As we move on from week to week and task to task, the code that you have already completed, will stay on the template but will not run, this is possible by adding eval=FALSE to the corresponding code chunk. Note that the libraries need to be linked to this program as well.
# Install and load necessary libraries
#install.packages("ggplot2") # Install ggplot2 for plotting, if you have already installed the packages, comment this out by enterring a # in front of this command
#install.packages("scales") # Install scales for formatting
#install.packages("moments") # Install moments for skewness and kurtosis
library(ggplot2) # Load ggplot2 library
library(scales) # Load scales libraryThis needs to be addressed here.
# Check the current working directory
getwd()## [1] "C:/Users/admin/OneDrive/Desktop/NCU PhD Data Science/DDS 8500/Week 4/EnohMDDS8500-4"# in the next line, change the directory to the place where you saved the
# data file, if you prefer you can save your data.csv file in the directory
# that command 7 indicated.
# for example your next line should like something similar to this: setwd("C:/Users/tsapara/Documents")
# Set the working directory to where the data file is located
# This ensures the program can access the file correctly
setwd("C:/Users/admin/OneDrive/Desktop/NCU PhD Data Science/DDS 8500/Week 4/EnohMDDS8500-4")
### Choose an already existing directory in your computer.# Read the CSV file
# The header parameter ensures column names are correctly read
# sep defines the delimiter (comma in this case)
# stringsAsFactors prevents automatic conversion of strings to factors
df <- read.csv("data.csv", header = TRUE, sep = ",", stringsAsFactors = TRUE)
##########################################################
# Define variables A and B based on your student ID
# A represents the first 3 digits, B represents the last 3 digits
A <- 199
B <- 267
Randomizer <- A + B # Randomizer ensures a consistent seed value for reproducibility
# Generate a random sample of 500 rows from the dataset
set.seed(Randomizer) # Set the seed for reproducibility
sample_size <- 500
df <- df[sample(nrow(df), sample_size, replace = TRUE), ] # Sample the dataset
write.csv(df, file = "my_data.csv", row.names = FALSE) # this command may take some time to run once it is done, it will create the desired data file locally in your directoryAs practice, you may want now to knit your file in an html. To do this, you should click on the knit button on the top panel, and wait for the rendering file. The HTML will open once it is done for you to review.
It is recommended to practice with RMD and download and review the following cheatsheets: https://rmarkdown.rstudio.com/lesson-15.HTML
In addition, you may want to alter some of the editor components and re-knit your file to gain some knowledge and understanding of RMD. For a complete tutorial, visit: https://rmarkdown.rstudio.com/lesson-2.html
df <- read.csv("my_data.csv", header = TRUE, sep = ",", stringsAsFactors = TRUE)Step 0. Now that you read the file, you want to learn few information about your data
The following commands will not be explained here, do your research, review your csv file and answer the questions related with this part of your code.
# Basic exploratory commands
nrow(df) # Number of rows in the dataset## [1] 500length(df) # Number of columns (or variables) in the dataset## [1] 15str(df) # Structure of the dataset (data types and a preview)## 'data.frame': 500 obs. of 15 variables:
## $ Gender : Factor w/ 4 levels "","Female","Male",..: 3 4 4 4 2 2 2 2 4 3 ...
## $ Age : int 29 28 NA NA 27 26 29 27 30 NA ...
## $ Height : int 175 185 165 165 158 160 NA 168 165 182 ...
## $ Weight : int 70 85 56 56 NA 55 65 65 NA 80 ...
## $ Education : Factor w/ 5 levels "","Bachelor's",..: 5 3 5 5 2 2 2 2 3 5 ...
## $ Income : Factor w/ 15 levels "","32000","35000",..: 12 1 11 11 6 8 10 7 2 14 ...
## $ MaritalStatus: Factor w/ 3 levels "","Married","Single": 2 3 3 3 3 3 3 3 3 2 ...
## $ Employment : Factor w/ 4 levels "","Employed",..: 2 4 4 1 4 2 2 2 4 2 ...
## $ Score : Factor w/ 15 levels "","5.5","5.7",..: 12 3 12 12 1 5 9 6 2 14 ...
## $ Rating : Factor w/ 5 levels "","5.7","A","B",..: 5 3 4 4 3 4 4 4 3 5 ...
## $ Category : Factor w/ 5 levels "","A","Art","Music",..: 5 5 4 4 3 3 4 3 5 3 ...
## $ Color : Factor w/ 5 levels "","Blue","Green",..: 4 3 2 2 3 2 2 2 3 4 ...
## $ Hobby : Factor w/ 6 levels "","Green","Photography",..: 6 3 6 6 5 5 5 4 4 3 ...
## $ Happiness : Factor w/ 10 levels "","6","6.5","7",..: 9 2 8 8 3 4 2 4 NA 9 ...
## $ Location : Factor w/ 5 levels "","6","City",..: 5 4 5 5 3 4 3 3 4 5 ...summary(df) # Summary statistics for each column## Gender Age Height Weight Education
## : 3 Min. :25.00 Min. :155.0 Min. :54.00 : 26
## Female:200 1st Qu.:27.00 1st Qu.:165.0 1st Qu.:65.00 Bachelor's :184
## Male :205 Median :28.00 Median :175.0 Median :70.00 High School: 97
## Other : 92 Mean :28.41 Mean :172.9 Mean :70.39 Master's :102
## 3rd Qu.:30.00 3rd Qu.:182.0 3rd Qu.:80.00 PhD : 90
## Max. :34.00 Max. :190.0 Max. :90.00 NA's : 1
## NA's :28 NA's :32 NA's :46
## Income MaritalStatus Employment Score Rating
## 45000 : 87 : 15 : 16 6.2 : 89 : 5
## 60000 : 86 Married:208 Employed :374 7.8 : 73 5.7: 2
## : 79 Single :277 Single : 2 : 70 A :157
## 48000 : 68 Unemployed:108 6.1 : 56 B :191
## 65000 : 52 5.7 : 55 C :145
## 50000 : 25 7.5 : 33
## (Other):103 (Other):124
## Category Color Hobby Happiness Location
## : 10 : 5 : 5 7 :158 : 5
## A : 2 Blue :200 Green : 2 9 : 83 6 : 2
## Art :178 Green :138 Photography: 88 6 : 78 City :215
## Music :125 Red :155 Reading :143 8 : 64 Rural :178
## Sports:185 Sports: 2 Swimming :101 8.5 : 58 Suburb:100
## Traveling :161 (Other): 51
## NA's : 8Please answer the following questions, by typing information after the question.
Question 1
What type of variables does your file include?
Answer 1: Categorical (Gender, Education, MaritalStatus, Employment, Rating, Category, Color, Hobby, Location) and Numerical (Age, Height, Weight) variables
str(df)## 'data.frame': 500 obs. of 15 variables:
## $ Gender : Factor w/ 4 levels "","Female","Male",..: 3 4 4 4 2 2 2 2 4 3 ...
## $ Age : int 29 28 NA NA 27 26 29 27 30 NA ...
## $ Height : int 175 185 165 165 158 160 NA 168 165 182 ...
## $ Weight : int 70 85 56 56 NA 55 65 65 NA 80 ...
## $ Education : Factor w/ 5 levels "","Bachelor's",..: 5 3 5 5 2 2 2 2 3 5 ...
## $ Income : Factor w/ 15 levels "","32000","35000",..: 12 1 11 11 6 8 10 7 2 14 ...
## $ MaritalStatus: Factor w/ 3 levels "","Married","Single": 2 3 3 3 3 3 3 3 3 2 ...
## $ Employment : Factor w/ 4 levels "","Employed",..: 2 4 4 1 4 2 2 2 4 2 ...
## $ Score : Factor w/ 15 levels "","5.5","5.7",..: 12 3 12 12 1 5 9 6 2 14 ...
## $ Rating : Factor w/ 5 levels "","5.7","A","B",..: 5 3 4 4 3 4 4 4 3 5 ...
## $ Category : Factor w/ 5 levels "","A","Art","Music",..: 5 5 4 4 3 3 4 3 5 3 ...
## $ Color : Factor w/ 5 levels "","Blue","Green",..: 4 3 2 2 3 2 2 2 3 4 ...
## $ Hobby : Factor w/ 6 levels "","Green","Photography",..: 6 3 6 6 5 5 5 4 4 3 ...
## $ Happiness : Factor w/ 10 levels "","6","6.5","7",..: 9 2 8 8 3 4 2 4 NA 9 ...
## $ Location : Factor w/ 5 levels "","6","City",..: 5 4 5 5 3 4 3 3 4 5 ...Question 2
Specific data types?
