Error when launching shiny app in cytofkit:

launchShinyAPP_GUI("F:/Sync/cytometry/resultsQC/cytofkit.RData")

Error in FUN(X[[i]], ...) :
  cannot open file '~/R/win-library/3.3/reshape2/data/Rdata.rdb': No such file or directory

The file does exist:

> list.files('~/R/win-library/3.3/reshape2/data/')
[1] "Rdata.rdb" "Rdata.rds" "Rdata.rdx"

Session info:

R version 3.3.0 (2016-05-03)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

locale:
[1] LC_COLLATE=English_United Kingdom.1252  LC_CTYPE=English_United Kingdom.1252    LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C                            LC_TIME=English_United Kingdom.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] reshape2_1.4.1 gplots_3.0.1   shiny_0.13.2   cytofkit_1.4.3 plyr_1.8.4     ggplot2_2.1.0 

loaded via a namespace (and not attached):
 [1] gtools_3.5.0        splines_3.3.0       lattice_0.20-33     tcltk_3.3.0         pcaPP_1.9-60        colorspace_1.2-6    htmltools_0.3.5    
 [8] stats4_3.3.0        mgcv_1.8-12         flowCore_1.38.2     e1071_1.6-7         BiocGenerics_0.18.0 matrixStats_0.50.2  foreach_1.4.3      
[15] robustbase_0.92-6   stringr_1.0.0       munsell_0.4.3       pdist_1.2           gtable_0.2.0        caTools_1.17.1      mvtnorm_1.0-5      
[22] codetools_0.2-14    VGAM_1.0-2          Biobase_2.32.0      permute_0.9-0       doParallel_1.0.10   httpuv_1.3.3        parallel_3.3.0     
[29] class_7.3-14        DEoptimR_1.0-4      Rcpp_0.12.5         KernSmooth_2.23-15  xtable_1.8-2        corpcor_1.6.8       scales_0.4.0       
[36] gdata_2.17.0        vegan_2.3-5         graph_1.50.0        mime_0.4            RANN_2.5            digest_0.6.9        stringi_1.1.1      
[43] Rtsne_0.10          grid_3.3.0          tools_3.3.0         bitops_1.0-6        magrittr_1.5        cluster_2.0.4       rrcov_1.3-11       
[50] Matrix_1.2-6        MASS_7.3-45         reshape_0.8.5       iterators_1.0.8     R6_2.1.2            igraph_1.0.1        nlme_3.1-128   

Traceback:

