Data Literacy: Introduction to R

.title[
# Data Literacy: Introduction to R
]
.subtitle[
## Data Visualization - Part 1
]
.author[
### Veronika Batzdorfer
]
.date[
### 2025-05-24
]

---

---

## We'll start rather basic

---

## Content of the visualization sessions
.pull-left[
**`Base R` visualization**
- Standard plotting procedures in R
- very short
]

.pull-right[
**`tidyverse`/`ggplot2` visualization**
- Modern interface to graphics
- grammar of graphics
]

There's more that we won't cover:
- [`lattice`](https://cran.r-project.org/web/packages/lattice/index.html) plots, for example

---

---
### Data for this session

``` r
amount_topcountries <- stackoverflow_survey_single_response %>%
 filter(!is.na(country)) %>% 
 group_by(country) %>%
 summarise(Amount = n()) %>%
 arrange(desc(Amount)) %>%
 head(20) %>% # Top 20 first
 mutate(country = reorder(country, Amount)) %>% # Reorder country factor
 ungroup()

amount_topcountries %>% head()
```

```
## # A tibble: 6 × 2
## country Amount
## <fct> <int>
## 1 United States of America 11095
## 2 Germany 4947
## 3 India 4231
## 4 United Kingdom of Great Britain and Northern Ireland 3224
## 5 Ukraine 2672
## 6 France 2110
```

---

## Graphics in `R`

The `graphics` package is already included.

``` r
#-- Adjust plot margins--
par(mar = c(10, 4, 4, 2))

barplot(
  height = amount_topcountries$Amount,
  names.arg = amount_topcountries$country,
  las = 2,                          # Rotate labels for readability
  main = "Top 20 Countries by Count",
  ylab = "Number of Records",
  cex.names = 0.9,                 # Resize labels
  border = NA                      # Remove border 
)

#-- Reset margins after the plot
par(mar = c(5, 4, 4, 2))
```
]

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-3-1.png" style="display: block; margin: auto;" />
]

---

## Let's start from the beginning
The most basic function to plot in R is `plot()`.

``` r
options(scipen = 999) #prevent exponent. scientific notation

plot_df <- stackoverflow_survey_single_response %>% 
 filter(!is.na(converted_comp_yearly),
 !is.na(years_code))

plot(plot_df$years_code,
     plot_df$converted_comp_yearly)
```
]

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-4-1.png" style="display: block; margin: auto;" />
]

---

## Adding to the plot: titles & labels

``` r
plot(
  jitter(df$years_coding, 2), 
  jitter(df$participation_frequency, 2),
  pch = 1,
  main = 
    "Relationship Experience and Participation Frequency on SO",
  xlab = 
    "Year of Experience",
  ylab = 
    "SO Participation Frequency"
)
```
]

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-5-1.png" style="display: block; margin: auto;" />
]

---

## Where to go from here with `base R` graphics?

.pull-left[
Using similar procedures, we can add more and more stuff to our plot or edit its elements:
- regression lines
- legends
- annotations
- colors
- etc. 
]

.pull-right[
We can also create different *plot types*, such as
- histograms
- barplots
- boxplots
- densities
- pie charts
- etc.
]

---

## Example: A simple boxplot

``` r
boxplot(
  df$years_coding ~
    df$participation_frequency         
)
```
]

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-6-1.png" style="display: block; margin: auto;" />
]

---

## The `par()` and `dev.off()` functions for plots
.highlight[`par()`] stands for graphical parameters and is called before the actual plotting function. It prepares the graphics device in `R`. The most commonly used options are for "telling" the device that 2, 3, 4, or `x` plots have to be printed.

We can, e.g., use `mfrow` for specifying how many rows (the first value in the vector) and columns (the second value in the vector) we aim to plot.

``` r
par(mfrow = c(2, 2))
```

One caveat of using this function is that we actively have to turn off the device before generating another independent plot.

``` r
dev.off()
```

---

## Exporting plots

It's nice that `R` provides such pleasant plotting opportunities. However, to include them in our papers, we need to export them. As said in the beginning, numerous export formats are available in `R`.

