Task 3 Import the csv data set “Professorial Salaries” into R:
salary= read.csv("C:\\Users\\69412\\Desktop\\Professorial Salaries.csv")
task 4 Describe the salary data set
4.1 how many variable are there in the “salary” data set?
dim(salary)
## [1] 397 9
4.2 list the variable names
names(salary)
## [1] "ID" "Rank" "Discipline" "Yrs.since.phd"
## [5] "Yrs.service" "Sex" "NPubs" "Ncits"
## [9] "Salary"
4.3 list the first 6 observations
head(salary)
## ID Rank Discipline Yrs.since.phd Yrs.service Sex NPubs Ncits Salary
## 1 1 Prof B 19 18 Male 18 50 139750
## 2 2 Prof B 20 16 Male 3 26 173200
## 3 3 AsstProf B 4 3 Male 2 50 79750
## 4 4 Prof B 45 39 Male 17 34 115000
## 5 5 Prof B 40 41 Male 11 41 141500
## 6 6 AssocProf B 6 6 Male 6 37 97000
task 5 creat a new variable ” salary.level”
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
salary = salary %>% mutate(salary.level
= case_when(Salary >= 130000~ "High",
Salary>= 100000 & Salary< 130000~ "Medium",
Salary<100000~ "Low"))
task 6 Create a new variable “high.salary”
library(tidyverse)
salary = salary %>% mutate(high.salary
= case_when(Salary >= 130000~ "1",
Salary<130000~ "0"))
task 7 create a Create a new variable “salary.aud” as professor’s
salaries in AUD (USD/AUD ratio = 1.53).
library(tidyverse)
salary = salary %>% mutate(salary.aud = Salary*1.53)
task 8 Select a subset of participants that includes male professors
in the theoretical department who had high salaries.
Men.A.High = salary %>% filter(Sex ==
"Male", Discipline == "A",
salary.level == "High")
task 9 Create a new data set that includes 4 variables (ID, Rank,
Sex and Salary)
library(tidyverse)
var.select2 = salary %>% select(ID, Rank, Salary, Sex)