> traceback()
14: FUN(X[[i]], ...)
13: vapply(same, exists, NA, where = where, mode = "function", inherits = FALSE)
12: same.isFn(i)
11: checkConflicts(package, pkgname, pkgpath, nogenerics, ns)
10: library(reshape)
9: ..stacktraceon..({
       library(ggplot2)
       library(gplots)
       library(reshape2)
       library(reshape)
       library(plyr)
       library(VGAM)
       visuaPlot <- function(obj, xlab, ylab, zlab, pointSize = 1, 
           addLabel = TRUE, labelSize = 1, selectSamples, removeOutlier = TRUE) {
           data <- cbind(obj$allExpressionData, do.call(cbind, obj$dimReducedRes))
           data <- as.data.frame(data)
           clusterMethods <- names(obj$clusterRes)
           for (cname in clusterMethods) {
               data[[cname]] <- as.factor(obj$clusterRes[[cname]])
           }
           row.names(data) <- row.names(obj$expressionData)
           samples <- sub("_[0-9]*$", "", row.names(obj$expressionData))
           data <- data[samples %in% selectSamples, ]
           nsamples <- samples[samples %in% selectSamples]
           data$sample <- nsamples
           sample_num <- length(unique(nsamples))
           if (sample_num >= 8) {
               shape_value <- LETTERS[1:sample_num]
           }
           else {
               shape_value <- c(1:sample_num) + 15
           }
           if (zlab %in% clusterMethods) {
               cluster_num <- length(unique(data[[zlab]]))
               col_legend_row <- ceiling(cluster_num/15)
               size_legend_row <- ceiling(sample_num/4)
               shapeLab <- "sample"
               gp <- ggplot(data, aes_string(x = xlab, y = ylab, 
                   colour = zlab, shape = shapeLab)) + geom_point(size = pointSize) + 
                   scale_shape_manual(values = shape_value) + scale_colour_manual(values = rainbow(cluster_num)) + 
                   xlab(xlab) + ylab(ylab) + guides(colour = guide_legend(nrow = col_legend_row, 
                   override.aes = list(size = 4)), shape = guide_legend(nrow = size_legend_row, 
                   override.aes = list(size = 4))) + theme_bw() + 
                   theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
               if (addLabel) {
                   edata <- data[, c(xlab, ylab, zlab)]
                   colnames(edata) <- c("x", "y", "z")
                   center <- aggregate(cbind(x, y) ~ z, data = edata, 
                     median)
                   gp <- gp + annotate("text", label = center[, 
                     1], x = center[, 2], y = center[, 3], size = labelSize, 
                     colour = "black")
               }
               gp <- gp + theme(legend.position = "bottom", axis.text = element_text(size = 14), 
                   axis.title = element_text(size = 18, face = "bold"))
           }
           else {
               title <- zlab
               data <- data[, c(xlab, ylab, zlab)]
               if (removeOutlier) 
                   data[, zlab] <- remove_outliers(data[, zlab])
               zlab <- "Expression"
               colnames(data) <- c(xlab, ylab, zlab)
               gp <- ggplot(data, aes_string(x = xlab, y = ylab, 
                   colour = zlab)) + geom_point(size = pointSize) + 
                   theme_bw() + scale_colour_gradient2(low = "blue", 
                   mid = "white", high = "red", midpoint = median(data[[zlab]])) + 
                   theme(legend.position = "right") + xlab(xlab) + 
                   ylab(ylab) + ggtitle(title) + theme(panel.grid.major = element_blank(), 
                   panel.grid.minor = element_blank()) + theme(axis.text = element_text(size = 14), 
                   axis.title = element_text(size = 18, face = "bold"))
           }
           return(gp)
       }
       heatMap <- function(data, clusterMethod = "DensVM", type = "mean", 
           selectSamples, cex_row_label = 1, cex_col_label = 1, 
           scaleMethod = "none") {
           exprs <- data$expressionData
           samples <- sub("_[0-9]*$", "", row.names(exprs))
           exprs <- exprs[samples %in% selectSamples, ]
           ifMultiFCS <- length(selectSamples) > 1
           dataj <- data$clusterRes[[clusterMethod]][samples %in% 
               selectSamples]
           exprs_cluster <- data.frame(exprs, cluster = dataj)
           if (type == "mean") {
               cluster_stat <- aggregate(. ~ cluster, data = exprs_cluster, 
                   mean)
               rownames(cluster_stat) <- paste("cluster_", cluster_stat$cluster, 
                   sep = "")
               cluster_stat <- cluster_stat[, -which(colnames(cluster_stat) == 
                   "cluster")]
           }
           else if (type == "median") {
               cluster_stat <- aggregate(. ~ cluster, data = exprs_cluster, 
                   median)
               rownames(cluster_stat) <- paste("cluster_", cluster_stat$cluster, 
                   sep = "")
               cluster_stat <- cluster_stat[, -which(colnames(cluster_stat) == 
                   "cluster")]
           }
           else if (type == "percentage" && ifMultiFCS) {
               sampleName <- sub("_[0-9]*$", "", row.names(exprs))
               clusterCounts <- as.data.frame(table(sampleName, 
                   dataj))
               colnames(clusterCounts) <- c("sample", "cluster", 
                   "cellCount")
               sampleCellCount <- as.data.frame(table(sampleName))
               colnames(sampleCellCount) <- c("sample", "totalCellCount")
               clust_cellCount <- merge(clusterCounts, sampleCellCount, 
                   by = "sample")
