Introduction to the Tidyverse

class: center, middle, inverse, title-slide

# Introduction to the Tidyverse
## Data visualisation
### Olivier Gimenez
### last updated: 2022-05-22

---

# Visualization with ggplot2

* The package ggplot2 implements a **g**rammar of **g**raphics

* Operates on data.frames or tibbles, not vectors like base R

* Explicitly differentiates between the data and its representation

---

# The ggplot2 grammar

---

# Data

See Introduction to data wrangling with `dplyr` at https://bit.ly/3wF5S7f

```r
citations <- read_csv('https://raw.githubusercontent.com/oliviergimenez/intro_tidyverse/master/journal.pone.0166570.s001.CSV') %>%
  rename(journal = 'Journal identity',
         impactfactor = '5-year journal impact factor',
         pubyear = 'Year published',
         colldate = 'Collection date',
         pubdate = 'Publication date',
         nbtweets = 'Number of tweets',
         woscitations = 'Number of Web of Science citations') %>%
  mutate(journal = as.factor(journal))
```

```
## Rows: 1599 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): Journal identity, Issue, Authors, Collection date, Publication date
## dbl (7): 5-year journal impact factor, Year published, Volume, Number of twe...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
```

---

# Scatterplots

```r
*citations %>%
* ggplot() +
  aes(x = nbtweets, y = woscitations) +
  geom_point()
```
* Pass in the data frame as your first argument

---

# Scatterplots

```r
citations %>%
  ggplot() +
* aes(x = nbtweets, y = woscitations) +
  geom_point()
```
* Pass in the data frame as your first argument
* Aesthetics maps the data onto plot characteristics, here x and y axes

---

# Scatterplots

```r
citations %>%
  ggplot() +
  aes(x = nbtweets, y = woscitations) +
* geom_point()
```
* Pass in the data frame as your first argument
* Aesthetics maps the data onto plot characteristics, here x and y axes
* Display the data geometrically as points

---

# Scatterplots

```r
citations %>%
  ggplot() +
  aes(x = nbtweets, y = woscitations) +
  geom_point()
```

---

# Scatterplots, with colors

```r
citations %>%
  ggplot() +
  aes(x = nbtweets, y = woscitations) +
* geom_point(color = "red")
```

---

# Scatterplots, with species-specific colors

```r
citations %>%
  ggplot() +
* aes(x = nbtweets, y = woscitations, color = journal) +
  geom_point()
```

* Placing color inside aesthetic maps it to the data

---

# Pick a few journals

```r
citations_ecology <- citations %>%
  mutate(journal = str_to_lower(journal)) %>% # all journals names lowercase
  filter(journal %in%
           c('journal of animal ecology','journal of applied ecology','ecology')) # filter
citations_ecology
```

```
## # A tibble: 216 × 12
##    journal impactfactor pubyear Volume Issue Authors   colldate pubdate nbtweets
##    <chr>          <dbl>   <dbl>  <dbl> <chr> <chr>     <chr>    <chr>      <dbl>
##  1 ecology         6.16    2014     95 12    Magliane… 3/19/20… 12/1/2…        1
##  2 ecology         6.16    2014     95 12    Soinen    3/19/20… 12/1/2…        6
##  3 ecology         6.16    2014     95 12    Graham a… 3/19/20… 12/1/2…        1
##  4 ecology         6.16    2014     95 11    White et… 3/19/20… 11/1/2…        9
##  5 ecology         6.16    2014     95 11    Einarson… 3/19/20… 11/1/2…       15
##  6 ecology         6.16    2014     95 11    Haav and… 3/19/20… 11/1/2…        2
##  7 ecology         6.16    2014     95 10    Dodds et… 3/19/20… 10/1/2…        1
##  8 ecology         6.16    2014     95 10    Brown et… 3/19/20… 10/1/2…        1
##  9 ecology         6.16    2014     95 10    Wright e… 3/19/20… 10/1/2…        0
## 10 ecology         6.16    2014     95 9     Ramahlo … 3/19/20… 9/1/20…       27
## # … with 206 more rows, and 3 more variables: `Number of users` <dbl>,
## #   `Twitter reach` <dbl>, woscitations <dbl>
```

---

# Scatterplots, with species-specific shapes

```r
citations_ecology %>%
  ggplot() +
* aes(x = nbtweets, y = woscitations, shape = journal) +
  geom_point(size=2)
```

---

# Scatterplots, lines instead of points

```r
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets, y = woscitations) +
* geom_line() +
  scale_x_log10()
```

---

# Scatterplots, lines with sorting beforehand

```r
citations_ecology %>%
* arrange(woscitations) %>%
  ggplot() +
  aes(x = nbtweets, y = woscitations) +
  geom_line() +
  scale_x_log10()
```

---

# Scatterplots, add points

```r
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets, y = woscitations) +
  geom_line() +
* geom_point() +
  scale_x_log10()
```

---

# Scatterplots, add linear trend

```r
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets, y = woscitations) +
  geom_point() +
* geom_smooth(method = "lm") +
  scale_x_log10()
```

---

# Scatterplots, add smoother

```r
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets, y = woscitations) +
  geom_point() +
* geom_smooth() +
  scale_x_log10()
```

---

# aes or not aes?

* If we are to establish a link between the values of a variable and a graphical feature, ie a mapping, then we need an aes().

* Otherwise, the graphical feature is modified irrespective of the data, then we do not need an aes().

