Data Literacy: Introduction to R

class: center, middle, inverse, title-slide

.title[
# Data Literacy: Introduction to R
]
.subtitle[
## Data Visualization - Part 1
]
.author[
### Veronika Batzdorfer
]
.date[
### 2024-11-22
]

---

layout: true

---

## We'll start rather basic

.footnote[https://twitter.com/katjaberlin/status/1290667772779913218]

---

## 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
library(tidyverse)
library(dplyr)

stackoverflow_survey_questions <- read_csv("./data/stackoverflow_survey_questions.csv")
stackoverflow_survey_single_response <- read_csv("./data/stackoverflow_survey_single_response.csv")
qname_levels_single_response_crosswalk <- read_csv("./data/qname_levels_single_response_crosswalk.csv")

df <- stackoverflow_survey_single_response %>%
 rename(
 response_id = response_id,
 main_branch = main_branch,
 age_group = age,
 remote_work = remote_work,
 education_level = ed_level,
 years_coding = years_code,
 years_pro_coding = years_code_pro,
 dev_type = dev_type,
 org_size = org_size,
 purchase_influence = purchase_influence,
 build_vs_buy = buildvs_buy,
 visit_frequency = so_visit_freq,
 has_account = so_account,
 participation_frequency = so_part_freq,
 community_belief = so_comm,
 ai_usage = ai_select,
 ai_sentiment = ai_sent,
 ai_acc = ai_acc,
 ai_complexity = ai_complex,
 ai_threat = ai_threat,
 survey_length = survey_length,
 survey_ease = survey_ease,
 yearly_compensation = converted_comp_yearly,
 r_used = r_used,
 r_want_to_use = r_want_to_use
 )
```

---

```r
# Recode main_branch
df <- df %>%
 mutate(
 main_branch = case_when(
 main_branch == 1 ~ "Developer by profession",
 main_branch == 2 ~ "Learning to code",
 main_branch == 3 ~ "Not primarily a developer",
 main_branch == 4 ~ "Hobbyist",
 main_branch == 5 ~ "Former developer",
 TRUE ~ as.character(main_branch)
 )
 )

# Recode age_group
df <- df %>%
 mutate(
 age_group = case_when(
 age_group == 1 ~ "18-24",
 age_group == 2 ~ "25-34",
 age_group == 3 ~ "35-44",
 age_group == 4 ~ "45-54",
 age_group == 5 ~ "55-64",
 age_group == 6 ~ "65+",
 age_group == 7 ~ "Prefer not to say",
 age_group == 8 ~ "Under 18",
 TRUE ~ as.character(age_group)
 )
 )

# Recode remote_work
df <- df %>%
 mutate(
 remote_work = case_when(
 remote_work == 1 ~ "Hybrid",
 remote_work == 2 ~ "In-person",
 remote_work == 3 ~ "Remote",
 TRUE ~ as.character(remote_work)
 )
 )

# Recode education_level
df <- df %>%
 mutate(
 education_level = case_when(
 education_level == 1 ~ "Associate degree",
 education_level == 2 ~ "Bachelor’s degree",
 education_level == 3 ~ "Master’s degree",
 education_level == 4 ~ "Primary/elementary school",
 education_level == 5 ~ "Professional degree",
 education_level == 6 ~ "Secondary school",
 education_level == 7 ~ "Some college",
 education_level == 8 ~ "Other",
 TRUE ~ as.character(education_level)
 )
 )

# Recode so_visit_freq
df <- df %>%
 mutate(
 visit_frequency = case_when(
 visit_frequency == 1 ~ "A few times per month or weekly",
 visit_frequency == 2 ~ "A few times per week",
 visit_frequency == 3 ~ "Daily or almost daily",
 visit_frequency == 4 ~ "Less than once per month or monthly",
 visit_frequency == 5 ~ "Multiple times per day",
 TRUE ~ as.character(visit_frequency)
 )
 )

df <- df %>%
 mutate(
 ai_sentiment = case_when(
 ai_sentiment == 1 ~ "Favorable",
 ai_sentiment == 2 ~ "Indifferent",
 ai_sentiment == 3 ~ "Unfavorable",
 ai_sentiment == 4 ~ "Unsure",
 ai_sentiment == 5 ~ "Very favorable",
 ai_sentiment == 6 ~ "Very unfavorable",
 TRUE ~ as.character(ai_sentiment)
 )
 )

