Data vizualisation with R (session 4)

class: center, middle, inverse, title-slide

.title[
# Data vizualisation with R (session 4)
]
.subtitle[
## M2 Statistics and Econometrics
]
.author[
### <a href="http://www.thibault.laurent.free.fr">Thibault Laurent</a>
]
.institute[
### Toulouse School of Economics, CNRS
]
.date[
### Last update: 2023-10-04
]

---

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background-image: url(https://cran.r-project.org/Rlogo.svg)
background-size: contain

# Table of contents

#### 1. Nice tools for writting a report
#### 2. Base graphics VS **ggplot2**
#### 3. Original statistical graphics
#### 4. Interactive graphics

---
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background-image: url(https://cran.r-project.org/Rlogo.svg)
background-size: contain

# Before starting

---
background-image: url(https://cran.r-project.org/Rlogo.svg)
background-size: 100px
background-position: 90% 8%

# Packages and software versions

This document has been compiled under this version: R version 4.3.1 (2023-06-16). Install the following packages and load them:

```r
install.packages(c("broom", "gapminder", "ggcorrplot", "ggiraph", "ggpol", "ggridges", "ggtern", "kableExtra", "knitr", "plotly", "RColorBrewer", "shiny", "stargazer", "survival", "survminer", "visreg", "vcd"), dependencies = TRUE)
```

```r
require("gapminder") # use data from https://www.gapminder.org/
require("ggcorrplot") # coorelation matrix with ggplot2 style
require("ggiraph") # interactive plot
require("ggridges")   # multi density plot 
require("ggpol")      # ggplot2 extensions
require("ggtern")     # ternary diagram
require("kableExtra") # include nice Table in Markdown document
require("plotly")     # interactive graphics
require("RColorBrewer") # color palettes 
require("stargazer")   # inlude latex/html regression table
require("ggplot2")     # data visualization
require("visreg")    # visualization of regression models
require("shiny")    # shiny App
require("survival")  # survival data analysis
require("survminer") # visualization of survival data analysis
require("vcd")       # visualization of categorical data
```

---
class: inverse, middle, center
background-image: url(https://cran.r-project.org/Rlogo.svg)
background-size: contain

# 1. Nice tools for writting a report

---

# Include tables in a **R** Markdown document

* **R** Markdown is very useful for writing reports which contain text, codes and results of codes.

* Use function `kable()` in `knitr` package permits to print a table

````markdown
```{r, results = 'asis'}
knitr::kable(head(iris[1:5, ]))
```
````

```r
kableExtra::kbl(head(iris[1:5, ]))
```

<table>
 <thead>
 <tr>
 <th style="text-align:right;"> Sepal.Length </th>
 <th style="text-align:right;"> Sepal.Width </th>
 <th style="text-align:right;"> Petal.Length </th>
 <th style="text-align:right;"> Petal.Width </th>
 <th style="text-align:left;"> Species </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:right;"> 5.1 </td>
 <td style="text-align:right;"> 3.5 </td>
 <td style="text-align:right;"> 1.4 </td>
 <td style="text-align:right;"> 0.2 </td>
 <td style="text-align:left;"> setosa </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 4.9 </td>
 <td style="text-align:right;"> 3.0 </td>
 <td style="text-align:right;"> 1.4 </td>
 <td style="text-align:right;"> 0.2 </td>
 <td style="text-align:left;"> setosa </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 4.7 </td>
 <td style="text-align:right;"> 3.2 </td>
 <td style="text-align:right;"> 1.3 </td>
 <td style="text-align:right;"> 0.2 </td>
 <td style="text-align:left;"> setosa </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 4.6 </td>
 <td style="text-align:right;"> 3.1 </td>
 <td style="text-align:right;"> 1.5 </td>
 <td style="text-align:right;"> 0.2 </td>
 <td style="text-align:left;"> setosa </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 5.0 </td>
 <td style="text-align:right;"> 3.6 </td>
 <td style="text-align:right;"> 1.4 </td>
 <td style="text-align:right;"> 0.2 </td>
 <td style="text-align:left;"> setosa </td>
 </tr>
</tbody>
</table>

---
# Another example from **kableExtra**

```r
vs_dt <- iris[1:5, ]
vs_dt[1:4] <- lapply(vs_dt[1:4], function(x) {
 cell_spec(x, bold = T, color = spec_color(x, end = 0.9),
 font_size = spec_font_size(x))
})
vs_dt[5] <- cell_spec(vs_dt[[5]], color = "white", bold = T,
 background = spec_color(1:5, end = 0.9, option = "A", direction = -1))
kbl(vs_dt, escape = F, align = "c") %>% kable_classic("striped", full_width = F)
```