Answer 2: integer or int : Age, Height, Weight Character or factor: Gender, Education, MaritalStatus, Employment, Rating, Category, Color, Hobby, Location
Question 3
Are they read properly?
Answer 3: No, not all of them. Specifically, Income, Score, and Happiness should be numeric but are read as character.
Question 4
Are there any issues ?
Answer 4:
Yes, there are significant data quality issues such as data Shift in Some rows, and rows which contain text in numeric columns. For example:
Income contains “High School”. Score contains “Unemployed”. Happiness contains “Photography”.
Question 5
Does your file includes both NAs and blanks?
Answer 5: Yes, the file contains both blanks and NAs
# Counts the total number of NA values
sum(is.na(df))## [1] 115# Counts the total number of empty strings (blanks)
sum(df == "", na.rm = TRUE)## [1] 254Question 6
How many NAs do you have and
Answer 6:
we have 115 NAs
Question 7
How many blanks?
Answer 7:
254 blanks
Step 1: Handling both blanks and NAs is not simple so first we want to eliminate some of those, let’s eliminate the blanks and change them to NAs
#
# Step 1: # Handling both blanks and NAs is not simple so first we want to eliminate
# some of those, let's eliminate the blanks and change them to NAs
#
# Replace blanks with NAs across the dataset
# This ensures that blank values are consistently treated as missing data
df[df == ""] <- NA
# Convert specific columns to factors
# This step ensures categorical variables are treated correctly after replacing blanks
factor_columns <- c("Gender", "Education", "Rating", "MaritalStatus", "Category",
"Employment", "Color", "Hobby", "Location")
df[factor_columns] <- lapply(df[factor_columns], function(col) as.factor(as.character(col)))Step 2: Count NAs in the entire dataset
#
# Step 2: Count NAs in the entire dataset
# Count the total number of NAs in the dataset
total_nas <- sum(is.na(df))
total_nas # Print the total number of missing values## [1] 369Please answer the following questions, by typing information after the question.
Question 8
Explain what the printed number is, what is the information that relays and how can you use it in your analysis?
Answer 8: In the command, df[df == “”] <- NA, we treated all the blanks as NAs. That eliminates all the blanks and converts them to NAs. Performing the command total_nas now adds the blanks and the NAs, making 369 NAs
Step 3: Count rows with NAs.
#
# Step 3: Count rows with NAs
#
# Count rows with at least one NA
rows_with_nas <- sum(rowSums(is.na(df)) > 0)
Percent_row_NA <- percent(rows_with_nas / nrow(df)) # Percentage of rows with NAs
rows_with_nas## [1] 258Percent_row_NA## [1] "52%"Question 9
How large is the proportion of the rows with NAs, we can drop up to 5%?
Answer 9:
There are 256 rows with NAs, making 52% of the dataset.We can but but I would not recommend dropping 5% of th dataset.
Question 10
Do you think that would be wise to drop the above percent?
Answer 10:
It is not appropriate to simply remove 5% of the data to address missing values.
Question 11
How this will affect your dataset?
Answer 11: Eliminating rows with missing data can result in the loss of critical information in variables such as income. For example, this approach may exclude unemployed individuals from the analysis, leading to a significant loss of insight. The resulting dataset would be biased toward employed individuals, potentially producing inaccurate conclusions about the overall population. Such exclusion can substantially alter the statistical outcomes. This approach complicates the detection of genuine patterns or relationships within the data. Additionally, confidence intervals may widen, and the reliability of the results may decrease.
Step 4: Count columns with NAs
#
# Step 4: Count columns with NAs
# Count columns with at least one NA
cols_with_nas <- sum(colSums(is.na(df)) > 0)
Percent_col_NA <- percent(cols_with_nas / length(df)) # Percentage of columns with NAs
cols_with_nas## [1] 15Percent_col_NA## [1] "100%"Question 12
How large is the proportion of the cols with NAs, we never want to drop entire columnes as this would mean that we will loose variables and associations but do you think that would be wise to drop the above percent?
Answer 12: All columns, 15 (100%) contain missing data.
Question 13
How this will affect your dataset?
Answer 13:
Dropping any column from the dataset is not recommended, as 100% of the columns contain missing data. Removing these columns would result in an empty dataset for analysis. Eliminating an entire column solely due to a small proportion of missing values is not recommended. This approach would discard all valid data within that variable, which may be essential for the analysis. For example, removing the “Income” variable because 5% of respondents did not report it could result in the loss of critical information.
Step 5: Replace NAs with appropriate values (mean for numeric and integer,mode for factor, “NA” for character)
In later weeks we will learn how to replace the NAs properly based on the descriptive statistics and you will discuss this code.For now, you can assume that by setting the mean of the variable for numeric and mode for categorical it is correct - this is not always the case of course but the code will become much more complicated in that case.
#
# Step 5: Replace NAs with appropriate values (mean for numeric and integer,
# mode for factor, "NA" for character)
# In later weeks we will learn how to replace the NAs properly based on the
# descriptive statistics and you will discuss this code.
# for now, you can assume that by setting the mean of the variable for numeric
# and mode for categorical it is correct - this is not always the case of course
# but the code will become much more complicated in that case.
# Replace NAs with appropriate values
# Numeric: Replace with the mean if sufficient data is available
# Categorical: Replace with the mode (most common value)
# Character: Replace with the string "NA"
df <- lapply(df, function(col) {
if (is.numeric(col) || is.integer(col)) { # Numeric or integer columns
if (sum(!is.na(col)) > 10) {
col[is.na(col)] <- mean(col, na.rm = TRUE) # Replace with mean
} else {
col[is.na(col)] <- approx(seq_along(col), col, n = length(col))[["y"]][is.na(col)] # Interpolation
}
} else if (is.factor(col)) { # Factor columns
mode_val <- names(sort(-table(col)))[1] # Mode (most common value)
col[is.na(col)] <- mode_val
} else if (is.character(col)) { # Character columns
col[is.na(col)] <- "NA" # Replace with "NA"
}
return(col) # Return the modified column
})
df <- as.data.frame(df) # Convert the list back to a dataframe
#
# following the above method to impute, has now changed some of the statistics
# Check the updated dataset and ensure no remaining NAs
summary(df)## Gender Age Height Weight Education
## Female:200 Min. :25.00 Min. :155.0 Min. :54.00 Bachelor's :211
## Male :208 1st Qu.:27.00 1st Qu.:165.0 1st Qu.:65.00 High School: 97
## Other : 92 Median :28.00 Median :175.0 Median :70.00 Master's :102
## Mean :28.41 Mean :172.9 Mean :70.39 PhD : 90
## 3rd Qu.:30.00 3rd Qu.:182.0 3rd Qu.:80.00
## Max. :34.00 Max. :190.0 Max. :90.00
##
## Income MaritalStatus Employment Score Rating
## 45000 :166 Married:208 Employed :390 6.2 :159 5.7: 2
## 60000 : 86 Single :292 Single : 2 7.8 : 73 A :157
## 48000 : 68 Unemployed:108 6.1 : 56 B :196
## 65000 : 52 5.7 : 55 C :145
## 50000 : 25 7.5 : 33
## 52000 : 23 5.5 : 25
## (Other): 80 (Other): 99
## Category Color Hobby Happiness Location
## A : 2 Blue :205 Green : 2 7 :181 6 : 2
## Art :178 Green :138 Photography: 88 9 : 83 City :220
## Music :125 Red :155 Reading :143 6 : 78 Rural :178
## Sports:195 Sports: 2 Swimming :101 8 : 64 Suburb:100
## Traveling :166 8.5 : 58
## 6.5 : 15
## (Other): 21Essay Question
Run summary(df) and compare with the previous statistics. Do you observe any undesired changes? Explain in detail. Are there any more NA’s in your file? What is the information that is printed by the summary? How can this be interpreted? what are your observations? Verify the effects of imputation, and explain in detail. Compare the updated summary with the earlier statistics and note changes. Explain everything that you obsevrve.
