Data analysis II

# Data analysis II
## Visualisation, practice
### Laurent Bergé
### University of Bordeaux, BxSE
### 09/12/2021

---

1. Intro
2. [A bit of graphical code in R](#sec_r_code)
3. [A functional approach to graphs](#sec_functional)
4. [More graphical code in R](sec_r_more)

---

# A tale of two paths...

![](images/roads_choice.png)

---

# Pros and cons: Base R

- the principle is easy to grasp: we simply overlay successive forms on top of each other

- low level operations `$=$` everything is possible

]

- because everything is an overlay on stg already in place, the first plot is critical: you need to plan everything in advance!!! .color2.fs-18[Doing simple stuff can be surprisingly difficult.]

- many commands which are not really intuitive. .color2.fs-18[After 10 years, I still look up `?par` regularly.]

]

---

# Messing up the first plot in Base R

You miss most of the show!

![](images/danse_cropped.gif)

---

# Disclaimer

I'm a `ggplot2` noob, base R is my home. I have a lot of sympathy for it.

![](images/roads_lb_choice.png)

---

# Pros and cons: ggplot2

- much more user friendly: stacks all your layers before creating the graph and makes all the computations for you. .color2.fs-18[You don't need to overthink how to set the stage any more.]

- millions of contributed packages

]

- since there is pre-processing, it is sometimes difficult to do exactly what you want (e.g. for a very precise publication graph). .color2.fs-18[To be confirmed, it's only second-hand experience!]

]

---

# Wait! There are other paths!

![](images/roads_other_choice.png)

---

# Ready made solutions

- there are numerous packages out there which make good graphs with a user-friendly interface (ie with minimal user input)

- example: [ggpubr](https://rpkgs.datanovia.com/ggpubr/index.html) or [fplot](https://cran.r-project.org/web/packages/fplot/index.html) for distributions; [ggcorrplot](https://cran.r-project.org/web/packages/ggcorrplot/index.html) for correlations; [highcharter](https://jkunst.com/highcharter/) for many things, etc.

---

# Pros and cons: Ready made solutions

### Pros

- in a single line of code you get a graph of (.color2[usually]) very decent quality

- very good for exploratory graphs

]

### Cons

- the level of customization is limited, this is especially problematic for presentations/publications in which you want a very high level of customization

]

---

# Direct edition

- I strongly encourage you to learn how to work with [inkscape](https://inkscape.org/).footnote[{`\$\star\$`}Or any equivalent image edition software, but .color1[inkscape] is both free and powerful (.color2[yeah!]).]

- it's just crazy how fast you can edit/create images

- that's indispensable in your skill set, you'll save so much time!

???

later: add gif edition of the castle image

---

# Pros and cons: Direct edition

### Pros

- you can do exactly what you want, as precisely as you want

- with practice, you can edit very rapidly

]

### Cons

- the direct edition only comes after the first creation of the graph: hence you have to navigate across software

- cannot be automated: all the work that you do with one graph, you'd have to do it again for a graph with new data

]

---

# Short introduction to Base R

---
# Data content

R dispose of multiple functions to display data:

- `plot()`: CORE graphical function
- `points()`, `lines()`, `abline()`, `text()`: used to plot additional data
- `density()`, `hist()`, `boxplot()`, etc...

---

# Plot

- The function `plot()` is the main graphical function of R (more precisely, it's a method).

- By default it is a scatterplot between two variables, but it can be used to do much more than that.

- Some functions preprocess the data, like `density()`, and modify completely the behavior of `plot()` when you apply it to the preprocessed data. More on that later.

- When you apply `plot()`, it creates a new graphic and the previous one is lost (of course there are exceptions...). To add several pieces of information, you'll need to use other functions.

