Samenvatting R-studio-cheatsheat

Data<readRDS(‘/pad’) Packages: car, haven, alpha(data[, c("y1_lpoc1",
hmisc, lsr, psych "y1_lpoc2", "y1_lpoc3",
Install.package "y1_lpoc4")])
library regressive:
View model1 <- lm(delinq_tot ~
Attributes(data$....) vrouw, data=data)
Table(data$....) summary(model1)
Missende warden: data$gem_vaardig <-
Data$...[data$.. ==-99] <- NA rowMeans(data[, c("y1_lpoc1",
Onder 0 missing: "y1_lpoc2", "y1_lpoc3",
Data[data<0] <- NA "y1_lpoc4")], na.rm = TRUE)
saveRDS(data, file = “nieuwe naam”) sd(data$....., na.rm = T)
berekenen data$.....-data$....
gemiddeld: mean(data$.., na.rm =
TRUE)
summary(data$...)
min(data$.., na.rm= TRUE)
procent: 100*prop.table(table(data$...))
prop.table(table(data$....))*100

Hercoderen: Chi-kwadraat:
data$huiswerktijd <- chisq.test(data$...., data$....)
recode(as.numeric(data$......),
"c(3,4,5)='1'; c(1,2)='2'") specifiek filteren:
data$opl <- labelled(data$...., labels = jaar1995_data <-
c("vmbo" = 1, "havo" = 2, "vwo"=3), data[data$.....== 1995, ]
label = "Schooltype in 3 categorieen")
data$y1_workh2 <-
recode(as.numeric(data$......), "NA='0'")
Ifelse Correlatie( 2 ratio):
data$bornin1993 <- ifelse(data$y1_doby Cor1 <-
== 1993, 1, 0) rcorr(as.matrix(data[,c(“("y1_",
GELIJK AAN: ifelse(xx == yy, yes, no) "y1_", "y1_")]))
• NIET GELIJK AAN: ifelse(xx != yy, yes,
no) Rwaarde: cor1$r
• OF: ifelse(xx == yy | xx == zz, yes, no) pwaarde: cor1$P
• EN: ifelse(xx == yy & xx == zz, yes,
no) Regresie: model <-
• ONDER: ifelse(xx < 5, yes, no) lm(y1_isei08f ~
• BOVEN OF GELIJK AAN: ifelse(xx >= 5, vader_migratieachtergrond, data
yes, no) = data) summary(model)
data$workm_dag <- Proportietabellen:
data$y1_workh_new*60/7 prop.table(table(data$.....,
round(mean(data$...., na.rm=T),2) data$.....), margin = 2)
round(sd(data$......, na.rm=T),2) 1: rij 2: kollom
Drieweg kruistabel
nrow prop.table(table(data$....,
rowMeans(data_zonen[, c("y1_sspm", data$....., data$...), c(2,3))
"y1_sspsc", "y1_sspe")], na.rm = TRUE)