Data 607 Week 1

Overview:

This dataset examines daily work-from-home behaviors and how they relate to employee burnout and productivity. It includes about 1,800 daily records with information on work hours, screen time, meetings, breaks, sleep, and burnout levels across both weekdays and weekends. The data can be used to study burnout risk and how work habits affect productivity and well-being.

Link: https://www.kaggle.com/datasets/sonalshinde123/work-from-home-employee-burnout-dataset?resource=download

install.packages("tidyverse")

## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.5'
## (as 'lib' is unspecified)

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ forcats   1.0.1     ✔ stringr   1.6.0
## ✔ ggplot2   4.0.1     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.2
## ✔ purrr     1.2.0     
## ── 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

data_url <- "https://raw.githubusercontent.com/bb2955/Data-607/main/work_from_home_burnout_dataset.csv"

df <- readr::read_csv(data_url, show_col_types = FALSE)

install.packages("conflicted")

## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.5'
## (as 'lib' is unspecified)

library(conflicted)

conflict_prefer_all("dplyr", quiet = TRUE)

problems(df)

## # A tibble: 0 × 5
## # ℹ 5 variables: row <int>, col <int>, expected <chr>, actual <chr>, file <chr>

colnames(df)

##  [1] "user_id"              "day_type"             "work_hours"          
##  [4] "screen_time_hours"    "meetings_count"       "breaks_taken"        
##  [7] "after_hours_work"     "sleep_hours"          "task_completion_rate"
## [10] "burnout_score"        "burnout_risk"

head(df)

## # A tibble: 6 × 11
##   user_id day_type work_hours screen_time_hours meetings_count breaks_taken
##     <dbl> <chr>         <dbl>             <dbl>          <dbl>        <dbl>
## 1       1 Weekday        9.59             11.9               4            2
## 2       1 Weekend        7.38             10.3               4            1
## 3       1 Weekend        6.31              8.92              1            2
## 4       1 Weekday        8.34             10.7               4            1
## 5       1 Weekend        6.97              9.83              1            2
## 6       1 Weekday        7.24              9.09              1            4
## # ℹ 5 more variables: after_hours_work <dbl>, sleep_hours <dbl>,
## #   task_completion_rate <dbl>, burnout_score <dbl>, burnout_risk <chr>

glimpse(df)

## Rows: 1,800
## Columns: 11
## $ user_id              <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2…
## $ day_type             <chr> "Weekday", "Weekend", "Weekend", "Weekday", "Week…
## $ work_hours           <dbl> 9.59, 7.38, 6.31, 8.34, 6.97, 7.24, 8.09, 7.15, 8…
## $ screen_time_hours    <dbl> 11.86, 10.33, 8.92, 10.70, 9.83, 9.09, 11.64, 9.9…
## $ meetings_count       <dbl> 4, 4, 1, 4, 1, 1, 6, 3, 1, 0, 3, 3, 0, 3, 1, 0, 3…
## $ breaks_taken         <dbl> 2, 1, 2, 1, 2, 4, 3, 4, 2, 4, 5, 3, 5, 1, 5, 3, 1…
## $ after_hours_work     <dbl> 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0…
## $ sleep_hours          <dbl> 7.55, 6.69, 8.87, 8.13, 5.85, 7.53, 5.04, 5.89, 6…
## $ task_completion_rate <dbl> 91.2, 82.0, 80.6, 70.0, 67.1, 69.1, 58.4, 46.9, 4…
## $ burnout_score        <dbl> 19.17, 29.70, 32.93, 45.47, 51.61, 54.16, 68.83, …
## $ burnout_risk         <chr> "Low", "Low", "Low", "Low", "Low", "Low", "Low", …

summary(df)