---

#  Main `plot` arguments

Main plot arguments relating to data:

- `x, y`: the data
- `xlim, ylim`: the limits of the plotting region
- `col, pch, lty, lwd`: color, symbol, line type and line width
- `type`: the type of plot
- `log`: whether to put the x/y axes to logarithm

---

#  Plot: type

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-1-1.png)

---

#  Plot: type = "n"

Using `type = "n"` hides the data, but EVERYTHING else is there. Can be useful when constructing complex graphics: i.e. when setting the stage.

```r
plot(1:5, type = "n")
```

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-2-1.png)

---

#  Plot: limits

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-3-1.png)

---

#  Plot: pch

```r
plot(1:20, pch = 1:20)
grid()
```

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-4-1.png)

---

#  Plot: cex

```r
plot(1:5, pch = 16, cex = 1:5, main = "cex: modify point size")
```

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-5-1.png)

---

#  Plot: lty and lwd

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-6-1.png)

---

#  Plot: col I

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-7-1.png)

---

#  Plot: col II

Lot of color possibilities:

- Custom colors: `rgb()`,`hsv()`, etc 
- "Nice" colors: package `RColorBrewer`
- Color interpolation:
    - `rainbow(n)`, `heat.colors(n)`, etc, create vectors of `n` colors.
    - `colorRampPalette(c("white", "blue"))(5)`: create a vector of 5 colors between the colors white and blue.

- Nice introduction to R colors in the [R-stats UBC course](https://stat545.com/colors.html)

---

#  Exercise: Plot

Generate a 100 periods Brownian motion `$x_{t+1} = x_{t} + \epsilon_{t}$`, `$\epsilon_{t}\sim N(0,1)$`.

1. Plot its evolution with both a solid line and filled points (in the same graph).
2. This time display only the points and use the function `rainbow()` to set the color of each point.

---

#  Adding data points

To add points/lines onto an existing plot:

- `lines()`
- `points()`

It behaves as the function `plot()` and contains the same arguments (col, lty, cex, lwd, pch).

---

#  Lines & points

```r
plot(1:5, ylim = c(-2, 5))
lines(1:5 - 1)
points(1:5 - 2)
```

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-8-1.png)

---

#  Exercise: Plot & line

In the following graph, the functions `plot()`, `lines()` and `points()` have been called. Can you say to what command refers each graphical information, and in what order they have been called?
![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-9-1.png)

---

#  Exercise: Plot & lines

Re-generate the previous Brownian motion.

1. Plot it with both line and dots.

2. Generate another Brownian motion with `$\epsilon_{t}\sim N(0, 4)$`.  
Plot the two motions on a single graph, the second one should be of `"firebrick"` color, have thick and dashed line and be of triangle symbol.

---

#  abline I

The function `abline()` draws lines. Its arguments are:

- `h`: coordinate of horizontal line
- `v`: coordinate of vertical line
- `a, b`: intercept (a) and slope of a straight line. Shorthan exist: can take the result of an OLS regression (function `lm()`) instead.

---

#  abline II

```r
plot(iris$Sepal.Length, iris$Petal.Width)
abline(lm(Petal.Width ~ Sepal.Length, iris))
abline(h = c(1, 2), v = c(5, 7), col = "gray", lty = 3)
```

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-10-1.png)

---

#  Exercise: abline

You want to illustrate the relation between the variables "Sepal.Length" and "Petal.Width" for each species of the `iris` data.

1. Plot the scatterplot between the two variables with one color per species.
2. Draw the regression lines for each group with the appropriate color.

---

#  Text I

You can add text to an existing plot with the function `text()`. The most important arguments are:

- x, y: coordinates of the text
- labels: the text to be displayed
- pos: the position of the text relative to the coordinate. pos = 0: as is, pos = 1: below, 2: left, 3: top, 4: right.
- As usual, other graphical parameters apply: cex (size), col, etc.

---

#  Text II

```r
plot(5:1, col = "firebrick", pch = 18, xlim = c(0, 6))
text(1, 5, "pos = default")
text(2:5, 4:1, paste0("pos = ", 1:4), pos = 1:4)
```

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-11-1.png)

---

#  Exercise: text

1. As in the previous exercise, plot the scatterplot between the two variables with one color per species for the variables "Sepal.Length" and "Petal.Width" of the `iris` data.
2. Add the Species names *in the middle* of the points for each species in the right color and with large font.

---

# A functional approach to graphs

---

# From pain we learn

Base R can be surprisingly painful for doing seemingly simple stuff.

Base R is so painful, that if you stick to it, it will make you a good programmer (or a masochist!).

Remember though: it's not just painful, it's also .bold1[extremely powerful]!

---

# Hard coding

It's very easy to write code that is specific to your current data! In fact, it's usually the first thing we do, and it works well.

