r4ds_14

1. In code that doesn’t use stringr, you’ll often see paste() and paste0(). What’s the difference between the two functions? What stringr function are they equivalent to? How do the functions differ in their handling of NA?

2. In your own words, describe the difference between the sep and collapse arguments to str_c().

# sep
str_c("a", "b", "c", sep = ", ")

## [1] "a, b, c"

# collapse
str_c(c("a", "b", "c"), collapse = ", ")

## [1] "a, b, c"

3. Use str_length() and str_sub() to extract the middle character from a string. What will you do if the string has an even number of characters?

x <- "honey bee"
mid_x <- str_length(x) / 2 + 0.5
str_sub(x, mid_x, mid_x)

## [1] "y"

y <- "bumble bee"
mid_y <- str_length(y) / 2
str_sub(y, mid_y, mid_y + 1)

## [1] "le"

4. What does str_wrap() do? When might you want to use it?

str_warp()：指定の文字数で改行する.

5. What does str_trim() do? What’s the opposite of str_trim()?

str_trim()：whitespaceを文字列の始まりと終わりから削除する.
str_pad()：str_trim()の逆.

6. Write a function that turns (e.g.) a vector c(“a”, “b”, “c”) into the string a, b, and c. Think carefully about what it should do if given a vector of length 0, 1, or 2.

q6 <- function(x){
  len <- length(x)
  if(len <= 1){
    str_c(x, collapse = ", ")
  }
  else{
      str_c(str_c(x[-len], collapse = ", "), x[len], sep = ", and ")
  }
}
q6(c(""))

## [1] ""

q6(c("a"))

## [1] "a"

q6(c("a", "b"))

## [1] "a, and b"

q6(c("a", "b", "c"))

## [1] "a, b, and c"

1. Explain why each of these strings don’t match a \: "\", "\\", "\\\".

\はescapeなので, \をescapeするには, \\とする必要があるが,
\\は\と認識されるので、\\\\とする必要がある.

2. How would you match the sequence "'\?

x <- "a \"'\\b"
writeLines(x)

## a "'\b

str_view(x, "\"'\\\\")

3. What patterns will the regular expression \..\..\.. match? How would you represent it as a string?

.◯.◯.◯のような文字列とマッチする.

 x <- "a.b.c.d"
str_view(x, "\\..\\..\\..")

1. How would you match the literal string "$^$"?

x <- "a$^$a"
str_view(x, "\\$\\^\\$")

2. Given the corpus of common words in stringr::words, create regular expressions that find all words that:

1: Start with “y”.

str_view(words, "^y", match = TRUE)

2: End with “x”

str_view(words, "x$", match = TRUE)

3: Are exactly three letters long. (Don’t cheat by using str_length()!)

str_view(words, "\\b...\\b", match = TRUE)

4: Have seven letters or more.

str_view(words, ".......", match = TRUE)

1. Create regular expressions to find all words that:

1: Start with a vowel.

x <- c("apple", "banana")
str_view(x, "^[aiueo]", match = TRUE)

2: That only contain consonants. (Hint: thinking about matching “not”-vowels.)

x <- c("apple", "banana")
str_view(x, "^[^aiueo]", match = TRUE)

3: End with ed, but not with eed.

x <- c("seed", "played")
str_view(x, "^[e]ed$", match = TRUE)

4: End with ing or ise.

x <- c("eating", "noise")
str_view(x, "ing$ise$")

2. Empirically verify the rule “i before e except after c”.

3. Is “q” always followed by a “u”?

str_view(words, "q[^u]", match = TRUE)

4. Write a regular expression that matches a word if it’s probably written in British English, not American English.

5. Create a regular expression that will match telephone numbers as commonly written in your country.

"(\\d\\d\\d|\\d\\d)-(\\d\\d\\d|\\d\\d\\d\\d)-(\\d\\d\\d\\d)"

1. Describe the equivalents of ?, +, * in {m,n} form.

? : {0, 1}
+ : {1, }
* : {0, }

2. Describe in words what these regular expressions match: (read carefully to see if I’m using a regular expression or a string that defines a regular expression.)

1: ^.*$ -> 全ての文字列
2: "\\{.+\\}" -> {}に挟まれた１文字以上の文字列
3: \d{4}-\d{2}-\d{2} -> 4桁の数字 - 2桁の数字 - 2桁の数字 4: "\\\\{4}" -> \\\\

3. Create regular expressions to find all words that:

1: Start with three consonants.

str_view(words, "^[^aiueo]{3}", match = TRUE)

2: Have three or more vowels in a row.

str_view(words, "[aiueo]{3, }", match = TRUE)

3: Have two or more vowel-consonant pairs in a row.

str_view(words, "([aiueo][^aiueo]){2, }", match = TRUE)

1. Describe, in words, what these expressions will match:

1: (.)\1\1 -> 同じ文字が３回連続.
2: "(.)(.)\\2\\1" -> 2文字が逆の順番で連続. 3: (..)\1 -> 同じ２文字が２回連続.
4: "(.).\\1.\\1" ->　abacaとかxyxzxみたいな文字列
5: "(.)(.)(.).*\\3\\2\\1" -> abcfghjcbaとかwxyrtyuiyxwみたいな文字列.
##### 2. Construct regular expressions to match words that:
1: Start and end with the same character.

str_view(words, "(.).*\\1$", match = TRUE)

2: Contain a repeated pair of letters (e.g. “church” contains “ch” repeated twice.)

str_view(words, "(..).*\\1", match = TRUE)

3: Contain one letter repeated in at least three places (e.g. “eleven” contains three “e”s.)

str_view(words, "(.).*\\1.*\\1", match = FALSE)

1. For each of the following challenges, try solving it by using both a single regular expression, and a combination of multiple str_detect() calls.

1: Find all words that start or end with x.

# single
str_view(words, "^x.*x$", match = TRUE)
# combine
words[str_detect(words, "^x")] %>% str_view("x$", match = TRUE)

2: Find all words that start with a vowel and end with a consonant.

# single
str_view(words, "^[aiueo].*[^aiueo]$", match = TRUE)
# combine
words[str_detect(words, "^[aiueo]")] %>% str_view("[^aiueo]$", match = TRUE)

3: Are there any words that contain at least one of each different vowel?

2. What word has the highest number of vowels? What word has the highest proportion of vowels? (Hint: what is the denominator?)

df <- tibble(
  word = words
)
df <- df %>% mutate(number = str_count(word, "[aiueo]"), prop = number / str_length(word))
# highest number
df %>% filter(number == max(df$number))

## # A tibble: 8 x 3
##   word        number  prop
##   <chr>        <int> <dbl>
## 1 appropriate      5 0.455
## 2 associate        5 0.556
## 3 available        5 0.556
## 4 colleague        5 0.556
## 5 encourage        5 0.556
## 6 experience       5 0.5  
## 7 individual       5 0.5  
## 8 television       5 0.5

# prop
df %>% filter(prop == max(df$prop))

## # A tibble: 1 x 3
##   word  number  prop
##   <chr>  <int> <dbl>
## 1 a          1     1

1. In the previous example, you might have noticed that the regular expression matched “flickered”, which is not a colour. Modify the regex to fix the problem.

color <- c("red", "orange", "yellow", "green", "blue", "purple")
color_match <- str_c(color, collapse = "|")

2. From the Harvard sentences data, extract:

1: The first word from each sentence.

str_extract(sentences, "^[a-zA-Z]+")

2: All words ending in ing.

str_extract_all(sentences, "[a-zA-Z]+ing")

3: All plurals.

str_extract_all(sentences, "[a-zA-Z]{3,}s")