R and its package repository are great. I could find almost all kinds of functions, various tricks and tips online. However, as I worked on R and R studio long enough. I happen to create custom functions which I find it convenient to use for my purpose. I slowly began to realize that I should share some of them with others in case they find it helpful.
The following functions are implemented (broad category wise)
interpolatemax.advmin.advsum.advmov.avgmov.sdrm.na
clr: Remove variables from the environmentreset
catn: cat() function with new line already attached in the end.hrprint.warnstr.list### Productivity ### Summaryget.empty.columns: Get empty columns (NA or blank) in a data.framestat.summarysummary.advsummary.non.numsummary.numsummaryLM
Just download the util_Global.R file.
source("util_Global.R")
install("tidyverse")
## [1] "tidyverse not loaded."
## [1] "tidyverse is already installed!"
## [1] "tidyverse has been loaded now."Source it locally and you are good to go.
clr() is motivated from Matlab function clr Its an advance and short
way of writing rm(list = ls()) #### syntax
clr(mode = "notall", except = NULL)
# To delete all the variables except functions!
clr()
# To delete all the variables except functions!
temp = 1
clr( except = "temp")# To clean everything except function clr(), and clc()
clr("all")Select out columns/rows based on unfit values #### syntax
drop_unfit_cols(data, ..., contains = "", na.rm = TRUE)df <- tibble(c1 = c("a", "", "c", "d", NA, "f", "g", "h"),
c2 = c(NA, NA, 3, 4, NA, 6, 7, 8),
c3 = c("A", "", "C", "D", NA, "#Value!", "G", "H"),
c4 = c(NA, NA, NA, NA, NA, NA, NA, NA),
c5 = c(0.001, NA, -99, 1, NA, NA, 1000, NA),
c6 = c("", "", "", "", "", "", "", NA),
c7 = c("#Value!", "", "", "#Value!", NA, "#Value!", "", "#Value!"),
c8 = c(-99, NA, -99, NA, NA, NA, -99, NA))
df
## # A tibble: 8 × 8
## c1 c2 c3 c4 c5 c6 c7 c8
## <chr> <dbl> <chr> <lgl> <dbl> <chr> <chr> <dbl>
## 1 "a" NA "A" NA 0.001 "" "#Value!" -99
## 2 "" NA "" NA NA "" "" NA
## 3 "c" 3 "C" NA -99 "" "" -99
## 4 "d" 4 "D" NA 1 "" "#Value!" NA
## 5 <NA> NA <NA> NA NA "" <NA> NA
## 6 "f" 6 "#Value!" NA NA "" "#Value!" NA
## 7 "g" 7 "G" NA 1000 "" "" -99
## 8 "h" 8 "H" NA NA <NA> "#Value!" NA
# Filterning blank or NA filled columns
df %>% drop_unfit_cols()
## # A tibble: 8 × 6
## c1 c2 c3 c5 c7 c8
## <chr> <dbl> <chr> <dbl> <chr> <dbl>
## 1 "a" NA "A" 0.001 "#Value!" -99
## 2 "" NA "" NA "" NA
## 3 "c" 3 "C" -99 "" -99
## 4 "d" 4 "D" 1 "#Value!" NA
## 5 <NA> NA <NA> NA <NA> NA
## 6 "f" 6 "#Value!" NA "#Value!" NA
## 7 "g" 7 "G" 1000 "" -99
## 8 "h" 8 "H" NA "#Value!" NA
# Filterning blank or NA filled rows
df %>% drop_unfit_rows()