.leftcol-45.fs-12[

```r
plot(iris$Petal.Length, iris$Sepal.Length, 
	 col = iris$Species, pch = 20, cex = 2)

text(1.5, 5, "Setosa", 
	 font = 2, cex = 4)
text(4, 6, "Versicolor", 
	 font = 2, cex = 4, col = 2)
text(6, 7, "Virginica", 
	 font = 2, cex = 4, col = 3)
```
]

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-13-1.png)

]

---

# Hard coding: The problem

If your data changes, even slightly, your code is messed up.

Changing `Sepal.Length` into `Sepal.Width` loses the legend:

.leftcol-45.fs-12[

```r
plot(iris$Petal.Length, iris$Sepal.Width, 
	 col = iris$Species, pch = 20, cex = 2)

text(1.5, 5, "Setosa", 
	 font = 2, cex = 4)
text(4, 6, "Versicolor", 
	 font = 2, cex = 4, col = 2)
text(6, 7, "Virginica", 
	 font = 2, cex = 4, col = 3)
```
]

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-15-1.png)

]

---

# Hard coding: The problem

If your data changes, even slightly, your code is messed up.

---

# Hard coding: The routine

If you want to replicate a hard coded graph to a new data set you:

1. copy paste the code 
2. change the data
3. make the adjustments so the graph looks as you wish with the new data

I think I don't need to write that each of these three steps are .bold1[highly error-prone], and can cost dearly..footnote[{`\$\star\$`} Think of the situation where you advise your boss based on a graph, only to realize a couple of hours later that you simply badly updated a copy-pasted piece of code... .color2[and off you go!]]

---

# Hard coding: The solution

- very simple: don't hard code! <svg aria-hidden="true" role="img" viewBox="0 0 496 512" style="height:1em;width:0.97em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M248 8C111 8 0 119 0 256s111 248 248 248 248-111 248-248S385 8 248 8zm0 448c-110.3 0-200-89.7-200-200S137.7 56 248 56s200 89.7 200 200-89.7 200-200 200zm117.8-146.4c-10.2-8.5-25.3-7.1-33.8 3.1-20.8 25-51.5 39.4-84 39.4s-63.2-14.3-84-39.4c-8.5-10.2-23.7-11.5-33.8-3.1-10.2 8.5-11.5 23.6-3.1 33.8 30 36 74.1 56.6 120.9 56.6s90.9-20.6 120.9-56.6c8.5-10.2 7.1-25.3-3.1-33.8zM168 240c17.7 0 32-14.3 32-32s-14.3-32-32-32-32 14.3-32 32 14.3 32 32 32zm160-60c-25.7 0-55.9 16.9-59.9 42.1-1.7 11.2 11.5 18.2 19.8 10.8l9.5-8.5c14.8-13.2 46.2-13.2 61 0l9.5 8.5c8.5 7.4 21.6.3 19.8-10.8-3.8-25.2-34-42.1-59.7-42.1z"/></svg>

- OK, here comes some tips

---

# Tip 1: Define global variables!

- whenever a variable is repeated *twice*, use a global variable.footnote[Uppercase letters work well to identify parameters.] defined at the beginning of the piece of code

```r
plot(iris$Petal.Length, iris$Sepal.Width, 
	 col = iris$Species, pch = 20, cex = 2)

text(1.5, 5, "Setosa", 
	 font = 2, cex = 4)

text(4, 6, "Versicolor", 
	 font = 2, cex = 4, col = 2)

text(6, 7, "Virginica", 
	 font = 2, cex = 4, col = 3)
```
]

]

```r
FONT = 2
CEX = 4

plot(iris$Petal.Length, iris$Sepal.Width, 
	 col = iris$Species, pch = 20, cex = 2)

text(1.5, 5, "Setosa", 
	 font = FONT, cex = CEX)

text(4, 6, "Versicolor", 
	 font = FONT, cex = CEX, col = 2)

text(6, 7, "Virginica", 
	 font = FONT, cex = CEX, col = 3)
```
]
]

---

# Tip 2: Lay bare how you think!

- when you decide to place some text here, or a legend there, how do you take the decision?

- you decide based on heuristics (.color2[although you may not even notice there was a decision process!])

