Subsetting Data R

[Mengkelyu]

选列（选择变量）

Selecting (Keeping) Variables

# select variables v1, v2, v3
myvars <- c("v1", "v2", "v3")
newdata <- mydata[myvars]

# another method
myvars <- paste("v", 1:3, sep="")
newdata <- mydata[myvars]

# select 1st and 5th thru 10th variables
newdata <- mydata[c(1,5:10)]

Excluding (DROPPING) Variables

# exclude variables v1, v2, v3
myvars <- names(mydata) %in% c("v1", "v2", "v3")
newdata <- mydata[!myvars]

# exclude 3rd and 5th variable
newdata <- mydata[c(-3,-5)]

# exclude 3rd and 5th column
newdata <- mydata[,-c(1,2)]

# delete variables v3 and v5
mydata$v3 <- mydata$v5 <- NULL

选行（选择观察值（Obsrevations））

# first 5 observations
newdata <- mydata[1:5,]

# based on variable values
newdata <- mydata[ which(mydata$gender=='F' & mydata$age > 65), ]

# exclude 3rd and 5th row
newdata <- mydata[c(-3,-5),]

# or
attach(mydata)
newdata <- mydata[ which(gender=='F' & age > 65),]
detach(mydata)

用subset同时选行和选列

# using subset function
newdata <- subset(mydata, age >= 20 | age < 10, select=c(ID, Weight))

# using subset function, remove ID and Weight
newdata <- subset(mydata, age >= 20 | age < 10, select=-c(ID, Weight))

# using subset function (part 2)
newdata <- subset(mydata, sex=="m" & age > 25,
select=weight:income)

Random Samples

# take a random sample of size 50 from a dataset mydata
# sample without replacement
mysample <- mydata[sample(1:nrow(mydata), 50,
   replace=FALSE),]

[Mengkelyu]

什么时候可以用 - 反选， 什么时候不能用 - 反选

例子一：这个例子里面，我们选择了前三行作为测试集，用了后面的行作为训练集

split <- c(1,2,3)
train <-  mydata[split,]
test <-  mydata[-split,]

例子二：这个例子里面，我们选择了 ID < 50 作为训练集，其它为测试集

split <- mydata$ID < 50
# 这里split的结果是一个包含了True和False的数列!!!
train <-  mydata[split,]
test <-  mydata[!split,]

下面的例子都是从网上找到的可以replicate的代码
例子一：

data(mtcars)

## 75% of the sample size
smp_size <- floor(0.75 * nrow(mtcars))

## set the seed to make your partition reproducible
set.seed(123)
train_ind <- sample(seq_len(nrow(mtcars)), size = smp_size)
#  注释：这里用sample生成的是从最大行数里面随便选的一些行数
train <- mtcars[train_ind, ]
test <- mtcars[-train_ind, ]

例子二：

n = nrow(dataset)
split = sample(c(TRUE, FALSE), n, replace=TRUE, prob=c(0.75, 0.25))
# 注释：这里用sample生成的是用True和False组成的，长度为原本数据集长度的变量
training = dataset[split, ]
testing = dataset[!split, ]