Author: Alexander Walker, Philipp Schauberger
Date: 2021-06-15

# Introduction

#### Alexander Walker, Philipp Schauberger

#### 2021-06-15

## Basic Examples

### write.xlsx

The simplest way to write to a workbook is write.xlsx(). By default, write.xlsx calls writeData. If asTable is TRUE write.xlsx will write x as an Excel table.

    ## write to working directory
    library(openxlsx)
    write.xlsx(iris, file = "writeXLSX1.xlsx")
    write.xlsx(iris, file = "writeXLSXTable1.xlsx", asTable = TRUE)

### write list of data.frames to xlsx-file

    ## write a list of data.frames to individual worksheets using list names as worksheet names
    l <- list("IRIS" = iris, "MTCARS" = mtcars)
    write.xlsx(l, file = "writeXLSX2.xlsx")
    write.xlsx(l, file = "writeXLSXTable2.xlsx", asTable = TRUE)

### write.xlsx also accepts styling parameters

#### The simplest way is to set default options and set column class

    options("openxlsx.borderColour" = "#4F80BD")
    options("openxlsx.borderStyle" = "thin")
    options("openxlsx.dateFormat" = "mm/dd/yyyy")
    options("openxlsx.datetimeFormat" = "yyyy-mm-dd hh:mm:ss")
    options("openxlsx.numFmt" = NULL) ## For default style rounding of numeric columns

    df <- data.frame("Date" = Sys.Date()-0:19, "LogicalT" = TRUE,
                     "Time" = Sys.time()-0:19*60*60,
                     "Cash" = paste("$",1:20), "Cash2" = 31:50,
                     "hLink" = "https://CRAN.R-project.org/",
                     "Percentage" = seq(0, 1, length.out=20),
                     "TinyNumbers" = runif(20) / 1E9,  stringsAsFactors = FALSE)

    class(df$Cash) <- "currency"
    class(df$Cash2) <- "accounting"
    class(df$hLink) <- "hyperlink"
    class(df$Percentage) <- "percentage"
    class(df$TinyNumbers) <- "scientific"

    write.xlsx(df, "writeXLSX3.xlsx")
    write.xlsx(df, file = "writeXLSXTable3.xlsx", asTable = TRUE)

## Workbook styles

### define a style for column headers

    hs <- createStyle(fontColour = "#ffffff", fgFill = "#4F80BD",
                      halign = "center", valign = "center", textDecoration = "Bold",
                      border = "TopBottomLeftRight", textRotation = 45)

    write.xlsx(iris, file = "writeXLSX4.xlsx", borders = "rows", headerStyle = hs)
    write.xlsx(iris, file = "writeXLSX5.xlsx", borders = "columns", headerStyle = hs)

    write.xlsx(iris, "writeXLSXTable4.xlsx", asTable = TRUE,
    headerStyle = createStyle(textRotation = 45))

### When writing a list, the stylings will apply to all list elements

    l <- list("IRIS" = iris, "colClasses" = df)
    write.xlsx(l, file = "writeXLSX6.xlsx", borders = "columns", headerStyle = hs)
    write.xlsx(l, file = "writeXLSXTable5.xlsx", asTable = TRUE, tableStyle = "TableStyleLight2")

    openXL("writeXLSX6.xlsx")
    openXL("writeXLSXTable5.xlsx")

### write.xlsx returns the workbook object for further editing

    wb <- write.xlsx(iris, "writeXLSX6.xlsx")
    setColWidths(wb, sheet = 1, cols = 1:5, widths = 20)
    saveWorkbook(wb, "writeXLSX6.xlsx", overwrite = TRUE)

## Workbook creation walk-through

### create workbook and set default border Colour and style

    require(ggplot2)
    wb <- createWorkbook()
    options("openxlsx.borderColour" = "#4F80BD")
    options("openxlsx.borderStyle" = "thin")
    modifyBaseFont(wb, fontSize = 10, fontName = "Arial Narrow")

### Add Sheets

    addWorksheet(wb, sheetName = "Motor Trend Car Road Tests", gridLines = FALSE)
    addWorksheet(wb, sheetName = "Iris", gridLines = FALSE)

### write data to sheet 1

    freezePane(wb, sheet = 1, firstRow = TRUE, firstCol = TRUE) ## freeze first row and column
    writeDataTable(wb, sheet = 1, x = mtcars,
    colNames = TRUE, rowNames = TRUE,
    tableStyle = "TableStyleLight9")

    setColWidths(wb, sheet = 1, cols = "A", widths = 18)

### write data to sheet 2

iris data.frame is added as excel table on sheet 2.

    writeDataTable(wb, sheet = 2, iris, startCol = "K", startRow = 2)

    qplot(data=iris, x = Sepal.Length, y= Sepal.Width, colour = Species)
    insertPlot(wb, 2, xy=c("B", 16)) ## insert plot at cell B16

    means <- aggregate(x = iris[,-5], by = list(iris$Species), FUN = mean)
    vars <- aggregate(x = iris[,-5], by = list(iris$Species), FUN = var)

### add write group means

    headSty <- createStyle(fgFill="#DCE6F1", halign="center", border = "TopBottomLeftRight")
    writeData(wb, 2, x = "Iris dataset group means", startCol = 2, startRow = 2)
    writeData(wb, 2, x = means, startCol = "B", startRow=3, borders="rows", headerStyle = headSty)

### add write group variances

    writeData(wb, 2, x = "Iris dataset group variances", startCol = 2, startRow = 9)
    writeData(wb, 2, x= vars, startCol = "B", startRow=10, borders="columns",
    headerStyle = headSty)

    setColWidths(wb, 2, cols=2:6, widths = 12) ## width is recycled for each col
    setColWidths(wb, 2, cols=11:15, widths = 15)

### add style mean & variance table headers

    s1 <- createStyle(fontSize=14, textDecoration=c("bold", "italic"))
    addStyle(wb, 2, style = s1, rows=c(2,9), cols=c(2,2))

### save workbook

    saveWorkbook(wb, "basics.xlsx", overwrite = TRUE) ## save to working directory

## Gallery

    ## inspired by xtable gallery
    #https://CRAN.R-project.org/package=xtable/vignettes/xtableGallery.pdf

    ## Create a new workbook
    wb <- createWorkbook()
    data(tli, package = "xtable")

    ## data.frame
    test.n <- "data.frame"
    my.df <- tli[1:10, ]
    addWorksheet(wb = wb, sheetName = test.n)
    writeData(wb = wb, sheet = test.n, x = my.df, borders = "n")

    ## matrix
    test.n <- "matrix"
    design.matrix <- model.matrix(~ sex * grade, data = my.df)
    addWorksheet(wb = wb, sheetName = test.n)
    writeData(wb = wb, sheet = test.n, x = design.matrix)

    ## aov
    test.n <- "aov"
    fm1 <- aov(tlimth ~ sex + ethnicty + grade + disadvg, data = tli)
    addWorksheet(wb = wb, sheetName = test.n)
    writeData(wb = wb, sheet = test.n, x = fm1)

    ## lm
    test.n <- "lm"
    fm2 <- lm(tlimth ~ sex*ethnicty, data = tli)
    addWorksheet(wb = wb, sheetName = test.n)
    writeData(wb = wb, sheet = test.n, x = fm2)

    ## anova 1
    test.n <- "anova"
    my.anova <- anova(fm2)
    addWorksheet(wb = wb, sheetName = test.n)
    writeData(wb = wb, sheet = test.n, x = my.anova)

    ## anova 2
    test.n <- "anova2"
    fm2b <- lm(tlimth ~ ethnicty, data = tli)
    my.anova2 <- anova(fm2b, fm2)
    addWorksheet(wb = wb, sheetName = test.n)
    writeData(wb = wb, sheet = test.n, x = my.anova2)

