Introduction to R

# Introduction to R
## Week 2: Making figures
### Louisa Smith
### July 20 - July 24

---

---
# #goals
.pull-left[
<img src="02-slides_files/figure-html/goals-plot-1-1.png" width="500px" height="500px" style="display: block; margin: auto;" />
]
.pull-right[

<img src="02-slides_files/figure-html/goals-plot-2-1.png" width="500px" height="500px" style="display: block; margin: auto;" />
]

---
# Basic structure of a ggplot

```
ggplot(data = {data}) +
      <geom>(aes(x = {xvar}, y = {yvar}, <characteristic> = {othvar}, ...),
             <characteristic> = "value", ...) + ...
```
.midi[
- `{data}`: must be a dataframe (or tibble!)
- `{xvar}` and `{yvar}` are the names (unquoted) of the variables on the x- and y-axes
- `{othvar}` is some other unquoted variable name that defines a grouping or other characteristic you want to map to an aesthetic
- `<geom>`: the geometric feature you want to use; e.g., point (scatterplot), line, histogram, bar, etc.
- `<characteristic>`: you can map `{othvar}` or a fixed `"value"` to any of a number of aesthetic features of the figure; e.g., color, shape, size, linetype, etc.
- `"value"`: a fixed value that defines some characteristic of the figure; e.g., "red", 10, "dashed"
- ... : there are numerous other options to discover!
]

---
class:middle, center
# ggplot builds figures by adding on pieces via a particular "*g*rammar of *g*raphics"
![](../img/grammar.png)

---
count: false
.left-code[

```r
*ggplot(data = nlsy)
```
]
.right-plot[
![](02-slides_files/figure-html/goals-plot-1a-1.png)
]

---
count: false

```r
ggplot(data = nlsy) +
* geom_bar(aes(x = eyesight,
*         fill = factor(eyesight)))
```
]
.right-plot[
![](02-slides_files/figure-html/goals-plot-1b-1.png)
]

---
count: false

```r
ggplot(data = nlsy) +
  geom_bar(aes(x = eyesight, 
          fill = factor(eyesight))) +
* facet_grid(
*   cols = vars(glasses),
*   labeller = labeller(glasses = c(
*     "0" = "Doesn't wear glasses",
*     "1" = "Wears glasses/contacts")))
```
]
.right-plot[
![](02-slides_files/figure-html/goals-plot-1c-1.png)
]

---
count: false

---
count: false

---
count: false

---
count: false

```r
ggplot(data = nlsy) +
  geom_bar(aes(x = eyesight, 
          fill = factor(eyesight))) +
  facet_grid(
    cols = vars(glasses),
    labeller = labeller(glasses = c(
      "0" = "Doesn't wear glasses",
      "1" = "Wears glasses/contacts"))) +
  scale_fill_brewer(palette = "Spectral",
                    direction = -1) +
  scale_x_continuous(
    labels = c("Excellent", "Very Good",
               "Good", "Fair", "Poor"),
    breaks = c(1, 2, 3, 4, 5)) +
  theme_minimal() +
* theme(legend.position = "none",
*       axis.text.x = element_text(
*     angle = 45, vjust = 1, hjust = 1)
*   )
```
]
.right-plot[
![](02-slides_files/figure-html/goals-plot-1g-1.png)
]

---
count: false

```r
ggplot(data = nlsy) +
  geom_bar(aes(x = eyesight, 
          fill = factor(eyesight))) +
  facet_grid(
    cols = vars(glasses),
    labeller = labeller(glasses = c(
      "0" = "Doesn't wear glasses",
      "1" = "Wears glasses/contacts"))) +
  scale_fill_brewer(palette = "Spectral",
                    direction = -1) +
  scale_x_continuous(
    labels = c("Excellent", "Very Good",
               "Good", "Fair", "Poor"),
    breaks = c(1, 2, 3, 4, 5)) +
  theme_minimal() +
  theme(legend.position = "none",
        axis.text.x = element_text(
      angle = 45, vjust = 1, hjust = 1)
    ) +
* labs(title = "Eyesight in NLSY",
*      x = "Eyesight quality",
*      y = NULL)
```
]
.right-plot[
![](02-slides_files/figure-html/goals-plot-1h-1.png)
]

