Skip to content
Snippets Groups Projects
Verified Commit 7cbabb25 authored by Laurent Modolo's avatar Laurent Modolo
Browse files

session_2: update support

parent 68d2bd0e
No related branches found
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
session_2/session_2.Rmd
+ 102
− 42
View file @ 7cbabb25
......@@ -121,9 +121,9 @@ Instead of continuing to learn more about R programming, in this session we are
We make this choice for three reasons:
- Rendering nice plots is direclty rewarding
- Rendering nice plots is directly 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
- We will come back to R programming later, when you have all the necessary tools to visualize your results.
The objectives of this session will be to:
......@@ -135,7 +135,7 @@ The objectives of this session will be to:
## Tidyverse
The `tidyverse` is a collection of R packages designed for data science that include `ggplot2`.
The `tidyverse` package is a collection of R packages designed for data science that include `ggplot2`.
All packages share an underlying design philosophy, grammar, and data structures (plus the same shape of logo).
......@@ -148,13 +148,13 @@ All packages share an underlying design philosophy, grammar, and data structures
install.packages("tidyverse")
```
Luckily for your `tidyverse` is preinstalled on your Rstudio server. So you just have to load the ` library`
Luckily for you, `tidyverse` is preinstalled on your Rstudio server. So you just have to load the ` library`
```{R load_tidyverse}
library("tidyverse")
```
### Toy data set `mpg`
## Toy data set `mpg`
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.
......@@ -168,7 +168,7 @@ You can use the `?` command to know more about this dataset.
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.
This command also work for file URL
This command also works for file URL
```{r mpg_download, cache=TRUE, message=FALSE}
new_mpg <- read_csv(
......@@ -176,34 +176,50 @@ new_mpg <- read_csv(
)
```
You can check the number of line and column of the data with `dim`:
You can check the number of lines and columns of the data with `dim`:
```{r mpg_inspect2, include=TRUE}
dim(new_mpg)
```
To visualize the data in Rstudio you can use the command `View`
To visualize the data in Rstudio you can use the command. `View`
```R
View(new_mpg)
```
### New script
Or by simply calling the variable.
Like for simple data type calling a variable print it.
But complex data type like `new_mpg` can use complex print function.
Like in the last session, instead of typing your commands direclty in the console, you are going to write them in an R script.
```{r mpg_inspect3, include=TRUE}
new_mpg
```
Here we can see that `new_mpg` is a `tibble` we will come back to `tibble` later.
## New script
Like in the last session, instead of typing your commands directly in the console, you are going to write them in an R script.
![](./img/formationR_session2_scriptR.png)
# First plot with `ggplot2`
We are going to make the simpliest plot possible to study the relationship between two variables: the scatterplot.
We are going to make the simplest plot possible to study the relationship between two variables: the scatterplot.
The following command generate a plot between engine size `displ` and fuel efficiency `hwy`.
The following command generates a plot between engine size `displ` and fuel efficiency `hwy` present the `new_mpg` `tibble`.
```{r new_mpg_plot_a, cache = TRUE, fig.width=8, fig.height=4.5}
ggplot(data = mpg) +
ggplot(data = new_mpg) +
geom_point(mapping = aes(x = displ, y = hwy))
```
<div class="pencadre">
Are cars with bigger engines less fuel efficient ?
</div>
`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.
```
......@@ -214,9 +230,13 @@ ggplot(data = <DATA>) +
- you begin a plot with the function `ggplot()`
- you complete your graph by adding one or more layers
- `geom_point()` adds a layer with a scatterplot
- each geom function in `ggplot2` takes a `mapping` argument
- each **geom **function in `ggplot2` takes a `mapping` argument
- the `mapping` argument is always paired with `aes()`
<div class="pencadre">
What happend when you use only the command `ggplot(data = mpg)` ?
</div>
<div class="pencadre">
Make a scatterplot of `hwy` ( fuel efficiency ) vs. `cyl` ( number of cylinders ).
......@@ -226,7 +246,7 @@ Make a scatterplot of `hwy` ( fuel efficiency ) vs. `cyl` ( number of cylinders
<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)) +
ggplot(data = new_mpg, mapping = aes(x = hwy, y = cyl)) +
geom_point()
```
......@@ -249,7 +269,7 @@ Try the following aesthetic:
## `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)) +
ggplot(data = new_mpg, mapping = aes(x = displ, y = hwy, color = class)) +
geom_point()
```
......@@ -257,28 +277,28 @@ ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = class)) +
## `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)) +
ggplot(data = new_mpg, mapping = aes(x = displ, y = hwy, size = class)) +
geom_point()
```
## `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)) +
ggplot(data = new_mpg, mapping = aes(x = displ, y = hwy, alpha = class)) +
geom_point()
```
## `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)) +
ggplot(data = new_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:
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}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
ggplot(data = new_mpg, mapping = aes(x = displ, y = hwy)) +
geom_point(color = "blue", shape=0)
```
......@@ -292,25 +312,25 @@ What’s gone wrong with this code? Why are the points not blue?
</div>
```{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")) +
ggplot(data = new_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)) +
ggplot(data = new_mpg, mapping = aes(x = displ, y = hwy)) +
geom_point(color = "blue")
```
</p>
</details>
## mapping a **continuous** variable to a 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)) +
ggplot(data = new_mpg, mapping = aes(x = displ, y = hwy, color = cyl)) +
geom_point()
```
......@@ -321,7 +341,7 @@ What happens if you map an aesthetic to something other than a variable name, li
<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)) +
ggplot(data = new_mpg, mapping = aes(x = displ, y = hwy, color = displ < 5)) +
geom_point()
```
</p>
......@@ -329,14 +349,14 @@ ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = displ < 5)) +
# 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.
You can create multiple plots at once by faceting. For this you can use the command `facet_wrap`.
This command takes a formula as input.
We will come back to formulas in R later, for now, you 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)) +
ggplot(data = new_mpg, mapping = aes(x = displ, y = hwy)) +
geom_point() +
facet_wrap(~class, nrow = 2)
```
......@@ -348,7 +368,7 @@ Now try to facet your plot by `fl + class`
<details><summary>Solution</summary>
<p>
Formulas allow your to express complex relationship between variables in R !
Formulas allow you 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)) +
......@@ -363,14 +383,14 @@ ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
There are different ways to represent the information :
```{r new_mpg_plot_o, cache = TRUE, fig.width=8, fig.height=4.5}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
ggplot(data = new_mpg, mapping = aes(x = displ, y = hwy)) +
geom_point()
```
\
```{r new_mpg_plot_p, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
ggplot(data = new_mpg, mapping = aes(x = displ, y = hwy)) +
geom_smooth()
```
......@@ -379,7 +399,7 @@ ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
We can add as many layers as we want
```{r new_mpg_plot_q, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
ggplot(data = new_mpg, mapping = aes(x = displ, y = hwy)) +
geom_point() +
geom_smooth()
```
......@@ -389,28 +409,29 @@ ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
We can make `mapping` layer specific
```{r new_mpg_plot_s, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
ggplot(data = new_mpg, mapping = aes(x = displ, y = hwy)) +
geom_point(mapping = aes(color = class)) +
geom_smooth()
```
\
We can use different `data` for different layer (You will lean more on `filter()` later)
We can use different `data` for different layers (you will lean more on `filter()` later)
```{r new_mpg_plot_t, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
ggplot(data = new_mpg, mapping = aes(x = displ, y = hwy)) +
geom_point(mapping = aes(color = class)) +
geom_smooth(data = filter(mpg, class == "subcompact"))
```
# Challenge !
## First challenge
<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)) +
ggplot(data = new_mpg, mapping = aes(x = displ, y = hwy, color = drv)) +
geom_point(show.legend = FALSE) +
geom_smooth(se = FALSE)
```
......@@ -420,6 +441,43 @@ ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) +
- What does the `se` argument to `geom_smooth()` do?
</div>
## Second challenge
<div class="pencadre">
How being a `2seater` car impact the engine size versus fuel efficiency relationship ?
Make a plot *colorizing* this information
</div>
<details><summary>Solution</summary>
<p>
```{r new_mpg_plot_color_2seater, cache = TRUE, fig.width=8, fig.height=4.5}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point() +
geom_point(data = filter(mpg, class == "2seater"), color = "red")
```
</p>
</details>
<div class="pencadre">
Write a `function` called `plot_color_2seater` that can take as sol argument the variable `mpg` and plot the same graph.
</div>
<details><summary>Solution</summary>
<p>
```{r new_mpg_plot_color_2seater_fx, cache = TRUE, fig.width=8, fig.height=4.5}
plot_color_2seater <- function(mpg) {
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
geom_point() +
geom_point(data = filter(mpg, class == "2seater"), color = "red")
}
plot_color_2seater(mpg)
```
</p>
</details>
## Third challenge
<div class="pencadre">
......@@ -432,10 +490,12 @@ ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) +
geom_smooth(mapping = aes(linetype = drv))
```
## Third challenge
```{r new_mpg_plot_v, cache = TRUE, fig.width=8, fig.height=4.5, message=FALSE}
<details><summary>Solution</summary>
<p>
```{r new_mpg_plot_v, eval=F}
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) +
geom_point() +
geom_smooth(mapping = aes(linetype = drv))
```
\ No newline at end of file
```
</p>
</details>
\ No newline at end of file
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment