gapminder.xlsx")

This dataset contains 4 variables of interest for us to display per country:

• gdpPercap the GDP par capita (US$, inflation-adjusted)
• lifeExp the life expectancy at birth, in years
• pop the population size
• contient a factor with 5 levels

Using `ggplot2`, build a scatterplot of the `gdpPercap` vs `lifeExp`. Add the `pop` and `continent` information to this plot.

Solution

```{r gapminder_plot_a} ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) + geom_point() ```

What's wrong ? You can use the `scale_x_log10()` to display the `gdpPercap` on the `log10` scale.

Solution

```{r gapminder_plot_b} ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) + geom_point() + scale_x_log10() ```

We would like to add the `year` information to the plots. We could use a `facet_wrap`, but instead we are going to use the `gganimate` package.

For this we need to add a transition_time layer that will take as an argument year to our plot.

Solution

```{r gapminder_plot_c} ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) + geom_point() + scale_x_log10() + transition_time(year) + labs(title = 'Year: {as.integer(frame_time)}') ```