[R Course] Data Wrangling with R

R Courses Data Wrangling

Learn how to rename, create and delete variables. Also learn how to convert text case, replace or remove specifics characters. Finally, learn how to handle missing values.

Thierry Warin https://warin.ca/aboutme.html (HEC Montréal and CIRANO (Canada))https://www.hec.ca/en/profs/thierry.warin.html
05-24-2019

Syntax

Tidy Data

R follows a set of conventions that makes one layout of tabular data much easier to work with than others. Your data will be easier to work with in R if it follows three rules:

Loading packages

To have access to the functions of specifics packages, you have two ways of doing it:

The first option allow you to load your package by calling the function library() and then use functions from it as you want without having to reload the package.

library(package)

This second option allow you to have access to the wanted package and function. But you can’t use another function from the same package without using this same syntax or by using the previous one.

package::function()

Loading data

Now, let’s load the data on which we will do some data wrangling.

dataFull <- gsheet::gsheet2tbl("https://docs.google.com/spreadsheets/d/1uLaXke-KPN28-ESPPoihk8TiXVWp5xuNGHW7w7yqLCc/edit#gid=329712902")
rank company country industrial.sector RD.usd sales.usd year
1 VOLKSWAGEN Germany Automobiles Parts 14712470286 NA 2015
2 DAIMLER Germany Automobiles Parts 6335781792 145635513797 2015
3 ROBERT BOSCH Germany Automobiles Parts 5653984389 54892540624 2015
4 SANOFI France Pharmaceuticals Biotechnology 5396067608 37868911705 2015
5 BMW Germany Automobiles Parts 5120208790 90159856973 2015
6 SIEMENS Germany Electronic Electrical Equipment 4908268479 80649456021 2015

Tbl class

To convert data to tbl class (tbl’s are easier to examine than data frames):

dplyr::tbl_df(dataFull)

Dense summary

To obtain an information dense summary of tbl data:

dplyr::glimpse(dataFull)

Rows: 24,334
Columns: 7
$ rank              <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13…
$ company           <chr> "VOLKSWAGEN", "DAIMLER", "ROBERT BOSCH", …
$ country           <chr> "Germany", "Germany", "Germany", "France"…
$ industrial.sector <chr> "Automobiles Parts", "Automobiles Parts",…
$ RD.usd            <dbl> 14712470286, 6335781792, 5653984389, 5396…
$ sales.usd         <dbl> NA, 145635513797, 54892540624, 3786891170…
$ year              <dbl> 2015, 2015, 2015, 2015, 2015, 2015, 2015,…

Renaming Variables

This section will present you how to rename columns with different functions.

Names

names(dataFull)[2] <- "Company"

names(dataFull)[names(dataFull) == "country"] <- "Country"

Colnames

colnames(dataFull)[colnames(dataFull)=="industrial.sector"] <- "Industry"

Rename

dataFull <- dplyr::rename(dataFull, Year = year)
rank Company Country Industry RD.usd sales.usd Year
1 VOLKSWAGEN Germany Automobiles Parts 14712470286 NA 2015
2 DAIMLER Germany Automobiles Parts 6335781792 145635513797 2015
3 ROBERT BOSCH Germany Automobiles Parts 5653984389 54892540624 2015
4 SANOFI France Pharmaceuticals Biotechnology 5396067608 37868911705 2015
5 BMW Germany Automobiles Parts 5120208790 90159856973 2015
6 SIEMENS Germany Electronic Electrical Equipment 4908268479 80649456021 2015

Creating Variables

To add a new column to your dataset, enter the name of your new column after the $ symbol and assign a value to it. The new column will have the same value with this technique and be placed at the end of your dataframe!

dataFull$newColumn <- 42
rank Company Country Industry RD.usd sales.usd Year newColumn
1 VOLKSWAGEN Germany Automobiles Parts 14712470286 NA 2015 42
2 DAIMLER Germany Automobiles Parts 6335781792 145635513797 2015 42
3 ROBERT BOSCH Germany Automobiles Parts 5653984389 54892540624 2015 42
4 SANOFI France Pharmaceuticals Biotechnology 5396067608 37868911705 2015 42
5 BMW Germany Automobiles Parts 5120208790 90159856973 2015 42
6 SIEMENS Germany Electronic Electrical Equipment 4908268479 80649456021 2015 42

A new column called ‘newColumn’ will be created containing the value 42.

