191 lines
5.8 KiB
Plaintext
191 lines
5.8 KiB
Plaintext
# Programming with strings
|
|
|
|
```{r}
|
|
library(stringr)
|
|
library(tidyr)
|
|
library(tibble)
|
|
```
|
|
|
|
### Extract
|
|
|
|
```{r}
|
|
colours <- c("red", "orange", "yellow", "green", "blue", "purple")
|
|
colour_match <- str_c(colours, collapse = "|")
|
|
colour_match
|
|
|
|
more <- sentences[str_count(sentences, colour_match) > 1]
|
|
str_extract_all(more, colour_match)
|
|
```
|
|
|
|
If you use `simplify = TRUE`, `str_extract_all()` will return a matrix with short matches expanded to the same length as the longest:
|
|
|
|
```{r}
|
|
|
|
str_extract_all(more, colour_match, simplify = TRUE)
|
|
|
|
x <- c("a", "a b", "a b c")
|
|
str_extract_all(x, "[a-z]", simplify = TRUE)
|
|
```
|
|
|
|
We don't talk about matrices here, but they are useful elsewhere.
|
|
|
|
### Exercises
|
|
|
|
1. From the Harvard sentences data, extract:
|
|
|
|
1. The first word from each sentence.
|
|
2. All words ending in `ing`.
|
|
3. All plurals.
|
|
|
|
## Grouped matches
|
|
|
|
Earlier in this chapter we talked about the use of parentheses for clarifying precedence and for backreferences when matching.
|
|
You can also use parentheses to extract parts of a complex match.
|
|
For example, imagine we want to extract nouns from the sentences.
|
|
As a heuristic, we'll look for any word that comes after "a" or "the".
|
|
Defining a "word" in a regular expression is a little tricky, so here I use a simple approximation: a sequence of at least one character that isn't a space.
|
|
|
|
```{r}
|
|
noun <- "(a|the) ([^ ]+)"
|
|
|
|
has_noun <- sentences %>%
|
|
str_subset(noun) %>%
|
|
head(10)
|
|
has_noun %>%
|
|
str_extract(noun)
|
|
```
|
|
|
|
`str_extract()` gives us the complete match; `str_match()` gives each individual component.
|
|
Instead of a character vector, it returns a matrix, with one column for the complete match followed by one column for each group:
|
|
|
|
```{r}
|
|
has_noun %>%
|
|
str_match(noun)
|
|
```
|
|
|
|
(Unsurprisingly, our heuristic for detecting nouns is poor, and also picks up adjectives like smooth and parked.)
|
|
|
|
## Spitting
|
|
|
|
Use `str_split()` to split a string up into pieces.
|
|
For example, we could split sentences into words:
|
|
|
|
```{r}
|
|
sentences %>%
|
|
head(5) %>%
|
|
str_split(" ")
|
|
```
|
|
|
|
Because each component might contain a different number of pieces, this returns a list.
|
|
If you're working with a length-1 vector, the easiest thing is to just extract the first element of the list:
|
|
|
|
```{r}
|
|
"a|b|c|d" %>%
|
|
str_split("\\|") %>%
|
|
.[[1]]
|
|
```
|
|
|
|
Otherwise, like the other stringr functions that return a list, you can use `simplify = TRUE` to return a matrix:
|
|
|
|
```{r}
|
|
sentences %>%
|
|
head(5) %>%
|
|
str_split(" ", simplify = TRUE)
|
|
```
|
|
|
|
You can also request a maximum number of pieces:
|
|
|
|
```{r}
|
|
fields <- c("Name: Hadley", "Country: NZ", "Age: 35")
|
|
fields %>% str_split(": ", n = 2, simplify = TRUE)
|
|
```
|
|
|
|
Instead of splitting up strings by patterns, you can also split up by character, line, sentence and word `boundary()`s:
|
|
|
|
```{r}
|
|
x <- "This is a sentence. This is another sentence."
|
|
str_view_all(x, boundary("word"))
|
|
|
|
str_split(x, " ")[[1]]
|
|
str_split(x, boundary("word"))[[1]]
|
|
```
|
|
|
|
## Replace with function
|
|
|
|
## Locations
|
|
|
|
`str_locate()` and `str_locate_all()` give you the starting and ending positions of each match.
|
|
These are particularly useful when none of the other functions does exactly what you want.
|
|
You can use `str_locate()` to find the matching pattern, `str_sub()` to extract and/or modify them.
|
|
|
|
## stringi
|
|
|
|
stringr is built on top of the **stringi** package.
|
|
stringr is useful when you're learning because it exposes a minimal set of functions, which have been carefully picked to handle the most common string manipulation functions.
|
|
stringi, on the other hand, is designed to be comprehensive.
|
|
It contains almost every function you might ever need: stringi has `r length(getNamespaceExports("stringi"))` functions to stringr's `r length(getNamespaceExports("stringr"))`.
|
|
|
|
If you find yourself struggling to do something in stringr, it's worth taking a look at stringi.
|
|
The packages work very similarly, so you should be able to translate your stringr knowledge in a natural way.
|
|
The main difference is the prefix: `str_` vs. `stri_`.
|
|
|
|
### Exercises
|
|
|
|
1. Find the stringi functions that:
|
|
|
|
a. Count the number of words.
|
|
b. Find duplicated strings.
|
|
c. Generate random text.
|
|
|
|
2. How do you control the language that `stri_sort()` uses for sorting?
|
|
|
|
### Exercises
|
|
|
|
1. What do the `extra` and `fill` arguments do in `separate()`?
|
|
Experiment with the various options for the following two toy datasets.
|
|
|
|
```{r, eval = FALSE}
|
|
tibble(x = c("a,b,c", "d,e,f,g", "h,i,j")) %>%
|
|
separate(x, c("one", "two", "three"))
|
|
|
|
tibble(x = c("a,b,c", "d,e", "f,g,i")) %>%
|
|
separate(x, c("one", "two", "three"))
|
|
```
|
|
|
|
2. Both `unite()` and `separate()` have a `remove` argument.
|
|
What does it do?
|
|
Why would you set it to `FALSE`?
|
|
|
|
3. Compare and contrast `separate()` and `extract()`.
|
|
Why are there three variations of separation (by position, by separator, and with groups), but only one unite?
|
|
|
|
4. In the following example we're using `unite()` to create a `date` column from `month` and `day` columns.
|
|
How would you achieve the same outcome using `mutate()` and `paste()` instead of unite?
|
|
|
|
```{r, eval = FALSE}
|
|
events <- tribble(
|
|
~month, ~day,
|
|
1 , 20,
|
|
1 , 21,
|
|
1 , 22
|
|
)
|
|
|
|
events %>%
|
|
unite("date", month:day, sep = "-", remove = FALSE)
|
|
```
|
|
|
|
5. You can also pass a vector of integers to `sep`. `separate()` will interpret the integers as positions to split at.
|
|
Positive values start at 1 on the far-left of the strings; negative value start at -1 on the far-right of the strings.
|
|
Use `separate()` to represent location information in the following tibble in two columns: `state` (represented by the first two characters) and `county`.
|
|
Do this in two ways: using a positive and a negative value for `sep`.
|
|
|
|
```{r}
|
|
baker <- tribble(
|
|
~location,
|
|
"FLBaker County",
|
|
"GABaker County",
|
|
"ORBaker County",
|
|
)
|
|
baker
|
|
```
|