Load country-wide data by gender and age

Load country-wide data by gender and age (age in 6 bins)

Usage

pv_cr_monthlypay_age_gender(path, sheet = 2)

Arguments

path

path(s) to file(s), Will be file with "CR_YYQD and either "PLS" or "MZS" in the name.

sheet

sheet number; you should be able to leave this as default (2) if using files downloaded from ISPV

Value

a tibble

Examples

pv_cr_monthlypay_age_gender(system.file("extdata", "CR_204_MZS.xlsx", package = "ispv"))
#> # A tibble: 21 × 20
#>    category  fte_t…¹ pay_m…² pay_m…³ pay_d1 pay_q1 pay_q3 pay_d9 pay_m…⁴ pay_m…⁵
#>    <chr>       <dbl>   <dbl>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>   <dbl>   <dbl>
#>  1 CELKEM -… 2893.    31567.    105. 18402. 23915. 42283. 60110.  37789.    105.
#>  2 do 20 let    7.10  23743.    109. 16501. 20063. 28256. 33070.  24587.    109.
#>  3 20 – 29 …  433.    29978.    104. 18603. 23843. 37716. 47923.  32397.    105.
#>  4 30 – 39 …  655.    34142.    106. 18948. 25589. 46332. 66049.  40227.    106.
#>  5 40 – 49 …  907.    32394.    105. 18468. 24073. 44434. 66188.  40220.    106.
#>  6 50 – 59 …  665.    30178.    104. 18012. 23033. 40165. 56233.  36315.    105.
#>  7 60 a víc…  226.    30006.    103. 17921. 22549. 40234. 57129.  36040.    103.
#>  8 MUŽI      1733.    34083.    103. 18976. 25844. 45626. 66143.  41070.    104.
#>  9 do 20 let    4.78  24243.    106. 16662. 20352. 28960. 33913.  25089.    107 
#> 10 20 – 29 …  269.    31296.    103. 18741. 24735. 39148. 49859.  33609.    104.
#> # … with 11 more rows, 10 more variables: bonus_perc <dbl>,
#> #   supplements_perc <dbl>, compensation_perc <dbl>, hours_per_month <dbl>,
#> #   file <chr>, group <chr>, sfera <chr>, period <chr>, year <chr>,
#> #   quarter <chr>, and abbreviated variable names ¹​fte_thous, ²​pay_median,
#> #   ³​pay_median_yoy, ⁴​pay_mean, ⁵​pay_mean_yoy
pv_cr_monthlypay_age_gender(system.file("extdata", "CR_204_PLS.xlsx", package = "ispv"))
#> # A tibble: 21 × 20
#>    category  fte_t…¹ pay_m…² pay_m…³ pay_d1 pay_q1 pay_q3 pay_d9 pay_m…⁴ pay_m…⁵
#>    <chr>       <dbl>   <dbl>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>   <dbl>   <dbl>
#>  1 CELKEM -… 648.     39900.    110. 25228. 32199. 49028. 61312.  42555.    110.
#>  2 do 20 let   0.393  24277.    117. 14945. 18396. 29113. 35222.  24793.    115.
#>  3 20 – 29 …  58.2    35075.    110. 24872. 29473. 41026. 49294.  36506.    112.
#>  4 30 – 39 … 113.     39780.    109. 26796. 32429. 47538. 58401.  41946.    110.
#>  5 40 – 49 … 208.     40647.    110. 25499. 32583. 49793. 62191.  43097.    110.
#>  6 50 – 59 … 195.     40792.    109. 24521. 32806. 50367. 63173.  43495.    110.
#>  7 60 a víc…  73.5    41487.    109. 24411. 32656. 50713. 65708.  44356.    109.
#>  8 MUŽI      220.     43134.    108. 27744. 34438. 53347. 67127.  46509.    108.
#>  9 do 20 let   0.164  20642.    121. 14945. 15376. 25900. 29018.  21586.    114.
#> 10 20 – 29 …  23.5    35892.    109. 25046. 30055. 41693. 48638.  36839.    110.
#> # … with 11 more rows, 10 more variables: bonus_perc <dbl>,
#> #   supplements_perc <dbl>, compensation_perc <dbl>, hours_per_month <dbl>,
#> #   file <chr>, group <chr>, sfera <chr>, period <chr>, year <chr>,
#> #   quarter <chr>, and abbreviated variable names ¹​fte_thous, ²​pay_median,
#> #   ³​pay_median_yoy, ⁴​pay_mean, ⁵​pay_mean_yoy