Deleting Variables

To delete a column it’s simple. Assign the value “NULL” to the column and it’s done!

dataFull$newColumn <- NULL

Note: The column called ‘newColumn’ is deleted.

You can delete multiple columns at once by using this code :

dataFull[,5:7] <- NULL

Note: The columns 5 to 7 will be removed. We will actually keep these columns for the rest of the course.

Converting Text Case

Upper case

Put in upper case:

dataFull$Industry <- stringr::str_to_upper(dataFull$Industry)
rank Company Country Industry RD.usd sales.usd Year
1 VOLKSWAGEN Germany AUTOMOBILES PARTS 14712470286 NA 2015
2 DAIMLER Germany AUTOMOBILES PARTS 6335781792 145635513797 2015
3 ROBERT BOSCH Germany AUTOMOBILES PARTS 5653984389 54892540624 2015
4 SANOFI France PHARMACEUTICALS BIOTECHNOLOGY 5396067608 37868911705 2015
5 BMW Germany AUTOMOBILES PARTS 5120208790 90159856973 2015
6 SIEMENS Germany ELECTRONIC ELECTRICAL EQUIPMENT 4908268479 80649456021 2015

Lower case

Put in lowercase:

dataFull$Company <- stringr::str_to_lower(dataFull$Company)
rank Company Country Industry RD.usd sales.usd Year
1 volkswagen Germany AUTOMOBILES PARTS 14712470286 NA 2015
2 daimler Germany AUTOMOBILES PARTS 6335781792 145635513797 2015
3 robert bosch Germany AUTOMOBILES PARTS 5653984389 54892540624 2015
4 sanofi France PHARMACEUTICALS BIOTECHNOLOGY 5396067608 37868911705 2015
5 bmw Germany AUTOMOBILES PARTS 5120208790 90159856973 2015
6 siemens Germany ELECTRONIC ELECTRICAL EQUIPMENT 4908268479 80649456021 2015

Title case

To put the first lette of “principal” words in capital and common words like “for” in lowercase:

dataFull$Company <- stringr::str_to_title(dataFull$Company)
rank Company Country Industry RD.usd sales.usd Year
1 Volkswagen Germany AUTOMOBILES PARTS 14712470286 NA 2015
2 Daimler Germany AUTOMOBILES PARTS 6335781792 145635513797 2015
3 Robert Bosch Germany AUTOMOBILES PARTS 5653984389 54892540624 2015
4 Sanofi France PHARMACEUTICALS BIOTECHNOLOGY 5396067608 37868911705 2015
5 Bmw Germany AUTOMOBILES PARTS 5120208790 90159856973 2015
6 Siemens Germany ELECTRONIC ELECTRICAL EQUIPMENT 4908268479 80649456021 2015

The function is intended for English text only.

Sentence case

Only put the first letter in capital:

dataFull$Country <- stringr::str_to_sentence(dataFull$Country)
rank Company Country Industry RD.usd sales.usd Year
1 Volkswagen Germany AUTOMOBILES PARTS 14712470286 NA 2015
2 Daimler Germany AUTOMOBILES PARTS 6335781792 145635513797 2015
3 Robert Bosch Germany AUTOMOBILES PARTS 5653984389 54892540624 2015
4 Sanofi France PHARMACEUTICALS BIOTECHNOLOGY 5396067608 37868911705 2015
5 Bmw Germany AUTOMOBILES PARTS 5120208790 90159856973 2015
6 Siemens Germany ELECTRONIC ELECTRICAL EQUIPMENT 4908268479 80649456021 2015

Replace

Specifics

To replace all instances:

dataFull$Country <- gsub("UK", "United Kingdom", dataFull$Country)
rank Company Country Industry RD.usd sales.usd Year
1 Volkswagen Germany AUTOMOBILES PARTS 14712470286 NA 2015
2 Daimler Germany AUTOMOBILES PARTS 6335781792 145635513797 2015
3 Robert Bosch Germany AUTOMOBILES PARTS 5653984389 54892540624 2015
4 Sanofi France PHARMACEUTICALS BIOTECHNOLOGY 5396067608 37868911705 2015
5 Bmw Germany AUTOMOBILES PARTS 5120208790 90159856973 2015
6 Siemens Germany ELECTRONIC ELECTRICAL EQUIPMENT 4908268479 80649456021 2015

