使用类函数在 read.table 中指定 colClasses

Question

有没有办法使用read.table()读取里面的全部或部分文件，使用类函数获取列类型，修改列类型，然后重新读取文件？

基本上我的列是零填充整数，我喜欢将其视为字符串。如果我让read.table() 做它的事情，它当然会假设这些是数字并去掉前导零并使列类型为整数。事情是我有相当多的列，所以虽然我可以创建一个字符向量来指定每个列，但我只想从 R 的最佳猜测中更改几个。我想做的是阅读前几行：

myTable <- read.table("//myFile.txt", sep="\t", quote="\"", header=TRUE, stringsAsFactors=FALSE, nrows = 5)

然后获取列类：

colTypes <- sapply(myTable, class)

更改几个列类型，即：

colTypes[1] <- "character"

然后使用修改后的列类型重新读取文件：

myTable <- read.table("//myFile.txt", sep="\t", quote="\"", colClasses=colTypes, header=TRUE, stringsAsFactors=FALSE, nrows = 5)

虽然这似乎是一件非常合理的事情，而且colTypes = c("character") 工作正常，但当我真正尝试它时，我得到了：

scan() expected 'an integer', got '"000001"'

class(colTypes) 和 class(c("character")) 都返回 "character" 那么有什么问题呢？

Answer 1

您使用read.tables colClasses = 参数来指定要分类为characters 的列。例如：

txt <- 
"var1, var2, var3
 0001, 0002, 1
 0003, 0004, 2"
df <- 
read.table(
    text = txt,
    sep = ",",
    header = TRUE,
    colClasses = "character") ## read all as characters
df    
df2 <- 
read.table(
    text = txt, 
    sep = ",",
    header = TRUE,
    colClasses = c("character", "character", "double")) ## the third column is numeric 
df2

[更新...] 或者，您可以使用矢量设置和重新设置 colClasses...

df <- 
read.table(
    text = txt, 
    sep = ",",
    header = TRUE)
df

## they're all currently read as integer
myColClasses <-   
sapply(df, class)

## create a vector of column names for zero padded variable
zero_padded <-    
c("var1", "var2")

## if a name is in zero_padded, return "character", else leave it be
myColClasses <-   
ifelse(names(myColClasses) %in% zero_padded, 
       "character", 
       myColClasses)

## read in with colClasses set to myColClasses
df2 <- 
read.table(
    text = txt, 
    sep = ",",
    colClasses = myColClasses,
    header = TRUE)
df2