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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Introduction to the Tidyverse</title>
<meta charset="utf-8" />
<meta name="author" content="Olivier Gimenez" />
<script src="libs/header-attrs-2.11/header-attrs.js"></script>
<link href="libs/font-awesome-5.15.3/css/all.min.css" rel="stylesheet" />
<link href="libs/font-awesome-5.15.3/css/v4-shims.min.css" rel="stylesheet" />
<link rel="stylesheet" href="css/rutgers-tidyverse.css" type="text/css" />
<link rel="stylesheet" href="css/rutgers-fonts_og.css" type="text/css" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
# Introduction to the Tidyverse
## How to be a tidy data scientist
### Olivier Gimenez
### Novembre 2021
---
# **Tidyverse**
- **Ordocosme** in 🇫🇷 with _Tidy_ for "bien rangé" and _verse_ for "univers"
- A collection of R 📦 developed by H. Wickham and others at Rstudio
<img src="img/wickham_president.jpg" width="50%" style="display: block; margin: auto;" />
---
# **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
<img src="img/tidydata.png" width="80%" style="display: block; margin: auto;" />
---
# **Tidyverse** is a collection of R 📦
* `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 📦
* [`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
<img src="img/data-science-workflow.png" width="100%" style="display: block; margin: auto;" />
---
class: middle
# Workflow in data science, with **Tidyverse**
<img src="img/01_tidyverse_data_science.png" width="90%" style="display: block; margin: auto;" />
---
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)&nbsp;📦
```r
# 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)
<img src="img/paper_workflow.png" width="85%" style="display: block; margin: auto;" />
---
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 🏎
---
# Import data
```r
citations_raw <- readr::read_csv('https://raw.githubusercontent.com/oliviergimenez/intro_tidyverse/master/journal.pone.0166570.s001.CSV')
citations_raw
```
```
## # A tibble: 1,599 × 12
## `Journal identity` `5-year journal im… `Year published` Volume Issue Authors `Collection dat… `Publication da…
## <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr>
## 1 Ecology Letters 16.7 2014 17 12 Morin et … 2/1/2016 9/16/2014
## 2 Ecology Letters 16.7 2014 17 12 Jucker et… 2/1/2016 10/13/2014
## 3 Ecology Letters 16.7 2014 17 12 Calcagno … 2/1/2016 10/21/2014
## 4 Ecology Letters 16.7 2014 17 11 Segre et … 2/1/2016 8/28/2014
## 5 Ecology Letters 16.7 2014 17 11 Kaufman e… 2/1/2016 8/28/2014
## 6 Ecology Letters 16.7 2014 17 10 Nasto et … 2/2/2016 7/28/2014
## 7 Ecology Letters 16.7 2014 17 10 Tschirren… 2/2/2016 8/6/2014
## 8 Ecology Letters 16.7 2014 17 9 Barnechi … 2/2/2016 6/17/2014
## 9 Ecology Letters 16.7 2014 17 9 Pinto-San… 2/2/2016 6/12/2014
## 10 Ecology Letters 16.7 2014 17 9 Clough et… 2/2/2016 7/17/2014
## # … with 1,589 more rows, and 4 more variables: Number of tweets <dbl>, Number of users <dbl>, Twitter reach <dbl>,
## # Number of Web of Science citations <dbl>
```
---
class: inverse, center, middle
# Tidy, transform
---
# Rename columns
```r
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
```
```
