Create your own (synthetic) data!
You need sample data for learning/testing/teaching?
Create your own … using {explore}
The R package {explore} offers prepared synthetic data. You can use create_data_*() functions to create this synthetic data.
The number of observations can be controlled by parameter obs. If you want to create reproducible synthetic data, use parameter seed (random seed number)
Person
create_data_person() creates a dataset containing synthetic data of people:
library(explore)
data <- create_data_person(obs = 1000, seed = 10)
describe(data)
# A tibble: 15 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 age int 0 0 80 16 55.4 95
2 gender chr 0 0 3 NA NA NA
3 eye_color chr 0 0 3 NA NA NA
4 shoe_size dbl 0 0 39 30.2 41.8 52.2
5 iq dbl 0 0 110 37 99.3 173
6 education int 0 0 101 0 50.0 100
7 income dbl 0 0 220 0 62.5 143
8 handset chr 0 0 3 NA NA NA
9 pet chr 0 0 4 NA NA NA
10 favorite_pizza chr 0 0 6 NA NA NA
11 favorite_icecream chr 0 0 7 NA NA NA
12 likes_garlic int 0 0 2 0 0.6 1
13 likes_sushi int 0 0 2 0 0.32 1
14 likes_beatles int 0 0 2 0 0.5 1
15 likes_beer int 0 0 2 0 0.64 1
We get a dataframe containing 1000 observations and 15 variables. Some of these variables have completely random values, some have build in correlations.
data |> explore(age, target = likes_beer)
data |> explore(gender, target = likes_beer)
The pattern age/likes_beer is just a random noise, but gender/likes_beer is a build in correlation.
App
create_data_app() creates a dataset containing synthetic data of apps in an appstore:
data <- create_data_app(obs = 1000, seed = 10)
describe(data)
# A tibble: 7 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 os chr 0 0 3 NA NA NA
2 free dbl 0 0 2 0 0.6 1
3 downloads int 0 0 968 72 6943. 23362
4 rating dbl 0 0 5 1 3.39 5
5 type chr 0 0 10 NA NA NA
6 updates dbl 0 0 147 0 48.2 100
7 screen_sizes dbl 0 0 5 1 2.56 5
data |> explore(downloads, target = os)
Buy
create_data_buy() creates a dataset containing synthetic data of customer who buy (or not buy) a product:
data <- create_data_buy(obs = 1000, seed = 10)
describe(data)
# A tibble: 13 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 period int 0 0 1 202012 202012 202012
2 buy int 0 0 2 0 0.5 1
3 age int 0 0 69 16 52.4 97
4 city_ind int 0 0 2 0 0.52 1
5 female_ind int 0 0 2 0 0.52 1
6 fixedvoice_ind int 0 0 2 0 0.1 1
7 fixeddata_ind int 0 0 1 1 1 1
8 fixedtv_ind int 0 0 2 0 0.41 1
9 mobilevoice_ind int 0 0 2 0 0.6 1
10 bbi_speed_ind int 0 0 2 0 0.63 1
11 bbi_usg_gb int 0 0 85 8 164. 100000
12 hh_single int 0 0 2 0 0.31 1
data |> explain_tree(target = buy)
Churn
create_data_churn() creates a dataset containing synthetic data of customer churn:
data <- create_data_churn(obs = 1000, seed = 10)
describe(data)
# A tibble: 9 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 price dbl 0 0 26 4 19.1 29
2 type chr 0 0 3 NA NA NA
3 usage dbl 0 0 157 0 56.7 150
4 shared dbl 0 0 2 0 0.37 1
5 device chr 0 0 3 NA NA NA
6 newsletter dbl 0 0 2 0 0.49 1
7 language chr 0 0 4 NA NA NA
8 duration int 0 0 101 0 50.8 100
9 churn dbl 0 0 2 0 0.36 1
data |> explain_tree(target = churn)
Unfair
