『Data Science』R语言学习笔记，基础语法

Main Arguments:

• file
• header
• sep,columns separate,like ,.
• colClasses,the data class types of the column.
• nrows,number of the rows.
• comment.character,a character vector indicating the class of each column in the dataset.
• skip,the number of lines to skip from the beginning.
• stringsAsFactors,should character variables be coded as factors?

Usages:

read.table(file,header = FALSE,sep = "",quote = ""'",dec = ".",numerals = c("allow.loss","warn.loss","no.loss"),row.names,col.names,as.is = !stringsAsFactors,na.strings = "NA",colClasses = NA,nrows = -1,skip = 0,check.names = TRUE,fill = !blank.lines.skip,strip.white = FALSE,blank.lines.skip = TRUE,comment.char = "#",allowEscapes = FALSE,flush = FALSE,stringsAsFactors = default.stringsAsFactors(),fileEncoding = "",encoding = "unknown",text,skipNul = FALSE)

read.csv(file,header = TRUE,sep = ",",quote = """,fill = TRUE,comment.char = "",...)

read.csv2(file,sep = ";",dec = ",...)

read.delim(file,sep = "t",...)

read.delim2(file,...)

Writing Data

Description: write.table prints its required argument x (after converting it to a data frame if it is not one nor a matrix) to a file or connection.

Main Points:

• write.table
• writeLines
• dump
• dput
• save
• serialize

Usages:

write.table(x,file = "",append = FALSE,quote = TRUE,sep = " ",eol = "n",na = "NA",row.names = TRUE,col.names = TRUE,qmethod = c("escape","double"),fileEncoding = "")

write.csv(...)
write.csv2(...)

Reading Large Tables

• Read the help page for read.table,which contains many hints.
• Make a rough calculation of the memory required to store your dataset. If the dataset is larger than the amount of RAM on your computer,you can probably stop right here.
• Set comment.char = "" if there are no commented lines in your file.
• Use the colClasses argument. Specifying this option instead of using the default can make read.table run MUCH faster,often twice as fast. In order to use this option,you have to know the class of each column in your data frame. If all of the columns are "numeric",for example,then you can just set colClasses = "numeric". A quick an dirty way to figure out the classes of each column is the following:

> initial <- read.table("db.txt",nrows = 100,sep = "t")
> classes <- sapply(initial,class)
> tabAll <- read.table("db.txt",colClasses = classes)

• Set nrows. This doesn't make R run faster but it helps with memory usage. A mild overestimate is okay. You can use the Unix tool wc to calculate the number of lines in a file.

Reading Data Formats

dput and dget

> y <- data.frame(a = 1,b = "a") ## Create a `data.frame` object for `dput`
> dput(y)                         ## `dput` the object created before

structure(list(a = 1,b = structure(1L,.Label = "a",class = "factor")),.Names = c("a","b"),row.names = c(NA,-1L),class = "data.frame")

> dput(y,file = 'y.R')           ## `dput` the object created before into a file which named 'y.R'
> new.y <- dget('y.R')            ## get the data store in the file 'y.R'
> new.y                           ## print the data in the 'y.R'

  a b
1 1 a

dump

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