Load country-wide data by ISCO (profession classification) at 4 and 5 digits
Arguments
- path
path(s) to file(s), Will be file with "CR_YYQD and either "PLS" or "MZS" in the name.
- sheet
which sheet to open. Will be 1 (the default) in files with only one sheet ("CR_204_MZS_M8r.xlsx") and 7 in comprehensive file ("CR_204_MZS.xlsx")
Examples
pv_cr_monthlypay_isco(system.file("extdata", "CR_204_MZS_M8r.xlsx", package = "ispv"), 1)
#> # A tibble: 871 × 21
#> isco_full fte_t…¹ pay_m…² pay_d1 pay_q1 pay_q3 pay_d9 pay_m…³ bonus…⁴ suppl…⁵
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1120 Nej… 5.42 91993. 34693. 5.21e4 1.90e5 3.15e5 144016. 24.2 0.65
#> 2 11201 Ne… 1.18 200585. 60919. 1.07e5 3.15e5 4.73e5 244336. 26.1 0.81
#> 3 11202 Ne… 2.53 103946. 34886. 5.82e4 1.94e5 2.95e5 145212. 24.2 0.56
#> 4 11203 Ne… 1.28 52900. 30031. 4.18e4 9.10e4 1.10e5 66750. 19.6 0.58
#> 5 1211 Říd… 7.49 88015. 39263. 5.44e4 1.31e5 1.95e5 108521. 19.5 0.7
#> 6 12111 Ek… 2.53 93133. 42640. 6.38e4 1.50e5 2.22e5 121126. 21.6 0.65
#> 7 12112 Ří… 2.83 78829. 35594. 4.89e4 1.14e5 1.70e5 96291. 19.2 0.98
#> 8 12113 Ří… 1.47 91182. 45845. 6.14e4 1.25e5 1.90e5 109671. 16.6 0.34
#> 9 1212 Říd… 2.57 88028. 39440. 5.24e4 1.31e5 1.95e5 107242. 17.5 0.6
#> 10 12121 Pe… 0.614 107879. 54997. 7.60e4 1.68e5 2.47e5 140092. 19.8 0.64
#> # … with 861 more rows, 11 more variables: compensation_perc <dbl>,
#> # hours_per_month <dbl>, estimate_quality <chr>, file <chr>, sfera <chr>,
#> # period <chr>, year <chr>, quarter <chr>, isco_digits <dbl>, isco_id <chr>,
#> # isco_name <chr>, and abbreviated variable names ¹fte_thous, ²pay_median,
#> # ³pay_mean, ⁴bonus_perc, ⁵supplements_perc
pv_cr_monthlypay_isco(system.file("extdata", "CR_204_MZS.xlsx", package = "ispv"), 7)
#> # A tibble: 467 × 21
#> isco_full fte_t…¹ pay_m…² pay_d1 pay_q1 pay_q3 pay_d9 pay_m…³ bonus…⁴ suppl…⁵
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1120 Nej… 5.42 91993. 34693. 5.21e4 1.90e5 3.15e5 144016. 24.2 0.65
#> 2 11201 Ne… 1.18 200585. 60919. 1.07e5 3.15e5 4.73e5 244336. 26.1 0.81
#> 3 11202 Ne… 2.53 103946. 34886. 5.82e4 1.94e5 2.95e5 145212. 24.2 0.56
#> 4 1211 Říd… 7.49 88015. 39263. 5.44e4 1.31e5 1.95e5 108521. 19.5 0.7
#> 5 12111 Ek… 2.53 93133. 42640. 6.38e4 1.50e5 2.22e5 121126. 21.6 0.65
#> 6 12112 Ří… 2.83 78829. 35594. 4.89e4 1.14e5 1.70e5 96291. 19.2 0.98
#> 7 12113 Ří… 1.47 91182. 45845. 6.14e4 1.25e5 1.90e5 109671. 16.6 0.34
#> 8 1212 Říd… 2.57 88028. 39440. 5.24e4 1.31e5 1.95e5 107242. 17.5 0.6
#> 9 12122 Ří… 1.39 79872. 39535. 4.94e4 1.22e5 1.89e5 98617. 16.2 0.67
#> 10 1219 Ost… 6.53 65974. 29301. 4.26e4 9.73e4 1.47e5 81229. 20.2 1.2
#> # … with 457 more rows, 11 more variables: compensation_perc <dbl>,
#> # hours_per_month <dbl>, estimate_quality <chr>, file <chr>, sfera <chr>,