---
count: false

```r
ggplot(data = nlsy) +
  geom_bar(aes(x = eyesight, 
          fill = factor(eyesight))) +
  facet_grid(
    cols = vars(glasses),
    labeller = labeller(glasses = c(
      "0" = "Doesn't wear glasses",
      "1" = "Wears glasses/contacts"))) +
  scale_fill_brewer(palette = "Spectral",
                    direction = -1) +
  scale_x_continuous(
    labels = c("Excellent", "Very Good",
               "Good", "Fair", "Poor"),
    breaks = c(1, 2, 3, 4, 5)) +
  theme_minimal() +
  theme(legend.position = "none",
        axis.text.x = element_text(
      angle = 45, vjust = 1, hjust = 1)
    ) +
  labs(title = "Eyesight in NLSY",
       x = "Eyesight quality",
       y = NULL) +
* coord_cartesian(expand = FALSE)
```
]
.right-plot[
![](02-slides_files/figure-html/goals-plot-1i-1.png)
]

---
count: false
.left-code[

```r
*ggplot(data = nlsy, aes(x = income,
*   y = age_bir, col = factor(sex))
*) 
```
]
.right-plot[
![](02-slides_files/figure-html/goals-plot-2a-1.png)
]

---
count: false

```r
ggplot(data = nlsy, aes(x = income, 
    y = age_bir, col = factor(sex))
) +
* geom_point(alpha = 0.1)
```
]
.right-plot[
![](02-slides_files/figure-html/goals-plot-2b-1.png)
]

---
count: false

```r
ggplot(data = nlsy, aes(x = income, 
    y = age_bir, col = factor(sex))
) +
  geom_point(alpha = 0.1) +
* scale_color_brewer(palette = "Set1",
*   name = "Sex",
*   labels = c("Male", "Female"))
```
]
.right-plot[
![](02-slides_files/figure-html/goals-plot-2c-1.png)
]

---
count: false

---
count: false

---
count: false

```r
ggplot(data = nlsy, aes(x = income, 
    y = age_bir, col = factor(sex))
) +
  geom_point(alpha = 0.1) +
  scale_color_brewer(palette = "Set1",
    name = "Sex",
    labels = c("Male", "Female")) +
  scale_x_log10(labels = 
                  scales::dollar) +
  geom_smooth(aes(
    group = factor(sex)), 
    method = "lm") +
* facet_grid(rows = vars(race_eth),
*   labeller = labeller(race_eth = c(
*   "1" = "Hispanic",
*   "2" = "Black",
*   "3" = "Non-Black, Non-Hispanic")))
```
]
.right-plot[
![](02-slides_files/figure-html/goals-plot-2f-1.png)
]

---
count: false

```r
ggplot(data = nlsy, aes(x = income, 
    y = age_bir, col = factor(sex))
) +
  geom_point(alpha = 0.1) +
  scale_color_brewer(palette = "Set1",
    name = "Sex",
    labels = c("Male", "Female")) +
  scale_x_log10(labels = 
                  scales::dollar) +
  geom_smooth(aes(
    group = factor(sex)), 
    method = "lm") +
  facet_grid(rows = vars(race_eth),
    labeller = labeller(race_eth = c(
    "1" = "Hispanic",
    "2" = "Black",
    "3" = "Non-Black, Non-Hispanic"))) +
* theme_minimal()
```
]
.right-plot[
![](02-slides_files/figure-html/goals-plot-2g-1.png)
]

