員工敬業度分析
HR 分析：用 R 探索員工資料
Ben Teusch
HR Analytics Consultant
什麼是員工敬業度？
- 敬業員工：投入、熱情，並對工作與職場有承諾的人。（Gallup）
1 http://news.gallup.com/poll/180404/gallup-daily-employee-engagement.aspx
什麼是員工敬業度？
調查資料
head(survey)
# A tibble: 6 × 5
employee_id department engagement salary vacation_days_taken
<dbl> <chr> <dbl> <dbl> <dbl>
1 1 Sales 3 103264. 7
2 2 Engineering 3 80709. 12
3 4 Engineering 3 60737. 12
4 5 Engineering 3 99116. 7
5 7 Engineering 3 51022. 18
6 8 Engineering 3 98400. 9
複習 mutate()
survey %>%
mutate(max_salary = max(salary))
# A tibble: 1,470 × 6
employee_id department engagement salary vacation_days_taken max_salary
<dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 1 Sales 3 103264. 7 164073.
2 2 Engineering 3 80709. 12 164073.
3 4 Engineering 3 60737. 12 164073.
4 5 Engineering 3 99116. 7 164073.
5 7 Engineering 3 51022. 18 164073.
6 8 Engineering 3 98400. 9 164073.
7 10 Engineering 3 57106. 18 164073.
8 11 Engineering 1 55065. 4 164073.
9 12 Engineering 4 77158. 12 164073.
10 13 Engineering 2 48365. 14 164073.
# i 1,460 more rows
# i Use `print(n = ...)` to see more rows
x <- 5
if(x < 10){ "True" } else { "False" }
"True"
z <- c(5, 8, 11, 14)if(z < 10){ "True" } else { "False" }
Error in if (z < 10) { : the condition has length > 1
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 × 6
employee_id department engagement salary vacation_days_taken takes_vacation
<dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 1 Sales 3 103264. 7 No
2 2 Engineering 3 80709. 12 Yes
3 4 Engineering 3 60737. 12 Yes
4 5 Engineering 3 99116. 7 No
5 7 Engineering 3 51022. 18 Yes
6 8 Engineering 3 98400. 9 No
7 10 Engineering 3 57106. 18 Yes
8 11 Engineering 1 55065. 4 No
9 12 Engineering 4 77158. 12 Yes
10 13 Engineering 2 48365. 14 Yes
# i 1,460 more rows
# i Use `print(n = ...)` to see more rows
多個彙總統計
survey %>%
group_by(department) %>%
summarize(max_salary = max(salary))
# A tibble: 3 × 2
department max_salary
<chr> <dbl>
1 Engineering 164073.
2 Finance 127013.
3 Sales 143105.
多個彙總統計
survey %>%
group_by(department) %>%
summarize(max_salary = max(salary),
min_salary = min(salary),
avg_salary = mean(salary))
# A tibble: 3 × 4
department max_salary min_salary avg_salary
<chr> <dbl> <dbl> <dbl>
1 Engineering 164073. 45530. 73576.
2 Finance 127013. 45714. 76652.
3 Sales 143105. 46134. 75074.
一起來練習吧！
HR 分析：用 R 探索員工資料
