使用“lapply”对数据框 (R) 中的所有列进行缩排序

我正在尝试应用Winsorize()函数使用lapply来自library(DescTools)包裹。我目前拥有的是；

data$col1 <- Winsorize(data$col1)

这本质上用基于分位数的值替换了极值，替换了以下数据：

> data$col1
 [1]   -0.06775798   **-0.55213508**   -0.12338265
 [4]    0.04928349    **0.47524313**    0.04782829
 [7]   -0.05070639 **-112.67126382**    0.12657896
[10]   -0.12886632

> Winsorize(data$col1)
 [1] -0.06775798 **-0.37884540** -0.12338265  0.04928349
 [5]  **0.26038103**  0.04782829 -0.05070639 **-0.37884540**
 [9]  0.12657896 -0.12886632

我有一个for loop它可以在 data.frame 的所有列上执行此操作col1, col2, col3, col4，不过，我知道lapply是一个更好的选择，所以我尝试将其合并到lapply功能但似乎无法让它工作。如果有人能指出我正确的方向，我将不胜感激。

数据;

data <- structure(list(EQ.TA = c(-0.0677579847115102, -0.552135083517749, 
-0.123382654164705, 0.0492834931482554, 0.475243125304193, 0.0478282913638668, 
-0.050706389027946, -112.671263815473, 0.126578956975704, -0.128866322940619
), NI.EQ = c(3.64670235329765, 1.66115713369585, 0.209424623633739, 
0.340430636358184, -0.248411254566261, -12.1709277350516, 1.06888235737433, 
0.0515582237132515, 0.177323118521857, 0.419879195374698), NI.TA = c(-0.24709320230217, 
-0.917183132749265, -0.0258393659113752, 0.0167776109344148, 
-0.118055740980805, -0.582114677880617, -0.0541991646381309, 
-5.80913022585296, 0.0224453753901758, -0.0541082879872031), 
    TL.TA = c(1.06775798471151, 1.55213508351775, 1.12338265416471, 
    0.950716506851745, 0.524756874695807, 0.952171708636133, 
    1.05070638902795, 113.671263815473, 0.873421043024296, 1.12886632294062
    )), .Names = c("EQ.TA", "NI.EQ", "NI.TA", "TL.TA"), row.names = c(NA, 
10L), class = "data.frame")

You can lapply整体上data.frame并重新分配它，如下所示：

library(DescTools)
data[]<-lapply(data, Winsorize)

data
#          EQ.TA       NI.EQ       NI.TA      TL.TA
#1   -0.06775798  2.75320700 -0.24709320  1.0677580
#2   -0.55213508  1.66115713 -0.91718313  1.5521351
#3   -0.12338265  0.20942462 -0.02583937  1.1233827
#4    0.04928349  0.34043064  0.01677761  0.9507165
#5    0.31834425 -0.24841125 -0.11805574  0.6816558
#6    0.04782829 -6.80579532 -0.58211468  0.9521717
#7   -0.05070639  1.06888236 -0.05419916  1.0507064
#8  -62.21765589  0.05155822 -3.60775403 63.2176559
#9    0.12657896  0.17732312  0.01989488  0.8734210
#10  -0.12886632  0.41987920 -0.05410829  1.1288663