# Recode ai_threat
df <- df %>%
 mutate(
 ai_threat = case_when(
 ai_threat == 1 ~ "I'm not sure",
 ai_threat == 2 ~ "No",
 ai_threat == 3 ~ "Yes",
 TRUE ~ as.character(ai_threat)
 )
 )

# Recode ai_complex
df <- df %>%
 mutate(
 ai_complexity = case_when(
 ai_complexity == 1 ~ "Bad at complex tasks",
 ai_complexity == 2 ~ "Good",
 ai_complexity == 3 ~ "Neither ",
 ai_complexity == 4 ~ "Very poor at complex tasks",
 ai_complexity == 5 ~ "Very well at complex tasks",
 TRUE ~ as.character(ai_complexity)
 )
 )

# Recode ai_acc
df <- df %>%
 mutate(
 ai_acc = case_when(
 ai_acc == 1 ~ "Highly distrust",
 ai_acc == 2 ~ "Highly trust",
 ai_acc == 3 ~ "Neither trust nor distrust",
 ai_acc == 4 ~ "Somewhat distrust",
 ai_acc == 5 ~ "Somewhat trust",
 TRUE ~ as.character(ai_acc)
 )
 )
```

---

## Graphics in `R`

The `graphics` package is already included for that.

.pull-left[

```r
main_branch_counts <- table(df$main_branch)

# Adjust plot margins to ensure labels fit
par(mar = c(8, 4, 4, 2))  # Increase bottom margin

# Create the bar plot with smaller font size
barplot(
  main_branch_counts,
  main = "Distribution of Main Branch (Skill Level)",
  xlab = "",
  ylab = "Count",
  col = "darkgreen",
  border = "black",
  las = 2,           
  cex.names = 0.8,   
  cex.main = 0.9,    
  cex.lab = 0.9,     
  cex.axis = 0.8     
)

# 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-2-1.png" style="display: block; margin: auto;" />
]

---

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

.pull-left[

```r
options(scipen = 999)
plot(df$yearly_compensation)
```
]

.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;" />
]

---

#### Adding to the plot: titles & labels

.pull-left[

```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-4-1.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

.pull-left[

```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-5-1.png" style="display: block; margin: auto;" />
]

---

## The `par()` and `dev.off()` functions for plots
`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()
```

---

## A personal note on `base R` plotting

But to be honest: I do not use all the other functions that often. The syntax is sometimes cumbersome with all the `par()`  or `dev.off()` calls, and manipulating parameters simply feels somewhat "outdated".

In the following, we will turn towards more modern techniques using the `ggplot2` package. Yet, we still believe that it is worthwhile to become comfortable with `base R` plotting since `ggplot2`, e.g., may sometimes be "too much" for simple quick data exploration.

**As so often, in the end, it's also a matter of taste.**

---

## 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`

.tinyish[
.pull-left[

```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-12-1.png" style="display: block; margin: auto;" />
]

---

## Boxplots as in `base R`

.tinyish[
.pull-left[

```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-13-1.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)

.pull-left[
We may want to get used to it as this format has some benefits:
- 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*.

.pull-left[
`ggplot()` is the most basic command to create a plot:

```r
ggplot()
```
]

.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;" />
]

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

---

## What now? Data input!

.pull-left[

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

.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;" />
]

**Still nothing there...**

---

## `aes`thetics!

.pull-left[
`ggplot` requires information about the variables to plot.

```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-16-1.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-17-1.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-18-1.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

.pull-left[
We can add different colors for different groups in our data.

```r
df %>%
  filter(!is.na(ai_threat)) %>%
  ggplot(aes(
    x = years_coding, 
    y = participation_frequency, 
    group = ai_threat
  )) +
  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;" />
]

---

## Manipulating group `aes`thetics

.pull-left[
We can also change the colors that are used in the plot.

```r
df %>%
  filter(!is.na(ai_threat)) %>%
ggplot(
  aes(
    x = years_coding, 
    y = participation_frequency, 
    group = ai_threat,
    color = ai_threat)) +
  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;" />
]

The legend is drawn automatically, that's handy!

---

## Using another color palette

.pull-left[

.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;" />
]

---

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

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

Have this difference in mind when you use `scale_color_brewer` or `scale_fill_brewer` in your plots. Manipulating these colors and their corresponding legends in an elaborate plot can get a little tricky.

