README.Rmd

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output: github_document
---

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knitr::opts_chunk$set(
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```{r , include = FALSE}
library(tidyverse)
library(TidyWrappers)
```

# TidyWrappers 

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cat(
badger::badge_devel("cparsania/tidywrappers" , color = "blue"),
badger::badge_lifecycle()
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TidyWrappers is an R package to deal with an object `tbl`. It provides handy wrapper functions on top of `dplyr` verbs to get, count, convert, keep, remove and replace data from a `tbl` object.  


## Motivation

The way R is being used, has changed drastically in last few years due to the availability of [`tidyverse`](https://www.tidyverse.org/packages/){target="_blank"} and more specifically [`dplyr`](https://dplyr.tidyverse.org){target="_blank"} (all credits to [Hadley Wickham](http://hadley.nz) and [RStudio](https://rstudio.com) team). Rich documentation, stable release and excellent functionalities of these packages have provoked significant number of R users to switch from traditional `dataframes` to intelligible data structure, `tibble` (aka `tbl`). [`dplyr`](https://dplyr.tidyverse.org){target="_blank"} provides users with rich utilities like `select`, `mutate`, `filter`, `group_by` , `summarise` and `arrange` for easy manipulation of a `tbl`.  However, for newbies it takes a while to become familiar with [`tidy data`](https://vita.had.co.nz/papers/tidy-data.pdf){target="_blank"} and [`dplyr`](https://dplyr.tidyverse.org){target="_blank"} core and helper functions.  

`TidyWrappers` contains set of functions, which save you little from writing and implementing  core [`dplyr`](https://dplyr.tidyverse.org){target="_blank"} verbs while manipulating data from `tbl`. Additionaly, redundant lines of code can be avoided using functions given in `TidyWrappers`. Current version of `TidyWrappers` containes more than 30 functions, which are wrapped on top of various [`dplyr`](https://dplyr.tidyverse.org){target="_blank"} functions. See below in examples.

## Install

Install current version of TidyWrappers on your system.

```{r, eval=FALSE}
# install.packages("devtools")
library(devtools)
devtools::install_github("cparsania/TidyWrappers", dependencies = TRUE)

```



## Examples 
```{r , message=FALSE}
library(dplyr)
library(TidyWrappers)
library(magrittr)

  tbl <- tibble::tibble(a = letters[1:6], b = NA_character_, x = c(0,1,2,0,0,NA) , y = c(0,1,2,0,3,5) , z = c(0,1,2,0,3,5) )
  tbl
  
  ## keep rows having  0 across all numeric columns. 
  
  # using dplyr
  tbl %>% dplyr::filter_if(is.numeric , dplyr::all_vars( . == 0) )
  
  # using TidyWrappers
  tbl %>% TidyWrappers::tbl_keep_rows_zero_all()

  ## remove rows having  0 across all numeric columns. 
  
  # using dplyr 
  tbl %>% dplyr::filter_if(is.numeric , dplyr::any_vars( . > 0) )
  
  # using TidyWrappers 
  tbl %>% TidyWrappers::tbl_remove_rows_zero_all()
  
  ## Convert log2 to all numeric columns optionally adding numeric fraction to original values. 
  
  # using dplyr 
  tbl %>% dplyr::mutate_if(is.numeric , ~ log2 (. +  1))
 
  # using TidyWrappers
  tbl %>% TidyWrappers::tbl_convert_log2(frac = 1)

  
  ## Remove columns / variables  having atleaset one NA
  
  # using dplyr
  tbl %>% dplyr::select_if( ~ ( (is.na(.)) %>% any(., na.rm = T)  %>% `!`) ) 
  
  # using TidyWrappers
  tbl %>% TidyWrappers::tbl_remove_vars_NA_any()
  
  ## Remove columns / variables  having all values are NA
  
  # using dplyr
  tbl %>% dplyr::select_if( ~ ( (is.na(.)) %>% all(., na.rm = T)  %>% `!`) ) 
  
  # using TidyWrappers
  tbl %>% TidyWrappers::tbl_remove_vars_NA_all()
  
  
```

## Reference

Follow page [Reference](https://cparsania.github.io/TidyWrappers/reference/) to see full list of functions.