README.Rmd

---
title: "`featr` package"
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(featr)
```

`featr` stands for "feature" (oh, really?) and pronounced the same way.
There is a bunch of functions to produce not-so-common tasks such as 
renaming variables in the data frame or list of data frames,
one-hot-encoding with custom parameters etc. 
Please see [Examples](#examples) section

## Installation

To install `featr` please use `devtools`:

```r
devtools::install_github("pavel-filatov/featr")
```

Please note: you may need to install development version of `rlang` package:
```r
devtools::install_github("r-lib/rlang")
```

## Examples 

#### `cor_index()` 

`cor_index()` fucntion makes a correlation index: it gets each pair of variables, calculate 
correlation between them, and turn it all to data frame. 
Then the function sort data frame by absolute value of correlation.
```{r}
featr::cor_index(mtcars)
```


#### `identity_index()`

For each pair of variables in input data frame `identity_index()` calculates three numbers:

  - **`identical`** - number of observations where first and second variables are equal;
  - **`non_identical`** - number of observations where first and second variables aren't equal;
  - **`other`** all the other cases (e.g. when one of the entries is `NA`).
  
**`identitity_index`** is computed as ratio 
$$identity\ index = \frac{identical}{identical + non\_identical}$$


That's how the fuction works:
```{r}
set.seed(42)
df <- tibble(letter1 = sample(c(letters[1:3], NA, NaN), 100, TRUE),
             letter2 = sample(c(letters[1:3], NA), 100, TRUE),
             letter3 = sample(c(letters[1:3], NA), 100, TRUE),
             num1 = sample(1:4, 100, TRUE),
             num2 = sample(1:4, 100, TRUE),
             num3 = sample(c(1:3, NA), 100, TRUE))

identity_index(df)
```

You see that variables `letter1` and `letter3` have 22 equal, 
40 different observations and 38 cases where at least on of them has `NA`.


`.na_include` flag lets you consider as identical cases where `var1 == NA` and `var2 == NA`.
The same way if `var1 == NA` and `var2 != NA` it will count this case as `non_identical`.

```{r}
identity_index(df, .na_include = TRUE)
```

Now `letter1` and `letter3` have 6 more identical cases. 


#### `make_shift()`

`make_shift()` is great option when you need to make a lot of lags (move down) 
and leads (move up) for one or more variables. 
This function captures variables to apply shifting and vector of window sizes and 
returns a list of quoted expressions:

```{r}
make_shift(mpg, qsec, .n = -2:2)
```

To make new variables you simply need to unqoute the function output
inside the `dplyr::mutate()` call **using `!!!`** (bang-bang-bang) opearator:

```{r}
select(mtcars, mpg, qsec) %>% 
  mutate(!!!make_shift(mpg, qsec, .n = -1:2))
```