---

## 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_dark()`
- `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-22-1.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(~ai_threat, ncol = 3) +
  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-23-1.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-24-1.png" style="display: block; margin: auto;" />
]

---

## A note on plotting options

.pull-left[
Working with combined aesthetics and different data inputs can become challenging.

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;" />
]

.right[
Source: https://de.wikipedia.org/wiki/Der_Schrei#/media/File:The_Scream.jpg
]

---

## `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-26-1.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-27-1.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

.pull-left[

```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-30-1.png" style="display: block; margin: auto;" />
]

---

## Fancier barplots: Relative frequencies

.pull-left[

```r
library(scales)

df %>% 
  ggplot(
    aes(
      x = age_group, 
      fill = age_group
    )
  ) + 
  geom_bar(
    aes(
      y = (..count..)/sum(..count..)
    )
  ) +
  scale_y_continuous(
    labels = percent
  ) +
  ylab("Relative Frequencies")+
  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-31-1.png" style="display: block; margin: auto;" />
]

---

## Fancier barplots: Percentages & formatting

.pull-left[

```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-32-1.png" style="display: block; margin: auto;" />
]

---

## Correlation plots

.pull-left[

```r
library(GGally)

# Remove missing values
df_clean <- df %>%
 filter(!is.na(ai_sentiment) & !is.na(ai_acc) & !is.na(ai_complexity) & !is.na(ai_threat))

# to factors
df_clean <- df_clean %>%
 mutate(
 ai_sentiment = factor(ai_sentiment, levels = c("Very unfavorable", "Somewhat unfavorable", "Neutral", "Somewhat favorable", "Very favorable")),
 ai_acc = factor(ai_acc, levels = c("Very distrustful", "Somewhat distrustful", "Neutral", "Somewhat trust", "Very trust")),
 ai_complexity = factor(ai_complexity, levels = c("Bad at complex tasks", "Neutral", "Good at complex tasks")),
 ai_threat = factor(ai_threat, levels = c("No", "Yes"))
 )

cor_matrix <- df_clean %>%
 mutate_all(factor) %>%
 mutate_all(~as.integer(factor(.))) %>%
 cor(method = "spearman", use = "complete.obs") # Complete case analysis

print(cor_matrix)

