The goal of {fakir} is to provide fake datasets that can be used to teach R.
The full documentation is in this {pkgdown} site: https://thinkr-open.github.io/fakir/
- Video presentation of {fakir} by Abdul Majed: https://www.youtube.com/watch?v=EhhljL5zaWs
Installation
# From r-universe.dev (No need for GITHUB_PAT)
install.packages("fakir",
repos = c("thinkropen" = "https://thinkr-open.r-universe.dev"))
# With {remotes} using GitHub API
# install.packages("remotes")
remotes::install_github("ThinkR-open/fakir")Characteristics
This package is designed for teaching data wrangling and data visualisation:
- some datasets follow the tidy-data principles, others don’t.
- Some missing values are set for numeric and categorical variables
- Some variables values are correlated
These datasets are suitable to introduce to the {tidyverse} and to provide examples for main functions.
Supported languages are, for now, French and US English.
Examples
Fake support ticket base :
- All tickets
library(fakir)
fake_ticket_client(vol = 10)
#> # A tibble: 10 × 25
#> ref num_client first last job age region id_dpt departement
#> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
#> 1 DOSS-AMQN-002 79 Jovan O'Ke… Gene… 22 Langu… 30 Gard
#> 2 DOSS-NCKJ-010 69 Miss Lean… Emer… 68 Île-d… 78 Yvelines
#> 3 DOSS-GPBE-009 120 Odell Stok… Engi… 24 Auver… 15 Cantal
#> 4 DOSS-GRLN-001 31 Loren Lars… <NA> NA Midi-… 81 Tarn
#> 5 DOSS-LEPJ-004 59 Maybelle Maye… Furt… 18 Franc… 70 Haute-Saône
#> 6 DOSS-DUCL-005 118 Jamarion Ober… Engi… 18 Breta… 22 Côtes-d'Ar…
#> 7 DOSS-OCED-003 77 Lee Scha… Admi… NA Corse 2A Corse-du-S…
#> 8 DOSS-KXSJ-007 65 Demetric Auer Cont… 21 Bourg… 71 Saône-et-L…
#> 9 DOSS-UITD-006 141 Wilfrid Harv… Educ… 53 Prove… 13 Bouches-du…
#> 10 DOSS-SHKL-008 182 Addyson Nien… Earl… 65 Bourg… 21 <NA>
#> # … with 16 more variables: cb_provider <chr>, name <chr>, entry_date <dttm>,
#> # fidelity_points <dbl>, priority_encoded <dbl>, priority <fct>,
#> # timestamp <date>, year <dbl>, month <dbl>, day <int>, supported <chr>,
#> # supported_encoded <int>, type <chr>, type_encoded <int>, state <fct>,
#> # source_call <fct>- Separate tickets and client databases
tickets_db <- fake_ticket_client(vol = 100, split = TRUE)
tickets_db
#> $clients
#> # A tibble: 200 × 14
#> num_client first last job age region id_dpt departement cb_provider
#> * <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr>
#> 1 1 Solomon Heaney Civil… 53 Rhône-… 73 Savoie Diners Clu…
#> 2 2 Karma Willi… Scien… 81 Centre 41 Loir-et-Ch… VISA 13 di…
#> 3 3 Press Kulas Anima… NA <NA> 01 Ain <NA>
#> 4 4 Laken McDer… <NA> NA Centre 18 Cher <NA>
#> 5 5 Sydnie Jasko… Horti… 30 Basse-… 50 <NA> <NA>
#> 6 6 Clayton Runol… Commi… NA Bretag… 56 Morbihan Diners Clu…
#> 7 7 Roberta Purdy… Finan… 60 <NA> 69 Rhône <NA>
#> 8 8 Dr. Ronal… Astro… 30 Langue… 48 Lozère <NA>
#> 9 9 Miss Alond… Occup… 18 Franch… 25 Doubs Diners Clu…
#> 10 10 Vernice Ondri… Clini… 19 Bourgo… 58 Nièvre <NA>
#> # … with 190 more rows, and 5 more variables: name <chr>, entry_date <dttm>,
#> # fidelity_points <dbl>, priority_encoded <dbl>, priority <fct>
#>
#> $tickets
#> # A tibble: 100 × 10
