diff --git a/session_2/session_2.Rmd b/session_2/session_2.Rmd
index 6c22ae24f9939631460033b5e2b2aa6f2d20e2a7..2b97249be9988cffa27fd06ceceabb361128ef73 100644
--- a/session_2/session_2.Rmd
+++ b/session_2/session_2.Rmd
@@ -1,232 +1,210 @@
---
-title: "R#2: introduction to Tidyverse"
+title: "R.2: introduction to Tidyverse"
author: "Laurent Modolo [laurent.modolo@ens-lyon.fr](mailto:laurent.modolo@ens-lyon.fr), Hélène Polvèche [hpolveche@istem.fr](mailto:hpolveche@istem.fr)"
-date: "Mars 2020"
+date: "2021"
output:
- html_document: default
- pdf_document: default
+ rmdformats::downcute:
+ self_contain: false
+ use_bookdown: true
+ default_style: "dark"
+ lightbox: true
+ css: "../src/style.css"
---
-<style type="text/css">
-h3 { /* Header 3 */
- position: relative ;
- color: #729FCF ;
- left: 5%;
-}
-h2 { /* Header 2 */
- color: darkblue ;
- left: 10%;
-}
-h1 { /* Header 1 */
- color: #034b6f ;
-}
-#pencadre{
- border:1px;
- border-style:solid;
- border-color: #034b6f;
- background-color: #EEF3F9;
- padding: 1em;
- text-align: center ;
- border-radius : 5px 4px 3px 2px;
-}
-legend{
- color: #034b6f ;
-}
-#pquestion {
- color: darkgreen;
- font-weight: bold;
-
-}
-}
-</style>
+
```{r setup, include=FALSE}
+rm(list=ls())
knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_chunk$set(comment = NA)
+```
+```{r klippy, echo=FALSE, include=TRUE}
+klippy::klippy(
+ position = c('top', 'right'),
+ color = "white",
+ tooltip_message = 'Click to copy',
+ tooltip_success = 'Copied !')
+```
+```{r download_data, include=FALSE, eval=FALSE}
+library("tidyverse")
+tmp <- tempfile(fileext = ".zip")
+download.file("http://www.fueleconomy.gov/feg/epadata/vehicles.csv.zip",
+ tmp,
+ quiet = TRUE)
+unzip(tmp, exdir = "data-raw")
+new_class_level <- c(
+ "Compact Cars",
+ "Large Cars",
+ "Midsize Cars",
+ "Midsize Cars",
+ "Midsize Cars",
+ "Compact Cars",
+ "Minivan",
+ "Minivan",
+ "Pickup Trucks",
+ "Pickup Trucks",
+ "Pickup Trucks",
+ "Sport Utility Vehicle",
+ "Sport Utility Vehicle",
+ "Compact Cars",
+ "Special Purpose Vehicle",
+ "Special Purpose Vehicle",
+ "Special Purpose Vehicle",
+ "Special Purpose Vehicle",
+ "Special Purpose Vehicle",
+ "Special Purpose Vehicle",
+ "Sport Utility Vehicle",
+ "Sport Utility Vehicle",
+ "Pickup Trucks",
+ "Pickup Trucks",
+ "Pickup Trucks",
+ "Pickup Trucks",
+ "Sport Utility Vehicle",
+ "Sport Utility Vehicle",
+ "Compact Cars",
+ "Two Seaters",
+ "Vans",
+ "Vans",
+ "Vans",
+ "Vans"
+)
+new_fuel_level <- c(
+ "gas",
+ "Diesel",
+ "Regular",
+ "gas",
+ "gas",
+ "Regular",
+ "Regular",
+ "Hybrid",
+ "Hybrid",
+ "Regular",
+ "Regular",
+ "Hybrid",
+ "Hybrid"
+)
+read_csv("data-raw/vehicles.csv") %>%
+ select(
+ "id",
+ "make",
+ "model",
+ "year",
+ "VClass",
+ "trany",
+ "drive",
+ "cylinders",
+ "displ",
+ "fuelType",
+ "highway08",
+ "city08"
+ ) %>%
+ rename(
+ "class" = "VClass",
+ "trans" = "trany",
+ "drive" = "drive",
+ "cyl" = "cylinders",
+ "displ" = "displ",
+ "fuel" = "fuelType",
+ "hwy" = "highway08",
+ "cty" = "city08"
+ ) %>%
+ filter(drive != "") %>%
+ drop_na() %>%
+ arrange(make, model, year) %>%
+ mutate(class = factor(as.factor(class), labels = new_class_level)) %>%
+ mutate(fuel = factor(as.factor(fuel), labels = new_fuel_level)) %>%
+ write_csv("mpg.csv")
+```
-library(tidyverse)
-# tmp <- tempfile(fileext = ".zip")
-# download.file("http://www.fueleconomy.gov/feg/epadata/vehicles.csv.zip",
-# tmp,
-# quiet = TRUE)
-# unzip(tmp, exdir = "data-raw")
-# new_class_level <- c(
-# "Compact Cars",
-# "Large Cars",
-# "Midsize Cars",
-# "Midsize Cars",
-# "Midsize Cars",