ggcorr(cor_matrix, label = TRUE, label_size = 2)
```
]

.pull-right[

```
##                          response_id  main_branch    age_group  remote_work
## response_id              1.000000000  0.164323319 -0.148279931  0.045541077
## main_branch              0.164323319  1.000000000 -0.082842878 -0.081439566
## age_group               -0.148279931 -0.082842878  1.000000000  0.134707320
## remote_work              0.045541077 -0.081439566  0.134707320  1.000000000
## education_level         -0.063259472  0.042774246  0.044058911 -0.002923373
## years_coding            -0.057852605 -0.028833301  0.697523531  0.069588683
## years_pro_coding        -0.073903343 -0.103291289  0.791243278  0.174225412
## dev_type                 0.018511718  0.078342432  0.012291304 -0.096248425
## org_size                -0.042346948  0.093091663 -0.029415088  0.098455694
## purchase_influence      -0.046379997  0.059371083 -0.035070729 -0.027534405
## build_vs_buy             0.051007494 -0.031386031 -0.013012664  0.023620009
## country                 -0.097186361 -0.076478901  0.135455735  0.103262618
## currency                -0.053975491 -0.072631419  0.058415678  0.155750503
## comp_total               0.029753830 -0.055667145  0.081770863  0.046390647
## visit_frequency          0.014274239  0.016976935 -0.129076794  0.048497872
## has_account                       NA           NA           NA           NA
## participation_frequency  0.034169039 -0.006159126  0.001479647  0.009595035
## community_belief        -0.045390953 -0.141106771  0.069556561  0.041645185
## ai_usage                          NA           NA           NA           NA
## ai_sentiment             0.102007178  0.016621799  0.021355449  0.007902663
## ai_acc                            NA           NA           NA           NA
## ai_complexity                     NA           NA           NA           NA
## ai_threat                0.064963455  0.004569483 -0.040697208  0.137603407
## survey_length            0.079190004 -0.062120609 -0.040178396  0.058054343
## survey_ease             -0.021310080  0.026167318 -0.003773888 -0.066514850
## yearly_compensation      0.022360855 -0.037501771  0.430395995  0.063041050
## r_used                   0.034817385 -0.037505365  0.088003502 -0.010698923
## r_want_to_use           -0.002606607 -0.033469491  0.108917316 -0.046601549
##                         education_level years_coding years_pro_coding
## response_id                -0.063259472 -0.057852605     -0.073903343
## main_branch                 0.042774246 -0.028833301     -0.103291289
## age_group                   0.044058911  0.697523531      0.791243278
## remote_work                -0.002923373  0.069588683      0.174225412
## education_level             1.000000000  0.108325079      0.036936340
## years_coding                0.108325079  1.000000000      0.877682312
## years_pro_coding            0.036936340  0.877682312      1.000000000
## dev_type                    0.064007000  0.059515867      0.083546204
## org_size                    0.124333724 -0.065635299     -0.036543985
## purchase_influence         -0.015699942 -0.167274964     -0.122405400
## build_vs_buy                0.020076005  0.012249343      0.011391147
## country                    -0.153027285  0.075065867      0.100023733
## currency                   -0.183735555 -0.012142301      0.017882032
## comp_total                 -0.093786063  0.044297143      0.112663841
## visit_frequency             0.037175223 -0.107707615     -0.130732523
## has_account                          NA           NA               NA
## participation_frequency     0.028908681  0.035955272      0.050614769
## community_belief           -0.016438711  0.046208533      0.065453844
## ai_usage                             NA           NA               NA
## ai_sentiment               -0.097540100 -0.061711654      0.048786156
## ai_acc                               NA           NA               NA
## ai_complexity                        NA           NA               NA
## ai_threat                  -0.137655167 -0.099787155     -0.136182635
## survey_length               0.039336578 -0.008812184     -0.020357660
## survey_ease                -0.049303289 -0.021059869      0.024911413
## yearly_compensation        -0.039976803  0.449317464      0.469264679
## r_used                      0.035780570  0.058272674      0.002107932
## r_want_to_use               0.015218577  0.108337652      0.043369851
##                             dev_type     org_size purchase_influence
## response_id              0.018511718 -0.042346948       -0.046379997
## main_branch              0.078342432  0.093091663        0.059371083
## age_group                0.012291304 -0.029415088       -0.035070729
## remote_work             -0.096248425  0.098455694       -0.027534405
## education_level          0.064007000  0.124333724       -0.015699942
## years_coding             0.059515867 -0.065635299       -0.167274964
## years_pro_coding         0.083546204 -0.036543985       -0.122405400
## dev_type                 1.000000000  0.083498975       -0.064158718
## org_size                 0.083498975  1.000000000       -0.098442456
## purchase_influence      -0.064158718 -0.098442456        1.000000000
## build_vs_buy            -0.042264261 -0.014514943        0.006545222
## country                 -0.068286874 -0.186177751        0.054106541
## currency                 0.014175689 -0.028133024        0.092433319
## comp_total               0.004035800  0.041898075        0.134824831
## visit_frequency         -0.053486393 -0.092671855       -0.017782554
## has_account                       NA           NA                 NA
## participation_frequency -0.045339854 -0.042455110       -0.050442897
## community_belief         0.008340008 -0.045408900       -0.016103889
## ai_usage                          NA           NA                 NA
