Project 3 Question 1 N Gallardo

How to access a column in R:

We have the dataset Satisfaction_Set_1. We want to extract the Satisfaction data as our main column.

To do so:

Sat<-Satisfaction_Set_1_$Satisfaction
mean(Sat)

[1] 6.75

median(Sat)

[1] 7

Now, we would like to create a specific column for the ages of the HR department:

hr_ages<-Satisfaction_Set_1_$Age[Satisfaction_Set_1_$Department == "HR"]

Next, we would like to convert the Age and Satisfaction columns into a matrix:

age_satisfaction_matrix <- as.matrix(
  Satisfaction_Set_1_[, c("Age", "Satisfaction")]
)
summary(age_satisfaction_matrix)

      Age         Satisfaction 
 Min.   :22.00   Min.   :4.00  
 1st Qu.:28.00   1st Qu.:6.00  
 Median :32.00   Median :7.00  
 Mean   :33.95   Mean   :6.75  
 3rd Qu.:39.25   3rd Qu.:8.00  
 Max.   :50.00   Max.   :9.00  

Our next step will be to create a matric where each row now will represent a department and their respective means:

department_means <- tapply(
  Satisfaction_Set_1_$Satisfaction,
  Satisfaction_Set_1_$Department,
  mean
)

We want to assign this data to a data frame. We also want to summarize the Gender and Department columns:

View(Satisfaction_Set_1_)
dim(Satisfaction_Set_1_)

[1] 20  5

summary(Satisfaction_Set_1_[, c("Gender", "Department")])

       Gender       Department
 Length   :20   Length   :20  
 N.unique : 3   N.unique : 4  
 N.blank  : 0   N.blank  : 0  
 Min.nchar: 4   Min.nchar: 2  
 Max.nchar:10   Max.nchar: 9  

To filter the dataset by 30 and 40-year-olds, we will use the following:

age_30_40 <- subset(Satisfaction_Set_1_, Age >= 30 & Age <= 40)
print(age_30_40)

# A tibble: 9 × 5
  RespondentID   Age Gender     Satisfaction Department
         <dbl> <dbl> <chr>             <dbl> <chr>     
1            2    34 Male                  6 IT        
2            5    30 Female                9 HR        
3            7    40 Male                  4 Finance   
4            8    35 Non-binary            6 Marketing 
5           11    31 Female                9 Marketing 
6           12    38 Male                  6 IT        
7           16    33 Female                8 IT        
8           17    39 Male                  7 HR        
9           20    31 Male                  5 IT        

To create a histogram of the Age column:

hist(Satisfaction_Set_1_$Age)

Now, we want to plot a histogram of the Satisfaction ratings:

hist(Satisfaction_Set_1_$Satisfaction)

Now, we will create a boxplot reflectiong the Satisfaction ratings by Department:

boxplot(Satisfaction~Department, data=Satisfaction_Set_1_)

Next, we want to generate a boxplot of Age by Gender:

boxplot(Age~Gender, data=Satisfaction_Set_1_)

We now want to identify the outliers, if any, in the Satisfaction ratings using the IQR method:

Q1<-quantile(Satisfaction_Set_1_$Satisfaction, 0.25)
Q3<-quantile(Satisfaction_Set_1_$Satisfaction, 0.75)
IQR_value<-IQR(Satisfaction_Set_1_$Satisfaction)

Finally, we want to create a boxplot of Satisfaction ratings by Department, with identification if there are any departments with outliers in the Satisfaction ratings:

boxplot(Satisfaction ~ Department, data=Satisfaction_Set_1_)

The outlier is identified by the round point outside of the boxplot boundaries. For Finance, this is an outlier value of 4.