Numbers

dataFull$code <- "Abc12d345efg6"
rank Company Country Industry RD.usd sales.usd Year code
1 Volkswagen Germany AUTOMOBILES PARTS 14712470286 NA 2015 Abc12d345efg6
2 Daimler Germany AUTOMOBILES PARTS 6335781792 145635513797 2015 Abc12d345efg6
3 Robert Bosch Germany AUTOMOBILES PARTS 5653984389 54892540624 2015 Abc12d345efg6
4 Sanofi France PHARMACEUTICALS BIOTECHNOLOGY 5396067608 37868911705 2015 Abc12d345efg6
5 Bmw Germany AUTOMOBILES PARTS 5120208790 90159856973 2015 Abc12d345efg6
6 Siemens Germany ELECTRONIC ELECTRICAL EQUIPMENT 4908268479 80649456021 2015 Abc12d345efg6

To replace all numbers:

dataFull$code <- gsub("[0-9]*", "", dataFull$code)
rank Company Country Industry RD.usd sales.usd Year code
1 Volkswagen Germany AUTOMOBILES PARTS 14712470286 NA 2015 Abcdefg
2 Daimler Germany AUTOMOBILES PARTS 6335781792 145635513797 2015 Abcdefg
3 Robert Bosch Germany AUTOMOBILES PARTS 5653984389 54892540624 2015 Abcdefg
4 Sanofi France PHARMACEUTICALS BIOTECHNOLOGY 5396067608 37868911705 2015 Abcdefg
5 Bmw Germany AUTOMOBILES PARTS 5120208790 90159856973 2015 Abcdefg
6 Siemens Germany ELECTRONIC ELECTRICAL EQUIPMENT 4908268479 80649456021 2015 Abcdefg

Letters

dataFull$code2 <- "Abc12D345eF6g"
rank Company Country Industry RD.usd sales.usd Year code code2
1 Volkswagen Germany AUTOMOBILES PARTS 14712470286 NA 2015 Abcdefg Abc12D345eF6g
2 Daimler Germany AUTOMOBILES PARTS 6335781792 145635513797 2015 Abcdefg Abc12D345eF6g
3 Robert Bosch Germany AUTOMOBILES PARTS 5653984389 54892540624 2015 Abcdefg Abc12D345eF6g
4 Sanofi France PHARMACEUTICALS BIOTECHNOLOGY 5396067608 37868911705 2015 Abcdefg Abc12D345eF6g
5 Bmw Germany AUTOMOBILES PARTS 5120208790 90159856973 2015 Abcdefg Abc12D345eF6g
6 Siemens Germany ELECTRONIC ELECTRICAL EQUIPMENT 4908268479 80649456021 2015 Abcdefg Abc12D345eF6g

To replace all letters in lowercase:

dataFull$code2 <- gsub("[a-z]*", "", dataFull$code2)
rank Company Country Industry RD.usd sales.usd Year code code2
1 Volkswagen Germany AUTOMOBILES PARTS 14712470286 NA 2015 Abcdefg A12D345F6
2 Daimler Germany AUTOMOBILES PARTS 6335781792 145635513797 2015 Abcdefg A12D345F6
3 Robert Bosch Germany AUTOMOBILES PARTS 5653984389 54892540624 2015 Abcdefg A12D345F6
4 Sanofi France PHARMACEUTICALS BIOTECHNOLOGY 5396067608 37868911705 2015 Abcdefg A12D345F6
5 Bmw Germany AUTOMOBILES PARTS 5120208790 90159856973 2015 Abcdefg A12D345F6
6 Siemens Germany ELECTRONIC ELECTRICAL EQUIPMENT 4908268479 80649456021 2015 Abcdefg A12D345F6

To replace all letters in capital:

dataFull$code2 <- gsub("[A-Z]*", "", dataFull$code2)
rank Company Country Industry RD.usd sales.usd Year code code2
1 Volkswagen Germany AUTOMOBILES PARTS 14712470286 NA 2015 Abcdefg 123456
2 Daimler Germany AUTOMOBILES PARTS 6335781792 145635513797 2015 Abcdefg 123456
3 Robert Bosch Germany AUTOMOBILES PARTS 5653984389 54892540624 2015 Abcdefg 123456
4 Sanofi France PHARMACEUTICALS BIOTECHNOLOGY 5396067608 37868911705 2015 Abcdefg 123456
5 Bmw Germany AUTOMOBILES PARTS 5120208790 90159856973 2015 Abcdefg 123456
6 Siemens Germany ELECTRONIC ELECTRICAL EQUIPMENT 4908268479 80649456021 2015 Abcdefg 123456