    ## glm
    test.n <- "glm"
    fm3 <- glm(disadvg ~ ethnicty*grade, data = tli, family = binomial())
    addWorksheet(wb = wb, sheetName = test.n)
    writeData(wb = wb, sheet = test.n, x = fm3)

    ## prcomp
    test.n <- "prcomp"
    pr1 <- prcomp(USArrests)
    addWorksheet(wb = wb, sheetName = test.n)
    writeData(wb = wb, sheet = test.n, x = pr1)

    ## summary.prcomp
    test.n <- "summary.prcomp"
    addWorksheet(wb = wb, sheetName = test.n)
    writeData(wb = wb, sheet = test.n, x = summary(pr1))

    ## simple table
    test.n <- "table"
    data(airquality)
    airquality$OzoneG80 <- factor(airquality$Ozone > 80,
    levels = c(FALSE, TRUE),
    labels = c("Oz <= 80", "Oz > 80"))
    airquality$Month <- factor(airquality$Month,
    levels = 5:9,
    labels = month.abb[5:9])
    my.table <- with(airquality, table(OzoneG80,Month) )
    addWorksheet(wb = wb, sheetName = test.n)
    writeData(wb = wb, sheet = test.n, x = my.table)

    ## survdiff 1
    library(survival)
    test.n <- "survdiff1"
    addWorksheet(wb = wb, sheetName = test.n)
    x <- survdiff(Surv(futime, fustat) ~ rx, data = ovarian)
    writeData(wb = wb, sheet = test.n, x = x)

    ## survdiff 2
    test.n <- "survdiff2"
    addWorksheet(wb = wb, sheetName = test.n)
    expect <- survexp(futime ~ ratetable(age=(accept.dt - birth.dt),
          sex=1,year=accept.dt,race="white"), jasa, cohort=FALSE,
          ratetable=survexp.usr)
    x <- survdiff(Surv(jasa$futime, jasa$fustat) ~ offset(expect))
    writeData(wb = wb, sheet = test.n, x = x)

    ## coxph 1
    test.n <- "coxph1"
    addWorksheet(wb = wb, sheetName = test.n)
    bladder$rx <- factor(bladder$rx, labels = c("Pla","Thi"))
    x <- coxph(Surv(stop,event) ~ rx, data = bladder)
    writeData(wb = wb, sheet = test.n, x = x)

    ## coxph 2
    test.n <- "coxph2"
    addWorksheet(wb = wb, sheetName = test.n)
    x <- coxph(Surv(stop,event) ~ rx + cluster(id), data = bladder)
    writeData(wb = wb, sheet = test.n, x = x)

    ## cox.zph
    test.n <- "cox.zph"
    addWorksheet(wb = wb, sheetName = test.n)
    x <- cox.zph(coxph(Surv(futime, fustat) ~ age + ecog.ps,  data=ovarian))
    writeData(wb = wb, sheet = test.n, x = x)

    ## summary.coxph 1
    test.n <- "summary.coxph1"
    addWorksheet(wb = wb, sheetName = test.n)
    x <- summary(coxph(Surv(stop,event) ~ rx, data = bladder))
    writeData(wb = wb, sheet = test.n, x = x)

    ## summary.coxph 2
    test.n <- "summary.coxph2"
    addWorksheet(wb = wb, sheetName = test.n)
    x <- summary(coxph(Surv(stop,event) ~ rx + cluster(id), data = bladder))
    writeData(wb = wb, sheet = test.n, x = x)

    ## view without saving
    openXL(wb)

## Further Examples

### Stock Price

    require(ggplot2)

    wb <- createWorkbook()

    ## read historical prices from yahoo finance
    ticker <- "CBA.AX"
    csv.url <- paste("http://ichart.finance.yahoo.com/table.csv?s=",
    ticker, "&a=01&b=9&c=2009&d=01&e=9&f=2014&g=d&ignore=.csv")
    prices <- read.csv(url(csv.url), as.is = TRUE)
    prices$Date <- as.Date(prices$Date)
    close <- prices$Close
    prices$logReturns = c(0, log(close[2:length(close)]/close[1:(length(close)-1)]))

