15 15. stringr tutorial

This is an overview of the stringr package that is part of the “tidyverse” family of packages. The info in this section comes from this youtube playlist: https://www.youtube.com/watch?v=oIu5jK8DeX8&list=PLiC1doDIe9rDwsUhd3FtN1XGCV2ES1xZ2

See these resources for more info about the entire tidyverse family of packages.

See these links for more info about the stringr package

https://stringr.tidyverse.org/

See these links for more info about other related tidyverse packagaes

tibble package: https://tibble.tidyverse.org/
magrittr: https://magrittr.tidyverse.org/
lubridate package: https://lubridate.tidyverse.org/
hms package: https://hms.tidyverse.org/
dplyr package: https://dplyr.tidyverse.org/
- Also see this playlist for info about dplyr: https://www.youtube.com/watch?v=THGFXV4RW8U&list=PLiC1doDIe9rC8RgWPAWqDETE-VbKOWfWl

15.1 Setting up the package for use

15.1.1 Download the code with install.packages() function

Before using functions from any package, you must install the package using the install.packages() function (see more below).

The install.packages() function, downloads the code for the R package(s) to your computer from a “repository” known as CRAN - the Comprehensive R Archive Network. CRAN is actually supported by different websites that are funded and managed by different institutions. Each of these sources of package info is known as a CRAN “mirror”. If you don’t specify where the the install.packages function should get the package, a window might popup asking your to choose a “CRAN mirror”. Alternatively, you can specify the mirror you want in the repos argument directly in the call to install.packages. This is what we do below.

You can install the stringr package with the following command.

# The following is the list of major CRAN mirrors. You do NOT need to do this 
# part. It is done for this website since the code to generate this website
# is NOT run interactively and if a popup should appear asking us to choose 
# a "mirror" the website would not be generated correctly. Therefore we
# use this code to specify a list of mirror from which install.packages()
# can choose one. If the first one is not available (e.g. it is "down") then
# install.packages() will try the 2nd one, etc.

CRANrepos = c(
    "https://mirror.las.iastate.edu/CRAN/",       # Iowa State University, Iowa
    "http://ftp.ussg.iu.edu/CRAN/",               # Indiana University, Indiana
    "https://repo.miserver.it.umich.edu/cran/", # University of Michigan
    "https://cran.wustl.edu/",              # Washington University, Missouri
    "https://archive.linux.duke.edu/cran/",     # Duke University, NC
    "https://cran.case.edu/",                   # Case Western Reserve University, OH
    "https://ftp.osuosl.org/pub/cran/",         # Oregon State University
    "http://lib.stat.cmu.edu/R/CRAN/",    # Carnegie Mellon University, PA
    "https://cran.mirrors.hoobly.com/",         # Hoobly Classifieds, PA
    "https://mirrors.nics.utk.edu/cran/") # Nat. Inst. 4 Computational Sci, TN

# NOTE: If you are running this command interactively you can leave out the
# repos argument.
install.packages("stringr", repos=CRANrepos)

Installing package into '/home/yitz/R/x86_64-pc-linux-gnu-library/4.5'
(as 'lib' is unspecified)

Note that stringr is part of the tidyverse family of packages. You can install any tidyverse package by itself or install the entire set of tidyverse packages with the command install.packages("tidyverse")

15.1.2 Calling functions from stringr

At this point the code is downloaded to your computer. You can now use the functions in stringr but you will have to prefix each call to a function with stringr::

For example the stringr::str_length() function returns the length of the strings (i.e. character values) that are passed to it.

stuff = c("Hi", "there.", "How are you?")
stringr::str_length(stuff)

[1]  2  6 12

Notice that I must include stringr::

# Determine the length of each value in the vector
stuff = c("Hi", "there.", "How are you?")
str_length(stuff)

Error in str_length(stuff): could not find function "str_length"

15.1.3 library(stringr) or require(stringr)

You can use the library or require commands to avoid needing to write stringr::

library(stringr)   # require(stringr) will also work 

stuff = c("Hi", "there.", "How are you?")
str_length(stuff)

[1]  2  6 12

Note that stringr is part of the tidyverse family of packages. If you’ve already installed the entire tidyverser set of packages with install.packages("tidyverse") you could then call library(tidyverse) to “load” the entire tidyverse into your R session.

