feddelegrand7 / radous

Query Random User Data From the Random User Generator API

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:

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:

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:

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:

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:

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:

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.

radous's Issues

Bad responses from the randomuser.me API cause test failure

Hello,

I recently submitted the janitor package to CRAN and they noted that it seemed to cause new failures in radous. After testing your package, I think the issue is that once in a while, a call to the randomuser.me API fails, displaying an error page that says "Uh oh, something bad happened". I tried https://randomuser.me/api/?seed=2&results=10&format=csv and it worked 4 times, then errored the 5th. Trying a few minutes later, it succeeded 16 times, then errored on the 17th.

Here's the sort of failure I'm seeing when I run your tests - I don't think it depends what version of janitor I'm using:

>   testthat::expect_identical(final,get_data(n = 10, seed = "123"))
No encoding supplied: defaulting to UTF-8.
Warning: 2 parsing failures.
row col  expected    actual         file
  2  -- 1 columns 2 columns literal data
  5  -- 1 columns 2 columns literal data

Error: `final` not identical to get_data(n = 10, seed = "123").
Names: 1 string mismatch
Attributes: < Names: 1 string mismatch >
Attributes: < Length mismatch: comparison on first 2 components >
Attributes: < Component 2: Modes: numeric, list >
Attributes: < Component 2: names for current but not for target >
Attributes: < Component 2: Attributes: < target is NULL, current is list > >
Attributes: < Component 2: target is numeric, current is tbl_df >
Length mismatch: comparison on first 1 components
Component 1: Lengths (10, 8) differ (string compare on first 8)

This is the same as the failing tests on CRAN, which I'm guessing are also caused by this issue.

To fix, maybe have your function try the API call, then if it returns this "oh no" error page, it tries again a certain number of times? Or if there's another issue causing the API failure, like rate limit on a token you're using, address it there so it doesn't fail.