From 00f811810e3848f476a2c509b87735d4c6909a00 Mon Sep 17 00:00:00 2001
From: hpolvech <helene.polveche@ens-lyon.fr>
Date: Wed, 25 Mar 2020 12:57:38 +0100
Subject: [PATCH] session3: until position part
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+---
+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"
+output:
+ html_document: default
+ pdf_document: default
+---
+<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}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+The goal of this practical is to practices advanced features of `ggplot2`.
+
+The objectives of this session will be to:
+
+- learn about statistical transformations
+- practices position adjustments
+- change the coordinate systems
+
+ \
+
+# `ggplot2` statistical transformations
+
+ \
+
+```{r packageloaded, include=TRUE, message=FALSE}
+library("tidyverse")
+```
+
+ \
+
+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
+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}
+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!
+
+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()`:
+
+```{r diamonds_stat_count, include=TRUE, fig.width=8, fig.height=4.5, message=FALSE}
+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.
+
+```{r 3_a, include=TRUE, fig.width=8, fig.height=4.5}
+demo <- tribble(
+ ~cut, ~freq,
+ "Fair", 1610,
+ "Good", 4906,
+ "Very Good", 12082,
+ "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!)
+
+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).
+```{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?
+
+```{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.
+
+ \
+
+- You might want to draw greater attention to the statistical transformation in your code.
+
+```{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()
+
+
+ggplot(data = diamonds, mapping = aes(x = cut, y = depth)) +
+ stat_summary(
+ fun.min = min,
+ fun.max = max,
+ fun = median
+ )
+```
+
+
+# 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}
+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()
+```
+
+
+
+You can also use `fill` with another variable:
+
+```{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()
+```
+
+
+
+The stacking is performed by the position adjustment `position`
+
+### 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
+
+```{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")
+```
+
+
+
+```{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()
+```
+
+```{r dia_jitter3, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
+ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) +
+ geom_jitter()
+```
+
+### 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.
+
+
+```{r dia_boxplot, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
+ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) +
+ geom_boxplot()
+```
+
+
+
+```{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()
+```
+
+
+
+```{r diamonds_bar, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
+bar <- ggplot(data = diamonds) +
+ geom_bar(
+ mapping = aes(x = cut, fill = cut),
+ show.legend = FALSE,
+ width = 1
+ ) +
+ theme(aspect.ratio = 1) +
+ labs(x = NULL, y = NULL)
+```
+**3_d**
+
+
+
+```{r diamonds_bar_plot, echo=F, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
+bar
+```
+
+**3_d**
+
+
+```{r diamonds_bar_flip, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
+bar + coord_flip()
+```
+
+
+
+```{r mpg_jitter_noquickmap, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
+ggplot(data = mpg) +
+ geom_jitter(mapping = aes(x = cty, y = hwy))
+```
+
+
+
+```{r mpg_jitter_quickmap, cache = TRUE, fig.width=3.5, fig.height=3.5, message=FALSE}
+ggplot(data = mpg) +
+ geom_jitter(mapping = aes(x = cty, y = hwy)) +
+ coord_quickmap()
+```
+
+
+
+```{r mpg_jitter_log, cache = TRUE, fig.width=8.5, fig.height=3.5, message=FALSE}
+ggplot(data = mpg) +
+ geom_jitter(mapping = aes(x = cty, y = hwy)) +
+ scale_y_log10() +
+ scale_x_log10()
+```
+
+
+```{r diamonds_bar_polar, cache = TRUE, fig.width=5, fig.height=3.5, message=FALSE}
+bar + coord_polar()
+```
+
+## Coordinate systems challenges
+
+- Turn a stacked bar chart into a pie chart using `coord_polar()`.
+- What does `labs()` do? Read the documentation.
+- What does the plot below tell you about the relationship between `city` and highway `mpg`? Why is `coord_fixed()` important? What does `geom_abline()` do?
+
+```{r mpg_point_fixed, eval = F, cache = TRUE, fig.width=4.5, fig.height=3.5, message=FALSE}
+ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
+ geom_point() +
+ geom_abline() +
+ coord_fixed()
+```
+
+## Coordinate systems challenges
+
+```{r diamonds_barplot_pos_fill_polar, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
+ggplot(data = diamonds) +
+ geom_bar(mapping = aes(x = cut, fill = clarity),
+ position = "fill") +
+ coord_polar()
+```
+
+## Coordinate systems challenges
+
+```{r mpg_point_nofixed_plot, eval = T, cache = TRUE, fig.width=8, fig.height=3.5, message=FALSE}
+ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
+ geom_point() + geom_abline()
+```
+
+## Coordinate systems challenges
+
+```{r mpg_point_fixed_plot, eval = T, cache = TRUE, fig.width=8, fig.height=3.5, message=FALSE}
+ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
+ geom_point() + geom_abline() + coord_fixed()
+```
--
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