README.md
In prlitics/dumdum: An Easy Way to Make Dummy (Binary) Variables
dumdum 
Make dummy variables easily in R
Dummy variables (binary, 0/1 variables) are a frequent part of analyses
in the social sciences. But making them can be an arduous process in
base R, which tends rely on numerous ifelse()statements. This can be
especially trying and headache-inducing if you have a variable with
several values (e.g., race, education level, employment status,
political party identification, etc.).
dumdum cuts down on the work, gives you the ability to easily rename
the new, dummied variables, and also makes selecting reference
categories
simple. It also allows you to make dummies across many variables at
once.
There are currently a few other packages that helps users make dummy
variables such as ml, caret, tidymodels, and fastdummies. What
sets dumdum apart is that it was designed with social-scientists and
non-machine learning folk in mind. Its functions are flexible and
intuitive. It also requires no package dependencies; all that’s
necessary to have installed are the base R packages!
Installation
You can install the latest version from GitHub
with:
# install.packages("devtools")
devtools::install_github("prlitics/dumdum")
or
# install.packages("remotes")
remotes::install_github("prlitics/dumdum")
Functions
There are two functions in dumdum
dummify() which allows you to make dummy variables from a
specified dataframe and variable. (This is the main function in
dumdum)dummify_across() a wrapper for dummify() that lets users dummify
multiple variables at once.
Background in-depth look at dummify()
dummify() has 4 arguments; 2 mandatory and 2 options. The options are
set to NULL as default.
dummify(data, var, reference = NULL, dumNames = NULL)
data & var requirements
- Currently,
dummify() accepts data frame objects in data.
var accepts either the name of the column or the the column index.dummify currently accepts integer, factor, and character vectors.
reference and dumNames options.
reference allows you to decide if you want to leave out a
reference category from your values.
- If you pass
TRUE, it will leave out the first value it
encounters.
- If you pass the name of a value (say “Adelie”—a penguin
species), it will leave that value out.
- If you pass a vector of names (say
Adelie, Gentoo), it will
leave out all of the specified values.
dumNames has dummify() rename the variables for you, so you
don’t have to do it afterwards.
- The standard naming convention goes
variableName_DUM_Value. So
for a column for Adelie penguins it will be
species_DUM_Adelie. All of the Adelie penguins will have a 1
for this variable and the other penguins will have a 0.
*dumNames can accept a character list that will rename all
the columns in the order of the list (). *`dumNames` can also
accept a named list so that you don't have to worry about making
sure the names are in the right order. ()
Examples
These examples are going to use the Palmer
Penguins dataset
because there are a number of “dummy-able” variables in it. (And, also,
like, penguins!!)
library(dumdum)
penguins<-palmerpenguins::penguins
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year |
| :------ | :-------- | ---------------: | --------------: | ------------------: | ------------: | :----- | ---: |
| Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 |
| Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 |
| Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 |
| Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 |
| Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 |
| Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 |
dummify
Let’s say that you want to make a dummy variable for the penguins’ sex
because you plan to run a regression where you check to see if sex is
predictive of body mass. To make dummy variables out of species, you
could do this with dummify():
penguins<- palmerpenguins::penguins
penguins_dummied<-dummify(data = penguins, var = "sex")
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | sex_DUM_male | sex_DUM_female | sex_DUM_NA |
| :------ | :-------- | ---------------: | --------------: | ------------------: | ------------: | :----- | ---: | -------------: | ---------------: | -----------: |
| Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 | 1 | 0 | 0 |
| Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 | 0 | 1 | 0 |
| Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 | 0 | 1 | 0 |
| Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 | NA | NA | 1 |
| Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 | 0 | 1 | 0 |
| Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 | 1 | 0 | 0 |
If you wanted to set “male” as the reference category, you could do:
penguins_dummied<-dummify(data = penguins, var = "sex", reference = "male")
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | sex_DUM_female | sex_DUM_NA |
| :------ | :-------- | ---------------: | --------------: | ------------------: | ------------: | :----- | ---: | ---------------: | -----------: |
| Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 | 0 | 0 |
| Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 | 1 | 0 |
| Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 | 1 | 0 |
| Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 | NA | 1 |
| Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 | 1 | 0 |
| Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 | 0 | 0 |
The default naming convention is to make sure that the user knows what
the 1 is in reference to in that column. You can also rename the
columns.
