How many participants/variables are there in the “salary” data set?
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorssalary = read.csv("C:\\Users\\12884742\\Downloads\\Professorial Salaries.csv")
dim(salary)## [1] 397 9List the variable names
names(salary)## [1] "ID" "Rank" "Discipline" "Yrs.since.phd"
## [5] "Yrs.service" "Sex" "NPubs" "Ncits"
## [9] "Salary"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 97000Create a new variable “salary.level”: - If Salary USD 130,000 then salary.level = “High” - If Salary USD 100,000 and Salary< USD 100,000 then salary.level = “Medium” - If Salary< USD 100,000 then salary.level = “Low”
salary = salary %>% mutate(salary.level = case_when(Salary>=130000~ "High", Salary<130000 & Salary>=100000~ "Medium", Salary <100000~ "Low"))
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
## salary.level
## 1 High
## 2 High
## 3 Low
## 4 Medium
## 5 High
## 6 LowCreate a new variable “high.salary”: - If Salary USD 130,000 then high.salary = 1 - If Salary< USD 130,000 then high.salary = 0
salary = salary %>% mutate(salary.high = case_when(Salary>=130000~1, Salary<130000~0))
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
## salary.level salary.high
## 1 High 1
## 2 High 1
## 3 Low 0
## 4 Medium 0
## 5 High 1
## 6 Low 0Create a new variable “salary.aud” as professor’s salaries in AUD (USD/AUD ratio = 1.53)
salary = salary %>% mutate(salary.aud = Salary*1.53)
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
## salary.level salary.high salary.aud
## 1 High 1 213817.5
## 2 High 1 264996.0
## 3 Low 0 122017.5
## 4 Medium 0 175950.0
## 5 High 1 216495.0
## 6 Low 0 148410.0Select a subset of participants that includes male professors in the theoretical department who had high salaries
male.A.high = subset(salary, Sex=="Male" & Discipline=="A" & salary.high==1)
head(male.A.high)## ID Rank Discipline Yrs.since.phd Yrs.service Sex NPubs Ncits Salary
## 27 27 Prof A 35 23 Male 20 27 134885
## 110 110 Prof A 40 31 Male 50 55 131205
## 117 117 Prof A 30 29 Male 19 83 148500
## 127 127 Prof A 28 26 Male 2 50 155500
## 135 135 Prof A 35 25 Male 30 28 168635
## 136 136 Prof A 20 18 Male 21 31 136000
## salary.level salary.high salary.aud
## 27 High 1 206374.1
## 110 High 1 200743.6
## 117 High 1 227205.0
## 127 High 1 237915.0
## 135 High 1 258011.6
## 136 High 1 208080.0Create a new data set that includes 4 variables (ID, Rank, Sex and Salary)
id.rank.sex.salary = subset(salary, select = c(ID, Rank, Sex, Salary))
head(id.rank.sex.salary)## ID Rank Sex Salary
## 1 1 Prof Male 139750
## 2 2 Prof Male 173200
## 3 3 AsstProf Male 79750
## 4 4 Prof Male 115000
## 5 5 Prof Male 141500
## 6 6 AssocProf Male 97000Save all your work and publish it into your Rpubs account - Well, you’re reading it!