## # A tibble: 1,599 × 12
## journal impactfactor pubyear Volume Issue Authors colldate pubdate nbtweets `Number of user… `Twitter reach`
## <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 Ecology … 16.7 2014 17 12 Morin et … 2/1/2016 9/16/2… 18 16 29877
## 2 Ecology … 16.7 2014 17 12 Jucker et… 2/1/2016 10/13/… 15 12 5997
## 3 Ecology … 16.7 2014 17 12 Calcagno … 2/1/2016 10/21/… 5 4 1667
## 4 Ecology … 16.7 2014 17 11 Segre et … 2/1/2016 8/28/2… 9 8 3482
## 5 Ecology … 16.7 2014 17 11 Kaufman e… 2/1/2016 8/28/2… 3 3 1329
## 6 Ecology … 16.7 2014 17 10 Nasto et … 2/2/2016 7/28/2… 27 23 41906
## 7 Ecology … 16.7 2014 17 10 Tschirren… 2/2/2016 8/6/20… 6 6 12223
## 8 Ecology … 16.7 2014 17 9 Barnechi … 2/2/2016 6/17/2… 19 18 22020
## 9 Ecology … 16.7 2014 17 9 Pinto-San… 2/2/2016 6/12/2… 26 23 23003
## 10 Ecology … 16.7 2014 17 9 Clough et… 2/2/2016 7/17/2… 44 42 131788
## # … with 1,589 more rows, and 1 more variable: woscitations <dbl>
```
---
# Create (or modify) columns
```r
citations <- dplyr::mutate(citations_temp, journal = as.factor(journal))
citations
```
```
## # A tibble: 1,599 × 12
## journal impactfactor pubyear Volume Issue Authors colldate pubdate nbtweets `Number of user… `Twitter reach`
## <fct> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 Ecology … 16.7 2014 17 12 Morin et … 2/1/2016 9/16/2… 18 16 29877
## 2 Ecology … 16.7 2014 17 12 Jucker et… 2/1/2016 10/13/… 15 12 5997
## 3 Ecology … 16.7 2014 17 12 Calcagno … 2/1/2016 10/21/… 5 4 1667
## 4 Ecology … 16.7 2014 17 11 Segre et … 2/1/2016 8/28/2… 9 8 3482
## 5 Ecology … 16.7 2014 17 11 Kaufman e… 2/1/2016 8/28/2… 3 3 1329
## 6 Ecology … 16.7 2014 17 10 Nasto et … 2/2/2016 7/28/2… 27 23 41906
## 7 Ecology … 16.7 2014 17 10 Tschirren… 2/2/2016 8/6/20… 6 6 12223
## 8 Ecology … 16.7 2014 17 9 Barnechi … 2/2/2016 6/17/2… 19 18 22020
## 9 Ecology … 16.7 2014 17 9 Pinto-San… 2/2/2016 6/12/2… 26 23 23003
## 10 Ecology … 16.7 2014 17 9 Clough et… 2/2/2016 7/17/2… 44 42 131788
## # … with 1,589 more rows, and 1 more variable: woscitations <dbl>
```
---
# Create (or modify) columns
```r
levels(citations$journal)
```
```
## [1] "Animal Conservation" "Conservation Letters" "Diversity and Distributions"
## [4] "Ecological Applications" "Ecology" "Ecology Letters"
## [7] "Evolution" "Evolutionary Applications" "Fish and Fisheries"
## [10] "Functional Ecology" "Global Change Biology" "Global Ecology and Biogeography"
## [13] "Journal of Animal Ecology" "Journal of Applied Ecology" "Journal of Biogeography"
## [16] "Limnology and Oceanography" "Mammal Review" "Methods in Ecology and Evolution"
## [19] "Molecular Ecology Resources" "New Phytologist"
```
---
class: inverse, center, middle
# Give your code some air
---
# Cleaner code with "pipe" operator `%>%`
```r
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))
```
```