---

## Exporting with *RStudio*

---

## Saving plots via a command
Alternatively, you can also export plots with the commands `png()`, `pdf()` or `jpeg()`, for example. For this purpose, you first have to wrap the plot call between one of those functions and a `dev.off()`call.

``` r
png("Plot.png")
hist(df$years_coding)
dev.off()
```

``` r
pdf("Plot.pdf")
hist(df$years_coding)
dev.off()
```

``` r
jpeg("Plot.jpeg")
hist(df$years_coding)
dev.off()
```

---

## What is `ggplot2`?
`ggplot2` is another `R` package for creating plots and is part of the `tidyverse`.

It uses the *grammar of graphics*. Some things to note about `ggplot2`:
- it is well-suited for multi-dimensional data
- it expects data (frames) as input
- components of the plot are added as layers

``` r
plot_call +
  layer_1 +
  layer_2 +
  ... +
  layer_n
```

---
## `ggplot2` examples 
.pull-left[
<img src="data:image/png;base64,#https://github.com/jobreu/r-intro-gesis-2021/blob/main/content/img/143_radar_chart_multi_indiv_2.png?raw=true" style="display: block; margin: auto;" />
]

.pull-right[
<img src="data:image/png;base64,#https://github.com/jobreu/r-intro-gesis-2021/blob/main/content/img/21_ggplot2_ddensity_plot.png?raw=true" style="display: block; margin: auto;" />
]

Sources: https://www.r-graph-gallery.com/wp-content/uploads/2016/05/143_radar_chart_multi_indiv_2.png and https://www.r-graph-gallery.com/wp-content/uploads/2015/09/21_ggplot2_ddensity_plot.png

---

## `ggplot2` examples 
.pull-left[
<img src="data:image/png;base64,#https://github.com/jobreu/r-intro-gesis-2021/blob/main/content/img/51_scatterplot_linear_model_with_CI_ggplot2.png?raw=true" style="display: block; margin: auto;" />
]

.pull-right[
<img src="data:image/png;base64,#https://github.com/jobreu/r-intro-gesis-2021/blob/main/content/img/328_Hexbin_map_USA_4.png?raw=true" style="display: block; margin: auto;" />
]

Sources: https://www.r-graph-gallery.com/wp-content/uploads/2015/11/51_scatterplot_linear_model_with_CI_ggplot2-300x300.png and https://www.r-graph-gallery.com/wp-content/uploads/2017/12/328_Hexbin_map_USA_4-300x200.png

---

## Barplots as in `base R`

``` r
ggplot(df , aes(x = age_group)) + 
  geom_bar()
```
]
]

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-13-1.png" style="display: block; margin: auto;" />
]

---

## Boxplots as in `base R`

``` r
ggplot(
  df , 
  aes(
    x = as.factor(main_branch), 
    y = years_coding )
) + 
  geom_boxplot()
```
]
]

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-14-1.png" style="display: block; margin: auto;" />
]

---

## Components of a plot
According to Wickham (2010, 8)* a layered plot consists of the following components:

* http://dx.doi.org/10.1198/jcgs.2009.07098

- data and aesthetic mappings,
- geometric objects,
- scales,
- and facet specification

``` r
plot_call +
  data +
  aesthetics +
  geometries +
  scales +
  facets
```

---

## Data requirements
You can use one single data frame to create a plot in `ggplot2`. This creates a smooth workflow from data wrangling to the final presentation of the results.

Source: http://r4ds.had.co.nz

---

## Why the long format? 🐴
`ggplot2` prefers data in long format (**NB**: of course, only if this is possible and makes sense for the data set at hand)

- every element we aim to plot is an observation
- no thinking required how a specific variable relates to an observation
- most importantly, the long format is more parsimonious
- it requires less memory and less disk space
]

.pull-right[
<img src="data:image/png;base64,#https://github.com/jobreu/r-intro-gesis-2021/blob/main/content/img/long.png?raw=true" width="40%" style="display: block; margin: auto;" />
Source: https://github.com/gadenbuie/tidyexplain#tidy-data
]

---

## Before we start
The architecture of building plots in `ggplot` is similar to standard `R` graphics. There is an initial plotting call, and subsequently, more stuff is added to the plot.

