[英]R data.table - Apply function A to some columns and function B to some others
I want to aggregate datatable's row, but the aggragation function depends on the name of the column. 我想聚合数据表的行,但是aggragation函数取决于列的名称。
For example, if column name is: 例如,如果列名是:
variable1
or variable2
, then apply the mean()
function. variable1
或variable2
,然后应用mean()
函数。 variable3
, then apply the max()
function. variable3
,然后应用max()
函数。 variable4
, then apply the sd()
function. variable4
,然后应用sd()
函数。 My datatables always have a datetime
column: I want to aggregate rows by time. 我的数据表总是有一个
datetime
列:我想按时间聚合行。 However, the number of "data" column can vary. 但是,“数据”列的数量可以变化。
I know how to do that with the same aggregation function (eg mean()
) for all columns: 我知道如何使用相同的聚合函数(例如
mean()
)为所有列做到这一点:
dt <- dt[, lapply(.SD, mean),
by = .(datetime = floor_date(datetime, timeStep))]
Or for only a subset of columns: 或者仅针对列的子集:
cols <- c("variable1", "variable2")
dt <- dt[ ,(cols) := lapply(.SD, mean),
by = .(datetime = floor_date(datetime, timeStep)),
.SDcols = cols]
What I would like to do is something like: 我想做的是:
colsToMean <- c("variable1", "variable2")
colsToMax <- c("variable3")
colsToSd <- c("variable4")
dt <- dt[ ,{(colsToMean) := lapply(.SD???, mean),
(colsToMax) := lapply(.SD???, max),
(colsToSd) := lapply(.SD???, sd)},
by = .(datetime = floor_date(datetime, timeStep)),
.SDcols = (colsToMean, colsToMax, colsToSd)]
I looked at data.table in R - apply multiple functions to multiple columns which gave me the idea to use a custom function: 我查看了R中的data.table - 将多个函数应用到多个列 ,这让我有了使用自定义函数的想法:
myAggregate <- function(x, columnName) {
FUN = getAggregateFunction(columnName) # Return mean() or max() or sd()
return FUN(x)
}
dt <- dt[, lapply(.SD, myAggregate, ???columName???),
by = .(datetime = floor_date(datetime, timeStep))]
But I don't know how to pass the current column name to myAggregate()
... 但我不知道如何将当前列名传递给
myAggregate()
...
Here is one way to do it with Map
or mapply
: 以下是使用
Map
或mapply
执行此操作的一种方法:
Let's make some toy data first: 让我们先制作一些玩具数据:
dt <- data.table(
variable1 = rnorm(100),
variable2 = rnorm(100),
variable3 = rnorm(100),
variable4 = rnorm(100),
grp = sample(letters[1:5], 100, replace = T)
)
colsToMean <- c("variable1", "variable2")
colsToMax <- c("variable3")
colsToSd <- c("variable4")
Then, 然后,
scols <- list(colsToMean, colsToMax, colsToSd)
funs <- rep(c(mean, max, sd), lengths(scols))
# summary
dt[, Map(function(f, x) f(x), funs, .SD), by = grp, .SDcols = unlist(scols)]
# or replace the original values with summary statistics as in OP
dt[, unlist(scols) := Map(function(f, x) f(x), funs, .SD), by = grp, .SDcols = unlist(scols)]
Another option with GForce on: GForce的另一个选择:
scols <- list(colsToMean, colsToMax, colsToSd)
funs <- rep(c('mean', 'max', 'sd'), lengths(scols))
jexp <- paste0('list(', paste0(funs, '(', unlist(scols), ')', collapse = ', '), ')')
dt[, eval(parse(text = jexp)), by = grp, verbose = TRUE]
# Detected that j uses these columns: variable1,variable2,variable3,variable4
# Finding groups using forderv ... 0.000sec
# Finding group sizes from the positions (can be avoided to save RAM) ... 0.000sec
# Getting back original order ... 0.000sec
# lapply optimization is on, j unchanged as 'list(mean(variable1), mean(variable2), max(variable3), sd(variable4))'
# GForce optimized j to 'list(gmean(variable1), gmean(variable2), gmax(variable3), gsd(variable4))'
# Making each group and running j (GForce TRUE) ... 0.000sec
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