<table class=" lightable-classic lightable-striped" style='font-family: "Arial Narrow", "Source Sans Pro", sans-serif; width: auto !important; margin-left: auto; margin-right: auto;'>
 <thead>
 <tr>
 <th style="text-align:center;"> Sepal.Length </th>
 <th style="text-align:center;"> Sepal.Width </th>
 <th style="text-align:center;"> Petal.Length </th>
 <th style="text-align:center;"> Petal.Width </th>
 <th style="text-align:center;"> Species </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:center;"> 5.1 </td>
 <td style="text-align:center;"> 3.5 </td>
 <td style="text-align:center;"> 1.4 </td>
 <td style="text-align:center;"> 0.2 </td>
 <td style="text-align:center;"> setosa </td>
 </tr>
 <tr>
 <td style="text-align:center;"> 4.9 </td>
 <td style="text-align:center;"> 3 </td>
 <td style="text-align:center;"> 1.4 </td>
 <td style="text-align:center;"> 0.2 </td>
 <td style="text-align:center;"> setosa </td>
 </tr>
 <tr>
 <td style="text-align:center;"> 4.7 </td>
 <td style="text-align:center;"> 3.2 </td>
 <td style="text-align:center;"> 1.3 </td>
 <td style="text-align:center;"> 0.2 </td>
 <td style="text-align:center;"> setosa </td>
 </tr>
 <tr>
 <td style="text-align:center;"> 4.6 </td>
 <td style="text-align:center;"> 3.1 </td>
 <td style="text-align:center;"> 1.5 </td>
 <td style="text-align:center;"> 0.2 </td>
 <td style="text-align:center;"> setosa </td>
 </tr>
 <tr>
 <td style="text-align:center;"> 5 </td>
 <td style="text-align:center;"> 3.6 </td>
 <td style="text-align:center;"> 1.4 </td>
 <td style="text-align:center;"> 0.2 </td>
 <td style="text-align:center;"> setosa </td>
 </tr>
</tbody>
</table>

More informations [here](https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html).

---
# Print the result of a linear regression model

One can use function `kbl()` after applying function `tidy()` on the `lm` class of object

```r
lm(Sepal.Length ~ Species, data = iris) %>%
  broom::tidy() %>%
  kbl()
```

<table>
 <thead>
 <tr>
 <th style="text-align:left;"> term </th>
 <th style="text-align:right;"> estimate </th>
 <th style="text-align:right;"> std.error </th>
 <th style="text-align:right;"> statistic </th>
 <th style="text-align:right;"> p.value </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:left;"> (Intercept) </td>
 <td style="text-align:right;"> 5.006 </td>
 <td style="text-align:right;"> 0.0728022 </td>
 <td style="text-align:right;"> 68.761639 </td>
 <td style="text-align:right;"> 0 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Speciesversicolor </td>
 <td style="text-align:right;"> 0.930 </td>
 <td style="text-align:right;"> 0.1029579 </td>
 <td style="text-align:right;"> 9.032819 </td>
 <td style="text-align:right;"> 0 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Speciesvirginica </td>
 <td style="text-align:right;"> 1.582 </td>
 <td style="text-align:right;"> 0.1029579 </td>
 <td style="text-align:right;"> 15.365506 </td>
 <td style="text-align:right;"> 0 </td>
 </tr>
</tbody>
</table>

More informations [here](https://zief0002.github.io/book-8252/pretty-printing-tables-in-markdown.html).

---
# Compare different regression models

.pull-left[

`stargazer()` from `stargazer` permits to print several regression table

```r
mod_1 <- lm(Sepal.Length ~ Petal.Length, data = iris)
mod_2 <- lm(Sepal.Length ~ Petal.Width, data = iris)
stargazer::stargazer(mod_1, mod_2, type = "html", 
 title = "Regression results", header = F)
```

]

.pull-right[

<table style="text-align:center"><caption>Regression results</caption>
<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td colspan="2">Dependent variable:</td></tr>
<tr><td></td><td colspan="2" style="border-bottom: 1px solid black"></td></tr>
<tr><td style="text-align:left"></td><td colspan="2">Sepal.Length</td></tr>
<tr><td style="text-align:left"></td><td>(1)</td><td>(2)</td></tr>
<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Petal.Length</td><td>0.409***</td><td></td></tr>
<tr><td style="text-align:left"></td><td>(0.019)</td><td></td></tr>
<tr><td style="text-align:left"></td><td></td><td></td></tr>
<tr><td style="text-align:left">Petal.Width</td><td></td><td>0.889***</td></tr>
<tr><td style="text-align:left"></td><td></td><td>(0.051)</td></tr>
<tr><td style="text-align:left"></td><td></td><td></td></tr>
<tr><td style="text-align:left">Constant</td><td>4.307***</td><td>4.778***</td></tr>
<tr><td style="text-align:left"></td><td>(0.078)</td><td>(0.073)</td></tr>
<tr><td style="text-align:left"></td><td></td><td></td></tr>
<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Observations</td><td>150</td><td>150</td></tr>
<tr><td style="text-align:left">R2</td><td>0.760</td><td>0.669</td></tr>
<tr><td style="text-align:left">Adjusted R2</td><td>0.758</td><td>0.667</td></tr>
<tr><td style="text-align:left">Residual Std. Error (df = 148)</td><td>0.407</td><td>0.478</td></tr>
<tr><td style="text-align:left">F Statistic (df = 1; 148)</td><td>468.550***</td><td>299.167***</td></tr>
<tr><td colspan="3" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Note:</td><td colspan="2" style="text-align:right">*p<0.1; **p<0.05; ***p<0.01</td></tr>
</table>

