https://github.com/wfmackey/abscorr

This package has been superseded by runapp/strayr. Please see https://github.com/runapp-aus/strayr.
https://github.com/wfmackey/abscorr

australia correspondence data-science r

Last synced: 11 days ago
JSON representation

This package has been superseded by runapp/strayr. Please see https://github.com/runapp-aus/strayr.

• Host: GitHub
• URL: https://github.com/wfmackey/abscorr
• Owner: wfmackey
• Created: 2019-09-06T02:54:28.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-06-03T13:02:51.000Z (over 3 years ago)
• Last Synced: 2024-12-24T19:54:58.935Z (11 days ago)
• Topics: australia, correspondence, data-science, r
• Language: R
• Homepage:
• Size: 189 KB
• Stars: 3
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd

Awesome Lists containing this project

README

        
---

output: github_document

---

```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

data_path <- "man/data/egdata.csv"

```

# abscorr

`abscorr` is an R package that helps you deal with ABS correspondences. 

**For the moment**, `abscorr` provides easy access to common ABS structures, like

the Australian and New Zealand Standard Classification of Occupations (ANZSCO) 

and the Australian Standard Classification of Education (ASCED). 

**In the future**, it will contain correspondence functions 

(kind of like `absmapsdata::get_correspondence_absmaps`) for imperfect correspondence matching.

Installation and examples below.

I'll be adding commonly-used structures as I use them myself. If you'd like 

to request one, let me know via a Github issue or email at [[email protected]](mailto:[email protected]).

## Installation

You can install the current version of `abscorr` from [GitHub](https://github.com/) with:

``` r

# install.packages("remotes")

remotes::install_github("wfmackey/abscorr")

```

## Example

```{r example, message=FALSE}

library(tidyverse)

library(abscorr)

glimpse(anzsco)

glimpse(asced_foe)

```

These tibbles can be tweaked and joined to your existing datasets. For example,

given a dataset of Australians by four-digit field of education and age group:

```{r}

data <- read_csv(data_path)

glimpse(data)

```

We can retrieve the four-digit fields and their corresponding two-digit fields

from `abscorr::asced_foe`:

```{r}

join_foe2 <- abscorr::asced_foe %>% 

  select(foe2, foe4) %>%   # just keep the variables you want

  distinct()               # only keep unique observations

join_foe2

```

And join with our original dataset:

```{r}

data %>% 

  left_join(join_foe2)

```

Beaut.