如何在 r 的循环中调用不同的数据帧并向它们添加不同的计算结果？

Question

如何自动执行以下步骤？ 我有以下我想做的示例 - 最后得到一个由较小数据帧组成的数据帧，这些数据帧在前面的步骤中自动生成。这些较小的数据帧还需要在聚合之前在其中完成计算。我可以使用长脚本手动完成所有操作，但似乎无法弄清楚如何正确组合 list()、apply() 或 for() 循环以获得我想要的结果（不确定这些是这里的最佳选择） . 请指教。 谢谢！

########### 详细代码中的我的问题

# DATASET

    a <- c(2.0, 2.4,  2.1,  2.2,  2.3)
    b <- c(4.0, 0,  4.5,  4.4,  4.8)
    c <- c(0.3, 0.2,  2.0,  2.1,  2.3)
    d <- c(5.0, 4.8,  4.8,  4.9,  5.0)
    test.data <- data.frame(rbind(a,b,c,d))

#STEP 1: create separate dfs and do different calculations by column in each
#LONG WAY, MANUAL
# calculates % difference between each value with respect to first value in row 
# in df1, then second value in row for df2, etc.

    nc <- ncol(test.data)
    df1 <- (test.data[,1:nc] - test.data[[1]])/(test.data[[1]])*100
    df2 <- (test.data[,1:nc] - test.data[[2]])/(test.data[[2]])*100
    df3 <- (test.data[,1:nc] - test.data[[3]])/(test.data[[3]])*100
    df4 <- (test.data[,1:nc] - test.data[[4]])/(test.data[[4]])*100
    df5 <- (test.data[,1:nc] - test.data[[5]])/(test.data[[5]])*100

# some results from above give Inf (since divided by zero), so set those to NA
df1[df1==Inf] <- NA
df2[df2==Inf] <- NA 
df3[df3==Inf] <- NA 
df4[df4==Inf] <- NA 
df4[df4==Inf] <- NA
df5[df5==Inf] <- NA

#next will filter each calculated %-value by the specified percent difference filter
# and save the results in separate associated dataframes.

percent.diff <- 30

df.A1 <- data.frame(ifelse(df1 > -percent.diff & df1 < percent.diff, 1, 0))
df.A2 <- data.frame(ifelse(df2 > -percent.diff & df2 < percent.diff, 1, 0))
df.A3 <- data.frame(ifelse(df3 > -percent.diff & df3 < percent.diff, 1, 0))
df.A4 <- data.frame(ifelse(df4 > -percent.diff & df4 < percent.diff, 1, 0))
df.A5 <- data.frame(ifelse(df5 > -percent.diff & df5 < percent.diff, 1, 0))

#next add ID columns to each of the newly created dataframes
obs <- 4
#add row and df ID variables to each of the above
df.A1["df.cat"] <- 1
df.A1["row"] <- 1:obs

df.A2["df.cat"] <- 2
df.A2["row"] <- 1:obs

df.A3["df.cat"] <- 3
df.A3["row"] <- 1:obs

df.A4["df.cat"] <- 4
df.A4["row"] <- 1:obs

df.A5["df.cat"] <- 5
df.A5["row"] <- 1:obs

#combine the individual dataframes with IDs into a single dataframe.
Combo.df <-list(df.A1, df.A2, df.A3, df.A4, df.A5)
All.df <- Reduce(rbind, Combo.df)

最终输出应如下所示（仅显示前几行）

    X1 X2 X3 X4 X5 df.cat row
a   1  1  1  1  1      1   1
b   1  0  1  1  1      1   2
c   1  0  0  0  0      1   3
d   1  1  1  1  1      1   4
a1  1  1  1  1  1      2   1
b1  1  1  1  1  1      2   2
c1  0  1  0  0  0      2   3
d1  1  1  1  1  1      2   4
a2  1  1  1  1  1      3   1
b2  1  0  1  1  1      3   2
c2  0  0  1  1  1      3   3
d2  1  1  1  1  1      3   4

尝试自动执行上述步骤失败 #

 a) created the number of dataframes I will need
        num.reps <- 5
        obs <- 4
        n.cols <- 5

        lst <- replicate(num.reps,data.frame(matrix(NA, nrow = obs, ncol = n.cols)), simplify=FALSE)
        names(lst) <- paste0('df', 1:num.reps)
        list2env(lst, envir = .GlobalEnv)

        # b) fill dataframes (not sure how to call up dataframe by sequential names in loop)
        # THIS DOES NOT WORK
        f.diff.calc <- function(i)
        {df[[i]] <-(df[,1:nc] - df[[i]])/(df[[i]])*100}
        diff.calc.list <- replicate(5, f.diff.calc(list))
        #Error in `[.data.frame`(df, , 1:nc) : undefined columns selected 

Answer 1

这是对您的代码的简化，据我所知，它可以满足您的需求。

fun1 <- function(col, DF = test.data){
  res <- 100*(DF - DF[[col]])/DF[[col]]
  is.na(res) <- is.infinite(as.matrix(res))
  res
}

fun2 <- function(DF, percent.diff = 30){
  data.frame(ifelse(-percent.diff < DF & DF < percent.diff, 1, 0))
}

df_list <- lapply(seq_len(ncol(test.data)), fun1)
names(df_list) <- paste0("df", seq_along(df_list))


#next will filter each calculated %-value by the specified percent difference filter
# and save the results in a list of dataframes.

percent.diff <- 30

df.A_list <- lapply(df_list, fun2)

#next add ID columns to each of the newly created dataframes

tmp <- names(df.A_list)

df.A_list <- lapply(seq_along(df.A_list), function(i){
  df.A_list[[i]][["df.cat"]] <- i
  df.A_list[[i]][["row"]] <- seq_len(nrow(df.A_list[[i]]))
  df.A_list[[i]]
})
names(df.A_list) <- tmp

# combine the results in one dataframe
All.df <- do.call(rbind, df.A_list)

Answer 2

好吧，我真诚地认为，通过更多的研究，您可以解决它。此外，我无法重新创建您获得的确切输出，但我能够匹配使用您的代码获得的输出。

这是代码的自动化版本。

a <- c(2.0, 2.4,  2.1,  2.2,  2.3)
b <- c(4.0, 0,  4.5,  4.4,  4.8)
c <- c(0.3, 0.2,  2.0,  2.1,  2.3)
d <- c(5.0, 4.8,  4.8,  4.9,  5.0)
test.data <- data.frame(rbind(a,b,c,d))

#STEP 1: create separate dfs and do different calculations by column in each
#LONG WAY, MANUAL
# calculates % difference between each value with respect to first value in row 
# in df1, then second value in row for df2, etc.

nc <- ncol(test.data)

calc<-function(x,percent.diff=30,i){
  x[x==Inf] <- NA
  obs<-4

  x.A<- data.frame(ifelse(x > -percent.diff & x < percent.diff, 1, 0))
  x.A$df.cat<-i
  x.A$row<-1:obs
  return(x.A)
}

output<-data.frame()
for(i in 1:5){
  assign(paste('df',i,sep=""),(test.data[,1:nc] - test.data[[i]])/(test.data[[i]])*100)
}

for(i in 1:5){
  output<-rbind.data.frame(output,calc(x = get(paste('df',i,sep="")),percent.diff = 30,i=i))
}