From 68a83da0181899a2cb0f2aa97aa9a088d550dd72 Mon Sep 17 00:00:00 2001
From: Laurent Modolo <laurent.modolo@ens-lyon.fr>
Date: Fri, 3 Sep 2021 11:57:05 +0200
Subject: [PATCH] README.md: update program
---
README.md | 4 +
session_3/{HTML_tuto_s3.Rmd => session_3.Rmd} | 131 ++++++------------
2 files changed, 48 insertions(+), 87 deletions(-)
rename session_3/{HTML_tuto_s3.Rmd => session_3.Rmd} (77%)
diff --git a/README.md b/README.md
index 9bb00ae..c2d7cf6 100644
--- a/README.md
+++ b/README.md
@@ -28,3 +28,7 @@ Groups:
- Anissa Guillemin
# program
+
+- Session 1: first step with R and RStudio
+- Session 2: my first plot with `ggplot2`
+- Session 3: more fun with `ggplot2`
diff --git a/session_3/HTML_tuto_s3.Rmd b/session_3/session_3.Rmd
similarity index 77%
rename from session_3/HTML_tuto_s3.Rmd
rename to session_3/session_3.Rmd
index 4612e49..5295b4f 100644
--- a/session_3/HTML_tuto_s3.Rmd
+++ b/session_3/session_3.Rmd
@@ -1,47 +1,32 @@
---
-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)"
-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, cache=TRUE}
+
+```{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 !')
```
-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:
@@ -49,43 +34,31 @@ The objectives of this session will be to:
- practices position adjustments
- 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}
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`.
- Use the `help` and `view` command to explore this data set.
- Try the `str` command, which information are displayed ?
-```R
+```{r str_diamon}
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()` :
```{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)) +
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:
- You might want to override the default stat.
@@ -124,14 +95,15 @@ demo <- tribble(
"Premium", 13791,
"Ideal", 21551
)
+```
-# (Don't worry that you haven't seen <- or tribble() before. You might be able
-# to guess at their meaning from the context, and you will learn exactly what
-# they do soon!)
+(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!)
+```{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).
@@ -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.
- \
-
- 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}
-# 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)) +
stat_summary()
@@ -172,8 +142,6 @@ ggplot(data = diamonds, mapping = aes(x = cut, y = depth)) +
# Position adjustments
- \
-
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}
@@ -181,8 +149,6 @@ ggplot(data = diamonds, mapping = aes(x = cut, color = cut)) +
geom_bar()
```
- \
-
or, more usefully, `fill`:
```{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)) +
geom_bar()
```
-
-
You can also use `fill` with another variable:
```{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)) +
geom_bar()
```
-
-
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}
ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) +
geom_bar( position = "fill")
```
-### dodge
+## dodge
```{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)) +
geom_bar( position = "dodge")
```
-### jitter
+## jitter
```{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)) +
geom_bar( position = "jitter")
```
-
-
```{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()
@@ -236,14 +196,13 @@ ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) +
geom_jitter()
```
-### violin
+## violin
```{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.
@@ -294,5 +253,3 @@ bar
bar + coord_polar()
```
-
-##See you to Session#4 : "data transformation"
\ No newline at end of file
--
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