penguins_dummied<-dummify(data = penguins, var = "sex", reference = "male", dumNames = c("f","unknown"))
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | f | unknown |
| :------ | :-------- | ---------------: | --------------: | ------------------: | ------------: | :----- | ---: | -: | ------: |
| Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 | 0 | 0 |
| Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 | 1 | 0 |
| Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 | 1 | 0 |
| Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 | NA | 1 |
| Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 | 1 | 0 |
| Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 | 0 | 0 |
If you didn’t want to worry about putting the list of column names in
the right order:
penguins_dummied<-dummify(data = penguins, var = "sex", reference = "male", dumNames = c("f"="female","unknown"=NA))
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | f | unknown |
| :------ | :-------- | ---------------: | --------------: | ------------------: | ------------: | :----- | ---: | -: | ------: |
| Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 | 0 | 0 |
| Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 | 1 | 0 |
| Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 | 1 | 0 |
| Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 | NA | 1 |
| Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 | 1 | 0 |
| Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 | 0 | 0 |
dummify_across
Let’s say that there are multiple variables that you want to dummy
across. In the case of the penguins, you might want to dummy species, as
well as the island and sex. You can do so with dummify_across().
dummify_across() is a wrapper for dummify() that allows you to pass
multiple variables at once. Like dummify(), you specify a data frame
object and you specify a set of variables (vars) that you want to be
dummified. These can either be names or column indices.
penguins_dummied<-dummify_across(data = penguins, vars = c("sex","species","island"))
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | sex_DUM_male | sex_DUM_female | sex_DUM_NA | species_DUM_Adelie | species_DUM_Gentoo | species_DUM_Chinstrap | island_DUM_Torgersen | island_DUM_Biscoe | island_DUM_Dream |
| :------ | :-------- | ---------------: | --------------: | ------------------: | ------------: | :----- | ---: | -------------: | ---------------: | -----------: | -------------------: | -------------------: | ----------------------: | ---------------------: | ------------------: | -----------------: |
| Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 | NA | NA | 1 | 1 | 0 | 0 | 1 | 0 | 0 |
| Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
You can also pass along whether or not you want dummify_across() to
leave out a reference column for the variables you selected:
penguins_dummied<-dummify_across(data = penguins, vars = c("sex","species","island"), reference = TRUE)
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | sex_DUM_female | sex_DUM_NA | species_DUM_Gentoo | species_DUM_Chinstrap | island_DUM_Biscoe | island_DUM_Dream |
| :------ | :-------- | ---------------: | --------------: | ------------------: | ------------: | :----- | ---: | ---------------: | -----------: | -------------------: | ----------------------: | ------------------: | -----------------: |
| Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 | 0 | 0 | 0 | 0 | 0 | 0 |
| Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 | 1 | 0 | 0 | 0 | 0 | 0 |
| Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 | 1 | 0 | 0 | 0 | 0 | 0 |
| Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 | NA | 1 | 0 | 0 | 0 | 0 |
| Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 | 1 | 0 | 0 | 0 | 0 | 0 |
| Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 | 0 | 0 | 0 | 0 | 0 | 0 |
Currently, dummify_across() will only leave out the first encountered
variable as a reference. Future updates to the package will allow you to
specify which variables you want to have reference categories for–as
well as the values for those references.
Playing nice with pipes
I personally am a huge fan of the
tidyverse; it’s what allowed me to get
my feet wet with R before I could truly dive into it. I know a lot of
potential dumdum users would also use the tidyverse, so it was
important to me that dumdum functions were pipe-able.
