# Chapter 24 String processing

One of the most common data wrangling challenges involves extracting numeric data contained in character strings and converting them into the numeric representations required to make plots, compute summaries, or fit models in R. Also common is processing unorganized text into meaningful variable names or categorical variables. Many of the string processing challenges a data scientist faces are unique and often unexpected. It is therefore quite ambitious to write a comprehensive section on this topic. Here we use a series of case studies that help us demonstrate how string processing is a necessary step for many data wrangling challenges. Specifically, we describe the process of converting the not yet shown original raw data from which we extracted the murders, heights, and research_funding_rates example into the data frames we have studied in this book.

By going over these case studies, we will cover some of the most common tasks in string processing including extracting numbers from strings, removing unwanted characters from text, finding and replacing characters, extracting specific parts of strings, converting free form text to more uniform formats, and splitting strings into multiple values.

Base R includes functions to perform all these tasks. However, they don’t follow a unifying convention, which makes them a bit hard to memorize and use. The stringr package basically repackages this functionality, but uses a more consistent approach of naming functions and ordering their arguments. For example, in stringr, all the string processing functions start with str_. This means that if you type str_ and hit tab, R will auto-complete and show all the available functions. As a result, we don’t necessarily have to memorize all the function names. Another advantage is that in the functions in this package the string being processed is always the first argument, which means we can more easily use the pipe. Therefore, we will start by describing how to use the functions in the stringr package.

Most of the examples will come from the second case study which deals with self-reported heights by students and most of the chapter is dedicated to learning regular expressions (regex), and functions in the stringr package.

## 24.1 The stringr package

library(tidyverse)
library(stringr)

In general, string processing tasks can be divided into detecting, locating, extracting, or replacing patterns in strings. We will see several examples. The table below includes the functions available to you in the stringr package. We split them by task. We also include the R-base equivalent when available.

All these functions take a character vector as first argument. Also, for each function, operations are vectorized: the operation gets applied to each string in the vector.

Finally, note that in this table we mention groups. These will be explained in Section 24.5.10.

str_detect Detect Is the pattern in the string? grepl
str_which Detect Returns the index of entries that contain the pattern. grep
str_subset Detect Returns the subset of strings that contain the pattern. grep with value = TRUE
str_locate Locate Returns positions of first occurrence of pattern in a string. regexpr
str_locate_all Locate Returns position of all occurrences of pattern in a string. gregexpr
str_view Locate Show the first part of the string that matches pattern.
str_view_all Locate Show me all the parts of the string that match the pattern.
str_extract Extract Extract the first part of the string that matches the pattern.
str_extract_all Extract Extract all parts of the string that match the pattern.
str_match Extract Extract first part of the string that matches the groups and the patterns defined by the groups.
str_match_all Extract Extract all parts of the string that matches the groups and the patterns defined by the groups.
str_sub Extract Extract a substring. substring
str_split Extract Split a string into a list with parts separated by pattern. strsplit
str_split_fixed Extract Split a string into a matrix with parts separated by pattern. strsplit with fixed = TRUE
str_count Describe Count number of times a pattern appears in a string.
str_length Describe Number of character in string. nchar
str_replace Replace Replace first part of a string matching a pattern with another.
str_replace_all Replace Replace all parts of a string matching a pattern with another. gsub
str_to_upper Replace Change all characters to upper case. toupper
str_to_lower Replace Change all characters to lower case. tolower
str_to_title Replace Change first character to upper and rest to lower.
str_replace_na Replace Replace all NAs to a new value.
str_trim Replace Remove white space from start and end of string.
str_c Manipulate Join multiple strings. paste0
str_conv Manipulate Change the encoding of the string.
str_sort Manipulate Sort the vector in alphabetical order. sort
str_order Manipulate Index needed to order the vector in alphabetical order. order
str_trunc Manipulate Truncate a string to a fixed size.
str_pad Manipulate Add white space to string to make it a fixed size.
str_dup Manipulate Repeat a string. rep then paste
str_wrap Manipulate Wrap things into formatted paragraphs.
str_interp Manipulate String interpolation. sprintf

## 24.2 Case study 1: US murders data

In this section we introduce some of the more simple string processing challenges with the following datasets as an example:

library(rvest)
url <- paste0("https://en.wikipedia.org/w/index.php?title=",
"Gun_violence_in_the_United_States_by_state",
"&direction=prev&oldid=810166167")
html_node("table") |>
html_table() |>
setNames(c("state", "population", "total", "murder_rate"))

The code above shows the first step in constructing the dataset

library(dslabs)
data(murders)

In general, string processing involves a string and a pattern. In R, we usually store strings in a character vector such as murders$population. The first three strings in this vector defined by the population variable are: murders_raw$population[1:3]
#> [1] "4,853,875" "737,709"   "6,817,565"

The usual coercion does not work here:

as.numeric(murders_raw$population[1:3]) #> Warning: NAs introduced by coercion #> [1] NA NA NA This is because of the commas ,. The string processing we want to do here is remove the pattern, ,, from the strings in murders_raw$population and then coerce to numbers. We can use the str_detect function to see that two of the three columns have commas in the entries:

commas <- function(x) any(str_detect(x, ","))
murders_raw |> summarize_all(commas)
#> # A tibble: 1 × 4
#>   state population total murder_rate
#>   <lgl> <lgl>      <lgl> <lgl>
#> 1 FALSE TRUE       TRUE  FALSE

We can then use the str_replace_all function to remove them:

test_1 <- str_replace_all(murders_raw$population, ",", "") test_1 <- as.numeric(test_1) We can then use mutate_all to apply this operation to each column, since it won’t affect the columns without commas. It turns out that this operation is so common that readr includes the function parse_number specifically meant to remove non-numeric characters before coercing: test_2 <- parse_number(murders_raw$population)
identical(test_1, test_2)
#> [1] TRUE

So we can obtain our desired table using:

murders_new <- murders_raw |> mutate_at(2:3, parse_number)
#> # A tibble: 6 × 4
#>   state      population total murder_rate
#>   <chr>           <dbl> <dbl>       <dbl>
#> 1 Alabama       4853875   348         7.2
#> 2 Alaska         737709    59         8
#> 3 Arizona       6817565   309         4.5
#> 4 Arkansas      2977853   181         6.1
#> 5 California   38993940  1861         4.8
#> # … with 1 more row

This case is relatively simple compared to the string processing challenges that we typically face in data science. The next example is a rather complex one and it provides several challenges that will permit us to learn many string processing techniques.

