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base.R
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base.R
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## Vector
x <- c(1, 2, 3, 4, 5, 6) ## vector of variable
y <- c(7, 8, 9, 10, 11, 12)
x + y
x * y
sqrt(x) ## root
sum(x)
diff(x) ## difference
mean(x) ## mean
var(x) ## variance
sd(x) ## standard deviation
median(x) ## median
IQR(x) ## inter-quantile range
max(x) ## max value
which.max(x) ## order of max value
max(x, y) ## max value among x & y
length(x)
## Slice
x[2] ## 2 번째
x[-2] ## 2 번째만 빼고
x[1:3] ## 1-3 번째
x[c(1, 2, 3)] ## 동일
x[c(1, 3, 4, 5, 6)] ## 1, 3, 4, 5, 6 번째
x >= 4 ## 각 항목이 4 이상인지 TRUE/FALSE
sum(x >= 4) ## TRUE 1, FALSE 0 인식
x[x >= 4] ## TRUE 인 것들만, 즉 4 이상인 것들
sum(x[x >= 4]) ## 4 이상인 것들만 더하기.
x %in% c(1, 3, 5) ## 1, 3, 5 중 하나에 속하는지 TRUE/FALSE
x[x %in% c(1, 3, 5)]
## Make vector
v1 <- seq(-5, 5, by = .2); v1 ## Sequence
v2 <- rep(1, 3); v2 ## Repeat
v3 <- rep(c(1, 2, 3), 2); v3 ## Repeat for vector
v4 <- rep(c(1, 2, 3), each = 2); v4 ## Repeat for vector : each
## for
for (i in 1:3){
print(i)
}
i <- 0
for (j in c(1, 2, 4, 5, 6)){
i <- i + j
}
i
## if/else
x <- 5
if (x >= 3 ){
x <- x + 3
}
x
x <- 5
if (x >= 10){
print("High")
} else if (x >= 5){
print("Medium")
} else {
print("Low")
}
## ifelse
x <- 1:6
y <- ifelse(x >= 4, "Yes", "No") ## ifelse (조건,참일때,거짓일때)
y
## function
x <- c(1:10, 12, 13, NA, NA, 15, 17) ## 결측치가 포함되어 있다면..
mean(x)
mean0 <- function(x){
mean(x, na.rm = T)
} ## mean함수의 na.rm 옵션을 TRUE로 바꿈. default는 F
mean0 <- function(x){mean(x, na.rm = T)} ## 한줄에 쓸 수도 있다.
mean0(x)
twomean <- function(x1, x2){
a <- (x1 + x2)/2
a
}
twomean(4, 6)
## Apply: apply, sapply, lapply
mat <- matrix(1:20, nrow = 4, byrow = T) ## 4행 5열, byrow = T : 행부터 채운다.
mat
out <- NULL ## 빈 벡터, 여기에 하나씩 붙여넣는다.
