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Fix for CRAN ERRORs - moved to https API

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  1. 6
      DESCRIPTION
  2. 1
      NEWS.md
  3. 2
      R/epi_query.r
  4. 38
      README.Rmd
  5. 188
      README.md
  6. BIN
      README_files/figure-gfm/unnamed-chunk-4-1.png
  7. 14
      cran-comments.md

6
DESCRIPTION
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@ -1,8 +1,8 @@
Package: epidata
Type: Package
Title: Tools to Retrieve Economic Policy Institute Data Library Extracts
Version: 0.1.0
Date: 2017-01-08
Version: 0.2.0
Date: 2018-03-29
Authors@R: c(person("Bob", "Rudis", email = "bob@rud.is", role = c("aut", "cre")))
Maintainer: Bob Rudis <bob@rud.is>
Encoding: UTF-8
@ -29,4 +29,4 @@ Imports:
tidyr,
readr,
stringi
RoxygenNote: 6.0.1
RoxygenNote: 6.0.1.9000

1
NEWS.md
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@ -2,6 +2,7 @@
* WIP
* Added new `get_` functions for new data sources provided by the EPI
* Fixed issues with different return values for some hidden API calls
* Updated to use new https
0.1.0
* Passes CRAN checks

2
R/epi_query.r
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@ -4,7 +4,7 @@ epi_query <- function(args) {
qs <- paste(sprintf("%s=%s", names(args), args), collapse="&")
httr::POST(
"http://www.epi.org/wp-admin/admin-ajax.php",
"https://www.epi.org/wp-admin/admin-ajax.php",
httr::add_headers(`X-Requested-With`="XMLHttpRequest"),
encode="form",
body=list(action="epi_getdata", queryString = qs)) -> res

38
README.Rmd
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@ -2,15 +2,21 @@
output: rmarkdown::github_document
---
```{r message=FALSE, warning=FALSE, error=FALSE, include=FALSE}
options(width=120)
```
[![Travis-CI Build Status](https://travis-ci.org/hrbrmstr/epidata.svg?branch=master)](https://travis-ci.org/hrbrmstr/epidata)
`epidata` : Tools to Retrieve Economic Policy Institute Data Library Extracts
# epidata
Tools to Retrieve Economic Policy Institute Data Library Extracts
The [Economic Policy Institute](http://www.epi.org/data/) provides researchers, media, and
the public with easily accessible, up-to-date, and comprehensive historical data on the
American labor force. It is compiled from Economic Policy Institute analysis of government
data sources. Use it to research wages, inequality, and other economic indicators over
time and among demographic groups. Data is usually updated monthly.
## Description
The [Economic Policy Institute](http://www.epi.org/data/) provides researchers, media, and the public with easily accessible, up-to-date, and comprehensive historical data on the American labor force. It is compiled from Economic Policy Institute analysis of government data sources. Use it to research wages, inequality, and other economic indicators over time and among demographic groups. Data is usually updated monthly.
## What's Inside The Tin?
The following functions are implemented:
@ -45,17 +51,13 @@ The following functions are implemented:
- `get_wage_decomposition`: Retreive Wage Decomposition
- `get_wage_ratios`: Retreive the level of inequality within the hourly wage distribution.
### Installation
## Installation
```{r eval=FALSE}
devtools::install_github("hrbrmstr/epidata")
```
```{r message=FALSE, warning=FALSE, error=FALSE, include=FALSE}
options(width=120)
```
### Usage
## Usage
```{r message=FALSE, warning=FALSE, error=FALSE}
library(epidata)
@ -70,7 +72,7 @@ get_underemployment()
get_median_and_mean_wages("gr")
```
### Extended Example
## Extended Example
```{r message=FALSE, warning=FALSE, error=FALSE, fig.width=10, fig.height=8, fig.retina=2}
library(tidyverse)
@ -112,15 +114,5 @@ ggplot(df, aes(rate, median)) +
caption="Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata") +
theme_ipsum_rc(grid="XY")
```
### Test Results
```{r message=FALSE, warning=FALSE, error=FALSE}
library(epidata)
library(testthat)
date()
test_dir("tests/")
```

