按r分组和缩放/归一化r中的列 [英] group by and scale/normalize a column in r

问题描述

我有一个数据框如下所示：

I have a dataframe that looks like this:

  Store Temperature Unemployment Sum_Sales
1     1       42.31        8.106   1643691
2     1       38.51        8.106   1641957
3     1       39.93        8.106   1611968
4     1       46.63        8.106   1409728
5     1       46.50        8.106   1554807
6     1       57.79        8.106   1439542

我不知道在R中是如何分组和申请。所以对于每个商店（分组），我想要归一化/缩放两列（sum_sales和temperature）。

What I can't figure out in R is how to group by and apply. So for each store (grouped), I want to normalize/scale two columns (sum_sales and temperature).

我想要的输出如下：

  Store Temperature Unemployment Sum_Sales
1     1       1.000        8.106   1.00000
2     1       0.000        8.106   0.94533
3     1       0.374        8.106   0.00000
4     2       0.012        8.106   0.00000
5     2       0.000        8.106   1.00000
6     2       1.000        8.106   0.20550

这是我创建的规范化功能：

Here is the normalizing function that I created:

 normalit<-function(m){
   (m - min(m))/(max(m)-min(m))
 }

我正在使用dply包，似乎无法弄清楚将该功能分组并应用于列。我试过这样的东西，并得到一个错误：

I'm using the dply package and can't seem to figure out how to group by and apply that function to a column. I tried something like this and get an error:

df2 <- df %.%
  group_by('Store') %.%
  summarise(Temperature = normalit(Temperature), Sum_Sales = normalit(Sum_Sales)))

任何建议/帮助将不胜感激。谢谢。

Any suggestions/help would be greatly appreciated. Thanks.

推荐答案

问题是你使用错误的dplyr动词。总结将为每个变量创建一个结果。你想要的是突变mutate更改变量并返回与原始长度相同的结果。请参阅 http://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html 。以下两种方法使用dplyr：

The issue is that you are using the wrong dplyr verb. Summarize will create one result per group per variable. What you want is mutate. Mutate changes variables and returns a result of the same length as the original. See http://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html. Below two approaches using dplyr:

df %>%
    group_by(Store) %>%
    mutate(Temperature = normalit(Temperature), Sum_Sales = normalit(Sum_Sales))

df %>%
    group_by(Store) %>%
    mutate_each(funs(normalit), Temperature, Sum_Sales)

注意：Store变量在您的数据和所需结果之间是不同的。我假设@jlhoward得到了正确的数据。

Note: The Store variable is different between your data and desired result. I assumed that @jlhoward got the right data.

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