#> ref num_client year month day timestamp supported type state
#> <chr> <chr> <dbl> <dbl> <int> <date> <chr> <chr> <fct>
#> 1 DOSS-GFEL-0028 1 2015 11 27 2015-11-27 Non Insta… Term…
#> 2 DOSS-UWYV-0016 22 2019 9 18 2019-09-18 Non Insta… Atte…
#> 3 DOSS-DKFC-0073 9 2019 10 23 2019-10-23 Non Insta… Term…
#> 4 DOSS-SAYJ-0047 8 2019 11 7 2019-11-07 Non Box Atte…
#> 5 DOSS-GSMZ-0080 30 2019 11 24 2019-11-24 Oui Insta… Inte…
#> 6 DOSS-UIOZ-0085 10 2019 12 6 2019-12-06 Oui Insta… Atte…
#> 7 DOSS-DSMI-0065 37 2020 1 3 2020-01-03 Non Ligne Atte…
#> 8 DOSS-JOYV-0029 37 2020 2 23 2020-02-23 Non Box Atte…
#> 9 DOSS-WPSG-0013 24 2020 3 1 2020-03-01 Non <NA> En c…
#> 10 DOSS-NHFG-0036 12 2020 3 18 2020-03-18 Non Insta… Atte…
#> # … with 90 more rows, and 1 more variable: source_call <fct>
ggplot(tickets_db$clients) +
aes(entry_date, fidelity_points) +
geom_point() +
geom_smooth()
ggplot(tickets_db$tickets) + aes(type) + geom_bar()
ggplot(tickets_db$tickets) + aes(state) + geom_bar()
- Join with internal {sf} spatial dataset
clients_map <- tickets_db$clients %>%
group_by(id_dpt) %>%
summarise(
number_of_clients = n(),
average_fidelity = mean(fidelity_points, na.rm = TRUE)
) %>%
full_join(fra_sf, by = "id_dpt") %>%
st_sf()
ggplot(clients_map) +
geom_sf(aes(fill = average_fidelity)) +
scale_fill_viridis_c() +
coord_sf(
crs = 2154,
datum = 4326
)
Fake products
- Create a fake dataset of connected wearables
fake_products(10)
#> # A tibble: 10 × 8
#> name brand color price body_location category sent_from id
#> <chr> <chr> <chr> <int> <chr> <chr> <chr> <int>
#> 1 Step and Dis… Larkin, … Pink 3 Waist Industri… Taiwan 1
#> 2 Biking Track… Larkin, … OliveD… 5 Waist Pets and… United S… 2
#> 3 Wearable Tra… Moen, Mo… Beige 4 Feet Lifestyle Netherla… 3
#> 4 Multifunctio… Weimann,… Yellow… 5 Head Lifestyle China 4
#> 5 Action Camer… Blanda, … Moccas… 10 Brain Lifestyle Italy 5
#> 6 Strapless He… Mann and… DeepPi… 4 Head Medical Finland 6
#> 7 Action Camer… Mann and… Maroon 5 Feet Entertai… Finland 7
#> 8 Microcontrol… Moen, Mo… Pink 3 Neck Fitness Finland 8
#> 9 Microcontrol… Moen, Mo… Plum 5 Arms Awesome France 9
#> 10 Wearable AUS… Moen, Mo… DarkRed 9 Torso Medical Italy 10Fake website visits
fake_visits(from = "2017-01-01", to = "2017-01-31")
#> # A tibble: 31 × 8
#> timestamp year month day home about blog contact
#> * <date> <dbl> <dbl> <int> <int> <int> <int> <int>
#> 1 2017-01-01 2017 1 1 369 220 404 210
#> 2 2017-01-02 2017 1 2 159 250 414 490
#> 3 2017-01-03 2017 1 3 436 170 498 456
#> 4 2017-01-04 2017 1 4 NA 258 526 392
#> 5 2017-01-05 2017 1 5 362 NA 407 291
#> 6 2017-01-06 2017 1 6 245 145 576 90
#> 7 2017-01-07 2017 1 7 NA NA 484 167
#> 8 2017-01-08 2017 1 8 461 103 441 NA
#> 9 2017-01-09 2017 1 9 337 113 673 379
#> 10 2017-01-10 2017 1 10 NA 169 308 139
#> # … with 21 more rowsFake questionnaire on mean of transport / goal
- All answers
fake_sondage_answers(n = 10)
#> # A tibble: 30 × 12
#> id_individu age sexe region id_departement nom_departement
#> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 ID-NYDZ-010 NA <NA> Corse 2A Corse-du-Sud
#> 2 ID-NYDZ-010 NA <NA> Corse 2A Corse-du-Sud
#> 3 ID-NYDZ-010 NA <NA> Corse 2A Corse-du-Sud