## 24.3 Case study 2: self-reported heights

The dslabs package includes the raw data from which the heights dataset was obtained. You can load it like this:

data(reported_heights)

These heights were obtained using a web form in which students were asked to enter their heights. They could enter anything, but the instructions asked for height in inches, a number. We compiled 1,095 submissions, but unfortunately the column vector with the reported heights had several non-numeric entries and as a result became a character vector:

class(reported_heights$height) #> [1] "character" If we try to parse it into numbers, we get a warning: x <- as.numeric(reported_heights$height)
#> Warning: NAs introduced by coercion

Although most values appear to be height in inches as requested:

#> [1] 75 70 68 74 61 65

we do end up with many NAs:

sum(is.na(x))
#> [1] 81

We can see some of the entries that are not successfully converted by using filter to keep only the entries resulting in NAs:

reported_heights |>
mutate(new_height = as.numeric(height)) |>
filter(is.na(new_height)) |>
#>             time_stamp    sex                 height new_height
#> 1  2014-09-02 15:16:28   Male                  5' 4"         NA
#> 2  2014-09-02 15:16:37 Female                  165cm         NA
#> 3  2014-09-02 15:16:52   Male                    5'7         NA
#> 4  2014-09-02 15:16:56   Male                  >9000         NA
#> 5  2014-09-02 15:16:56   Male                   5'7"         NA
#> 6  2014-09-02 15:17:09 Female                   5'3"         NA
#> 7  2014-09-02 15:18:00   Male 5 feet and 8.11 inches         NA
#> 8  2014-09-02 15:19:48   Male                   5'11         NA
#> 9  2014-09-04 00:46:45   Male                  5'9''         NA
#> 10 2014-09-04 10:29:44   Male                 5'10''         NA

We immediately see what is happening. Some of the students did not report their heights in inches as requested. We could discard these data and continue. However, many of the entries follow patterns that, in principle, we can easily convert to inches. For example, in the output above, we see various cases that use the format x'y'' with x and y representing feet and inches, respectively. Each one of these cases can be read and converted to inches by a human, for example 5'4'' is 5*12 + 4 = 64. So we could fix all the problematic entries by hand. However, humans are prone to making mistakes, so an automated approach is preferable. Also, because we plan on continuing to collect data, it will be convenient to write code that automatically does this.

A first step in this type of task is to survey the problematic entries and try to define specific patterns followed by a large groups of entries. The larger these groups, the more entries we can fix with a single programmatic approach. We want to find patterns that can be accurately described with a rule, such as “a digit, followed by a feet symbol, followed by one or two digits, followed by an inches symbol”.

To look for such patterns, it helps to remove the entries that are consistent with being in inches and to view only the problematic entries. We thus write a function to automatically do this. We keep entries that either result in NAs when applying as.numeric or are outside a range of plausible heights. We permit a range that covers about 99.9999% of the adult population. We also use suppressWarnings to avoid the warning message we know as.numeric will gives us.

not_inches <- function(x, smallest = 50, tallest = 84){
inches <- suppressWarnings(as.numeric(x))
ind <- is.na(inches) | inches < smallest | inches > tallest
ind
}

We apply this function and find the number of problematic entries:

problems <- reported_heights |>
filter(not_inches(height)) |>
pull(height)
length(problems)
#> [1] 292

We can now view all the cases by simply printing them. We don’t do that here because there are length(problems), but after surveying them carefully, we see that three patterns can be used to define three large groups within these exceptions.

1. A pattern of the form x'y or x' y'' or x'y" with x and y representing feet and inches, respectively. Here are ten examples:

#> 5' 4" 5'7 5'7" 5'3" 5'11 5'9'' 5'10'' 5' 10 5'5" 5'2"

2. A pattern of the form x.y or x,y with x feet and y inches. Here are ten examples:

#> 5.3 5.5 6.5 5.8 5.6 5,3 5.9 6,8 5.5 6.2

3. Entries that were reported in centimeters rather than inches. Here are ten examples:

#> 150 175 177 178 163 175 178 165 165 180

Once we see these large groups following specific patterns, we can develop a plan of attack. Remember that there is rarely just one way to perform these tasks. Here we pick one that helps us teach several useful techniques. But surely there is a more efficient way of performing the task.

Plan of attack: we will convert entries fitting the first two patterns into a standardized one. We will then leverage the standardization to extract the feet and inches and convert to inches. We will then define a procedure for identifying entries that are in centimeters and convert them to inches. After applying these steps, we will then check again to see what entries were not fixed and see if we can tweak our approach to be more comprehensive.

At the end, we hope to have a script that makes web-based data collection methods robust to the most common user mistakes.

To achieve our goal, we will use a technique that enables us to accurately detect patterns and extract the parts we want: regular expressions (regex). But first, we quickly describe how to escape the function of certain characters so that they can be included in strings.

## 24.4 How to escape when defining strings

To define strings in R, we can use either double quotes:

s <- "Hello!"

or single quotes:

s <- 'Hello!'

Make sure you choose the correct single quote since using the back quote will give you an error:

s <- Hello

Now, what happens if the string we want to define includes double quotes? For example, if we want to write 10 inches like this 10"? In this case you can’t use:

s <- "10""

because this is just the string 10 followed by a double quote. If you type this into R, you get an error because you have an unclosed double quote. To avoid this, we can use the single quotes:

s <- '10"'

If we print out s we see that the double quotes are escaped with the backslash \.

s
#> [1] "10\""

In fact, escaping with the backslash provides a way to define the string while still using the double quotes to define strings:

s <- "10\""

In R, the function cat lets us see what the string actually looks like:

cat(s)
#> 10"

Now, what if we want our string to be 5 feet written like this 5'? In this case, we can use the double quotes:

s <- "5'"
cat(s)
#> 5'

So we’ve learned how to write 5 feet and 10 inches separately, but what if we want to write them together to represent 5 feet and 10 inches like this 5'10"? In this case, neither the single nor double quotes will work. This:

s <- '5'10"'

closes the string after 5 and this:

s <- "5'10""

closes the string after 10. Keep in mind that if we type one of the above code snippets into R, it will get stuck waiting for you to close the open quote and you will have to exit the execution with the esc button.

In this situation, we need to escape the function of the quotes with the backslash \. You can escape either character like this:

s <- '5\'10"'
cat(s)
#> 5'10"

or like this:

s <- "5'10\""
cat(s)
#> 5'10"

Escaping characters is something we often have to use when processing strings.

## 24.5 Regular expressions

A regular expression (regex) is a way to describe specific patterns of characters of text. They can be used to determine if a given string matches the pattern. A set of rules has been defined to do this efficiently and precisely and here we show some examples. We can learn more about these rules by reading a detailed tutorials84 85. This RStudio cheat sheet86 is also very useful.

The patterns supplied to the stringr functions can be a regex rather than a standard string. We will learn how this works through a series of examples.

Throughout this section you will see that we create strings to test out our regex. To do this, we define patterns that we know should match and also patterns that we know should not. We will call them yes and no, respectively. This permits us to check for the two types of errors: failing to match and incorrectly matching.

### 24.5.1 Strings are a regexp

Technically any string is a regex, perhaps the simplest example is a single character. So the comma , used in the next code example is a simple example of searching with regex.

pattern <- ","
str_detect(murders_raw$total, pattern) We suppress the output which is logical vector telling us which entries have commas. Above, we noted that an entry included a cm. This is also a simple example of a regex. We can show all the entries that used cm like this: str_subset(reported_heights$height, "cm")
#> [1] "165cm"  "170 cm"

### 24.5.2 Special characters

Now let’s consider a slightly more complicated example. Which of the following strings contain the pattern cm or inches?

yes <- c("180 cm", "70 inches")
no <- c("180", "70''")
s <- c(yes, no)
str_detect(s, "cm") | str_detect(s, "inches")
#> [1]  TRUE  TRUE FALSE FALSE

However, we don’t need to do this. The main feature that distinguishes the regex language from plain strings is that we can use special characters. These are characters with a meaning. We start by introducing | which means or. So if we want to know if either cm or inches appears in the strings, we can use the regex cm|inches:

str_detect(s, "cm|inches")
#> [1]  TRUE  TRUE FALSE FALSE

Another special character that will be useful for identifying feet and inches values is \d which means any digit: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9. The backslash is used to distinguish it from the character d. In R, we have to escape the backslash \ so we actually have to use \\d to represent digits. Here is an example:

yes <- c("5", "6", "5'10", "5 feet", "4'11")
no <- c("", ".", "Five", "six")
s <- c(yes, no)
pattern <- "\\d"
str_detect(s, pattern)
#> [1]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE

We take this opportunity to introduce the str_view function, which is helpful for troubleshooting as it shows us the first match for each string:

str_view(s, pattern)

and str_view_all shows us all the matches, so 3'2 has two matches and 5'10 has three.

str_view_all(s, pattern)

There are many other special characters. We will learn some others below, but you can see most or all of them in the cheat sheet87 mentioned earlier.

Finally, a useful special character is \w which stands for word character and it matches any letter, number, or underscore. It is equivalent to [a-zA-Z0-9_].

### 24.5.3 Character classes

Character classes are used to define a series of characters that can be matched. We define character classes with square brackets []. So, for example, if we want the pattern to match only if we have a 5 or a 6, we use the regex [56]:

str_view(s, "[56]")

Suppose we want to match values between 4 and 7. A common way to define character classes is with ranges. So, for example, [0-9] is equivalent to \\d. The pattern we want is therefore [4-7].

yes <- as.character(4:7)
no <- as.character(1:3)
s <- c(yes, no)
str_detect(s, "[4-7]")
#> [1]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE

However, it is important to know that in regex everything is a character; there are no numbers. So 4 is the character 4 not the number four. Notice, for example, that [1-20] does not mean 1 through 20, it means the characters 1 through 2 or the character 0. So [1-20] simply means the character class composed of 0, 1, and 2.

Keep in mind that characters do have an order and the digits do follow the numeric order. So 0 comes before 1 which comes before 2 and so on. For the same reason, we can define lower case letters as [a-z], upper case letters as [A-Z], and [a-zA-z] as both.

### 24.5.4 Anchors

What if we want a match when we have exactly 1 digit? This will be useful in our case study since feet are never more than 1 digit so a restriction will help us. One way to do this with regex is by using anchors, which let us define patterns that must start or end at a specific place. The two most common anchors are ^ and $which represent the beginning and end of a string, respectively. So the pattern ^\\d$ is read as “start of the string followed by one digit followed by end of string”.

This pattern now only detects the strings with exactly one digit:

pattern <- "^\\d$" yes <- c("1", "5", "9") no <- c("12", "123", " 1", "a4", "b") s <- c(yes, no) str_view_all(s, pattern) The 1 does not match because it does not start with the digit but rather with a space, which is actually not easy to see. ### 24.5.5 Quantifiers For the inches part, we can have one or two digits. This can be specified in regex with quantifiers. This is done by following the pattern with curly brackets containing the number of times the previous entry can be repeated. We use an example to illustrate. The pattern for one or two digits is: pattern <- "^\\d{1,2}$"
yes <- c("1", "5", "9", "12")
no <- c("123", "a4", "b")
str_view(c(yes, no), pattern)

In this case, 123 does not match, but 12 does. So to look for our feet and inches pattern, we can add the symbols for feet ' and inches " after the digits.

With what we have learned, we can now construct an example for the pattern x'y\" with x feet and y inches.

pattern <- "^[4-7]'\\d{1,2}\"$" The pattern is now getting complex, but you can look at it carefully and break it down: • ^ = start of the string • [4-7] = one digit, either 4,5,6 or 7 • ' = feet symbol • \\d{1,2} = one or two digits • \" = inches symbol •$ = end of the string

Let’s test it out:

yes <- c("5'7\"", "6'2\"",  "5'12\"")
no <- c("6,2\"", "6.2\"","I am 5'11\"", "3'2\"", "64")
str_detect(yes, pattern)
#> [1] TRUE TRUE TRUE
str_detect(no, pattern)
#> [1] FALSE FALSE FALSE FALSE FALSE

For now, we are permitting the inches to be 12 or larger. We will add a restriction later as the regex for this is a bit more complex than we are ready to show.

### 24.5.6 White space \s

Another problem we have are spaces. For example, our pattern does not match 5' 4" because there is a space between ' and 4 which our pattern does not permit. Spaces are characters and R does not ignore them:

identical("Hi", "Hi ")
#> [1] FALSE

In regex, \s represents white space. To find patterns like 5' 4, we can change our pattern to:

pattern_2 <- "^[4-7]'\\s\\d{1,2}\"$" str_subset(problems, pattern_2) #> [1] "5' 4\"" "5' 11\"" "5' 7\"" However, this will not match the patterns with no space. So do we need more than one regex pattern? It turns out we can use a quantifier for this as well. ### 24.5.7 Quantifiers: *, ?, + We want the pattern to permit spaces but not require them. Even if there are several spaces, like in this example 5' 4, we still want it to match. There is a quantifier for exactly this purpose. In regex, the character * means zero or more instances of the previous character. Here is an example: yes <- c("AB", "A1B", "A11B", "A111B", "A1111B") no <- c("A2B", "A21B") str_detect(yes, "A1*B") #> [1] TRUE TRUE TRUE TRUE TRUE str_detect(no, "A1*B") #> [1] FALSE FALSE The above matches the first string which has zero 1s and all the strings with one or more 1. We can then improve our pattern by adding the * after the space character \s. There are two other similar quantifiers. For none or once, we can use ?, and for one or more, we can use +. You can see how they differ with this example: data.frame(string = c("AB", "A1B", "A11B", "A111B", "A1111B"), none_or_more = str_detect(yes, "A1*B"), nore_or_once = str_detect(yes, "A1?B"), once_or_more = str_detect(yes, "A1+B")) #> string none_or_more nore_or_once once_or_more #> 1 AB TRUE TRUE FALSE #> 2 A1B TRUE TRUE TRUE #> 3 A11B TRUE FALSE TRUE #> 4 A111B TRUE FALSE TRUE #> 5 A1111B TRUE FALSE TRUE We will actually use all three in our reported heights example, but we will see these in a later section. ### 24.5.8 Not To specify patterns that we do not want to detect, we can use the ^ symbol but only inside square brackets. Remember that outside the square bracket ^ means the start of the string. So, for example, if we want to detect digits that are preceded by anything except a letter we can do the following: pattern <- "[^a-zA-Z]\\d" yes <- c(".3", "+2", "-0","*4") no <- c("A3", "B2", "C0", "E4") str_detect(yes, pattern) #> [1] TRUE TRUE TRUE TRUE str_detect(no, pattern) #> [1] FALSE FALSE FALSE FALSE Another way to generate a pattern that searches for everything except is to use the upper case of the special character. For example \\D means anything other than a digit, \\S means anything except a space, and so on. ### 24.5.9 Lookarounds Lookarounds provide a way to ask for one or more conditions to be satisfied without moving the search forward or matching it. For example, you might want to check for multiple conditions and if they are matched, then return the pattern or part of the pattern that matched. An example: check if a string satisfies the conditions for a password and if it does return the password. Suppose the conditions are 1) 8-16 word characters, 2) starts with a letter, and 3) has at least one digit. There are four types of lookarounds: lookahead (?=pattern), lookbehind (?<=pattern), negative lookahead (?!pattern), and negative lookbehind (?<!pattern). You can concatenate them to check for multiple conditions so for our example we can write it like this: pattern <- "(?=\\w{8,16})(?=^[a-z|A-Z].*)(?=.*\\d+.*).*" yes <- c("Ihatepasswords1", "password1234") no <- c("sh0rt", "Ihaterpasswords", "7X%9,NyrYG92b7") str_detect(yes, pattern) #> [1] TRUE TRUE str_detect(no, pattern) #> [1] FALSE FALSE FALSE str_extract(yes, pattern) #> [1] "Ihatepasswords1" "password1234" ### 24.5.10 Groups Groups are a powerful aspect of regex that permits the extraction of values. Groups are defined using parentheses. They don’t affect the pattern matching per se. Instead, it permits tools to identify specific parts of the pattern so we can extract them. We want to change heights written like 5.6 to 5'6. To avoid changing patterns such as 70.2, we will require that the first digit be between 4 and 7 [4-7] and that the second be none or more digits \\d*. Let’s start by defining a simple pattern that matches this: pattern_without_groups <- "^[4-7],\\d*$"

We want to extract the digits so we can then form the new version using a period. These are our two groups, so we encapsulate them with parentheses:

pattern_with_groups <-  "^([4-7]),(\\d*)$" We encapsulate the part of the pattern that matches the parts we want to keep for later use. Adding groups does not affect the detection, since it only signals that we want to save what is captured by the groups. Note that both patterns return the same result when using str_detect: yes <- c("5,9", "5,11", "6,", "6,1") no <- c("5'9", ",", "2,8", "6.1.1") s <- c(yes, no) str_detect(s, pattern_without_groups) #> [1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE str_detect(s, pattern_with_groups) #> [1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE Once we define groups, we can use the function str_match to extract the values these groups define: str_match(s, pattern_with_groups) #> [,1] [,2] [,3] #> [1,] "5,9" "5" "9" #> [2,] "5,11" "5" "11" #> [3,] "6," "6" "" #> [4,] "6,1" "6" "1" #> [5,] NA NA NA #> [6,] NA NA NA #> [7,] NA NA NA #> [8,] NA NA NA Notice that the second and third columns contain feet and inches, respectively. The first column is the part of the string matching the pattern. If no match occurred, we see an NA. Now we can understand the difference between the functions str_extract and str_match: str_extract extracts only strings that match a pattern, not the values defined by groups: str_extract(s, pattern_with_groups) #> [1] "5,9" "5,11" "6," "6,1" NA NA NA NA ## 24.6 Search and replace with regex Earlier we defined the object problems containing the strings that do not appear to be in inches. We can see that not too many of our problematic strings match the pattern: pattern <- "^[4-7]'\\d{1,2}\"$"
sum(str_detect(problems, pattern))
#> [1] 14

To see why this is, we show some examples that expose why we don’t have more matches:

problems[c(2, 10, 11, 12, 15)] |> str_view(pattern)

An initial problem we see immediately is that some students wrote out the words “feet” and “inches”. We can see the entries that did this with the str_subset function:

str_subset(problems, "inches")
#> [1] "5 feet and 8.11 inches" "Five foot eight inches"
#> [3] "5 feet 7inches"         "5ft 9 inches"
#> [5] "5 ft 9 inches"          "5 feet 6 inches"

We also see that some entries used two single quotes '' instead of a double quote ".

str_subset(problems, "''")
#>  [1] "5'9''"   "5'10''"  "5'10''"  "5'3''"   "5'7''"   "5'6''"
#>  [7] "5'7.5''" "5'7.5''" "5'10''"  "5'11''"  "5'10''"  "5'5''"

To correct this, we can replace the different ways of representing inches and feet with a uniform symbol. We will use ' for feet, whereas for inches we will simply not use a symbol since some entries were of the form x'y. Now, if we no longer use the inches symbol, we have to change our pattern accordingly:

pattern <- "^[4-7]'\\d{1,2}$" If we do this replacement before the matching, we get many more matches: problems |> str_replace("feet|ft|foot", "'") |> # replace feet, ft, foot with ' str_replace("inches|in|''|\"", "") |> # remove all inches symbols str_detect(pattern) |> sum() #> [1] 48 However, we still have many cases to go. Note that in the code above, we leveraged the stringr consistency and used the pipe. For now, we improve our pattern by adding \\s* in front of and after the feet symbol ' to permit space between the feet symbol and the numbers. Now we match a few more entries: pattern <- "^[4-7]\\s*'\\s*\\d{1,2}$"
problems |>
str_replace("feet|ft|foot", "'") |> # replace feet, ft, foot with '
str_replace("inches|in|''|\"", "") |> # remove all inches symbols
str_detect(pattern) |>
sum()
#> [1] 53

We might be tempted to avoid doing this by removing all the spaces with str_replace_all. However, when doing such an operation we need to make sure that it does not have unintended effects. In our reported heights examples, this will be a problem because some entries are of the form x y with space separating the feet from the inches. If we remove all spaces, we will incorrectly turn x y into xy which implies that a 6 1 would become 61 inches instead of 73 inches.

The second large type of problematic entries were of the form x.y, x,y and x y. We want to change all these to our common format x'y. But we can’t just do a search and replace because we would change values such as 70.5 into 70'5. Our strategy will therefore be to search for a very specific pattern that assures us feet and inches are being provided and then, for those that match, replace appropriately.

### 24.6.1 Search and replace using groups

Another powerful aspect of groups is that you can refer to the extracted values in a regex when searching and replacing.