#> # period <chr>, year <chr>, quarter <chr>, isco_digits <dbl>, isco_id <chr>,
#> # isco_name <chr>, and abbreviated variable names ¹fte_thous, ²pay_median,
#> # ³pay_mean, ⁴bonus_perc, ⁵supplements_perc
pv_cr_monthlypay_isco(system.file("extdata", "CR_204_PLS_M8r.xlsx", package = "ispv"), 1)
#> # A tibble: 664 × 21
#> isco_full fte_t…¹ pay_m…² pay_d1 pay_q1 pay_q3 pay_d9 pay_m…³ bonus…⁴ suppl…⁵
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0110 Gen… 6.60 56520. 42206. 48122. 6.57e4 7.40e4 57230. 1.21 6.65
#> 2 01102 Vy… 2.38 68335. 60819. 62950. 7.37e4 8.15e4 69755. 1.51 6.51
#> 3 01103 Ni… 4.18 50305. 40397. 45022. 5.59e4 5.99e4 49610. 0.98 6.76
#> 4 0210 Pod… 8.57 32639. 29070 30786. 3.56e4 3.87e4 33186. 1.17 5.41
#> 5 0310 Zam… 9.85 42321. 23871. 38351. 4.61e4 4.96e4 40149. 0.77 7.78
#> 6 03101 Pr… 8.13 43607. 38477. 40636. 4.70e4 5.01e4 43941. 0.72 8.45
#> 7 1112 Nej… 2.25 72287. 48419. 60775. 9.25e4 1.22e5 80163. 14.2 29.6
#> 8 11123 Ne… 0.222 114740. 71977. 90507. 1.51e5 1.69e5 120286. 16 37.5
#> 9 11124 Ne… 0.0409 101794. 34390. 48523. 1.41e5 1.86e5 101743. 16.0 35.4
#> 10 11125 Ne… 1.59 72377. 51034. 61947. 8.87e4 1.09e5 77628. 15.0 28.5
#> # … with 654 more rows, 11 more variables: compensation_perc <dbl>,
#> # hours_per_month <dbl>, file <chr>, estimate_quality <lgl>, sfera <chr>,
#> # period <chr>, year <chr>, quarter <chr>, isco_digits <dbl>, isco_id <chr>,
#> # isco_name <chr>, and abbreviated variable names ¹fte_thous, ²pay_median,
#> # ³pay_mean, ⁴bonus_perc, ⁵supplements_perc
pv_cr_monthlypay_isco(system.file("extdata", "CR_204_PLS.xlsx", package = "ispv"), 7)
#> # A tibble: 289 × 21
#> isco_full fte_t…¹ pay_m…² pay_d1 pay_q1 pay_q3 pay_d9 pay_m…³ bonus…⁴ suppl…⁵
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0110 Gen… 6.60 56520. 42206. 48122. 6.57e4 7.40e4 57230. 1.21 6.65
#> 2 01102 Vy… 2.38 68335. 60819. 62950. 7.37e4 8.15e4 69755. 1.51 6.51
#> 3 01103 Ni… 4.18 50305. 40397. 45022. 5.59e4 5.99e4 49610. 0.98 6.76
#> 4 0210 Pod… 8.57 32639. 29070 30786. 3.56e4 3.87e4 33186. 1.17 5.41
#> 5 0310 Zam… 9.85 42321. 23871. 38351. 4.61e4 4.96e4 40149. 0.77 7.78
#> 6 03101 Pr… 8.13 43607. 38477. 40636. 4.70e4 5.01e4 43941. 0.72 8.45
#> 7 1112 Nej… 2.25 72287. 48419. 60775. 9.25e4 1.22e5 80163. 14.2 29.6
#> 8 11123 Ne… 0.222 114740. 71977. 90507. 1.51e5 1.69e5 120286. 16 37.5
#> 9 11125 Ne… 1.59 72377. 51034. 61947. 8.87e4 1.09e5 77628. 15.0 28.5
#> 10 1113 Pře… 0.185 55204. 35375. 45016. 6.55e4 7.69e4 56255. 10.6 25.8
#> # … with 279 more rows, 11 more variables: compensation_perc <dbl>,
#> # hours_per_month <dbl>, file <chr>, estimate_quality <lgl>, sfera <chr>,
#> # period <chr>, year <chr>, quarter <chr>, isco_digits <dbl>, isco_id <chr>,
#> # isco_name <chr>, and abbreviated variable names ¹fte_thous, ²pay_median,
#> # ³pay_mean, ⁴bonus_perc, ⁵supplements_perc