15.2 stringr: Basic String Manipulation

chr_data <- c("Data", "Daft", "YouTube", "channel",
             "learn", "and", "have", "FUN!")

# Check the length of a string
str_length("Hi there! How are you?")

[1] 22

str_length(chr_data)

[1] 4 4 7 7 5 3 4 4

# Convert string letters to uppercase
str_to_upper(chr_data)

[1] "DATA"    "DAFT"    "YOUTUBE" "CHANNEL" "LEARN"   "AND"     "HAVE"   
[8] "FUN!"

# Convert string letters to lowercase
str_to_lower(chr_data)

[1] "data"    "daft"    "youtube" "channel" "learn"   "and"     "have"   
[8] "fun!"

# Convert string to title (first letter uppercase)
str_to_title(chr_data)

[1] "Data"    "Daft"    "Youtube" "Channel" "Learn"   "And"     "Have"   
[8] "Fun!"

# Convert string to sentence (only first letter of first word uppercase)
str_to_sentence("make me into a SENTENCE!")

[1] "Make me into a sentence!"

# Trim whitespace
str_trim("  Trim Me!   ")

[1] "Trim Me!"

# Pad strings with whitespace
str_pad("Pad Me!", width = 15, side="both")

[1] "    Pad Me!    "

# Truncate strings to a given length
str_trunc("If you have a long string, you might want to truncate it!", 
          width = 50)

[1] "If you have a long string, you might want to tr..."

15.3 stringr: Split and Join Strings

# Split strings
str_split("Split Me!", pattern = " ")

[[1]]
[1] "Split" "Me!"

food <- c(
  "apples and oranges and pears and bananas",
  "pineapples and mangos and guavas"
)

stringr::str_split(food, " and ")

[[1]]
[1] "apples"  "oranges" "pears"   "bananas"

[[2]]
[1] "pineapples" "mangos"     "guavas"

# Join strings (equivalent to base R paste())
str_c("Join", "Me!", sep="_")

[1] "Join_Me!"

# Join strings (equivalent to base R paste())
str_c(c("Join", "vectors"), c("Me!", "too!"), sep="_")

[1] "Join_Me!"     "vectors_too!"

# Collapse a vector of strings into a single string
str_c(c("Turn", "me", "into", "one", "string!"), collapse= " ")

[1] "Turn me into one string!"

# Convert NA values in character vector to string "NA"
str_replace_na(c("Make", NA, "strings!"))

[1] "Make"     "NA"       "strings!"

15.4 stringr: Sorting Strings

sort_data <- c("sort", "me", "please!")

# Get vector of indicies that would sort a string alphabetically
str_order(sort_data)

[1] 2 3 1

# Use discovered ordering to extract data in sorted order
sort_data[str_order(sort_data)]

[1] "me"      "please!" "sort"

# Directly extract sorted strings
str_sort(sort_data)

[1] "me"      "please!" "sort"

# Extract in reverse sorted order
str_sort(sort_data, decreasing = TRUE)

[1] "sort"    "please!" "me"

15.5 stringr: String Interpolation

first <- c("Luke", "Han", "Jean-Luc")
last <- c("Skywalker", "Solo", "Picard")

# Interpolate (insert variable values) into strings with str_glue()
str_glue("My name is {first}. {first} {last}.")

My name is Luke. Luke Skywalker.
My name is Han. Han Solo.
My name is Jean-Luc. Jean-Luc Picard.

minimum_age <- 18
over_minimum <- c(5, 17, 33)

# Interpolate the result of an execution into a string
str_glue("{first} {last} is {minimum_age + over_minimum} years old.")