Answer
total_nas <- sum(is.na(df))
total_nas## [1] 0# Count rows with at least one NA
rows_with_nas <- sum(rowSums(is.na(df)) > 0)
Percent_row_NA <- percent(rows_with_nas / nrow(df)) # Percentage of rows with NAs
rows_with_nas## [1] 0Percent_row_NA## [1] "0%"summary(df)## Gender Age Height Weight Education
## Female:200 Min. :25.00 Min. :155.0 Min. :54.00 Bachelor's :211
## Male :208 1st Qu.:27.00 1st Qu.:165.0 1st Qu.:65.00 High School: 97
## Other : 92 Median :28.00 Median :175.0 Median :70.00 Master's :102
## Mean :28.41 Mean :172.9 Mean :70.39 PhD : 90
## 3rd Qu.:30.00 3rd Qu.:182.0 3rd Qu.:80.00
## Max. :34.00 Max. :190.0 Max. :90.00
##
## Income MaritalStatus Employment Score Rating
## 45000 :166 Married:208 Employed :390 6.2 :159 5.7: 2
## 60000 : 86 Single :292 Single : 2 7.8 : 73 A :157
## 48000 : 68 Unemployed:108 6.1 : 56 B :196
## 65000 : 52 5.7 : 55 C :145
## 50000 : 25 7.5 : 33
## 52000 : 23 5.5 : 25
## (Other): 80 (Other): 99
## Category Color Hobby Happiness Location
## A : 2 Blue :205 Green : 2 7 :181 6 : 2
## Art :178 Green :138 Photography: 88 9 : 83 City :220
## Music :125 Red :155 Reading :143 6 : 78 Rural :178
## Sports:195 Sports: 2 Swimming :101 8 : 64 Suburb:100
## Traveling :166 8.5 : 58
## 6.5 : 15
## (Other): 21df## Gender Age Height Weight Education Income MaritalStatus
## 1 Male 29.00000 175.0000 70.00000 PhD 60000 Married
## 2 Other 28.00000 185.0000 85.00000 High School 45000 Single
## 3 Other 28.41102 165.0000 56.00000 PhD 55000 Single
## 4 Other 28.41102 165.0000 56.00000 PhD 55000 Single
## 5 Female 27.00000 158.0000 70.39207 Bachelor's 42000 Single
## 6 Female 26.00000 160.0000 55.00000 Bachelor's 48000 Single
## 7 Female 29.00000 172.9124 65.00000 Bachelor's 52000 Single
## 8 Female 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 9 Other 30.00000 165.0000 70.39207 High School 32000 Single
## 10 Male 28.41102 182.0000 80.00000 PhD 70000 Married
## 11 Male 28.00000 178.0000 78.00000 High School 38000 Married
## 12 Male 29.00000 175.0000 70.00000 PhD 60000 Married
## 13 Other 28.00000 185.0000 85.00000 High School 35000 Single
## 14 Male 30.00000 182.0000 80.00000 Master's 65000 Married
## 15 Male 31.00000 180.0000 70.39207 Bachelor's 50000 Married
## 16 Other 30.00000 165.0000 70.39207 High School 32000 Single
## 17 Male 30.00000 182.0000 80.00000 Master's 65000 Married
## 18 Male 31.00000 180.0000 70.39207 Bachelor's 50000 Married
## 19 Female 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 20 Female 26.00000 160.0000 55.00000 Bachelor's 48000 Single
## 21 Male 30.00000 182.0000 80.00000 Master's 65000 Married
## 22 Female 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 23 Female 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 24 Other 28.00000 185.0000 85.00000 High School 45000 Single
## 25 Female 29.00000 172.9124 65.00000 Bachelor's 52000 Single
## 26 Female 26.00000 160.0000 55.00000 Bachelor's 48000 Single
## 27 Male 32.00000 178.0000 75.00000 Master's 50000 Married
## 28 Male 32.00000 178.0000 75.00000 Master's 50000 Married
## 29 Male 32.00000 178.0000 75.00000 Master's 50000 Married
## 30 Male 34.00000 190.0000 90.00000 Master's 60000 Single
## 31 Male 32.00000 178.0000 75.00000 Master's 50000 Married
## 32 Other 28.00000 185.0000 85.00000 High School 45000 Single
## 33 Male 32.00000 178.0000 75.00000 Master's 50000 Married
## 34 Male 28.00000 178.0000 78.00000 High School 38000 Married
## 35 Male 34.00000 190.0000 90.00000 Master's 60000 Married
## 36 Female 26.00000 160.0000 55.00000 Bachelor's 48000 Single
## 37 Male 30.00000 182.0000 80.00000 Master's 65000 Married
## 38 Other 30.00000 165.0000 70.39207 High School 45000 Single
## 39 Other 30.00000 165.0000 70.39207 High School 32000 Single
## 40 Male 34.00000 190.0000 90.00000 Master's 60000 Single
## 41 Male 30.00000 182.0000 80.00000 Master's 65000 Married
## 42 Male 32.00000 175.0000 70.00000 PhD 60000 Married
## 43 Other 28.00000 185.0000 85.00000 High School 45000 Single
## 44 Female 26.00000 160.0000 55.00000 Bachelor's 48000 Single
## 45 Female 26.00000 165.0000 62.00000 Bachelor's 48000 Single
## 46 Male 30.00000 182.0000 80.00000 Master's 65000 Married
## 47 Male 30.00000 182.0000 80.00000 Master's 65000 Married
## 48 Other 28.00000 185.0000 85.00000 High School 45000 Single
## 49 Male 29.00000 175.0000 70.00000 PhD 60000 Married
## 50 Female 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 51 Other 28.00000 185.0000 85.00000 High School 35000 Single
## 52 Male 28.00000 178.0000 78.00000 High School 38000 Married
## 53 Female 25.00000 165.0000 58.00000 Bachelor's 45000 Single
## 54 Other 28.00000 185.0000 85.00000 High School 45000 Single
## 55 Male 32.00000 178.0000 75.00000 Master's 50000 Married
## 56 Male 32.00000 178.0000 75.00000 Master's 45000 Married
## 57 Other 28.00000 185.0000 85.00000 High School 45000 Single
## 58 Female 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 59 Male 30.00000 182.0000 80.00000 Master's 65000 Married
## 60 Female 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 61 Female 25.00000 165.0000 58.00000 Bachelor's 45000 Single
## 62 Male 34.00000 190.0000 90.00000 Master's 60000 Married
## 63 Male 28.00000 178.0000 78.00000 High School 38000 Married
## 64 Male 30.00000 180.0000 75.00000 Master's 60000 Married
## 65 Female 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 66 Female 29.00000 172.9124 65.00000 Bachelor's 52000 Single
## 67 Male 29.00000 175.0000 70.00000 PhD 60000 Married
## 68 Other 30.00000 165.0000 70.39207 High School 32000 Single
## 69 Other 30.00000 165.0000 70.39207 High School 32000 Single
## 70 Female 26.00000 160.0000 55.00000 Bachelor's 48000 Single
## 71 Other 30.00000 165.0000 70.39207 High School 32000 Single
## 72 Female 29.00000 172.9124 