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.
The way R is being used, has changed drastically in last few years due
to the availability of
tidyverse and more specifically
dplyr (all credits to Hadley
Wickham and RStudio 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 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 and
dplyr core and helper functions.
TidyWrappers contains set of functions, which save you little from
writing and implementing core dplyr
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
functions. See below in examples.
Install current version of TidyWrappers on your system.
# install.packages("devtools")
library(devtools)
devtools::install_github("cparsania/TidyWrappers", dependencies = TRUE)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
#> # A tibble: 6 x 5
#> a b x y z
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 a <NA> 0 0 0
#> 2 b <NA> 1 1 1
#> 3 c <NA> 2 2 2
#> 4 d <NA> 0 0 0
#> 5 e <NA> 0 3 3
#> 6 f <NA> NA 5 5
## keep rows having 0 across all numeric columns.
# using dplyr
tbl %>% dplyr::filter_if(is.numeric , dplyr::all_vars( . == 0) )
#> # A tibble: 2 x 5
#> a b x y z
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 a <NA> 0 0 0
#> 2 d <NA> 0 0 0
# using TidyWrappers
tbl %>% TidyWrappers::tbl_keep_rows_zero_all()
#> # A tibble: 2 x 5
#> a b x y z
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 a <NA> 0 0 0
#> 2 d <NA> 0 0 0
## remove rows having 0 across all numeric columns.
# using dplyr
tbl %>% dplyr::filter_if(is.numeric , dplyr::any_vars( . > 0) )
#> # A tibble: 4 x 5
#> a b x y z
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 b <NA> 1 1 1
#> 2 c <NA> 2 2 2
#> 3 e <NA> 0 3 3
#> 4 f <NA> NA 5 5
# using TidyWrappers
tbl %>% TidyWrappers::tbl_remove_rows_zero_all()
#> # A tibble: 4 x 5
#> a b x y z
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 b <NA> 1 1 1
#> 2 c <NA> 2 2 2
#> 3 e <NA> 0 3 3
#> 4 f <NA> NA 5 5
## Convert log2 to all numeric columns optionally adding numeric fraction to original values.
# using dplyr
tbl %>% dplyr::mutate_if(is.numeric , ~ log2 (. + 1))
#> # A tibble: 6 x 5
#> a b x y z
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 a <NA> 0 0 0
#> 2 b <NA> 1 1 1
#> 3 c <NA> 1.58 1.58 1.58
#> 4 d <NA> 0 0 0
#> 5 e <NA> 0 2 2
#> 6 f <NA> NA 2.58 2.58
# using TidyWrappers
tbl %>% TidyWrappers::tbl_convert_log2(frac = 1)
#> # A tibble: 6 x 5
#> a b x y z
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 a <NA> 0 0 0
#> 2 b <NA> 1 1 1
#> 3 c <NA> 1.58 1.58 1.58
#> 4 d <NA> 0 0 0
#> 5 e <NA> 0 2 2
#> 6 f <NA> NA 2.58 2.58
## Remove columns / variables having atleaset one NA
# using dplyr
tbl %>% dplyr::select_if( ~ ( (is.na(.)) %>% any(., na.rm = T) %>% `!`) )
#> # A tibble: 6 x 3
#> a y z
#> <chr> <dbl> <dbl>
#> 1 a 0 0
#> 2 b 1 1
#> 3 c 2 2
#> 4 d 0 0
#> 5 e 3 3
#> 6 f 5 5
# using TidyWrappers
tbl %>% TidyWrappers::tbl_remove_vars_NA_any()
#> # A tibble: 6 x 3
#> a y z
#> <chr> <dbl> <dbl>
#> 1 a 0 0
#> 2 b 1 1
#> 3 c 2 2
#> 4 d 0 0
#> 5 e 3 3
#> 6 f 5 5
## Remove columns / variables having all values are NA
# using dplyr
tbl %>% dplyr::select_if( ~ ( (is.na(.)) %>% all(., na.rm = T) %>% `!`) )
#> # A tibble: 6 x 4
#> a x y z
#> <chr> <dbl> <dbl> <dbl>
#> 1 a 0 0 0
#> 2 b 1 1 1
#> 3 c 2 2 2
#> 4 d 0 0 0
#> 5 e 0 3 3
#> 6 f NA 5 5
# using TidyWrappers
tbl %>% TidyWrappers::tbl_remove_vars_NA_all()
#> # A tibble: 6 x 4
#> a x y z
#> <chr> <dbl> <dbl> <dbl>
#> 1 a 0 0 0
#> 2 b 1 1 1
#> 3 c 2 2 2
#> 4 d 0 0 0
#> 5 e 0 3 3
#> 6 f NA 5 5Follow page Reference to see full list of functions.