]

---
# 👩‍🎓 Training

### Exercise 4.1

* Insert in a Markdown document the correlation matrix of the numeric variables of the `iris` data.

* Insert the table of results of a regression analysis of the `iris` data

---
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background-image: url(https://cran.r-project.org/Rlogo.svg)
background-size: contain

# 2. Base graphics VS **ggplot2**

---
# Base graphics VS **ggplot2**

There are two main approaches for doing graphics

I. Base functions programming.

II. Use package `ggplot2` which uses its own syntax.

Comparisons of the two approaches:
* Most of the time, the two approaches are not complementary.  
* Objective of this section: obtain the same graphics by using the two approaches.
* Some references: 
    + [Comparing ggplot2 and R Base Graphics](https://flowingdata.com/2016/03/22/comparing-ggplot2-and-r-base-graphics/)
    + [Graphics with R (in French)](http://www.thibault.laurent.free.fr/cours/R_intro/chapitre_4.html)
    + [Univariate Graphs](https://rkabacoff.github.io/datavis/Univariate.html)
* Package `esquisse` allows to obtain `ggplot2` codes using an interface

**Remark:** actually, there is a third approach by using `lattice` package (see [this link](https://www.londonr.org/wp-content/uploads/sites/2/presentations/LondonR_-_lattice_vs_ggplot2_-_Richard_Pugh_and_Andy_Nicholls_-_20130910.pdf) for more informations), but we will not present this solution
---
# Base graphics syntax

1. (optionnal): use `par()` function for defining the margin, the size of the labels, etc.
2. Open a device with a first-level plot function like `plot()`, `hist()`, `barplot()`, `boxplot()`, etc.
3. Customize the plot by using functions like `points()`, `lines()`, `text()`, `legend()`

```r
op <- par(oma = c(1, 1, 0, 1), las = 1)
boxplot(Sepal.Length ~ Species, data = iris)
points(as.numeric(iris$Species) + rnorm(150, 0, 0.1), iris$Sepal.Length)
points(c(1, 2, 3), tapply(iris$Sepal.Length, iris$Species, mean), 
 col = "red", pch = 16, cex = 2)
```

![](my_course_4_files/figure-html/unnamed-chunk-7-1.png)

```r
par(op)
```

---
# **ggplot2** syntax

1. use `ggplot()` function for specifying:
    + data frame containing the data to be plotted
    + the mapping of the variables to visual properties of the graph within the `aes()` function.
2. Add the operator `+` followed by the geometric objects `Geoms` (points, lines, bars, etc.) that can be placed on a graph using functions that look `geom_xxx()`
3. Add the operator `+` followed by  the `scales` control which define how variables are mapped to the visual characteristics of the plot. Use functions which look `scale_xxx()`
4. Add the operator `+` followed by  the  `facets` which reproduce a graph for each level of a given variable. Use functions that look `facet_xxx()`

.pull-left[

```r
data("diamonds")
ggplot(diamonds, 
       aes(x = carat,
           y = price)) + 
  geom_point() + 
  ggtitle("My scatter plot")
```

]

.pull-right[
![](my_course_4_files/figure-html/unnamed-chunk-9-1.png)

]

---
# Barplot with **ggplot2**

```r
ggplot(diamonds) +
  aes(x = cut) +
  geom_bar(stat = "count") + 
  xlab("quality degree") +
  ylab("Size") +
  ggtitle("Quality diamonds")
```

![](my_course_4_files/figure-html/unnamed-chunk-10-1.png)

---
# Barplot with graphic base

.pull-left[
We first need to define the contingency table (`table` object)

```r
tab_cut <- table(diamonds$cut)
```
We then use `barplot()` function and `abline()` to get the horizontal lines like with `ggplot2`

```r
par(las = 1)
barplot(tab_cut,
    col = "#AFC0CB",
    border = FALSE,
    main = "Quality diamonds",
    xlab = "quality degree",
    ylab = "size",
    cex.axis = 0.8)
abline(h = seq(0, 20000, by = 2500), 
  col = "lightgray", lty = "dotted")
```
]