65.00000 Bachelor's 52000 Single
## 73 Other 30.00000 165.0000 70.39207 High School 32000 Single
## 74 Male 30.00000 182.0000 80.00000 Master's 65000 Married
## 75 Female 26.00000 160.0000 55.00000 Bachelor's 48000 Single
## 76 Female 26.00000 160.0000 55.00000 Bachelor's 48000 Single
## 77 Other 28.00000 185.0000 85.00000 High School 45000 Single
## 78 Female 26.00000 160.0000 55.00000 Bachelor's 48000 Single
## 79 Female 25.00000 165.0000 58.00000 Bachelor's 45000 Single
## 80 Female 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 81 Female 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 82 Male 32.00000 175.0000 70.00000 PhD 60000 Married
## 83 Male 30.00000 182.0000 80.00000 Master's 65000 Married
## 84 Male 29.00000 175.0000 70.00000 PhD 60000 Married
## 85 Female 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 86 Male 30.00000 182.0000 80.00000 Master's 65000 Married
## 87 Male 31.00000 180.0000 70.39207 Bachelor's 50000 Married
## 88 Female 27.00000 158.0000 70.39207 Bachelor's 42000 Single
## 89 Male 30.00000 182.0000 80.00000 Master's 65000 Married
## 90 Male 28.00000 178.0000 78.00000 High School 38000 Married
## 91 Male 28.41102 182.0000 80.00000 PhD 70000 Married
## 92 Female 28.00000 165.0000 60.00000 High School 42000 Married
## 93 Female 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 94 Male 30.00000 182.0000 80.00000 Master's 65000 Married
## 95 Female 29.00000 172.9124 65.00000 Bachelor's 52000 Single
## 96 Female 25.00000 165.0000 58.00000 Bachelor's 45000 Single
## 97 Other 28.00000 185.0000 85.00000 High School 45000 Single
## 98 Female 26.00000 160.0000 55.00000 Bachelor's 48000 Single
## 99 Female 26.00000 160.0000 55.00000 Bachelor's 48000 Single
## 100 Male 29.00000 175.0000 70.00000 PhD 60000 Married
## 101 Male 30.00000 180.0000 75.00000 Master's 60000 Married
## 102 Other 28.00000 172.9124 85.00000 High School 45000 Single
## 103 Female 26.00000 160.0000 55.00000 Bachelor's 48000 Single
## 104 Male 28.00000 178.0000 78.00000 High School 38000 Married
## 105 Male 28.41102 182.0000 80.00000 PhD 45000 Married
## 106 Female 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 107 Male 31.00000 180.0000 70.39207 Bachelor's 50000 Married
## 108 Male 29.00000 175.0000 70.00000 PhD 60000 Married
## 109 Male 29.00000 175.0000 70.00000 PhD 60000 Married
## 110 Male 30.00000 182.0000 80.00000 Master's 65000 Married
## 111 Male 30.00000 182.0000 80.00000 Master's 65000 Married
## 112 Female 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 113 Female 26.00000 160.0000 55.00000 Bachelor's 48000 Single
## 114 Female 25.00000 165.0000 58.00000 Bachelor's 45000 Single
## 115 Female 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 116 Female 29.00000 172.9124 65.00000 Bachelor's 52000 Single
## 117 Male 28.00000 178.0000 78.00000 High School 38000 Married
## 118 Other 28.41102 165.0000 56.00000 PhD 55000 Single
## 119 Female 29.00000 172.9124 65.00000 Bachelor's 52000 Single
## 120 Male 29.00000 175.0000 70.00000 PhD 60000 Married
## 121 Male 28.41102 182.0000 80.00000 PhD 70000 Married
## 122 Female 28.00000 165.0000 60.00000 High School 42000 Married
## 123 Female 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 124 Male 31.00000 180.0000 70.39207 Bachelor's 50000 Married
## 125 Female 26.00000 160.0000 55.00000 Bachelor's 48000 Single
## 126 Male 29.00000 175.0000 70.00000 PhD 60000 Married
## 127 Other 28.00000 172.9124 85.00000 High School 45000 Single
## 128 Female 26.00000 165.0000 62.00000 Bachelor's 48000 Single
## 129 Male 30.00000 180.0000 75.00000 Master's 60000 Married
## 130 Male 29.00000 175.0000 70.00000 PhD 60000 Married
## 131 Other 28.00000 185.0000 85.00000 High School 45000 Single
## 132 Female 26.00000 160.0000 55.00000 Bachelor's 48000 Single
## 133 Female 26.00000 160.0000 55.00000 Bachelor's 48000 Single
## 134 Female 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 135 Other 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 136 Female 25.00000 165.0000 58.00000 Bachelor's 45000 Single
## 137 Male 32.00000 178.0000 75.00000 Master's 50000 Married
## 138 Other 30.00000 165.0000 70.39207 High School 32000 Single
## 139 Male 32.00000 175.0000 70.00000 PhD 60000 Married
## 140 Female 29.00000 172.9124 65.00000 Bachelor's 52000 Single
## 141 Male 29.00000 175.0000 70.00000 PhD 60000 Married
## 142 Male 28.41102 182.0000 80.00000 PhD 45000 Married
## 143 Male 34.00000 190.0000 90.00000 Master's 60000 Married
## 144 Male 30.00000 182.0000 80.00000 Master's 65000 Married
## 145 Female 26.00000 160.0000 55.00000 Bachelor's 48000 Single
## 146 Female 27.00000 168.0000 65.00000 Bachelor's 45000 Single
## 147 Female 26.00000 160.0000 55.00000 Bachelor's 48000 Single
## 148 Male 28.41102 182.0000 80.00000 PhD 45000 Married
## 149 Male 30.00000 182.0000 80.00000 Master's 65000 Married
## 150 Male 30.00000 182.0000 80.00000 Master's 65000 Married
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## Employment Score Rating Category Color Hobby Happiness
