38 changed files with 539 additions and 76 deletions
@ -1,5 +1,9 @@ |
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0.1.0 |
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0.2.0 |
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* WIP |
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* Added new `get_` functions for new data sources provided by the EPI |
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* Fixed issues with different return values for some hidden API calls |
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|
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0.1.0 |
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* Passes CRAN checks |
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* Complete EPI SWADL hidden API coverage |
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* Initial release |
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|
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@ -0,0 +1,47 @@ |
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#' Retreive Health Insurance Coverage |
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#' |
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#' Employer-sponsored health insurance (ESI) coverage shows the share of workers who |
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#' received health insurance from their own job for which their employer paid for at |
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#' least some of their health insurance coverage. |
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#' |
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#' Population sample: Private-sector workers age 18–64 & at least 20 hours/week and 26 weeks/year |
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#' |
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#' @param by \code{NULL} or character string with any combination of \code{g} (Gender), |
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#' \code{r} (Race), \code{e} (Education), \code{d} (Percentile), \code{l} (Entry-level) |
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#' i.e. if you want to retrieve unemployment data by gender and race, you would set this |
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#' parameter to "\code{gr}". |
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#' @return \code{tbl_df} with data filtered by the selected criteria. |
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#' @references \href{http://www.epi.org/data/}{Economic Policy Institute Data Library} |
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#' @note Data source: CPS ASEC |
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#' @export |
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#' @examples |
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#' get_health_insurance_coverage() |
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#' |
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#' get_health_insurance_coverage("r") |
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#' |
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#' get_health_insurance_coverage("gr") |
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get_health_insurance_coverage <- function(by=NULL) { |
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params <- list(subject="healthcov") |
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if (!is.null(by)) { |
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params <- make_params(params, by, c("g", "r", "e", "d", "l")) |
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} |
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names(params) <- gsub("^l$", "el", names(params)) |
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|
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res <- epi_query(params) |
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|
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cols <- stringi::stri_trans_tolower(res$columns$name) |
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cols <- stringi::stri_replace_all_regex(cols, "[[:space:]" %s+% |
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rawToChar(as.raw(c(0xe2, 0x80, 0x93))) %s+% "-]+", |
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"_") |
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out <- setNames(as_data_frame(res$data), cols) |
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out <- dplyr::mutate_all(out, "clean_cols") |
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out <- suppressMessages(readr::type_convert(out)) |
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|
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cite <- html_text(read_html(res$meta$source %||% "<p>Economic Policy Institute</p>")) |
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message(sprintf('Note: %s\nCitation: "%s"', res$meta$notes %||% "None", cite)) |
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|
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out |
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|
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} |
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@ -0,0 +1,47 @@ |
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#' Retreive Pension Coverage |
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#' |
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#' Employer-provided pension coverage shows the share of workers included in an |
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#' employer-provided plan for which the employer paid for at least some of their pension |
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#' coverage. |
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#' |
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#' Population sample: Private-sector workers age 18–64 & at least 20 hours/week and 26 weeks/year |
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#' |
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#' @param by \code{NULL} or character string with any combination of \code{g} (Gender), |
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#' \code{r} (Race), \code{e} (Education), \code{d} (Percentile), \code{l} (Entry-level) |
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#' i.e. if you want to retrieve pension data by gender and race, you would set this |
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#' parameter to "\code{gr}". |
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#' @return \code{tbl_df} with data filtered by the selected criteria. |
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#' @references \href{http://www.epi.org/data/}{Economic Policy Institute Data Library} |
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#' @note Data source: CPS ASEC |
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#' @export |
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#' @examples |
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#' get_health_insurance_coverage() |
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#' |
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#' get_health_insurance_coverage("r") |
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#' |
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#' get_health_insurance_coverage("gr") |
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get_pension_coverage <- function(by=NULL) { |
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params <- list(subject="pensioncov") |
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if (!is.null(by)) { |
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params <- make_params(params, by, c("g", "r", "e", "d", "l")) |
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} |
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names(params) <- gsub("^l$", "el", names(params)) |
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res <- epi_query(params) |
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|
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cols <- stringi::stri_trans_tolower(res$columns$name) |
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cols <- stringi::stri_replace_all_regex(cols, "[[:space:]" %s+% |
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rawToChar(as.raw(c(0xe2, 0x80, 0x93))) %s+% "-]+", |
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"_") |
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out <- setNames(as_data_frame(res$data), cols) |
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out <- dplyr::mutate_all(out, "clean_cols") |
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out <- suppressMessages(readr::type_convert(out)) |
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|
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cite <- html_text(read_html(res$meta$source %||% "<p>Economic Policy Institute</p>")) |
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message(sprintf('Note: %s\nCitation: "%s"', res$meta$notes %||% "None", cite)) |
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out |
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} |
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@ -0,0 +1,44 @@ |
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#' Retreive Productivity and hourly compensation |
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#' |
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#' Productivity is how much workers produce per hour, or the growth of output of goods and |
