- data.table
https://github.com/Rdatatable/data.table
https://www.rstudio.com/resources/cheatsheets/
知乎 | 张敬信 | 【R语言新书】2.7 数据处理神器：data.table包
知乎 | 老俊俊 | data.table 让你高效快速地处理数据
“data.table 高度抽象的语法无疑增加了学习成本，但它的高效性能和处理大数据能力，使得非常有必要学习它。当然，读者如果既想要 data.table 的高性能，又想要 tidyverse 的整洁语法，也可以借助一些衔接二者的中间包，如 dtplyr, tidyfst 等。”
dtplyr
https://github.com/tidyverse/dtplyr
https://dtplyr.tidyverse.org/tidyfst
https://github.com/hope-data-science/tidyfst
知乎 | 黄天元 | R语言高效数据框操作：tidyfst （注意有专栏）
知乎 | 黄小伟 | R最快且比dplyr最高效的大数据处理R包：tidyfst
创建
-
data.table(): as.data.table()
读取
-
fread("file.csv")
select = c("a", "b")): 读取指定的列
写出
fwrite(dt, "file.csv")
行操作
dt[1:2,]dt[a > 5,]dt[, c := 1:.N, by = b]dt[, c := shift(a, 1), by = b]dt[, c := shift(a, 1, type = "lead"), by = b]
>、<
>=<=
is.na()
!is.na()
%in%
|、&、!
%like%
%between%
列操作
dt[, c(2)]dt[, .(b, c)]dt[, .(x = sum(a))]dt[, c := 1+2]-
dt[,`:=`(c = 1, d = 2)] dt[, c := NULL]dt[, b := as.integer(b)]dt[, lapply(.SD, mean). SDcols = c("a", "b")]-
cols <- c("a")
dt[, paste0(cols , "_m") := lapply(.SD, mean)]
分组
dt[, j, by = .(a)]dt[, j, keyby = .(a)]dt[, .(c = sum(b)), by = a)]dt[, c := sum(b), by = a]dt[, .SD[1], by = a]dt[, .SD[.N], by = a]
dt[...][...]
函数
setorder(dt, a, -b)-
unique[dt, by = c("a", "b")]: 去重 -
uniqueN(dt, by = c("a", "b")): 计数 -
setnames(dt, c("a", "b"), c("x", "y")): 重命名 setkey(dt, a, b)
data.table中 以set为前缀的函数和操作符:=不需要<-就可以改变数据。
例如，setDT(df)等同于df < - as.data.table(df)。
合并
dt_a[dt_b, on = .(b = y)]dt_a[dt_b, on = .(b = y, c > z)]rbind(dt_a, dt_b)cbind(dt_a, dt_b)
重塑
-
dcast(): 长变宽 -
melt(): 宽变长
dcast(dt,
id - y,
value.var = c("a", "b"))
melt(dt,
id.vars = c("id"),
measure.vars = patterns("^a", "^b"),
variable.name = "y",
value.name = c("a", "b"))
