R：如何讀取帶有data.table :: fread的CSV文件，其逗號為十進制，並指向千位分隔符=“。”

Question

我有幾個CSV文件，其中包含本地德語風格的數字，即逗號作為小數分隔符，點數為千分隔符，例如10.380,45。 CSV文件中的值以“;”分隔。 這些文件還包含類字符，日期，日期和時間以及邏輯的列。

read.table函數的問題是，您可以使用dec =“，”指定小數分隔符，但不能指定千位分隔符。 （如果我錯了，請糾正我）

我知道預處理是一種解決方法，但我想以某種方式編寫代碼，其他人可以在沒有我的情況下使用它。

通過設置我自己的類，我找到了一種通過read.csv2以我想要的方式讀取CSV文件的方法，如下例所示。 基於最優雅的方式加載csv點與R中的千位分隔符

# Create test example
df_test_write <- cbind.data.frame(c("a","b","c","d","e","f","g","h","i","j",rep("k",times=200)),
                            c("5.200,39","250,36","1.000.258,25","3,58","5,55","10.550,00","10.333,00","80,33","20.500.000,00","10,00",rep("3.133,33",times=200)),
                            c("25.03.2015","28.04.2015","03.05.2016","08.08.2016","08.08.2016","08.08.2016","08.08.2016","08.08.2016","08.08.2016","08.08.2016",rep("08.08.2016",times=200)),
                            stringsAsFactors=FALSE)
colnames(df_test_write) <- c("col_text","col_num","col_date")

# write test csv
write.csv2(df_test_write,file="Test.csv",quote=FALSE,row.names=FALSE)

#### read with read.csv2 ####

# First, define your own class

#define your own numeric class
setClass('myNum')
#define conversion
setAs("character","myNum", function(from) as.numeric(gsub(",","\\.",gsub("\\.","",from))))

# own date class
library(lubridate)
setClass('myDate')
setAs("character","myDate",function(from) dmy(from))

# Read the csv file, in colClasses the columns class can be defined
df_test_readcsv <- read.csv2(paste0(getwd(),"/Test.csv"),
                       stringsAsFactors = FALSE,
                       colClasses = c(
                         col_text = "character",
                         col_num = "myNum",
                         col_date = "myDate"
                       )
                )

我現在的問題是，不同的數據集最多有200列和350000行。 使用上層解決方案，我需要40到60秒才能加載一個CSV文件，我想加快速度。

通過我的研究，我從data.table包中找到了fread() ，這非常快。 加載CSV文件大約需要3到5秒。

不幸的是，也沒有可能指定千分隔符。 所以我嘗試使用我的解決方案與colClasses，但似乎有問題，你不能使用個人類與fread https://github.com/Rdatatable/data.table/issues/491

另見我的以下測試代碼：

##### read with fread ####
library(data.table)

# Test without colclasses
df_test_readfread1 <- fread(paste0(getwd(),"/Test.csv"),
                            stringsAsFactors = FALSE,
                            dec = ",",
                            sep=";",
                            verbose=TRUE)
str(df_test_readfread1)

# PROBLEM: In my real dataset it turns the number into an numeric column, 
# unforunately it sees the "." as decimal separator, so it turns e.g. 10.550, 
# into 10.5
# Here it keeps everything as character

# Test with colclasses
df_test_readfread2 <- fread(paste0(getwd(),"/Test.csv"),
                            stringsAsFactors = FALSE,
                            colClasses = c(
                              col_text = "character",
                              col_num = "myNum",
                              col_date = "myDate"
                            ),
                            sep=";",
                            verbose=TRUE)
str(df_test_readfread2)

# Keeps everything as character

所以我的問題是：有沒有辦法讀取數字值為10.380,45與fread的CSV文件？

（或者：讀取具有此類數值的CSV的最快方法是什么？）

Answer 1

首先刪除所有逗號。

filepath<-paste0(getwd(),"/Test.csv")
filestring<-readChar(filepath, file.info(filepath)$size)
filestring<-gsub('.','',filestring,fixed=TRUE)
fread(filestring)

Answer 2

我自己從未使用過包裝，但它來自Hadley Wickham，應該是好東西

https://cran.r-project.org/web/packages/readr/readr.pdf

它應該處理語言環境：

locale(date_names = "en", date_format = "%AD", time_format = "%AT", decimal_mark = ".", grouping_mark = ",", tz = "UTC", encoding = "UTF-8", asciify = FALSE)

decimal_mark和grouping_mark就是你要找的東西

編輯形式PhiSeu：解決方案

感謝您的建議，這里有兩個解決方案，其中read_csv2() readr包中的read_csv2() 。 對於我的350000行CSV文件，大約需要8秒，這比read.csv2解決方案快得多。 （來自hadley和RStudio的另一個有用的軟件包，謝謝）

library(readr)

# solution 1 with specified columns
df_test_readr <- read_csv2(paste0(getwd(),"/Test.csv"),
                           locale = locale("de"),
                           col_names = TRUE,
                           cols(
                             col_text = col_character(),
                             col_num = col_number(), # number is automatically regcognized through locale=("de")
                             col_date2 = col_date(format ="%d.%m.%Y") # Date specification
                           )
                           )

# solution 2 with overall definition of date format
df_test_readr <- read_csv2(paste0(getwd(),"/Test.csv"),
                           locale = locale("de",date_format = "%d.%m.%Y"), # specifies the date format for the whole file
                           col_names = TRUE
)