Question

我的数据包含numeric和非numeric列，如下所示：

mydt
          vnum1 vint1 vfac1 vch1
 1: -0.30159484     8     3    E
 2: -0.09833430     8     1    D
 3: -2.15963282     1     3    D
 4:  0.03904374     5     2    B
 5:  1.54928970     4     1    C
 6: -0.73873654     5     1    A
 7: -0.68594479     9     2    B
 8:  1.35765612     1     2    E
 9:  1.46958351     2     1    B
10: -0.89623979     2     4    E

如何仅选择numeric列并使用mean计算data.table？

我试过这个：

mydt[ , lapply(.SD, mean), ]
#        vnum1 vint1 vfac1 vch1
# 1: -0.046491   4.5    NA   NA
# Warning messages:
# 1: In mean.default(X[[3L]], ...) :
#  argument is not numeric or logical: returning NA
# 2: In mean.default(X[[4L]], ...) :
#  argument is not numeric or logical: returning NA


dput(mydt)
structure(list(vnum1 = c(-0.301594844692861, -0.0983343040483769, 
-2.15963282153076, 0.03904374068617, 1.54928969700272, -0.738736535236348, 
-0.685944791146016, 1.35765612481877, 1.46958350568506, -0.896239790653183
), vint1 = c(8L, 8L, 1L, 5L, 4L, 5L, 9L, 1L, 2L, 2L), vfac1 = structure(c(3L, 
1L, 3L, 2L, 1L, 1L, 2L, 2L, 1L, 4L), .Label = c("1", "2", "3", 
"4"), class = "factor"), vch1 = structure(c(5L, 4L, 4L, 2L, 3L, 
1L, 2L, 5L, 2L, 5L), .Label = c("A", "B", "C", "D", "E"), class = "factor")), .Names = c("vnum1", 
"vint1", "vfac1", "vch1"), class = c("data.table", "data.frame"
), row.names = c(NA, -10L), .internal.selfref = <pointer: 0x991c070>)

我也尝试了以下方法，但它不起作用：

 mydt[ , lapply(.SD, is.numeric),]
 #    vnum1 vint1 vfac1  vch1
 # 1:  TRUE  TRUE FALSE FALSE

mydt[,mydt[,lapply(.SD, is.numeric),]]
#    vnum1 vint1 vfac1  vch1
# 1:  TRUE  TRUE FALSE FALSE

mydt[ , mydt[ , lapply(.SD, is.numeric) , ], with = F]
# Error in Math.data.frame(j) : 
#  non-numeric variable in data frame: vnum1vint1vfac1vch1

mydt[ , c(mydt[ , lapply(.SD, is.numeric)), ], with = F]
# Error: unexpected ')' in "mydt[,c(mydt[,lapply(.SD, is.numeric))"

正如@Arun所建议的那样，我尝试过以下但无法获得一个子集：

 xx <- mydt[ , lapply(.SD, is.numeric), ]
 xx
 #    vnum1 vint1 vfac1  vch1
 # 1:  TRUE  TRUE FALSE FALSE

 mydt[ , lapply(.SD, mean), .SDcols = xx]
 # Error in `[.data.table`(mydt, , lapply(.SD, mean), .SDcols = xx) : 
 # .SDcols should be column numbers or names

根据@David的建议，我尝试了以下内容，但非数字列的值为NULL。我想获得mydt的子集，以便甚至不列出其他列。

mydt[ , lapply(.SD, function(x) if(is.numeric(x)) mean(x))]
#       vnum1 vint1 vfac1 vch1
# 1: -0.046491   4.5  NULL NULL

我缺少data.frame：

sapply(mydf, is.numeric)
# vnum1 vint1 vfac1  vch1 
#  TRUE  TRUE FALSE FALSE 

mydf[sapply(mydf, is.numeric)]
#         vnum1 vint1
#1  -0.30159484     8
#2  -0.09833430     8
#3  -2.15963282     1
#4   0.03904374     5
#5   1.54928970     4
#6  -0.73873654     5
#7  -0.68594479     9
#8   1.35765612     1
#9   1.46958351     2
#10 -0.89623979     2


sapply(mydf[sapply(mydf, is.numeric)], mean)
#    vnum1     vint1 
#-0.046491  4.500000

行。感谢David的评论，以下作品：

mydt[, sapply(mydt, is.numeric), with = FALSE][,sapply(.SD, mean),]
#    vnum1     vint1 
# -0.046491  4.500000 

mydt[, sapply(mydt, is.numeric), with = FALSE]
#          vnum1 vint1
# 1: -0.30159484     8
# 2: -0.09833430     8
# 3: -2.15963282     1
# 4:  0.03904374     5
# ...

Answer 1

在SO上搜索.SDcols，我登上this answer，我认为很好地解释了如何使用它。

cols = sapply(mydt, is.numeric)
cols = names(cols)[cols]
mydt[, lapply(.SD, mean), .SDcols = cols]
#        vnum1 vint1
# 1: -0.046491   4.5

执行mydt[, sapply(mydt, is.numeric), with = FALSE]并不是那么有效，因为它子集你的data.table与那些列并且会产生（深层）副本 - 不必要地使用更多内存。

使用colMeans将data.table强制转换为matrix，这对内存效率的影响不大。

Answer 2

您可以在一行中使用以下格式而无需使用 sapply：

mydt[, lapply(.SD, mean), .SDcols = is.numeric]

此外，如果您正在处理真实数据，那么您的数据很有可能 na 值。以下是 NA 情况下的工作方式：

mydt[, lapply(.SD, function(i) mean(i, na.rm = T)), .SDcols = is.numeric]

Answer 3

我遇到了同样的问题，下面的代码也可能有帮助。

data("mtcars")
mtcars$X1 <- factor(mtcars$gear, levels = c(4,3,5)); str(mtcars) #create an non numeric column X1
my.mean <- function(x){ if(is.numeric(x)) c(mean(x), median(x))} 
my.df <- setNames(as.data.frame(unlist(lapply(mtcars, FUN = my.mean))), "values"); my.df
my.df$names <- rep(c("mean","median"), times = length(row.names(my.df))/2); my.df
my.df$variables <-  rownames(my.df); my.df
library(stringr)
my.df$variables <- str_remove(my.df$variables, "[12]"); my.df 

data_wide <- spread(my.df,  names, values)
data_wide

> data_wide
   variables       mean  median
1         am   0.406250   0.000
2       carb   2.812500   2.000
3        cyl   6.187500   6.000
4       disp 230.721875 196.300
5       drat   3.596563   3.695
6       gear   3.687500   4.000
7         hp 146.687500 123.000
8        mpg  20.090625  19.200
9       qsec  17.848750  17.710
10        vs   0.437500   0.000
11        wt   3.217250   3.325

使用data.table计算所有数字列的摘要统计信息（例如均值）

3 个答案: