Description
Query Random User Data from the Random User Generator API.
Description
Generate random user data from the Random User Generator API. For more information, see <https://randomuser.me/>.
README.md
radous
radous allows you to generate random user data from the Random User Generator API which can be useful in many situations :
- Teaching;
- Testing a function;
- Testing an application (Shiny, Dash or others)
You can generate up to 5000 observations in one query.
Installation
You can radous from CRAN with:
install.packages("radous")
Usage
radous is extremely simple to use and has one function: get_data().
Suppose we want to generate 10 random user data:
library(radous)
get_data(n = 10)
# A tibble: 10 x 34
gender name_title name_first name_last location_street~ location_street~
<chr> <chr> <chr> <chr> <dbl> <chr>
1 male Mr Phoenix King 2697 West Quay
2 female Miss Venla Lehto 1288 Rotuaari
3 female Miss Ingeborg Hatlevik 549 Revierstredet
4 male Mr Salazar Duarte 3498 Rua Primeiro de~
5 female Miss Addison Johnson 7674 Hardy Street
6 male Mr Bartholom~ Bräuer 5196 Erlenweg
7 female Miss Elise Renard 7354 Rue Louis-Garra~
8 male Mr Stanley Sims 1481 Shady Ln Dr
9 female Miss Beatrice Gagné 2575 Maple Ave
10 female Miss Florence Garza 8604 New Street
# ... with 28 more variables: location_city <chr>, location_state <chr>,
# location_country <chr>, location_postcode <chr>,
# location_coordinates_latitude <dbl>, location_coordinates_longitude <dbl>,
# location_timezone_offset <chr>, location_timezone_description <chr>,
# email <chr>, login_uuid <chr>, login_username <chr>, login_password <chr>,
# login_salt <chr>, login_md5 <chr>, login_sha1 <chr>, login_sha256 <chr>,
# dob_date <dttm>, dob_age <dbl>, registered_date <dttm>,
# registered_age <dbl>, phone <chr>, cell <chr>, id_name <chr>,
# id_value <chr>, picture_large <chr>, picture_medium <chr>,
# picture_thumbnail <chr>, nat <chr>
If you want to generate always the same set of users, you can use the seed argument:
get_data(n = 5, seed = "1990")
# A tibble: 5 x 34
gender name_title name_first name_last location_street~ location_street~
<chr> <chr> <chr> <chr> <dbl> <chr>
1 female Mrs Bernice Duncan 9327 N Stelling Rd
2 female Mrs Kine Otnes 6127 Tjernveien
3 female Ms Keira King 7676 Mt Wellington H~
4 female Mrs Amelia Young 7477 Simcoe St
5 male Mr Júnio Santos 4240 Rua Pará
# ... with 28 more variables: location_city <chr>, location_state <chr>,
# location_country <chr>, location_postcode <chr>,
# location_coordinates_latitude <dbl>, location_coordinates_longitude <dbl>,
# location_timezone_offset <chr>, location_timezone_description <chr>,
# email <chr>, login_uuid <chr>, login_username <chr>, login_password <chr>,
# login_salt <chr>, login_md5 <chr>, login_sha1 <chr>, login_sha256 <chr>,
# dob_date <dttm>, dob_age <dbl>, registered_date <dttm>,
# registered_age <dbl>, phone <chr>, cell <chr>, id_name <chr>,
# id_value <chr>, picture_large <chr>, picture_medium <chr>,
# picture_thumbnail <chr>, nat <chr>
Let’s run the above code again to check if we get the same info:
get_data(n = 5, seed = "1990")
# A tibble: 5 x 34
gender name_title name_first name_last location_street~ location_street~
<chr> <chr> <chr> <chr> <dbl> <chr>
1 female Mrs Bernice Duncan 9327 N Stelling Rd
2 female Mrs Kine Otnes 6127 Tjernveien
3 female Ms Keira King 7676 Mt Wellington H~
4 female Mrs Amelia Young 7477 Simcoe St
5 male Mr Júnio Santos 4240 Rua Pará
# ... with 28 more variables: location_city <chr>, location_state <chr>,
# location_country <chr>, location_postcode <chr>,
# location_coordinates_latitude <dbl>, location_coordinates_longitude <dbl>,
# location_timezone_offset <chr>, location_timezone_description <chr>,
# email <chr>, login_uuid <chr>, login_username <chr>, login_password <chr>,
# login_salt <chr>, login_md5 <chr>, login_sha1 <chr>, login_sha256 <chr>,
# dob_date <dttm>, dob_age <dbl>, registered_date <dttm>,
# registered_age <dbl>, phone <chr>, cell <chr>, id_name <chr>,
# id_value <chr>, picture_large <chr>, picture_medium <chr>,
# picture_thumbnail <chr>, nat <chr>
If you need some user images, it’s easy to get:
library(dplyr)
random_image <- get_data(n = 1) %>% select(picture_large) %>% pull()
htmltools::img(src = random_image, height = "150px", width = "150px")
Note that All randomly generated photos come from the authorized section of UI Faces.