Luke Skywalker is 23 years old.
Han Solo is 35 years old.
Jean-Luc Picard is 51 years old.

num <- c(1:5)

# Interpolate the result of function calls
str_glue("The square root of {num} is {round(sqrt(num), 3)}.")

The square root of 1 is 1.
The square root of 2 is 1.414.
The square root of 3 is 1.732.
The square root of 4 is 2.
The square root of 5 is 2.236.

fuel_efficiency <- 30

# Interpolate strings using data from a data frame
mtcars %>% rownames_to_column("Model") %>%
         filter(mpg > fuel_efficiency) %>%
         str_glue_data("The {Model} gets {mpg} mpg.")

Error in rownames_to_column(., "Model"): could not find function "rownames_to_column"

15.6 stringr: String Matching

head(data,8)

       author score
1  butt_ghost     3
2 buntaro_pup     1
3  iidealized     2
4   [deleted]     1
5   stathibus     6
6 soulslicer0     2
7 swiftsecond     1
                                                                                                                                                                                                                       body
1                                                                                      Hdf5. It's structured, it's easy to get data in and out, and it's fast. Plus it will scale if you ever get up there in dataset size.
2                                                                                                                                                                                                          yep, good point.
3                                                            Google must have done (and is doing) serious internal research in ranking. I've heard they're pretty good at that and they've even made some money doing it :P
4                                                                                                                                                                                                                 [deleted]
5                                                                                                   Sebastian Thrun's book, Probabilistic Robotics, goes through this in great detail. Get it, read it, make it your bible.
6 This. Such a legendary book. Kalman filters, particle filters, recursive Bayesian filters and a whole bunch of other stuff. I learnt so much. Read these 3 for starts from the book, then come back and ask the questions
7                                                                                                                                                                                                   Do you still need help?

# Detecting the presence of a pattern in strings
str_detect(data$body[1:100], pattern="deep")

  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE    NA    NA    NA    NA    NA
 [13]    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
 [25]    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
 [37]    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
 [49]    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
 [61]    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
 [73]    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
 [85]    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
 [97]    NA    NA    NA    NA

# Get the indicies of matched strings
str_inds <- str_which(data$body[1:100], pattern="deep")
str_inds

integer(0)

# Extract matched strings using detected indicies
data$body[str_inds]

character(0)

# Count the number of matches
str_count(data$body[1:100], "deep")

  [1]  0  0  0  0  0  0  0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
 [26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
 [51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
 [76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

# Get the position of matches
str_locate_all(data$body[1], "deep")

[[1]]
     start end

# Get a list of the first match found in each string as a vector
str_extract(data$body[1:3], "deep|the|and")

[1] "and" NA    "and"

# Get a list of the first match found in each string as matrix
str_match(data$body[1:3], "deep|the|and")

     [,1] 
[1,] "and"
[2,] NA   
[3,] "and"

# Get a list of the all matches found in each string as list of matricies
str_match_all(data$body[1:3], "deep|the|and")

[[1]]
     [,1] 
[1,] "and"
[2,] "and"
[3,] "the"

[[2]]
     [,1]

[[3]]
     [,1] 
[1,] "and"
[2,] "the"
[3,] "and"
[4,] "the"

15.7 stringr: Subset and Replace Strings

head(data,8)

       author score
1  butt_ghost     3
2 buntaro_pup     1
3  iidealized     2
4   [deleted]     1
5   stathibus     6
6 soulslicer0     2
7 swiftsecond     1
                                                                                                                                                                                                                       body
1                                                                                      Hdf5. It's structured, it's easy to get data in and out, and it's fast. Plus it will scale if you ever get up there in dataset size.
2                                                                                                                                                                                                          yep, good point.
3                                                            Google must have done (and is doing) serious internal research in ranking. I've heard they're pretty good at that and they've even made some money doing it :P
4                                                                                                                                                                                                                 [deleted]
5                                                                                                   Sebastian Thrun's book, Probabilistic Robotics, goes through this in great detail. Get it, read it, make it your bible.
6 This. Such a legendary book. Kalman filters, particle filters, recursive Bayesian filters and a whole bunch of other stuff. I learnt so much. Read these 3 for starts from the book, then come back and ask the questions
7                                                                                                                                                                                                   Do you still need help?

