Rを使った人事データ分析に関してのスライドです。

1. R

2. 3
R R Studio

3. 2
1 2

4. R
R
https://www.r-project.org/

5. R

6. R (Mac
Widows Mac OS

7. R (Win
Widows Mac OS
USB

8. R studio R
https://www.rstudio.com/

9. R studio R

10. R studio R

11. Tools
→
Global Option
R studio R

12. R studio
Source
Console
Environment
File Plot

13. R

14. ” # ”
#

15. R
R script
ctrl + enter R enter
console
5 + 5
10 * 3
plot(1:10) plot(1:100)
?summary

16. read.csv error
install.packages("readxl")
library(readxl)
x <-
read_excel("/Users/omotetakanori/Desktop/test_mac/class
Data.xlsx")
readxl install (library
x excel

17. head(x)
x 6
head (x,10)

19. tests %>% select(Japanese, Mathematics) %>% cor()
tests %>% select(Japanese:Mathematics) %>% cor()

20. #Data
tests <- read_excel("rhrData.xlsx")
library(dplyr)
#Data
tests %>% head(3)
#
library(ggplot2)
tests %>% ggplot(aes(x=Expense,y=Sales)) + geom_point() + geom_smooth(method =
"lm")

21. #
lm.Sales.Expense <- lm(Sales~Expense,data = tests)
#
summary(lm.Sales.Expense)
Call:
lm(formula = Sales ~ Expense, data = tests)
Residuals:
Min 1Q Median 3Q Max
-1558.1 -514.3 163.8 366.9 1300.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1260.693 418.680 3.011 0.00750 **
Expense 7.073 2.117 3.340 0.00364 **
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
Residual standard error: 709.3 on 18 degrees of freedom
Multiple R-squared: 0.3827, Adjusted R-squared: 0.3484
F-statistic: 11.16 on 1 and 18 DF, p-value: 0.003642

22. 0
0
0

23. ( )
#
library(readxl)
tokei <- read_excel("TokeiSeminar.xlsx")
lm.tokeiseminar <- lm(FutureSales ~
Sex+ Academic + Interview + IQ + MBTI + Abroad + Leadership + Athelete, data = tokei)
summary(lm.tokeiseminar)

24. 1. Data
2. 2000 1
3.
4. 2000
mutate
Environment
#Data
tests <- read_excel("rhrData.xlsx")
#if sales 2000>=
#1.
tests <- tests %>% mutate(IsSalesOver2000= Sales >= 2000)

25. predict xlab ylab
1. Data
2. 2000 1
3.
4. 2000
# 2000
pred.Salesover2000 <- predict (glm.Sales.NoCl.Ex,data = tests.over2000)
plot(pred.Salesover2000, xlab = "SalesName", ylab = "2000overProb")

26. 1 2

28. 0
0
50
100
150
200
250
0 5 10 15 20
Sales
58V N 96 3
N 1 S N S 9 5
4 2

29. (p)
p
-1.5
-1
-0.5
0
0.5
1
1.5
2
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2

30. # (PrincipalComponentAnalysis)
library(dplyr)
#
tests<- read_excel('classData.xlsx')
testsPr <- tests %>%
select(English:Mathematics)
rownames(testsPr) <-tests$Name
testsPr <- prcomp(testsPr)
testsPr %>% biplot()

34. k-means
1
k
5
2
k
3
k
4
k
5
2