.pull-right[
![](my_course_4_files/figure-html/unnamed-chunk-13-1.png)

]

---
# Plotting a time serie

We create a vector of numeric values:

```r
serie <- c(161.31, 154.00, 161.94, 160.23, 173.20, 170.21, 
 163.97, 161.70, 144.91, 145.31, 140.50, 139.58, 135.60, 
 124.40, 132.24, 150.51, 146.56, 153.00, 151.78, 160.65, 
 158.32, 158.06, 153.50, 161.95, 167.00, 175.00, 180.48, 
 173.82, 160.05, 152.80, 153.58, 145.00, 142.98, 145.35)
```

We create a vector of type `Date`:

```r
date_serie <- seq(as.Date("2015/1/1"), by = "month", length.out = 34)
```

For using `ggplot2`, user needs to create a `data.frame` (or `tibble`) object (which is not the case with the **R** base code which allows the use of vectors in most of the functions):

```r
serie_df <- data.frame(date_serie = date_serie, serie = serie)
```

---
# Plot a time serie with **ggplot2**

```r
ggplot(serie_df) +             
  aes(x = date_serie, y = serie) + 
  geom_line(linetype = 2, colour = "blue")  + 
  xlab("Months") +
  ylab("Index") +
  ggtitle("Evolution")
```

![](my_course_4_files/figure-html/unnamed-chunk-17-1.png)

---
# Plot a time serie with **R** base code

```r
par(las = 1)
plot(serie ~ date_serie, data = serie_df, type = "l", col = "royalblue", lty = 2,
  main = "Evolution", xlab = "Months", ylab = "Index", cex.axis = 0.8)
abline(h = seq(130, 180, by = 10), v = date_serie[seq(1, 32, 6)], col = "lightgray", lty = "dotted")
```

![](my_course_4_files/figure-html/unnamed-chunk-18-1.png)

---
# Histogram and density plot with **ggplot2**

```r
ggplot(diamonds) +
 aes(x = price) +
  geom_histogram(aes(y = after_stat(density)), fill = "blue", colour = "black", bins = 30) + 
  geom_density(colour = "red", adjust = 2) +
  stat_function(fun = dnorm, args = c(mean = mean(diamonds$price), sd = sd(diamonds$price))) + 
  xlab("Price") +
  ggtitle("Distribution price")
```

![](my_course_4_files/figure-html/unnamed-chunk-19-1.png)

---
# Histogram and density plot with with **R** base code

```r
par(las = 1, cex.axis = 0.8, cex.lab = 0.8)
hist(diamonds$price, freq = F, col = "lightblue", nclass = 30, xlab = "Price", main = "Distribution price")
lines(density(diamonds$price),col = "red")
x_seq <- seq(-1000, 20000, by = 100)
lines(x_seq, dnorm(x_seq, mean(diamonds$price), sd(diamonds$price)))
abline(h = seq(0, 0.0005, by = 0.00005), v = seq(0, 20000, by = 2500), col = "lightgray", lty = "dotted")
```

![](my_course_4_files/figure-html/unnamed-chunk-20-1.png)

---
# Scatter plot with **ggplot2**

We select first a sample of the observations:

```r
set.seed(123) # fix a seed
diam_ech <- diamonds[sample(nrow(diamonds), 5000, replace = F), ] 
ggplot(diam_ech) + 
 aes(x = carat, y = price) + 
 geom_point() + 
 geom_smooth(method = "loess") + # Non parametric regression
 geom_smooth(method = "lm", col = "red") + # Linear regression
 xlab("Carat") +
 ylab("Price") +
 ggtitle("Relationship between Y and X")
```

![](my_course_4_files/figure-html/unnamed-chunk-21-1.png)

---
# Scatter plot with **R** base code

```r
par(las = 1, cex.axis = 0.8, cex.lab = 0.8)
plot(price ~ carat, data = diam_ech, pch = 16, cex = 0.7, xlab = "carat", 
 ylab= "prix", main = "Scatter plot")
abline(lm(price ~ carat, data = diam_ech), col = "red", lwd = 3)
# values to predict
x_carat <- seq(0, 4.5, 0.01)
lines(x_carat, predict(loess(price ~ carat, data = diam_ech), data.frame(carat = x_carat)), 
 col = "blue", lwd = 3)
abline(h = seq(0, 20000, by = 5000), v = seq(0, 4, by = 0.5), col = "lightgray", lty = "dotted")
```

![](my_course_4_files/figure-html/unnamed-chunk-22-1.png)

---
# Parallel boxplot with **ggplot2**

```r
ggplot(diam_ech) +              
  aes(x = color, y = price) +  
  geom_boxplot() 
```

![](my_course_4_files/figure-html/unnamed-chunk-23-1.png)

---
# Parallel boxplot with **R** base code

```r
par(las = 1, cex.axis = 0.8, cex.lab = 0.8)
boxplot(price ~ color, data = diam_ech, pch = 16, cex = 0.7, xlab = "carat", ylab= "prix")
abline(h = seq(0, 20000, by = 2500), v = seq(0, 5, by = 1), col = "lightgray", lty = "dotted")
```

![](my_course_4_files/figure-html/unnamed-chunk-24-1.png)

---
# What is conditionnal graphics?