Teaching with radous 👨🏫
The generated data has 34 variables (columns) with different types of information that you can play with. The data frame is particularly suited for teaching the tidyverse, here some examples:
Select
Here we select columns that are related to users’ location:
Here we select columns that are related to users’ location:
library(tidyverse)
df <- get_data(n = 500, seed = "123")
df %>% select(contains("location"))
# A tibble: 500 x 10
location_street~ location_street~ location_city location_state
<dbl> <chr> <chr> <chr>
1 9120 Rua Três Parnaíba Roraima
2 3420 Armagh Street Taupo West Coast
3 7871 Hämeentie Tyrnävä Åland
4 9456 Henry Street Kilkenny Wexford
5 8290 Bulevardi Tervo Tavastia Prop~
6 4794 Rue Bossuet Lamone Zug
7 1201 Richmond Road Brighton and~ Buckinghamshi~
8 2483 Cedar St Beaumont British Colum~
9 659 Vatan Cd Hakkâri Elazig
10 4841 Bagdat Cd Bursa Karaman
# ... with 490 more rows, and 6 more variables: location_country <chr>,
# location_postcode <chr>, location_coordinates_latitude <dbl>,
# location_coordinates_longitude <dbl>, location_timezone_offset <chr>,
# location_timezone_description <chr>
Filter
Getting the users that are US citizens:
Getting the users that are US citizens:
df %>% filter(nat == "US")
# A tibble: 25 x 34
gender name_title name_first name_last location_street~ location_street~
<chr> <chr> <chr> <chr> <dbl> <chr>
1 female Miss Bella Palmer 6951 First Street
2 male Mr Joseph Gardner 8106 Eason Rd
3 female Mrs Marlene James 4385 Spring St
4 male Mr Raymond Day 6389 Spring Hill Rd
5 male Mr Lester Marshall 9574 White Oak Dr
6 male Mr Wyatt Stevens 3341 Ash Dr
7 female Ms Linda James 4549 Spring St
8 female Ms Darlene Lee 4457 Hunters Creek Dr
9 male Mr Nathaniel Henderson 6333 W Pecan St
10 male Mr Sean Stephens 3079 Dogwood Ave
# ... with 15 more rows, and 28 more variables: location_city <chr>,
# location_state <chr>, location_country <chr>, location_postcode <chr>,
# location_coordinates_latitude <dbl>, location_coordinates_longitude <dbl>,
# location_timezone_offset <chr>, location_timezone_description <chr>,
# email <chr>, login_uuid <chr>, login_username <chr>, login_password <chr>,
# login_salt <chr>, login_md5 <chr>, login_sha1 <chr>, login_sha256 <chr>,
# dob_date <dttm>, dob_age <dbl>, registered_date <dttm>,
# registered_age <dbl>, phone <chr>, cell <chr>, id_name <chr>,
# id_value <chr>, picture_large <chr>, picture_medium <chr>,
# picture_thumbnail <chr>, nat <chr>
relocate
Relocating the last column nat to the beginning:
Relocating the last column nat to the beginning:
df %>% relocate(nat, before = gender)
# A tibble: 500 x 34
nat gender name_title name_first name_last location_street~
<chr> <chr> <chr> <chr> <chr> <dbl>
1 BR male Mr Heldo Campos 9120
2 NZ female Mrs Peyton Jackson 3420
3 FI female Ms Viivi Ruona 7871
4 IE female Mrs Kaitlin Steward 9456
5 FI female Miss Linnea Pulkkinen 8290
6 CH female Madame Valentine Le Gall 4794
7 GB female Mrs Suzanna Miller 1201
8 CA male Mr Antoine Thompson 2483
9 TR female Miss Latife Kurutluo~ 659
10 TR male Mr Vedat Aydan 4841
# ... with 490 more rows, and 28 more variables: location_street_name <chr>,
# location_city <chr>, location_state <chr>, location_country <chr>,
# location_postcode <chr>, location_coordinates_latitude <dbl>,
# location_coordinates_longitude <dbl>, location_timezone_offset <chr>,
# location_timezone_description <chr>, email <chr>, login_uuid <chr>,
# login_username <chr>, login_password <chr>, login_salt <chr>,
# login_md5 <chr>, login_sha1 <chr>, login_sha256 <chr>, dob_date <dttm>,
# dob_age <dbl>, registered_date <dttm>, registered_age <dbl>, phone <chr>,
# cell <chr>, id_name <chr>, id_value <chr>, picture_large <chr>,
# picture_medium <chr>, picture_thumbnail <chr>
group_by & summarise
Calculating median age by gender:
Calculating median age by gender:
df %>% group_by(gender) %>%
summarise(median_age = median(dob_age))
`summarise()` ungrouping output (override with `.groups` argument)
# A tibble: 2 x 2
gender median_age
<chr> <dbl>
1 female 51.5
2 male 49
count, arrange & desc
Getting the number of users per country of residence:
Getting the number of users per country of residence:
df %>%
count(location_country) %>%
arrange(desc(n))
# A tibble: 17 x 2
location_country n
<chr> <int>
1 Ireland 36
2 Turkey 36
3 Spain 35
4 Canada 34
5 France 33
6 Australia 32
7 Finland 32
8 Iran 32
9 New Zealand 30
10 Norway 30
11 Germany 29
12 United Kingdom 26
13 United States 25
14 Netherlands 24
15 Switzerland 24
16 Brazil 22
17 Denmark 20
filter & str_detect
Filtering out the users that have a cell number that begins with 081:
Filtering out the users that have a cell number that begins with 081:
df %>% select(1:3, cell) %>%
filter(str_detect(cell, "081"))
# A tibble: 36 x 4
gender name_title name_first cell
<chr> <chr> <chr> <chr>
1 female Mrs Kaitlin 081-087-1612
2 male Mr Jason 081-584-4669
3 male Mr Arnold 081-470-7126
4 male Mr Brent 081-614-3193
5 female Mrs Fiona 081-779-4190
6 female Mrs Megan 081-511-0321
7 female Ms Izzie 081-850-4070
8 male Mr Leslie 081-172-5148
9 male Mr Edgar 081-206-3946
10 female Ms Deborah 081-984-3691
# ... with 26 more rows
Code of Conduct
Please note that the radous project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.