index.Rmd

---
title: "Introduction to the Tidyverse"
subtitle: "How to be a tidy data scientist"
author: "Olivier Gimenez"
date: "Novembre 2021"
output:
  xaringan::moon_reader:
    css: ["css/rutgers-tidyverse.css", "css/rutgers-fonts_og.css"]
    lib_dir: "libs"
    nature:
      highlightStyle: github
      highlightLines: true
      countIncrementalSlides: false
# to convert html output into pdf,
# xaringan::decktape("file://.../filename.html", "filename.pdf", docker = FALSE)
---

```{r global_options, include=FALSE}
knitr::opts_chunk$set(
  fig.path   = 'chunks/',
  cache.path = "cache/",
  dpi        = 300
)
library(tidyverse)
theme_set(theme_grey(base_size = 16))
```

# **Tidyverse**

- **Ordocosme** in `r emo::ji("fr")` with _Tidy_ for "bien rangé" and _verse_ for "univers"

- A collection of R `r emo::ji("package")` developed by H. Wickham and others at Rstudio

```{r echo=FALSE, out.width = "50%", fig.align="center"}
knitr::include_graphics("img/wickham_president.jpg")
```


---

# **Tidyverse**

* "A framework for managing data that aims at making the cleaning and preparing steps [muuuuuuuch] easier" (Julien Barnier).

* Main characteristics of a tidy dataset:
    - each variable is a column
    - each observation is a raw
    - each value is in a different cell

```{r echo=FALSE, out.width = "80%", fig.align="center"}
knitr::include_graphics("img/tidydata.png")
```


---

# **Tidyverse** is a collection of R `r emo::ji("package")`


* `ggplot2` - visualising stuff

* `dplyr`, `tidyr` - data manipulation

* `purrr` - advanced programming

* `readr` - import data

* `tibble` - improved data.frame format

* `forcats` - working w/ factors

* `stringr` - working w/ chain of characters

---

# **Tidyverse** is a collection of R `r emo::ji("package")`

* [`ggplot2` - visualising stuff](https://ggplot2.tidyverse.org/)

* [`dplyr`, `tidyr` - data manipulation](https://dplyr.tidyverse.org/)

* `purrr` - advanced programming

* [`readr` - import data](https://readr.tidyverse.org/)

* [`tibble` - improved data.frame format](https://tibble.tidyverse.org/)

* [`forcats` - working w/ factors](https://forcats.tidyverse.org/)

* [`stringr` - working w/ chain of characters](https://stringr.tidyverse.org/)



---
class: middle


# Workflow in data science

```{r, echo=FALSE, out.width = '100%', fig.align="center"}
knitr::include_graphics("img/data-science-workflow.png")
```

---
class: middle

# Workflow in data science, with **Tidyverse**

```{r, echo=FALSE, out.width = '90%', fig.align="center"}
knitr::include_graphics("img/01_tidyverse_data_science.png")
```

---
background-image: url(https://github.com/rstudio/hex-stickers/raw/master/SVG/tidyverse.svg?sanitize=true)
background-size: 100px
background-position: 90% 3%

# Load [tidyverse](www.tidyverse.org) `r emo::ji("package")`

```{r, eval=FALSE}
# install.packages("tidyverse")
library(tidyverse)
```

---
class: middle

## Case study:
# [Using Twitter to predict citation rates of ecological research](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0166570)

```{r, echo=FALSE, out.width = '85%', fig.align="center"}
knitr::include_graphics("img/paper_workflow.png")
```

---
class: inverse, center, middle

# Import


---

# Import data

**readr::read_csv** function:

* ~~keeps input types as is (no conversion to factor)~~ (since `R` 4.0.0)

* creates `tibbles` instead of `data.frame`
     - no names to rows
     - allows column names with special characters (see next slide)
     - more clever on screen display than w/ data.frames (see next slide)
     - [no partial matching on column names](https://stackoverflow.com/questions/58513997/how-to-make-r-stop-accepting-partial-matches-for-column-names)
     - warning if attempt to access unexisting column

* is daaaaaamn fast `r emo::ji("racing_car")`

---

# Import data

```{r message=FALSE, warning=FALSE}
citations_raw <- readr::read_csv('https://raw.githubusercontent.com/oliviergimenez/intro_tidyverse/master/journal.pone.0166570.s001.CSV')
citations_raw
```

