README.Rmd

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# quickinspectR

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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

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The goal of **quickinspectR** is to make it easier (and quicker) for beginner R programmers to graphically inspect their data. The package is centered around an expanding collection of simple `inspect` functions that help users visualize their data.

Currently, this package supports:

-   `inspect_normality`: graphically inspect the distribution of numeric variables in your data.
-   `inspect_balance`: graphically inspect the class balance (or lack thereof) in your data.
-   `inspect_missing`: graphically inspect missingness in your data.

## Installation

You can install the development version of **quickinspectR** from [GitHub](https://github.com/andrewfullerton/quickinspectR) with:

``` r
# install.packages("devtools")
devtools::install_github("andrewfullerton/quickinspectR")
```

## Examples

By design, all **quickinspectR** functions require only one argument: a data frame or a tibble (`data`). Unless otherwise specified, functions will display all the relevant variables contained in the data frame and default to easy-to-read plot styling. 

To get started, load the package.

```{r loadlibrary}
library(quickinspectR)
```

If you want to see how the numeric variables in your data are distributed (e.g. check if they are skewed), you can use `inspect_normality`.

```{r example1}
inspect_normality(data = iris)
```

If you want to check for class imbalance (e.g. unequal distribution of classes within categorical variables), you can use `inspect_balance`. **Tip:** if you're only interested in a few key variables in your data, then you can use the `vars` argument to manually specify which variables will be displayed.

```{r example2}
inspect_balance(data = palmerpenguins::penguins, vars = c("species", "island"))
```

If you want to quickly see if you're missing any data (and more importantly: where you're missing data), then you can use `inspect_missing`. 

```{r example3}
inspect_missing(data = palmerpenguins::penguins)
```

Even though all `inspect` functions will run with `data` as their sole argument, there may be times when you want to stylize your plots a bit more. To make this more accessible, each `inspect` function contains several additional (but completely optional) arguments to enable basic plot customization. Here's an example using `inspect_missing`:

```{r example4}
inspect_missing(data = palmerpenguins::penguins,
                na_colour = "purple", # Change colour used to represent missing values
                fill_colour = "darkgreen", # Change colour used to represent non-missing values
                title = "Missing values in penguins dataset") # Add title
```

And while advanced plot customization is not the goal of **quickinspectR**, all its functions are built on top of **ggplot2** and support more advanced customization via additional arguments. To learn more about this, you can read more in the "Advanced usage" section of [this vignette](https://andrewfullerton.github.io/quickinspectR/articles/using-quickinspectR.html). 

*Statement of data usage: This package and its documentation make use of the `iris` and `airquality` datasets from the `datasets` package as well as the `penguins` dataset from the `palmerpenguins` package*

## Thanks for checking out quickinspectR!