Brian_Singh_DATA607_Wk3

DATA 607 – Week Three - R Character Manipulation and Date Processing

Load libraries and import data

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.6     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.4     ✓ stringr 1.4.0
## ✓ readr   2.1.2     ✓ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(readr)
library(curl)

## Using libcurl 7.64.1 with LibreSSL/2.8.3

## 
## Attaching package: 'curl'

## The following object is masked from 'package:readr':
## 
##     parse_date

library(ggplot2)
library(dplyr)

majors<-read.csv(curl("https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/majors-list.csv"))

1. Identify the majors that contain either “DATA” or “STATISTICS”

tibble(Major = majors$Major) %>%
    tidyr::extract(Major,c("Data/Stats"),"(DATA|STATISTICS)",remove=FALSE) %>%
    drop_na()

## # A tibble: 3 × 2
##   Major                                         `Data/Stats`
##   <chr>                                         <chr>       
## 1 MANAGEMENT INFORMATION SYSTEMS AND STATISTICS STATISTICS  
## 2 COMPUTER PROGRAMMING AND DATA PROCESSING      DATA        
## 3 STATISTICS AND DECISION SCIENCE               STATISTICS

There are two majors containing “Statistics”: Management Information Systems and Statistics and Statistics and Decision Science; and one major containing “Data”: Computer Programming and Data Processing.

2. Transform strings to vector

original_strings <- '[1] "bell pepper"  "bilberry"     "blackberry"   "blood orange"
[5] "blueberry"    "cantaloupe"   "chili pepper" "cloudberry"  
[9] "elderberry"   "lime"         "lychee"       "mulberry"    
[13] "olive"        "salal berry"'
original_strings

## [1] "[1] \"bell pepper\"  \"bilberry\"     \"blackberry\"   \"blood orange\"\n[5] \"blueberry\"    \"cantaloupe\"   \"chili pepper\" \"cloudberry\"  \n[9] \"elderberry\"   \"lime\"         \"lychee\"       \"mulberry\"    \n[13] \"olive\"        \"salal berry\""

convert_to_vector <- unlist(str_extract_all(original_strings, pattern = "\"([a-z]+.[a-z]+)\""))
convert_to_vector <- str_remove_all(convert_to_vector, "\"")
convert_to_vector <- convert_to_vector %>%
    str_c(collapse = ", ")
convert_to_vector

## [1] "bell pepper, bilberry, blackberry, blood orange, blueberry, cantaloupe, chili pepper, cloudberry, elderberry, lime, lychee, mulberry, olive, salal berry"

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

(.)\1\1

This would match any character followed by two repetitions of itself ““.

“(.)(.)\2\1”

This will match two characters, e.g. “xy” as the .., followed by the second character, “y”, and then the first character, “x”. In this example, xyyx.

(..)\1

This would match any two characters repeated once if this was enclosed by ““.

“(.).\1.\1”

This will return a match on a string that has the first character, third and fifth as the same. The 2nd and 4th characters can be any character.

“(.)(.)(.).*\3\2\1”

This will match three of the same characters in a row and then any amount of characters, then the first three characters in reverse order.

4. Construct regular expressions to match words that:

A: Start and end with the same character.

“^(.)((.*\1\()|\\1?\))”

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

“([A-Za-z][A-Za-z]).*\1”)

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

“([A-Za-z][A-Za-z]).*\1”