    ## Create plot of price series and add to worksheet
    ggplot(data = prices, aes(as.Date(Date), as.numeric(Close))) +
    geom_line(colour="royalblue2") +
    labs(x = "Date", y = "Price", title = ticker) +
    geom_area(fill = "royalblue1",alpha = 0.3) +
    coord_cartesian(ylim=c(min(prices$Close)-1.5, max(prices$Close)+1.5))

    ## Add worksheet and write plot to sheet
    addWorksheet(wb, sheetName = "CBA")
    insertPlot(wb, sheet = 1, xy = c("J", 3))

    ## Histogram of log returns
    ggplot(data = prices, aes(x = logReturns)) + geom_bar(binwidth=0.0025) +
    labs(title = "Histogram of log returns")

    ## currency
    class(prices$Close) <- "currency" ## styles as currency in workbook

    ## write historical data and  histogram of returns
    writeDataTable(wb, sheet = "CBA", x = prices)
    insertPlot(wb, sheet = 1, startRow=25, startCol = "J")

    ## Add conditional formatting to show where logReturn > 0.01 using default style
    conditionalFormat(wb, sheet = 1, cols = 1:ncol(prices), rows = 2:(nrow(prices)+1),
    rule = "$H2 > 0.01")

    ## style log return col as a percentage
    logRetStyle <- createStyle(numFmt = "percentage")

    addStyle(wb, 1, style = logRetStyle, rows = 2:(nrow(prices) + 1),
    cols = "H", gridExpand = TRUE)

    setColWidths(wb, sheet=1, cols = c("A", "F", "G", "H"), widths = 15)

    ## save workbook to working directory
    saveWorkbook(wb, "stockPrice.xlsx", overwrite = TRUE)
    openXL("stockPrice.xlsx")

### Image Compression using PCA

    require(openxlsx)
    require(jpeg)
    require(ggplot2)

    plotFn <- function(x, ...){
      colvec <- grey(x)
      colmat <- array(match(colvec, unique(colvec)), dim = dim(x)[1:2])
      image(x = 0:(dim(colmat)[2]), y = 0:(dim(colmat)[1]), z = t(colmat[nrow(colmat):1, ]),
        col = unique(colvec), xlab = "", ylab = "", axes = FALSE, asp = 1,
        bty ="n", frame.plot=F, ann=FALSE)
    }

    ## Create workbook and add a worksheet, hide gridlines
    wb <- createWorkbook("Einstein")
    addWorksheet(wb, "Original Image", gridLines = FALSE)

    A <- readJPEG(file.path(path.package("openxlsx"), "einstein.jpg"))
    height <- nrow(A); width <- ncol(A)

    ## write "Original Image" to cell B2
    writeData(wb, 1, "Original Image", xy = c(2,2))

    ## write Object size to cell B3
    writeData(wb, 1, sprintf("Image object size: %s bytes",
                             format(object.size(A+0)[[1]], big.mark=',')),
              xy = c(2,3))  ## equivalent to startCol = 2, startRow = 3

    ## Plot image
    par(mar=rep(0, 4), xpd = NA); plotFn(A)

    ## insert plot currently showing in plot window
    insertPlot(wb, 1, width, height, units="px", startRow= 5, startCol = 2)

    ## SVD of covariance matrix
    rMeans <- rowMeans(A)
    rowMeans <- do.call("cbind", lapply(1:ncol(A), function(X) rMeans))
    A <- A - rowMeans
    E <- svd(A %*% t(A) / (ncol(A) - 1)) # SVD on covariance matrix of A
    pve <- data.frame("Eigenvalues" = E$d,
                      "PVE" = E$d/sum(E$d),
                      "Cumulative PVE" = cumsum(E$d/sum(E$d)))