---
count: false

```r
ggplot(data = nlsy, aes(x = income, 
    y = age_bir, col = factor(sex))
) +
  geom_point(alpha = 0.1) +
  scale_color_brewer(palette = "Set1",
    name = "Sex",
    labels = c("Male", "Female")) +
  scale_x_log10(labels = 
                  scales::dollar) +
  geom_smooth(aes(
    group = factor(sex)), 
    method = "lm") +
  facet_grid(rows = vars(race_eth),
    labeller = labeller(race_eth = c(
    "1" = "Hispanic",
    "2" = "Black",
    "3" = "Non-Black, Non-Hispanic"))) +
  theme_minimal() +
* theme(legend.position = "top")
```
]
.right-plot[
![](02-slides_files/figure-html/goals-plot-2h-1.png)
]

---
count: false

```r
ggplot(data = nlsy, aes(x = income, 
    y = age_bir, col = factor(sex))
) +
  geom_point(alpha = 0.1) +
  scale_color_brewer(palette = "Set1",
    name = "Sex",
    labels = c("Male", "Female")) +
  scale_x_log10(labels = 
                  scales::dollar) +
  geom_smooth(aes(
    group = factor(sex)), 
    method = "lm") +
  facet_grid(rows = vars(race_eth),
    labeller = labeller(race_eth = c(
    "1" = "Hispanic",
    "2" = "Black",
    "3" = "Non-Black, Non-Hispanic"))) +
  theme_minimal() +
  theme(legend.position = "top") +
* labs(title = "Relationship between income and age at first birth",
*   subtitle = "by sex and race",
*   x = "Income",
*   y = "Age at first birth")
```
]
.right-plot[
![](02-slides_files/figure-html/goals-plot-2j-1.png)
]

---

# Basic example

```
ggplot(data = {data}) +
      <geom>(aes(x = {xvar}, y = {yvar}, <characteristic> = {othvar}, ...),
             <characteristic> = "value", ...) +
      ...
```

---
count:true
# Basic example

```
ggplot(data = `nlsy`) +
      <geom>(aes(x = {xvar}, y = {yvar}, <characteristic> = {othvar}, ...),
             <characteristic> = "value", ...) +
      ...
```
.large[The `data = ` argument must be a dataframe (or tibble)]

---
count:true
# Basic example

```
ggplot(data = nlsy) +
      `geom_point`(aes(x = {xvar}, y = {yvar}, <characteristic> = {othvar}, ...),
             <characteristic> = "value", ...) +
      ...
```
.large[
`geom_point()` gives us a scatterplot
]

.center[
.go[Other helpful "geoms" include `geom_line()`, `geom_bar()`, `geom_histogram()`, `geom_boxplot()`]]

---
<img src="../img/geoms.png" width="95%" style="display: block; margin: auto;" />

.footnote[Image via https://nbisweden.github.io/RaukR-2019/ggplot/presentation/ggplot_presentation.html]

---
count:true

# Basic example

```
ggplot(data = nlsy) +
      geom_point(aes(x = `income`, y = `age_bir`, <characteristic> = {othvar}, ...),
             <characteristic> = "value", ...) +
      ...
```

`geom_point()` requires an `x = ` and a `y = ` variable

Other geoms require other arguments
- For example, `geom_histogram()` only requires an `x = ` variable

---
count:true

# Basic example

```
ggplot(data = nlsy, `aes(x = income, y = age_bir, <characteristic> = {othvar}, ...)`) +
      geom_point(<characteristic> = "value", ...) +
      ...
```

We could also put the aesthetics (the variables that are being mapped to the plot) in the initial `ggplot()` function

- This will be helpful when we want multiple geoms (say, points and a line)

---
.left-code[

```r
ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir))
```
.large[
What if we want to change the color of the points?
]]
.right-plot[
![](02-slides_files/figure-html/point-ex1-1.png)
]

---
count:true
.left-code[

```r
ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir),
*            color = "blue")
```
.large[When we put `color = ` *outside* the `aes()`, it means we're giving it a specific color value that applies to all the points.]

]
.right-plot[
![](02-slides_files/figure-html/point-ex2-1.png)
]

---

count:true
.left-code[

```r
ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir),
*            color = "#3d93c8")
```
.center[.go[One of my favorite color resources: https://www.color-hex.com
]]]
.right-plot[
![](02-slides_files/figure-html/point-ex3-1.png)
]

---
count:true
.left-code[

```r
ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir,
*                color = eyesight))
```
.large[

When we put `color = ` *inside* the `aes()` -- with no quotation marks -- it means we're telling it how it should assign colors.

