README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
basic_exists <- file.exists(Sys.getenv("EPIEXTRACTS_CPSBASIC_DIR"))
org_exists <- file.exists(Sys.getenv("EPIEXTRACTS_CPSORG_DIR"))
```

# epiextractr

<!-- badges: start -->
<!-- badges: end -->

epiextractr makes it easy to use the [EPI microdata extracts](https://microdata.epi.org/) in R.

## Example
Load a selection of variables from the 2019-2021 EPI CPS ORG extracts:
```{r, eval = org_exists}
library(epiextractr)
load_org(2019:2021, year, female, wage, orgwgt)
```
```{r, echo = FALSE, eval = !org_exists}
cat("
# A tibble: 824,963 × 4
    year orgwgt female      wage
   <int>  <dbl> <int+lbl>  <dbl>
 1  2019 11367. 1 [Female] 14   
 2  2019  6541. 1 [Female] 20.9 
 3  2019  6327. 0 [Male]    7.65
 4  2019  6327. 0 [Male]    7.65
 5  2019 11262. 1 [Female] 10   
 6  2019  7867. 1 [Female] 28.8 
 7  2019 11262. 1 [Female] 11   
 8  2019  7943. 0 [Male]   NA   
 9  2019  6092. 1 [Female] NA   
10  2019  7738. 0 [Male]   NA   
# … with 824,953 more rows
")
```

## Installation and basic usage

First, install the current version of the package from R-Universe:
```{r, eval = FALSE}
install.packages("epiextractr", repos = c("https://economic.r-universe.dev", "https://cloud.r-project.org"))
```

Then download the CPS microdata using `download_cps()`. For example,
```{r, eval = FALSE}
download_cps("org", "C:\data\cps")
```
will download the latest EPI CPS ORG extracts in .feather format from https://microdata.epi.org and place them in the directory `C:\data\cps`.

After the data is downloaded, load a selection of CPS data for your analysis:
```{r, eval = FALSE}
load_cps("org", 2000:2019, year, orgwgt, wage, wbho, .extracts_dir = "C:\data\cps")
```

See `vignette("epiextractr")` for more examples.