## 1 Employed 7.8 C Sports Red Traveling 9
## 2 Unemployed 5.7 A Sports Green Photography 6
## 3 Unemployed 7.8 B Music Blue Traveling 8.5
## 4 Employed 7.8 B Music Blue Traveling 8.5
## 5 Unemployed 6.2 A Art Green Swimming 6.5
## 6 Employed 6.1 B Art Blue Swimming 7
## 7 Employed 7.1 B Music Blue Swimming 6
## 8 Employed 6.2 B Art Blue Reading 7
## 9 Unemployed 5.5 A Sports Green Reading 7
## 10 Employed 8.9 C Art Red Photography 9
## 11 Employed 6.7 A Sports Red Photography 8
## 12 Employed 7.8 C Sports Red Traveling 9
## 13 Unemployed 5.7 A Sports Green Photography 6
## 14 Employed 6.2 C Sports Red Traveling 8.5
## 15 Employed 6.5 A Art Green Reading 8
## 16 Unemployed 5.5 A Sports Green Reading 7
## 17 Employed 6.2 C Music Red Traveling 7
## 18 Employed 6.5 A Art Green Reading 8
## 19 Employed 6.2 B Art Blue Reading 7
## 20 Employed 6.1 B Art Blue Swimming 7
## 21 Employed 6.2 C Music Red Traveling 8.5
## 22 Employed 6.2 B Art Blue Reading 7
## 23 Employed 6.2 B Art Blue Reading 7
## 24 Unemployed 5.7 A Sports Green Photography 6
## 25 Employed 7.1 B Music Blue Swimming 6
## 26 Employed 6.1 B Art Blue Swimming 7
## 27 Employed 7.5 A Sports Blue Reading 8
## 28 Employed 7.5 A Sports Blue Reading 8
## 29 Employed 7.5 A Sports Blue Reading 8
## 30 Employed 8.2 B Music Blue Traveling 7.5
## 31 Employed 7.5 A Sports Blue Reading 8
## 32 Unemployed 5.7 A Sports Green Photography 6
## 33 Employed 7.5 A Sports Blue Reading 8
## 34 Employed 6.7 A Sports Red Photography 8
## 35 Employed 8.2 B Music Blue Traveling 7.5
## 36 Employed 6.1 B Art Blue Swimming 7
## 37 Employed 6.2 C Music Red Traveling 8.5
## 38 Unemployed 5.5 A Sports Green Reading 7
## 39 Unemployed 5.5 A Sports Green Reading 7
## 40 Employed 8.2 B Music Blue Traveling 7.5
## 41 Employed 6.2 C Music Red Traveling 8.5
## 42 Employed 7.8 C Sports Red Traveling 9
## 43 Unemployed 5.7 A Sports Green Photography 6
## 44 Employed 6.1 B Art Blue Swimming 7
## 45 Employed 5.9 A Art Green Reading 7
## 46 Employed 6.2 C Music Red Traveling 8.5
## 47 Employed 6.2 C Music Red Traveling 8.5
## 48 Unemployed 5.7 A Sports Green Photography 6
## 49 Employed 7.8 C Sports Red Traveling 9
## 50 Employed 6.2 B Art Blue Reading 7
## 51 Unemployed 5.7 A Sports Green Photography 6
## 52 Employed 6.7 A Sports Red Photography 8
## 53 Employed 6.2 B Music Green Traveling 7
## 54 Unemployed 5.7 A Sports Green Photography 6
## 55 Employed 7.5 A Sports Blue Reading 8
## 56 Employed 7.5 A Sports Blue Reading 8
## 57 Unemployed 5.7 A Sports Green Photography 6
## 58 Employed 6.2 B Art Blue Reading 7
## 59 Employed 6.2 C Music Red Traveling 8.5
## 60 Employed 6.2 B Art Blue Reading 7
## 61 Employed 6.2 B Music Green Traveling 7
## 62 Employed 8.2 B Music Blue Traveling 7.5
## 63 Employed 6.7 A Sports Red Photography 8
## 64 Employed 7.5 C Music Blue Reading 8
## 65 Employed 6.2 B Art Blue Reading 7
## 66 Employed 7.1 B Music Blue Swimming 6
## 67 Employed 7.8 C Sports Red Traveling 9
## 68 Unemployed 5.5 A Sports Green Reading 7
## 69 Unemployed 5.5 A Sports Green Reading 7
## 70 Employed 6.1 B Art Blue Swimming 7
## 71 Unemployed 5.5 A Sports Green Reading 7
## 72 Employed 7.1 B Music Blue Swimming 6
## 73 Unemployed 5.5 A Sports Green Reading 7
## 74 Employed 6.2 C Music Red Traveling 8.5
## 75 Employed 6.1 B Art Blue Swimming 7
## 76 Employed 6.1 B Art Blue Swimming 7
## 77 Unemployed 5.7 A Sports Green Photography 6
## 78 Employed 6.1 B Art Blue Swimming 7
## 79 Unemployed 6.2 B Music Green Traveling 7
## 80 Employed 6.2 B Art Blue Reading 7
## 81 Employed 6.2 B Art Blue Reading 7
## 82 Employed 7.8 C Sports Red Traveling 9
## 83 Employed 6.2 C Music Red Traveling 8.5
## 84 Employed 7.8 C Sports Red Traveling 9
## 85 Employed 6.2 B Art Blue Reading 7
## 86 Employed 6.2 C Music Red Traveling 8.5
## 87 Employed 6.5 A Art Green Reading 8
## 88 Unemployed 6.2 A Art Green Swimming 6.5
## 89 Employed 6.2 C Music Red Traveling 8.5
## 90 Employed 6.7 A Sports Red Photography 8
## 91 Employed 8.9 C Art Red Photography 9
## 92 Unemployed 5.5 A Sports Red Traveling 8
## 93 Employed 6.2 B Sports Blue Reading 7
## 94 Employed 6.2 C Sports Red Traveling 8.5
## 95 Employed 7.1 B Music Blue Swimming 6
## 96 Employed 6.2 B Music Green Traveling 7
## 97 Unemployed 5.7 A Sports Green Photography 6
## 98 Employed 6.1 B Art Blue Swimming 7
## 99 Employed 6.1 B Art Blue Swimming 7
## 100 Employed 7.8 C Sports Red Traveling 9
## 101 Employed 7.5 C Music Blue Reading 8
## 102 Unemployed 5.7 A Sports Green Photography 6
## 103 Employed 6.1 B Art Blue Swimming 7
## 104 Employed 6.7 A Sports Red Photography 8
## 105 Employed 8.9 C Art Red Photography 9
## 106 Employed 6.2 B Art Blue Reading 7
## 107 Employed 6.5 A Art Green Reading 8
## 108 Employed 7.8 C Sports Red Traveling 9
## 109 Employed 7.8 C Sports Red Traveling 9
## 110 Employed 6.2 C Music Red Traveling 8.5
## 111 Employed 6.2 C Music Red Traveling 8.5
## 112 Employed 6.2 B Art Blue Reading 7
## 113 Employed 6.1 B Art Blue Swimming 7
## 114 Unemployed 6.2 B Music Green Traveling 7
## 115 Employed 6.2 B Art Blue Reading 7
## 116 Employed 7.1 B Music Blue Swimming 6
## 117 Employed 6.7 A Sports Red Photography 8
## 118 Unemployed 7.8 B Music Blue Traveling 8.5
## 119 Employed 7.1 B Music Blue Swimming 6
## 120 Employed 7.8 C Sports Red Traveling 9
## 121 Employed 8.9 C Art Red Photography 9
## 122 Unemployed 5.5 A Sports Red Traveling 8
## 123 Employed 6.2 B Art Blue Reading 7
## 124 Employed 6.5 A Art Green Reading 8
## 125 Employed 6.1 B Art Blue Swimming 7
## 126 Employed 7.8 C Sports Red Traveling 9
## 127 Unemployed 5.7 A Sports Green Photography 6
## 128 Employed 5.9 A Art Green Reading 7
## 129 Employed 7.5 C Music Blue Reading 8
## 130 Employed 7.8 C Sports Red Traveling 9
## 131 Unemployed 5.7 A Sports Green Photography 6
## 132 Employed 6.1 B Art Blue Swimming 7
## 133 Employed 6.1 B Art Blue Swimming 7
## 134 Employed 6.2 B Art Blue Reading 7
## 135 Employed 6.2 B Art Green Reading 7
## 136 Employed 6.2 B Music Green Traveling 7
## 137 Employed 7.5 A Sports Blue Reading 8
## 138 Unemployed 5.5 A Sports Green Reading 7
## 139 Employed 7.8 C Sports Red Traveling 9
## 140 Employed 7.1 B Music Blue Swimming 6
## 141 Employed 7.8 C Sports Red Traveling 9
## 142 Employed 8.9 C Art Red Photography 9
## 143 Employed 8.2 B Music Blue Traveling 7.5
## 144 Employed 6.2 C Music Red Traveling 8.5
## 145 Employed 6.1 B Art Blue Swimming 7
## 146 Employed 6.2 B Art Blue Reading 7
## 147 Employed 6.1 B Art Blue Swimming 7
## 