**Objective:** it consists in representing the distributions on sub-populations (for example, a population can be splitted with respect to a qualitative variable). It can be done differently:

.pull-left[
Represent all the sub-distributions in the same graphic
![](my_course_4_files/figure-html/unnamed-chunk-25-1.png)
]

.pull-right[
Repeat the same graphic for each sub-sample (can be called `facets`).
![](my_course_4_files/figure-html/unnamed-chunk-26-1.png)

]

---
# Conditionnal density plot with **ggplot2**

It is very easy to plot conditionnal graphics with **ggplot2**. Note that these 3 rows of code represent actually a lot of **R** base codes. Initial graphical parameters are already chosen.

```r
ggplot(diam_ech) + 
  aes(x = price, fill = cut) + 
  geom_density(alpha = 0.5)
```

![](my_course_4_files/figure-html/unnamed-chunk-27-1.png)

---
# Conditionnal density plot with **R** base code

It can be boring to make conditional grahics with **R** base code but ... it is still possible to make some elegant graphics and maybe easier to change optional parameters.

.pull-left[

```r
list_price <- split(diam_ech$price, diam_ech$cut)
list_density <- lapply(list_price, density)
par(las = 1, cex.axis = 0.8, cex.lab = 0.8)
plot(range(unlist(lapply(
 list_density, function(l) range(l$x)))),
 range(unlist(lapply(list_density, 
 function(l) range(l$y)))),
 type = "n", xlab = "price", ylab = "density")
col_pal <- c("#F8766D", "#A3A500", "#00BF7D", "#00B0F6", "#E76BF3")
dont_print <- mapply(lines, list_density, col = col_pal, lwd = 2)
abline(h = seq(0, 4*10^(-4), by = 10^(-4)), 
 v = seq(0, 25000, by = 5000), 
 col = "lightgray", lty = "dotted")
legend("topright", legend = names(list_density),
 col = col_pal, lwd = 2, cex = 0.8)
```

]

.pull-right[

![](my_course_4_files/figure-html/unnamed-chunk-29-1.png)

]

---
# Conditionnal scatter plot with **ggplot2**

.pull-left[

```r
ggplot(diam_ech) + 
  aes(x = carat, y = price) + 
  geom_point() + 
  geom_smooth(aes(colour = cut)) +
  theme_bw() +                    
  xlab("Carat") +                  
  ylab("price (in USD)") +        
  ggtitle("Scatter plot") +  
  scale_colour_brewer(name = "Qualité", 
  labels = c("A--", "A-", "A", "A+", "A++"), 
  palette = "Greens")     
```

]

.pull-right[
![](my_course_4_files/figure-html/unnamed-chunk-31-1.png)

]

---
# Conditionnal scatter plot with **R** base code

.pull-left[

```r
par(las = 1, cex.axis = 0.8, cex.lab = 0.8)
plot(price ~ carat, data = diam_ech, pch = 16, cex = 0.7, xlab = "carat", 
 ylab = "price (in USD)", main = "Scatter plot")
abline(h = seq(0, 20000, by = 5000), v = seq(0, 4, by = 0.5), 
 col = "lightgray", lty = "dotted")
list_df <- split(diam_ech, diam_ech$cut)
x_carat <- seq(0, 4.5, 0.01)
list_loess <- lapply(list_df, 
 function(obj) predict(loess(price ~ carat, 
 data = obj), data.frame(carat = x_carat)))
require("RColorBrewer") 
col_pal <- brewer.pal(length(list_price), "Greens")
dont_print <- mapply(lines, rep(list(x_carat), 5), list_loess, 
 col = col_pal, lwd = 3)
legend("topright", legend = c("A--", "A-", "A", "A+", "A++"),
 col = col_pal, lwd = 2, cex = 0.8)
```

]

.pull-right[

![](my_course_4_files/figure-html/unnamed-chunk-33-1.png)
]

---
# Conditionnal parallel boxplot with **ggplot2**

```r
ggplot(diam_ech) +              
  aes(x = color, y = price, fill = cut) +  
  geom_boxplot()    
```

![](my_course_4_files/figure-html/unnamed-chunk-34-1.png)

