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The explore package offers a simplified way to describe data.
describe_tbl() describes the table (number of rows,
columns, …)describe_all() returns a description of all variables
as data framedescribe() returns a description of of a specific
variable (or a description of all variables if no specific variable is
passed to the function)describe_cat() returns a description of of a specific
variable (forcing to handle the variable as categorical)describe_num() returns a description of of a specific
variable (forcing to handle the variable as numerical)We use synthetic data in this example
library(dplyr)
library(explore)
data <- create_data_buy(obs = 100)
glimpse(data)
#> Rows: 100
#> Columns: 13
#> $ period <int> 202012, 202012, 202012, 202012, 202012, 202012, 202012…
#> $ buy <int> 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, …
#> $ age <int> 48, 68, 45, 50, 59, 60, 66, 56, 70, 47, 71, 40, 47, 92…
#> $ city_ind <int> 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, …
#> $ female_ind <int> 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, …
#> $ fixedvoice_ind <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, …
#> $ fixeddata_ind <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
#> $ fixedtv_ind <int> 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, …
#> $ mobilevoice_ind <int> 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, …
#> $ mobiledata_prd <chr> "NO", "NO", "NO", "NO", "NO", "MOBILE STICK", "MOBILE …
#> $ bbi_speed_ind <int> 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, …
#> $ bbi_usg_gb <int> 79, 60, 82, 52, 54, 64, 52, 73, 36, 90, 78, 103, 52, 2…
#> $ hh_single <int> 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …data %>% describe_tbl()
#> 100 observations with 13 variables
#> 0 observations containing missings (NA)
#> 0 variables containing missings (NA)
#> 2 variables with no variancedata %>% describe_all()
#> # A tibble: 13 × 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 0.53 1
#> 3 age int 0 0 41 24 52.6 92
#> 4 city_ind int 0 0 2 0 0.49 1
#> 5 female_ind int 0 0 2 0 0.53 1
#> 6 fixedvoice_ind int 0 0 2 0 0.08 1
#> 7 fixeddata_ind int 0 0 1 1 1 1
#> 8 fixedtv_ind int 0 0 2 0 0.43 1
#> 9 mobilevoice_ind int 0 0 2 0 0.68 1
#> 10 mobiledata_prd chr 0 0 3 NA NA NA
#> 11 bbi_speed_ind int 0 0 2 0 0.6 1
#> 12 bbi_usg_gb int 0 0 56 10 1064. 100000
#> 13 hh_single int 0 0 2 0 0.29 1data %>%
describe_all() %>%
filter(unique == 1)
#> # A tibble: 2 × 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 fixeddata_ind int 0 0 1 1 1 1data %>% describe(age)
#> variable = age
#> type = integer
#> na = 0 of 100 (0%)
#> unique = 41
#> min|max = 24 | 92
#> q05|q95 = 33.85 | 71
#> q25|q75 = 45 | 60
#> median = 52.5
#> mean = 52.55data %>% describe(buy)
#> variable = buy
#> type = integer
#> na = 0 of 100 (0%)
#> unique = 2
#> 0 = 47 (47%)
#> 1 = 53 (53%)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.