Here we're plotting the values according to eyesight, where 1 is excellent and 5 is poor.

- But they're kind of hard to distinguish!
]
]

---

count:true
.left-code[

```r
ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir,
               color = eyesight)) +
*scale_color_gradient(low = "green",
*                   high = "purple")
```
.large[We can map the values of `eyesight` to a different continuous scale using `scale_color_gradient()`

You can read lots more about this function [here](https://ggplot2.tidyverse.org/reference/scale_gradient.html), so you don't have to have such ugly color scales! 
]
]
.right-plot[
![](02-slides_files/figure-html/point-ex7-1.png)
]

---
count:true
.left-code[

```r
ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir,
            color = eyesight))
```
.large[Returning to the nice blues, we think: But wait! The variable `eyesight` isn't really continuous: it has 5 discrete values.
]
]
.right-plot[
![](02-slides_files/figure-html/point-ex8-1.png)
]

---
count:true
.left-code[

```r
ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir,
            color = `factor(eyesight)`))
```

.large[Returning to the nice blues, we think: But wait! The variable `eyesight` isn't really continuous: it has 5 discrete values.

We can make R treat it as a "factor", or categorical variable, with the `factor()` function
]
.center[.go[We'll see lots more on factors later!]]
]
.right-plot[
![](02-slides_files/figure-html/point-ex9-1.png)
]

---
count:true
.left-code[

```r
ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir,
            color = factor(eyesight))) +
*scale_color_manual(
*   values = c("blue", "purple", "red",
*              "green", "yellow"))
```
.large[Now if we want to change the color scheme, we have to use a different function.

Before we used `scale_color_gradient`, now `scale_color_manual`.

- There are a lot of options that follow the same naming scheme.]

]
.right-plot[
![](02-slides_files/figure-html/point-ex10-1.png)
]

---
count:true
.left-code[

```r
ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir,
            color = factor(eyesight))) +
*scale_color_brewer(palette = "Set1")
```
.large[There are tons of different options in R for color palettes.

You can play around with those in the `RColorBrewer` package here: http://colorbrewer2.org]

- You can access the scales in that package with `scale_color_brewer()`, or see them all after installing the package with `RColorBrewer::display.brewer.all()`

]
.right-plot[
![](02-slides_files/figure-html/point-ex11-1.png)
]

---
count:true
.left-code[

```r
ggplot(data = nlsy) +
geom_point(aes(x = income, y = age_bir,
            color = factor(eyesight))) +
scale_color_brewer(palette = "Set1", 
*           name = "Eyesight",
*           labels = c("Excellent",
*                      "Very Good",
*                      "Good",
*                      "Fair",
*                      "Poor"))
```
.large[Each of the `scale_color_x()` functions has a lot of the same arguments.]

.center[.go[Make sure if you are labelling a factor variable in a plot like this that you get the names right!]]

]
.right-plot[
![](02-slides_files/figure-html/point-ex12-1.png)
]

---
class: inverse

.hand-large[
Your turn...
]
.exercise[
Exercises 2.1: Make a fancy scatterplot showing the relationship between sleep on weekdays and on weekends.
]

---
# Facets

One of the most useful features of `ggplot2` is the ability to "facet" a graph by splitting it up according to the values of some variable.

You might use this to show results for a lot of outcomes or exposures at once, for example, or see how some relationship differs by something like age or geographic region

---
.left-code[

```r
ggplot(data = nlsy) +
  `geom_bar(aes`(x = nsibs)) +
  labs(x = "Number of siblings")
```
.large[We'll introduce bar graphs at the same time!

Notice how we only need an `x = ` argument - the y-axis is automatically the count with this geom.
]
]
.right-plot[
![](02-slides_files/figure-html/bar-ex-1.png)
]

---
count:true
.left-code[

```r
ggplot(data = nlsy) +
  geom_bar(aes(x = nsibs)) +
  labs(x = "Number of siblings") +
* facet_grid(cols = vars(region))
```
.large[The `facet_grid()` function splits up the data according to a variable(s).