---
class: inverse, center, middle

# Tidy, transform

---

# Rename columns

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
citations_temp <- dplyr::rename(citations_raw,
       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')
citations_temp
```

---

# Create (or modify) columns

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
citations <- dplyr::mutate(citations_temp, journal = as.factor(journal))
citations
```

---

# Create (or modify) columns

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
levels(citations$journal)
```

---
class: inverse, center, middle

# Give your code some air

---

# Cleaner code with "pipe" operator `%>%`

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
citations_raw %>%
  dplyr::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') %>%
  dplyr::mutate(journal = as.factor(journal))
```

---

# Name object

```{r message=FALSE, warning=FALSE, paged.print=FALSE, eval = FALSE}
citations <- citations_raw %>% #<<
  dplyr::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') %>%
  dplyr::mutate(journal = as.factor(journal))
```

---

# Syntax with pipe

* Verb(Subject,Complement) replaced by Subject %>% Verb(Complement)


* No need to name unimportant intermediate variables


* Clear syntax (readability)

```{r echo=FALSE, out.width = "40%", fig.align="center"}
knitr::include_graphics("img/logo_pipe.png")
```

---

# Base R from [Lise Vaudor's blog](http://perso.ens-lyon.fr/lise.vaudor/)

```{r, eval = FALSE}
white_and_yolk       <- crack(egg, add_seasoning)
omelette_batter      <- beat(white_and_yolk)
omelette_with_chives <- cook(omelette_batter,add_chives)
```

```{r echo=FALSE, message=FALSE, warning=FALSE, paged.print=FALSE, out.width = "500px", fig.align = "center"}
knitr::include_graphics("img/piping_successive.jpg")
```

---

# Piping from [Lise Vaudor's blog](http://perso.ens-lyon.fr/lise.vaudor/)

```{r, eval = FALSE}
egg %>%
  crack(add_seasoning) %>%
  beat() %>%
  cook(add_chives) -> omelette_with_chives
```

```{r echo=FALSE, message=FALSE, warning=FALSE, paged.print=FALSE, out.width = "250px", fig.align = "center"}
knitr::include_graphics("img/piping_piped.png")
```


---
class: inverse, center, middle

# Tidy, transform

---

# Select columns

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
citations %>%
  dplyr::select(journal, impactfactor, nbtweets)
```

---

# Drop columns

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
citations %>%
  dplyr::select(-Volume, -Issue, -Authors)
```


---

# Split a column in several columns

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
citations %>%
  tidyr::separate(pubdate, c('month', 'day', 'year'), sep = '/')
```

---

# Transform in Date format...

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
citations %>%
  dplyr::mutate(
    pubdate  = lubridate::mdy(pubdate),
    colldate = lubridate::mdy(colldate))
```

---

# ...for easy manipulation of dates

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
citations %>%
  dplyr::mutate(
    pubdate  = lubridate::mdy(pubdate),
    colldate = lubridate::mdy(colldate),
    pubyear2 = lubridate::year(pubdate)) #<<
```

* Check out `?lubridate::lubridate` for more functions

---

# How to join tables together?

<blockquote class="twitter-tweet" data-lang="fr"><p lang="en" dir="ltr">More <a href="https://twitter.com/hashtag/dplyr?src=hash&amp;ref_src=twsrc%5Etfw">#dplyr</a> 🔧 gifs!  It took me a hella long time to wrap my head around the different types of joins when I first started learning them, so here&#39;s a few examples with some excellent mini datasets from <a href="https://twitter.com/hashtag/dplyr?src=hash&amp;ref_src=twsrc%5Etfw">#dplyr</a> designed specifically for this purpose! <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> <a href="https://twitter.com/hashtag/tidyverse?src=hash&amp;ref_src=twsrc%5Etfw">#tidyverse</a> <a href="https://t.co/G56fWmIZSq">pic.twitter.com/G56fWmIZSq</a></p>&mdash; Nic Crane (@nic_crane) <a href="https://twitter.com/nic_crane/status/1064237554910806016?ref_src=twsrc%5Etfw">18 novembre 2018</a></blockquote>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>


[![Watch the video](mp4/dplyr_join.mp4)](mp4/dplyr_join.mp4)


---

## <https://www.garrickadenbuie.com/project/tidyexplain/>

