Using the 173 majors listed in fivethirtyeight.com’s College Majors dataset [https://fivethirtyeight.com/features/the-economic-guide-to-picking-a-college-major/], provide code that identifies the majors that contain either “DATA” or “STATISTICS”
Answer:
# retrieve the csv file from GitHub
urlfile = "https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/majors-list.csv"
collegemajors <- read_csv(url(urlfile), show_col_types = FALSE)
# filter the Major column of the collegemajors database to include only strings that contain the words "data" or "statistics"
data_or_stats <- collegemajors %>%
filter(str_detect(Major, "DATA | STATISTICS"))
# select only the major column to show the filtered majors
filtered_majors <- data_or_stats %>%
select(Major) %>%
distinct(Major)
# change the tibble to a data frame
filtered_majors <- data.frame(filtered_majors)
# show the data frame
filtered_majors## Major
## 1 MANAGEMENT INFORMATION SYSTEMS AND STATISTICS
## 2 COMPUTER PROGRAMMING AND DATA PROCESSINGWrite code that transforms the data below:
[1] “bell pepper” “bilberry” “blackberry” “blood orange” [5]
“blueberry” “cantaloupe” “chili pepper” “cloudberry”
[9] “elderberry” “lime” “lychee” “mulberry”
[13] “olive” “salal berry”
Into a format like this:
c(“bell pepper”, “bilberry”, “blackberry”, “blood orange”, “blueberry”, “cantaloupe”, “chili pepper”, “cloudberry”, “elderberry”, “lime”, “lychee”, “mulberry”, “olive”, “salal berry”)
Answer:
library(stringr)
# recreate the character vector
fruit_vector <- c(
"bell pepper", "bilberry", "blackberry", "blood orange",
"blueberry", "cantaloupe", "chili pepper", "cloudberry",
"elderberry", "lime", "lychee", "mulberry",
"olive", "salal berry"
)
# convert the character vector into a string
fruit_string <- str_c("c(", str_c('"', fruit_vector, '"', collapse = ", "), ")", sep = "")
# Remove backslashes from the fruit string
formatted_string <- gsub("\\\\", "", fruit_string, fixed = TRUE)
# Print the fruit string
cat(formatted_string, sep = "\n")## c("bell pepper", "bilberry", "blackberry", "blood orange", "blueberry", "cantaloupe", "chili pepper", "cloudberry", "elderberry", "lime", "lychee", "mulberry", "olive", "salal berry")Describe, in words, what these expressions will match: a. (.)\1\1 b. “(.)(.)\2\1” c. (..)\1 d. “(.).\1.\1” e. “(.)(.)(.).*\3\2\1”
Answer:
(.)\1\1 will match any 3 consecutive characters in a string. For example, “aaa”.
“(.)(.)\2\1” will match the a string where the first and second characters are repeated in reverse order. For example, “abba”, “1221”, etc.
(..)\1 captures 2 consecutive characters and stores them in the same capturing group. This will match any expression that have a repeated pair of characters (i.e. banana)
“(.).\1.\1” captures 3 consecutive characters from a string. The first character is captured by the group, the second character is matched by the “.”, and the third character is matched by the final “.”. This will match any expression that has similar 3-character sequences, i.e. “axaxa”, “cbcbc”, or “12121”.
“(.)(.)(.).\3\2\1” captures 3 characters into 3 separate capturing groups, then the ”.” outside the “()” matches 0 or more characters. “\3\2\1” matches the same characters as previously captured, but in reverse order. Examples: “xyz123zyx”, “123456321”, or “hannah”.
Construct regular expressions to match words that:
Start and end with the same character.
Contain a repeated pair of letters (e.g. “church” contains “ch” repeated twice.)
Contain one letter repeated in at least three places (e.g. “eleven” contains three “e”s.)
Answer:
Start and end with the same character: “(.).*\1” Example:
stringx <- "abcba"
str_view(stringx, "(.).*\\1")## [1] │ <abcba>Contain a repeated pair of letters (e.g. “church” contains “ch” repeated twice): “(..).*\1”
church <- "church"
str_view(church, "(..).*\\1")## [1] │ <church>Contain one letter repeated in at least three places (e.g. “eleven” contains three “e”s.): ”
eleven <- "eleven"
str_view(eleven, "([a-z]).*\\1.*\\1")## [1] │ <eleve>nTitle: “The Economic Guide to Picking a College Major.” Author/Organization: FiveThirtyEight. URL: https://fivethirtyeight.com/features/the-economic-guide-to-picking-a-college-major/
Title: GitHub Repository for FiveThirtyEight College Majors Data Author/Organization: FiveThirtyEight URL: https://github.com/fivethirtyeight/data/blob/15f210532b2a642e85738ddefa7a2945d47e2585/college-majors/majors-list.csv