Here we've split it by region into columns.
]
]
.right-plot[
![](02-slides_files/figure-html/bar-ex1-1.png)
]

---
count:true
.left-code[

```r
ggplot(data = nlsy) +
  geom_bar(aes(x = nsibs)) +
  labs(x = "Number of siblings") +
  facet_grid(`rows` = vars(region))
```
.large[Since this is hard to read, we'll probably want to split by rows instead.
]
]
.right-plot[
![](02-slides_files/figure-html/bar-ex2-1.png)
]

---
count:true
.left-code[

```r
ggplot(data = nlsy) +
  geom_bar(aes(x = nsibs)) +
  labs(x = "Number of siblings") +
  facet_grid(rows = vars(region),
             `margins = TRUE`)
```
.large[We can also add a row for all of the data together.]

]
.right-plot[
![](02-slides_files/figure-html/bar-ex3-1.png)
]

---
count:true
.left-code[

```r
ggplot(data = nlsy) +
  geom_bar(aes(x = nsibs)) +
  labs(x = "Number of siblings") +
  facet_grid(rows = vars(region),
             margins = TRUE,
             `scales = "free_y"`)
```
.large[This squishes the other rows though! We can allow them all to have their own axis limits with the `scales = ` argument.

Other options are "free_x" if we want to allow the x-axis scale to vary, or just "free" to combine both.
]

]
.right-plot[
![](02-slides_files/figure-html/bar-ex4-1.png)
]

---
count:true
.left-code[

```r
ggplot(data = nlsy) +
  geom_bar(aes(x = nsibs)) +
  labs(x = "Number of siblings") +
* facet_wrap(vars(region))
```

.large[We can use `facet_wrap()` instead, if we want to use both multiple rows and columns for all the values of a variable.
]
]
.right-plot[
![](02-slides_files/figure-html/bar-ex5-1.png)
]

---
count:true
.left-code[

```r
ggplot(data = nlsy) +
  geom_bar(aes(x = nsibs)) +
  labs(x = "Number of siblings") +
  facet_wrap(vars(region),
*            ncol = 3)
```

]
.right-plot[
![](02-slides_files/figure-html/bar-ex6-1.png)
]

---
# Wait, these look like histograms!

When we have a variable with a lot of possible values, we may want to bin them with a histogram

```r
ggplot(nlsy) +
  geom_histogram(aes(x = income))
```

---

# `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

We used discrete values with `geom_bar()`, but with `geom_histogram()` we're combining values: the default is into 30 bins.

This is one of the most common warning messages I get in R!
<br>
<img src="https://www.washingtonpost.com/pbox.php?url=http://www.washingtonpost.com/news/volokh-conspiracy/wp-content/uploads/sites/14/2015/08/Warning-2.gif&w=1484&op=resize&opt=1&filter=antialias&t=20170517" width="50%" style="display: block; margin: auto;" />

---
.left-code[

```r
ggplot(data = nlsy) +
  geom_histogram(aes(x = income), 
*                bins = 10)
```

]
.right-plot[
![](02-slides_files/figure-html/bar-hist-1.png)
]

---
count:true
.left-code[

```r
ggplot(data = nlsy) +
  geom_histogram(aes(x = income), 
*                bins = 100)
```

]
.right-plot[
![](02-slides_files/figure-html/bar-hist2-1.png)
]

---
count:true
.left-code[

```r
ggplot(data = nlsy) +
  geom_histogram(aes(x = income), 
*                binwidth = 1000)
```

]
.right-plot[
![](02-slides_files/figure-html/bar-hist3-1.png)
]

---
count:true
.left-code[

```r
ggplot(data = nlsy) +
  geom_histogram(aes(x = income),
                 bins = 100) +
* scale_x_log10()
```

There are a lot of `scale_x_()` and `scale_y_()` functions for you to explore!

.center[.go[The naming schemes work similarly to the `scale_color` ones, just with different options!]]