##     user_id         day_type           work_hours     screen_time_hours
##  Min.   :  1.00   Length:1800        Min.   : 3.000   Min.   : 4.510   
##  1st Qu.: 45.75   Class :character   1st Qu.: 4.430   1st Qu.: 7.240   
##  Median : 90.50   Mode  :character   Median : 6.445   Median : 9.210   
##  Mean   : 90.50                      Mean   : 6.515   Mean   : 9.271   
##  3rd Qu.:135.25                      3rd Qu.: 8.510   3rd Qu.:11.310   
##  Max.   :180.00                      Max.   :12.170   Max.   :15.700   
##  meetings_count    breaks_taken   after_hours_work  sleep_hours    
##  Min.   : 0.000   Min.   :1.000   Min.   :0.0000   Min.   : 4.500  
##  1st Qu.: 1.000   1st Qu.:2.000   1st Qu.:0.0000   1st Qu.: 6.280  
##  Median : 2.000   Median :3.000   Median :0.0000   Median : 6.990  
##  Mean   : 1.941   Mean   :3.029   Mean   :0.3589   Mean   : 6.996  
##  3rd Qu.: 3.000   3rd Qu.:4.000   3rd Qu.:1.0000   3rd Qu.: 7.750  
##  Max.   :10.000   Max.   :5.000   Max.   :1.0000   Max.   :10.800  
##  task_completion_rate burnout_score    burnout_risk      
##  Min.   : 40.00       Min.   :  2.50   Length:1800       
##  1st Qu.: 62.30       1st Qu.: 25.37   Class :character  
##  Median : 74.50       Median : 39.27   Mode  :character  
##  Mean   : 72.31       Mean   : 44.01                     
##  3rd Qu.: 83.70       3rd Qu.: 58.20                     
##  Max.   :107.20       Max.   :143.92

df_clean <- df %>%
  rename(
    id = user_id,
    num_meetings = meetings_count,
    num_breaks = breaks_taken,
    burnout_score = burnout_score,   # keep same but shown for clarity
    burnout_risk = burnout_risk
  )

df_clean <- df_clean %>%
  mutate(
    day_type = case_when(
      day_type %in% c("W", "wkday", "weekday") ~ "Weekday",
      day_type %in% c("E", "wknd", "weekend") ~ "Weekend",
      TRUE ~ as.character(day_type)
    ),
    after_hours_work = case_when(
      after_hours_work %in% c("Y", "Yes", "1") ~ "Yes",
      after_hours_work %in% c("N", "No", "0")  ~ "No",
      TRUE ~ as.character(after_hours_work)
    ),
    burnout_risk = case_when(
      burnout_risk %in% c("L", "low") ~ "Low",
      burnout_risk %in% c("M", "med", "medium") ~ "Medium",
      burnout_risk %in% c("H", "high") ~ "High",
      TRUE ~ as.character(burnout_risk)
    ),
    day_type = factor(day_type),
    after_hours_work = factor(after_hours_work),
    burnout_risk = factor(burnout_risk, levels = c("Low", "Medium", "High"))
  )

df %>% summarise(
  day_type_vals = paste(sort(unique(day_type)), collapse = ", "),
  after_hours_vals = paste(sort(unique(after_hours_work)), collapse = ", "),
  burnout_risk_vals = paste(sort(unique(burnout_risk)), collapse = ", ")
)

## # A tibble: 1 × 3
##   day_type_vals    after_hours_vals burnout_risk_vals
##   <chr>            <chr>            <chr>            
## 1 Weekday, Weekend 0, 1             High, Low, Medium

table(df_clean$day_type)

## 
## Weekday Weekend 
##     876     924

table(df_clean$after_hours_work)

## 
##   No  Yes 
## 1154  646

table(df_clean$burnout_risk)

## 
##    Low Medium   High 
##   1527    253     20

hist(
  df_clean$burnout_score,
  main = "Distribution of Burnout Score",
  xlab = "Burnout Score"
)

plot(
  df_clean$work_hours,
  df_clean$burnout_score,
  xlab = "Work Hours",
  ylab = "Burnout Score",
  main = "Work Hours vs Burnout Score"
)

boxplot(
  burnout_score ~ after_hours_work,
  data = df_clean,
  xlab = "After-Hours Work",
  ylab = "Burnout Score",
  main = "Burnout Score by After-Hours Work"
)

Conclusion:

The exploratory analysis suggests that working after hours and longer work hours are associated with slightly higher burnout scores, but these relationships show substantial variability. This indicates that burnout is influenced by multiple factors, and future work should include additional variables and more advanced models to better understand and predict burnout risk. To update and extend this work, additional variables such as job role, workload intensity (task difficulty or deadlines), and stress or mental health indicators would be useful because burnout is often influenced by more than just hours worked. Including manager support, flexibility level, and long-term trends over time would also help explain the variability seen in the graphs and lead to more accurate burnout prediction and interpretation.