Character

dataFull$CompanyWithoutDash <- gsub("\\-"," ", dataFull$Company)
rank Company Country Industry RD.usd sales.usd Year code code2 CompanyWithoutDash
1 Volkswagen Germany AUTOMOBILES PARTS 14712470286 NA 2015 Abcdefg 123456 Volkswagen
2 Daimler Germany AUTOMOBILES PARTS 6335781792 145635513797 2015 Abcdefg 123456 Daimler
3 Robert Bosch Germany AUTOMOBILES PARTS 5653984389 54892540624 2015 Abcdefg 123456 Robert Bosch
4 Sanofi France PHARMACEUTICALS BIOTECHNOLOGY 5396067608 37868911705 2015 Abcdefg 123456 Sanofi
5 Bmw Germany AUTOMOBILES PARTS 5120208790 90159856973 2015 Abcdefg 123456 Bmw
6 Siemens Germany ELECTRONIC ELECTRICAL EQUIPMENT 4908268479 80649456021 2015 Abcdefg 123456 Siemens
7 Astrazeneca Uk PHARMACEUTICALS BIOTECHNOLOGY 4669863663 24102098903 2015 Abcdefg 123456 Astrazeneca
8 Glaxosmithkline Uk PHARMACEUTICALS BIOTECHNOLOGY 4487751988 33165406723 2015 Abcdefg 123456 Glaxosmithkline
9 Ericsson Sweden TECHNOLOGY HARDWARE EQUIPMENT 4324815865 27217621479 2015 Abcdefg 123456 Ericsson
10 Bayer Germany PHARMACEUTICALS BIOTECHNOLOGY 4136760891 47365856130 2015 Abcdefg 123456 Bayer
11 Fiat Chrysler The netherlands AUTOMOBILES PARTS 4109847835 107753145565 2015 Abcdefg 123456 Fiat Chrysler
12 Airbus The netherlands AEROSPACE DEFENCE 4054900347 68082180525 2015 Abcdefg 123456 Airbus
13 Nokia Finland TECHNOLOGY HARDWARE EQUIPMENT 3047903524 17033721315 2015 Abcdefg 123456 Nokia
14 Boehringer Ingelheim Germany PHARMACEUTICALS BIOTECHNOLOGY 2976135377 14933381616 2015 Abcdefg 123456 Boehringer Ingelheim
15 Sap Germany SOFTWARE COMPUTER SERVICES 2587017450 19691385536 2015 Abcdefg 123456 Sap

Note: It removes a dash character

dataFull$CompanyFirstName <- gsub("\\-.*"," ", dataFull$Company)
rank Company Country Industry RD.usd sales.usd Year code code2 CompanyWithoutDash CompanyFirstName
1 Volkswagen Germany AUTOMOBILES PARTS 14712470286 NA 2015 Abcdefg 123456 Volkswagen Volkswagen
2 Daimler Germany AUTOMOBILES PARTS 6335781792 145635513797 2015 Abcdefg 123456 Daimler Daimler
3 Robert Bosch Germany AUTOMOBILES PARTS 5653984389 54892540624 2015 Abcdefg 123456 Robert Bosch Robert Bosch
4 Sanofi France PHARMACEUTICALS BIOTECHNOLOGY 5396067608 37868911705 2015 Abcdefg 123456 Sanofi Sanofi
5 Bmw Germany AUTOMOBILES PARTS 5120208790 90159856973 2015 Abcdefg 123456 Bmw Bmw
6 Siemens Germany ELECTRONIC ELECTRICAL EQUIPMENT 4908268479 80649456021 2015 Abcdefg 123456 Siemens Siemens
7 Astrazeneca Uk PHARMACEUTICALS BIOTECHNOLOGY 4669863663 24102098903 2015 Abcdefg 123456 Astrazeneca Astrazeneca
8 Glaxosmithkline Uk PHARMACEUTICALS BIOTECHNOLOGY 4487751988 33165406723 2015 Abcdefg 123456 Glaxosmithkline Glaxosmithkline
9 Ericsson Sweden TECHNOLOGY HARDWARE EQUIPMENT 4324815865 27217621479 2015 Abcdefg 123456 Ericsson Ericsson
10 Bayer Germany PHARMACEUTICALS BIOTECHNOLOGY 4136760891 47365856130 2015 Abcdefg 123456 Bayer Bayer
11 Fiat Chrysler The netherlands AUTOMOBILES PARTS 4109847835 107753145565 2015 Abcdefg 123456 Fiat Chrysler Fiat Chrysler
12 Airbus The netherlands AEROSPACE DEFENCE 4054900347 68082180525 2015 Abcdefg 123456 Airbus Airbus
13 Nokia Finland TECHNOLOGY HARDWARE EQUIPMENT 3047903524 17033721315 2015 Abcdefg 123456 Nokia Nokia
14 Boehringer Ingelheim Germany PHARMACEUTICALS BIOTECHNOLOGY 2976135377 14933381616 2015 Abcdefg 123456 Boehringer Ingelheim Boehringer Ingelheim
15 Sap Germany SOFTWARE COMPUTER SERVICES 2587017450 19691385536 2015 Abcdefg 123456 Sap Sap