```{r echo=FALSE, out.width = "70%", fig.align="center"}
knitr::include_graphics("img/left-join.gif")
```




---
class: inverse, center, middle

# Easy character manipulation

---

# Select rows corresponding to papers with more than 3 authors

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
citations %>%
  dplyr::filter(stringr::str_detect(Authors, 'et al')) #<<
```

---

# Get column with rows corresponding to papers with more than 3 authors

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
citations %>%
  dplyr::filter(stringr::str_detect(Authors, 'et al')) %>% #<<
  dplyr::select(Authors) #<<
```


---

# Select rows corresponding to papers with less than 3 authors

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
citations %>%
  dplyr::filter(!stringr::str_detect(Authors, 'et al')) #<<
```


---

# Get column with rows corresponding to papers with less than 3 authors

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
citations %>%
  dplyr::filter(!stringr::str_detect(Authors, 'et al')) %>% #<<
  dplyr::select(Authors) #<<
```


---

# Get column with rows corresponding to papers with less than 3 authors

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
citations %>%
  dplyr::filter(!stringr::str_detect(Authors, 'et al')) %>%
  dplyr::pull(Authors) %>% #<<
  head(10)
```


---

# Select rows corresponding to papers with less than 3 authors in journal with IF < 5

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
citations %>%
  dplyr::filter(!stringr::str_detect(Authors, 'et al'), impactfactor < 5) #<<
```

---

# Convert words to lowercase

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
citations %>%
  dplyr::mutate(authors_lowercase = stringr::str_to_lower(Authors)) %>% #<<
  dplyr::select(authors_lowercase)
```

---

# Remove all spaces in journal names

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
citations %>%
  dplyr::mutate(journal = stringr::str_remove_all(journal, " ")) %>% #<<
  dplyr::select(journal) %>%
  unique() %>%
  head(5)
```


---

# Explore `r emo::ji("package")` stringr and regular expressions

* Check out the [vignette on stringr](https://cran.r-project.org/web/packages/stringr/vignettes/stringr.html) for more examples on character manipulation and pattern matching functions.

* Check out the [vignette on regular expressions](https://stringr.tidyverse.org/articles/regular-expressions.html) which are a concise and flexible tool for describing patterns in strings.


---
class: inverse, center, middle

# Basic exploratory data analysis

---

# Count

```{r}
citations %>% dplyr::count(journal, sort = TRUE)
```

---

# Count

```{r}
citations %>%
  dplyr::count(journal, pubyear) %>%
  head()
```

---

# Count sum of tweets per journal

```{r}
citations %>%
  dplyr::count(journal, wt = nbtweets, sort = TRUE)
```

---

# Group by variable to calculate stats

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
citations %>%
  dplyr::group_by(journal) %>% #<<
  dplyr::summarise(avg_tweets = mean(nbtweets)) %>% #<<
  head(10)
```


---

# Order stuff

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
citations %>%
  dplyr::group_by(journal) %>%
  dplyr::summarise(avg_tweets = mean(nbtweets)) %>%
  dplyr::arrange(dplyr::desc(avg_tweets)) %>% # decreasing order (wo desc for increasing) #<<
  head(10)
```


---

# What if we want to work on several columns?

```{r echo=FALSE, out.width = "85%", fig.align="center"}
knitr::include_graphics("img/dplyr_across.png")
```

---

# Compute mean across all numeric columns for each journal

```{r}
citations %>%
  dplyr::group_by(journal) %>% #<<
  dplyr::summarize(dplyr::across(where(is.numeric), mean)) %>% #<<
  head()
```

---

## <https://github.com/courtiol/Rguides>

```{r echo=FALSE, out.width = "85%", fig.align="center"}
knitr::include_graphics("img/dplyr_guide_for_one_table_part2.png")
```



---

# Tidying tibbles

```{r echo=FALSE, out.width = "70%", fig.align="center"}
knitr::include_graphics("img/original-dfs-tidy.png")
```

---

## Going from **long** to **wide** format and vice-versa

```{r echo=FALSE, out.width = "70%", fig.align="center"}
knitr::include_graphics("img/tidyr-longer-wider.gif")
```


---
class: inverse, center, middle

# Visualize


---

# 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