-# "Compact Cars",
-# "Minivan",
-# "Minivan",
-# "Pickup Trucks",
-# "Pickup Trucks",
-# "Pickup Trucks",
-# "Sport Utility Vehicle",
-# "Sport Utility Vehicle",
-# "Compact Cars",
-# "Special Purpose Vehicle",
-# "Special Purpose Vehicle",
-# "Special Purpose Vehicle",
-# "Special Purpose Vehicle",
-# "Special Purpose Vehicle",
-# "Special Purpose Vehicle",
-# "Sport Utility Vehicle",
-# "Sport Utility Vehicle",
-# "Pickup Trucks",
-# "Pickup Trucks",
-# "Pickup Trucks",
-# "Pickup Trucks",
-# "Sport Utility Vehicle",
-# "Sport Utility Vehicle",
-# "Compact Cars",
-# "Two Seaters",
-# "Vans",
-# "Vans",
-# "Vans",
-# "Vans"
-# )
-# new_fuel_level <- c(
-# "gas",
-# "Diesel",
-# "Regular",
-# "gas",
-# "gas",
-# "Regular",
-# "Regular",
-# "Hybrid",
-# "Hybrid",
-# "Regular",
-# "Regular",
-# "Hybrid",
-# "Hybrid"
-# )
-# read_csv("data-raw/vehicles.csv") %>%
-# select(
-# "id",
-# "make",
-# "model",
-# "year",
-# "VClass",
-# "trany",
-# "drive",
-# "cylinders",
-# "displ",
-# "fuelType",
-# "highway08",
-# "city08"
-# ) %>%
-# rename(
-# "class" = "VClass",
-# "trans" = "trany",
-# "drive" = "drive",
-# "cyl" = "cylinders",
-# "displ" = "displ",
-# "fuel" = "fuelType",
-# "hwy" = "highway08",
-# "cty" = "city08"
-# ) %>%
-# filter(drive != "") %>%
-# drop_na() %>%
-# arrange(make, model, year) %>%
-# mutate(class = factor(as.factor(class), labels = new_class_level)) %>%
-# mutate(fuel = factor(as.factor(fuel), labels = new_fuel_level)) %>%
-# write_csv("2_data.csv")
+# Introduction
-```
+In the last session, we have gone through the basis of R.
+Instead of continuing to learn more about R programming, in this session we are going to jump directly to rendering plots.
+
+We make this choice for three reasons:
+- Rendering nice plots is direclty rewarding
+- You will be able to apply what you learn in this session to your own data (given that they are *correctly formated*)
+- We will come back to R programming later, when you have all the necessary tools to visualize your results
-The goal of this practical is to familiarize yourself with `ggplot2`.
The objectives of this session will be to:
-- Create basic plot with `ggplot2`
+- Create basic plot with the `ggplot2` `library`
- Understand the `tibble` type
- Learn the different aesthetics in R plots
-- Compose graphics
-
-
-<div id='pencadre'>
-
-**Write the commands in the grey box in the terminal.**
-
-**The expected results will always be printed in a white box here.**
-
-**You can `copy-paste` but I advise you to practice writing directly in the terminal. To validate the line at the end of your command: press `Return`.**
-</div>
-
+- Compose complex graphics
## Tidyverse
-The tidyverse is a collection of R packages designed for data science.
+The `tidyverse` is a collection of R packages designed for data science that include `ggplot2`.
-All packages share an underlying design philosophy, grammar, and data structures.
+All packages share an underlying design philosophy, grammar, and data structures (plus the same shape of logo).
<center>
{width=500px}
</center>
- \
+`tidyverse` is a meta library, which can be long to install with the following command:
```R
install.packages("tidyverse")
```
-```R
+Luckily for your `tidyverse` is preinstalled on your Rstudio server. So you just have to load the ` library`
+
+```{R load_tidyverse}
library("tidyverse")
```
-
- \
### Toy data set `mpg`
-This dataset contains a subset of the fuel economy data that the EPA makes available on http://fueleconomy.gov . It contains only models which had a new release every year between 1999 and 2008.