## # A tibble: 1,599 × 12
## journal impactfactor pubyear Volume Issue Authors colldate pubdate nbtweets `Number of user… `Twitter reach`
## <fct> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 Ecology … 16.7 2014 17 12 Morin et … 2/1/2016 9/16/2… 18 16 29877
## 2 Ecology … 16.7 2014 17 12 Jucker et… 2/1/2016 10/13/… 15 12 5997
## 3 Ecology … 16.7 2014 17 12 Calcagno … 2/1/2016 10/21/… 5 4 1667
## 4 Ecology … 16.7 2014 17 11 Segre et … 2/1/2016 8/28/2… 9 8 3482
## 5 Ecology … 16.7 2014 17 11 Kaufman e… 2/1/2016 8/28/2… 3 3 1329
## 6 Ecology … 16.7 2014 17 10 Nasto et … 2/2/2016 7/28/2… 27 23 41906
## 7 Ecology … 16.7 2014 17 10 Tschirren… 2/2/2016 8/6/20… 6 6 12223
## 8 Ecology … 16.7 2014 17 9 Barnechi … 2/2/2016 6/17/2… 19 18 22020
## 9 Ecology … 16.7 2014 17 9 Pinto-San… 2/2/2016 6/12/2… 26 23 23003
## 10 Ecology … 16.7 2014 17 9 Clough et… 2/2/2016 7/17/2… 44 42 131788
## # … with 1,589 more rows, and 1 more variable: woscitations <dbl>
```
---
# Name object
```r
*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)
<img src="img/logo_pipe.png" width="40%" style="display: block; margin: auto;" />
---
# Base R from [Lise Vaudor's blog](http://perso.ens-lyon.fr/lise.vaudor/)
```r
white_and_yolk <- crack(egg, add_seasoning)
omelette_batter <- beat(white_and_yolk)
omelette_with_chives <- cook(omelette_batter,add_chives)
```
<img src="img/piping_successive.jpg" width="500px" style="display: block; margin: auto;" />
---
# Piping from [Lise Vaudor's blog](http://perso.ens-lyon.fr/lise.vaudor/)
```r
egg %>%
crack(add_seasoning) %>%
beat() %>%
cook(add_chives) -> omelette_with_chives
```
<img src="img/piping_piped.png" width="250px" style="display: block; margin: auto;" />
---
class: inverse, center, middle
# Tidy, transform
---
# Select columns
```r
citations %>%
dplyr::select(journal, impactfactor, nbtweets)
```
```
## # A tibble: 1,599 × 3
## journal impactfactor nbtweets
## <fct> <dbl> <dbl>
## 1 Ecology Letters 16.7 18
## 2 Ecology Letters 16.7 15
## 3 Ecology Letters 16.7 5
## 4 Ecology Letters 16.7 9
## 5 Ecology Letters 16.7 3
## 6 Ecology Letters 16.7 27
## 7 Ecology Letters 16.7 6
## 8 Ecology Letters 16.7 19
## 9 Ecology Letters 16.7 26
## 10 Ecology Letters 16.7 44
## # … with 1,589 more rows
```
---
# Drop columns
```r
citations %>%
dplyr::select(-Volume, -Issue, -Authors)
```
```
## # A tibble: 1,599 × 9
## journal impactfactor pubyear colldate pubdate nbtweets `Number of users` `Twitter reach` woscitations
## <fct> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Ecology Letters 16.7 2014 2/1/2016 9/16/2014 18 16 29877 3
## 2 Ecology Letters 16.7 2014 2/1/2016 10/13/2014 15 12 5997 8
## 3 Ecology Letters 16.7 2014 2/1/2016 10/21/2014 5 4 1667 1
## 4 Ecology Letters 16.7 2014 2/1/2016 8/28/2014 9 8 3482 2
## 5 Ecology Letters 16.7 2014 2/1/2016 8/28/2014 3 3 1329 1
## 6 Ecology Letters 16.7 2014 2/2/2016 7/28/2014 27 23 41906 9
## 7 Ecology Letters 16.7 2014 2/2/2016 8/6/2014 6 6 12223 6
## 8 Ecology Letters 16.7 2014 2/2/2016 6/17/2014 19 18 22020 9
## 9 Ecology Letters 16.7 2014 2/2/2016 6/12/2014 26 23 23003 5
## 10 Ecology Letters 16.7 2014 2/2/2016 7/17/2014 44 42 131788 4
## # … with 1,589 more rows
```
---
# Split a column in several columns
```r
citations %>%
tidyr::separate(pubdate, c('month', 'day', 'year'), sep = '/')
```
```
## # A tibble: 1,599 × 14
## journal impactfactor pubyear Volume Issue Authors colldate month day year nbtweets `Number of user…
## <fct> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Ecology Letters 16.7 2014 17 12 Morin et … 2/1/2016 9 16 2014 18 16