However, in `base R`, it is sometimes tricky to find out how to add (or remove) certain plot elements. For example, think of removing the axis ticks in the scatter plot.

We will systematically explore which elements are used in `ggplot` in this session.

---

## Creating your own plot
We do not want to give a lecture on the theory behind data visualization (if you want that, we suggest having a look at the excellent book [*Fundamentals of Data Visualization*](https://serialmentor.com/dataviz/) by Claus O. Wilke).

Creating plots is all about practice... and 'borrowing' code from others.

Three components are important:
- Plot initiation and data input
- aesthetics definition
- so-called geoms

---

## Plot initiation
Now, let's start from the beginning and have a closer look at the *grammar of graphics*.

``` r
ggplot()
```
]

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-15-1.png" style="display: block; margin: auto;" />
]

**But it doesn't show anything...**

---

## What now? Data input!

``` r
ggplot(data = df )
```
]

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-16-1.png" style="display: block; margin: auto;" />
]

**Still nothing there...**

---

## `aes`thetics!

``` r
ggplot(data = df ) +
  aes(x = years_coding, y = yearly_compensation)
```
]

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-17-1.png" style="display: block; margin: auto;" />
]

**That's a little bit better, right?**

---

## `geom`s!
.pull-left[
Finally, `ggplot` needs information *how* to plot the variables.

``` r
ggplot(data = df ) +
  aes(x = years_coding, y = yearly_compensation) +
  geom_point()
```
]

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-18-1.png" style="display: block; margin: auto;" />
]

**A scatter plot!**

---

## Add a fancy `geom`
.pull-left[
We can also add more than one `geom`.

``` r
ggplot(data = df) +
  aes(x = years_coding, y = yearly_compensation) +
  geom_jitter() +
  geom_smooth(method = "lm", se = FALSE)
```
]

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-19-1.png" style="display: block; margin: auto;" />
]

**A regression line!** (without confidence intervals; the regression behind this operation is run automatically)

---

## Going further: adding group `aes`thetics

``` r
df %>%
  filter(!is.na(ai_threat)) %>%
  ggplot(aes(
    x = years_coding, 
    y = participation_frequency, 
    group = main_branch 
  )) +
  geom_smooth(method = "lm", se = FALSE)
```
]

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-20-1.png" style="display: block; margin: auto;" />
]

---

## Manipulating group `aes`thetics

``` r
df %>%
  filter(!is.na(ai_threat)) %>%
ggplot(
  aes(
    x = years_coding, 
    y = participation_frequency, 
    group = main_branch,
    color = main_branch)) +
  geom_smooth(method = "lm", se = FALSE) 
```
]

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-21-1.png" style="display: block; margin: auto;" />
]

The legend is drawn automatically, that's handy!

---

## Using another color palette

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-22-1.png" style="display: block; margin: auto;" />
]

---

## Difference between `color` and `fill`
Notably, there are two components of the plot or `geom` associated with colors: `color` and `fill`.

Generally, .highlight[`color`] refers to the geometry borders, such as a line. .highlight[`fill`] refers to a geometry area, such as a polygon.

Remember when using `scale_color_brewer` or `scale_fill_brewer` in your plots.

---

## Colors and `theme`s
One particular strength of `ggplot2` lies in its immense theming capabilities. The package has some built-in theme functions that makes theming a plot fairly easy, e.g.,
- `theme_bw()`
- `theme_apa()`
- `theme_void()`
- etc.

See: https://ggplot2.tidyverse.org/reference/ggtheme.html

---

## Alternative to being too colorful: facets
.pull-left[

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-23-1.png" style="display: block; margin: auto;" />
]

---

## The `theme()` argument in general
The most direct interface for manipulating your theme is the `theme()` argument. Here you can change the appearance of:
- axis labels
- captions and titles
- legend
- grid layout
- the wrapping strips
- ...