## ai_sentiment             0.066028207 -0.005249052        0.084059666
## ai_acc                            NA           NA                 NA
## ai_complexity                     NA           NA                 NA
## ai_threat               -0.072714868 -0.002764507        0.056734516
## survey_length           -0.104552281  0.032902715       -0.142042800
## survey_ease             -0.032040297  0.008952926       -0.044444639
## yearly_compensation      0.104943751 -0.150360307        0.002308850
## r_used                   0.095639273  0.053534615        0.141820049
## r_want_to_use            0.017423375  0.127361747        0.118096004
##                         build_vs_buy     country    currency  comp_total
## response_id              0.051007494 -0.09718636 -0.05397549  0.02975383
## main_branch             -0.031386031 -0.07647890 -0.07263142 -0.05566714
## age_group               -0.013012664  0.13545574  0.05841568  0.08177086
## remote_work              0.023620009  0.10326262  0.15575050  0.04639065
## education_level          0.020076005 -0.15302729 -0.18373556 -0.09378606
## years_coding             0.012249343  0.07506587 -0.01214230  0.04429714
## years_pro_coding         0.011391147  0.10002373  0.01788203  0.11266384
## dev_type                -0.042264261 -0.06828687  0.01417569  0.00403580
## org_size                -0.014514943 -0.18617775 -0.02813302  0.04189808
## purchase_influence       0.006545222  0.05410654  0.09243332  0.13482483
## build_vs_buy             1.000000000  0.04980502  0.12757616 -0.09365334
## country                  0.049805022  1.00000000  0.75526817  0.18215559
## currency                 0.127576160  0.75526817  1.00000000  0.20566388
## comp_total              -0.093653342  0.18215559  0.20566388  1.00000000
## visit_frequency          0.015756942 -0.03891360 -0.06879638  0.03649558
## has_account                       NA          NA          NA          NA
## participation_frequency  0.104131761 -0.03962814 -0.07997838 -0.01809675
## community_belief         0.019564032  0.05563624  0.01271872  0.02057449
## ai_usage                          NA          NA          NA          NA
## ai_sentiment            -0.052978270 -0.08627177 -0.08013472  0.09373389
## ai_acc                            NA          NA          NA          NA
## ai_complexity                     NA          NA          NA          NA
## ai_threat               -0.031555778 -0.05812340 -0.05380360  0.01392942
## survey_length            0.137700454 -0.12127842 -0.05721348  0.08199237
## survey_ease             -0.039207320  0.06493793 -0.01033303  0.04194963
## yearly_compensation     -0.035137365  0.44317477  0.30792209  0.22566212
## r_used                  -0.024704927  0.17059583  0.10417857 -0.01822880
## r_want_to_use           -0.054227233  0.13318225  0.09510533 -0.04770680
##                         visit_frequency has_account participation_frequency
## response_id                  0.01427424          NA            0.0341690390
## main_branch                  0.01697693          NA           -0.0061591258
## age_group                   -0.12907679          NA            0.0014796465
## remote_work                  0.04849787          NA            0.0095950348
## education_level              0.03717522          NA            0.0289086811
## years_coding                -0.10770762          NA            0.0359552719
## years_pro_coding            -0.13073252          NA            0.0506147687
## dev_type                    -0.05348639          NA           -0.0453398539
## org_size                    -0.09267185          NA           -0.0424551095
## purchase_influence          -0.01778255          NA           -0.0504428971
## build_vs_buy                 0.01575694          NA            0.1041317611
## country                     -0.03891360          NA           -0.0396281380
## currency                    -0.06879638          NA           -0.0799783755
## comp_total                   0.03649558          NA           -0.0180967507
## visit_frequency              1.00000000          NA           -0.1352814075
## has_account                          NA           1                      NA
## participation_frequency     -0.13528141          NA            1.0000000000
## community_belief             0.19937032          NA           -0.0365081788
## ai_usage                             NA          NA                      NA
## ai_sentiment                 0.03433023          NA           -0.0531274802
## ai_acc                               NA          NA                      NA
## ai_complexity                        NA          NA                      NA
## ai_threat                    0.03672628          NA            0.0244884595
## survey_length               -0.01405976          NA           -0.1038068403
## survey_ease                  0.10317334          NA           -0.0811395153
## yearly_compensation         -0.15609534          NA            0.0835903849
## r_used                      -0.07746261          NA           -0.1261998129
## r_want_to_use               -0.06912702          NA            0.0005877856
##                         community_belief ai_usage ai_sentiment ai_acc
## response_id                 -0.045390953       NA  0.102007178     NA
## main_branch                 -0.141106771       NA  0.016621799     NA
## age_group                    0.069556561       NA  0.021355449     NA
## remote_work                  0.041645185       NA  0.007902663     NA
## education_level             -0.016438711       NA -0.097540100     NA
## years_coding                 0.046208533       NA -0.061711654     NA
## years_pro_coding             0.065453844       NA  0.048786156     NA
## dev_type                     0.008340008       NA  0.066028207     NA
## org_size                    -0.045408900       NA -0.005249052     NA
## purchase_influence          -0.016103889       NA  0.084059666     NA