for (i in 1:nrow(mat)){
out <- c(out, mean(mat[i, ]))
}
out
sapply(1:nrow(mat), function(x){mean(mat[x, ])}) ## Return vector
lapply(1:nrow(mat), function(x){mean(mat[x, ])}) ## Return list type
unlist(lapply(1:nrow(mat), function(x){mean(mat[x, ])})) ## Same to sapply
apply(mat, 1, mean) ## 1: 행
rowMeans(mat) ## 동일
rowSums(mat) ## 행별로 합
apply(mat, 2, mean) ## 2: 열
colMeans(mat) ## 열별로 합
## Practice 1
x <- 1:6
y <- 7:12
## With data
getwd() ## 현재 디렉토리
setwd("data") ## 디렉토리 설정
## 동일
setwd("/cloud/project/data")
getwd()
ex <- read.csv("example_g1e.csv")
head(ex)
ex <- read.csv("https://raw.githubusercontent.com/jinseob2kim/lecture-snuhlab/master/data/example_g1e.csv")
head(ex)
#install.packages(c("readxl", "haven")) ## install packages
library(readxl) ## for xlsx
ex.excel <- read_excel("example_g1e.xlsx", sheet = 1) ## 1st sheet
library(haven) ## for SAS/SPSS/STATA
ex.sas <- read_sas("example_g1e.sas7bdat") ## SAS
ex.spss <- read_sav("example_g1e.sav") ## SPSS
head(ex.spss)
write.csv(ex, "example_g1e_ex.csv", row.names = F)
#write_sas(ex.sas, "example_g1e_ex.sas7bdat")
#write_sav(ex.spss, "example_g1e_ex.sav")
## See data
head(ex) ## 처음 6행
tail(ex) ## 마지막 6행
head(ex, 10) ## 처음 10행
str(ex)
names(ex)
dim(ex) ## row, column
nrow(ex) ## row
ncol(ex) ## column
class(ex)
class(ex.spss)
summary(ex)
## See variables
ex$EXMD_BZ_YYYY ## data.frame style
ex[, "EXMD_BZ_YYYY"] ## matrix style
ex[["EXMD_BZ_YYYY"]] ## list style
ex[, 1] ## matrix style with order
ex[[1]] ## list style with order
ex[, c("EXMD_BZ_YYYY", "RN_INDI", "BMI")] ## matrix syle with names
ex[, c(1, 2, 16)] ## matrix syle with names
ex[, names(ex)[c(1, 2, 16)]] ## same
ex$EXMD_BZ_YYYY[1:50] ## data.frame style
ex[1:50, 1] ## matrix style
ex[[1]][1:50] ## list style
unique(ex$EXMD_BZ_YYYY) ## unique value
length(unique(ex$EXMD_BZ_YYYY)) ## number of unique value
table(ex$EXMD_BZ_YYYY) ## table
## New variable
mean(ex$BMI) ## mean
BMI_cat <- (ex$BMI >= 25) ## TRUE of FALSE
table(BMI_cat)
rows <- which(ex$BMI >= 25) ## row numbers
head(rows)
values <- ex$BMI[ex$BMI >= 25] ## values
head(values)
length(values)
BMI_HGHT_and <- (ex$BMI >= 25 & ex$HGHT >= 175) ## and
BMI_HGHT_or <- (ex$BMI >= 25 | ex$HGHT >= 175) ## or
ex$zero <- 0 ## variable with 0
ex$BMI_cat <- (ex$BMI >= 25) ## T/F
ex$BMI_cat <- as.integer(ex$BMI >= 25) ## 0, 1
ex$BMI_cat <- as.character(ex$BMI >= 25) ## "0", "1"
ex$BMI_cat <- ifelse(ex$BMI >= 25, "1", "0") ## same
table(ex$BMI_cat)
ex[, "BMI_cat"] <- (ex$BMI >= 25) ## matrix style
ex[["BMI_cat"]] <- (ex$BMI >= 25) ## list style
## Set class
vars.cat <- c("RN_INDI", "Q_PHX_DX_STK", "Q_PHX_DX_HTDZ", "Q_PHX_DX_HTN", "Q_PHX_DX_DM", "Q_PHX_DX_DLD", "Q_PHX_DX_PTB",
"Q_HBV_AG", "Q_SMK_YN", "Q_DRK_FRQ_V09N")
vars.cat <- names(ex)[c(2, 4:12)] ## same