188
README.md
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@ -1,40 +1,73 @@
[![Travis-CI Build Status](https://travis-ci.org/hrbrmstr/epidata.svg?branch=master)](https://travis-ci.org/hrbrmstr/epidata)
[![Travis-CI Build
Status](https://travis-ci.org/hrbrmstr/epidata.svg?branch=master)](https://travis-ci.org/hrbrmstr/epidata)
`epidata` : Tools to Retrieve Economic Policy Institute Data Library Extracts
# epidata
The [Economic Policy Institute](http://www.epi.org/data/) provides researchers, media, and the public with easily accessible, up-to-date, and comprehensive historical data on the American labor force. It is compiled from Economic Policy Institute analysis of government data sources. Use it to research wages, inequality, and other economic indicators over time and among demographic groups. Data is usually updated monthly.
Tools to Retrieve Economic Policy Institute Data Library Extracts
## Description
The [Economic Policy Institute](http://www.epi.org/data/) provides
researchers, media, and the public with easily accessible, up-to-date,
and comprehensive historical data on the American labor force. It is
compiled from Economic Policy Institute analysis of government data
sources. Use it to research wages, inequality, and other economic
indicators over time and among demographic groups. Data is usually
updated monthly.
## What’s Inside The Tin?
The following functions are implemented:
- `get_annual_wages_and_work_hours`: Retreive CPS ASEC Annual Wages and Work Hours
- `get_black_white_wage_gap`: Retreive the percent by which hourly wages of black workers are less than hourly wages of white workers
- `get_college_wage_premium`: Retreive the percent by which hourly wages of college graduates exceed those of otherwise equivalent high school graduates
- `get_employment_to_population_ratio`: Retreive the share of the civilian noninstitutional population that is employed
- `get_gender_wage_gap`: Retreive the percent by which hourly wages of female workers are less than hourly wages of male workers
- `get_health_insurance_coverage`: Retreive Health Insurance Coverage
- `get_hispanic_white_wage_gap`: Retreive the percent by which hourly wages of Hispanic workers are less than hourly wages of white workers
- `get_labor_force_participation_rate`: Retreive the share of the civilian noninstitutional population that is in the labor force
- `get_long_term_unemployment`: Retreive the share of the labor force that has been unemployed for six months or longer
- `get_median_and_mean_wages`: Retreive the hourly wage in the middle of the wage distribution
- `get_pension_coverage`: Retreive Pension Coverage
- `get_non_high_school_wage_penalty`: Retreive the percent by which hourly wages of workers without a high school diploma (or equivalent) are less than wages of otherwise equivalent workers who have graduated from high school
- `get_underemployment`: Retreive the share of the labor force that is "underemployed"
- `get_unemployment`: Retreive the share of the labor force without a job
- `get_unemployment_by_state`: Retreive the share of the labor force without a job (by state)
- `get_union_coverage`: Retreive Union Coverage
- `get_wages_by_education`: Retreive the average hourly wages of workers disaggregated by the highest level of education attained
- `get_wages_by_percentile`: Retreive wages at ten distinct points in the wage distribution
- `get_wage_decomposition`: Retreive Wage Decomposition
- `get_wage_ratios`: Retreive the level of inequality within the hourly wage distribution.
### Installation
- `get_annual_wages_and_work_hours`: Retreive CPS ASEC Annual Wages
and Work Hours
- `get_black_white_wage_gap`: Retreive the percent by which hourly
wages of black workers are less than hourly wages of white workers
- `get_college_wage_premium`: Retreive the percent by which hourly
wages of college graduates exceed those of otherwise equivalent high
school graduates
- `get_employment_to_population_ratio`: Retreive the share of the
civilian noninstitutional population that is employed
- `get_gender_wage_gap`: Retreive the percent by which hourly wages of