148 Employed 8.9 C Art Red Photography 9
## 149 Employed 6.2 C Music Red Traveling 8.5
## 150 Employed 6.2 C Music Red Traveling 8.5
## 151 Unemployed 6.2 A Art Green Swimming 6.5
## 152 Employed 7.5 A Sports Blue Reading 8
## 153 Employed 6.1 B Art Blue Swimming 7
## 154 Employed 6.1 B Art Blue Swimming 7
## 155 Employed 7.5 A Sports Blue Reading 8
## 156 Unemployed 6.2 B Music Green Traveling 7
## 157 Employed 6.1 B Art Blue Swimming 7
## 158 Unemployed 6.2 A Art Green Swimming 6.5
## 159 Employed 7.1 B Music Blue Swimming 6
## 160 Unemployed 5.7 A Sports Green Photography 6
## 161 Employed 6.1 B Art Blue Swimming 7
## 162 Unemployed 5.7 A Sports Green Photography 6
## 163 Employed 6.2 B Art Blue Reading 7
## 164 Employed 8.2 B Music Blue Traveling 7.5
## 165 Employed 7.8 C Sports Red Traveling 9
## 166 Employed 7.8 C Sports Red Traveling 9
## 167 Employed 8.9 C Art Red Photography 9
## 168 Employed 7.8 C Sports Red Traveling 9
## 169 Employed 6.5 A Art Green Reading 8
## 170 Employed 7.8 C Sports Red Traveling 9
## 171 Unemployed 5.7 A Sports Green Photography 6
## 172 Employed 8.2 B Music Blue Traveling 7.5
## 173 Single Unemployed 5.7 A Sports Green Photography
## 174 Employed 7.5 A Sports Blue Reading 8
## 175 Unemployed 6.2 A Art Green Swimming 6.5
## 176 Unemployed 5.7 A Sports Green Photography 6
## 177 Employed 6.2 C Music Red Traveling 8.5
## 178 Unemployed 5.7 A Sports Green Photography 6
## 179 Employed 6.2 C Music Red Traveling 8.5
## 180 Employed 6.2 C Music Red Traveling 8.5
## 181 Employed 6.2 B Art Blue Reading 7
## 182 Unemployed 7.8 B Music Blue Traveling 8.5
## 183 Employed 6.2 B Art Blue Reading 7
## 184 Employed 8.9 C Art Red Photography 9
## 185 Employed 7.8 C Sports Red Traveling 9
## 186 Employed 6.7 A Sports Red Photography 8
## 187 Employed 8.2 B Music Blue Traveling 7.5
## 188 Unemployed 5.7 A Sports Green Photography 6
## 189 Unemployed 5.5 A Sports Green Reading 7
## 190 Employed 8.9 C Art Red Photography 9
## 191 Employed 6.2 B Art Blue Reading 7
## 192 Unemployed 5.7 A Sports Green Photography 6
## 193 Employed 6.7 A Sports Red Photography 8
## 194 Employed 6.1 B Art Blue Swimming 7
## 195 Employed 7.1 B Music Blue Swimming 6
## 196 Employed 6.2 B Art Blue Reading 7
## 197 Unemployed 5.7 A Sports Green Photography 6
## 198 Employed 6.1 B Art Blue Swimming 7
## 199 Employed 7.8 C Sports Red Traveling 9
## 200 Employed 6.2 C Music Red Traveling 8.5
## 201 Unemployed 5.7 A Sports Green Photography 6
## 202 Employed 6.5 A Art Green Reading 8
## 203 Employed 6.2 B Art Green Reading 7
## 204 Employed 7.8 C Sports Red Traveling 9
## 205 Unemployed 6.2 B Music Green Traveling 7
## 206 Employed 7.8 C Sports Red Traveling 9
## 207 Unemployed 5.7 A Sports Green Photography 6
## 208 Employed 6.5 A Art Green Reading 8
## 209 Unemployed 6.2 A Art Green Swimming 6.5
## 210 Unemployed 5.7 A Sports Green Photography 6
## 211 Employed 5.7 A Sports Green Photography 6
## 212 Unemployed 5.7 A Sports Green Photography 6
## 213 Single Unemployed 5.7 A Sports Green Photography
## 214 Employed 6.2 C Music Red Traveling 8.5
## 215 Unemployed 5.5 A Sports Green Reading 7
## 216 Employed 7.8 C Sports Red Traveling 9
## 217 Employed 6.1 B Art Blue Swimming 7
## 218 Employed 7.1 B Music Blue Swimming 6
## 219 Employed 7.1 B Music Blue Swimming 6
## 220 Unemployed 5.5 A Sports Green Reading 7
## 221 Employed 6.2 C Music Red Traveling 8.5
## 222 Unemployed 5.7 A Sports Green Photography 6
## 223 Employed 6.1 B Art Blue Swimming 7
## 224 Employed 7.5 C Music Blue Reading 8
## 225 Employed 6.1 B Art Blue Swimming 7
## 226 Employed 5.9 A Art Green Reading 7
## 227 Employed 6.2 C Music Red Traveling 8.5
## 228 Employed 7.8 C Sports Red Traveling 9
## 229 Employed 6.2 C Music Red Traveling 8.5
## 230 Employed 6.2 C Music Red Traveling 8.5
## 231 Employed 7.8 C Sports Red Traveling 9
## 232 Employed 7.8 C Sports Red Traveling 9
## 233 Unemployed 5.7 A Sports Green Photography 6
## 234 Employed 7.8 C Sports Red Traveling 9
## 235 Employed 6.2 B Art Blue Reading 7
## 236 Unemployed 5.7 A Sports Green Photography 6
## 237 Employed 6.1 B Art Blue Swimming 7
## 238 Employed 6.2 B Music Green Traveling 7
## 239 Employed 6.2 B Art Blue Reading 7
## 240 Employed 7.3 B Music Blue Swimming 8.2
## 241 Employed 6.2 B Art Blue Reading 7
## 242 Employed 7.5 A Sports Blue Reading 8
## 243 Unemployed 6.2 A Art Green Swimming 6.5
## 244 Unemployed 6.2 A Art Green Swimming 6.5
## 245 Employed 6.2 B Art Blue Reading 7
## 246 Employed 7.8 C Sports Red Traveling 9
## 247 Unemployed 5.7 A Sports Green Photography 6
## 248 Employed 5.9 A Art Green Reading 7
## 249 Employed 6.7 A Sports Red Photography 8
## 250 Employed 6.2 B Art Blue Reading 7
## 251 Employed 6.2 B Art Blue Reading 7
## 252 Employed 7.3 B Music Blue Swimming 8.2
## 253 Unemployed 5.5 A Sports Red Traveling 8
## 254 Employed 6.2 C Music Red Traveling 8.5
## 255 Unemployed 5.5 A Sports Green Reading 7
## 256 Employed 7.8 C Sports Red Traveling 9
## 257 Employed 7.8 C Sports Red Traveling 9
## 258 Employed 6.7 A Sports Red Photography 8
## 259 Employed 6.2 C Music Red Traveling 8.5
## 260 Employed 5.9 A Art Green Reading 7
## 261 Employed 7.8 C Sports Red Traveling 9
## 262 Employed 8.9 C Art Red Photography 9
## 263 Unemployed 6.2 A Art Green Swimming 6.5
## 264 Employed 7.8 C Sports Red Traveling 9
## 265 Employed 7.5 C Music Blue Reading 8
## 266 Unemployed 5.5 A Sports Green Reading 7
## 267 Employed 7.8 C Sports Red Traveling 9
## 268 Unemployed 6.2 A Art Green Swimming 6.5
## 269 Employed 7.3 B Music Blue Swimming 8.2
## 270 Unemployed 5.7 A Sports Green Photography 6
## 271 Employed 6.2 B Art Green Reading 7
## 272 Employed 7.8 C Sports Red Traveling 9
## 273 Employed 6.2 B Art Blue Reading 7
## 274 Unemployed 