Week 3 Assignment
Yanyi Li
2024-09-18
Overview
Normalization
# import data
df <- read.csv('https://raw.githubusercontent.com/yli1048/yli1048/refs/heads/607/Employee%20Sample%20Data.csv', header=TRUE)
glimpse(df)## Rows: 19
## Columns: 14
## $ EEID <chr> "E02387", "E04105", "E02572", "E02832", "E01639", "E0064…
## $ Full.Name <chr> "Emily Davis", "Theodore Dinh", "Luna Sanders", "Penelop…
## $ Job.Title <chr> "Sr. Manger", "Technical Architect", "Director", "Comput…
## $ Department <chr> "IT", "IT", "Finance", "IT", "Finance", "Sales", "IT", "…
## $ Business.Unit <chr> "Research & Development", "Manufacturing", "Speciality P…
## $ Gender <chr> "Female", "Male", "Female", "Female", "Male", "Male", "F…
## $ Ethnicity <chr> "Black", "Asian", "Caucasian", "Caucasian", "Asian", "As…
## $ Age <int> 55, 59, 50, 26, 55, 57, 27, 25, 29, 34, 36, 27, 59, 51, …
## $ Hire.Date <chr> "4/8/2016", "11/29/1997", "10/26/2006", "9/27/2019", "11…
## $ Annual.Salary <int> 141604, 99975, 163099, 84913, 95409, 50994, 119746, 4133…
## $ Bonus.. <chr> "15%", "0%", "20%", "7%", "0%", "0%", "10%", "0%", "6%",…
## $ Country <chr> "United States", "China", "United States", "United State…
## $ City <chr> "Seattle", "Chongqing", "Chicago", "Chicago", "Phoenix",…
## $ Exit.Date <chr> "10/16/2021", "", "", "", "", "", "", "5/20/2021", "", "…#Create data frame for employee information
df1 <- df[, c(1:5, 9, 14)]
print(df1)## EEID Full.Name Job.Title Department
## 1 E02387 Emily Davis Sr. Manger IT
## 2 E04105 Theodore Dinh Technical Architect IT
## 3 E02572 Luna Sanders Director Finance
## 4 E02832 Penelope Jordan Computer Systems Manager IT
## 5 E01639 Austin Vo Sr. Analyst Finance
## 6 E00644 Joshua Gupta Account Representative Sales
## 7 E01550 Ruby Barnes Manager IT
## 8 E04332 Luke Martin Analyst Finance
## 9 E04533 Easton Bailey Manager Accounting
## 10 E03838 Madeline Walker Sr. Analyst Finance
## 11 E00591 Savannah Ali Sr. Manger Human Resources
## 12 E03344 Camila Rogers Controls Engineer Engineering
## 13 E00530 Eli Jones Manager Human Resources
## 14 E04239 Everleigh Ng Sr. Manger Finance
## 15 E03496 Robert Yang Sr. Analyst Accounting
## 16 E00549 Isabella Xi Vice President Marketing
## 17 E00163 Bella Powell Director Finance
## 18 E00884 Camila Silva Sr. Manger Marketing
## 19 E04116 David Barnes Director IT
## Business.Unit Hire.Date Exit.Date
## 1 Research & Development 4/8/2016 10/16/2021
## 2 Manufacturing 11/29/1997
## 3 Speciality Products 10/26/2006
## 4 Manufacturing 9/27/2019
## 5 Manufacturing 11/20/1995
## 6 Corporate 1/24/2017
## 7 Corporate 7/1/2020
## 8 Manufacturing 5/16/2020 5/20/2021
## 9 Manufacturing 1/25/2019
## 10 Speciality Products 6/13/2018
## 11 Manufacturing 2/11/2009
## 12 Speciality Products 10/21/2021
## 13 Manufacturing 3/14/1999
## 14 Research & Development 6/10/2021
## 15 Speciality Products 11/4/2017 3/9/2020
## 16 Research & Development 3/13/2013
## 17 Research & Development 3/4/2002
## 18 Speciality Products 12/1/2003
## 19 Corporate 11/3/2013#Create data frame for employee's salary
df2 <- df[, c(1, 10, 11)]
print(df2)## EEID Annual.Salary Bonus..
