This notebook is your first lab assignment. Please be sure to submit your work before the due date.You are expected to submit a knitted version of your worksheet which can be an html, pdf or word file.
If you worked as a team one of the team members submission will be enough as long as it has all the names at the header section.
BalanceGig is a company that matches independent workers for short-term engagements with businesses in the construction, automotive, and high-tech industries. The ‘gig’ employees work only for a short period of time, often on a particular project. A manager at BalanceGig extracts the employee data from their most recent work engagement, including the hourly wage (HourlyWage), the client’s industry (Industry), and the employee’s job satisfaction (Job). The manager suspects that data about the gig employees are sometimes incomplete, perhaps due to the short engagement and transient nature of the employees. She would like to find the number of missing observations. In addition, she would like information on the number of employees who worked in the automotive industry and earned more than $30 per hour.
# Read the Gig data file and display the number of records and the variables.
library(readxl)
myDataGig<-read_excel("Gig.xlsx")
dim(myDataGig)## [1] 604 4num_records <- nrow(myDataGig)
cat("Number of records:", num_records, "\n")## Number of records: 604num_variables <- ncol(myDataGig)
cat("Number of Variables:", num_variables, "\n")## Number of Variables: 4# Calculate the mean, median maximum and minimum hourly wages.
mean(hourly_wages<-mean(myDataGig$HourlyWage, na.rm = TRUE))## [1] 40.12287median(hourly_wages<-median(myDataGig$HourlyWage, na.rm = TRUE))## [1] 41.91max(hourly_wages<-max(myDataGig$HourlyWage, na.rm = TRUE))## [1] 51min(hourly_wages<-min(myDataGig$HourlyWage, na.rm = TRUE))## [1] 24.28R stores missing values as NA, and we use the is.na function to identify the observations with missing values. R labels observations with missing values as “True” and observations without missing values as “False”
#List only observations that have missing values:
missing_data <- is.na(myDataGig)
missing_data## EmployeeID HourlyWage Industry Job
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## [604,] FALSE FALSE FALSE FALSEFor a large data set, having to look through all observations is inconvenient. Alternately, we can use the which function together with the is.na function to identify “which” observations contain missing values.
#Check which records have missing values for hourly wages, industry and job.
missing_wages<-which(is.na(myDataGig$HourlyWage))
missing_wages## [1] 10 34 59 81 104 177 224 597missing_industry<- which(is.na(myDataGig$Industry))
missing_industry## [1] 24 139 361 378 441 446 479 500 531 565missing_job<- which(is.na(myDataGig$Job))
missing_job## [1] 21 58 66 89 108 175 212 253 291 347 355 387 388 555 577 593To identify and count the number of employees with multiple selection criteria, we use the which and length functions.
We use the ‘na.omit’ function to remove observations with missing values and store the resulting data set into omissionData data frame. How can you make sure that all observations with missing values were omitted? (Hint: check number of observations)
#use this chunk to remove observations with missing values and to check and make sure that observations with missing values were omitted.
omissionData <- na.omit(myDataGig)
dim(omissionData)## [1] 570 4We will calculate the average value using the ‘mean’ function. The option na.rm=TRUE ignores the missing values when calculating the average values.
To impute the missing values in the HourlyWage variable, we again use the ‘is.na’ function to identify the missing values and replace them with the means calculated in previous step.Check and make sure that all missing values for the HourlyWage were replaced by the mean value.(Hint: you can use length and whic functions to identify observations with HourlyWage equal to its mean value)
#Use this chunk to impute missing values and to make sure that all missing values were replaced by the mean value
myDataGig$HourlyWage[is.na(myDataGig$HourlyWage)]=hourly_wages
myDataGig## # A tibble: 604 × 4
## EmployeeID HourlyWage Industry Job
## <dbl> <dbl> <chr> <chr>
## 1 1 32.8 Construction Analyst
## 2 2 46 Automotive Engineer
## 3 3 43.1 Construction Sales Rep
## 4 4 48.1 Automotive Other
## 5 5 43.6 Automotive Accountant
## 6 6 47.0 Construction Engineer
## 7 7 43.0 Construction Sales Rep
## 8 8 41.0 Construction Programmer
## 9 9 38.9 Construction Consultant
## 10 10 24.3 Construction Accountant
## # ℹ 594 more rowsTo identify and count the number of employees with multiple selection criteria, we use the which and length functions.
#Find the number of employees working in the automotive industry and earning more than $30:(Hint: you can use the length and which functions)
num_employees<-length(which(myDataGig$Industry == "Automotive"&myDataGig$HourlyWage>30))
num_employees## [1] 180