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#' services minus depreciation per hour worked. Compensation is made up of both nonwage |
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#' payments and wages. |
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#' |
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#' Wages are in 2015 dollars. Median compensation is calculated using hourly wage medians |
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#' from the CPS ORG and compensation from NIPA. |
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#' |
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#' Population sample: All workers & Production and nonsupervisory workers |
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#' |
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#' @param by \code{NULL} or character string of \code{g} (Gender) |
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#' @return \code{tbl_df} with data filtered by the selected criteria. |
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#' @references \href{http://www.epi.org/data/}{Economic Policy Institute Data Library} |
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#' @note Data source: NIPA (compensation) | BLS Productivity Data |
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#' @export |
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#' @examples |
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#' get_productivity_and_hourly_compensation() |
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#' |
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#' get_productivity_and_hourly_compensation("g") |
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get_productivity_and_hourly_compensation <- function(by=NULL) { |
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params <- list(subject="prodpay") |
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if (!is.null(by)) { |
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params <- make_params(params, by, "g") |
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} |
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res <- epi_query(params) |
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cols <- stringi::stri_trans_tolower(res$columns$name) |
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cols <- stringi::stri_replace_all_regex(cols, "[[:space:]" %s+% |
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rawToChar(as.raw(c(0xe2, 0x80, 0x93))) %s+% "-]+", |
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"_") |
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out <- setNames(as_data_frame(res$data), cols) |
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out <- dplyr::mutate_all(out, "clean_cols") |
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out <- suppressMessages(readr::type_convert(out)) |
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|
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cite <- html_text(read_html(res$meta$source %||% "<p>Economic Policy Institute</p>")) |
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message(sprintf('Note: %s\nCitation: "%s"', res$meta$notes %||% "None", cite)) |
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out |
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} |
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@ -0,0 +1,32 @@ |
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#' Retreive Union Coverage |
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#' |
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#' The union coverage rate shows the percentage of the workforce covered by a collective |
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#' bargaining agreement. |
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#' |
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#' @return \code{tbl_df} |
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#' @references \href{http://www.epi.org/data/}{Economic Policy Institute Data Library} |
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#' @note Data source: CPS ORG | Hirsch and Macpherson (2003) |
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#' @export |
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#' @examples |
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#' get_union_coverage() |
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get_union_coverage <- function() { |
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params <- list(subject="unioncov") |
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res <- epi_query(params) |
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cols <- stringi::stri_trans_tolower(res$columns$name) |
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cols <- stringi::stri_replace_all_regex(cols, "[\\(\\)]", "") |
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cols <- stringi::stri_replace_all_regex(cols, "[[:space:]" %s+% |
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rawToChar(as.raw(c(0xe2, 0x80, 0x93))) %s+% "-]+", |
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"_") |
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out <- setNames(as_data_frame(res$data), cols) |
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out <- dplyr::mutate_all(out, "clean_cols") |
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out <- suppressMessages(readr::type_convert(out)) |
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|
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cite <- html_text(read_html(res$meta$source %||% "<p>Economic Policy Institute</p>")) |
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message(sprintf('Note: %s\nCitation: "%s"', res$meta$notes %||% "None", cite)) |
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out |
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} |
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@ -0,0 +1,42 @@ |
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#' Retreive Wage Decomposition |
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#' |
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#' Wage inequality data shows the overall wage inequality and the within-group and |
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#' between-group wage inequality over time. These measures allow an examination of how |
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#' much of the change in overall wage inequality in particular periods was due to changes |
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#' in within-group and between-group wage inequality. |
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#' |
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#' Population sample: Wage and salary workers age 18–64 |
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#' |
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#' @param by \code{NULL} or character string of \code{g} (Gender) |
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#' @return \code{tbl_df} with data filtered by the selected criteria. |
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#' @references \href{http://www.epi.org/data/}{Economic Policy Institute Data Library} |
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#' @note Data source: CPS ORG |
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#' @export |
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#' @examples |
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#' get_wages_by_percentile() |
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#' |
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#' get_wages_by_percentile("g") |
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get_wage_decomposition <- function(by=NULL) { |
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params <- list(subject="wageineq") |
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if (!is.null(by)) { |
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params <- make_params(params, by, "g") |
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} |
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res <- epi_query(params) |
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cols <- stringi::stri_trans_tolower(res$columns$name) |
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cols <- stringi::stri_replace_all_regex(cols, "[[:space:]" %s+% |
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rawToChar(as.raw(c(0xe2, 0x80, 0x93))) %s+% "-]+", |
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"_") |
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out <- setNames(as_data_frame(res$data), cols) |
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out <- dplyr::mutate_all(out, "clean_cols") |
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out <- suppressMessages(readr::type_convert(out)) |
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|
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cite <- html_text(read_html(res$meta$source %||% "<p>Economic Policy Institute</p>")) |
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message(sprintf('Note: %s\nCitation: "%s"', res$meta$notes %||% "None", cite)) |
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out |
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} |
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@ -0,0 +1,34 @@ |
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#' Retreive CPS ASEC Annual Wages and Work Hours |
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#' |
