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R Data Analysis/Rを使った人事データ分析入門
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R Data Analysis/Rを使った人事データ分析入門
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)
18.
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
27.
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()
31.
32.
33.
34.
k-means 1 k 5 2 k 3 k 4 k 5 2
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