Note: It removes everything after a dash character

dataFull$CompanyLastName <- gsub(".*\\-","", dataFull$Company)
rank Company Country Industry RD.usd sales.usd Year code code2 CompanyWithoutDash CompanyFirstName CompanyLastName
1 Volkswagen Germany AUTOMOBILES PARTS 14712470286 NA 2015 Abcdefg 123456 Volkswagen Volkswagen Volkswagen
2 Daimler Germany AUTOMOBILES PARTS 6335781792 145635513797 2015 Abcdefg 123456 Daimler Daimler Daimler
3 Robert Bosch Germany AUTOMOBILES PARTS 5653984389 54892540624 2015 Abcdefg 123456 Robert Bosch Robert Bosch Robert Bosch
4 Sanofi France PHARMACEUTICALS BIOTECHNOLOGY 5396067608 37868911705 2015 Abcdefg 123456 Sanofi Sanofi Sanofi
5 Bmw Germany AUTOMOBILES PARTS 5120208790 90159856973 2015 Abcdefg 123456 Bmw Bmw Bmw
6 Siemens Germany ELECTRONIC ELECTRICAL EQUIPMENT 4908268479 80649456021 2015 Abcdefg 123456 Siemens Siemens Siemens
7 Astrazeneca Uk PHARMACEUTICALS BIOTECHNOLOGY 4669863663 24102098903 2015 Abcdefg 123456 Astrazeneca Astrazeneca Astrazeneca
8 Glaxosmithkline Uk PHARMACEUTICALS BIOTECHNOLOGY 4487751988 33165406723 2015 Abcdefg 123456 Glaxosmithkline Glaxosmithkline Glaxosmithkline
9 Ericsson Sweden TECHNOLOGY HARDWARE EQUIPMENT 4324815865 27217621479 2015 Abcdefg 123456 Ericsson Ericsson Ericsson
10 Bayer Germany PHARMACEUTICALS BIOTECHNOLOGY 4136760891 47365856130 2015 Abcdefg 123456 Bayer Bayer Bayer
11 Fiat Chrysler The netherlands AUTOMOBILES PARTS 4109847835 107753145565 2015 Abcdefg 123456 Fiat Chrysler Fiat Chrysler Fiat Chrysler
12 Airbus The netherlands AEROSPACE DEFENCE 4054900347 68082180525 2015 Abcdefg 123456 Airbus Airbus Airbus
13 Nokia Finland TECHNOLOGY HARDWARE EQUIPMENT 3047903524 17033721315 2015 Abcdefg 123456 Nokia Nokia Nokia
14 Boehringer Ingelheim Germany PHARMACEUTICALS BIOTECHNOLOGY 2976135377 14933381616 2015 Abcdefg 123456 Boehringer Ingelheim Boehringer Ingelheim Boehringer Ingelheim
15 Sap Germany SOFTWARE COMPUTER SERVICES 2587017450 19691385536 2015 Abcdefg 123456 Sap Sap Sap