vars.cat <- c("RN_INDI", grep("Q_", names(ex), value = T)) ## same: extract variables starting with "Q_"
vars.conti <- setdiff(names(ex), vars.cat) ## Exclude categorical variables
vars.conti <- names(ex)[!(names(ex) %in% vars.cat)] ## same: !- not, %in%- including
for (vn in vars.cat){ ## for loop: as.factor
ex[, vn] <- as.factor(ex[, vn])
}
for (vn in vars.conti){ ## for loop: as.numeric
ex[, vn] <- as.numeric(ex[, vn])
}
summary(ex)
table(as.numeric(ex$Q_PHX_DX_STK))
table(as.numeric(as.character(ex$Q_PHX_DX_STK)))
## Date
addDate <- paste(ex$HME_YYYYMM, "01", sep = "") ## add day- use `paste`
ex$HME_YYYYMM <- as.Date(addDate, format = "%Y%m%d") ## set format
head(ex$HME_YYYYMM)
class(ex$HME_YYYYMM)
## NA
tapply(ex$LDL, ex$EXMD_BZ_YYYY, mean) ## measure/group/function
tapply(ex$LDL, ex$EXMD_BZ_YYYY,
function(x){
mean(x, na.rm = T)
})
summary(lm(LDL ~ HDL, data = ex))
## Practice 2
ex.naomit <- na.omit(ex)
nrow(ex.naomit)
getmode <- function(v){
uniqv <- unique(v)
uniqv[which.max(tabulate(match(v, uniqv)))]
}
getmode(ex$Q_PHX_DX_STK)
## Subset
ex1 <- ex.naomit ## simple name
ex1.2012 <- ex1[ex1$EXMD_BZ_YYYY >= 2012, ]
table(ex1.2012$EXMD_BZ_YYYY)
ex1.2012 <- subset(ex1, EXMD_BZ_YYYY >= 2012) ## subset
table(ex1.2012$EXMD_BZ_YYYY)
## Group by
aggregate(ex1[, c("WSTC", "BMI")], list(ex1$Q_PHX_DX_HTN), mean)
aggregate(cbind(WSTC, BMI) ~ Q_PHX_DX_HTN, data = ex1, mean) ## same
aggregate(cbind(WSTC, BMI) ~ Q_PHX_DX_HTN, data = ex, mean)
aggregate(ex1[, c("WSTC", "BMI")], list(ex1$Q_PHX_DX_HTN, ex1$Q_PHX_DX_DM), mean)
aggregate(cbind(WSTC, BMI) ~ Q_PHX_DX_HTN + Q_PHX_DX_DM, data = ex1, mean)
aggregate(cbind(WSTC, BMI) ~ Q_PHX_DX_HTN + Q_PHX_DX_DM, data = ex1, function(x){c(mean = mean(x), sd = sd(x))})
aggregate(. ~ Q_PHX_DX_HTN + Q_PHX_DX_DM, data = ex1, function(x){c(mean = mean(x), sd = sd(x))})
## Sort
ord <- order(ex1$HGHT) ## 작은 순서대로 순위
head(ord)
head(ex1$HGHT[ord]) ## Sort
ord.desc <- order(-ex1$HGHT) ## descending
head(ex1$HGHT[ord.desc])
ex1.sort <- ex1[ord, ]
head(ex1.sort)
## Wide to long, long to wide format
library(reshape2)
long <- melt(ex1, id = c("EXMD_BZ_YYYY", "RN_INDI"), measure.vars = c("BP_SYS", "BP_DIA"), variable.name = "BP_type", value.name = "BP")
long
library(reshape2)
long <- melt(ex1, id = c("EXMD_BZ_YYYY", "RN_INDI"), measure.vars = c("BP_SYS", "BP_DIA"), variable.name = "BP_type", value.name = "BP")
long %>% paged_table(options = list(rownames.print = F))
wide <- dcast(long, EXMD_BZ_YYYY + RN_INDI ~ BP_type, value.var = "BP")
head(wide)
## Merge
ex1.Q <- ex1[, c(1:3, 4:12)]
ex1.measure <- ex1[, c(1:3, 13:ncol(ex1))]
head(ex1.Q)
head(ex1.measure)
# all = T: Full, all.x = T: Left, all.y: Right, all = F: inner join
ex1.merge <- merge(ex1.Q, ex1.measure, by = c("EXMD_BZ_YYYY", "RN_INDI", "HME_YYYYMM"), all = T)
head(ex1.merge)