    ## write eigenvalues to worksheet
    addWorksheet(wb, "Principal Component Analysis")
    hs <- createStyle(fontColour = "#ffffff", fgFill = "#4F80BD",
                      halign = "CENTER", textDecoration = "Bold",
                      border = "TopBottomLeftRight", borderColour = "#4F81BD")

    writeData(wb, 2, x="Proportions of variance explained by Eigenvector" ,startRow = 2)
    mergeCells(wb, sheet=2, cols=1:4, rows=2)

    setColWidths(wb, 2, cols = 1:3, widths = c(14, 12, 15))
    writeData(wb, 2, x=pve, startRow = 3, startCol = 1, borders="rows", headerStyle=hs)

    ## Plots
    pve <- cbind(pve, "Ind" = 1:nrow(pve))
    ggplot(data = pve[1:20,], aes(x = Ind, y = 100*PVE)) +
      geom_bar(stat="identity", position = "dodge") +
      xlab("Principal Component Index") + ylab("Proportion of Variance Explained") +
      geom_line(size = 1, col = "blue") + geom_point(size = 3, col = "blue")

    ## Write plot to worksheet 2
    insertPlot(wb, 2, width = 5, height = 4, startCol = "E", startRow = 2)

    ## Plot of cumulative explained variance
    ggplot(data = pve[1:50,], aes(x = Ind, y = 100*Cumulative.PVE)) +
      geom_point(size=2.5) + geom_line(size=1) + xlab("Number of PCs") +
      ylab("Cumulative Proportion of Variance Explained")
    insertPlot(wb, 2, width = 5, height = 4, xy= c("M", 2))

    ## Reconstruct image using increasing number of PCs
    nPCs <- c(5, 7, 12, 20, 50, 200)
    startRow <- rep(c(2, 24), each = 3)
    startCol <- rep(c("B", "H", "N"), 2)

    ## create a worksheet to save reconstructed images to
    addWorksheet(wb, "Reconstructed Images", zoom = 90)

    for(i in 1:length(nPCs)){

      V <- E$v[, 1:nPCs[i]]
      imgHat <- t(V) %*% A  ## project img data on to PCs
      imgSize <- object.size(V) + object.size(imgHat) + object.size(rMeans)

      imgHat <- V %*% imgHat + rowMeans  ## reconstruct from PCs and add back row means
      imgHat <- round((imgHat - min(imgHat)) / (max(imgHat) - min(imgHat))*255) # scale
      plotFn(imgHat/255)

      ## write strings to worksheet 3
      writeData(wb, "Reconstructed Images",
                sprintf("Number of principal components used:  %s",
                        nPCs[[i]]), startCol[i], startRow[i])

      writeData(wb, "Reconstructed Images",
                sprintf("Sum of component object sizes: %s bytes",
                        format(as.numeric(imgSize), big.mark=',')), startCol[i], startRow[i]+1)

      ## write reconstruced image
      insertPlot(wb, "Reconstructed Images", width, height, units="px",
                 xy = c(startCol[i], startRow[i]+3))

    }

    # hide grid lines
    showGridLines(wb, sheet = 3, showGridLines = FALSE)

    ## Make text above images BOLD
    boldStyle <- createStyle(textDecoration="BOLD")

    ## only want to apply style to specified cells (not all combinations of rows & cols)
    addStyle(wb, "Reconstructed Images", style=boldStyle,
             rows = c(startRow, startRow+1), cols = rep(startCol, 2),
             gridExpand = FALSE)

    ## save workbook to working directory
    saveWorkbook(wb, "Image dimensionality reduction.xlsx", overwrite = TRUE)

    ## remove example files for cran test
    if (identical(Sys.getenv("NOT_CRAN", unset = "true"), "false")) {
    file_list<-list.files(pattern="\\.xlsx",recursive = T)
    file_list<-fl[!grepl("inst/extdata",file_list)&!grepl("man/",file_list)]

    if(length(file_list)>0){
    rm(file_list)
    }