README.Rmd

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output: github_document
---

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```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
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```

# iNZightTools

![R-CMD-check](https://github.com/iNZightVIT/iNZightTools/workflows/R-CMD-check/badge.svg)
[![Coverage status](https://codecov.io/gh/iNZightVIT/iNZightTools/branch/dev/graph/badge.svg)](https://codecov.io/github/iNZightVIT/iNZightTools?branch=dev)
[![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](http://www.gnu.org/licenses/gpl-3.0)
[![CRAN](https://www.r-pkg.org/badges/version/iNZightTools)](https://CRAN.R-project.org/package=iNZightTools)

Package consisting of a set of helper functions for doing data science with _iNZight_. These functions are designed to work well with a graphical user interface (GUI), but many^[with others being modified in time] are functional for direct use through _R_.

## Installation

The current release version is available on CRAN:

```{r,eval=F}
install.packages("iNZightTools")
```

The development version can be downloaded from GitHub:

```{r,eval=F}
remotes::install_github("iNZightVIT/iNZightTools@dev")
```

## Basic usage

The package itself doesn't have any one specific use, but the functions can be broken down into various workflows.

```{r}
library(iNZightTools)
```

Most of the functions return not only the resulting data, but attach the _tidyverse_ code used to generate it. This is useful for GUIs that display code history (e.g., _iNZight_) or when learning to code.

### Data import

Importing data is done using the `smart_read()` function, which can read CSV, Excel, Stata, SAS, RData, and a few other formats based on the file extension.

```{r,warning=FALSE}
data <- smart_read(system.file("extdata/cas500.xls", package = "iNZightTools"))
str(data)
tidy_all_code(code(data))
```

### Surveys

Being an important but tricker data type to work with, _iNZightTools_ includes methods for easily importing surveys using a specification format. For details, check out https://inzight.nz/docs/survey-specification.html

### Other

There are many other data manipulation-focussed functions, such as filter, renaming variables, etc.

```{r}
filter_num(data, "height", "<", 150)
```