The regex special character for the i-th group is \\i. So \\1 is the value extracted from the first group, \\2 the value from the second and so on. As a simple example, note that the following code will replace a comma with period, but only if it is between two digits:

pattern_with_groups <-  "^([4-7]),(\\d*)$" yes <- c("5,9", "5,11", "6,", "6,1") no <- c("5'9", ",", "2,8", "6.1.1") s <- c(yes, no) str_replace(s, pattern_with_groups, "\\1'\\2") #> [1] "5'9" "5'11" "6'" "6'1" "5'9" "," "2,8" "6.1.1" We can use this to convert cases in our reported heights. We are now ready to define a pattern that helps us convert all the x.y, x,y and x y to our preferred format. We need to adapt pattern_with_groups to be a bit more flexible and capture all the cases. pattern_with_groups <-"^([4-7])\\s*[,\\.\\s+]\\s*(\\d*)$"

Let’s break this one down:

• ^ = start of the string
• [4-7] = one digit, either 4, 5, 6, or 7
• \\s* = none or more white space
• [,\\.\\s+] = feet symbol is either ,, . or at least one space
• \\s* = none or more white space
• \\d* = none or more digits
• $= end of the string We can see that it appears to be working: str_subset(problems, pattern_with_groups) |> head() #> [1] "5.3" "5.25" "5.5" "6.5" "5.8" "5.6" and will be able to perform the search and replace: str_subset(problems, pattern_with_groups) |> str_replace(pattern_with_groups, "\\1'\\2") |> head() #> [1] "5'3" "5'25" "5'5" "6'5" "5'8" "5'6" Again, we will deal with the inches-larger-than-twelve challenge later. ## 24.7 Testing and improving Developing the right regex on the first try is often difficult. Trial and error is a common approach to finding the regex pattern that satisfies all desired conditions. In the previous sections, we have developed a powerful string processing technique that can help us catch many of the problematic entries. Here we will test our approach, search for further problems, and tweak our approach for possible improvements. Let’s write a function that captures all the entries that can’t be converted into numbers remembering that some are in centimeters (we will deal with those later): not_inches_or_cm <- function(x, smallest = 50, tallest = 84){ inches <- suppressWarnings(as.numeric(x)) ind <- !is.na(inches) & ((inches >= smallest & inches <= tallest) | (inches/2.54 >= smallest & inches/2.54 <= tallest)) !ind } problems <- reported_heights |> filter(not_inches_or_cm(height)) |> pull(height) length(problems) #> [1] 200 Let’s see what proportion of these fit our pattern after the processing steps we developed above: converted <- problems |> str_replace("feet|foot|ft", "'") |> # convert feet symbols to ' str_replace("inches|in|''|\"", "") |> # remove inches symbols str_replace("^([4-7])\\s*[,\\.\\s+]\\s*(\\d*)$", "\\1'\\2")# change format

pattern <- "^[4-7]\\s*'\\s*\\d{1,2}$" index <- str_detect(converted, pattern) mean(index) #> [1] 0.615 Note how we leveraged the pipe, one of the advantages of using stringr. This last piece of code shows that we have matched well over half of the strings. Let’s examine the remaining cases: converted[!index] #> [1] "6" "165cm" "511" "6" #> [5] "2" ">9000" "5 ' and 8.11 " "11111" #> [9] "6" "103.2" "19" "5" #> [13] "300" "6'" "6" "Five ' eight " #> [17] "7" "214" "6" "0.7" #> [21] "6" "2'33" "612" "1,70" #> [25] "87" "5'7.5" "5'7.5" "111" #> [29] "5' 7.78" "12" "6" "yyy" #> [33] "89" "34" "25" "6" #> [37] "6" "22" "684" "6" #> [41] "1" "1" "6*12" "87" #> [45] "6" "1.6" "120" "120" #> [49] "23" "1.7" "6" "5" #> [53] "69" "5' 9 " "5 ' 9 " "6" #> [57] "6" "86" "708,661" "5 ' 6 " #> [61] "6" "649,606" "10000" "1" #> [65] "728,346" "0" "6" "6" #> [69] "6" "100" "88" "6" #> [73] "170 cm" "7,283,465" "5" "5" #> [77] "34" Four clear patterns arise: 1. Many students measuring exactly 5 or 6 feet did not enter any inches, for example 6', and our pattern requires that inches be included. 2. Some students measuring exactly 5 or 6 feet entered just that number. 3. Some of the inches were entered with decimal points. For example 5'7.5''. Our pattern only looks for two digits. 4. Some entries have spaces at the end, for example 5 ' 9. Although not as common, we also see the following problems: 1. Some entries are in meters and some of these use European decimals: 1.6, 1,70. 2. Two students added cm. 3. A student spelled out the numbers: Five foot eight inches. It is not necessarily clear that it is worth writing code to handle these last three cases since they might be rare enough. However, some of them provide us with an opportunity to learn a few more regex techniques, so we will build a fix. For case 1, if we add a '0 after the first digit, for example, convert all 6 to 6'0, then our previously defined pattern will match. This can be done using groups: yes <- c("5", "6", "5") no <- c("5'", "5''", "5'4") s <- c(yes, no) str_replace(s, "^([4-7])$", "\\1'0")
#> [1] "5'0" "6'0" "5'0" "5'"  "5''" "5'4"

The pattern says it has to start (^) with a digit between 4 and 7 and end there ($). The parenthesis defines the group that we pass as \\1 to generate the replacement regex string. We can adapt this code slightly to handle the case 2 as well, which covers the entry 5'. Note 5' is left untouched. This is because the extra ' makes the pattern not match since we have to end with a 5 or 6. We want to permit the 5 or 6 to be followed by 0 or 1 feet sign. So we can simply add '{0,1} after the ' to do this. However, we can use the none or once special character ?. As we saw above, this is different from * which is none or more. We now see that the fourth case is also converted: str_replace(s, "^([56])'?$", "\\1'0")
#> [1] "5'0" "6'0" "5'0" "5'0" "5''" "5'4"

Here we only permit 5 and 6, but not 4 and 7. This is because 5 and 6 feet tall is quite common, so we assume those that typed 5 or 6 really meant 60 or 72 inches. However, 4 and 7 feet tall are so rare that, although we accept 84 as a valid entry, we assume 7 was entered in error.

We can use quantifiers to deal with case 3. These entries are not matched because the inches include decimals and our pattern does not permit this. We need to allow the second group to include decimals not just digits. This means we must permit zero or one period . then zero or more digits. So we will be using both ? and *. Also remember that, for this particular case, the period needs to be escaped since it is a special character (it means any character except line break). Here is a simple example of how we can use *.

