@BBKim给出了最好的答案，但它可以做得更短。我很惊讶居然还没人想到。

<- data.frame(x = rnorm(10,30， .2)， y = runif(10,3,5)) 应用(dat, 2, function(x) (x - mean(x)) / sd(x))

再说一次，尽管这是一个老问题，但它非常相关!我发现了一个简单的方法来规范化某些列，而不需要任何包:

normFunc <- function(x){(x-mean(x, na.rm = T))/sd(x, na.rm = T)}

例如

x<-rnorm(10,14,2)
y<-rnorm(10,7,3)
z<-rnorm(10,18,5)
df<-data.frame(x,y,z)

df[2:3] <- apply(df[2:3], 2, normFunc)

您将看到y和z列已经规范化。不需要软件包:-)

我假设你想要的是均值为0，标准差为1。如果你的数据在一个数据框架中，所有的列都是数值的，你可以简单地调用数据上的缩放函数来做你想做的事情。

dat <- data.frame(x = rnorm(10, 30, .2), y = runif(10, 3, 5))
scaled.dat <- scale(dat)

# check that we get mean of 0 and sd of 1
colMeans(scaled.dat)  # faster version of apply(scaled.dat, 2, mean)
apply(scaled.dat, 2, sd)

使用内置函数是有品位的。比如这只猫:

'插入'包提供了预处理数据的方法(例如居中和缩放)。你也可以使用下面的代码:

library(caret)
# Assuming goal class is column 10
preObj <- preProcess(data[, -10], method=c("center", "scale"))
newData <- predict(preObj, data[, -10])

详情:http://www.inside-r.org/node/86978

意识到这个问题已经很老了，一个答案已经被接受了，我将提供另一个答案供参考。

缩放是有限的，因为它缩放所有变量。下面的解决方案只允许缩放特定的变量名，同时保持其他变量不变(变量名可以动态生成):

library(dplyr)

set.seed(1234)
dat <- data.frame(x = rnorm(10, 30, .2), 
                  y = runif(10, 3, 5),
                  z = runif(10, 10, 20))
dat

dat2 <- dat %>% mutate_at(c("y", "z"), ~(scale(.) %>% as.vector))
dat2

这给了我这个:

> dat
          x        y        z
1  29.75859 3.633225 14.56091
2  30.05549 3.605387 12.65187
3  30.21689 3.318092 13.04672
4  29.53086 3.079992 15.07307
5  30.08582 3.437599 11.81096
6  30.10121 4.621197 17.59671
7  29.88505 4.051395 12.01248
8  29.89067 4.829316 12.58810
9  29.88711 4.662690 19.92150
10 29.82199 3.091541 18.07352

and

> dat2 <- dat %>% mutate_at(c("y", "z"), ~(scale(.) %>% as.vector))
> dat2
          x          y           z
1  29.75859 -0.3004815 -0.06016029
2  30.05549 -0.3423437 -0.72529604
3  30.21689 -0.7743696 -0.58772361
4  29.53086 -1.1324181  0.11828039
5  30.08582 -0.5946582 -1.01827752
6  30.10121  1.1852038  0.99754666
7  29.88505  0.3283513 -0.94806607
8  29.89067  1.4981677 -0.74751378
9  29.88711  1.2475998  1.80753470
10 29.82199 -1.1150515  1.16367556

编辑1(2016):处理朱利安的评论:规模的输出是Nx1矩阵，所以理想情况下，我们应该添加一个as。向量，将矩阵类型转换回向量类型。谢谢朱利安!

编辑2(2019):引用杜乔·A。对于最新的dplyr(0.8版本)，您需要更改dplyr::funcs with list，如dat %>% mutate_each_(list(~scale(.) %>% as.vector)， vars=c("y"，"z"))

EDIT 3(2020):感谢@mj_whales:旧的解决方案已弃用，现在我们需要使用mutate_at。

BBMisc包中的normalize函数对我来说是合适的工具，因为它可以处理NA值。

下面是如何使用它:

给定以下数据集，

    ASR_API     <- c("CV",  "F",    "IER",  "LS-c", "LS-o")
    Human       <- c(NA,    5.8,    12.7,   NA, NA)
    Google      <- c(23.2,  24.2,   16.6,   12.1,   28.8)
    GoogleCloud <- c(23.3,  26.3,   18.3,   12.3,   27.3)
    IBM     <- c(21.8,  47.6,   24.0,   9.8,    25.3)
    Microsoft   <- c(29.1,  28.1,   23.1,   18.8,   35.9)
    Speechmatics    <- c(19.1,  38.4,   21.4,   7.3,    19.4)
    Wit_ai      <- c(35.6,  54.2,   37.4,   19.2,   41.7)
    dt     <- data.table(ASR_API,Human, Google, GoogleCloud, IBM, Microsoft, Speechmatics, Wit_ai)
> dt
   ASR_API Human Google GoogleCloud  IBM Microsoft Speechmatics Wit_ai
1:      CV    NA   23.2        23.3 21.8      29.1         19.1   35.6
2:       F   5.8   24.2        26.3 47.6      28.1         38.4   54.2
3:     IER  12.7   16.6        18.3 24.0      23.1         21.4   37.4
4:    LS-c    NA   12.1        12.3  9.8      18.8          7.3   19.2
5:    LS-o    NA   28.8        27.3 25.3      35.9         19.4   41.7

规范化的值可以这样得到:

> dtn <- normalize(dt, method = "standardize", range = c(0, 1), margin = 1L, on.constant = "quiet")
> dtn
   ASR_API      Human     Google GoogleCloud         IBM  Microsoft Speechmatics      Wit_ai
1:      CV         NA  0.3361245   0.2893457 -0.28468670  0.3247336  -0.18127203 -0.16032655
2:       F -0.7071068  0.4875320   0.7715885  1.59862532  0.1700986   1.55068347  1.31594762
3:     IER  0.7071068 -0.6631646  -0.5143923 -0.12409420 -0.6030768   0.02512682 -0.01746131
4:    LS-c         NA -1.3444981  -1.4788780 -1.16064578 -1.2680075  -1.24018782 -1.46198764
5:    LS-o         NA  1.1840062   0.9323361 -0.02919864  1.3762521  -0.15435044  0.32382788

手工计算方法忽略包含NAs的列:

> dt %>% mutate(normalizedHuman = (Human - mean(Human))/sd(Human)) %>% 
+ mutate(normalizedGoogle = (Google - mean(Google))/sd(Google)) %>% 
+ mutate(normalizedGoogleCloud = (GoogleCloud - mean(GoogleCloud))/sd(GoogleCloud)) %>% 
+ mutate(normalizedIBM = (IBM - mean(IBM))/sd(IBM)) %>% 
+ mutate(normalizedMicrosoft = (Microsoft - mean(Microsoft))/sd(Microsoft)) %>% 
+ mutate(normalizedSpeechmatics = (Speechmatics - mean(Speechmatics))/sd(Speechmatics)) %>% 
+ mutate(normalizedWit_ai = (Wit_ai - mean(Wit_ai))/sd(Wit_ai))
  ASR_API Human Google GoogleCloud  IBM Microsoft Speechmatics Wit_ai normalizedHuman normalizedGoogle
1      CV    NA   23.2        23.3 21.8      29.1         19.1   35.6              NA        0.3361245
2       F   5.8   24.2        26.3 47.6      28.1         38.4   54.2              NA        0.4875320
3     IER  12.7   16.6        18.3 24.0      23.1         21.4   37.4              NA       -0.6631646
4    LS-c    NA   12.1        12.3  9.8      18.8          7.3   19.2              NA       -1.3444981
5    LS-o    NA   28.8        27.3 25.3      35.9         19.4   41.7              NA        1.1840062
  normalizedGoogleCloud normalizedIBM normalizedMicrosoft normalizedSpeechmatics normalizedWit_ai
1             0.2893457   -0.28468670           0.3247336            -0.18127203      -0.16032655
2             0.7715885    1.59862532           0.1700986             1.55068347       1.31594762
3            -0.5143923   -0.12409420          -0.6030768             0.02512682      -0.01746131
4            -1.4788780   -1.16064578          -1.2680075            -1.24018782      -1.46198764
5             0.9323361   -0.02919864           1.3762521            -0.15435044       0.32382788

(normalizedHuman是一个NAs列表…)

对于计算具体列的选择，可以采用通用的方法，如下所示:

data_vars <- df_full %>% dplyr::select(-ASR_API,-otherVarNotToBeUsed)
meta_vars <- df_full %>% dplyr::select(ASR_API,otherVarNotToBeUsed)
data_varsn <- normalize(data_vars, method = "standardize", range = c(0, 1), margin = 1L, on.constant = "quiet")
dtn <- cbind(meta_vars,data_varsn)