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#' Annual, weekly, and hourly wages and work hours show the average wages and work hours |
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#' of wage and salary workers using data from the CPS ASEC (also known as the March CPS). |
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#' Note that this data is not directly comparable to the CPS ORG data in median/average |
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#' hourly wage. |
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#' |
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#' @return \code{tbl_df} |
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#' @references \href{http://www.epi.org/data/}{Economic Policy Institute Data Library} |
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#' @note CPS ASEC | Murphy and Welch (1989) |
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#' @export |
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#' @examples |
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#' get_annual_wages_and_work_hours() |
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get_annual_wages_and_work_hours <- function() { |
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params <- list(subject="hours") |
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res <- epi_query(params) |
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|
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cols <- stringi::stri_trans_tolower(res$columns$name) |
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cols <- stringi::stri_replace_all_regex(cols, "[\\(\\)]", "") |
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cols <- stringi::stri_replace_all_regex(cols, "[[:space:]" %s+% |
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rawToChar(as.raw(c(0xe2, 0x80, 0x93))) %s+% "-]+", |
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"_") |
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out <- setNames(as_data_frame(res$data), cols) |
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out <- dplyr::mutate_all(out, "clean_cols") |
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out <- suppressMessages(readr::type_convert(out)) |
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|
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cite <- html_text(read_html(res$meta$source %||% "<p>Economic Policy Institute</p>")) |
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message(sprintf('Note: %s\nCitation: "%s"', res$meta$notes %||% "None", cite)) |
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out |
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|
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} |
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After Width: | Height: | Size: 282 KiB |
@ -0,0 +1,26 @@ |
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% Generated by roxygen2: do not edit by hand |
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% Please edit documentation in R/wages_and_hours.r |
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\name{get_annual_wages_and_work_hours} |
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\alias{get_annual_wages_and_work_hours} |
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\title{Retreive CPS ASEC Annual Wages and Work Hours} |
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\usage{ |
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get_annual_wages_and_work_hours() |
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} |
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\value{ |
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\code{tbl_df} |
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} |
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\description{ |
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Annual, weekly, and hourly wages and work hours show the average wages and work hours |
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of wage and salary workers using data from the CPS ASEC (also known as the March CPS). |
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Note that this data is not directly comparable to the CPS ORG data in median/average |
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hourly wage. |
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} |
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\note{ |
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CPS ASEC | Murphy and Welch (1989) |
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} |
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\examples{ |
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get_annual_wages_and_work_hours() |
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} |
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\references{ |
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\href{http://www.epi.org/data/}{Economic Policy Institute Data Library} |
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} |
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@ -0,0 +1,38 @@ |
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% Generated by roxygen2: do not edit by hand |
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% Please edit documentation in R/health.r |
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\name{get_health_insurance_coverage} |
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\alias{get_health_insurance_coverage} |
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\title{Retreive Health Insurance Coverage} |
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\usage{ |
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get_health_insurance_coverage(by = NULL) |
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} |
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\arguments{ |
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\item{by}{\code{NULL} or character string with any combination of \code{g} (Gender), |
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\code{r} (Race), \code{e} (Education), \code{d} (Percentile), \code{l} (Entry-level) |
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i.e. if you want to retrieve unemployment data by gender and race, you would set this |
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parameter to "\code{gr}".} |
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} |
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\value{ |
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\code{tbl_df} with data filtered by the selected criteria. |
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} |
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\description{ |
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Employer-sponsored health insurance (ESI) coverage shows the share of workers who |
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received health insurance from their own job for which their employer paid for at |
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least some of their health insurance coverage. |
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} |
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\details{ |
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Population sample: Private-sector workers age 18–64 & at least 20 hours/week and 26 weeks/year |
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} |
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\note{ |
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Data source: CPS ASEC |
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} |
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\examples{ |
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get_health_insurance_coverage() |
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get_health_insurance_coverage("r") |
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|
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get_health_insurance_coverage("gr") |
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} |
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\references{ |
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\href{http://www.epi.org/data/}{Economic Policy Institute Data Library} |
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} |
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@ -0,0 +1,38 @@ |
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% Generated by roxygen2: do not edit by hand |
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% Please edit documentation in R/pension.r |
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\name{get_pension_coverage} |
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\alias{get_pension_coverage} |
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\title{Retreive Pension Coverage} |
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\usage{ |
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get_pension_coverage(by = NULL) |
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} |
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\arguments{ |
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\item{by}{\code{NULL} or character string with any combination of \code{g} (Gender), |
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\code{r} (Race), \code{e} (Education), \code{d} (Percentile), \code{l} (Entry-level) |
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i.e. if you want to retrieve pension data by gender and race, you would set this |
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parameter to "\code{gr}".} |
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} |
