leoluyi

## === 第一次用R需安裝套件 ===
list.of.packages <- c("ggplot2", "xlsx", "FactoMineR","showtext",
                      "Cairo", "ggthemes","directlabels","dplyr")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)


# 內建圖
ca_plot <- function (df,label=c("印象","品牌"),textSize=10,
                     x_limits=c(-0.5,0.6),.alpha=0.7,

leoluyi

library(data.table)
library(xlsx)
library(dplyr)
library(pipeR)
library(car)

## 讀取資料
fileNameA <- '3337_A卷.csv' # (手動更改參數)
fileNameB <- '3371_B卷.csv' # (手動更改參數)
# options(java.parameters = "-Xmx16g") # read.xlsx增加記憶體

leoluyi

## === 第一次用R需安裝套件 ===
list.of.packages <- c("plyr", "dplyr", "sjmisc")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
rm(list.of.packages, new.packages)

combine_pipe_table <- function (
  .data, .var_start, .var_end, .MR_num, .step)
{
  #   .var_start    # 表格起始變數名

leoluyi

# forked from hadley/recode.R
# https://gist.github.com/hadley/2751ba61d1c7f4eaacab

recode_to <- function(df, ..., match = c("first", "last")) {
  match <- match.arg(match)

  cases <- lapply(list(...), as.case)
  if (identical(match, "last")) cases <- rev(cases)

  n <- nrow(df)

leoluyi

write.Hmisc.SPSS = function(data, datafile, codefile) {
  ## Write an SPSS file from R with variable labels from the Hmisc package
  # source: 
  # http://stackoverflow.com/questions/10181730/information-from-label-attribute-in-r-to-variable-labels-in-spss/10261534#10261534

  # EXAMPLE DATA (see: http://stackoverflow.com/q/10181730/1270695)
  #
  # If you do not want to alter your original file, as in the example above, 
  # and if you are connected to the internet while you are using this function, 
  # you can try this self-contained function:

leoluyi

## === 第一次用R需安裝套件 ===
list.of.packages <- 
  c("plyr", "xlsx", "tools","dplyr","openxlsx",
    "sampling", "magrittr", "sjmisc")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
rm(list.of.packages, new.packages)

## -- 更新 package ----
if(packageDescription("openxlsx")$Version < "2.3.11") {

leoluyi

### Using the Windows Clipboard, or Passing Data Quickly From Excel to R and Back Again
## http://www.r-bloggers.com/using-the-windows-clipboard-or-passing-data-quickly-from-excel-to-r-and-back-again/

# Copy data out of R
copy.table <- function(obj, size = 4096) {
  clip <- paste('clipboard-', size, sep = '')
  f <- file(description = clip, open = 'w')
  write.table(obj, f, row.names = FALSE, sep = '\t')
  close(f)  
}

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multi.freq.table = function(data, sep="+", dropzero=FALSE, clean=TRUE) {
  ## 共選分析
  # Takes boolean multiple-response data and tabulates it according
  #   to the possible combinations of each variable.
  #
  # See: http://stackoverflow.com/q/11348391/1270695
  
  counts = data.frame(table(data))
  N = ncol(counts)
  counts$Combn = apply(counts[-N] == 1, 1, 

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write.spss <-
  function(data, datafile, codefile, varlabels=NULL) {
    # EXAMPLE DATA (see: http://stackoverflow.com/q/10181730/1270695)
    #
    # If you do not want to alter your original file, as in the example above, 
    # and if you are connected to the internet while you are using this function, 
    # you can try this self-contained function:
    #
    # df <- data.frame(id = c(1:6), 
    #                  p.code = c(1, 5, 4, NA, 0, 5),  

leoluyi

combine_pipe_table <- function (
  .data, .var,
  start = grep(.var, colnames(.data)), 
  Qcount, MRnum, step, key)
{
#   .var    # 表格起始變數名
#   start  # 開始欄
#   Qcount # piping來源題數
#   MRnum  # 複選題數
#   step   # piping相同題數