5.7 A Sports Green Photography 6
## 275 Employed 6.2 B Art Blue Reading 7
## 276 Employed 6.2 C Music Red Traveling 8.5
## 277 Employed 7.8 C Sports Red Traveling 9
## 278 Employed 6.2 B Art Blue Reading 7
## 279 Employed 7.3 B Music Blue Swimming 8.2
## 280 Unemployed 7.8 B Music Blue Traveling 8.5
## 281 Employed 7.8 C Sports Blue Traveling 9
## 282 Unemployed 6.2 B Music Green Traveling 7
## 283 Employed 6.1 B Art Blue Swimming 7
## 284 Unemployed 6.2 B Music Green Traveling 7
## 285 Employed 6.7 A Sports Red Photography 8
## 286 Employed 7.8 C Sports Red Traveling 9
## 287 Unemployed 5.7 A Sports Green Photography 6
## 288 Employed 6.1 B Art Blue Swimming 7
## 289 Employed 6.7 A Sports Red Photography 8
## 290 Employed 7.8 C Sports Red Traveling 9
## 291 Employed 6.2 B Sports Blue Reading 7
## 292 Employed 6.2 B Sports Blue Traveling 7
## 293 Employed 6.1 B Art Blue Swimming 7
## 294 Unemployed 5.7 A Sports Green Photography 6
## 295 Employed 7.1 B Music Blue Swimming 6
## 296 Employed 7.1 B Music Blue Swimming 6
## 297 Employed 7.1 B Music Blue Swimming 6
## 298 Employed 7.8 C Sports Red Traveling 9
## 299 Employed 6.2 C Music Red Traveling 8.5
## 300 Employed 7.3 B Music Blue Swimming 8.2
## 301 Employed 6.2 B Art Blue Reading 7
## 302 Unemployed 5.7 A Sports Green Photography 6
## 303 Unemployed 5.7 A Sports Green Photography 6
## 304 Employed 6.2 B Art Green Reading 7
## 305 Employed 6.2 B Art Blue Reading 7
## 306 Employed 8.2 B Music Blue Traveling 7.5
## 307 Employed 7.1 B Music Blue Swimming 6
## 308 Unemployed 5.7 A Sports Green Photography 6
## 309 Employed 7.1 B Music Blue Swimming 6
## 310 Employed 7.8 C Sports Red Traveling 9
## 311 Employed 6.2 C Music Red Traveling 8.5
## 312 Unemployed 5.5 A Sports Green Reading 7
## 313 Employed 7.8 C Sports Red Traveling 9
## 314 Employed 5.9 A Art Green Reading 7
## 315 Employed 8.9 C Art Red Photography 9
## 316 Employed 6.2 B Art Blue Reading 7
## 317 Employed 6.2 C Music Red Traveling 8.5
## 318 Employed 6.2 B Art Blue Reading 7
## 319 Unemployed 5.7 A Sports Green Photography 6
## 320 Unemployed 5.7 A Sports Green Photography 6
## 321 Employed 7.8 C Sports Red Traveling 9
## 322 Unemployed 5.5 A Sports Red Traveling 8
## 323 Unemployed 5.7 A Sports Green Photography 6
## 324 Employed 6.2 C Music Red Traveling 8.5
## 325 Employed 5.9 A Art Green Reading 7
## 326 Employed 6.7 A Sports Red Photography 8
## 327 Unemployed 5.7 A Sports Green Photography 6
## 328 Unemployed 6.2 B Music Green Traveling 7
## 329 Employed 8.9 C Art Red Photography 9
## 330 Employed 7.8 C Sports Red Traveling 9
## 331 Employed 6.2 C Music Red Traveling 8.5
## 332 Employed 6.1 B Art Blue Swimming 7
## 333 Employed 6.1 B Art Blue Swimming 7
## 334 Employed 6.2 B Art Blue Reading 7
## 335 Employed 6.2 B Art Blue Reading 7
## 336 Employed 6.2 C Music Red Traveling 8.5
## 337 Employed 6.1 B Art Blue Swimming 7
## 338 Employed 6.2 C Music Red Traveling 8.5
## 339 Employed 6.1 B Art Blue Swimming 7
## 340 Employed 6.2 B Art Blue Reading 7
## 341 Employed 7.8 C Sports Red Traveling 9
## 342 Employed 6.1 B Art Blue Swimming 7
## 343 Employed 6.1 B Art Blue Swimming 7
## 344 Employed 5.9 A Art Green Reading 7
## 345 Employed 6.2 C Music Red Traveling 8.5
## 346 Employed 6.2 C Music Red Traveling 8.5
## 347 Unemployed 5.5 A Sports Green Reading 7
## 348 Employed 6.1 B Art Blue Swimming 7
## 349 Unemployed 6.2 B Music Green Traveling 7
## 350 Unemployed 5.5 A Sports Red Traveling 8
## 351 Employed 6.1 B Art Blue Swimming 7
## 352 Employed 6.1 B Art Blue Swimming 7
## 353 Employed 6.2 B Music Green Traveling 7
## 354 Employed 7.5 C Music Blue Reading 8
## 355 Employed 6.2 B Art Green Reading 7
## 356 Unemployed 6.2 B Music Green Traveling 7
## 357 Employed 8.9 C Art Red Photography 9
## 358 Employed 7.8 C Sports Red Traveling 9
## 359 Employed 7.5 A Sports Blue Reading 8
## 360 Employed 6.2 C Music Red Traveling 8.5
## 361 Employed 6.1 B Art Blue Swimming 7
## 362 Employed 7.5 A Sports Blue Reading 8
## 363 Employed 7.5 C Music Blue Reading 8
## 364 Employed 7.3 B Music Blue Swimming 8.2
## 365 Employed 6.2 B Art Blue Reading 7
## 366 Employed 8.2 B Music Blue Traveling 7.5
## 367 Employed 6.2 B Art Blue Reading 7
## 368 Unemployed 5.7 A Sports Green Photography 6
## 369 Employed 7.8 C Sports Red Traveling 9
## 370 Unemployed 6.2 B Music Green Traveling 7
## 371 Employed 7.8 C Sports Red Traveling 9
## 372 Employed 6.1 B Art Blue Swimming 7
## 373 Employed 7.5 A Sports Blue Reading 8
## 374 Employed 7.8 C Sports Red Traveling 9
## 375 Employed 7.8 C Sports Red Traveling 9
## 376 Employed 7.8 C Sports Red Traveling 9
## 377 Employed 6.2 B Art Blue Reading 7
## 378 Employed 6.1 B Art Blue Swimming 7
## 379 Employed 6.2 C Music Red Traveling 8.5
## 380 Employed 6.2 C Music Red Traveling 8.5
## 381 Unemployed 5.5 A Sports Red Traveling 8
## 382 Employed 6.2 C Music Red Traveling 8.5
## 383 Employed 6.2 B Art Green Reading 7
## 384 Employed 6.1 B Art Blue Swimming 7
## 385 Employed 7.1 B Music Blue Swimming 6
## 386 Employed 7.8 C Sports Red Traveling 9
## 387 Unemployed 5.5 A Sports Green Reading 7
## 388 Employed 6.1 B Art Blue Swimming 7
## 389 Unemployed 6.2 A Art Green Swimming 6.5
## 390 Employed 5.7 A Sports Green Photography 6
## 391 Employed 7.5 A Sports Blue Reading 8
## 392 Unemployed 6.2 B Music Green Traveling 7
## 393 Employed 6.2 B Art Blue Reading 7
## 394 Employed 6.2 B Sports Blue Traveling 7
## 395 Employed 6.1 B Art Blue Swimming 7
## 396 Employed 8.2 B Music Blue Traveling 7.5
## 397 Employed 7.8 C Sports Red Traveling 9
## 398 Employed 7.5 A Sports Blue Reading 8
## 399 Employed 6.2 C Music Red Traveling 8.5
## 400 Employed 6.2 B Art Blue Reading 7
## 401 Employed 6.2 B Art Blue Reading 7
## 402 Employed 5.7 A Sports