library(magrittr)
pen_df <- penguins %>%
dummify("sex")
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | sex_DUM_male | sex_DUM_female | sex_DUM_NA |
| :------ | :-------- | ---------------: | --------------: | ------------------: | ------------: | :----- | ---: | -------------: | ---------------: | -----------: |
| Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 | 1 | 0 | 0 |
| Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 | 0 | 1 | 0 |
| Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 | 0 | 1 | 0 |
| Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 | NA | NA | 1 |
| Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 | 0 | 1 | 0 |
| Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 | 1 | 0 | 0 |
pen_df <- penguins %>%
dummify_across(c("sex","island","species"))
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | sex_DUM_male | sex_DUM_female | sex_DUM_NA | island_DUM_Torgersen | island_DUM_Biscoe | island_DUM_Dream | species_DUM_Adelie | species_DUM_Gentoo | species_DUM_Chinstrap |
| :------ | :-------- | ---------------: | --------------: | ------------------: | ------------: | :----- | ---: | -------------: | ---------------: | -----------: | ---------------------: | ------------------: | -----------------: | -------------------: | -------------------: | ----------------------: |
| Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 | NA | NA | 1 | 1 | 0 | 0 | 1 | 0 | 0 |
| Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
Bugs or suggestions
If you have any bugs or suggestions, let me
know! Always happy for
constructive feedback.
Acknowledgements
Huge thanks to Sabrina Marasa, who
tested the package on the Mac version of R.
License
This function is distributed under a MIT
license.
Citation
#>
#> To cite dumdum in publications use:
#>
#> Licari, P. R. (2020). dumdum: Make dummy variables easily in R.
#> version 0.8.0. https://github.com/prlitics/dumdum/.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {dumdum: Make dummy variables easily in R},
#> author = {Peter Licari},
#> year = {2020},
#> url = {https://github.com/prlitics/dumdum},
#> note = {version 0.8.0},
#> }
References
Data & packages used in this readme.
-
Horst AM, Hill AP, Gorman KB (2020). palmerpenguins: Palmer
Archipelago (Antarctica) penguin data. R package version 0.1.0.
https://allisonhorst.github.io/palmerpenguins/. doi:
10.5281/zenodo.3960218.
-
Stefan Milton Bache and Hadley Wickham (2014). magrittr: A
Forward-Pipe Operator for R. R package version 1.5.
https://CRAN.R-project.org/package=magrittr
prlitics/dumdum documentation built on Aug. 12, 2020, 12:54 a.m.
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dumdum
Make dummy variables easily in R
Dummy variables (binary, 0/1 variables) are a frequent part of analyses
in the social sciences. But making them can be an arduous process in
base R, which tends rely on numerous ifelse()statements. This can be
especially trying and headache-inducing if you have a variable with
several values (e.g., race, education level, employment status,
political party identification, etc.).
dumdum cuts down on the work, gives you the ability to easily rename
the new, dummied variables, and also makes selecting reference
categories
simple. It also allows you to make dummies across many variables at
once.
There are currently a few other packages that helps users make dummy
variables such as ml, caret, tidymodels, and fastdummies. What
sets dumdum apart is that it was designed with social-scientists and
non-machine learning folk in mind. Its functions are flexible and
intuitive. It also requires no package dependencies; all that’s
necessary to have installed are the base R packages!
Installation
You can install the latest version from GitHub with:
# install.packages("devtools")
devtools::install_github("prlitics/dumdum")
or
# install.packages("remotes")
remotes::install_github("prlitics/dumdum")
Functions
There are two functions in dumdum
dummify()which allows you to make dummy variables from a specified dataframe and variable. (This is the main function indumdum)dummify_across()a wrapper fordummify()that lets users dummify multiple variables at once.
Background in-depth look at dummify()
dummify() has 4 arguments; 2 mandatory and 2 options. The options are
set to NULL as default.
dummify(data, var, reference = NULL, dumNames = NULL)
data & var requirements
- Currently,
dummify()accepts data frame objects indata. varaccepts either the name of the column or the the column index.dummifycurrently accepts integer, factor, and character vectors.
reference and dumNames options.
referenceallows you to decide if you want to leave out a reference category from your values.- If you pass
TRUE, it will leave out the first value it encounters. - If you pass the name of a value (say “Adelie”—a penguin species), it will leave that value out.
- If you pass a vector of names (say
Adelie, Gentoo), it will leave out all of the specified values.