---
# Conditionnal parallel boxplot with **R** base code

.pull-left[

```r
col_pal <- c("#F8766D", "#A3A500", "#00BF7D", "#00B0F6", "#E76BF3")
par(las = 1, cex.axis = 0.8, cex.lab = 0.8,
 xpd = T, mar = par()$mar + c(0, 0, 0, 4))
boxplot(price ~ cut + color, data = diam_ech, xlab = "color", 
 ylab = "price", at = c(1:5, 7:11, 13:17, 19:23, 25:29, 31:35, 37:41),
 col = rep(col_pal, 7), pch = 16, xaxt = "n")
axis(1, at = c(3, 9, 15, 21, 27, 33, 39),
 labels = c("D", "E", "F", "G", "H", "I", "J"))
abline(h = seq(0, 20000, by = 2500), col = "lightgray", lty = "dotted")
legend(45, 15000, legend = c("A--", "A-", "A", "A+", "A++"),
 fill = col_pal)
```

]

.pull-right[

![](my_course_4_files/figure-html/unnamed-chunk-36-1.png)
]

---
# Facets with **ggplot2**

```r
ggplot(diam_ech) +
  aes(x = price) + 
  geom_density() +
  facet_wrap(~ cut) 
```

![](my_course_4_files/figure-html/unnamed-chunk-37-1.png)

---
# 👩‍🎓 Training

### Exercise 4.2

* Find the code which allows to obtain with **ggplot2** this figure:

* Find the code in **R** base code which allows to obtain this figure:

```r
data("diamonds")
ggplot(diamonds, 
       aes(x = carat,
           y = price)) + 
  geom_point() + 
  ggtitle("My scatter plot")
```

---
class: inverse, middle, center
background-image: url(https://cran.r-project.org/Rlogo.svg)
background-size: contain

# 3. Original statistical graphics

---
# Violin plots

It is the combination of density plot (in blue) and boxplot

```r
ggplot(diamonds, aes(x = cut, y = price)) +
  geom_violin(fill = "cornflowerblue") +
  geom_boxplot(width = .2, fill = "orange",
  outlier.color = "orange", outlier.size = 2) + 
  labs(title = "Price dist. by cut")
```

![](my_course_4_files/figure-html/unnamed-chunk-40-1.png)

---
# Combining jitter and boxplot

It is the combination of boxplot and jitter

```r
ggplot(diamonds, aes(x = cut, y = price, fill = cut)) +
  geom_boxjitter(color = "black", jitter.color = "darkgrey", errorbar.draw = TRUE) +
  theme_minimal() +
  theme(legend.position = "none")
```

![](my_course_4_files/figure-html/unnamed-chunk-41-1.png)

---
# Ridgeline plot

It represents the density plot on sub-sample

```r
ggplot(diam_ech) +
  aes(x = price, y = color, fill = color) +
  geom_density_ridges() + 
  theme_ridges() +
  labs("Price by levels color") +
  theme(legend.position = "none")
```

![](my_course_4_files/figure-html/unnamed-chunk-42-1.png)

---
# Comparing the means of sub-sample

We first compute in a `data.frame` the mean, the standard deviation, the standard error and the confidence interval of a numeric variable with respect to 2 categorical variables

```r
library(dplyr)
plotdata <- diamonds %>%
 group_by(color, cut) %>%
 summarize(n = n(),
 mean = mean(price),
 sd = sd(price),
 se = sd / sqrt(n),
 ci = qt(0.975, df = n - 1) * sd / sqrt(n))
```

---
# Represent error bars (1)

⚠️ we get different results with respect to the choosen criteria. Here the SD:

```r
ggplot(plotdata) + 
 aes(x = cut, y = mean, group = color, color = color) +
  geom_point(size = 3) +
  geom_line(linewidth = 1) +
  geom_errorbar(aes(ymin  = mean - sd, ymax = mean + sd), width = .1) + 
  labs(title = "standard deviation")
```

![](my_course_4_files/figure-html/unnamed-chunk-44-1.png)

---
# Represent error bars (2)

⚠️ we get different results with respect to the choosen criteria. Here the SE:

```r
ggplot(plotdata) + 
 aes(x = cut, y = mean, group = color, color = color) +
  geom_point(size = 3) +
  geom_line(linewidth = 1) +
  geom_errorbar(aes(ymin  = mean - se, ymax = mean + se), width = .1)  + 
  labs(title = "standard error")
```

![](my_course_4_files/figure-html/unnamed-chunk-45-1.png)

---
# Represent error bars (3)

⚠️ we get different results with respect to the choosen criteria. Here the CI:

```r
ggplot(plotdata) + 
 aes(x = cut, y = mean, group = color, color = color) +
  geom_point(size = 3) +
  geom_line(linewidth = 1) +
  geom_errorbar(aes(ymin  = mean - ci, ymax = mean + ci), width = .1)  + 
  labs(title = "confidence interval")
```

![](my_course_4_files/figure-html/unnamed-chunk-46-1.png)

---
# Cleveland dot chart

Compare the 2007 life expectancy for Asian country using the gapminder dataset.