So we can adapt our pattern, currently ^[4-7]\\s*'\\s*\\d{1,2}$to permit a decimal at the end: pattern <- "^[4-7]\\s*'\\s*(\\d+\\.?\\d*)$"

Case 4, meters using commas, we can approach similarly to how we converted the x.y to x'y. A difference is that we require that the first digit be 1 or 2:

yes <- c("1,7", "1, 8", "2, " )
no <- c("5,8", "5,3,2", "1.7")
s <- c(yes, no)
str_replace(s, "^([12])\\s*,\\s*(\\d*)$", "\\1\\.\\2") #> [1] "1.7" "1.8" "2." "5,8" "5,3,2" "1.7" We will later check if the entries are meters using their numeric values. We will come back to the case study after introducing two widely used functions in string processing that will come in handy when developing our final solution for the self-reported heights. ## 24.8 Trimming In general, spaces at the start or end of the string are uninformative. These can be particularly deceptive because sometimes they can be hard to see: s <- "Hi " cat(s) #> Hi identical(s, "Hi") #> [1] FALSE This is a general enough problem that there is a function dedicated to removing them: str_trim. str_trim("5 ' 9 ") #> [1] "5 ' 9" ## 24.9 Changing lettercase Notice that regex is case sensitive. Often we want to match a word regardless of case. One approach to doing this is to first change everything to lower case and then proceeding ignoring case. As an example, note that one of the entries writes out numbers as words Five foot eight inches. Although not efficient, we could add 13 extra str_replace calls to convert zero to 0, one to 1, and so on. To avoid having to write two separate operations for Zero and zero, One and one, etc., we can use the str_to_lower function to make all works lower case first: s <- c("Five feet eight inches") str_to_lower(s) #> [1] "five feet eight inches" Other related functions are str_to_upper and str_to_title. We are now ready to define a procedure that converts all the problematic cases to inches. ## 24.10 Case study 2: self-reported heights (continued) We now put all of what we have learned together into a function that takes a string vector and tries to convert as many strings as possible to one format. We write a function that puts together what we have done above. convert_format <- function(s){ s |> str_replace("feet|foot|ft", "'") |> str_replace_all("inches|in|''|\"|cm|and", "") |> str_replace("^([4-7])\\s*[,\\.\\s+]\\s*(\\d*)$", "\\1'\\2") |>
str_replace("^([56])'?$", "\\1'0") |> str_replace("^([12])\\s*,\\s*(\\d*)$", "\\1\\.\\2") |>
str_trim()
}

We can also write a function that converts words to numbers:

library(english)
words_to_numbers <- function(s){
s <- str_to_lower(s)
for(i in 0:11)
s <- str_replace_all(s, words(i), as.character(i))
s
}

Note that we can perform the above operation more efficiently with the function recode, which we learn about in Section 24.13. Now we can see which problematic entries remain:

converted <- problems |> words_to_numbers() |> convert_format()
remaining_problems <- converted[not_inches_or_cm(converted)]
pattern <- "^[4-7]\\s*'\\s*\\d+\\.?\\d*$" index <- str_detect(remaining_problems, pattern) remaining_problems[!index] #> [1] "511" "2" ">9000" "11111" "103.2" #> [6] "19" "300" "7" "214" "0.7" #> [11] "2'33" "612" "1.70" "87" "111" #> [16] "12" "yyy" "89" "34" "25" #> [21] "22" "684" "1" "1" "6*12" #> [26] "87" "1.6" "120" "120" "23" #> [31] "1.7" "86" "708,661" "649,606" "10000" #> [36] "1" "728,346" "0" "100" "88" #> [41] "7,283,465" "34" apart from the cases reported as meters, which we will fix below, they all seem to be cases that are impossible to fix. ### 24.10.1 The extract function The extract function is a useful tidyverse function for string processing that we will use in our final solution, so we introduce it here. In a previous section, we constructed a regex that lets us identify which elements of a character vector match the feet and inches pattern. However, we want to do more. We want to extract and save the feet and number values so that we can convert them to inches when appropriate. If we have a simpler case like this: s <- c("5'10", "6'1") tab <- data.frame(x = s) In Section 21.3 we learned about the separate function, which can be used to achieve our current goal: tab |> separate(x, c("feet", "inches"), sep = "'") #> feet inches #> 1 5 10 #> 2 6 1 The extract function from the tidyr package lets us use regex groups to extract the desired values. Here is the equivalent to the code above using separate but using extract: library(tidyr) tab |> extract(x, c("feet", "inches"), regex = "(\\d)'(\\d{1,2})") #> feet inches #> 1 5 10 #> 2 6 1 So why do we even need the new function extract? We have seen how small changes can throw off exact pattern matching. Groups in regex give us more flexibility. For example, if we define: s <- c("5'10", "6'1\"","5'8inches") tab <- data.frame(x = s) and we only want the numbers, separate fails: tab |> separate(x, c("feet","inches"), sep = "'", fill = "right") #> feet inches #> 1 5 10 #> 2 6 1" #> 3 5 8inches However, we can use extract. The regex here is a bit more complicated since we have to permit ' with spaces and feet. We also do not want the " included in the value, so we do not include that in the group: tab |> extract(x, c("feet", "inches"), regex = "(\\d)'(\\d{1,2})") #> feet inches #> 1 5 10 #> 2 6 1 #> 3 5 8 ### 24.10.2 Putting it all together We are now ready to put it all together and wrangle our reported heights data to try to recover as many heights as possible. The code is complex, but we will break it down into parts. We start by cleaning up the height column so that the heights are closer to a feet’inches format. We added an original heights column so we can compare before and after. Now we are ready to wrangle our reported heights dataset: pattern <- "^([4-7])\\s*'\\s*(\\d+\\.?\\d*)$"

smallest <- 50
tallest <- 84
new_heights <- reported_heights |>
mutate(original = height,
height = words_to_numbers(height) |> convert_format()) |>
extract(height, c("feet", "inches"), regex = pattern, remove = FALSE) |>
mutate_at(c("height", "feet", "inches"), as.numeric) |>
mutate(guess = 12 * feet + inches) |>
mutate(height = case_when(
is.na(height) ~ as.numeric(NA),
between(height, smallest, tallest) ~ height,  #inches
between(height/2.54, smallest, tallest) ~ height/2.54, #cm
between(height*100/2.54, smallest, tallest) ~ height*100/2.54, #meters
TRUE ~ as.numeric(NA))) |>
mutate(height = ifelse(is.na(height) &
inches < 12 & between(guess, smallest, tallest),
guess, height)) |>
select(-guess)

We can check all the entries we converted by typing:

new_heights |>
filter(not_inches(original)) |>
select(original, height) |>
arrange(height) |>
View()

A final observation is that if we look at the shortest students in our course:

#>            time_stamp    sex height feet inches original
#> 1 2017-07-04 01:30:25   Male   50.0   NA     NA       50
#> 2 2017-09-07 10:40:35   Male   50.0   NA     NA       50
#> 3 2014-09-02 15:18:30 Female   51.0   NA     NA       51
#> 4 2016-06-05 14:07:20 Female   52.0   NA     NA       52
#> 5 2016-06-05 14:07:38 Female   52.0   NA     NA       52
#> 6 2014-09-23 03:39:56 Female   53.0   NA     NA       53
#> 7 2015-01-07 08:57:29   Male   53.8   NA     NA    53.77

We see heights of 53, 54, and 55. In the originals, we also have 51 and 52. These short heights are rare and it is likely that the students actually meant 5'1, 5'2, 5'3, 5'4, and 5'5. Because we are not completely sure, we will leave them as reported. The object new_heights contains our final solution for this case study.