---

## Example: changing the grid layout & axis labels
.pull-left[

``` r
df %>%
  filter(!is.na(ai_threat)) %>%
ggplot(
  aes(
    x = years_coding, 
    y = participation_frequency)) +
 geom_smooth(
    color = "black", 
    method = "lm", 
    se = FALSE
    ) +
  facet_wrap(~main_branch, ncol = 3, nrow = 2) +
  theme_bw()+
  theme(
    panel.grid.major = 
      element_blank(), 
    panel.grid.minor = 
      element_blank(),
    strip.background = 
      element_rect(fill = "white")
  )
```
]

.pull-right[ 
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-24-1.png" style="display: block; margin: auto;" />
]

---

## Example: changing axis labels
.pull-left[

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-25-1.png" style="display: block; margin: auto;" />
]

---

## A note on plotting options

Particularly, plotting similar aesthetics which interfere with the automatic procedures can create conflicts.

Some 'favorites' include:
- Multiple legends
- and various color scales for similar `geoms`

]

.pull-right[
<img src="data:image/png;base64,#https://github.com/jobreu/r-intro-gesis-2021/blob/main/content/img/800px-The_Scream.jpg?raw=true" style="display: block; margin: auto;" />
]

---

## `ggplot` plots are 'simple' objects
In contrast to standard `R` plots, `ggplot2` outputs are standard objects like any other object in `R` (they are lists). So there is no graphics device involved from which we have to record our plot to re-use it later. We can just use it directly.

``` r
my_fancy_plot <- 
 ggplot(data = df,
 aes(
 x = years_coding, 
 y = participation_frequency
 )
 ) + 
 geom_point()

my_fancy_plot <- 
 my_fancy_plot +
 geom_smooth()
```

Additionally, there is also no need to call `dev.off()`

---

## It makes combining plots easy
As of today, there are now a lot of packages that help to combine `ggplot2`s fairly easily. For example, the [`cowplot` package](https://cran.r-project.org/web/packages/cowplot/index.html) provides a really flexible framework.

Yet, fiddling with this package can become quite complicated. A very easy-to-use package for combining `ggplot`s is [`patchwork` package](https://cran.r-project.org/web/packages/patchwork/index.html).

---

## Plotting side by side in one row
.pull-left[

``` r
library(patchwork)

my_barplot <- 
 ggplot(
 df , 
 aes(x = years_coding)
 ) + 
 geom_bar()

my_boxplot <- 
 ggplot(
 df , 
 aes(y = years_pro_coding)
 ) + 
 geom_boxplot()

my_barplot | my_boxplot
```
]

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-27-1.png" style="display: block; margin: auto;" />
]

---

## Plotting in two columns
.pull-left[

``` r
my_barplot / my_boxplot
```
]

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-28-1.png" style="display: block; margin: auto;" />
]

---

## There's more
You can also annotate plots with titles, subtitles, captions, and tags.

You can nest plots and introduce more complex layouts.

If you're interested in this, you should check out the [`patchwork` repository on *GitHub*](https://github.com/thomasp85/patchwork) as everything is really well-documented there.

---

## Exporting ggplot graphics
Exporting `ggplot2` graphics is fairly easy with the `ggsave()` function. It automatically detects the file format. You can also define the plot height, width, and dpi, which is particularly useful to produce high-class graphics for publications.

``` r
nice_plot <- 
 ggplot(
 df , 
 aes(x = years_coding)
 ) + 
 geom_bar()

ggsave("nice_plot.png", nice_plot, dpi = 300)
```

Or:

``` r
ggsave("nice_plot.tiff", nice_plot, dpi = 300)
```

---

## Visual exploratory data analysis

In the session on *Exploratory Data Analysis* (EDA), we have said that visualization should be part of EDA. We can use `ggplot2` for this, but there also are many packages out there that offer helpful visualization functions. We will look at two of those, `visdat` (for visualizing missing data patterns) and `GGAlly` (for visualizing correlations) in the following. Many of these packages build on `ggplot2` and their output can, hence, be further customized or extended using `ggplot2` or its extension packages.