```{r echo=FALSE, out.width = "30%", fig.align="center"}
knitr::include_graphics("img/ggplot2_logo.jpg")
```

---

# The ggplot2 grammar


 Grammar element   | What it is
 :---------------- | :-----------------------------
 **Data**          | The data frame being plotted
 **Geometrics**    | The geometric shape that will represent the data
                   | (e.g., point, boxplot, histogram)
 **Aesthetics**    | The aesthetics of the geometric object
                   | (e.g., color, size, shape)


```{r echo=FALSE, out.width = "30%", fig.align="center"}
knitr::include_graphics("img/ggplot2_logo.jpg")
```


---

# Scatterplots

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '400cm', out.height='400cm', fig.align = "center", eval=FALSE}
citations %>%  #<<
  ggplot() + #<<
  aes(x = nbtweets, y = woscitations) +
  geom_point()
```
* Pass in the data frame as your first argument

---

# Scatterplots

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '400cm', out.height='400cm',  fig.align = "center", eval=FALSE}
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 message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '400cm', out.height='400cm',  fig.align = "center", eval=FALSE}
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 message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '400cm', out.height='400cm', fig.align = "center"}
citations %>%
  ggplot() +
  aes(x = nbtweets, y = woscitations) +
  geom_point()
```


---

# Scatterplots, with colors

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '400cm', out.height='400cm',  fig.align = "center"}
citations %>%
  ggplot() +
  aes(x = nbtweets, y = woscitations) +
  geom_point(color = "red") #<<
```

---

# Scatterplots, with species-specific colors

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '400cm', out.height='400cm',  fig.align = "center"}
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 message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '400cm', out.height='400cm',  fig.align = "center"}
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
```


---

# Scatterplots, with species-specific shapes

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '400cm', out.height='400cm',  fig.align = "center"}
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets, y = woscitations, shape = journal) + #<<
  geom_point(size=2)
```

---

# Scatterplots, lines instead of points

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '350cm', out.height='350cm',  fig.align = "center"}
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets, y = woscitations) +
  geom_line() + #<<
  scale_x_log10()
```

---

# Scatterplots, lines with sorting beforehand

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '350cm', out.height='350cm',  fig.align = "center"}
citations_ecology %>%
  arrange(woscitations) %>% #<<
  ggplot() +
  aes(x = nbtweets, y = woscitations) +
  geom_line() +
  scale_x_log10()
```

---

# Scatterplots, add points

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '350cm', out.height='350cm',  fig.align = "center"}
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets, y = woscitations) +
  geom_line() +
  geom_point() + #<<
  scale_x_log10()
```

---

# Scatterplots, add linear trend

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '350cm', out.height='350cm',  fig.align = "center"}
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets, y = woscitations) +
  geom_point() +
  geom_smooth(method = "lm") + #<<
  scale_x_log10()
```

---

# Scatterplots, add smoother

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '350cm', out.height='350cm',  fig.align = "center"}
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().

```{r echo=FALSE, out.width = "30%", fig.align="center"}
knitr::include_graphics("img/ggplot2_logo.jpg")
```

---

# Histograms

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '400cm', out.height='400cm',  fig.align = "center"}
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets) +
  geom_histogram() #<<
```

---

# Histograms, with colors

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '400cm', out.height='400cm',  fig.align = "center"}
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets) +
  geom_histogram(fill = "orange") #<<
```

---

# Histograms, with colors

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '400cm', out.height='400cm',  fig.align = "center"}
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets) +
  geom_histogram(fill = "orange", color = "brown") #<<
```

---

# Histograms, with labels and title

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '350cm', out.height='350cm',  fig.align = "center"}
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 message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '300cm', out.height='300cm',  fig.align = "center"}
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 message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '350cm', out.height='350cm',  fig.align = "center"}
citations_ecology %>%
  ggplot() +
  aes(x = "", y = nbtweets) +
  geom_boxplot() + #<<
  scale_y_log10()
```

---

# Boxplots with colors

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '350cm', out.height='350cm',  fig.align = "center"}
citations_ecology %>%
  ggplot() +
  aes(x = "", y = nbtweets) +
  geom_boxplot(fill = "green") + #<<
  scale_y_log10()
```

---

# Boxplots with colors by species

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '300cm', out.height='300cm',  fig.align = "center"}
citations_ecology %>%
  ggplot() +
  aes(x = journal, y = nbtweets, fill = journal) + #<<
  geom_boxplot() +
  scale_y_log10()
```