female workers are less than hourly wages of male workers
- `get_health_insurance_coverage`: Retreive Health Insurance Coverage
- `get_hispanic_white_wage_gap`: Retreive the percent by which hourly
wages of Hispanic workers are less than hourly wages of white
workers
- `get_labor_force_participation_rate`: Retreive the share of the
civilian noninstitutional population that is in the labor force
- `get_long_term_unemployment`: Retreive the share of the labor force
that has been unemployed for six months or longer
- `get_median_and_mean_wages`: Retreive the hourly wage in the middle
of the wage distribution
- `get_pension_coverage`: Retreive Pension Coverage
- `get_non_high_school_wage_penalty`: Retreive the percent by which
hourly wages of workers without a high school diploma (or
equivalent) are less than wages of otherwise equivalent workers who
have graduated from high school
- `get_underemployment`: Retreive the share of the labor force that is
“underemployed”
- `get_unemployment`: Retreive the share of the labor force without a
job
- `get_unemployment_by_state`: Retreive the share of the labor force
without a job (by state)
- `get_union_coverage`: Retreive Union Coverage
- `get_wages_by_education`: Retreive the average hourly wages of
workers disaggregated by the highest level of education attained
- `get_wages_by_percentile`: Retreive wages at ten distinct points in
the wage distribution
- `get_wage_decomposition`: Retreive Wage Decomposition
- `get_wage_ratios`: Retreive the level of inequality within the
hourly wage distribution.
## Installation
``` r
devtools::install_github("hrbrmstr/epidata")
```
### Usage
## Usage
``` r
library(epidata)
@ -49,63 +82,63 @@ packageVersion("epidata")
get_black_white_wage_gap()
```
## # A tibble: 44 x 8
## # A tibble: 45 x 8
## date white_median white_average black_median black_average gap_median gap_average gap_regression_based
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1973 17.41 19.93 13.67 15.63 0.215 0.216 0.120
## 2 1974 16.94 19.46 13.51 15.38 0.203 0.210 0.107
## 3 1975 16.75 19.47 13.60 15.33 0.188 0.213 0.105
## 4 1976 16.94 19.63 13.62 15.94 0.196 0.188 0.089
## 5 1977 16.93 19.57 13.58 15.71 0.198 0.197 0.094
## 6 1978 16.93 19.68 13.55 15.92 0.200 0.191 0.092
## 7 1979 17.10 19.89 14.02 16.29 0.180 0.181 0.090
## 8 1980 16.79 19.47 13.67 15.93 0.185 0.182 0.092
## 9 1981 16.42 19.34 13.50 15.84 0.178 0.181 0.087
## 10 1982 16.68 19.51 13.34 15.65 0.200 0.198 0.103
## # ... with 34 more rows
## 1 1973 17.8 20.4 14.1 16.0 0.211 0.213 0.114
## 2 1974 17.4 19.9 13.8 15.7 0.204 0.211 0.103
## 3 1975 17.3 20.0 14.0 15.8 0.187 0.211 0.100
## 4 1976 17.3 20.0 13.9 16.3 0.195 0.188 0.0850
## 5 1977 17.5 20.1 14.0 16.2 0.195 0.195 0.0890
## 6 1978 17.3 20.1 13.8 16.3 0.200 0.191 0.0880
## 7 1979 17.5 20.3 14.3 16.6 0.180 0.181 0.0860
## 8 1980 17.2 19.9 14.0 16.3 0.185 0.182 0.0890
## 9 1981 16.8 19.7 13.8 16.2 0.178 0.181 0.0830
## 10 1982 17.0 19.9 13.6 16.0 0.200 0.198 0.100
## # ... with 35 more rows
``` r
get_underemployment()
```
## # A tibble: 325 x 2
## date all
## <date> <dbl>
## 1 1989-12-01 0.093
## 2 1990-01-01 0.093
## 3 1990-02-01 0.093
## 4 1990-03-01 0.094
## 5 1990-04-01 0.094
## 6 1990-05-01 0.094
## 7 1990-06-01 0.094
## 8 1990-07-01 0.094
## 9 1990-08-01 0.095
## 10 1990-09-01 0.095
## date all
## <date> <dbl>
## 1 1989-12-01 0.0930
## 2 1990-01-01 0.0930
## 3 1990-02-01 0.0930
## 4 1990-03-01 0.0940
## 5 1990-04-01 0.0940
## 6 1990-05-01 0.0940
## 7 1990-06-01 0.0940
## 8 1990-07-01 0.0940
## 9 1990-08-01 0.0950
## 10 1990-09-01 0.0950
## # ... with 315 more rows
``` r
get_median_and_mean_wages("gr")
```
## # A tibble: 44 x 25
## # A tibble: 45 x 25
## date median average men_median men_average women_median women_average white_median white_average black_median
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1973 16.74 19.30 20.14 22.60 12.63 14.48 17.41 19.93 13.67
