Install these packages:
install.packages(c("stringr", "forcats"))
Base R has some pretty decent string manipulation and searching functionality.
The primary way of working with strings is through
regular expressions. Regular
expressions, or regex in R, are a group of characters that have special meaning
and function. For instance, the . means wildcard and it will search for any
character (letter, number, symbol, etc). Or $ means end of the string or line.
If you want to see more details on regex in R, run ?regex to get help on it.
In general, the most commonly used functions (at least that I use and that I’ve
seen) are grep, grepl, sub, and gsub commands. Each of these take regex
characters ($ ^ * . + ? [] () | are a few; see ?regex for more). Let’s
begin with the base R functions!
fruits <- c("apple", "banana", "pear")
# replace `a` at beginning of string with nothing. `gsub` = global substitution
gsub("^a", "", fruits)
#> [1] "pple" "banana" "pear"
# replace letters `an` or `ar` with `bb`
gsub("(an|ar)", "bb", fruits)
#> [1] "apple" "bbbbba" "pebb"
# same as above but only first search and replace. `sub` = substitution
sub("(an|ar)", "bb", fruits)
#> [1] "apple" "bbbana" "pebb"
# search for fruits starting with a. Returns the number position of the search
# result.
grep("^a", fruits)
#> [1] 1
# same as above, but returns the actual value rather than the number
grep("^a", fruits, value = TRUE)
#> [1] "apple"
# same as above, but returns a vector of logical values
grepl("^a", fruits)
#> [1] TRUE FALSE FALSE
While base R is pretty powerful, it isn’t always easy to work with and there are
some major bits of functionality missing. That’s where the stringr package
(which is a subset of common functions from the stringi package) comes into
play. stringr greatly expands on string manipulation in R. Let’s go through
some.
library(stringr)
# month.name is a default variable in R. I'm putting it in a new variable to
# make it shorter to type out.
mth <- month.name
mth
#> [1] "January" "February" "March" "April" "May"
#> [6] "June" "July" "August" "September" "October"
#> [11] "November" "December"
# Almost every stringr function begins with `str_`.
# Convert cases (lower case, UPPER CASE)
str_to_lower(mth)
#> [1] "january" "february" "march" "april" "may"
#> [6] "june" "july" "august" "september" "october"
#> [11] "november" "december"
str_to_upper(mth)
#> [1] "JANUARY" "FEBRUARY" "MARCH" "APRIL" "MAY"
#> [6] "JUNE" "JULY" "AUGUST" "SEPTEMBER" "OCTOBER"
#> [11] "NOVEMBER" "DECEMBER"
# Find out the number of characters in each string of the vector
str_length(mth)
#> [1] 7 8 5 5 3 4 4 6 9 7 8 8
mth
#> [1] "January" "February" "March" "April" "May"
#> [6] "June" "July" "August" "September" "October"
#> [11] "November" "December"
# Combine (concatenate) the strings together. Similar to `paste`
str_c(mth, collapse = ", ")
#> [1] "January, February, March, April, May, June, July, August, September, October, November, December"
# Take a subset of the months based on a regex.
# for months starting with `J`
str_subset(mth, "^J")
#> [1] "January" "June" "July"
# for months ending in y
str_subset(mth, "y$")
#> [1] "January" "February" "May" "July"
# for months that contain either s, n, or d (only lower case)
str_subset(mth, "[snd]")
#> [1] "January" "June" "August"
# same as above, but all letters since months have been converted to lower case
str_subset(str_to_lower(mth), "[snd]")
#> [1] "january" "june" "august" "september" "november" "december"
# Because stringr is part of the tidyverse packages, you can also use the pipe
# `%>%` command with the commands, to make it easier to read.
mth %>%
str_to_lower() %>%
str_subset("[snd]")
#> [1] "january" "june" "august" "september" "november" "december"
# Using the fruit variable in the stringr package... different from the `fruits`
# we created above!
# str_detect converts to logical, for use in filtering, subsetting, etc.
# searches for all fruit beginning with `be`
str_detect(fruit, "^be")
#> [1] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [45] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [56] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [67] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [78] FALSE FALSE FALSE
# Find out the proportion (mean of values that are either 0 or 1) of fruit with
# at least one vowel at the end of the string.
str_detect(fruit, "[aeiou]$") %>%
mean()
#> [1] 0.35
# search for any fruit that does not end in an `a`
str_detect(fruit, "^.*[^a]$")
#> [1] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> [12] TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> [23] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
#> [34] TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> [45] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> [56] FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> [67] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE
#> [78] TRUE TRUE TRUE
# this is the same as above, but subsets instead
str_subset(fruit, "[^a]$")
#> [1] "apple" "apricot" "avocado"
#> [4] "bell pepper" "bilberry" "blackberry"
#> [7] "blackcurrant" "blood orange" "blueberry"
#> [10] "boysenberry" "breadfruit" "canary melon"
#> [13] "cantaloupe" "cherry" "chili pepper"
#> [16] "clementine" "cloudberry" "coconut"
#> [19] "cranberry" "cucumber" "currant"
#> [22] "damson" "date" "dragonfruit"
#> [25] "durian" "eggplant" "elderberry"
#> [28] "fig" "goji berry" "gooseberry"
#> [31] "grape" "grapefruit" "honeydew"
#> [34] "huckleberry" "jackfruit" "jambul"
#> [37] "jujube" "kiwi fruit" "kumquat"
#> [40] "lemon" "lime" "loquat"
#> [43] "lychee" "mandarine" "mango"
#> [46] "mulberry" "nectarine" "nut"
#> [49] "olive" "orange" "pamelo"
#> [52] "passionfruit" "peach" "pear"
#> [55] "persimmon" "physalis" "pineapple"
#> [58] "plum" "pomegranate" "pomelo"
#> [61] "purple mangosteen" "quince" "raisin"
#> [64] "rambutan" "raspberry" "redcurrant"
#> [67] "rock melon" "salal berry" "star fruit"
#> [70] "strawberry" "tamarillo" "tangerine"
#> [73] "ugli fruit" "watermelon"
