背景：文字資料欄位太多，只需要讀取有用欄位，以此降低記憶體壓力方便分析

方法一：read.table、read.csv

       直接使用自帶函式設定適當的引數（colClasses），該引數需要指定每列資料的型別（可以使用nrows引數讀取幾行檢視class），不需要的列指定為NULL即可。如下：

<span style="font-family:Courier New;font-size:14px;">dat <- structure(list(Year = 2009:2011, Jan = c(-41L, -41L, -21L), 
                      Feb = c(-27L, -27L, -27L), Mar = c(-25L, -25L, -2L), 
                      Apr = c(-31L, -31L, -6L), May = c(-31L, -31L, -10L), 
                      Jun = c(-39L, -39L, -32L), Jul = c(-25L, -25L, -13L), 
                      Aug = c(-15L, -15L, -12L), Sep = c(-30L, -30L, -27L), 
                      Oct = c(-27L, -27L, -30L), Nov = c(-21L, -21L, -38L), 
                      Dec = c(-25L, -25L, -29L)), 
                 .Names = c("Year", "Jan", "Feb", "Mar", "Apr", "May", "Jun", 
                            "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"), 
                 class = "data.frame",
                 row.names = c(NA, -3L))
write.table(dat, "test.txt", row.names=FALSE)

## 檢視每個列的class
df <- read.table("test.txt", nrow=2, header = TRUE)
apply(df, 2, class)
# Year       Jan       Feb       Mar       Apr       May       Jun       Jul       Aug       Sep       Oct       Nov       Dec 
# "integer" "integer" "integer" "integer" "integer" "integer" "integer" "integer" "integer" "integer" "integer" "integer" "integer"

df <- read.table("test.txt", 
                 colClasses = c(rep("integer", 7), rep("NULL", 6)), 
                 header = TRUE)
# > df
# Year Jan Feb Mar Apr May Jun
# 1 2009 -41 -27 -25 -31 -31 -39
# 2 2010 -41 -27 -25 -31 -31 -39
# 3 2011 -21 -27  -2  -6 -10 -32

write.csv(dat, "test.csv", row.names=FALSE)
df <- read.csv("test.csv", 
               colClasses = c(rep("integer", 7), rep("NULL", 6)), 
               header = TRUE)
# > df
# Year Jan Feb Mar Apr May Jun
# 1 2009 -41 -27 -25 -31 -31 -39
# 2 2010 -41 -27 -25 -31 -31 -39
# 3 2011 -21 -27  -2  -6 -10 -32 </span>
 

沒有安裝成功，好像也不怎麼支援了

方法三：使用package資料庫功能輔助（RJDBC）

實際就是用Java來解決這個問題，太複雜沒有去實現

<span style="font-family:Courier New;">library(RJDBC)
write.table(x=mtcars, file="mtcars.csv", sep=",", row.names=F, col.names=T)

path.to.jdbc.driver <- "jdbc//csvjdbc-1.0-18.jar"
drv <- JDBC("org.relique.jdbc.csv.CsvDriver", path.to.jdbc.driver)
conn <- dbConnect(drv, sprintf("jdbc:relique:csv:%s", getwd()))

head(dbGetQuery(conn, "select * from mtcars"), 3)
# mpg cyl disp  hp drat    wt  qsec vs am gear carb
# 1   21   6  160 110  3.9  2.62 16.46  0  1    4    4
# 2   21   6  160 110  3.9 2.875 17.02  0  1    4    4
# 3 22.8   4  108  93 3.85  2.32 18.61  1  1    4    1

head(dbGetQuery(conn, "select mpg, gear from mtcars"), 3)</span>
 

方法四：藉助linux工具命令實現

快速，方便；需要熟悉awk，cut的語法

cut功能比較單一適合處理分割整齊的資料，而awk的功能更加強大（awk使用1，awk使用2）

<span style="font-family:Courier New;font-size:14px;">dat <- structure(list(Year = 2009:2011, Jan = c(-41L, -41L, -21L), 
                      Feb = c(-27L, -27L, -27L), Mar = c(-25L, -25L, -2L), 
                      Apr = c(-31L, -31L, -6L), May = c(-31L, -31L, -10L), 
                      Jun = c(-39L, -39L, -32L), Jul = c(-25L, -25L, -13L), 
                      Aug = c(-15L, -15L, -12L), Sep = c(-30L, -30L, -27L), 
                      Oct = c(-27L, -27L, -30L), Nov = c(-21L, -21L, -38L), 
                      Dec = c(-25L, -25L, -29L)), 
                 .Names = c("Year", "Jan", "Feb", "Mar", "Apr", "May", "Jun", 
                            "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"), 
                 class = "data.frame",
                 row.names = c(NA, -3L))
# 使用製表符分割
write.table(dat, "test.txt", row.names=FALSE, sep = "\t")
df <- read.table(pipe("cut -f 1,5 test.txt"), header=TRUE)
df
system("cut -f 1,5 test.txt")
# Year Apr
# 1 2009 -31
# 2 2010 -31
# 3 2011  -6

# 使用空格分割
write.table(dat, "test.txt", row.names=FALSE, sep = " ")
df <- read.table(pipe("cut -d ' ' -f 1,5 test.txt"), header=TRUE)
df
system("cut -d ' ' -f 1,5 test.txt")
# Year Apr
# 1 2009 -31
# 2 2010 -31
# 3 2011  -6</span>
 

（1）資料量不是很大且對資料內容瞭解時，可以使用read.table指定合理的引數colClasses讀取。

（2）資料較大且要求速度建議藉助linux下的資料處理工具