create_data_unfair() creates a dataset containing synthetic data of people that can be used to test model fairness:
data <- create_data_unfair(obs = 1000, seed = 10)
describe(data)
# A tibble: 22 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 age int 0 0 80 16 55.4 95
2 gender chr 0 0 3 NA NA NA
3 eye_color chr 0 0 3 NA NA NA
4 shoe_size dbl 0 0 39 30.2 41.8 52.2
5 iq dbl 0 0 110 37 99.3 173
6 education int 0 0 101 0 50.0 100
7 income dbl 0 0 220 0 62.5 143
8 handset chr 0 0 3 NA NA NA
9 pet chr 0 0 4 NA NA NA
10 smoking dbl 0 0 2 0 0.29 1
11 name_arabic int 0 0 2 0 0.12 1
12 outfit chr 0 0 3 NA NA NA
13 glasses dbl 0 0 2 0 0.34 1
14 tatoos dbl 0 0 2 0 0.19 1
15 kids dbl 0 0 2 0 0.5 1
16 bad_debt dbl 0 0 3 0 0.24 2
17 credit_card chr 0 0 4 NA NA NA
18 left_handed dbl 0 0 2 0 0.21 1
19 skin_color chr 0 0 5 NA NA NA
20 religion chr 0 0 4 NA NA NA
21 internet_gb dbl 0 0 666 0 118. 442.
data |> explain_tree(target = internet_gb)
If we try to predict internet usage in gigabytes using all other variables in the dataset, we see some patterns. Some might make sense, some other might just cause unfairness and discrimination. This dataset is designed to be used as a educational playground for “Trustworthy AI”.
Random
create_data_random() creates a dataset containing random data:
data <- create_data_random(obs = 1000, vars = 10, seed = 10)
describe(data)
# A tibble: 12 x 8
variable type na na_pct unique min mean max
<chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
1 id int 0 0 1000 1 500. 1000
2 target_ind int 0 0 2 0 0.5 1
3 var_1 int 0 0 101 0 49.5 100
4 var_2 int 0 0 101 0 50.8 100
5 var_3 int 0 0 101 0 50.5 100
6 var_4 int 0 0 101 0 48.9 100
7 var_5 int 0 0 101 0 51.1 100
8 var_6 int 0 0 101 0 50.9 100
9 var_7 int 0 0 101 0 49.7 100
10 var_8 int 0 0 101 0 49.2 100
11 var_9 int 0 0 101 0 49.2 100
12 var_10 int 0 0 101 0 50.1 100
Empty
create_data_empty() creates an empty dataset. You can use add_var_random_*() functions to add random variables:
data <- create_data_empty(obs = 5000)
data <- data |>
add_var_random_01(name = "success", prob = c(0.3, 0.7), seed = 16) |>
add_var_random_dbl(name = "budget", min_val = 10, max_val = 100) |>
add_var_random_cat(name = "group", cat = LETTERS[1:5])
head(data)
success budget group
1 1 85.71165 C
2 1 92.23001 D
3 0 14.32489 C
4 0 91.91952 E
5 0 65.75793 B
6 1 28.00104 D
You can add prepared random variables too:
data <- data |>
add_var_random_starsign() |>
add_var_random_moon()
success budget group random_starsign random_moon
1 1 85.71165 C Leo Waning (-)
2 1 92.23001 D Leo Waning (-)
3 0 14.32489 C Aries Waning (-)
4 0 91.91952 E Pisces Waxing (+)
5 0 65.75793 B Pisces Waning (-)
6 1 28.00104 D Aries Waxing (+)
7 0 17.92046 C Virgo Waxing (+)
8 1 16.59915 D Libra Waning (-)
9 0 78.61641 C Aries Waxing (+)
10 0 15.86658 C Taurus Waning (-)
11 0 12.72608 E Pisces New ( )
12 0 71.91871 C Leo Waning (-)
13 1 22.82367 E Aquarius Waning (-)
14 1 19.63139 D Carpicorn Waning (-)
15 1 38.01133 E Carpicorn Waxing (+)
data |> explain_tree(target = success)
If we try to predict success using all other variables in the dataset, we see some patterns. But all variables are created just randomly.