]
.right-plot[
![](02-slides_files/figure-html/bar-hist4-1.png)
]

---
class: inverse

.hand-large[
Your turn...
]
.exercise[
Exercises 2.2: Make a fancy histogram showing the distribution of income in this data.
]

---
# Finally, themes to make our plots prettier

You probably recognize the ggplot theme. But did you know you can trick people into thinking you made your figures in Stata?

.pull-left[
<img src="02-slides_files/figure-html/unnamed-chunk-9-1.png" width="85%" style="display: block; margin: auto;" />
]

.pull-right[
<img src="02-slides_files/figure-html/unnamed-chunk-10-1.png" width="85%" style="display: block; margin: auto;" />
]

---
.left-code[

```r
*p <- ggplot(data = nlsy) +
  geom_boxplot(aes(
    x = factor(sleep_wknd), 
    y = sleep_wkdy,
    fill = factor(sleep_wknd))) +
  scale_fill_discrete(guide = FALSE) +
  labs(x = "hours slept on weekends",
       y = "hours slept on weekends",
       title = "The more people sleep on weekends, the more they\n sleep on weekdays",
       subtitle = "According to NLSY data")

p
```
Let's store our plot first.

Plots work just like other R objects, meaning we can use the assignment arrow.

.center[.go[Can you figure out what each chunk of this code is doing to the figure?]]
]
.right-plot[
![](02-slides_files/figure-html/theme-1.png)
]

---
count:true
.left-code[

```r
p +
* theme_minimal()
```
.large[We can change the overall theme

.center[.go[Since we stored the plot as `p`, it's easy to add on / try different things]]
]]
.right-plot[
![](02-slides_files/figure-html/theme2-1.png)
]

---
count:true
.left-code[

```r
p +
* theme_dark()
```
]
.right-plot[
![](02-slides_files/figure-html/theme3-1.png)
]

---
count:true
.left-code[

```r
p +
* theme_classic()
```
]
.right-plot[
![](02-slides_files/figure-html/theme4-1.png)
]

---
count:true
.left-code[

```r
p +
* theme_void()
```
]
.right-plot[
![](02-slides_files/figure-html/theme5-1.png)
]

---
count:true
.left-code[

```r
p +
* ggthemes::theme_fivethirtyeight()
```

.large[Other packages may contain themes.]
]
.right-plot[
![](02-slides_files/figure-html/themegga-1.png)
]

---
count:true
.left-code[

```r
p +
* ggthemes::theme_excel_new()
```

---
count:true
.left-code[

```r
p +
* ggthemes::theme_gdocs()
```
]
.right-plot[
![](02-slides_files/figure-html/themegga-1.png)
]

---
count:true
.left-code[

```r
p +
* louisahstuff::my_theme()
```
.center[.go[You can even make your own!]]

]
.right-plot[
![](02-slides_files/figure-html/theme6-1.png)
]

---
class:big-code
# Finally, save it!

If your data changes, you can easily run the whole script again:

```r
library(tidyverse)
dataset <- read_csv("dataset.csv")
ggplot(dataset) + 
  geom_point(aes(x = xvar, y = yvar))
`ggsave`(filename = "scatterplot.pdf")
```

The `ggsave()` function will automatically save the most recent plot in your output.

To be safe, you can store your plot, e.g., `p <- ggplot(...) + ...` and then

```r
ggsave(filename = "scatterplot.pdf", plot = p)
```

---
# More resources

.left-col[
- Cheat sheet: https://www.rstudio.com/resources/cheatsheets/#ggplot2
- Catalog: http://shiny.stat.ubc.ca/r-graph-catalog/
- Cookbook: http://www.cookbook-r.com/Graphs/
- Official package reference: https://ggplot2.tidyverse.org/index.html
- List of themes and instructions to make your own: https://www.datanovia.com/en/blog/ggplot-themes-gallery/
]

---

<img src="02-slides_files/figure-html/unnamed-chunk-13-1.png" width="55%" style="display: block; margin: auto auto auto 0;" />
]