The goal of forstringr is to enable complex string
manipulation in R. 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 begins with
str_.
You can install the development version of forstringr
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] 9str_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, before = TRUE, pattern = " ")
first_name
#> [1] "Aliko" "Mike" "Femi" "Arthur" "Abdulsamad"
#> [6] "Cletus" "Orji Uzor" "ABC" "Jimoh" "Tony"
revenue <- c("$159", "$587", "$891", "$207", "$793")
str_extract_part(revenue, before = FALSE, pattern = "$")
#> [1] "159" "587" "891" "207" "793"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