.pull-left[

```r
data(gapminder, package = "gapminder")

library(dplyr)
plotdata <- gapminder %>%
 filter(continent == "Asia" & year == 2007)
ggplot(plotdata) +
 aes(x = lifeExp, y = reorder(country, lifeExp)) +
 geom_point()
```

]

.pull-right[

![](my_course_4_files/figure-html/unnamed-chunk-48-1.png)
]

---
# Area chart
Compare the shares of different companies across the time
.pull-left[

```r
time_chart <- data.frame(
 year = rep(as.Date(c(2000, 2005, 2010)), each =3),
 market_share = c(20, 50, 30,
 30, 50, 20,
 50, 30, 20),
 comp = rep(c("a", "b", "c"), 3)
)

ggplot(time_chart) + 
  aes(x = year, y = market_share, fill = comp) +
  geom_area(color = "black") +
  labs(title = "Market share",
       subtitle = "2000 to 2010",
       x = "Year",
       y = "percentage",
       fill = "Company") +
  scale_fill_brewer(palette = "Set2") +
  theme_minimal()
```

]

.pull-right[

![](my_course_4_files/figure-html/unnamed-chunk-50-1.png)
]

---
# Ternary diagram for compositional data

Compare the shares of different companies across the time
.pull-left[

```r
library(tidyverse)
time_chart_wide <- pivot_wider(time_chart, values_from = market_share,
 names_from = comp)

library("ggtern")
ggtern(data = time_chart_wide, 
    mapping = aes(x = a, y = b, z = c)) +
    geom_point(size = 1.5)
```

]

.pull-right[

![](my_course_4_files/figure-html/unnamed-chunk-52-1.png)
]

---
# Correlation plot

```r
r <- cor(iris[, 1:4], use = "complete.obs")
ggcorrplot(r, hc.order = TRUE, type = "lower", lab = TRUE)
```

![](my_course_4_files/figure-html/unnamed-chunk-53-1.png)

---
# Linear regression (1)

The `visreg()` function takes (1) the model and (2) the variable of interest and plots the conditional relationship, controlling for the other variables (see [this link](https://journal.r-project.org/archive/2017/RJ-2017-046/RJ-2017-046.pdf) for more informations).

Example on a numeric variable:

```r
res_lm <- lm(Sepal.Length ~ Sepal.Width + Petal.Width + Species, data = iris)
visreg(res_lm, "Sepal.Width", gg = TRUE) 
```

![](my_course_4_files/figure-html/unnamed-chunk-54-1.png)

---
# Linear regression (2)

Example on a categorical variable:

```r
visreg(res_lm, "Species", gg = TRUE) 
```

![](my_course_4_files/figure-html/unnamed-chunk-55-1.png)

---
# Logistic regression

Model first:

```r
iris$binary <- factor(ifelse(iris$Species == "setosa", 1, 0))
res_glm <- glm(binary ~ Sepal.Length, family = binomial(link = "logit"), data = iris)
```

Plot the probability function with respect to one explanatory variable:

```r
visreg(res_glm, "Sepal.Length", gg = TRUE, scale="response")
```

![](my_course_4_files/figure-html/unnamed-chunk-57-1.png)

---
# Survival plot

Model first:

```r
sfit <- survfit(Surv(time, status) ~ sex, data = lung)
```

Plot the survival plot:

```r
ggsurvplot(sfit, conf.int = TRUE, pval = TRUE, legend.labs = c("M", "F"), legend.title = "Sex", 
  palette = c("cornflowerblue", "indianred3"),title = "Kaplan-Meier", xlab = "Time (days)")
```

![](my_course_4_files/figure-html/unnamed-chunk-59-1.png)

---
# Mosaic plot

The mosaic plot permits to appreciate the link between two categorical variables:

```r
tab <- xtabs(~cut + color, diamonds)
mosaic(tab, shade = TRUE, legend = TRUE)
```

![](my_course_4_files/figure-html/unnamed-chunk-60-1.png)

---
# 👩‍🎓 Training

### Exercise 4.3

* On the `lung` data used previously, make a mosaic plot between `status` and `sex` variable.

* On the `lung` data, make a ridge plot of variable `age` with respect to `status`.

* Make a correlation plot of variables `ph.karno`, `pat.karno`, `meal.cal`, `wt.loss` in the `lung` data.