---

## Plotting the structure of missing data

``` r
library(visdat)

vis_miss(df [,18:23])
```
]

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-31-1.png" style="display: block; margin: auto;" />
]

---

## Fancier barplots: Relative frequencies

``` r
library(scales)

df %>% 
  ggplot(
    aes(
      x = age_group, 
      fill = age_group                # Fill bars with color based on age group
    )
  ) + 
  geom_bar(                           # Create a bar plot
    aes(
      y = (..count..)/sum(..count..)  # Compute relative frequencies (proportions)
    )
  ) +
  scale_y_continuous(
    labels = percent                  # Format y-axis labels as percentages
  ) +
  ylab("Relative Frequencies")+
  theme_classic() +
  theme(legend.position = "none")     # Hide the legend
```
]

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-32-1.png" style="display: block; margin: auto;" />
]

---

## Fancier barplots: Percentages & formatting

``` r
df  %>% 
  filter(!is.na(ai_complexity)) %>% 
  ggplot(aes(x = ai_complexity, 
             fill = ai_complexity)) + 
  geom_bar(aes(y = (..count..)/sum(..count..))) +
  scale_y_continuous(labels = percent,
                     expand = expansion(mult = c(0, 0.1))) +
  ylab("Relative Frequencies") +
  xlab("")+
  theme_classic()+
  theme(legend.position = "none")
```
]

.pull-right[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-33-1.png" style="display: block; margin: auto;" />
]

---
## Are those particpating considering themselves as members?

``` r
survey <- stackoverflow_survey_single_response %>%
 mutate(
 so_comm = recode(as.character(so_comm),
 `1` = "Neutral",
 `2` = "No, not at all",
 `3` = "No, not really",
 `4` = "Not sure",
 `5` = "Yes, definitely",
 `6` = "Yes, somewhat"),
 
 so_part_freq = recode(as.character(so_part_freq),
 `1` = "A few times per month or weekly",
 `2` = "A few times per week",
 `3` = "Daily or almost daily",
 `4` = "I have never participated in Q&A on Stack Overflow",
 `5` = "Less than once per month or monthly",
 `6` = "Multiple times per day")
 )
```

---

``` r
library(scales)
library(ggrepel)

survey %>%
  select(country, so_comm, so_part_freq) %>%
  filter(!is.na(country), !is.na(so_comm), !is.na(so_part_freq)) %>%
  group_by(country) %>%
  summarise(
    ConsiderMember = mean(so_comm %in% c("Yes, definitely", "Yes, somewhat"), na.rm = TRUE),
#return a logical vector true/false for each row (country) if in freq. group and then calc. mean     
    particip = mean(so_part_freq %in% c(
      "Multiple times per day",
      "Daily or almost daily",
      "A few times per week",
      "A few times per month or weekly"), na.rm = TRUE),
    n = n()
  ) %>%
  filter(n > 700) %>%
  ggplot(aes(particip, ConsiderMember, label = country, col = ConsiderMember)) +
  geom_text_repel(size = 3, point.padding = 0.25) +
  geom_point(aes(size = n), alpha = 1) +
  scale_y_continuous(labels = percent_format()) +
  scale_x_continuous(labels = percent_format()) +
  scale_size_continuous(labels = comma_format()) +
  scale_color_gradientn(colors = viridis::viridis(50))+
  theme_minimal() +
  theme(legend.position = "none") +
  labs(
    x = "% who participated at least weekly",
    y = "% who consider themselves as SO Community",
    title = "Community Membership by Country and Stack Overflow Participation"
  )
```

<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/survey-1.png" style="display: block; margin: auto;" />
---

.tiny[
<img src="data:image/png;base64,#3_2_Data_Visualization_Part_1_files/figure-html/unnamed-chunk-35-1.png" style="display: block; margin: auto;" />
]

---

## Some additional resources

- [ggplot2 - Elegant Graphics for Data Analysis](https://www.springer.com/gp/book/9783319242750) by Hadley Wickham

- [Chapter 3](https://r4ds.had.co.nz/data-visualisation.html) in *R for Data Science*

- [Fundamentals of Data Visualization](https://serialmentor.com/dataviz/) by Claus O. Wilke

- [Data Visualization - A Practical Introduction](https://press.princeton.edu/titles/13826.html) by Kieran Healy

- [data-to-viz](https://www.data-to-viz.com/)

- [R Graph Gallery](https://www.r-graph-gallery.com/)

- [BBC Visual and Data Journalism cookbook for R graphics](https://bbc.github.io/rcookbook/#how_to_create_bbc_style_graphics)

- [List of `ggplot2` extensions](https://exts.ggplot2.tidyverse.org/)