如何将数组转换为R中的data.table并返回? [英] How to convert array to data.table in R and back?
问题描述
这是将数组转换为数据表的最直接方法吗?
require(data.table)
require(ggplot2)
# this returns a data.table with both array's dimensions and values
aaa <- array(rnorm(3*4*2), dim = c(3,4,2))
DT1 <- as.data.table(as.data.frame.table(aaa))
# the following does not work properly, because it only returns the array values
DT2 <- as.data.table(aaa)
# plot values aggregated by 3rd array dim
ggplot(DT1, aes(Var1, Freq, fill = Var3)) + geom_boxplot()
# sum values by 2nd array dim
DT1[ , sum(Freq), Var2]
EDIT1:
对不起,正确的意思是我得到的数据帧只有一列,因此我不知道值是从原始数组的哪个位置产生的。
的想法是将数组转换为平面表,这样更容易例如使用维度作为因子绘制变量,或按因子汇总值。
sorry, with "properly" I mean that I get a data frame with one column only, so that I don't know from which position in the original array a values has originated. The idea is to transform the array into a flat table, so that is easier to e.g. plot the variables using the dimensions as factors, or to aggregate values by factors. Would that be still possible with DT2?
EDIT2:
另一个有用的事情是将 data.table转换回原始数组。您是否知道通过定义用作维的列来将data.table强制转换为数组的函数?
one other useful thing would be to convert the data.table back into the original array. Do you know a function that coerces data.table to array, by defining which columns to use as dimensions?
aaa <- array(rnorm(3*4*2), dim = c(3,4,2), list(Var1 = LETTERS[1:3], Var2 = LETTERS[1:4], Var3 = LETTERS[1:2] ))
DT1 <- setDT(melt(aaa))
# convert DT1 back to aaa
array(data = DT1[ ,value],
dim = c(length(unique(DT1[ ,Var1])),
length(unique(DT1[ ,Var2])),
length(unique(DT1[ ,Var3]))),
dimnames = list(Var1 = unique(DT1[ ,Var1]),
Var2 = unique(DT1[ ,Var2]),
Var3 = unique(DT1[ ,Var3])))
谢谢!
推荐答案
仅适用于1.11.4和1.11.2版本,但不适用于某些较早的版本
返回相同的数据表,但 A = 1
, B = 2
, C = 3
以及行以不同的方式排序。因此,第二种方法是解决方法。
both approaches essentially return the same data.table but with A=1
, B=2
, C=3
in your second approach, and rows ordered in different ways. so the second approach is the way to go.
DT2 <- as.data.table(aaa)
head(DT2)
# V1 V2 V3 value
#1: 1 1 1 0.32337516
#2: 1 1 2 1.59189589
#3: 1 2 1 -1.48751756
#4: 1 2 2 -0.86749305
#5: 1 3 1 0.01017255
#6: 1 3 2 2.66571093
#compare
DT[order(Freq), ]
#and
DT2[order(value), ]
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