---
class: inverse, middle, center
background-image: url(https://cran.r-project.org/Rlogo.svg)
background-size: contain

# 4. Interactive Graphics

---
# **plotly** package (1)

* Interactive graphics with respect to library **plotly.js** (in **JavaScript**)
* More informations [here](https://plot.ly/r/)
* Syntax:
    + 1st argument: the `data.frame`
    + argument `x=` gives the name of the `$x$` variable; argument `y=` gives the name of the `$y$` variable; 
    + argument `color=` gives the name of the conditionnal variable
    + argument `type=` gives the type of graphic

```r
plot_ly(diam_ech, x = ~price, color = ~cut, type = "box")
```

<div class="plotly html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-843c054f80e4323a248f" style="width:360px;height:288px;"></div>
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---
# **plotly** package (2)

Another example with a scatterplot:

```r
plot_ly(diam_ech, x = ~carat, y = ~price, type = "scatter", 
   mode = "markers", hoverinfo = 'text',
   text = ~paste('Carat: ', carat, '\n Price: ', price, '\n Clarity: ', diam_ech$clarity),
   color = ~carat)
```

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---
# **plotly** with **ggplot2** style

Use function `ggplotly()` on a **ggplot2** syntax. Example:

```r
p <- ggplot(diam_ech) + 
 aes(x = color, y = price) + 
 geom_boxplot() 
ggplotly(p)
```

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---
# **ggiraph** with **ggplot2** style

A new library that allows to keep the aesthetics than origin **ggplot2**

```r
library(ggiraph)
data <- mtcars
data$carname <- row.names(data)
gg_point = ggplot(data = data) +
 geom_point_interactive(aes(x = wt, y = qsec, color = disp,
 tooltip = carname, data_id = carname)) + 
 theme_minimal()
girafe(ggobj = gg_point, options = list(opts_sizing(rescale = TRUE, width = .3)))
```

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Other packages for interactive graphics: **ggvis**(see [this link](https://ggvis.rstudio.com/)) and 
**rCharts** (see [this link](https://ramnathv.github.io/rCharts/))

---
# Short introduction to **Shiny**

#### What is shiny ?

* **Shiny**: Interactive web page
* Examples of shiny app: http://shiny.rstudio.com/gallery/ 
* More informations [here](https://www.londonr.org/wp-content/uploads/sites/2/presentations/LondonR_-_Workshop-Introduction_to_Shiny_-_Aimee_Gott_-_20150330.pdf)
* Create a new shiny App: File - New File - Shiny Web App...
* Possibility to create web pages. **RStudio** proposes to host a couple of App for free. See https://www.rstudio.com/products/shiny/shiny-server/ for more informations

---
# The two files

.pull-left[

```r
library(shiny)
# Define UI for application that draws a histogram
ui <- fluidPage(
 # Application title
 titlePanel("Old Faithful Geyser Data"),

# Sidebar with a slider input for number of bins 
  sidebarLayout(
    sidebarPanel(
      sliderInput("bins",
                  "Number of bins:",
                   min = 1,
                   max = 50,
                   value = 30)
        ),
  # Show a plot of the generated distribution
  mainPanel(
         plotOutput("distPlot")
       )
    )
)
```

]

.pull-right[

```r
# Define server logic required to draw a histogram
server <- function(input, output) {

output$distPlot <- renderPlot({
 # generate bins based on input$bins from ui.R
 x <- faithful[, 2]
 bins <- seq(min(x), max(x), length.out = input$bins + 1)

# draw the histogram with the specified number of bins
  hist(x, breaks = bins, col = 'darkgray', border = 'white')
 })
}
```
To run the application:

```r
shinyApp(ui = ui, server = server)
```
]

---
# Description of the two files

* *ui.R*: indicates how the screen should be organized. For example, on the left, we print the title, the ruler, etc. that the user can eventually modify. On the right, we decide to print the graphic.
* *server.R*: contains the codes which permits to plot a graphic by using the parameters defined by users in the interface.
* When these files are opened in **RStudio**, it is then possible to run the App by clicking on the button Run App.

---
# Description of the options

#### **ui.R** file
* *textInput()*: entering a string,
* *numericInput()*: entering a numeric,
* *selectInput()*: Create a select list input control,
* *sliderInput()*: Slider Input Widget,
* *radioButtons()*: Create radio buttons
* *fileInput()*: File Upload Control.

#### **server.R** file

* For plotting a graphic: use *renderPlot()* in **server.R** and *plotOutput()* in **ui.R**. 
* For printing text: use *renderPrint()* in **server.R** and *textOutput()* in **ui.R**. 
* For printing data table: use *renderDataTable()* in **server.R** and *dataTableOutput()* in **ui.R**. 
* For printing images: use *renderImage()* in **server.R** and *imageOutput()* in **ui.R**.