## # A tibble: 6 × 8
## c1 c2 c3 c4 c5 c6 c7 c8
## <chr> <dbl> <chr> <lgl> <dbl> <chr> <chr> <dbl>
## 1 a NA A NA 0.001 "" "#Value!" -99
## 2 c 3 C NA -99 "" "" -99
## 3 d 4 D NA 1 "" "#Value!" NA
## 4 f 6 #Value! NA NA "" "#Value!" NA
## 5 g 7 G NA 1000 "" "" -99
## 6 h 8 H NA NA <NA> "#Value!" NA
# Filterning blank or NA filled columns from a column range
df %>% drop_unfit_cols(c3:c8)
## # A tibble: 8 × 6
## c1 c2 c3 c5 c7 c8
## <chr> <dbl> <chr> <dbl> <chr> <dbl>
## 1 "a" NA "A" 0.001 "#Value!" -99
## 2 "" NA "" NA "" NA
## 3 "c" 3 "C" -99 "" -99
## 4 "d" 4 "D" 1 "#Value!" NA
## 5 <NA> NA <NA> NA <NA> NA
## 6 "f" 6 "#Value!" NA "#Value!" NA
## 7 "g" 7 "G" 1000 "" -99
## 8 "h" 8 "H" NA "#Value!" NA
# Filterning blank or NA filled rows from a column range
df %>% drop_unfit_rows(c3:c8)
## # A tibble: 6 × 8
## c1 c2 c3 c4 c5 c6 c7 c8
## <chr> <dbl> <chr> <lgl> <dbl> <chr> <chr> <dbl>
## 1 a NA A NA 0.001 "" "#Value!" -99
## 2 c 3 C NA -99 "" "" -99
## 3 d 4 D NA 1 "" "#Value!" NA
## 4 f 6 #Value! NA NA "" "#Value!" NA
## 5 g 7 G NA 1000 "" "" -99
## 6 h 8 H NA NA <NA> "#Value!" NA
# Filtering selected or NA filled columns from a column range
df %>% drop_unfit_cols(c3:c8, contains = list("", "#Value!", -99))
## # A tibble: 8 × 4
## c1 c2 c3 c5
## <chr> <dbl> <chr> <dbl>
## 1 "a" NA "A" 0.001
## 2 "" NA "" NA
## 3 "c" 3 "C" -99
## 4 "d" 4 "D" 1
## 5 <NA> NA <NA> NA
## 6 "f" 6 "#Value!" NA
## 7 "g" 7 "G" 1000
## 8 "h" 8 "H" NA
# Filtering selected or NA filled rows from a column range
df %>% drop_unfit_rows(c3:c8, contains = list("", "#Value!", -99))
## # A tibble: 5 × 8
## c1 c2 c3 c4 c5 c6 c7 c8
## <chr> <dbl> <chr> <lgl> <dbl> <chr> <chr> <dbl>
## 1 a NA A NA 0.001 "" "#Value!" -99
## 2 c 3 C NA -99 "" "" -99
## 3 d 4 D NA 1 "" "#Value!" NA
## 4 g 7 G NA 1000 "" "" -99
## 5 h 8 H NA NA <NA> "#Value!" NAloading packages every now or then you require a package to run before
you can use some R code you found online. Its typically starts with
loading a library() or require(). For example
If “package_name” is already install then you are good to go, however if it is not installed (which is the case sometimes), then you will get an error as following-
Error in library("package_name") :
there is no package called ‘package_name’
Then one needs install it manually either by using RStudio GUI or writing a command such as
install.packages("package_name")install("tidyverse")cat() command is used to display a message on console or in a file. catn() adds a new line in the end for convenience. Also this function adds the functionality of adding the color and an argument to when or when not to print. #### syntax
catn(..., file = "", sep = " ", fill = FALSE, labels = NULL, append = FALSE, console = TRUE, color = NULL)
# Color will only work on console!
catn("A message in blue!", color = "blue")
