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README.md: update program

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...@@ -28,3 +28,7 @@ Groups: ...@@ -28,3 +28,7 @@ Groups:
- Anissa Guillemin - Anissa Guillemin
# program # program
- Session 1: first step with R and RStudio
- Session 2: my first plot with `ggplot2`
- Session 3: more fun with `ggplot2`
session_3/HTML_tuto_s3.Rmd → session_3/session_3.Rmd
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--- ---
title: 'R#3: Transformations with ggplot2' 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)" 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: output:
html_document: default rmdformats::downcute:
pdf_document: default self_contain: false
use_bookdown: true
default_style: "dark"
lightbox: true
css: "../src/style.css"
--- ---
<style type="text/css">
h3 { /* Header 3 */ ```{r setup, include=FALSE}
position: relative ; rm(list=ls())
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, cache=TRUE}
knitr::opts_chunk$set(echo = TRUE) knitr::opts_chunk$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 !')
``` ```
The goal of this practical is to practices advanced features of `ggplot2`. # Introduction
In the last session, we have seen how to use `ggplot2` and [The Grammar of Graphics](https://www.amazon.com/Grammar-Graphics-Statistics-Computing/dp/0387245448/ref=as_li_ss_tl). The goal of this practical is to practices more advanced features of `ggplot2`.
The objectives of this session will be to: The objectives of this session will be to:
...@@ -49,43 +34,31 @@ The objectives of this session will be to: ...@@ -49,43 +34,31 @@ The objectives of this session will be to:
- practices position adjustments - practices position adjustments
- change the coordinate systems - change the coordinate systems
\
# `ggplot2` statistical transformations
\ The first step is to load the `tidyverse`.
<details><summary>Solution</summary>
<p>
```{r packageloaded, include=TRUE, message=FALSE} ```{r packageloaded, include=TRUE, message=FALSE}
library("tidyverse") library("tidyverse")
``` ```
</p>
</details>
\ Like in the previous sessions, it's good practice to create a new **.R** file to write your code instead of using directly the R terminal.
# `ggplot2` statistical transformations
We are going to use the `diamonds` data set included in `tidyverse`. We are going to use the `diamonds` data set included in `tidyverse`.
- Use the `help` and `view` command to explore this data set. - Use the `help` and `view` command to explore this data set.
- Try the `str` command, which information are displayed ? - Try the `str` command, which information are displayed ?
```R ```{r str_diamon}
str(diamonds) str(diamonds)
``` ```
```
## Classes 'tbl_df', 'tbl' and 'data.frame': 53940 obs. of 10 variables:
## $ carat : num 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
## $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
## $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
## $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
## $ depth : num 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
## $ table : num 55 61 65 58 58 57 57 55 61 61 ...
## $ price : int 326 326 327 334 335 336 336 337 337 338 ...
## $ x : num 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
## $ y : num 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
## $ z : num 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...
```
\
We saw scatterplot (`geom_point()`), smoothplot (`geom_smooth()`). Now barplot with `geom_bar()` : We saw scatterplot (`geom_point()`), smoothplot (`geom_smooth()`). Now barplot with `geom_bar()` :
```{r diamonds_barplot, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE} ```{r diamonds_barplot, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
...@@ -109,8 +82,6 @@ ggplot(data = diamonds, mapping = aes(x = cut)) + ...@@ -109,8 +82,6 @@ ggplot(data = diamonds, mapping = aes(x = cut)) +
stat_count() 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: 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:
- You might want to override the default stat. - You might want to override the default stat.
...@@ -124,14 +95,15 @@ demo <- tribble( ...@@ -124,14 +95,15 @@ demo <- tribble(
"Premium", 13791, "Premium", 13791,
"Ideal", 21551 "Ideal", 21551
) )
```
# (Don't worry that you haven't seen <- or tribble() before. You might be able (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 to guess at their meaning from the context, and you will learn exactly what
# they do soon!) they do soon!)
```{r 3_ab, include=TRUE, fig.width=8, fig.height=4.5}
ggplot(data = demo, mapping = aes(x = cut, y = freq)) + ggplot(data = demo, mapping = aes(x = cut, y = freq)) +
geom_bar(stat = "identity") geom_bar(stat = "identity")
``` ```
- You might want to override the default mapping from transformed variables to aesthetics ( e.g. proportion). - You might want to override the default mapping from transformed variables to aesthetics ( e.g. proportion).
