From f1402350d27b3afbf317653bdce67be9cd340507 Mon Sep 17 00:00:00 2001
From: Laurent Modolo <laurent.modolo@ens-lyon.fr>
Date: Tue, 7 Sep 2021 18:20:10 +0200
Subject: [PATCH] session_3: improve the Q & A system
---
session_3/session_3.Rmd | 129 +++++++++++++++++++++++++++++++---------
1 file changed, 102 insertions(+), 27 deletions(-)
diff --git a/session_3/session_3.Rmd b/session_3/session_3.Rmd
index 5295b4f..8e2b7e4 100644
--- a/session_3/session_3.Rmd
+++ b/session_3/session_3.Rmd
@@ -49,42 +49,59 @@ Like in the previous sessions, it's good practice to create a new **.R** file to
# `ggplot2` statistical transformations
+In the previous session, we have ploted the data as they are by using the variables 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.
+<div class="pencadre">
+
+- Use the `help` and `View` command to explore this data set.
+- How much records does this dataset contains ?
- Try the `str` command, which information are displayed ?
+</div>
+
```{r str_diamon}
str(diamonds)
```
-We saw scatterplot (`geom_point()`), smoothplot (`geom_smooth()`). Now barplot with `geom_bar()` :
+## Introduction to `geom_bar`
-```{r diamonds_barplot, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
+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}
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!
+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!**
-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()`.
-
+## **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()`:
+
+
+You can generally use **geoms** and **stats** interchangeably. For example, you can recreate the previous plot using `stat_count()` instead of `geom_bar()`:
-```{r diamonds_stat_count, include=TRUE, fig.width=8, fig.height=4.5, message=FALSE}
+```{r diamonds_stat_count, include=TRUE, fig.width=8, fig.height=4.5}
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:
+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.
+You might want to override the default stat.
+For example in the following `demo` dataset we allready have a varible for the **counts** per `cut`.
```{r 3_a, include=TRUE, fig.width=8, fig.height=4.5}
demo <- tribble(
@@ -101,36 +118,66 @@ demo <- tribble(
to guess at their meaning from the context, and you will learn exactly what
they do soon!)
+<div class="pencadre">
+So instead of using the default `geom_bar` parameter `stat = "count"` ty to use `"identity"`
+</div>
+
+<details><summary>Solution</summary>
+<p>
```{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")
```
+</p>
+</details>
+
+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).
```{r 3_b, include=TRUE, fig.width=8, fig.height=4.5}
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?
+<div class="pencadre">
+In our proportion bar chart, we need to set `group = 1`. Why?
+</div>
+<details><summary>Solution</summary>
+<p>
```{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.
+</p>
+</details>
+
+## 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 summarises the y values for each unique x
-value, to draw attention to the summary that you are computing:
+<div class="pencadre">
+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
+</div>
+<details><summary>Solution</summary>
+<p>
```{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,
@@ -138,54 +185,80 @@ ggplot(data = diamonds, mapping = aes(x = cut, y = depth)) +
fun = median
)
```
+</p>
+</details>
+# Coloring area plots
-# Position adjustments
-
-You can colour a bar chart using either the `color` aesthetic,
+<div class="pencadre">
+You can colour a bar chart using either the `color` aesthetic, or, more usefully, `fill`:
+Try both solutions on a `cut`, histogram.
+</div>
+<details><summary>Solution</summary>
+<p>
```{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()
```
-or, more usefully, `fill`:
-
```{r diamonds_barplot_fill, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
ggplot(data = diamonds, mapping = aes(x = cut, fill = cut)) +
geom_bar()
```
+</p>
+</details>
+<div class="pencadre">
You can also use `fill` with another variable:
+Try to color by `clarity`. Is `clarity` a continuous or categorial variable ?
+</div>
+<details><summary>Solution</summary>
+<p>
```{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()
```
+</p>
+</details>
+
+# Position adjustments
-The stacking is performed by the position adjustment `position`
+The stacking of the `fill` parameter is performed by the position adjustment `position`
-## fill
+<div class="pencadre">
+Try the following `position` parameter for `geom_bar`: `"fill"`, `"dodge"` and `"jitter"`
+</div>
+
+<details><summary>Solution</summary>
+<p>
```{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
-
```{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
-
```{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")
```
+</p>
+</details>
+`jitter` is often used for plotting points when they are stacked on top of each others.
+
+<div class="pencadre">
+Compare `geom_point` to `geom_jitter` to plot `cut` versus `depth` and color by `clarity`
+</div>
+
+<details><summary>Solution</summary>
+<p>
```{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()
@@ -195,6 +268,8 @@ ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) +
ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) +
geom_jitter()
```
+</p>
+</details>
## violin
--
GitLab