Green Photography 6
## 403 Unemployed 6.2 A Art Green Swimming 6.5
## 404 Employed 6.7 A Sports Red Photography 8
## 405 Employed 6.2 B Art Blue Reading 7
## 406 Employed 6.5 A Art Green Reading 8
## 407 Unemployed 5.7 A Sports Green Photography 6
## 408 Employed 6.1 B Art Blue Swimming 7
## 409 Employed 6.2 C Music Red Traveling 8.5
## 410 Employed 6.2 C Music Red Traveling 8.5
## 411 Employed 6.1 B Art Blue Swimming 7
## 412 Employed 7.5 A Sports Blue Reading 8
## 413 Unemployed 5.7 A Sports Green Photography 6
## 414 Employed 7.5 A Sports Blue Reading 8
## 415 Employed 6.2 B Art Green Reading 7
## 416 Employed 7.8 C Sports Red Traveling 9
## 417 Employed 7.8 C Sports Red Traveling 9
## 418 Employed 7.1 B Music Blue Swimming 6
## 419 Employed 5.9 A Art Green Reading 7
## 420 Employed 6.2 B Art Blue Reading 7
## 421 Employed 6.1 B Art Blue Swimming 7
## 422 Employed 7.8 B Music Blue Traveling 8.5
## 423 Unemployed 6.2 B Music Green Traveling 7
## 424 Employed 7.3 B Music Blue Swimming 8.2
## 425 Employed 7.8 C Sports Red Traveling 9
## 426 Employed 6.2 C Sports Red Traveling 8.5
## 427 Employed 7.5 A Sports Blue Reading 8
## 428 Employed 7.5 A Sports Blue Reading 8
## 429 Employed 7.5 A Sports Blue Reading 8
## 430 Employed 5.9 A Art Green Reading 7
## 431 Employed 6.1 B Art Blue Swimming 7
## 432 Employed 6.1 B Art Blue Swimming 7
## 433 Employed 6.2 C Music Red Traveling 8.5
## 434 Unemployed 6.2 A Art Green Swimming 6.5
## 435 Employed 6.1 B Art Blue Swimming 7
## 436 Employed 8.9 C Art Red Photography 9
## 437 Employed 6.2 B Art Blue Traveling 7
## 438 Employed 7.1 B Music Blue Swimming 6
## 439 Employed 5.7 A Sports Green Photography 6
## 440 Employed 6.2 B Art Blue Reading 7
## 441 Employed 6.2 C Music Red Traveling 8.5
## 442 Employed 6.2 C Music Red Traveling 8.5
## 443 Employed 6.2 B Art Blue Reading 7
## 444 Unemployed 5.7 A Sports Green Photography 6
## 445 Employed 6.2 B Art Blue Reading 7
## 446 Employed 6.2 B Art Blue Reading 7
## 447 Employed 6.2 B Art Blue Reading 7
## 448 Employed 8.2 B Music Blue Traveling 7.5
## 449 Employed 8.9 C Art Red Photography 9
## 450 Employed 5.9 A Art Green Reading 7
## 451 Employed 6.2 B Art Blue Reading 7
## 452 Employed 6.1 B Art Blue Swimming 7
## 453 Unemployed 5.7 A Sports Green Photography 6
## 454 Employed 7.1 B Music Blue Swimming 6
## 455 Unemployed 5.7 A Sports Green Photography 6
## 456 Unemployed 7.8 B Music Blue Traveling 8.5
## 457 Employed 7.8 C Sports Red Traveling 9
## 458 Employed 7.5 C Music Blue Reading 8
## 459 Employed 6.1 B Art Blue Swimming 7
## 460 Employed 6.2 C Sports Red Traveling 8.5
## 461 Employed 8.9 C Art Red Photography 9
## 462 Employed 6.2 B Art Green Reading 7
## 463 Employed 7.8 C Sports Red Traveling 9
## 464 Employed 5.9 A Art Green Reading 7
## 465 Unemployed 5.5 A Sports Green Reading 7
## 466 Employed 6.1 A Art Blue Swimming 7
## 467 Employed 7.8 C Sports Red Traveling 9
## 468 Employed 7.8 C Sports Red Traveling 9
## 469 Employed 6.2 B Art Blue Reading 7
## 470 Employed 7.5 C Music Blue Reading 8
## 471 Employed 6.2 B Sports Blue Reading 7
## 472 Unemployed 6.2 A Art Green Swimming 6.5
## 473 Unemployed 5.7 A Sports Green Photography 6
## 474 Employed 7.8 C Sports Red Traveling 9
## 475 Employed 6.2 B Art Blue Traveling 7
## 476 Employed 7.8 C Sports Red Traveling 9
## 477 Employed 7.8 C Sports Blue Traveling 9
## 478 Employed 7.8 C Sports Red Traveling 9
## 479 Employed 7.1 B Music Blue Swimming 6
## 480 Employed 7.5 A Sports Blue Reading 8
## 481 Employed 7.1 B Music Blue Swimming 6
## 482 Unemployed 5.7 A Sports Green Photography 6
## 483 Employed 7.8 C Sports Red Traveling 9
## 484 Unemployed 5.5 A Sports Green Reading 7
## 485 Employed 6.2 B Art Blue Reading 7
## 486 Unemployed 6.2 A Art Green Swimming 6.5
## 487 Employed 6.1 B Art Blue Swimming 7
## 488 Employed 6.7 A Sports Red Photography 8
## 489 Unemployed 5.7 A Sports Green Photography 6
## 490 Employed 7.8 C Sports Red Traveling 9
## 491 Employed 6.2 B Sports Blue Traveling 7
## 492 Employed 6.2 B Art Blue Reading 7
## 493 Unemployed 5.7 A Sports Green Photography 6
## 494 Employed 5.9 A Art Green Reading 7
## 495 Employed 6.2 B Art Green Reading 7
## 496 Employed 6.2 C Music Red Traveling 8.5
## 497 Employed 6.2 B Art Blue Reading 7
## 498 Employed 8.9 C Art Red Photography 9
## 499 Employed 6.2 B Art Blue Reading 7
## 500 Employed 7.5 C Music Blue Reading 8
## Location
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## 500 CityThe imputation strategy used in the code is a hybrid, rule-based approach that performs single imputation. This method fills in missing values across all missing percentages for mixed data types (Chungnoy & Songmuang, 2023). For numerical variables, the code imputes the mean if the column has more than 10 non-missing values and replaces all NAs with this mean. If the column has 10 or fewer data points, linear interpolation is used by drawing a straight line between existing points. For categorical (factor) variables, missing values are replaced with the most common category, while character variables are replaced with a literal “NA” category. Interpolation is a straightforward method that estimates missing values by assuming a straight-line relationship between known values. Linear interpolation is appropriate when the variable is a continuous time series or a sequence of ordered measurements, or when the data follows a natural sequence. This is not the case in our dataset, and this method is not the best estimate for missing data The imputation impacts the output. While replacing missing values with the observed mean kept the average of numeric variables the same, mode imputation increased the frequency of the most common categories. At the end, there are no more NAs.