...@@ -149,13 +121,11 @@ ggplot(data = diamonds, mapping = aes(x = cut, y = ..prop..)) + ...@@ -149,13 +121,11 @@ ggplot(data = diamonds, mapping = aes(x = cut, y = ..prop..)) +
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. 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.
\
- You might want to draw greater attention to the statistical transformation in your code. - You might want to draw greater attention to the statistical transformation in your code.
you might use stat_summary(), which summarises the y values for each unique x
value, to draw attention to the summary that you are computing:
```{r 3_c, include=TRUE, fig.width=8, fig.height=4.5, message=FALSE} ```{r 3_c, include=TRUE, fig.width=8, fig.height=4.5, message=FALSE}
# you might use stat_summary(), which summarises the y values for each unique x
# value, to draw attention to the summary that you are computing:
ggplot(data = diamonds, mapping = aes(x = cut, y = depth)) + ggplot(data = diamonds, mapping = aes(x = cut, y = depth)) +
stat_summary() stat_summary()
...@@ -172,8 +142,6 @@ ggplot(data = diamonds, mapping = aes(x = cut, y = depth)) + ...@@ -172,8 +142,6 @@ ggplot(data = diamonds, mapping = aes(x = cut, y = depth)) +
# Position adjustments # Position adjustments
\
You can colour a bar chart using either the `color` aesthetic, You can colour a bar chart using either the `color` aesthetic,
```{r diamonds_barplot_color, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE} ```{r diamonds_barplot_color, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
...@@ -181,8 +149,6 @@ ggplot(data = diamonds, mapping = aes(x = cut, color = cut)) + ...@@ -181,8 +149,6 @@ ggplot(data = diamonds, mapping = aes(x = cut, color = cut)) +
geom_bar() geom_bar()
``` ```
\
or, more usefully, `fill`: or, more usefully, `fill`:
```{r diamonds_barplot_fill, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE} ```{r diamonds_barplot_fill, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
...@@ -190,8 +156,6 @@ ggplot(data = diamonds, mapping = aes(x = cut, fill = cut)) + ...@@ -190,8 +156,6 @@ ggplot(data = diamonds, mapping = aes(x = cut, fill = cut)) +
geom_bar() geom_bar()
``` ```
You can also use `fill` with another variable: You can also use `fill` with another variable:
```{r diamonds_barplot_fill_clarity, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE} ```{r diamonds_barplot_fill_clarity, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
...@@ -199,33 +163,29 @@ ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + ...@@ -199,33 +163,29 @@ ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) +
geom_bar() geom_bar()
``` ```
The stacking is performed by the position adjustment `position` The stacking is performed by the position adjustment `position`
### fill ## fill
```{r diamonds_barplot_pos_fill, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE} ```{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)) + ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) +
geom_bar( position = "fill") geom_bar( position = "fill")
``` ```
### dodge ## dodge
```{r diamonds_barplot_pos_dodge, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE} ```{r diamonds_barplot_pos_dodge, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) +
geom_bar( position = "dodge") geom_bar( position = "dodge")
``` ```
### jitter ## jitter
```{r diamonds_barplot_pos_jitter, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE} ```{r diamonds_barplot_pos_jitter, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) +
geom_bar( position = "jitter") geom_bar( position = "jitter")
``` ```
```{r dia_jitter2, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE} ```{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)) + ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) +
geom_point() geom_point()
...@@ -236,14 +196,13 @@ ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) + ...@@ -236,14 +196,13 @@ ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) +
geom_jitter() geom_jitter()
``` ```
### violin ## violin
```{r dia_violon, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE} ```{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)) + ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) +
geom_violin() geom_violin()
``` ```
# Coordinate systems # 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. 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.
...@@ -294,5 +253,3 @@ bar ...@@ -294,5 +253,3 @@ bar
bar + coord_polar() bar + coord_polar()
``` ```
##See you to Session#4 : "data transformation"
\ No newline at end of file
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