README.Rmd

---
output:
  md_document:
    variant: markdown_github
---

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

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

**NOTE: This is a toy package created for expository purposes by Jenny Bryan, and edited by Nivretta Thatra for Stat 545 2016. It is not meant to actually be useful. If you want a package for factor handling, please see [forcats](https://cran.r-project.org/package=forcats).**

### foofactors

Factors are a very useful type of variable in R, but they can also drive you nuts. This package provides some helper functions for the care and feeding of factors.

### Installation

```{r installation, eval = FALSE}
library(tidyverse)
library("devtools")
devtools::install_github("nivretta/foofactors")

```
  
### Quick demo
###Keeping order of levels via `givenorder()`:

```{r}
library(foofactors)
z <- factor(c("b", "c", "a", "e", "b"))

```

Normally the levels will be ordered alphabetically.

```{r}
factor(z)
```

Sometimes you'd like to maintain the order levels in which they appear in the data.

```{r}
givenorder(z)
```

###Change some factors to characters via `character()`:

Check for factors where number of unique values = length, and change to character.

```{r}
m <- factor(c("a", "b", "c", "d"))
class(m)

m_character <- character(m)
class(m_character)
```

Otherwise print nochange.

```{r}
n <- factor(c("a", "b", "c", "d", "a"))
class(n)

character(n)

```

###Jenny's given functions

Binding two factors via `fbind()`:

```{r}
a <- factor(c("character", "hits", "your", "eyeballs"))
b <- factor(c("but", "integer", "where it", "counts"))
```

Simply catenating two factors leads to a result that most don't expect.

```{r}
c(a, b)
```

The `fbind()` function glues two factors together and returns factor.

```{r}
fbind(a, b)
```

Often we want a table of frequencies for the levels of a factor. The base `table()` function returns an object of class `table`, which can be inconvenient for downstream work. Processing with `as.data.frame()` can be helpful but it's a bit clunky.

```{r}
set.seed(1234)
x <- factor(sample(letters[1:5], size = 100, replace = TRUE))
table(x)
as.data.frame(table(x))
```

The `freq_out()` function returns a frequency table as a well-named `tbl_df`:

```{r}
freq_out(x)
```