先讀入契約人力問卷調查資料
檔案形式為sav檔，會先用sav檔讀入，在儲存成rda檔做分析
讀入sav檔並儲存成rda檔
問卷資料用contract_staff來存放
contract_staff <- read_spss("~/Documents/2021TextbookProject/契約人力問卷調查.sav")
#將sav檔儲存成rda檔
save(contract_staff, file = "~/Documents/2021TextbookProject/契約人力問卷調查.rda",compress = T)
rm(list=ls())讀入rda檔
檢查contract_staff的屬性
load("契約人力問卷調查.rda")
nrow(contract_staff)## [1] 231names(contract_staff)## [1] "ID" "A01" "A02"
## [4] "A03" "A04" "A05"
## [7] "A06" "A07" "A08"
## [10] "A09" "B10" "B11"
## [13] "B12" "B13" "B14"
## [16] "B15" "B16" "B17"
## [19] "B18" "B19" "C20"
## [22] "C21" "C22" "C23"
## [25] "C24" "C25" "C26"
## [28] "C27" "C28" "C29"
## [31] "C30" "C31" "C32"
## [34] "D33" "D34" "D35_1"
## [37] "D35_2" "D35_3" "D35_4"
## [40] "D35_5" "D35_6" "D35_7"
## [43] "D35_8" "D35_9" "D35_10"
## [46] "D35_11" "E36" "E37"
## [49] "E38" "E39" "E40"
## [52] "E41" "E42" "E43"
## [55] "E44" "E45" "E46"
## [58] "E47" "E48" "E49"
## [61] "E50" "E51" "E52"
## [64] "E53" "F01" "F02"
## [67] "F03" "F04" "F05"
## [70] "F06" "F07" "F08"
## [73] "G01" "薪資福利心理契約違反" "工作負荷心理契約違反"
## [76] "工作滿意度" "組織承諾" "年齡"
## [79] "H1" "H2" "H3"
## [82] "情感性承諾" "持續性承諾" "規範性承諾"
## [85] "filter_$" "G02" "G05"
## [88] "G06" "G07" "W4"
## [91] "education" "D1" "D2"
## [94] "D3" "D4" "male"
## [97] "marry"class(contract_staff)## [1] "data.frame"將變數名稱修改,並重新命名變數
#重新命名變數
contract_staff$G01[contract_staff$G01 == 1] <- "聘用人員"
contract_staff$G01[contract_staff$G01 == 2] <- "約僱人員"
contract_staff$G01[contract_staff$G01 == 3] <- "臨時人員"
contract_staff$W4[contract_staff$W4 == 1] <- "行政庶務"
contract_staff$W4[contract_staff$W4 == 2] <- "專業研究性"
contract_staff$W4[contract_staff$W4 == 3] <- "技術性"
contract_staff$W4[contract_staff$W4 == 4] <- "民眾服務"
contract_staff$H3[contract_staff$H3 == 1] <- "2萬-3萬"
contract_staff$H3[contract_staff$H3 == 2] <- "3萬-4萬"
contract_staff$H3[contract_staff$H3 == 3] <- "4萬-5萬"
contract_staff$H3[contract_staff$H3 == 4] <- "5萬-6萬"
contract_staff$H3[contract_staff$H3 == 5] <- "6萬以上"
contract_staff$education[contract_staff$education == 1] <- "高中高職或以下"
contract_staff$education[contract_staff$education == 2] <- "專科"
contract_staff$education[contract_staff$education == 3] <- "大學"
contract_staff$education[contract_staff$education == 4] <- "碩士以上"
contract_staff$H2[contract_staff$H2 == 1] <- "少於1年"
contract_staff$H2[contract_staff$H2 == 2] <- "1-9年"
contract_staff$H2[contract_staff$H2 == 3] <- "10-19年"
contract_staff$H2[contract_staff$H2 == 4] <- "20-29年"
contract_staff$H2[contract_staff$H2 == 5] <- "30年以上"
#查找變數在第幾列
which(colnames(contract_staff) == "G01")## [1] 73which(colnames(contract_staff) == "W4")## [1] 90which(colnames(contract_staff) == "F06")## [1] 70which(colnames(contract_staff) == "H3")## [1] 81which(colnames(contract_staff) == "education")## [1] 91which(colnames(contract_staff) == "F04")## [1] 68which(colnames(contract_staff) == "F05")## [1] 69which(colnames(contract_staff) == "H2")## [1] 80#重新命名變數名稱
names(contract_staff)[73] <- "契約人力"
names(contract_staff)[90] <- "工作性質"
names(contract_staff)[70] <- "薪資待遇"
names(contract_staff)[81] <- "月薪資區間"
names(contract_staff)[91] <- "學歷"
names(contract_staff)[68] <- "學歷程度"
names(contract_staff)[69] <- "現職機關工作年資"
names(contract_staff)[80] <- "工作年資區間"ggplot(contract_staff, aes(x = 工作年資區間, fill = 學歷)) +
geom_bar(position = "dodge") + facet_wrap(vars(契約人力)) + labs(y = "次數", size = 10) + geom_text(stat = "count", aes(label = ( ..count..)), vjust = -1, color = I("black"), size = 4)
ggplot(contract_staff, aes(x = 現職機關工作年資, y = 薪資待遇, colour = 契約人力))+
geom_point()+geom_smooth()工作年資與學歷的描述性統計。
ggplot(contract_staff, aes(x = 年齡, y = 薪資待遇, colour = 契約人力))+
geom_point()+geom_smooth()年齡與薪資待遇的差異。
ggplot(data = contract_staff, aes(x = 月薪資區間, y = 工作性質,
color = 契約人力, shape = 契約人力)) +
geom_jitter(alpha = 1/2, size = 3.5) +
geom_smooth(method = "lm", se = FALSE)月薪資與工作性質的差異。
constract.df = contract_staff[, c("薪資待遇", "學歷程度", "現職機關工作年資", "年齡")]
cor.mat = cor(constract.df, use = "complete", method = "pearson")
round(cor.mat, 4)## 薪資待遇 學歷程度 現職機關工作年資 年齡
## 薪資待遇 1.0000 0.3839 -0.0803 0.0136
## 學歷程度 0.3839 1.0000 -0.2219 -0.1962
## 現職機關工作年資 -0.0803 -0.2219 1.0000 0.8251
## 年齡 0.0136 -0.1962 0.8251 1.0000pairs(constract.df, main = "中文標題", family = "黑體-繁 中黑")薪資待遇、學歷、工作年資及年齡的相關性1。
GGally::ggpairs(data = constract.df)薪資待遇、學歷、工作年資及年齡的相關性2。
GGally::ggcorr(data = constract.df,
method = c("complete", "pearson"))薪資待遇、學歷、工作年資及年齡的相關性2。
my_fn <- function(data, mapping, ...){
p <- ggplot(data = data, mapping = mapping) +
geom_point() +
geom_smooth(method = lm, se = FALSE, fill = "blue", color = "blue", ...)
p
}
GGally::ggpairs(data = constract.df,
lower = list(continuous = my_fn)) + theme(text = element_text(family = "黑體-繁 中黑", size = 14))
ggplot(data = contract_staff, aes(x = 月薪資區間, y = 學歷)) + geom_jitter() +
facet_grid(工作性質 ~ 契約人力)