+This dataset contains a subset of the fuel economy data that the EPA makes available on [fueleconomy.gov](http://fueleconomy.gov).
+It contains only models which had a new release every year between 1999 and 2008.
+You can use the `?` command to know more about this dataset.
```{r mpg_inspect, include=TRUE}
?mpg
-mpg
```
-```{r mpg_inspect2, include=TRUE}
-dim(mpg)
-```
+But instead of using a dataset included in a R package, you may want to be able to use any dataset with the same format.
+For that we are going to use the command `read_csv` which is able to read a [csv](https://en.wikipedia.org/wiki/Comma-separated_values) file.
-```R
-View(mpg)
-```
-### New script
-
-
+This command also work for file URL
- \
+```{r mpg_download, cache=TRUE, message=FALSE}
+new_mpg <- read_csv(
+ "http://perso.ens-lyon.fr/laurent.modolo/R/mpg.csv"
+)
+```
-<!-- ### Updated version of the data -->
+You can check the number of line and column of the data with `dim`:
-<!-- `mpg` is loaded with tidyverse, we want to be able to read our own data from -->
+```{r mpg_inspect2, include=TRUE}
+dim(new_mpg)
+```
-<!-- \ -->
-<!-- http://perso.ens-lyon.fr/laurent.modolo/R/2_data.csv -->
+To visualize the data in Rstudio you can use the command `View`
-<!-- ```{r mpg_download, cache=TRUE, message=FALSE} -->
-<!-- new_mpg <- read_csv( -->
-<!-- "http://perso.ens-lyon.fr/laurent.modolo/R/2_data.csv" -->
-<!-- ) -->
+```R
+View(new_mpg)
+```
-<!-- ``` -->
+### New script
- \
-
- \
+Like in the last session, instead of typing your commands direclty in the console, you are going to write them in an R script.
+
# First plot with `ggplot2`
-Relationship between engine size `displ` and fuel efficiency `hwy`.
+We are going to make the simpliest plot possible to study the relationship between two variables: the scatterplot.
-```{r new_mpg_plot_a, cache = TRUE, fig.width=8, fig.height=4.5}
-ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
- geom_point()
+The following command generate a plot between engine size `displ` and fuel efficiency `hwy`.
+```{r new_mpg_plot_a, cache = TRUE, fig.width=8, fig.height=4.5}
+ggplot(data = mpg) +
+ geom_point(mapping = aes(x = displ, y = hwy))
```
-### Composition of plot with `ggplot2`
-
+`ggplot2` is a system for declaratively creating graphics, based on [The Grammar of Graphics](https://www.amazon.com/Grammar-Graphics-Statistics-Computing/dp/0387245448/ref=as_li_ss_tl). You provide the data, tell `ggplot2` how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
```
ggplot(data = <DATA>) +
@@ -239,43 +217,27 @@ ggplot(data = <DATA>) +
- each geom function in `ggplot2` takes a `mapping` argument
- the `mapping` argument is always paired with `aes()`
- \
-
-
-<div id="pquestion"> - Make a scatterplot of `hwy` ( fuel efficiency ) vs. `cyl` ( number of cylinders ). </div>
- \
-
- \
-
- \
-
- \
-
- \
-
- \
-
- \
-
- \
-
+<div class="pencadre">
+Make a scatterplot of `hwy` ( fuel efficiency ) vs. `cyl` ( number of cylinders ).
+</div>
-
+<details><summary>Solution</summary>
+<p>
```{r new_mpg_plot_b, cache = TRUE, fig.width=8, fig.height=4.5}
ggplot(data = mpg, mapping = aes(x = hwy, y = cyl)) +
geom_point()
```
- \
-
-
- \
+</p>
+<div class="pencadre">
+What seems to be the problem ?
+</div>
+</details>
# Aesthetic mappings
-
`ggplot2` will automatically assign a unique level of the aesthetic (here a unique color) to each unique value of the variable, a process known as scaling. `ggplot2` will also add a legend that explains which levels correspond to which values.