- If you pass
dumNameshasdummify()rename the variables for you, so you don’t have to do it afterwards.- The standard naming convention goes
variableName_DUM_Value. So for a column for Adelie penguins it will bespecies_DUM_Adelie. All of the Adelie penguins will have a 1 for this variable and the other penguins will have a 0. *dumNamescan accept a character list that will rename all the columns in the order of the list (). *`dumNames` can also accept a named list so that you don't have to worry about making sure the names are in the right order. ()
- The standard naming convention goes
Examples
These examples are going to use the Palmer Penguins dataset because there are a number of “dummy-able” variables in it. (And, also, like, penguins!!)
library(dumdum)
penguins<-palmerpenguins::penguins
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | | :------ | :-------- | ---------------: | --------------: | ------------------: | ------------: | :----- | ---: | | Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 | | Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 | | Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 | | Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 | | Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 | | Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 |
dummify
Let’s say that you want to make a dummy variable for the penguins’ sex
because you plan to run a regression where you check to see if sex is
predictive of body mass. To make dummy variables out of species, you
could do this with dummify():
penguins<- palmerpenguins::penguins
penguins_dummied<-dummify(data = penguins, var = "sex")
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | sex_DUM_male | sex_DUM_female | sex_DUM_NA | | :------ | :-------- | ---------------: | --------------: | ------------------: | ------------: | :----- | ---: | -------------: | ---------------: | -----------: | | Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 | 1 | 0 | 0 | | Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 | 0 | 1 | 0 | | Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 | 0 | 1 | 0 | | Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 | NA | NA | 1 | | Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 | 0 | 1 | 0 | | Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 | 1 | 0 | 0 |
If you wanted to set “male” as the reference category, you could do:
penguins_dummied<-dummify(data = penguins, var = "sex", reference = "male")
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | sex_DUM_female | sex_DUM_NA | | :------ | :-------- | ---------------: | --------------: | ------------------: | ------------: | :----- | ---: | ---------------: | -----------: | | Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 | 0 | 0 | | Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 | 1 | 0 | | Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 | 1 | 0 | | Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 | NA | 1 | | Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 | 1 | 0 | | Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 | 0 | 0 |
The default naming convention is to make sure that the user knows what
the 1 is in reference to in that column. You can also rename the
columns.
penguins_dummied<-dummify(data = penguins, var = "sex", reference = "male", dumNames = c("f","unknown"))
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | f | unknown | | :------ | :-------- | ---------------: | --------------: | ------------------: | ------------: | :----- | ---: | -: | ------: | | Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 | 0 | 0 | | Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 | 1 | 0 | | Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 | 1 | 0 | | Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 | NA | 1 | | Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 | 1 | 0 | | Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 | 0 | 0 |
If you didn’t want to worry about putting the list of column names in the right order:
penguins_dummied<-dummify(data = penguins, var = "sex", reference = "male", dumNames = c("f"="female","unknown"=NA))
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | f | unknown | | :------ | :-------- | ---------------: | --------------: | ------------------: | ------------: | :----- | ---: | -: | ------: | | Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 | 0 | 0 | | Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 | 1 | 0 | | Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 | 1 | 0 | | Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 | NA | 1 | | Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 | 1 | 0 | | Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 | 0 | 0 |
dummify_across
Let’s say that there are multiple variables that you want to dummy
across. In the case of the penguins, you might want to dummy species, as
well as the island and sex. You can do so with dummify_across().
dummify_across() is a wrapper for dummify() that allows you to pass
multiple variables at once. Like dummify(), you specify a data frame
object and you specify a set of variables (vars) that you want to be
dummified. These can either be names or column indices.
penguins_dummied<-dummify_across(data = penguins, vars = c("sex","species","island"))
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | sex_DUM_male | sex_DUM_female | sex_DUM_NA | species_DUM_Adelie | species_DUM_Gentoo | species_DUM_Chinstrap | island_DUM_Torgersen | island_DUM_Biscoe | island_DUM_Dream | | :------ | :-------- | ---------------: | --------------: | ------------------: | ------------: | :----- | ---: | -------------: | ---------------: | -----------: | -------------------: | -------------------: | ----------------------: | ---------------------: | ------------------: | -----------------: | | Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | | Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | | Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | | Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 | NA | NA | 1 | 1 | 0 | 0 | 1 | 0 | 0 | | Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | | Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
You can also pass along whether or not you want dummify_across() to
leave out a reference column for the variables you selected:
penguins_dummied<-dummify_across(data = penguins, vars = c("sex","species","island"), reference = TRUE)
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | sex_DUM_female | sex_DUM_NA | species_DUM_Gentoo | species_DUM_Chinstrap | island_DUM_Biscoe | island_DUM_Dream | | :------ | :-------- | ---------------: | --------------: | ------------------: | ------------: | :----- | ---: | ---------------: | -----------: | -------------------: | ----------------------: | ------------------: | -----------------: | | Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 | 0 | 0 | 0 | 0 | 0 | 0 | | Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 | 1 | 0 | 0 | 0 | 0 | 0 | | Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 | 1 | 0 | 0 | 0 | 0 | 0 | | Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 | NA | 1 | 0 | 0 | 0 | 0 | | Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 | 1 | 0 | 0 | 0 | 0 | 0 | | Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 | 0 | 0 | 0 | 0 | 0 | 0 |
Currently, dummify_across() will only leave out the first encountered
variable as a reference. Future updates to the package will allow you to
specify which variables you want to have reference categories for–as
well as the values for those references.
Playing nice with pipes
I personally am a huge fan of the
tidyverse; it’s what allowed me to get
my feet wet with R before I could truly dive into it. I know a lot of
potential dumdum users would also use the tidyverse, so it was
important to me that dumdum functions were pipe-able.
library(magrittr)
pen_df <- penguins %>%
dummify("sex")
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | sex_DUM_male | sex_DUM_female | sex_DUM_NA | | :------ | :-------- | ---------------: | --------------: | ------------------: | ------------: | :----- | ---: | -------------: | ---------------: | -----------: | | Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 | 1 | 0 | 0 | | Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 | 0 | 1 | 0 | | Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 | 0 | 1 | 0 | | Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 | NA | NA | 1 | | Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 | 0 | 1 | 0 | | Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 | 1 | 0 | 0 |
pen_df <- penguins %>%
dummify_across(c("sex","island","species"))
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex | year | sex_DUM_male | sex_DUM_female | sex_DUM_NA | island_DUM_Torgersen | island_DUM_Biscoe | island_DUM_Dream | species_DUM_Adelie | species_DUM_Gentoo | species_DUM_Chinstrap | | :------ | :-------- | ---------------: | --------------: | ------------------: | ------------: | :----- | ---: | -------------: | ---------------: | -----------: | ---------------------: | ------------------: | -----------------: | -------------------: | -------------------: | ----------------------: | | Adelie | Torgersen | 39.1 | 18.7 | 181 | 3750 | male | 2007 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | | Adelie | Torgersen | 39.5 | 17.4 | 186 | 3800 | female | 2007 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | | Adelie | Torgersen | 40.3 | 18.0 | 195 | 3250 | female | 2007 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | | Adelie | Torgersen | NA | NA | NA | NA | NA | 2007 | NA | NA | 1 | 1 | 0 | 0 | 1 | 0 | 0 | | Adelie | Torgersen | 36.7 | 19.3 | 193 | 3450 | female | 2007 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | | Adelie | Torgersen | 39.3 | 20.6 | 190 | 3650 | male | 2007 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
Bugs or suggestions
If you have any bugs or suggestions, let me know! Always happy for constructive feedback.
Acknowledgements
Huge thanks to Sabrina Marasa, who tested the package on the Mac version of R.
License
This function is distributed under a MIT license.
Citation
#>
#> To cite dumdum in publications use:
#>
#> Licari, P. R. (2020). dumdum: Make dummy variables easily in R.
#> version 0.8.0. https://github.com/prlitics/dumdum/.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {dumdum: Make dummy variables easily in R},
#> author = {Peter Licari},
#> year = {2020},
#> url = {https://github.com/prlitics/dumdum},
#> note = {version 0.8.0},
#> }
References
Data & packages used in this readme.
-
Horst AM, Hill AP, Gorman KB (2020). palmerpenguins: Palmer Archipelago (Antarctica) penguin data. R package version 0.1.0. https://allisonhorst.github.io/palmerpenguins/. doi: 10.5281/zenodo.3960218.
-
Stefan Milton Bache and Hadley Wickham (2014). magrittr: A Forward-Pipe Operator for R. R package version 1.5. https://CRAN.R-project.org/package=magrittr
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