- the game is to extract the (often implicit) rules that made you take a decision.footnote[{`\$\star\$`} You can .color1[always] find an heuristic that led to your decision.]

- if you achieve to make the heuristic explicit: you win since now you can automatize it!

---

# Tip 2: Lay bare how you think!

Remember when I asked to put the names *in the middle* of the points?

```r
FONT = 2
CEX = 4

plot(iris$Petal.Length, iris$Sepal.Width, 
	 col = iris$Species, pch = 20, cex = 2)

text(1.5, 5, "Setosa", 
	 font = FONT, cex = CEX)

text(4, 6, "Versicolor", 
	 font = FONT, cex = CEX, col = 2)

text(6, 7, "Virginica", 
	 font = FONT, cex = CEX, col = 3)
```
]
]

```r
FONT = 2
CEX = 4

plot(iris$Petal.Length, iris$Sepal.Width, 
	 col = iris$Species, pch = 20, cex = 2)

bary = aggregate(cbind(Petal.Length, Sepal.Width) ~ Species, 
				 iris, mean)

text(bary[1, 2], bary[1, 3], "Setosa", 
	 font = FONT, cex = CEX)

text(bary[2, 2], bary[2, 3], "Versicolor", 
	 font = FONT, cex = CEX, col = 2)

text(bary[3, 2], bary[3, 3], "Virginica", 
	 font = FONT, cex = CEX, col = 3)
```
]
]

---

# Tip 3: Loop whenever possible!

- whenever you repeat two statements: use a loop instead!

```r
FONT = 2
CEX = 4

plot(iris$Petal.Length, iris$Sepal.Width, 
	 col = iris$Species, pch = 20, cex = 2)

bary = aggregate(cbind(Petal.Length, Sepal.Width) ~ Species, 
				 iris, mean)

text(bary[1, 2], bary[1, 3], "Setosa", 
	 font = FONT, cex = CEX)

text(bary[2, 2], bary[2, 3], "Versicolor", 
	 font = FONT, cex = CEX, col = 2)

text(bary[3, 2], bary[3, 3], "Virginica", 
	 font = FONT, cex = CEX, col = 3)
```
]

]

```r
FONT = 2
CEX = 4

plot(iris$Petal.Length, iris$Sepal.Width, 
	 col = iris$Species, pch = 20, cex = 2)

categ_val = levels(iris$Species)

for(i in seq_along(categ_val)){
	data = iris[iris$Species == categ_val[i], ]
	text(mean(data$Petal.Length), 
		 mean(data$Sepal.Width), categ_val[i],
		 font = FONT, cex = CEX, col = i)
}
```

]

---

# Tip 4: Loop over the tips!

Apply recursively .strong1[Tip 1], .strong1[Tip 2] and .strong1[Tip 3] until you can't any more.

```r
FONT = 2
CEX = 4

plot(iris$Petal.Length, iris$Sepal.Width, 
	 col = iris$Species, pch = 20, cex = 2)

categ = levels(iris$Species)

for(i in seq_along(categ)){
	data = iris[iris$Species == categ[i], ]
	text(mean(data$Petal.Length), 
		 mean(data$Sepal.Width), categ[i],
		 font = FONT, cex = CEX, col = i)
}
```
]
]

```r
FONT = 2
CEX = 4
x = iris$Petal.Length
y = iris$Sepal.Width
categ = iris$Species

plot(x, y, col = categ, pch = 20, cex = 2)

categ_val = levels(categ)

for(i in seq_along(categ_val)){
	who = categ == categ_val[i]
	text(mean(x[who]), mean(y[who]), categ_val[i],
		 font = FONT, cex = CEX, col = i)
}
```
]
]

---

# Are the tips useful?

Can those tips be concretely helpful?

To know that, let's summon the .bold1[copy-paste demon].