## build_vs_buy                 0.019564032       NA -0.052978270     NA
## country                      0.055636240       NA -0.086271766     NA
## currency                     0.012718716       NA -0.080134718     NA
## comp_total                   0.020574490       NA  0.093733891     NA
## visit_frequency              0.199370317       NA  0.034330233     NA
## has_account                           NA       NA           NA     NA
## participation_frequency     -0.036508179       NA -0.053127480     NA
## community_belief             1.000000000       NA  0.073157593     NA
## ai_usage                              NA        1           NA     NA
## ai_sentiment                 0.073157593       NA  1.000000000     NA
## ai_acc                                NA       NA           NA      1
## ai_complexity                         NA       NA           NA     NA
## ai_threat                   -0.006273226       NA  0.027834784     NA
## survey_length               -0.052292011       NA -0.138985632     NA
## survey_ease                  0.040790072       NA  0.042868121     NA
## yearly_compensation          0.001123081       NA  0.053335425     NA
## r_used                       0.070398575       NA  0.010118369     NA
## r_want_to_use                0.036014985       NA  0.009029552     NA
##                         ai_complexity    ai_threat survey_length  survey_ease
## response_id                        NA  0.064963455   0.079190004 -0.021310080
## main_branch                        NA  0.004569483  -0.062120609  0.026167318
## age_group                          NA -0.040697208  -0.040178396 -0.003773888
## remote_work                        NA  0.137603407   0.058054343 -0.066514850
## education_level                    NA -0.137655167   0.039336578 -0.049303289
## years_coding                       NA -0.099787155  -0.008812184 -0.021059869
## years_pro_coding                   NA -0.136182635  -0.020357660  0.024911413
## dev_type                           NA -0.072714868  -0.104552281 -0.032040297
## org_size                           NA -0.002764507   0.032902715  0.008952926
## purchase_influence                 NA  0.056734516  -0.142042800 -0.044444639
## build_vs_buy                       NA -0.031555778   0.137700454 -0.039207320
## country                            NA -0.058123399  -0.121278424  0.064937932
## currency                           NA -0.053803597  -0.057213481 -0.010333035
## comp_total                         NA  0.013929415   0.081992369  0.041949629
## visit_frequency                    NA  0.036726280  -0.014059759  0.103173339
## has_account                        NA           NA            NA           NA
## participation_frequency            NA  0.024488460  -0.103806840 -0.081139515
## community_belief                   NA -0.006273226  -0.052292011  0.040790072
## ai_usage                           NA           NA            NA           NA
## ai_sentiment                       NA  0.027834784  -0.138985632  0.042868121
## ai_acc                             NA           NA            NA           NA
## ai_complexity                       1           NA            NA           NA
## ai_threat                          NA  1.000000000   0.010598349 -0.070979820
## survey_length                      NA  0.010598349   1.000000000 -0.024218036
## survey_ease                        NA -0.070979820  -0.024218036  1.000000000
## yearly_compensation                NA -0.086783071  -0.143368048 -0.055407127
## r_used                             NA -0.062806303  -0.047464792  0.020435381
## r_want_to_use                      NA -0.056047848   0.041600826  0.109722172
##                         yearly_compensation       r_used r_want_to_use
## response_id                     0.022360855  0.034817385 -0.0026066073
## main_branch                    -0.037501771 -0.037505365 -0.0334694907
## age_group                       0.430395995  0.088003502  0.1089173162
## remote_work                     0.063041050 -0.010698923 -0.0466015488
## education_level                -0.039976803  0.035780570  0.0152185771
## years_coding                    0.449317464  0.058272674  0.1083376524
## years_pro_coding                0.469264679  0.002107932  0.0433698511
## dev_type                        0.104943751  0.095639273  0.0174233746
## org_size                       -0.150360307  0.053534615  0.1273617474
## purchase_influence              0.002308850  0.141820049  0.1180960038
## build_vs_buy                   -0.035137365 -0.024704927 -0.0542272331
## country                         0.443174771  0.170595831  0.1331822527
## currency                        0.307922092  0.104178571  0.0951053306
## comp_total                      0.225662120 -0.018228798 -0.0477067964
## visit_frequency                -0.156095343 -0.077462610 -0.0691270205
## has_account                              NA           NA            NA
## participation_frequency         0.083590385 -0.126199813  0.0005877856
## community_belief                0.001123081  0.070398575  0.0360149848
## ai_usage                                 NA           NA            NA
## ai_sentiment                    0.053335425  0.010118369  0.0090295524
## ai_acc                                   NA           NA            NA
## ai_complexity                            NA           NA            NA
## ai_threat                      -0.086783071 -0.062806303 -0.0560478479
## survey_length                  -0.143368048 -0.047464792  0.0416008264
## survey_ease                    -0.055407127  0.020435381  0.1097221719
## yearly_compensation             1.000000000  0.124785798  0.0693379926
## r_used                          0.124785798  1.000000000  0.6642005029
## r_want_to_use                   0.069337993  0.664200503  1.0000000000
```

<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;" />
]

---

## 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/)