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\value{ |
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\code{tbl_df} with data filtered by the selected criteria. |
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} |
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\description{ |
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Employer-provided pension coverage shows the share of workers included in an |
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employer-provided plan for which the employer paid for at least some of their pension |
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coverage. |
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} |
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\details{ |
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Population sample: Private-sector workers age 18–64 & at least 20 hours/week and 26 weeks/year |
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} |
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\note{ |
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Data source: CPS ASEC |
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} |
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\examples{ |
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get_health_insurance_coverage() |
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|
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get_health_insurance_coverage("r") |
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|
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get_health_insurance_coverage("gr") |
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} |
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\references{ |
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\href{http://www.epi.org/data/}{Economic Policy Institute Data Library} |
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} |
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@ -0,0 +1,36 @@ |
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% Generated by roxygen2: do not edit by hand |
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% Please edit documentation in R/productivity.r |
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\name{get_productivity_and_hourly_compensation} |
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\alias{get_productivity_and_hourly_compensation} |
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\title{Retreive Productivity and hourly compensation} |
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\usage{ |
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get_productivity_and_hourly_compensation(by = NULL) |
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} |
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\arguments{ |
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\item{by}{\code{NULL} or character string of \code{g} (Gender)} |
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} |
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\value{ |
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\code{tbl_df} with data filtered by the selected criteria. |
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} |
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\description{ |
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Productivity is how much workers produce per hour, or the growth of output of goods and |
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services minus depreciation per hour worked. Compensation is made up of both nonwage |
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payments and wages. |
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} |
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\details{ |
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Wages are in 2015 dollars. Median compensation is calculated using hourly wage medians |
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from the CPS ORG and compensation from NIPA. |
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|
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Population sample: All workers & Production and nonsupervisory workers |
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} |
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\note{ |
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Data source: NIPA (compensation) | BLS Productivity Data |
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} |
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\examples{ |
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get_productivity_and_hourly_compensation() |
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|
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get_productivity_and_hourly_compensation("g") |
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} |
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\references{ |
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\href{http://www.epi.org/data/}{Economic Policy Institute Data Library} |
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} |
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@ -0,0 +1,24 @@ |
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% Generated by roxygen2: do not edit by hand |
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% Please edit documentation in R/union.r |
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\name{get_union_coverage} |
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\alias{get_union_coverage} |
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\title{Retreive Union Coverage} |
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\usage{ |
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get_union_coverage() |
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} |
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\value{ |
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\code{tbl_df} |
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} |
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\description{ |
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The union coverage rate shows the percentage of the workforce covered by a collective |
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bargaining agreement. |
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} |
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\note{ |
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Data source: CPS ORG | Hirsch and Macpherson (2003) |
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} |
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\examples{ |
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get_union_coverage() |
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} |
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\references{ |
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\href{http://www.epi.org/data/}{Economic Policy Institute Data Library} |
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} |
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@ -0,0 +1,34 @@ |
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% Generated by roxygen2: do not edit by hand |
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% Please edit documentation in R/wage_decomposition.r |
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\name{get_wage_decomposition} |
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\alias{get_wage_decomposition} |
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\title{Retreive Wage Decomposition} |
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\usage{ |
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get_wage_decomposition(by = NULL) |
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} |
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\arguments{ |
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\item{by}{\code{NULL} or character string of \code{g} (Gender)} |
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} |
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\value{ |
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\code{tbl_df} with data filtered by the selected criteria. |
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} |
|||
\description{ |
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Wage inequality data shows the overall wage inequality and the within-group and |
|||
between-group wage inequality over time. These measures allow an examination of how |
|||
much of the change in overall wage inequality in particular periods was due to changes |
|||
in within-group and between-group wage inequality. |
|||
} |
|||
\details{ |
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Population sample: Wage and salary workers age 18–64 |
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} |
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\note{ |
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Data source: CPS ORG |
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} |
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\examples{ |
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get_wages_by_percentile() |
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|
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get_wages_by_percentile("g") |
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} |
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\references{ |
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\href{http://www.epi.org/data/}{Economic Policy Institute Data Library} |
|||
} |
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