Employee safety

HR Analytics: Exploring Employee Data in R

Ben Teusch

HR Analytics Consultant

Employee safety

Why focus on safety?

to care for employees
decreased turnover
lower worker's comp costs and legal fees

Joining with two keys

accident_data

# A tibble: 2 × 3
   year employee_id accident_time
  <dbl>       <dbl> <chr>        
1  2017           1 Morning      
2  2016           4 Afternoon

hr_data

# A tibble: 2 × 3
   year employee_id location 
  <dbl>       <dbl> <chr>    
1  2016           1 Northwood
2  2017           1 Northwood

joined_data <- left_join(hr_data, safety_data, by = c("year", "employee_id"))

joined_data

# A tibble: 2 × 4
   year employee_id location  accident_time
  <dbl>       <dbl> <chr>     <chr>        
1  2016           1 Northwood <NA>         
2  2017           1 Northwood Morning

Dealing with NA

joined_data

# A tibble: 2 × 4
   year employee_id location  accident_time
  <dbl>       <dbl> <chr>     <chr>        
1  2016           1 Northwood <NA>         
2  2017           1 Northwood Morning

joined_data %>%
   filter(accident_time == NA)   # no results

joined_data %>% 
  filter(is.na(accident_time))   # use is.na() instead

# A tibble: 1 × 4
   year employee_id location  accident_time
  <dbl>       <dbl> <chr>     <chr>        
1  2016           1 Northwood <NA>

Why use is.na()?

5 == 5

TRUE

NA == NA

NA

is.na(NA)

TRUE

Let's practice!

HR Analytics: Exploring Employee Data in R