Write.xlsx：将输出转换为数字

Question

请参阅下面脚本末尾的write.xlsx(a, OutFile, row.names = F)。不是将数字数据输出到Excel文件，而是他将数据存储为Excel输出中的文本。将输出转换为数字的最无缝方式是什么？

rm(list = ls()) #clears the workspace

library(caret)
library(ggplot2)
library(scales)
library(foreach) 
library(doParallel)
library("xlsx")


Files = c("BBY", "BWLD", "CBRL", "CMG", "DLTR", "DNKN", "DSW", "FDO", "FIVE", "FL", "HD", "JCP", "JOSB",
          "JWN", "KSS", "LOW", "LQ", "M", "MW", "PLCE", "PLNT", "PNRA", "PZZA", "ROST", "SBUX", "SIX", "TGT",
          "TJX", "UA", "WEN", "CRI")

for(f in Files) {
  InFile  = paste("P:/R/R_Input/", f, "_Input.csv",  sep="")
  OutFile = paste("P:/Model_Output/", f, ".xlsx", sep="")
  Macro <- read.csv(InFile)

cbind.fill <- function(...){
  nm <- list(...) 
  nm <- lapply(nm, as.matrix)
  n <- max(sapply(nm, nrow)) 
  do.call(cbind, lapply(nm, function (x) 
    rbind(x, matrix(, n-nrow(x), ncol(x))))) 
}

# set up
options(scipen = 999)  #removes scientific notation
registerDoParallel(cores = 16) 

# read data
proj_path = "P:/R/R_Files"

# prep data
source("P:/R/R_Files/Var.R")  #Calls variables

# train set up
ctrl <- caret::trainControl(method = "timeslice", initialWindow = 8, horizon = 1, 
                            fixedWindow = FALSE, savePredictions = TRUE)

# Loads all variable names from Macro and Macro2
vars_macro = names(Macro)[!names(Macro) %in% c("qtrs", "y", "s1", "s2", "s3")]  #Returns names in Macro not in "qtrs", "y", "s1", "s2", "s3"
vars_macro2 = names(Macro2)[!names(Macro2) %in% c("y", "s1", "s2", "s3")]
vars_macro3 = names(Macro3)[!names(Macro3) %in% c("y", "s1", "s2", "s3")]
vars = c(vars_macro, vars_macro2, vars_macro3)

# run lm
lst = foreach(var = vars) %dopar% {

  if (var %in% vars_macro)  
    foo <- function(start, mod_formula) {
      myfit <- caret::train(mod_formula, data = Macro[start:14, ,drop = FALSE],
                            method = "lm", trControl = ctrl)
      c(myfit$pred)  ## return; drop dimension as a vector
    }
  if (var %in% vars_macro2)
    foo <- function(start, mod_formula) {
      myfit <- caret::train(mod_formula, data = Macro2[start:14, ,drop = FALSE],
                            method = "lm", trControl = ctrl)
      c(myfit$pred)  ## return; drop dimension as a vector
    }
  if (var %in% vars_macro3)
    foo <- function(start, mod_formula) {
      myfit <- caret::train(mod_formula, data = Macro3[start:14, ,drop = FALSE],
                            method = "lm", trControl = ctrl)
      c(myfit$pred)  ## return; drop dimension as a vector
    } 
  f = formula(paste0("y ~ ", var,  "+ s1 + s2 + s3"))
  Forecast <- sapply(1:6, foo, mod_formula = f)  

  F9 <- c(Forecast[[1,1]][1])
  F10 <- c(Forecast[[1,1]][2], Forecast[[1,2]][1])
  F11 <- c(Forecast[[1,1]][3], Forecast[[1,2]][2], Forecast[[1,3]][1])
  F12 <- c(Forecast[[1,1]][4], Forecast[[1,2]][3], Forecast[[1,3]][2],
           Forecast[[1,4]][1])
  F13 <- c(Forecast[[1,1]][5], Forecast[[1,2]][4], Forecast[[1,3]][3],
           Forecast[[1,4]][2], Forecast[[1,5]][1])
  F14 <- c(Forecast[[1,1]][6], Forecast[[1,2]][5], Forecast[[1,3]][4],
           Forecast[[1,4]][3], Forecast[[1,5]][2], Forecast[[1,6]][1])

  A <-c((mean(F9)/Macro[9:9,2:2]-1), (mean(F10)/Macro[10:10,2:2]-1), 
        (mean(F11)/Macro[11:11,2:2]-1), (mean(F12)/Macro[12:12,2:2]-1), 
        (mean(F13)/Macro[13:13,2:2]-1),(mean(F14)/Macro[14:14,2:2]-1))
  Temp <- mean(abs(A[0:5]))
  P <-c((mean(F9)/Macro[9:9,2:2]-1), (mean(F10)/Macro[10:10,2:2]-1), 
        (mean(F11)/Macro[11:11,2:2]-1), (mean(F12)/Macro[12:12,2:2]-1), 
        (mean(F13)/Macro[13:13,2:2]-1),(mean(F14)/Macro[14:14,2:2]-1),
        Temp,(mean(F14)/(1+mean(A[3:5])))/Macro[14:14,2:2]-1)
  #E <- scales::percent(P)
  C <- c(mean(F9),mean(F10),mean(F11), mean(F12), mean(F13), mean(F14),
         "abs error",mean(F14)/(1+mean(P[3:5])))
  data.frame(C, P)
}

# Summary            
model_error = as.character(sapply(lst, function(elt) elt$P[7]))
forecasts = as.numeric(as.character(sapply(lst, function(elt) elt$C[8])))
delta = as.character(sapply(lst, function(elt) elt$P[8]))
df = data.frame(Card = vars, Model_Avg_Error = model_error, 
                Forecast = forecasts,  Delta = delta)
df$blankVar = NA

df_macro1 = df[df$Card %in% vars_macro,]
df_macro1$blankVar = NA
df_macro2 = df[df$Card %in% vars_macro2,]
df_macro2 = df_macro2[order(df_macro2$Model_Avg_Error),]
df_macro2$blankVar = NA
df_macro3 = df[df$Card %in% vars_macro3,]
df_macro3 = df_macro3[order(df_macro3$Model_Avg_Error),]
df_macro3$blankVar = NA
df_macro4 = df[df$Card %in% names(Macro4),]
df_macro4 = df_macro4[order(df_macro4$Model_Avg_Error),]

df = df[order(df$Model_Avg_Error),]
a = cbind.fill(df_macro1, df_macro2, df_macro3, df, df_macro4)
# save

write.xlsx(a, OutFile, row.names = F)
closeAllConnections()
}