README.Rmd

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output: github_document
editor_options: 
  chunk_output_type: console
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```{r, include = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    fig.path = "man/figures/README-",
    out.width = "100%",
    eval = TRUE
)
devtools::load_all()
```

# wrang

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## Installation

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

```{r eval=FALSE}
# install.packages("remotes")
remotes::install_github("rappster/wrang")
```

## What?

Functionality for programmatic data wrangling based on [{dplyr}](https://github.com/tidyverse/dplyr), [{rlang}](https://github.com/r-lib/rlang) and friends

## Why?

In non-interactive use cases you typically need more programmatic approaches than hard-coding certain input for data wrangling tasks - e.g. column names or functions to be executed.

This package tries to come up with some out-of-the-box helpers for that.

### DISCLAIMER

It's one of those scratch-your-own-itch things where the code base might change as flesh my ideas out.

## How?

```{r, eval=FALSE}
library(wrang)
```

## Frequency table

```{r}
mtcars %>% summa_freq_table(cyl)
mtcars %>% summa_freq_table(cyl, gear, .sort = TRUE)

mtcars %>% summa_freq_table(cyl, .col_n_abs = "n",
    .col_n_rel = rel, .sort = TRUE)

mtcars %>% summa_freq_table(cyl, "gear")

cyl_ <- dplyr::quo(cyl)
gear_ <- dplyr::sym("gear")
mtcars %>% summa_freq_table(!!cyl_, !!gear_)
```

With specifying an "dependend variable" (not satisfied with the name yet, likely to change)

```{r}
# install.packages("palmerpenguins")
library(palmerpenguins)

penguins %>% summa_freq_table(species, island, sex, .digits_n_rel = 2)
```

Check that relative counts add up to `1`

```{r}
penguins %>% 
    summa_freq_table(species, island, sex, 
        .digits_n_rel = 2, .ungroup = FALSE) %>%
    dplyr::summarise(n_rel_total = sum(n_rel)) %>% 
    dplyr::ungroup()
```

## Handling NSE input

```{r}
foo <- function(
    data,
    col = "carb_sum",
    col_src = "carb",
    fn = purrr::partial(sum, na.rm = TRUE)
) {
    col <- dplyr::enquo(col) %>% handle_nse_input()
    col_src <- dplyr::enquo(col_src) %>% handle_nse_input()
    
    data %>% dplyr::summarize(
        !!col := fn(!!col_src)
    )
}
```

```{r}
mtcars %>% foo()
```

```{r}
mtcars %>% foo(
    col = "mpg_mean", 
    col_src = "mpg", 
    fn = purrr::partial(mean, na.rm = TRUE)
)
```

```{r}
mtcars %>% foo(
    col = mpg_mean_2, 
    col_src = "mpg", 
    fn = purrr::partial(mean, na.rm = TRUE)
)
```

```{r}
mtcars %>% foo(
    col = mpg_mean_2, 
    col_src = mpg, 
    fn = purrr::partial(mean, na.rm = TRUE)
)
```

## Other

```{r}
mtcars %>% dplyr::summarize(
    carb_sum = sum(carb, na.rm = TRUE),
    carb_mean = mean(carb, na.rm = TRUE)
)
```

```{r}
col_sum <- "carb_sum" %>% dplyr::sym()
col_mean <- "carb_mean" %>% dplyr::sym()
col_src <- "carb" %>% dplyr::sym()

mtcars %>% dplyr::summarize(
    !!col_sum := sum(!!col_src, na.rm = TRUE),
    !!col_mean := mean(!!col_src, na.rm = TRUE)
)
```