---

# Code without tips

```r
plot(iris$Petal.Length, iris$Sepal.Width, 
	 col = iris$Species, pch = 20, cex = 2)

text(1.5, 5, "Setosa", 
	 font = 2, cex = 4)

text(4, 6, "Versicolor", 
	 font = 2, cex = 4, col = 2)

text(6, 7, "Virginica", 
	 font = 2, cex = 4, col = 3)
```

---

# Code without tips: Summoning

![](images/man_summon.png)

---

# Code without tips: Outcome

The demon has .bold1[immense powers]

![](images/demon_powerful_code.png)

]

---

# Code with tips

```r
FONT = 2
CEX = 4
x = iris$Petal.Length
y = iris$Sepal.Width
categ = iris$Species

plot(x, y, col = categ, pch = 20, cex = 2)

categ_val = levels(categ)

for(i in seq_along(categ_val)){
	who = categ == categ_val[i]
	text(mean(x[who]), mean(y[who]), categ_val[i],
		 font = FONT, cex = CEX, col = i)
}
```

---

# Code with tips: Summoning

![](images/man_summon.png)

---

# Code with tips: Outcome

The demon is .bold1[weak]

![](images/demon_code_weak.png)

]

---

# Tips: Side benefits

If you've followed the tips, guess what:

```r
FONT = 2
CEX = 4
x = iris$Petal.Length
y = iris$Sepal.Width
categ = iris$Species

plot(x, y, col = categ, pch = 20, cex = 2)

categ_val = levels(categ)

for(i in seq_along(categ_val)){
	who = categ == categ_val[i]
	text(mean(x[who]), mean(y[who]), 
		 categ_val[i],
		 font = FONT, cex = CEX, col = i)
}
```

]

```r
scatter_name = function(x, y, categ, font = 2, cex = 4){
	
	plot(x, y, col = categ, pch = 20, cex = 2)
	
	categ_val = levels(categ)
	
	for(i in seq_along(categ_val)){
		who = categ == categ_val[i]
		text(mean(x[who]), mean(y[who]), categ_val[i],
			 font = font, cex = cex, col = i)
	}
}

scatter_name(iris$Petal.Length, 
			 iris$Sepal.Width,
			 iris$Species)
```

]

---

# Why create functions to make graphs?

1. guards you against, or limits, copy-paste problems

1. facilitates graph replications

1. you don't have to think to implementation details when running the function (.color2[reduces mental load])

1. it's very easy to include new features to the functions, and all the calls benefit from it

---

# Mental load

Code telling .bold1[.underline[what] you do] and **not** .bold1[.underline[how] you do it] increases productivity tremendously.

```r
FONT = 2
CEX = 4
x = iris$Petal.Length
y = iris$Sepal.Width
categ = iris$Species

plot(x, y, col = categ, 
	 pch = 20, cex = 2)

categ_val = levels(categ)

for(i in seq_along(categ_val)){
	who = categ == categ_val[i]
	text(mean(x[who]), mean(y[who]), 
		 categ_val[i], col = i,
		 font = FONT, cex = CEX)
}
```

]
]

```r
scatter_name(iris$Petal.Length, 
			 iris$Sepal.Width,
			 iris$Species)
```

]
]

The code on the right will .bold1[always be easier to understand] than the code on the left..footnote[{`\$\\star\$`} If in doubt, you just have to jump to the function definition and its associated documentation (.color2[that you didn't forget to write of course!]).]

---

# Why not create functions?

1. you have a presentation in 30 minutes and have to finish that graph

1. you're making a graph that you think will never replicate.footnote[{`\$\\star\$`} Note that this assumption is in general false. What we think is specific is often but an instance of a more general problem.]

1. the graph is really simple (.color2[in terms of lines of code!])

---

# Functions: Summary

- thinking in functions will change the way you code

- it will clarify your code: it will be easier to understand and share, and less error-prone

- due to the .color1[high fixed costs, 0 marginal cost] nature of functions, you'll gain *a lot* of productivity

---

# Functional programming: Application

Remember the scatterplot with different colors and a linear fit? Let's redo it.

1. plot the scatterplot between the variables "Sepal.Length" and "Petal.Width" for each species of the `iris` data, and add a linear fit

2. use `segments()` to shorten the fit to the width of the scatterplot

3. transform it into a function, with the appropriate arguments

4. add the argument `line_extend` giving how much the length of the segment should be extended, in % of the graph width (default is 0)

5. control the arguments given by the user

---

# More Base R graphs stuff

---

# Informative content

So far we've seen only data content. But there's much more to make a good graph: all the surrounding information!