## 2 1974 16.37 18.91 19.88 22.17 12.54 14.22 16.94 19.46 13.51
## 3 1975 16.26 18.87 20.01 22.09 12.59 14.32 16.75 19.47 13.60
## 4 1976 16.36 19.11 19.65 22.33 12.72 14.71 16.94 19.63 13.62
## 5 1977 16.28 19.00 20.09 22.33 12.66 14.54 16.93 19.57 13.58
## 6 1978 16.57 19.07 20.29 22.46 12.72 14.62 16.93 19.68 13.55
## 7 1979 16.36 19.30 20.55 22.75 12.82 14.82 17.10 19.89 14.02
## 8 1980 16.28 18.89 20.24 22.28 12.76 14.65 16.79 19.47 13.67
## 9 1981 15.85 18.75 19.77 22.09 12.69 14.62 16.42 19.34 13.50
## 10 1982 15.95 18.89 19.54 22.24 12.76 14.87 16.68 19.51 13.34
## # ... with 34 more rows, and 15 more variables: black_average <dbl>, hispanic_median <dbl>, hispanic_average <dbl>,
## 1 1973 17.2 19.8 20.6 23.1 12.9 14.8 17.8 20.4 14.1
## 2 1974 16.8 19.4 20.3 22.7 12.9 14.6 17.4 19.9 13.8
## 3 1975 16.7 19.4 20.5 22.7 13.0 14.7 17.3 20.0 14.0
## 4 1976 16.7 19.5 20.1 22.8 13.0 15.0 17.3 20.0 13.9
## 5 1977 16.8 19.5 20.7 23.0 13.0 14.9 17.5 20.1 14.0
## 6 1978 16.9 19.5 20.7 22.9 13.0 14.9 17.3 20.1 13.8
## 7 1979 16.7 19.7 21.0 23.2 13.1 15.1 17.5 20.3 14.3
## 8 1980 16.6 19.3 20.7 22.8 13.0 15.0 17.2 19.9 14.0
## 9 1981 16.2 19.1 20.2 22.6 13.0 14.9 16.8 19.7 13.8
## 10 1982 16.3 19.3 20.0 22.7 13.0 15.2 17.0 19.9 13.6
## # ... with 35 more rows, and 15 more variables: black_average <dbl>, hispanic_median <dbl>, hispanic_average <dbl>,
## # white_men_median <dbl>, white_men_average <dbl>, black_men_median <dbl>, black_men_average <dbl>,
## # hispanic_men_median <dbl>, hispanic_men_average <dbl>, white_women_median <dbl>, white_women_average <dbl>,
## # black_women_median <dbl>, black_women_average <dbl>, hispanic_women_median <dbl>, hispanic_women_average <dbl>
### Extended Example
## Extended Example
``` r
library(tidyverse)
@ -119,11 +152,11 @@ wages <- get_median_and_mean_wages()
glimpse(wages)
```
## Observations: 44
## Observations: 45
## Variables: 3
## $ date <int> 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 198...
## $ median <dbl> 16.74, 16.37, 16.26, 16.36, 16.28, 16.57, 16.36, 16.28, 15.85, 15.95, 15.91, 15.91, 16.00, 16.47, 1...
## $ average <dbl> 19.30, 18.91, 18.87, 19.11, 19.00, 19.07, 19.30, 18.89, 18.75, 18.89, 18.91, 18.99, 19.20, 19.66, 1...
## $ median <dbl> 17.16, 16.78, 16.73, 16.70, 16.76, 16.92, 16.71, 16.63, 16.18, 16.28, 16.26, 16.24, 16.34, 16.81, 1...
## $ average <dbl> 19.75, 19.36, 19.39, 19.51, 19.52, 19.47, 19.72, 19.31, 19.14, 19.29, 19.31, 19.38, 19.61, 20.06, 2...
``` r
glimpse(unemployment)
@ -163,24 +196,5 @@ ggplot(df, aes(rate, median)) +
theme_ipsum_rc(grid="XY")
```
<img src="README_files/figure-markdown_github-ascii_identifiers/unnamed-chunk-4-1.png" width="960" />
### Test Results
``` r
library(epidata)
library(testthat)
date()
```
## [1] "Tue Aug 1 18:06:05 2017"
``` r
test_dir("tests/")
```
## testthat results ========================================================================================================
## OK: 21 SKIPPED: 0 FAILED: 0
##
## DONE ===================================================================================================================
<img src="README_files/figure-gfm/unnamed-chunk-4-1.png" width="960" />
\`\`\`

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cran-comments.md
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## Test environments
* local OS X install, R 3.3.2, R-release
* local OS X install, R 3.4.4
* ubuntu 12.04 (on travis-ci), R-oldrel, R-devel, R-release
* win-builder (devel and release)
@ -8,7 +8,7 @@
0 errors | 0 warnings | 0 notes
* This is a new release.
* This is a bugfix release.
## Reverse dependencies
@ -16,9 +16,7 @@ This is a new release, so there are no reverse dependencies.
---
- Modified DESCRIPTION as per Kurt's submission feedback.
- examples and tests hit a live server but they httr::stop_on_status() on error and
they run under the CRAN time limit for tests. If you'd rather they be in a
\dontrun{} block or just not run or tested on CRAN that's cool and I'll modify
and re-submit.
- Big #ty to Kurt for reaching out. EPI changed their API endpoint
to https which caused the error. This has been fixed.
- Also added a few new API endpoints and updated others to account for
new fields being available.
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