## 24.11 String splitting

Another very common data wrangling operation is string splitting. To illustrate how this comes up, we start with an illustrative example. Suppose we did not have the function read_csv or read.csv available to us. We instead have to read a csv file using the base R function readLines like this:

filename <- system.file("extdata/murders.csv", package = "dslabs")

This function reads-in the data line-by-line to create a vector of strings. In this case, one string for each row in the spreadsheet. The first six lines are:

#> [1] "state,abb,region,population,total"
#> [2] "Alabama,AL,South,4779736,135"
#> [4] "Arizona,AZ,West,6392017,232"
#> [5] "Arkansas,AR,South,2915918,93"
#> [6] "California,CA,West,37253956,1257"

We want to extract the values that are separated by a comma for each string in the vector. The command str_split does exactly this:

x <- str_split(lines, ",")
#> [[1]]
#> [1] "state"      "abb"        "region"     "population" "total"
#>
#> [[2]]
#> [1] "Alabama" "AL"      "South"   "4779736" "135"

Note that the first entry has the column names, so we can separate that out:

col_names <- x[[1]]
x <- x[-1]

To convert our list into a data frame, we can use a shortcut provided by the map functions in the purrr package. The map function applies the same function to each element in a list. So if we want to extract the first entry of each element in x, we can write:

library(purrr)
#> [[1]]
#> [1] "Alabama"
#>
#> [[2]]

However, because this is such a common task, purrr provides a shortcut. If the second argument receives an integer instead of a function, it assumes we want that entry. So the code above can be written more efficiently like this:

map(x, 1)

To force map to return a character vector instead of a list, we can use map_chr. Similarly, map_int returns integers. So to create our data frame, we can use:

dat <- tibble(map_chr(x, 1),
map_chr(x, 2),
map_chr(x, 3),
map_chr(x, 4),
map_chr(x, 5)) |>
mutate_all(parse_guess) |>
setNames(col_names)
#> # A tibble: 6 × 5
#>   state      abb   region population total
#>   <chr>      <chr> <chr>       <dbl> <dbl>
#> 1 Alabama    AL    South     4779736   135
#> 2 Alaska     AK    West       710231    19
#> 3 Arizona    AZ    West      6392017   232
#> 4 Arkansas   AR    South     2915918    93
#> 5 California CA    West     37253956  1257
#> # … with 1 more row

If you learn more about the purrr package, you will learn that you perform the above with the following, more efficient, code:

dat <- x |>
transpose() |>
map( ~ parse_guess(unlist(.))) |>
setNames(col_names) |>
as_tibble()

It turns out that we can avoid all the work shown above after the call to str_split. Specifically, if we know that the data we are extracting can be represented as a table, we can use the argument simplify=TRUE and str_split returns a matrix instead of a list:

x <- str_split(lines, ",", simplify = TRUE)
col_names <- x[1,]
x <- x[-1,]
colnames(x) <- col_names
x |> as_tibble() |>
mutate_all(parse_guess) |>
#> # A tibble: 5 × 5
#>   state      abb   region population total
#>   <chr>      <chr> <chr>       <dbl> <dbl>
#> 1 Alabama    AL    South     4779736   135
#> 2 Alaska     AK    West       710231    19
#> 3 Arizona    AZ    West      6392017   232
#> 4 Arkansas   AR    South     2915918    93
#> 5 California CA    West     37253956  1257

## 24.12 Case study 3: extracting tables from a PDF

One of the datasets provided in dslabs shows scientific funding rates by gender in the Netherlands:

library(dslabs)
data("research_funding_rates")
research_funding_rates |>
select("discipline", "success_rates_men", "success_rates_women")
#>            discipline success_rates_men success_rates_women
#> 1   Chemical sciences              26.5                25.6
#> 2   Physical sciences              19.3                23.1
#> 3             Physics              26.9                22.2
#> 4          Humanities              14.3                19.3
#> 5  Technical sciences              15.9                21.0
#> 6   Interdisciplinary              11.4                21.8
#> 7 Earth/life sciences              24.4                14.3
#> 8     Social sciences              15.3                11.5
#> 9    Medical sciences              18.8                11.2

The data comes from a paper published in the Proceedings of the National Academy of Science (PNAS)88, a widely read scientific journal. However, the data is not provided in a spreadsheet; it is in a table in a PDF document. Here is a screenshot of the table:

(Source: Romy van der Lee and Naomi Ellemers, PNAS 2015 112 (40) 12349-1235389.)

We could extract the numbers by hand, but this could lead to human error. Instead, we can try to wrangle the data using R. We start by downloading the pdf document, then importing into R:

library("pdftools")
temp_file <- tempfile()
url <- paste0("https://www.pnas.org/content/suppl/2015/09/16/",
"1510159112.DCSupplemental/pnas.201510159SI.pdf")
txt <- pdf_text(temp_file)
file.remove(temp_file)

If we examine the object text, we notice that it is a character vector with an entry for each page. So we keep the page we want:

raw_data_research_funding_rates <- txt[2]

The steps above can actually be skipped because we include this raw data in the dslabs package as well:

data("raw_data_research_funding_rates")

Examining the object raw_data_research_funding_rates we see that it is a long string and each line on the page, including the table rows, are separated by the symbol for newline: \n. We therefore can create a list with the lines of the text as elements as follows:

tab <- str_split(raw_data_research_funding_rates, "\n")

Because we start off with just one element in the string, we end up with a list with just one entry.

tab <- tab[[1]]

By examining tab we see that the information for the column names is the third and fourth entries:

the_names_1 <- tab[3]
the_names_2 <- tab[4]

The first of these rows looks like this:

#>                                                       Applications, n
#>                   Awards, n                      Success rates, %

We want to create one vector with one name for each column. Using some of the functions we have just learned, we do this. Let’s start with the_names_1, shown above. We want to remove the leading space and anything following the comma. We use regex for the latter. Then we can obtain the elements by splitting strings separated by space. We want to split only when there are 2 or more spaces to avoid splitting Success rates. So we use the regex \\s{2,}

the_names_1 <- the_names_1 |>
str_trim() |>
str_replace_all(",\\s.", "") |>
str_split("\\s{2,}", simplify = TRUE)
the_names_1
#>      [,1]           [,2]     [,3]
#> [1,] "Applications" "Awards" "Success rates"

Now we will look at the_names_2:

#>                         Discipline              Total     Men      Women
#> n         Total    Men       Women          Total    Men      Women

Here we want to trim the leading space and then split by space as we did for the first line:

the_names_2 <- the_names_2 |>
str_trim() |>
str_split("\\s+", simplify = TRUE)
the_names_2
#>      [,1]         [,2]    [,3]  [,4]    [,5]    [,6]  [,7]    [,8]
#> [1,] "Discipline" "Total" "Men" "Women" "Total" "Men" "Women" "Total"
#>      [,9]  [,10]
#> [1,] "Men" "Women"

We can then join these to generate one name for each column:

tmp_names <- str_c(rep(the_names_1, each = 3), the_names_2[-1], sep = "_")
the_names <- c(the_names_2[1], tmp_names) |>
str_to_lower() |>
str_replace_all("\\s", "_")
the_names
#>  [1] "discipline"          "applications_total"  "applications_men"
#>  [4] "applications_women"  "awards_total"        "awards_men"
#>  [7] "awards_women"        "success_rates_total" "success_rates_men"
#> [10] "success_rates_women"