- `plot()` arguments: 
    - `xlab`, `ylab`: x/y axis labels
    - `sub`, `main`: subtitle and main title
    - `axes`: whether to draw the axes
    - `ann`: if `FALSE`, cleans all x/y labels
- `legend()`: adds a legend
- `title()`: adds axis labels and titles (close to previous plot arguments)
- `axis()`: function to draw the axes of a plot.
- mathematical formulas

---

# Plot: informative

```r
plot(-1:1, -1:1, xlab = "xlab", ylab = "ylab", main = "main", sub = "sub", type = "n")
text(0, 0, 'plot(-1:1, -1:1, xlab = "xlab", ylab = "ylab", main = "main", sub = "sub")')
```

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-30-1.png)

---

# Exercise: informative content

Do the scatterplot between variables "Sepal.Length" and "Petal.Width" of the `iris` data.

1. Put appropriate axes labels (i.e. add only the name of the variable).
2. Put the correlation in the title of the plot.

---

# Title

You can add a title after a plot is done with `title()`.

```r
plot(1:5)
title(main = "This is the title", sub = "This is the subtitle")
```

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-31-1.png)

---

# Title: mtext

Use `mtext` to add text in the margin of the graph. Can be used to insert a title.

```r
plot(1:5)
mtext("That's a basic graph", side = 3, 
	  line = 1, font = 2, adj = 0)
```

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-32-1.png)

---

# Adding a legend

You can add a legend to clarify the content of a plot. A legend is a piece of information appearing inside the plotting region.

Here are the main arguments:

- `x`, `y`: the location of the legend (top left corner). There exist shorthands! instead you can use "topleft", "right", etc.
- `legend`: the content of the legend (a character vector.)
- `pch`, `lty`, `col`, `lwd`: the `pch`, `lty`, `col`, `lwd` associated to the legend vector
- `bty`: whether or not to show the legend box ("o" is default, "n" removes it)

Keep in mind that there are many more arguments.

---

# Legend

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-33-1.png)

---

# Legend in the bottom I

How to have a legend in the bottom? Here's some ready-made code.

```r
legend_bottom = function(..., bty = "n"){
	# Original credits to: https://stackoverflow.com/questions/3932038/plot-a-legend-outside-of-the-plotting-area-in-base-graphics/3932558
	op = par(fig = c(0, 1, 0, 1), oma = c(0, 0, 0, 0), 
			 mar = c(0, 0, 0, 0), new = TRUE)
	
	on.exit(par(op))
	
	plot(0, 0, type = 'n', axes = FALSE, ann = FALSE)
	
	legend("bottom", horiz = TRUE, bty = bty, ...)
}
```

---

# Legend in the bottom II

```r
plot(iris$Sepal.Length, iris$Petal.Width, col = iris$Species, pch = 15)
legend_bottom(legend = levels(iris$Species), col = 1:3, pch = 15)
```

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-35-1.png)

Oh, yeah the legend ends up being too close to the label... Remember about ".color1[setting the stage]"? To make it nicer, you'd need to increase the bottom margin beforehand with, e.g., `par(mar = c(7, 4, 2, 2))` :-/ .comment[That's one of the reasons why `ggplot` is so much easier to handle.]

---

# Axes

You can modify the axes at will.

`axis(i)` draws the *ith* axis with, 1: bottom, 2: left, 3: top and 4: right.

```r
plot(1:5, axes = FALSE)
axis(1)
axis(4)
```

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-36-1.png)

---

# Axis

The function axis has the following main options:

- `side`: where to draw the axis (1: bottom, etc, 4:right)
- `at`: where the ticks are drawn
- `labels`: the labels at the ticks (usually numbers)
- `lwd`: line width of the horizontal line (if side is 1 or 3)
- `lwd.ticks`: the line width of the ticks. Default is equal to lwd
- `tck`: length of the ticks (in fraction of the plotting region)
- `las`: orientation of the text

Many other options.

---

# Axis example

```r
plot(1:5, axes = FALSE, ann = FALSE)
box() # draw a simple box
axis(1, 1:4, c("First", "Second", "Third", "Fourth"), cex.axis = .9)
axis(3, 5, "Fifth", lwd.ticks = 2)
axis(2, 1:3, c("One", "Two", "Three"), las = 2)
axis(2, 4, "Four", col.axis = "red")
axis(2, 5, "Five", col.ticks = "blue", lwd = 2)
```

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-37-1.png)

---

# Mathematical expressions

You can add mathematical expressions in graphics. Advice: for single mathematical formulas, use only function `substitute()`.

