The string (or character) data type typically requires moremanipulation to be helpful for data analysts. Thus, there is a need fora robust package that is up to the task.forstringr isa new package built on top of ‘stringr’ to execute various stringmanipulations in R programming. The main aim of ‘forstringr’ is tosimplify string manipulation for R beginners. This package combines itspower with the adaptability of other manipulation tools such as tidyrand dplyr. Like in the stringr package, most functions inforstringr begin withstr_. For a quick videotutorial, I gave a talk at Africa R users meetup, which you can findhere.
You can installforstringr package fromCRAN with:
install.packages("forstringr")or the development version fromGitHub with
if(!require("devtools")){install.packages("devtools")}devtools::install_github("gbganalyst/forstringr")This section provides a concise overview of the different functionsavailable in theforstringr package. These functions servevarious purposes and are designed to aid in string manipulationtasks.
length_omit_na()length_omitna() counts only non-missing elements of avector.
library(forstringr)#> Loading required package: stringrethnicity<-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] 9str_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 thatstr_title_case() is different fromstringr::str_to_title() which converts to title case, whereonly 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 leftside 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 rightside 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, thestr_mid()returns a specificnumber of characters from a text string, starting at the position youspecify, 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 resultsusing a specific index position, then, you will usestr_split_extract(). You can interpret it as follows:
Given a character string,S, extract the element at agiven position,k, from the result of splittingS 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{{}} usingstr_englue().For example, any value wrapped in{ } will be inserted intothe string and you automatically inserts a given variable name using{{ }}.
It is important to note thatstr_englue() must be usedinside a function.str_englue("{{ var }}") defuses theargumentvar and transforms it to a string using thedefault 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 extraspaces. Therefore, the functionstr_rm_whitespace_df()eliminates your data frame unnecessary leading, trailing, or otherwhitespaces.
# a dataframe with whitespacesrichest_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 whitespacesstr_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