## 2 Ecology Letters 16.7 2014 17 12 Jucker et… 2/1/2016 10 13 2014 15 12
## 3 Ecology Letters 16.7 2014 17 12 Calcagno … 2/1/2016 10 21 2014 5 4
## 4 Ecology Letters 16.7 2014 17 11 Segre et … 2/1/2016 8 28 2014 9 8
## 5 Ecology Letters 16.7 2014 17 11 Kaufman e… 2/1/2016 8 28 2014 3 3
## 6 Ecology Letters 16.7 2014 17 10 Nasto et … 2/2/2016 7 28 2014 27 23
## 7 Ecology Letters 16.7 2014 17 10 Tschirren… 2/2/2016 8 6 2014 6 6
## 8 Ecology Letters 16.7 2014 17 9 Barnechi … 2/2/2016 6 17 2014 19 18
## 9 Ecology Letters 16.7 2014 17 9 Pinto-San… 2/2/2016 6 12 2014 26 23
## 10 Ecology Letters 16.7 2014 17 9 Clough et… 2/2/2016 7 17 2014 44 42
## # … with 1,589 more rows, and 2 more variables: Twitter reach <dbl>, woscitations <dbl>
```
---
# Transform in Date format...
```r
citations %>%
dplyr::mutate(
pubdate = lubridate::mdy(pubdate),
colldate = lubridate::mdy(colldate))
```
```
## # A tibble: 1,599 × 12
## journal impactfactor pubyear Volume Issue Authors colldate pubdate nbtweets `Number of user… `Twitter reach`
## <fct> <dbl> <dbl> <dbl> <chr> <chr> <date> <date> <dbl> <dbl> <dbl>
## 1 Ecolog… 16.7 2014 17 12 Morin … 2016-02-01 2014-09-16 18 16 29877
## 2 Ecolog… 16.7 2014 17 12 Jucker… 2016-02-01 2014-10-13 15 12 5997
## 3 Ecolog… 16.7 2014 17 12 Calcag… 2016-02-01 2014-10-21 5 4 1667
## 4 Ecolog… 16.7 2014 17 11 Segre … 2016-02-01 2014-08-28 9 8 3482
## 5 Ecolog… 16.7 2014 17 11 Kaufma… 2016-02-01 2014-08-28 3 3 1329
## 6 Ecolog… 16.7 2014 17 10 Nasto … 2016-02-02 2014-07-28 27 23 41906
## 7 Ecolog… 16.7 2014 17 10 Tschir… 2016-02-02 2014-08-06 6 6 12223
## 8 Ecolog… 16.7 2014 17 9 Barnec… 2016-02-02 2014-06-17 19 18 22020
## 9 Ecolog… 16.7 2014 17 9 Pinto-… 2016-02-02 2014-06-12 26 23 23003
## 10 Ecolog… 16.7 2014 17 9 Clough… 2016-02-02 2014-07-17 44 42 131788
## # … with 1,589 more rows, and 1 more variable: woscitations <dbl>
```
---
# ...for easy manipulation of dates
```r
citations %>%
dplyr::mutate(
pubdate = lubridate::mdy(pubdate),
colldate = lubridate::mdy(colldate),
* pubyear2 = lubridate::year(pubdate))
```
```
## # A tibble: 1,599 × 13
## journal impactfactor pubyear Volume Issue Authors colldate pubdate nbtweets `Number of user… `Twitter reach`
## <fct> <dbl> <dbl> <dbl> <chr> <chr> <date> <date> <dbl> <dbl> <dbl>
## 1 Ecolog… 16.7 2014 17 12 Morin … 2016-02-01 2014-09-16 18 16 29877
## 2 Ecolog… 16.7 2014 17 12 Jucker… 2016-02-01 2014-10-13 15 12 5997
## 3 Ecolog… 16.7 2014 17 12 Calcag… 2016-02-01 2014-10-21 5 4 1667
## 4 Ecolog… 16.7 2014 17 11 Segre … 2016-02-01 2014-08-28 9 8 3482
## 5 Ecolog… 16.7 2014 17 11 Kaufma… 2016-02-01 2014-08-28 3 3 1329
## 6 Ecolog… 16.7 2014 17 10 Nasto … 2016-02-02 2014-07-28 27 23 41906
## 7 Ecolog… 16.7 2014 17 10 Tschir… 2016-02-02 2014-08-06 6 6 12223
## 8 Ecolog… 16.7 2014 17 9 Barnec… 2016-02-02 2014-06-17 19 18 22020
## 9 Ecolog… 16.7 2014 17 9 Pinto-… 2016-02-02 2014-06-12 26 23 23003
## 10 Ecolog… 16.7 2014 17 9 Clough… 2016-02-02 2014-07-17 44 42 131788
## # … with 1,589 more rows, and 2 more variables: woscitations <dbl>, pubyear2 <dbl>
```
* 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>
[](mp4/dplyr_join.mp4)
---
## <https://www.garrickadenbuie.com/project/tidyexplain/>
<img src="img/left-join.gif" width="70%" style="display: block; margin: auto;" />
---
class: inverse, center, middle
