員工安全

HR 分析：用 R 探索員工資料

Ben Teusch

HR Analytics Consultant

員工安全

為何重視安全？

關懷員工
降低離職率
降低職災補償與法律費用

以兩個鍵連接

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

處理 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>

為何用 is.na()？

5 == 5

TRUE

NA == NA

NA

is.na(NA)

TRUE

一起來練習吧！

HR 分析：用 R 探索員工資料