员工敬业度分析

HR 分析：用 R 探索员工数据

Ben Teusch

HR Analytics Consultant

什么是员工敬业度？

敬业员工：对其工作和职场投入、热情并富有承诺的人。（盖洛普）

¹ http://news.gallup.com/poll/180404/gallup-daily-employee-engagement.aspx

什么是员工敬业度？

survey 数据

head(survey)

# A tibble: 6 x 5
  employee_id  department engagement    salary vacation_days_taken
        <int>       <chr>      <int>     <dbl>               <int>
1           1       Sales          3 103263.64                   7
2           2 Engineering          2  80708.64                  12
3           4 Engineering          4  60737.05                  12
4           5 Engineering          3  99116.32                   7
5           7 Engineering          3  51021.64                  18
6           8 Engineering          5  98399.87                   9

回顾 mutate()

survey %>% 
  mutate(max_salary = max(salary))

# A tibble: 1,470 x 6
   employee_id  department engagement    salary vacation_days_taken max_salary
         <int>       <chr>      <int>     <dbl>               <int>      <dbl>
 1           1       Sales          3 103263.64                   7   164072.6
 2           2 Engineering          2  80708.64                  12   164072.6
 3           4 Engineering          4  60737.05                  12   164072.6
 4           5 Engineering          3  99116.32                   7   164072.6
 5           7 Engineering          3  51021.64                  18   164072.6
# ... with 1,465 more rows

x <- 5
if(x < 10){ "True" } else { "False" }

"True"

z <- c(5, 8, 11, 14)

if(z < 10){ "True" } else { "False" }

"True"
Warning message:
In if (z < 10) { :
  the condition has length > 1 and only the first element will be used

ifelse(z < 10, "Yes", "No")

"Yes"  "Yes"  "No" "No"

ifelse() + mutate()

survey %>% 
  mutate(takes_vacation = ifelse(vacation_days_taken > 10, "Yes", "No"))

# A tibble: 1,470 x 6
   employee_id engagement    salary vacation_days_taken takes_vacation
         <int>      <int>     <dbl>               <int>          <chr>
 1           1          3 103263.64                   7             No
 2           2          2  80708.64                  12            Yes
 3           4          4  60737.05                  12            Yes
 4           5          3  99116.32                   7             No
 5           7          3  51021.64                  18            Yes
 # ... with 1,465 more rows

多个 summarize

survey %>% 
  group_by(department) %>% 
  summarize(max_salary = max(salary))

# A tibble: 3 x 2
   department max_salary
        <chr>      <dbl>
1 Engineering   164072.6
2     Finance   127013.2
3       Sales   143105.5

多个 summarize

survey %>% 
  group_by(department) %>% 
  summarize(max_salary = max(salary),
            min_salary = min(salary),
            avg_salary = mean(salary))

# A tibble: 3 x 4
   department max_salary min_salary avg_salary
        <chr>      <dbl>      <dbl>      <dbl>
1 Engineering   164072.6   45529.69   73576.35
2     Finance   127013.2   45714.07   76651.66
3       Sales   143105.5   46133.67   75073.57

¡Vamos a practicar!

HR 分析：用 R 探索员工数据