多个变量值可用于函数和绑定结果 [英] Multiple Variable Values to Function and Cbind Results

问题描述

这是此问题的继续: 具有现有For-loop by行的For-Loop按列

This is a continuation of this question here: For-Loop By Columns with existing For-loop by Rows

我有一个数据集，其中使用了3个变量: adstock_rate，diminishing_rate和lag_number . 目前，每个号码仅设置为1个号码.

I have a dataset in which I am using 3 variables: adstock_rate, diminishing_rate, and lag_number . These are currently set to only 1 number each.

当前我正在使用以下数字:

Currently I am using the following numbers:

adstock_rate<-0.5
lag_number<-1
diminishing_rate<-0.6

最终输出是使用以下函数将新列附加到现有数据集的数据集.

The final output is a dataset with new columns attached to the existing dataset using the function below.

foo1 <- function(dot, lag_val = 1) {
     tmp <- dot
     for(i in (1 + lag_val): length(tmp)) {
           tmp[i] <- tmp[i] + adstock_rate * diminishing_rate * tmp[i - lag_val]
     }
     return(tmp)
   }


advertising_dataset %>%
       group_by(Region) %>%
       mutate_all(funs(adstocked = foo1(., lag_val = lag_number)))

这就是我想要做的:

我想将此函数应用于这些变量的不同值.以下是这些变量的组合:

I want to apply this function to different values to these variables. Below are the combinations of these variables:

adstock_rate = c(0.50, 0.60, 0.70)
lag_number = c(0,1)
diminishing_rate = c(0.50, 0.60)

combos<-expand.grid(adstock_rate,lag_number,diminishing_rate)
colnames(combos)[1]<-"AdStock_Rate"
colnames(combos)[2]<-"Lag_Number"
colnames(combos)[3]<-"Diminish_Rate"


head(combos)

   AdStock_Rate Lag_Number Diminish_Rate
1           0.5          0           0.5
2           0.6          0           0.5
3           0.7          0           0.5
4           0.5          1           0.5
5           0.6          1           0.5
6           0.7          1           0.5
7           0.5          0           0.6
8           0.6          0           0.6
9           0.7          0           0.6
10          0.5          1           0.6

我认为您将必须进行for循环或使用apply函数来降低组合数据集中的行列表.

I think you would have to make a for-loop or use the apply function to go down the list of rows in the combos dataset.

这是我的尝试:

for(j in combos){
foo1 <- function(dot, lag_val = 1) {
     tmp <- dot
     for(i in (1 + lag_val): length(tmp)) {
           tmp[i] <- tmp[i] + combos[j,1] * combos[j,3] * tmp[i - lag_val]
     }
     return(tmp)
   }


advertising_dataset %>%
       group_by(Region) %>%
       mutate_all(funs(adstocked = foo1(., lag_val = combos[j,2])))

##cbind to previous output
}

我还需要列名称具有数字值，例如adstock_0.5_1_0.6，其中0.5 =广告存货率，1 =滞后数和递减= 0.6.

I also need the column names to have the number values such as adstock_0.5_1_0.6 where 0.5 = adstock rate, 1 = lag number, and diminishing = 0.6.

希望这是有道理的.

如果您需要我提供更多信息，请告诉我.

Please let me know if you need me to provide any more info.

谢谢！

推荐答案

当我们循环浏览'combos'的行时，创建一个list，其length与'combos的行数相同'，用于存储for循环的输出

As we are looping through the rows of 'combos', create a list that have the same length as the number of rows of the 'combos' for storing the output from the for loop

lst <- vector("list", nrow(combos)) # initialize a list to store output

在'foo1'中添加更多参数以提高灵活性

Add some more parameters in the 'foo1' for more flexibility

foo1 <- function(dot, lag_val = 1, combos, ind) {
     tmp <- dot
     for(i in (1 + lag_val): length(tmp)) {
           tmp[i] <- tmp[i] + combos[ind,1] * combos[ind,3] * tmp[i - lag_val]
     }
     return(tmp)
   }

然后循环遍历"combos"行

and then loop through the rows of 'combos'

for(j in seq_len(nrow(combos))){

# assign the group by recursive output to each `list` element        


lst[[j]] <- advertising_dataset %>%
             group_by(Region) %>%
             mutate_all(funs(adstocked =
               foo1(., lag_val = combos[j,2], combos, ind = j)))

}
lst

尚不清楚我们是否需要list名称为'adstock_Rate_Number_Drate'.如果是那样的话，

It is not clear whether we need the list names to be 'adstock_Rate_Number_Drate' or not. If that is that case,

names(lst) <- paste0("adstock_", do.call(paste, c(combos, sep="_")))

将data.frame的list转换为具有"id"列以指示组合的单个data.frame

Convert the list of data.frame to a single data.frame having an 'id' column to indicate the combination

out <- bind_rows(lst, .id = 'id')
head(out, 3)
# A tibble: 3 x 6
# Groups:   Region [1]
#  id        Region advertising advertising2 advertising_adst… advertising2_ads…
#  <chr>      <dbl>       <dbl>        <dbl>             <dbl>             <dbl>
#1 adstock_…    500        118.         43.9              147.              54.9
#2 adstock_…    500        120.        231.               150.             289. 
#3 adstock_…    500        126.         76.8              157.              96.0

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