---

# Histograms

```r
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets) +
* geom_histogram()
```

---

# Histograms, with colors

```r
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets) +
* geom_histogram(fill = "orange")
```

---

# Histograms, with colors

```r
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets) +
* geom_histogram(fill = "orange", color = "brown")
```

---

# Histograms, with labels and title

```r
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets) +
  geom_histogram(fill = "orange", color = "brown") +
* labs(x = "Number of tweets",
*      y = "Count",
*      title = "Histogram of the number of tweets")
```

---

# Histograms, by species

```r
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets) +
  geom_histogram(fill = "orange", color = "brown") +
  labs(x = "Number of tweets",
       y = "Count",
       title = "Histogram of the number of tweets") + 
* facet_wrap(vars(journal))
```

---

# Boxplots

```r
citations_ecology %>%
  ggplot() +
  aes(x = "", y = nbtweets) +
* geom_boxplot() +
  scale_y_log10()
```

---

# Boxplots with colors

```r
citations_ecology %>%
  ggplot() +
  aes(x = "", y = nbtweets) +
* geom_boxplot(fill = "green") +
  scale_y_log10()
```

---

# Boxplots with colors by species

```r
citations_ecology %>%
  ggplot() +
* aes(x = journal, y = nbtweets, fill = journal) +
  geom_boxplot() +
  scale_y_log10()
```

---

# Get rid of the ticks on x axis

```r
citations_ecology %>%
  ggplot() +
  aes(x = journal, y = nbtweets, fill = journal) +
  geom_boxplot() +
  scale_y_log10() + 
* theme(axis.text.x = element_blank()) +
* labs(x = "")
```

---

# Boxplots, user-specified colors by species

```r
citations_ecology %>%
  ggplot() +
  aes(x = journal, y = nbtweets, fill = journal) +
  geom_boxplot() +
  scale_y_log10() +
* scale_fill_manual(
*   values = c("red", "blue", "purple")) +
  theme(axis.text.x = element_blank()) +
  labs(x = "")
```

---

# Boxplots, change legend settings

```r
citations_ecology %>%
  ggplot() +
  aes(x = journal, y = nbtweets, fill = journal) +
  geom_boxplot() +
  scale_y_log10() +
* scale_fill_manual(
    values = c("red", "blue", "purple"),
*   name = "Journal name",
*   labels = c("Ecology", "J Animal Ecology", "J Applied Ecology")) +
  theme(axis.text.x = element_blank()) +
  labs(x = "")
```

---

# Ugly bar plots

```r
citations %>%
  count(journal) %>%
  ggplot() +
  aes(x = journal, y = n) +
* geom_col()
```

---

# Idem, with flipping

```r
citations %>%
  count(journal) %>%
  ggplot() +
* aes(x = n, y = journal) +
  geom_col()
```

---

# Idem, with factors reordering and flipping

```r
citations %>%
  count(journal) %>%
  ggplot() +
* aes(x = n, y = fct_reorder(journal, n)) +
  geom_col()
```

---

# Further cleaning

```r
citations %>%
  count(journal) %>%
  ggplot() +
  aes(x = n, y = fct_reorder(journal, n)) +
  geom_col() + 
  labs(x = "counts", y = "")
```

---

# More about how to (tidy) work with factors

* [Be the boss of your factors](https://stat545.com/block029_factors.html) and 
* [forcats, forcats, vous avez dit forcats ?](https://thinkr.fr/forcats-forcats-vous-avez-dit-forcats/).

---

# Density plots

```r
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets, fill = journal) +
* geom_density() +
  scale_x_log10()
```

---

# Density plots, control transparency

```r
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets, fill = journal) +
* geom_density(alpha = 0.5) +
  scale_x_log10()
```

---

# Change default background

```r
# `B & W theme`
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets, fill = journal) +
  geom_density(alpha = 0.5) +
  scale_x_log10() +
* theme_bw()
```

---

# Change default background theme

```r
# `classic theme`
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets, fill = journal) +
  geom_density(alpha = 0.5) +
  scale_x_log10() +
* theme_classic()
```

---

# Change default background theme

```r
# `dark theme`
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets, fill = journal) +
  geom_density(alpha = 0.5) +
  scale_x_log10() +
* theme_dark()
```

---

# More on data visualisation with ggplot2

* [Portfolio](https://www.r-graph-gallery.com/portfolio/ggplot2-package/) of ggplot2 plots

* [Cédric Scherer's portfolio](https://cedricscherer.netlify.app/top/dataviz/) of data visualisations

* [Top](http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html) ggplot2 visualizations

* [Interactive](https://dreamrs.github.io/esquisse/) ggplot2 visualizations

* [Manipulate and visualise spatial data](https://oliviergimenez.github.io/intro_spatialR/#1) with the sf package

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background-size: 550px
background-position: 50% 50%

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# To dive deeper in data visualisation with the tidyverse

* [Learn the tidyverse](https://www.tidyverse.org/learn/): books, workshops and online courses

* My selection of books:
   - [R for Data Science](https://r4ds.had.co.nz/) et [Advanced R](http://adv-r.had.co.nz/)
   - [Introduction à R et au tidyverse](https://juba.github.io/tidyverse/)
   - [Fundamentals of Data visualization](https://clauswilke.com/dataviz/)
   - [Data Visualization: A practical introduction](http://socviz.co/)

* [Tidy Tuesdays videos](https://www.youtube.com/user/safe4democracy/videos) by D. Robinson

* Material of the [2-day workshop Data Science in the tidyverse](https://github.com/cwickham/data-science-in-tidyverse) held at the RStudio 2019 conference

* Material of the stat545 course on [Data wrangling, exploration, and analysis with R](https://stat545.com/) at the University of British Columbia

---

# The [RStudio Cheat Sheets](https://www.rstudio.com/resources/cheatsheets/)

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# The [RStudio Cheat Sheets](https://www.rstudio.com/resources/cheatsheets/)