               clust_cellCount$percentage <- round(clust_cellCount$cellCount/clust_cellCount$totalCellCount * 
                   100, 2)
               cluster_stat <- reshape::cast(clust_cellCount, sample ~ 
                   cluster, value = "percentage")
               percColNames <- cluster_stat$sample
               cluster_stat <- cluster_stat[, -which(colnames(cluster_stat) == 
                   "sample")]
               percRowNames <- paste("cluster_", colnames(cluster_stat), 
                   sep = "")
               cluster_stat <- t(as.matrix(cluster_stat))
               row.names(cluster_stat) <- percRowNames
               colnames(cluster_stat) <- percColNames
           }
           else {
               return(NULL)
           }
           cluster_stat <- as.matrix(cluster_stat)
           heatmap.2(cluster_stat, col = bluered, trace = "none", 
               symbreaks = FALSE, scale = scaleMethod, margins = c(8, 
                   8), cexRow = cex_row_label, cexCol = cex_col_label, 
               srtCol = 30, symkey = FALSE, keysize = 1, key.par = list(mgp = c(2, 
                   1, 0), mar = c(4, 3, 4, 0)), main = paste(clusterMethod, 
                   type, "heatmap", sep = " "))
       }
       progressionPlot <- function(data, orderCol = "isomap_1", 
           clusterCol = "cluster", trend_formula = "expression ~ sm.ns(Pseudotime, df=3)") {
           progressionData <- data$progressionRes
           if (!is.null(progressionData)) {
               data <- do.call(cbind, progressionData)
               markers <- colnames(progressionData[[1]])
               colnames(data) <- c(markers, "cluster", colnames(progressionData[[3]]))
               if (!is.data.frame(data)) 
                   data <- data.frame(data, check.names = FALSE)
               if (!all(markers %in% colnames(data))) 
                   stop("Unmatching markers found!")
               if (!(length(orderCol) == 1 && orderCol %in% colnames(data))) 
                   stop("Can not find orderCol in data")
               if (!(length(clusterCol) == 1 && clusterCol %in% 
                   colnames(data))) 
                   stop("Can not find clusterCol in data")
               orderValue <- data[[orderCol]]
               data <- data[order(orderValue), c(markers, clusterCol)]
               data$Pseudotime <- sort(orderValue)
               mdata <- melt(data, id.vars = c("Pseudotime", clusterCol))
               colnames(mdata) <- c("Pseudotime", clusterCol, "markers", 
                   "expression")
               mdata$markers <- factor(mdata$markers)
               mdata[[clusterCol]] <- factor(mdata[[clusterCol]])
               min_expr <- min(mdata$expression)
               vgamPredict <- ddply(mdata, .(markers), function(x) {
                   fit_res <- tryCatch({
                     vg <- suppressWarnings(vgam(formula = as.formula(trend_formula), 
                       family = VGAM::tobit(Lower = min_expr, lmu = "identitylink"), 
                       data = x, maxit = 30, checkwz = FALSE))
                     res <- VGAM::predict(vg, type = "response")
                     res[res < min_expr] <- min_expr
                     res
                   }, error = function(e) {
                     print("Error!")
                     print(e)
                     res <- rep(NA, nrow(x))
                     res
                   })
                   expectation = fit_res
                   data.frame(Pseudotime = x$Pseudotime, expectation = expectation)
               })
               color_by <- clusterCol
               plot_cols <- round(sqrt(length(markers)))
               cell_size <- 1
               x_lab <- orderCol
               y_lab <- "Expression"
               legend_title <- clusterCol
               monocle_theme_opts <- function() {
                   theme(strip.background = element_rect(colour = "white", 
                     fill = "white")) + theme(panel.border = element_blank(), 
                     axis.line = element_line()) + theme(panel.grid.minor.x = element_blank(), 
                     panel.grid.minor.y = element_blank()) + theme(panel.grid.major.x = element_blank(), 
                     panel.grid.major.y = element_blank()) + theme(panel.background = element_rect(fill = "white")) + 
                     theme(legend.position = "right") + theme(axis.title = element_text(size = 15))
               }
               q <- ggplot(aes(Pseudotime, expression), data = mdata)
               q <- q + geom_point(aes_string(color = color_by), 
                   size = I(cell_size))
               q <- q + geom_line(aes(Pseudotime, expectation), 
                   data = vgamPredict)
               q <- q + facet_wrap(~markers, ncol = plot_cols, scales = "free_y")
               q <- q + ylab(y_lab) + xlab(x_lab) + theme_bw()
               q <- q + guides(colour = guide_legend(title = legend_title, 
                   override.aes = list(size = cell_size * 3)))
               q <- q + monocle_theme_opts()
               return(q)
           }
           else {
               return(NULL)
           }
       }
       remove_outliers <- function(x, na.rm = TRUE, ...) {
           qnt <- quantile(x, probs = c(0.25, 0.75), na.rm = na.rm, 
               ...)
           H <- 1.5 * IQR(x, na.rm = na.rm)
           y <- x
           y[x < (qnt[1] - H)] <- qnt[1] - H
           y[x > (qnt[2] + H)] <- qnt[2] + H
           y
       }
   })
8: eval(expr, envir, enclos)
7: eval(exprs, envir)
6: sourceUTF8(file.path.ci(appDir, "global.R"))
5: appParts$onStart()
4: shiny::runApp(system.file("shiny", package = "cytofkit"))
3: cytofkitShinyAPP()
2: launchShinyAPP_GUI(okMessage)
1: cytofkit_GUI()