# similar to sub
# search any month that starts with J and ends with e and replace the middle
# letters with `HIII`.
str_replace(mth, "^(J).*(e)$", "\\1HIII\\2")
#> [1] "January" "February" "March" "April" "May"
#> [6] "JHIIIe" "July" "August" "September" "October"
#> [11] "November" "December"
# vector of sentences
head(sentences)
#> [1] "The birch canoe slid on the smooth planks."
#> [2] "Glue the sheet to the dark blue background."
#> [3] "It's easy to tell the depth of a well."
#> [4] "These days a chicken leg is a rare dish."
#> [5] "Rice is often served in round bowls."
#> [6] "The juice of lemons makes fine punch."
# you can split strings up by a specific character. `str_split` creates a list,
# so I use `[[1]]` or `[1]` to show only the first one.
str_split(sentences, " ")[[1]] # this outputs a vector
#> [1] "The" "birch" "canoe" "slid" "on" "the" "smooth"
#> [8] "planks."
str_split(sentences, " ")[1] # this outputs a list
#> [[1]]
#> [1] "The" "birch" "canoe" "slid" "on" "the" "smooth"
#> [8] "planks."
# or split by the letter `e`
str_split(sentences, "e")[[1]]
#> [1] "Th" " birch cano" " slid on th" " smooth planks."
# if you want to split by the word, use the `boundary` function.
str_split(sentences, boundary("word"))[[1]]
#> [1] "The" "birch" "canoe" "slid" "on" "the" "smooth" "planks"
# or by a sentence
str_split(sentences, boundary("sentence"))[[1]]
#> [1] "The birch canoe slid on the smooth planks."
# include in a pipe
str_split(sentences, boundary("sentence"))[[1]] %>%
# convert to Title Case
str_to_title()
#> [1] "The Birch Canoe Slid On The Smooth Planks."
String manipulation is great, but sometimes you need to convert to factor
variables for use in statistical models or when plotting (for instance when
using ggplot2). The forcats package makes it easier to work with factors.
Base R really only has one function for working with factors… the factor
command.
library(forcats)
str(mth)
#> chr [1:12] "January" "February" "March" "April" "May" ...
# This is base function. Compare this...
as.factor(mth)
#> [1] January February March April May June July
#> [8] August September October November December
#> 12 Levels: April August December February January July June March ... September
# with this...
as_factor(mth)
#> [1] January February March April May June July
#> [8] August September October November December
#> 12 Levels: January February March April May June July August ... December
# base R as.factor orders alphabetically, while forcats as_factor orders in
# appearance.
mth2 <- c(mth[10:12], mth[6:12], mth)
mth2
#> [1] "October" "November" "December" "June" "July"
#> [6] "August" "September" "October" "November" "December"
#> [11] "January" "February" "March" "April" "May"
#> [16] "June" "July" "August" "September" "October"
#> [21] "November" "December"
# you can also order by frequency:
fct_infreq(mth2)
#> [1] October November December June July August September
#> [8] October November December January February March April
#> [15] May June July August September October November
#> [22] December
#> 12 Levels: December November October August July June September ... May
# this more explicitly orders by appearance.
fct_inorder(mth2)
#> [1] October November December June July August September
#> [8] October November December January February March April
#> [15] May June July August September October November
#> [22] December
#> 12 Levels: October November December June July August ... May
# this counts the number of levels within the factors. It is a simple summary
fct_count(mth2, sort = TRUE)
#> # A tibble: 12 × 2
#> f n
#> <fctr> <int>
#> 1 December 3
#> 2 November 3
#> 3 October 3
#> 4 August 2
#> 5 July 2
#> 6 June 2
#> 7 September 2
#> 8 April 1
#> 9 February 1
#> 10 January 1
#> 11 March 1
#> 12 May 1
# if you want to convert levels that contains only a few (in this case, less the
# 10% of the total proportion), they will be converted to `Other`. This is a
# nice, quick function to group together uncommon values.
fct_lump(mth2, prop = 0.1)
#> [1] October November December Other Other Other Other
#> [8] October November December Other Other Other Other
#> [15] Other Other Other Other Other October November
#> [22] December
#> Levels: December November October Other
# or any level with only an n=1 in the category
fct_lump(mth2, n = 1)
#> [1] October November December Other Other Other Other
#> [8] October November December Other Other Other Other
#> [15] Other Other Other Other Other October November
#> [22] December
#> Levels: December November October Other
# or if you want them renamed to `None`
fct_lump(mth2, prop = 0.1, other_level = 'None')
#> [1] October November December None None None None
#> [8] October November December None None None None
#> [15] None None None None None October November
#> [22] December
#> Levels: December November October None
# this is also a really nice function to rename levels within a factor.
fct_recode(mth2,
Jan = "January",
Feb = "February",
"Dec ember" = "December"
)
#> [1] October November Dec ember June July August September
#> [8] October November Dec ember Jan Feb March April
#> [15] May June July August September October November
#> [22] Dec ember
#> 12 Levels: April August Dec ember Feb Jan July June March May ... September
Very good book on R and data (https://r4ds.had.co.nz). Also the tidyverse resource is good.
Factors (forcats):
Strings (stringr):