String Manipulation Package for Those Familiar with Microsoft Excel
Source:vignettes/forstringr.Rmd
forstringr.RmdThe 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")Functions in forstringr package
This section provides a concise overview of the different functions
available in the forstringr package. These functions serve
various purposes and are designed to aid in string manipulation
tasks.
length_omit_na()
length_omitna() counts only non-missing elements of a
vector.
library(forstringr)
#> Loading required package: stringr
ethnicity <- c("Hausa", NA, "Yoruba", "Ijaw", "Igbo", NA, "Ibibio", "Tiv", "Fulani", "Kanuri", "Others")
# count all the observations, including NAs.
length(ethnicity)
#> [1] 11
# count all the observations, without NAs.
length_omit_na(ethnicity)
#> [1] 9
str_title_case()
str_title_case() converts string to title case,
capitalizing only the first letter of each word while ignoring articles,
prepositions, and conjunctions.
Please note that str_title_case() is different from
stringr::str_to_title() which converts to title case, where
only the first letter of each word is capitalized.
words <- "the quick brown fox jumps over a lazy dog"
str_title_case(words) # from forstringr package
#> [1] "The Quick Brown Fox Jumps over a Lazy Dog"
str_to_title(words) # from stringr package
#> [1] "The Quick Brown Fox Jumps Over A Lazy Dog"
str_left()
Given a character vector, str_left() returns the left
side of a string. For examples:
str_right()
Given a character vector, str_right() returns the right
side of a string. For examples:
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_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 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