# Easy character manipulation
---
# Select rows corresponding to papers with more than 3 authors
```r
citations %>%
* dplyr::filter(stringr::str_detect(Authors, 'et al'))
```
```
## # A tibble: 1,280 × 12
## journal impactfactor pubyear Volume Issue Authors colldate pubdate nbtweets `Number of user… `Twitter reach`
## <fct> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 Ecology … 16.7 2014 17 12 Morin et … 2/1/2016 9/16/2… 18 16 29877
## 2 Ecology … 16.7 2014 17 12 Jucker et… 2/1/2016 10/13/… 15 12 5997
## 3 Ecology … 16.7 2014 17 12 Calcagno … 2/1/2016 10/21/… 5 4 1667
## 4 Ecology … 16.7 2014 17 11 Segre et … 2/1/2016 8/28/2… 9 8 3482
## 5 Ecology … 16.7 2014 17 11 Kaufman e… 2/1/2016 8/28/2… 3 3 1329
## 6 Ecology … 16.7 2014 17 10 Nasto et … 2/2/2016 7/28/2… 27 23 41906
## 7 Ecology … 16.7 2014 17 10 Tschirren… 2/2/2016 8/6/20… 6 6 12223
## 8 Ecology … 16.7 2014 17 9 Barnechi … 2/2/2016 6/17/2… 19 18 22020
## 9 Ecology … 16.7 2014 17 9 Pinto-San… 2/2/2016 6/12/2… 26 23 23003
## 10 Ecology … 16.7 2014 17 9 Clough et… 2/2/2016 7/17/2… 44 42 131788
## # … with 1,270 more rows, and 1 more variable: woscitations <dbl>
```
---
# Get column with rows corresponding to papers with more than 3 authors
```r
citations %>%
* dplyr::filter(stringr::str_detect(Authors, 'et al')) %>%
* dplyr::select(Authors)
```
```
## # A tibble: 1,280 × 1
## Authors
## <chr>
## 1 Morin et al
## 2 Jucker et al
## 3 Calcagno et al
## 4 Segre et al
## 5 Kaufman et al
## 6 Nasto et al
## 7 Tschirren et al
## 8 Barnechi et al
## 9 Pinto-Sanchez et al
## 10 Clough et al
## # … with 1,270 more rows
```
---
# Select rows corresponding to papers with less than 3 authors
```r
citations %>%
* dplyr::filter(!stringr::str_detect(Authors, 'et al'))
```
```
## # A tibble: 319 × 12
## journal impactfactor pubyear Volume Issue Authors colldate pubdate nbtweets `Number of user… `Twitter reach`
## <fct> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 Ecology … 16.7 2014 17 6 Neutle an… 2/15/20… 3/17/2… 8 7 10823
## 2 Ecology … 16.7 2014 17 5 Kellner a… 2/15/20… 2/20/2… 18 18 60309
## 3 Ecology … 16.7 2014 17 4 Griffin a… 2/15/20… 1/16/2… 4 4 9404
## 4 Ecology … 16.7 2014 17 3 Gremer an… 2/15/20… 1/17/2… 4 4 17927
## 5 Ecology … 16.7 2014 17 2 Cavieres 2/15/20… 10/17/… 16 15 18472
## 6 Ecology … 16.7 2014 17 2 Haegman a… 2/15/20… 12/5/2… 9 9 13211
## 7 Ecology … 16.7 2013 16 12 Kearney 2/15/20… 10/1/2… 13 13 37990
## 8 Ecology … 16.7 2013 16 9 Locey and… 2/15/20… 7/15/2… 28 24 51145
## 9 Ecology … 16.7 2013 16 8 Quintero … 2/15/20… 6/26/2… 120 120 686154
## 10 Ecology … 16.7 2013 16 3 Lesser an… 2/15/20… 12/22/… 9 9 12054
## # … with 309 more rows, and 1 more variable: woscitations <dbl>
```
---
# Get column with rows corresponding to papers with less than 3 authors
```r
citations %>%
* dplyr::filter(!stringr::str_detect(Authors, 'et al')) %>%
* dplyr::select(Authors)
```
```
## # A tibble: 319 × 1
## Authors
## <chr>
## 1 Neutle and Thorne
## 2 Kellner and Asner
## 3 Griffin and Willi
## 4 Gremer and Venable
## 5 Cavieres
## 6 Haegman and Loreau
## 7 Kearney
## 8 Locey and White
## 9 Quintero and Weins
## 10 Lesser and Jackson
## # … with 309 more rows
```
---
# Get column with rows corresponding to papers with less than 3 authors
```r
citations %>%
dplyr::filter(!stringr::str_detect(Authors, 'et al')) %>%
* dplyr::pull(Authors) %>%
head(10)
```
```
## [1] "Neutle and Thorne" "Kellner and Asner" "Griffin and Willi" "Gremer and Venable" "Cavieres"
## [6] "Haegman and Loreau" "Kearney" "Locey and White" "Quintero and Weins" "Lesser and Jackson"
```
---
# Select rows corresponding to papers with less than 3 authors in journal with IF < 5
```r
citations %>%
* dplyr::filter(!stringr::str_detect(Authors, 'et al'), impactfactor < 5)
```
```
## # A tibble: 77 × 12
## journal impactfactor pubyear Volume Issue Authors colldate pubdate nbtweets `Number of user… `Twitter reach`
## <fct> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 Molecular… 4.9 2014 14 6 Gautier 2/27/20… 5/14/2… 2 2 1015
## 2 Molecular… 4.9 2014 14 5 Gambel … 2/27/20… 3/7/20… 7 5 5302
## 3 Molecular… 4.9 2014 14 4 Kekkone… 2/27/20… 3/10/2… 4 4 2284
## 4 Molecular… 4.9 2014 14 3 Bhattac… 2/27/20… 12/8/2… 0 0 0
## 5 Molecular… 4.9 2014 14 1 Christi… 2/28/20… 10/25/… 0 0 0
## 6 Molecular… 4.9 2013 13 4 Villard… 2/28/20… 5/2/20… 0 0 0
## 7 Molecular… 4.9 2013 13 4 Wang 2/28/20… 4/25/2… 0 0 0
## 8 Molecular… 4.9 2012 12 1 Joly 2/28/20… 9/7/20… 3 3 1861
## 9 Animal Co… 3.21 2014 17 6 Plavsic 2/9/2016 4/17/2… 9 9 12822
## 10 Animal Co… 3.21 2014 17 Suppl… Knox an… 2/11/20… 11/13/… 1 1 206
## # … with 67 more rows, and 1 more variable: woscitations <dbl>
```
---
# Convert words to lowercase
```r
citations %>%
* dplyr::mutate(authors_lowercase = stringr::str_to_lower(Authors)) %>%
dplyr::select(authors_lowercase)
```
```
## # A tibble: 1,599 × 1
## authors_lowercase
## <chr>
## 1 morin et al
## 2 jucker et al
## 3 calcagno et al
## 4 segre et al
## 5 kaufman et al
## 6 nasto et al
## 7 tschirren et al
## 8 barnechi et al
## 9 pinto-sanchez et al
## 10 clough et al
## # … with 1,589 more rows
```
---
# Remove all spaces in journal names
```r
citations %>%
* dplyr::mutate(journal = stringr::str_remove_all(journal, " ")) %>%
dplyr::select(journal) %>%
unique() %>%
head(5)
```
```
## # A tibble: 5 × 1
## journal
## <chr>
## 1 EcologyLetters
## 2 GlobalChangeBiology
## 3 GlobalEcologyandBiogeography
## 4 MolecularEcologyResources
## 5 DiversityandDistributions
```
---
# Explore 📦 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)
```
```
## # A tibble: 20 × 2
## journal n
## <fct> <int>
## 1 New Phytologist 144
## 2 Ecology 108
## 3 Evolution 108
## 4 Global Change Biology 108
## 5 Global Ecology and Biogeography 108
## 6 Journal of Biogeography 108
## 7 Ecology Letters 106
## 8 Diversity and Distributions 105
## 9 Animal Conservation 102
## 10 Methods in Ecology and Evolution 90
## 11 Evolutionary Applications 74
## 12 Functional Ecology 54
## 13 Journal of Animal Ecology 54
## 14 Journal of Applied Ecology 54
## 15 Limnology and Oceanography 54
## 16 Molecular Ecology Resources 54
## 17 Conservation Letters 53
## 18 Ecological Applications 48
## 19 Fish and Fisheries 36
## 20 Mammal Review 31
```
---
# Count
```r
citations %>%
dplyr::count(journal, pubyear) %>%
head()
```
```
## # A tibble: 6 × 3
## journal pubyear n
## <fct> <dbl> <int>
## 1 Animal Conservation 2012 18
## 2 Animal Conservation 2013 18
## 3 Animal Conservation 2014 66
## 4 Conservation Letters 2012 17
## 5 Conservation Letters 2013 18
## 6 Conservation Letters 2014 18
```
---
# Count sum of tweets per journal
```r