#> 4 ID-PWLB-009 71 F Basse-Normandie 14 Calvados
#> 5 ID-PWLB-009 71 F Basse-Normandie 14 Calvados
#> 6 ID-PWLB-009 71 F Basse-Normandie 14 Calvados
#> 7 ID-NMQG-001 42 M Poitou-Charentes 86 <NA>
#> 8 ID-NMQG-001 42 M Poitou-Charentes 86 <NA>
#> 9 ID-NMQG-001 42 M Poitou-Charentes 86 <NA>
#> 10 ID-RJXN-002 71 O Auvergne 15 Cantal
#> # … with 20 more rows, and 6 more variables: question_date <dttm>, year <dbl>,
#> # type <chr>, distance_km <dbl>, transport <fct>,
#> # temps_trajet_en_heures <dbl>- Separate individuals and their answers
fake_sondage_answers(n = 10, split = TRUE)
#> $individus
#> # A tibble: 10 × 8
#> id_individu age sexe region id_departement nom_departement
#> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 ID-NYDZ-010 NA <NA> Pays de la Loire 49 Maine-et-Loire
#> 2 ID-PWLB-009 71 F Basse-Normandie 14 Calvados
#> 3 ID-NMQG-001 42 M Bourgogne 58 Nièvre
#> 4 ID-RJXN-002 71 O Centre 41 Loir-et-Cher
#> 5 ID-MROK-007 41 M Languedoc-Roussillon 48 Lozère
#> 6 ID-VMKS-004 33 O Poitou-Charentes 86 Vienne
#> 7 ID-XEMZ-003 81 O <NA> 77 Seine-et-Marne
#> 8 ID-EUDQ-005 44 M Champagne-Ardenne 51 <NA>
#> 9 ID-DCIZ-008 92 O Picardie 60 Oise
#> 10 ID-KPUS-006 57 O Picardie 60 Oise
#> # … with 2 more variables: question_date <dttm>, year <dbl>
#>
#> $answers
#> # A tibble: 30 × 5
#> id_individu type distance_km transport temps_trajet_en_heures
#> <chr> <chr> <dbl> <fct> <dbl>
#> 1 ID-NYDZ-010 travail 12.2 voiture 0.15
#> 2 ID-NYDZ-010 commerces 9.61 bus 1.01
#> 3 ID-NYDZ-010 loisirs 549. avion 0.27
#> 4 ID-PWLB-009 travail 11.9 voiture 0.14
#> 5 ID-PWLB-009 commerces 27.4 voiture 0.34
#> 6 ID-PWLB-009 loisirs 210. train 0.42
#> 7 ID-NMQG-001 travail 2.38 velo 0.43
#> 8 ID-NMQG-001 commerces 14.9 voiture 0.18
#> 9 ID-NMQG-001 loisirs 446. train 0.89
#> 10 ID-RJXN-002 travail 6.18 mobylette 0.75
#> # … with 20 more rowsfake transport use
answers <- fake_sondage_answers(n = 30)
answers
#> # A tibble: 90 × 12
#> id_individu age sexe region id_departement nom_departement
#> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 ID-MROK-007 NA M <NA> 16 <NA>
#> 2 ID-MROK-007 NA M <NA> 16 <NA>
#> 3 ID-MROK-007 NA M <NA> 16 <NA>
#> 4 ID-NYDZ-010 49 M Rhône-Alpes 07 Ardèche
#> 5 ID-NYDZ-010 49 M Rhône-Alpes 07 Ardèche
#> 6 ID-NYDZ-010 49 M Rhône-Alpes 07 Ardèche
#> 7 ID-HXOG-015 50 M <NA> 39 Jura
#> 8 ID-HXOG-015 50 M <NA> 39 Jura
#> 9 ID-HXOG-015 50 M <NA> 39 Jura
#> 10 ID-MZNB-024 70 F <NA> 51 Marne
#> # … with 80 more rows, and 6 more variables: question_date <dttm>, year <dbl>,
#> # type <chr>, distance_km <dbl>, transport <fct>,
#> # temps_trajet_en_heures <dbl>
ggplot(answers) + aes(age, log(distance_km), colour = type) + geom_point() +
geom_smooth() + facet_wrap(~type, scales = "free_y")
#> Warning: Removed 6 rows containing non-finite values (stat_smooth).
#> Warning: Removed 6 rows containing missing values (geom_point).
Prior work
This package is heavily inspired by {charlatan}.
Scott Chamberlain (2017). charlatan: Make Fake Data. R package version 0.1.0. https://CRAN.R-project.org/package=charlatan
Contribute
You can contribute to {fakir} in two ways:
COC
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
