従業員の安全

HR Analytics: Rで学ぶ従業員データの探索

Ben Teusch

HR Analytics Consultant

従業員の安全

なぜ安全性に注目するのか

従業員を大切にするため
離職率の低下
労災保険料や訴訟費用の削減

2つのキーで結合する

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

NA の扱い方

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>

is.na() を使う理由

5 == 5

TRUE

NA == NA

NA

is.na(NA)

TRUE

練習しましょう！

HR Analytics: Rで学ぶ従業員データの探索