# Get a string subset based on character position
str_sub(data$body[1], start=1, end=100)

[1] "Hdf5. It's structured, it's easy to get data in and out, and it's fast. Plus it will scale if you ev"

# Get a string subset based on words
word(data$body[1], start=1, end=10)

[1] "Hdf5. It's structured, it's easy to get data in and"

# Get the strings that contain a certain pattern
str_subset(data$body[1:100], pattern="deep")

character(0)

# Replace a substring with a new string by substring position
str_sub(data$body[1], start=1, end=100) <- str_to_upper(str_sub(data$body[1], 
                                                                start=1, 
                                                                end=100))
str_sub(data$body[1], start=1, end=100)

[1] "HDF5. IT'S STRUCTURED, IT'S EASY TO GET DATA IN AND OUT, AND IT'S FAST. PLUS IT WILL SCALE IF YOU EV"

# Replace first occurrence of a substring with a new string by matching
str_replace(data$body[1], pattern="deep|DEEP", replacement="multi-layer")

[1] "HDF5. IT'S STRUCTURED, IT'S EASY TO GET DATA IN AND OUT, AND IT'S FAST. PLUS IT WILL SCALE IF YOU EVer get up there in dataset size."

# Replace all occurrences of a substring with a new string by matching
str_replace_all(data$body[1], pattern="deep|DEEP", replacement="multi-layer")

[1] "HDF5. IT'S STRUCTURED, IT'S EASY TO GET DATA IN AND OUT, AND IT'S FAST. PLUS IT WILL SCALE IF YOU EVer get up there in dataset size."

15.8 stringr: Viewing Strings

# Basic printing
print(data$body[1:10])

 [1] "HDF5. IT'S STRUCTURED, IT'S EASY TO GET DATA IN AND OUT, AND IT'S FAST. PLUS IT WILL SCALE IF YOU EVer get up there in dataset size."                                                                                     
 [2] "yep, good point."                                                                                                                                                                                                         
 [3] "Google must have done (and is doing) serious internal research in ranking. I've heard they're pretty good at that and they've even made some money doing it :P"                                                           
 [4] "[deleted]"                                                                                                                                                                                                                
 [5] "Sebastian Thrun's book, Probabilistic Robotics, goes through this in great detail. Get it, read it, make it your bible."                                                                                                  
 [6] "This. Such a legendary book. Kalman filters, particle filters, recursive Bayesian filters and a whole bunch of other stuff. I learnt so much. Read these 3 for starts from the book, then come back and ask the questions"
 [7] "Do you still need help?"                                                                                                                                                                                                  
 [8] NA                                                                                                                                                                                                                         
 [9] NA                                                                                                                                                                                                                         
[10] NA

deep_learning_posts <- data$body[str_which(data$body, "deep learning")]

# View strings in HTML format with the first occurence of a pattern highlighted
str_view(deep_learning_posts, pattern="deep")

✖ Empty `string` provided.

# View strings in HTML format with the first all occurences highlighted
str_view_all(deep_learning_posts, pattern="deep")

Warning: `str_view_all()` was deprecated in stringr 1.5.0.
ℹ Please use `str_view()` instead.

✖ Empty `string` provided.

# Format strings into paragraphs of a given width with str_wrap()
wrapped <- str_wrap(data$body[str_which(data$body, "deep learning")][1], 
                    width = 50)
wrapped

[1] NA

# Print wrapped string with output obeying newlines
wrapped %>% cat()

NA

# Display wrapped paragraph as HTML, inserting paragraph breaks
str_wrap(data$body[str_which(data$body, "deep learning")][1], width = 50) %>%
str_replace_all("\n", "<br>") %>%
str_view_all(pattern = "deep")

[1] │ NA