citations %>%
dplyr::count(journal, wt = nbtweets, sort = TRUE)
```
```
## # A tibble: 20 × 2
## journal n
## <fct> <dbl>
## 1 Ecology Letters 1538
## 2 Animal Conservation 1268
## 3 Journal of Applied Ecology 1012
## 4 Methods in Ecology and Evolution 699
## 5 Global Change Biology 613
## 6 Conservation Letters 542
## 7 New Phytologist 509
## 8 Global Ecology and Biogeography 379
## 9 Ecology 335
## 10 Evolution 335
## 11 Journal of Animal Ecology 323
## 12 Fish and Fisheries 261
## 13 Evolutionary Applications 238
## 14 Journal of Biogeography 209
## 15 Diversity and Distributions 200
## 16 Mammal Review 166
## 17 Functional Ecology 155
## 18 Molecular Ecology Resources 139
## 19 Ecological Applications 125
## 20 Limnology and Oceanography 0
```
---
# Group by variable to calculate stats
```r
citations %>%
* dplyr::group_by(journal) %>%
* dplyr::summarise(avg_tweets = mean(nbtweets)) %>%
head(10)
```
```
## # A tibble: 10 × 2
## journal avg_tweets
## <fct> <dbl>
## 1 Animal Conservation 12.4
## 2 Conservation Letters 10.2
## 3 Diversity and Distributions 1.90
## 4 Ecological Applications 2.60
## 5 Ecology 3.10
## 6 Ecology Letters 14.5
## 7 Evolution 3.10
## 8 Evolutionary Applications 3.22
## 9 Fish and Fisheries 7.25
## 10 Functional Ecology 2.87
```
---
# Order stuff
```r
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)
```
```
## # A tibble: 10 × 2
## journal avg_tweets
## <fct> <dbl>
## 1 Journal of Applied Ecology 18.7
## 2 Ecology Letters 14.5
## 3 Animal Conservation 12.4
## 4 Conservation Letters 10.2
## 5 Methods in Ecology and Evolution 7.77
## 6 Fish and Fisheries 7.25
## 7 Journal of Animal Ecology 5.98
## 8 Global Change Biology 5.68
## 9 Mammal Review 5.35
## 10 New Phytologist 3.53
```
---
# What if we want to work on several columns?
<img src="img/dplyr_across.png" width="85%" style="display: block; margin: auto;" />
---
# Compute mean across all numeric columns for each journal
```r
citations %>%
* dplyr::group_by(journal) %>%
* dplyr::summarize(dplyr::across(where(is.numeric), mean)) %>%
head()
```
```
## # A tibble: 6 × 8
## journal impactfactor pubyear Volume nbtweets `Number of users` `Twitter reach` woscitations
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Animal Conservation 3.21 2013. 16.5 12.4 9.71 28345. 4.43
## 2 Conservation Letters 6.4 2013. 6.02 10.2 8.85 23234. 9.30
## 3 Diversity and Distributions 5.4 2013 19 1.90 1.77 2350. 10.2
## 4 Ecological Applications 5.06 2013 23 2.60 2.5 5727. 10.7
## 5 Ecology 6.16 2013 94 3.10 2.87 6176. 11.1
## 6 Ecology Letters 16.7 2013. 16.0 14.5 14.0 44748. 20.6
```
---
## <https://github.com/courtiol/Rguides>
<img src="img/dplyr_guide_for_one_table_part2.png" width="85%" style="display: block; margin: auto;" />
---
# Tidying tibbles
<img src="img/original-dfs-tidy.png" width="70%" style="display: block; margin: auto;" />
---
## Going from **long** to **wide** format and vice-versa
<img src="img/tidyr-longer-wider.gif" width="70%" style="display: block; margin: auto;" />
---
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
<img src="img/ggplot2_logo.jpg" width="30%" style="display: block; margin: auto;" />
---
# 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)
<img src="img/ggplot2_logo.jpg" width="30%" style="display: block; margin: auto;" />
---
# Scatterplots