theUrl <- "https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/all-ages.csv"
major <- read.table(file= theUrl, header=TRUE, sep=",")
summary(major)## Major_code Major Major_category Total
## Min. :1100 Length:173 Length:173 Min. : 2396
## 1st Qu.:2403 Class :character Class :character 1st Qu.: 24280
## Median :3608 Mode :character Mode :character Median : 75791
## Mean :3880 Mean : 230257
## 3rd Qu.:5503 3rd Qu.: 205763
## Max. :6403 Max. :3123510
## Employed Employed_full_time_year_round Unemployed
## Min. : 1492 Min. : 1093 Min. : 0
## 1st Qu.: 17281 1st Qu.: 12722 1st Qu.: 1101
## Median : 56564 Median : 39613 Median : 3619
## Mean : 166162 Mean : 126308 Mean : 9725
## 3rd Qu.: 142879 3rd Qu.: 111025 3rd Qu.: 8862
## Max. :2354398 Max. :1939384 Max. :147261
## Unemployment_rate Median P25th P75th
## Min. :0.00000 Min. : 35000 Min. :24900 Min. : 45800
## 1st Qu.:0.04626 1st Qu.: 46000 1st Qu.:32000 1st Qu.: 70000
## Median :0.05472 Median : 53000 Median :36000 Median : 80000
## Mean :0.05736 Mean : 56816 Mean :38697 Mean : 82506
## 3rd Qu.:0.06904 3rd Qu.: 65000 3rd Qu.:42000 3rd Qu.: 95000
## Max. :0.15615 Max. :125000 Max. :78000 Max. :210000#1. 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”
filtered_major <- major %>%
filter(grepl("DATA|STATISTICS", Major, ignore.case = TRUE))
print(filtered_major)## Major_code Major
## 1 2101 COMPUTER PROGRAMMING AND DATA PROCESSING
## 2 3702 STATISTICS AND DECISION SCIENCE
## 3 6212 MANAGEMENT INFORMATION SYSTEMS AND STATISTICS
## Major_category Total Employed Employed_full_time_year_round
## 1 Computers & Mathematics 29317 22828 18747
## 2 Computers & Mathematics 24806 18808 14468
## 3 Business 156673 134478 118249
## Unemployed Unemployment_rate Median P25th P75th
## 1 2265 0.09026422 60000 40000 85000
## 2 1138 0.05705405 70000 43000 102000
## 3 6186 0.04397714 72000 50000 100000#2 Write 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”)
first<- c("bell pepper", "bilberry", "blackberry", "blood orange", "blueberry", "cantaloupe", "chili pepper", "cloudberry", "elderberry", "lime", "lychee", "mulberry", "olive", "salal berry")
changed <- first
print(changed)## [1] "bell pepper" "bilberry" "blackberry" "blood orange" "blueberry"
## [6] "cantaloupe" "chili pepper" "cloudberry" "elderberry" "lime"
## [11] "lychee" "mulberry" "olive" "salal berry"#3 Describe, in words, what these expressions will match:
(.)\1\1 “(.)(.)\2\1” (..)\1 “(.).\1.\1” “(.)(.)(.).*\3\2\1” This regular expression will first match 3 identical characters consectively. The next line match any kind of word that is the same backwards as forward. The third line matches a string where the first two characters repeat at the end. The fourth line matches a 5 character string where the 1st, 3rd, and 5th characters are the same. And the 5th line matches a string that starts and ends with 3 charcters, but the last three characters is the first three characters in reverse
#4 Construct regular expressions to match words that:
1: Start and end with the same character. 2: Contain a repeated pair of letters (e.g. “church” contains “ch” repeated twice.) 3: Contain one letter repeated in at least three places (e.g. “eleven” contains three “e”s.) 1: (.)\1.(.)\2 2:(.)\1.(.)\2 3:(.)\1.(.)\2.(.)\3