Note: It removes everything after a dash character

Remove

dataFull$Country <- substr(dataFull$Country, 1, 2)
rank Company Country Industry RD.usd sales.usd Year code code2 CompanyWithoutDash CompanyFirstName CompanyLastName
1 Volkswagen Ge AUTOMOBILES PARTS 14712470286 NA 2015 Abcdefg 123456 Volkswagen Volkswagen Volkswagen
2 Daimler Ge AUTOMOBILES PARTS 6335781792 145635513797 2015 Abcdefg 123456 Daimler Daimler Daimler
3 Robert Bosch Ge AUTOMOBILES PARTS 5653984389 54892540624 2015 Abcdefg 123456 Robert Bosch Robert Bosch Robert Bosch
4 Sanofi Fr PHARMACEUTICALS BIOTECHNOLOGY 5396067608 37868911705 2015 Abcdefg 123456 Sanofi Sanofi Sanofi
5 Bmw Ge AUTOMOBILES PARTS 5120208790 90159856973 2015 Abcdefg 123456 Bmw Bmw Bmw
6 Siemens Ge ELECTRONIC ELECTRICAL EQUIPMENT 4908268479 80649456021 2015 Abcdefg 123456 Siemens Siemens Siemens

Note: It keeps the first two characters.

Handling Missing Values

Drop

To drop rows containing NA’s in columns:

dataDrop <- tidyr::drop_na(dataFull)
rank Company Country Industry RD.usd sales.usd Year code code2 CompanyWithoutDash CompanyFirstName CompanyLastName
2 Daimler Ge AUTOMOBILES PARTS 6335781792 145635513797 2015 Abcdefg 123456 Daimler Daimler Daimler
3 Robert Bosch Ge AUTOMOBILES PARTS 5653984389 54892540624 2015 Abcdefg 123456 Robert Bosch Robert Bosch Robert Bosch
4 Sanofi Fr PHARMACEUTICALS BIOTECHNOLOGY 5396067608 37868911705 2015 Abcdefg 123456 Sanofi Sanofi Sanofi
5 Bmw Ge AUTOMOBILES PARTS 5120208790 90159856973 2015 Abcdefg 123456 Bmw Bmw Bmw
6 Siemens Ge ELECTRONIC ELECTRICAL EQUIPMENT 4908268479 80649456021 2015 Abcdefg 123456 Siemens Siemens Siemens
7 Astrazeneca Uk PHARMACEUTICALS BIOTECHNOLOGY 4669863663 24102098903 2015 Abcdefg 123456 Astrazeneca Astrazeneca Astrazeneca

Replace

To replace NA’s by column with a chosen value:

dataReplace <- tidyr::replace_na(dataFull, list(sales.usd=0))
rank Company Country Industry RD.usd sales.usd Year code code2 CompanyWithoutDash CompanyFirstName CompanyLastName
1 Volkswagen Ge AUTOMOBILES PARTS 14712470286 0 2015 Abcdefg 123456 Volkswagen Volkswagen Volkswagen
2 Daimler Ge AUTOMOBILES PARTS 6335781792 145635513797 2015 Abcdefg 123456 Daimler Daimler Daimler
3 Robert Bosch Ge AUTOMOBILES PARTS 5653984389 54892540624 2015 Abcdefg 123456 Robert Bosch Robert Bosch Robert Bosch
4 Sanofi Fr PHARMACEUTICALS BIOTECHNOLOGY 5396067608 37868911705 2015 Abcdefg 123456 Sanofi Sanofi Sanofi
5 Bmw Ge AUTOMOBILES PARTS 5120208790 90159856973 2015 Abcdefg 123456 Bmw Bmw Bmw
6 Siemens Ge ELECTRONIC ELECTRICAL EQUIPMENT 4908268479 80649456021 2015 Abcdefg 123456 Siemens Siemens Siemens

NA’s in the column “sales.usd” will be filled by the value 0.


Citation

For attribution, please cite this work as

Warin (2019, May 24). Thierry Warin, PhD: [R Course] Data Wrangling with R. Retrieved from https://warin.ca/posts/rcourse-datawranglingwithr/

BibTeX citation

@misc{warin2019[r,
  author = {Warin, Thierry},
  title = {Thierry Warin, PhD: [R Course] Data Wrangling with R},
  url = {https://warin.ca/posts/rcourse-datawranglingwithr/},
  year = {2019}
}