## 1 E02387 141604 15%
## 2 E04105 99975 0%
## 3 E02572 163099 20%
## 4 E02832 84913 7%
## 5 E01639 95409 0%
## 6 E00644 50994 0%
## 7 E01550 119746 10%
## 8 E04332 41336 0%
## 9 E04533 113527 6%
## 10 E03838 77203 0%
## 11 E00591 157333 15%
## 12 E03344 109851 0%
## 13 E00530 105086 9%
## 14 E04239 146742 10%
## 15 E03496 97078 0%
## 16 E00549 249270 30%
## 17 E00163 175837 20%
## 18 E00884 154828 13%
## 19 E04116 186503 24%#Plot histogram
hist(df2$Annual.Salary, main = "Annual Salary", xlab = "Salary")
#Create data frame for workforce diversity
df3 <- df[, c(1, 6:8, 12, 13)]
print(df3)## EEID Gender Ethnicity Age Country City
## 1 E02387 Female Black 55 United States Seattle
## 2 E04105 Male Asian 59 China Chongqing
## 3 E02572 Female Caucasian 50 United States Chicago
## 4 E02832 Female Caucasian 26 United States Chicago
## 5 E01639 Male Asian 55 United States Phoenix
## 6 E00644 Male Asian 57 China Chongqing
## 7 E01550 Female Caucasian 27 United States Phoenix
## 8 E04332 Male Black 25 United States Miami
## 9 E04533 Male Caucasian 29 United States Austin
## 10 E03838 Female Caucasian 34 United States Chicago
## 11 E00591 Female Asian 36 United States Miami
## 12 E03344 Female Caucasian 27 United States Seattle
## 13 E00530 Male Caucasian 59 United States Austin
## 14 E04239 Female Asian 51 China Shanghai
## 15 E03496 Male Asian 31 United States Austin
## 16 E00549 Female Asian 41 United States Seattle
## 17 E00163 Female Black 65 United States Phoenix
## 18 E00884 Female Latino 64 United States Seattle
## 19 E04116 Male Caucasian 64 United States Columbus#Plot histogram
hist(df3$Age, main = "Age", xlab = "Age")
Character Manipulation
2. 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”
library(stringr)majors <- read.csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/majors-list.csv", header = TRUE)
glimpse(majors)## Rows: 174
## Columns: 3
## $ FOD1P <chr> "1100", "1101", "1102", "1103", "1104", "1105", "1106",…
## $ Major <chr> "GENERAL AGRICULTURE", "AGRICULTURE PRODUCTION AND MANA…
## $ Major_Category <chr> "Agriculture & Natural Resources", "Agriculture & Natur…#Subset data frame based on majors that have "DATA" or "STATISTICS"
majors[str_detect(majors$Major, "DATA|STATISTICS"),]## FOD1P Major Major_Category
## 44 6212 MANAGEMENT INFORMATION SYSTEMS AND STATISTICS Business
## 52 2101 COMPUTER PROGRAMMING AND DATA PROCESSING Computers & Mathematics
## 59 3702 STATISTICS AND DECISION SCIENCE Computers & MathematicsMajors that contains either “DATA” or “STATISTICS” are “COMPUTER PROGRAMMING AND DATA PROCESSING”, “MANAGEMENT INFORMATION SYSTEMS AND STATISTICS”, and “STATISTICS AND DECISION SCIENCE”.
3. Describe, in words, what these expressions will match:
(.)\1\1
The word contains the same character three times in a row. For example: AAA.
“(.)(.)\2\1”
Two consecutive characters are repeated in reverse order. For example: Alla.
(..)\1
Two consecutive characters are repeated. For example: Haha.
“(.).\1.\1”
The word has the same character in the first, third, and fifth positions. For example: Abaca.
“(.)(.)(.).*\3\2\1”
The first three characters are repeated in reverse order at the end, separated by any number of characters. For example: qwerewq.
4. Construct regular expressions to match words that:
Start and end with the same character.
str_subset(words, "^(.)((.*\\1$)|\\1?$)")## [1] "a" "america" "area" "dad" "dead"
## [6] "depend" "educate" "else" "encourage" "engine"
## [11] "europe" "evidence" "example" "excuse" "exercise"
## [16] "expense" "experience" "eye" "health" "high"
## [21] "knock" "level" "local" "nation" "non"
## [26] "rather" "refer" "remember" "serious" "stairs"
## [31] "test" "tonight" "transport" "treat" "trust"
## [36] "window" "yesterday"Contain a repeated pair of letters (e.g. “church” contains “ch” repeated twice.)
str_subset("church", "([A-Za-z][A-Za-z]).*\\1")## [1] "church"Contain one letter repeated in at least three places (e.g. “eleven” contains three “e”s.)
str_subset(words, "([a-z]).*\\1.*\\1")## [1] "appropriate" "available" "believe" "between" "business"
## [6] "degree" "difference" "discuss" "eleven" "environment"
## [11] "evidence" "exercise" "expense" "experience" "individual"
## [16] "paragraph" "receive" "remember" "represent" "telephone"
## [21] "therefore" "tomorrow"