Write a formula inside the function `substitute()` (a bit like in Latex). Some elements that can compose the formula:

- `x[i]` for subscript `$x_{i}$`
- `x^i` for superscript `$x^{i}$`
- `x %in% y`, `$x\in y$`
- `alpha`, `beta`, `gamma`, etc... 
- `paste(x, y, z)`: juxtapose the three components

See `?plotmath` for more details.

---

# Math

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-38-1.png)

---

# substitute

`substitute()` contains a second argument. It can be used to replace some variables with numbers:

```r
curve(sin(x)*sqrt(x), 1, 10000, log = "x", axes = FALSE, ann = FALSE)
title(main = substitute(sin(x)%*%sqrt(x)))
box() ; axis(2)
for(i in 0:4) axis(1, at = 10**i, substitute(10^p, list(p = i)), 
				   cex.axis = 2)
```

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-39-1.png)

---

# Graphical parameters

The function `par()` contains most graphical parameters (.color2[there are 72... <svg aria-hidden="true" role="img" viewBox="0 0 496 512" style="height:1em;width:0.97em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M248 8C111 8 0 119 0 256s111 248 248 248 248-111 248-248S385 8 248 8zm0 448c-110.3 0-200-89.7-200-200S137.7 56 248 56s200 89.7 200 200-89.7 200-200 200zm96-312c-44.2 0-80 35.8-80 80s35.8 80 80 80 80-35.8 80-80-35.8-80-80-80zm0 128c-26.5 0-48-21.5-48-48s21.5-48 48-48 48 21.5 48 48-21.5 48-48 48zm0-72c-13.3 0-24 10.7-24 24s10.7 24 24 24 24-10.7 24-24-10.7-24-24-24zm-112 24c0-44.2-35.8-80-80-80s-80 35.8-80 80 35.8 80 80 80 80-35.8 80-80zm-80 48c-26.5 0-48-21.5-48-48s21.5-48 48-48 48 21.5 48 48-21.5 48-48 48zm0-72c-13.3 0-24 10.7-24 24s10.7 24 24 24 24-10.7 24-24-10.7-24-24-24zm160 144H184c-13.2 0-24 10.8-24 24s10.8 24 24 24h128c13.2 0 24-10.8 24-24s-10.8-24-24-24z"/></svg>]).

You can change the graphical parameters directly with `par()`.

```r
par(cex = 2, lwd = 2)
plot(1:5, type = "o")
```

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-40-1.png)

---

# par

All plots will have these parameters as default. To reinitialize it, you can:

1. reinitialize it manually: `par(cex = 1, lwd = 1)`
2. Make a "save" when you modify it:

```r
op = par(cex = 2, lwd = 2) # save old params
# make the graphs
par(op) # reinitialize it
```

---

# par: other parameters

Some useful parameters:

- `mar`: the margins of the plot: the space between the axes and the edge of the plot. It's a vector of length 4 (1st is bottom, last is right).
- `cex`, `cex.axis`, `cex.lab`, `cex.main`, `cex.sub`: expansion factor for different situations
- `col`, `col.axis`, `col.lab`, `col.main`, `col.sub.`
- `bg`: color of the background (default is white)
- `family`: font family: can be "serif", "sans" and "mono"
- `las`: orientation of text (1: horizontal, 3:vertical)

See `?par` for more details.

---

# Multiple graphs

You can combine multiple graphs in one. Simplest way is to use `mfrow`:

```r
op = par(mfrow = c(2, 2))
for(i in 1:4) curve(sin(x) * x**i, -10, 10, ylab = paste0("sin(x) * x^", i))
par(op) # reset to an unique frame
```

![](2021-2022_M2-Data-2_Visu-2_practice_files/figure-html/unnamed-chunk-44-1.png)

---

# Conclusion

I'm afraid you won't be able to make nice graphs from this bare bones introduction!

It only brushes the topic but I hope that you could get some insights along the way! .comment[And especially that the functional programming approach convinced you!]

Cheers!