Try the following aesthetic:
@@ -284,7 +246,7 @@ Try the following aesthetic:
- `alpha`
- `shape`
-### Aesthetic mappings : `color`
+## `color` mapping
```{r new_mpg_plot_e, cache = TRUE, fig.width=8, fig.height=4.5}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = class)) +
@@ -292,30 +254,27 @@ ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = class)) +
```
-### Aesthetic mappings : `size`
+## `size` mapping
```{r new_mpg_plot_f, cache = TRUE, fig.width=8, fig.height=4.5, warning=FALSE}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, size = class)) +
geom_point()
```
-### Aesthetic mapping : `alpha`
+## `alpha` mapping
```{r new_mpg_plot_g, cache = TRUE, fig.width=8, fig.height=4.5, warning=FALSE}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, alpha = class)) +
geom_point()
```
-### Aesthetic mapping : `shape`
+## `shape` mapping
```{r new_mpg_plot_h, cache = TRUE, fig.width=8, fig.height=4.5, warning=FALSE}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, shape = class)) +
geom_point()
```
-
- \
-
You can also set the aesthetic properties of your geom manually. For example, we can make all of the points in our plot blue and squares:
```{r new_mpg_plot_i, cache = TRUE, fig.width=8, fig.height=4.5}
@@ -323,62 +282,58 @@ ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point(color = "blue", shape=0)
```
-
- \
+Here is a list of different shapes available in R:
<center>
{width=300px}
-
- \
-
-{width=100px}
</center>
-
-<div id="pquestion">- What’s gone wrong with this code? Why are the points not blue?</div>
-
-```R
-ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = "blue")) +
- geom_point()
-```
- \
-
- \
-
- \
-
- \
- \
-
- \
-
- \
+<div class="pencadre">
+What’s gone wrong with this code? Why are the points not blue?
+</div>
-```{r res2, cache = TRUE, echo=FALSE, fig.width=8, fig.height=4.5}
+```{r new_mpg_plot_not_blue, cache = TRUE, fig.width=8, fig.height=4.5}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = "blue")) +
geom_point()
```
- \
+<details><summary>Solution</summary>
+<p>
+```{r new_mpg_plot_blue, cache = TRUE, fig.width=8, fig.height=4.5}
+ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
+ geom_point(color = "blue")
+```
+</p>
+</details>
-- Map a **continuous** variable to color.
+## mapping a **continuous** variable to a color.
+
+You can also map continuous variable to a color
```{r continu, cache = TRUE, fig.width=8, fig.height=4.5}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = cyl)) +
geom_point()
```
-<div id="pquestion">- What happens if you map an aesthetic to something other than a variable name, like `color = displ < 5`?</div>
+<div class="pencadre">
+What happens if you map an aesthetic to something other than a variable name, like `color = displ < 5`?
+</div>
+
+<details><summary>Solution</summary>
+<p>
```{r condiColor, cache = TRUE, fig.width=8, fig.height=4.5}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = displ < 5)) +
geom_point()
```
-
- \
-
- \
+</p>
+</details>
# Facets
+You can create multiple plot at once by faceting. For this you can use the command `facet_wrap`.
+This command take a formula as input.
+We will come back to formulas in R later, for now, your have to know that formulas start with a `~` symbol.
+
+To make a scatterplot of `displ` versus `hwy` per car `class` you can use the following code:
```{r new_mpg_plot_k, cache = TRUE, fig.width=8, fig.height=4.5}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
@@ -386,13 +341,22 @@ ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
facet_wrap(~class, nrow = 2)
```
- \
+<div class="pencadre">
+Now try to facet your plot by `fl + class`
+</div>
+
+
+<details><summary>Solution</summary>
+<p>
+Formulas allow your to express complex relationship between variables in R !
```{r new_mpg_plot_l, cache = TRUE, fig.width=8, fig.height=4.5}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point() +
facet_wrap(~ fl + class, nrow = 2)
```
+</p>
+</details>
# Composition
@@ -442,20 +406,25 @@ ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
# Challenge !
-<div id="pquestion">- Run this code in your head and predict what the output will look like. Then, run the code in R and check your predictions.</div>
+<div class="pencadre">
+Run this code in your head and predict what the output will look like. Then, run the code in R and check your predictions.
+</div>
```R
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) +
- geom_point() +
+ geom_point(show.legend = FALSE) +
geom_smooth(se = FALSE)
```
-**http://perso.ens-lyon.fr/laurent.modolo/R/2_d**
+<div class="pencadre">
- What does `show.legend = FALSE` do?
- What does the `se` argument to `geom_smooth()` do?
+</div>
## Third challenge
-- Recreate the R code necessary to generate the following graph
+<div class="pencadre">
+Recreate the R code necessary to generate the following graph
+</div>
```{r new_mpg_plot_u, echo = FALSE, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) +