将 data.frame 转换为具有特定排序的数字和字符数据的行？

Question

Intro: Working in R, I often need to reorganize information from lists of data.frames to create a summary table. In this example, I start with a single data.frame, and I show my function that converts key information from the data.frame into a single row. Bearing in mind that my desired output requires the sorting of a mixture of numeric and character data, I can't help wondering if there is an easier technique to do this kind of thing.

My question: Can anyone provide advice, or better yet a solution, for a simpler technique to convert data.frames like these into rows, while respecting the specific sorting of the data?

#sample data
input_df <- data.frame(M1 = c("Age", "Weight", "Speed", "Range"),
  dogs = c(100, 120, 85, 105),
  cats = c(115, 89, 80, 111),
  birds = c(100, 90, 100, 104))

# desired summary row
desired_row <- data.frame(Model = "M1",
  dogs = "Weight (120)",
  cats = "Age (115), Range (111)",
  birds = "Range (104)",
  stringsAsFactors = F)
desired_row$Model <- factor(desired_row$Model)

# my function

  makeRow <- function(dat1) {

    # get model name
    mod <- data.frame(Model = names(dat1[1]))

    # make list of variables with model varible 
    d1 <- setNames(lapply(names(dat1)[-1], function(x) cbind(dat1[1], 
      dat1[x])), names(dat1)[-1])

    # create a sorted named vector, largest-to-smallest
    sorted_named_vec <- function(x) {
      sort(setNames(x[[2]], x[[1]]), decreasing = T)
    }
    d2 <- lapply(d1, sorted_named_vec)

    # implement a criterion to report only top indexes
    keep_tops <- function(x) {
      ifelse(x == max(x) | x >= 110 | (x > 102) & ((x - 
          100)/(max(x) - 100) > 0.33), x, "")
    }
    d3 <- lapply(d2, keep_tops)

    # remove blank character elements
    remove_blank_elements <- function(x) {
      x[nchar(x) > 0]
    }
    d4 <- lapply(d3, remove_blank_elements)

    # collapse variable name with top values and add parenthesis
    collapse_to_string <- function(x) {
      paste0(names(x), " (", x, "),", collapse = " ")
    }
    d5 <- lapply(d4, collapse_to_string)

    # remove the last comma
    remove_last_comma <- function(x) {
      gsub("\\,$", "", x)
    }
    d6 <- lapply(d5, remove_last_comma)

    # consturct a row from the list
    row <- cbind(mod, as.data.frame(d6, stringsAsFactors = F))

    row
  }

  # call
  row_output <- makeRow(dat1 = input_df)
  row_output

  # check output to desired
  identical(desired_row, row_output)

Answer 1

not sure if more efficient, but slightly less code and more direct approach imo.

  makeRow <- function(dat1) {
  #make data frame for row with model name
  d0 <- data.frame(mod = names(dat1)[1]) #col name changed later

  
  # implement a criterion to report only top indexes -> now return if true or false
  keep_tops <- function(x) {
    x == max(x) | x >= 110 | (x > 102) & ((x - 100)/(max(x) - 100) > 0.33)
  }
  vals =c() #empty -> for values of each cols
  # make list of variables with model variables(dat1 cols)
  #use the columns of the df directly
  for(col in 2:ncol(dat1)){
  #make temp df with each and evaluate what row to keep in the same line
    df = dat1[keep_tops(dat1[,col])==1,c(1,col)]
    df[,2] = paste0("(",df[,2],")") #add the () around the numbers
    val = apply(as.data.frame(apply(df, 1, paste0, collapse=" ")), 2, paste0, collapse=", ") #collapse rows, then cols
    vals = c(vals, val) #add this variable values to the values' list
  }
  # bind the first col made earlier with these values
  row <- cbind(d0, as.data.frame(t(vals), stringsAsFactors = F))
  colnames(row) =  colnames(dat1) #rename the columns to match
  
  row
}

# call
row_output <- makeRow(dat1 = input_df)
# check output to desired
identical(desired_row$birds, row_output$birds)

with your 'input_df', identical() was TRUE.