String Manipulation Package for Those Familiar with Microsoft Excel

The goal of ‘forstringr’ is to enable complex string manipulation in R, especially for those more familiar with the LEFT(), RIGHT(), and MID() functions in Microsoft Excel. The package combines the power of ‘stringr’ with other manipulation packages such as ‘dplyr’ and ‘tidyr’. Just like in the ‘stringr’ package, most functions in ‘forstringr’ begin with str_.

Installation

You can install forstringr package from CRAN with:

install.packages("forstringr")

or the development version from GitHub with:

if(!require("devtools")){
 install.packages("devtools")
}

devtools::install_github("gbganalyst/forstringr")

length_omit_na()

length_omitna() counts only non-missing elements of a vector.

library(forstringr)

ethnicity <- c("Hausa", NA, "Yoruba", "Ijaw", "Igbo", NA, "Ibibio", "Tiv", "Fulani", "Kanuri", "Others")

length(ethnicity) # Count all the observations, including the NAs.
#> [1] 11

length_omit_na(ethnicity)
#> [1] 9

str_left()

Given a character vector, str_left() returns the left side of a string. For examples:


str_left("Nigeria")
#> [1] "N"

str_left("Nigeria", n = 3)
#> [1] "Nig"

str_left(c("Female", "Male", "Male", "Female"))
#> [1] "F" "M" "M" "F"

str_right()

Given a character vector, str_right() returns the right side of a string. For examples:


str_right("July 20, 2022", 4)
#> [1] "2022"

str_right("Sale Price", n = 5)
#> [1] "Price"

str_mid()

Like in Microsoft Excel, the str_mid()returns a specific number of characters from a text string, starting at the position you specify, based on the number of characters you select.

str_mid("Super Eagle", 7, 5)
#> [1] "Eagle"

str_mid("Oyo Ibadan", 5, 6)
#> [1] "Ibadan"

str_split_extract()

If you want to split up a string into pieces and extract the results using a specific index position, then, you will use str_split_extract(). You can interpret it as interpret it as follows:

Given a character string, S, extract the element at a given position, k, from the result of splitting S by a given pattern, m. For example:

top_10_richest_nig <- c("Aliko Dangote", "Mike Adenuga", "Femi Otedola", "Arthur Eze", "Abdulsamad Rabiu", "Cletus Ibeto", "Orji Uzor Kalu", "ABC Orjiakor", "Jimoh Ibrahim", "Tony Elumelu")

first_name <- str_split_extract(top_10_richest_nig, " ", 1)

first_name
#>  [1] "Aliko"      "Mike"       "Femi"       "Arthur"     "Abdulsamad"
#>  [6] "Cletus"     "Orji"       "ABC"        "Jimoh"      "Tony"

str_extract_part()

Extract strings before or after a given pattern. For example:

first_name <- str_extract_part(top_10_richest_nig,  pattern = " ", before = TRUE)

first_name
#>  [1] "Aliko"      "Mike"       "Femi"       "Arthur"     "Abdulsamad"
#>  [6] "Cletus"     "Orji Uzor"  "ABC"        "Jimoh"      "Tony"

revenue <- c("$159", "$587", "$891", "$207", "$793")

str_extract_part(revenue, pattern = "$", before = FALSE)
#> [1] "159" "587" "891" "207" "793"

str_englue()

You can dynamically label ggplot2 plots with the glue operators [ or {{}} using str_englue(). For example, any value wrapped in { } will be inserted into the string and you automatically inserts a given variable name using {{ }}.

It is important to note that str_englue() must be used inside a function. str_englue("{{ var }}") defuses the argument var and transforms it to a string using the default name operation.

library(ggplot2)

histogram_plot <- function(df, var, binwidth) {
 df |>
   ggplot(aes(x = {{ var }})) +
   geom_histogram(binwidth = binwidth) +
   labs(title = str_englue("A histogram of {{var}} with binwidth {binwidth}"))
}

iris |> histogram_plot(Sepal.Length, binwidth = 0.1)

str_rm_whitespace_df()

Extra spaces are accidentally entered when working with survey data, and problems can arise when evaluating such data because of extra spaces. Therefore, the function str_rm_whitespace_df() eliminates your data frame unnecessary leading, trailing, or other whitespaces.

# A dataframe with whitespaces

richest_in_nigeria
#> # A tibble: 10 × 5
#>     Rank Name                   `Net worth`         Age `Source of Wealth`      
#>    <dbl> <chr>                  <chr>             <dbl> <chr>                   
#>  1     1 " Aliko Dangote"       "$14 Billion"        64 "  Cement and Sugar "   
#>  2     2 "Mike Adenuga"         "$7.9  Billion "     68 "Telecommunication,    …
#>  3     3 "Femi   Otedola"       "$5.9   Billion"     59 "Oil  and Gas"          
#>  4     4 " Arthur Eze"          "$5 Billion"         73 "Oil and Gas"           
#>  5     5 "Abdulsamad     Rabiu" "$3.7 Billion"       61 "Cement   and Sugar"    
#>  6     6 " Cletus Ibeto "       " $3.5 Billion"      69 "Automobile, Real Estat…
#>  7     7 "Orji Uzor Kalu"       "$3.2 Billion"       61 "Furniture,    Publishi…
#>  8     8 "ABC Orjiakor "        "  $1.2 Billion"     63 "Oil and Gas"           
#>  9     9 "  Jimoh Ibrahim"      "$1 Billion "        54 "Insurance, Oil and Gas…
#> 10    10 "Tony   Elumelu"       "$900    Million"    58 "  Banking  "
# A dataframe with no whitespaces

str_rm_whitespace_df(richest_in_nigeria)
#> # A tibble: 10 × 5
#>     Rank Name             `Net worth`    Age `Source of Wealth`                 
#>    <dbl> <chr>            <chr>        <dbl> <chr>                              
#>  1     1 Aliko Dangote    $14 Billion     64 Cement and Sugar                   
#>  2     2 Mike Adenuga     $7.9 Billion    68 Telecommunication, Oil, and Gas    
#>  3     3 Femi Otedola     $5.9 Billion    59 Oil and Gas                        
#>  4     4 Arthur Eze       $5 Billion      73 Oil and Gas                        
#>  5     5 Abdulsamad Rabiu $3.7 Billion    61 Cement and Sugar                   
#>  6     6 Cletus Ibeto     $3.5 Billion    69 Automobile, Real Estate            
#>  7     7 Orji Uzor Kalu   $3.2 Billion    61 Furniture, Publishing              
#>  8     8 ABC Orjiakor     $1.2 Billion    63 Oil and Gas                        
#>  9     9 Jimoh Ibrahim    $1 Billion      54 Insurance, Oil and Gas, Real Estate
#> 10    10 Tony Elumelu     $900 Million    58 Banking