Now we are ready to get the actual data. By examining the tab object, we notice that the information is in lines 6 through 14. We can use str_split again to achieve our goal:

new_research_funding_rates <- tab[6:14] |>
str_trim() |>
str_split("\\s{2,}", simplify = TRUE) |>
data.frame() |>
setNames(the_names) |>
mutate_at(-1, parse_number)
new_research_funding_rates |> as_tibble()
#> # A tibble: 9 × 10
#>   discipline         applications_tot… applications_men applications_wo…
#>   <chr>                          <dbl>            <dbl>            <dbl>
#> 1 Chemical sciences                122               83               39
#> 2 Physical sciences                174              135               39
#> 3 Physics                           76               67                9
#> 4 Humanities                       396              230              166
#> 5 Technical sciences               251              189               62
#> # … with 4 more rows, and 6 more variables: awards_total <dbl>,
#> #   awards_men <dbl>, awards_women <dbl>, success_rates_total <dbl>,
#> #   success_rates_men <dbl>, success_rates_women <dbl>

We can see that the objects are identical:

identical(research_funding_rates, new_research_funding_rates)
#> [1] TRUE

## 24.13 Recoding

Another common operation involving strings is recoding the names of categorical variables. Let’s say you have really long names for your levels and you will be displaying them in plots, you might want to use shorter versions of these names. For example, in character vectors with country names, you might want to change “United States of America” to “USA” and “United Kingdom” to UK, and so on. We can do this with case_when, although the tidyverse offers an option that is specifically designed for this task: the recode function.

Here is an example that shows how to rename countries with long names:

library(dslabs)
data("gapminder")

Suppose we want to show life expectancy time series by country for the Caribbean:

gapminder |>
filter(region == "Caribbean") |>
ggplot(aes(year, life_expectancy, color = country)) +
geom_line()

The plot is what we want, but much of the space is wasted to accommodate some of the long country names. We have four countries with names longer than 12 characters. These names appear once for each year in the Gapminder dataset. Once we pick nicknames, we need to change them all consistently. The recode function can be used to do this:

gapminder |> filter(region=="Caribbean") |>
mutate(country = recode(country,
Antigua and Barbuda = "Barbuda",
Dominican Republic = "DR",
St. Vincent and the Grenadines = "St. Vincent",
Trinidad and Tobago = "Trinidad")) |>
ggplot(aes(year, life_expectancy, color = country)) +
geom_line()

There are other similar functions in other R packages, such as recode_factor and fct_recoder in the forcats package.

## 24.14 Exercises

1. Complete all lessons and exercises in the https://regexone.com/ online interactive tutorial.

2. In the extdata directory of the dslabs package, you will find a PDF file containing daily mortality data for Puerto Rico from Jan 1, 2015 to May 31, 2018. You can find the file like this:

fn <- system.file("extdata", "RD-Mortality-Report_2015-18-180531.pdf",
package="dslabs")

Find and open the file or open it directly from RStudio. On a Mac, you can type:

system2("open", args = fn)

and on Windows, you can type:

system("cmd.exe", input = paste("start", fn))

Which of the following best describes this file:

1. It is a table. Extracting the data will be easy.
2. It is a report written in prose. Extracting the data will be impossible.
3. It is a report combining graphs and tables. Extracting the data seems possible.
4. It shows graphs of the data. Extracting the data will be difficult.

3. We are going to create a tidy dataset with each row representing one observation. The variables in this dataset will be year, month, day, and deaths. Start by installing and loading the pdftools package:

install.packages("pdftools")
library(pdftools)

Now read-in fn using the pdf_text function and store the results in an object called txt. Which of the following best describes what you see in txt?

1. A table with the mortality data.
2. A character string of length 12. Each entry represents the text in each page. The mortality data is in there somewhere.
3. A character string with one entry containing all the information in the PDF file.
4. An html document.

4. Extract the ninth page of the PDF file from the object txt, then use the str_split from the stringr package so that you have each line in a different entry. Call this string vector s. Then look at the result and choose the one that best describes what you see.

1. It is an empty string.
2. I can see the figure shown in page 1.
3. It is a tidy table.
4. I can see the table! But there is a bunch of other stuff we need to get rid of.

5. What kind of object is s and how many entries does it have?

6. We see that the output is a list with one component. Redefine s to be the first entry of the list. What kind of object is s and how many entries does it have?

7. When inspecting the string we obtained above, we see a common problem: white space before and after the other characters. Trimming is a common first step in string processing. These extra spaces will eventually make splitting the strings hard so we start by removing them. We learned about the command str_trim that removes spaces at the start or end of the strings. Use this function to trim s.

8. We want to extract the numbers from the strings stored in s. However, there are many non-numeric characters that will get in the way. We can remove these, but before doing this we want to preserve the string with the column header, which includes the month abbreviation. Use the str_which function to find the rows with a header. Save these results to header_index. Hint: find the first string that matches the pattern 2015 using the str_which function.

9. Now we are going to define two objects: month will store the month and header will store the column names. Identify which row contains the header of the table. Save the content of the row into an object called header, then use str_split to help define the two objects we need. Hints: the separator here is one or more spaces. Also, consider using the simplify argument.

10. Notice that towards the end of the page you see a totals row followed by rows with other summary statistics. Create an object called tail_index with the index of the totals entry.

11. Because our PDF page includes graphs with numbers, some of our rows have just one number (from the y-axis of the plot). Use the str_count function to create an object n with the number of numbers in each each row. Hint: you can write a regex for number like this \\d+.

12. We are now ready to remove entries from rows that we know we don’t need. The entry header_index and everything before it should be removed. Entries for which n is 1 should also be removed, and the entry tail_index and everything that comes after it should be removed as well.

13. Now we are ready to remove all the non-numeric entries. Do this using regex and the str_remove_all function. Hint: remember that in regex, using the upper case version of a special character usually means the opposite. So \\D means “not a digit”. Remember you also want to keep spaces.

14. To convert the strings into a table, use the str_split_fixed function. Convert s into a data matrix with just the day and death count data. Hints: note that the separator is one or more spaces. Make the argument n a value that limits the number of columns to the values in the 4 columns and the last column captures all the extra stuff. Then keep only the first four columns.

15. Now you are almost ready to finish. Add column names to the matrix, including one called day. Also, add a column with the month. Call the resulting object dat. Finally, make sure the day is an integer not a character. Hint: use only the first five columns.

16. Now finish it up by tidying tab with the pivot_longer_ function.

17. Make a plot of deaths versus day with color to denote year. Exclude 2018 since we do not have data for the entire year.

18. Now that we have wrangled this data step-by-step, put it all together in one R chunk, using the pipe as much as possible. Hint: first define the indexes, then write one line of code that does all the string processing.

19. Advanced: let’s return to the MLB Payroll example from the web scraping section. Use what you have learned in the web scraping and string processing chapters to extract the payroll for the New York Yankees, Boston Red Sox, and Oakland A’s and plot them as a function of time.