---

# Get rid of the ticks on x axis

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '300cm', out.height='300cm',  fig.align = "center"}
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 message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '300cm', out.height='300cm',  fig.align = "center"}
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 message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '270cm', out.height='270cm',  fig.align = "center"}
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 message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '350cm', out.height='350cm',  fig.align = "center"}
citations %>%
  count(journal) %>%
  ggplot() +
  aes(x = journal, y = n) +
  geom_col() #<<
```

---

# Idem, with flipping

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '350cm', out.height='350cm',  fig.align = "center"}
citations %>%
  count(journal) %>%
  ggplot() +
  aes(x = n, y = journal) + #<<
  geom_col()
```


---

# Idem, with factors reordering and flipping

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '350cm', out.height='350cm',  fig.align = "center"}
citations %>%
  count(journal) %>%
  ggplot() +
  aes(x = n, y = fct_reorder(journal, n)) + #<<
  geom_col()
```


---

# Further cleaning

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '350cm', out.height='350cm',  fig.align = "center"}
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 message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '350cm', out.height='350cm',  fig.align = "center"}
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets, fill = journal) +
  geom_density() +#<<
  scale_x_log10()
```

---

# Density plots, control transparency

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '350cm', out.height='350cm',  fig.align = "center"}
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets, fill = journal) +
  geom_density(alpha = 0.5) +#<<
  scale_x_log10()
```

---

# Change default background `B & W theme`

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '300cm', out.height='300cm',  fig.align = "center"}
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets, fill = journal) +
  geom_density(alpha = 0.5) +
  scale_x_log10() +
  theme_bw() #<<
```

---

# Change default background theme `classic theme`

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '300cm', out.height='300cm',  fig.align = "center"}
citations_ecology %>%
  ggplot() +
  aes(x = nbtweets, fill = journal) +
  geom_density(alpha = 0.5) +
  scale_x_log10() +
  theme_classic() #<<
```

---

# Change default background theme `dark theme`

```{r message=FALSE, warning=FALSE, paged.print=FALSE, out.width = '300cm', out.height='300cm',  fig.align = "center"}
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


```{r echo=FALSE, out.width = "30%", fig.align="center"}
knitr::include_graphics("img/ggplot2_logo.jpg")
```


---

background-image: url(https://github.com/rstudio/hex-stickers/raw/master/SVG/tidyverse.svg?sanitize=true)
background-size: 550px
background-position: 50% 50%


---

# To dive even deeper in 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 chief data scientist at DataCamp


* 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


* List of best R packages (with their description) on [data import, wrangling and visualization](https://www.computerworld.com/article/2921176/business-intelligence/great-r-packages-for-data-import-wrangling-visualization.html)

---

# [How to switch from base R to tidyverse?](https://www.significantdigits.org/2017/10/switching-from-base-r-to-tidyverse/)

```{r echo=FALSE, message=FALSE, warning=FALSE, paged.print=FALSE, out.width = "800px", fig.align = "center"}
knitr::include_graphics("img/switch_baseR_tidyverse.png")
```

---

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

```{r echo=FALSE, message=FALSE, warning=FALSE, paged.print=FALSE, out.width = "600px", fig.align = "center"}
knitr::include_graphics("img/cheatsheet_dplyr.png")
```


---
class: title-slide-final, middle
background-size: 55px
background-position: 9% 15%

# Thanks!

### I created these slides with [xaringan](https://github.com/yihui/xaringan) and [RMarkdown](https://rmarkdown.rstudio.com/) using the [rutgers css](https://github.com/jvcasillas/ru_xaringan) that I slightly modified.

### Credit: I used material from [Cécile Sauder](https://github.com/cecilesauder/RLadiesTidyverse), [Stephanie J. Spielman](http://sjspielman.org/bio5312_fall2017/) and [Julien Barnier](https://juba.github.io/tidyverse/).


|                                   |                                                                            |
| :-------------------------------- | :------------------------------------------------------------------------- |
| `r fontawesome::fa_i("envelope")` | **olivier.gimenez@cefe.cnrs.fr**                                           |
| `r fontawesome::fa_i("home")`     | [**https://oliviergimenez.github.io/**](https://oliviergimenez.github.io/) |
| `r fontawesome::fa_i("twitter")`  | [**@oaggimenez**](https://twitter.com/oaggimenez)                          |
| `r fontawesome::fa_i("github")`   | [**@oliviergimenez**](https://github.com/oliviergimenez)                   |