## [1] "crayon not loaded."
## [1] "crayon is already installed!"
## [1] "crayon has been loaded now."
## A message in blue!Get empty columns (NA or blank) in a data.frame In multiple data set
sometimes it happens that a critical column is there in the header name
but misses any of the data. Or We might be intrested that which columns
are completely empty once we group it. This functions provides a visual
summary of such sort. (In the outputs X represents an empty column)
df
## # A tibble: 8 × 8
## c1 c2 c3 c4 c5 c6 c7 c8
## <chr> <dbl> <chr> <lgl> <dbl> <chr> <chr> <dbl>
## 1 "a" NA "A" NA 0.001 "" "#Value!" -99
## 2 "" NA "" NA NA "" "" NA
## 3 "c" 3 "C" NA -99 "" "" -99
## 4 "d" 4 "D" NA 1 "" "#Value!" NA
## 5 <NA> NA <NA> NA NA "" <NA> NA
## 6 "f" 6 "#Value!" NA NA "" "#Value!" NA
## 7 "g" 7 "G" NA 1000 "" "" -99
## 8 "h" 8 "H" NA NA <NA> "#Value!" NA
df %>% get.empty.columns()
## [1] "knitr not loaded."
## [1] "knitr is already installed!"
## [1] "knitr has been loaded now."
##
##
## === === === === === === === ===
## c1 c2 c3 c4 c5 c6 c7 c8
## === === === === === === === ===
## X X
## === === === === === === === ===
rbind(df, df) %>% get.empty.columns(data = ., group.cols = c1)
##
##
## === === === === === === === ===
## c1 c2 c3 c4 c5 c6 c7 c8
## === === === === === === === ===
## X X X X X X X
## a X X X
## c X X X
## d X X X
## f X X X X
## g X X X
## h X X X X
## NA X X X X X X X
## === === === === === === === ===get current active source file name
get.source.file.name()
get.source.file.name()
## [1] "README.Rmd"str.list() command is used to display an internal structure of a list in a visual tree form on the console.
str.list(X, prefix1 = "", prefix2 = "", prefix3 = "", prefix4 = "", a =1)
l = list()
df <- data.frame(a = 1:10, b = 11:20)
l$data$df1 <- df
l$data$df2 <- df
l$plots$figure <- ggplot(df, aes(a, b)) + geom_point()
str.list(l)
## l
## ├─data
## │ ├─df1
## │ │ ├─a
## │ │ └─b
## │ └─df2
## │ ├─a
## │ └─b
## └─plots
## └─figure
## ├─data
## │ ├─a
## │ └─b
## ├─layers
## │ └─
## ├─scales
## ├─mapping
## │ ├─x
## │ └─y
## ├─theme
## ├─coordinates
## ├─facet
## ├─plot_env
## └─labels
## ├─x
## └─yCommand to save csv file and .Rdata with semi-automatic name in a predefined folder.
store.table(filename, data, lt, check = T, subfolder = "03-Tables", console = FALSE, fun_family = "csv", envir = rlang::caller_env(), ...)
l = list()
df <- data.frame(a = 1:10, b = 11:20)
l$data$df1 <- df
l$data$df2 <- df
store.table(filename = "test", data = df, lt = l)
## [1] "rlang not loaded."
## [1] "rlang is already installed!"
## [1] "rlang has been loaded now."
## Warning in pad.00(length(lt$tables) + 1): pad.00 is deprecated. Please use
## pad_decimal() instead.
# Following files will be stored in the folder named as `03-Folder`
# `01_test_[R].csv` `util_Global.Rdata`There are more than 650 color names in R, plot.color() shows which color name appears how. We can even filter out the colors which has some partial string
plot.colors(name = NULL)
plot.colors()
plot.colors("dark")
plot.colors("red")
###
pad_decimal() Pads a number or string with characters on left or right
side.
pad_decimal(3.14, n_left = 3, n_right = 4, char_left = " ", char_right = "0")
This is the main scripts which hosts all the custom functions. A list of function and their usage are mentioned below.
All the functions, which I no longer use, or found a way around are shifted here.
A file containing plain snippets. Copy these snippets, modify and add in your RStudio settings (Tools > Global Options > Code > Editing > Snippets > Edit Snippets button) for handy use. You can find more information about RStudio Snippets from here.
- Please feel free to raise a issue on github if you are find a bug, typo or suggest a feature.
- If you come across functions which supersedes any of functions presented here then please let me know by raising an issue.