使用R进行数据整理

• 数据读入
• 数据类型转化
• 数据转换

• base:read.csv,read.table
• readr:
• readxl
• data.table
• dplyr
• RODBC

包含在base包中常用的读取格式化数据的函数。base::read.csv是base::read.table 的sep=','的特例。

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

• readr做了统一的接口，并且省略了一些不必要的参数。
• 使用datasource函数，统一了数据来源

read_csv(file, 
         col_names = TRUE,
         col_types = NULL, #使用col_* 系列函数指定列的类型
         locale = default_locale(), 
         na = c("", "NA"), 
         comment = "",
         trim_ws = TRUE, #去除空白
         skip = 0, 
         n_max = -1, 
         progress = interactive())

require(readr)

## Loading required package: readr

dr1 <- datasource("a,b,c\n1,2,3")
dr2 <- datasource(system.file("extdata/mtcars.csv", package = "readr"))
head(read_csv(dr1),n = 2)

##   a b c
## 1 a b c
## 2 1 2 3

head(read_csv(dr2),n=2)

##   mpg cyl disp  hp drat   wt  qsec vs am gear carb
## 1 mpg cyl disp  hp drat   wt  qsec vs am gear carb
## 2  21   6  160 110  3.9 2.62 16.46  0  1    4    4

require(readr)
dr1 <- datasource("2015-01-02,1,a\n2015-02-03,2,b")
dat <- read_csv(dr1,col_names = FALSE,
                col_types = list(col_date(),
                              col_integer(),
                              col_character()))
dat$X1[1]-dat$X1[2]

## Time difference of -32 days

方便地读取Excel数据

能够快速地读取数据，数据为data.table格式的数据，可以快速地筛选处理。需要注意其[操作符。

require(data.table)
DT = data.table(x=rep(c("a","b","c"),each=3), y=c(1,3,6), v=1:9)
DT[y %between% c(3,6)] # y在3-6之间的行
DT[2] # 第二行
DT[,v] # v列
DT[,list(v)] # v列，注意和DT[,v]的区别
DT[2:3,sum(v)] # 第2-3行的v列求和
DT[2:5,cat(v,"\n")]  #
DT[c(FALSE,TRUE)] # 偶数行
DT[,2,with=FALSE] # 比较 DT[,2,with=TRUE]
setkey(DT,x) #  设置x列位key
DT['a'] #取出DT中x为a的行
DT[,sum(v),by=x]  # 按照x中值进行group
X = data.table(c("b","c"),foo=c(4,2))
X
DT[X] # 使用DT中的x进行join

• as.Date
• as.POSIXct
• xts以及相关的包

# cctCalls的列为count dateBin
cctCalls$dateBin <- as.POSIXct(x = cctCalls$dateBin)
full <- seq(from = as.POSIXct('2015-01-01'),
            by='1 hour',
            to = as.POSIXct('2016-05-30'))
cctCalls2 <- data.frame(Date=full, 
                         value=with(cctCalls, count[match(full, dateBin)]))


rownames(cctCalls2) <- cctCalls2$dateBin
cctCalls2 <- cctCalls2[,1:2]
cctCalls2TS <- as.xts(as.matrix(cctCalls2))