Hacking our way through UpSetR

(This article was first published on Rstats on LIBD rstats club, and kindly contributed to R-bloggers)

For our club meeting today we were going to summarize the Demystifying Data Science conference but we forgot that the videos are not released yet.

So we adjusted plans and decided to continue our work on the UpSetR (Gehlenborg, 2016) package by Nils Gehlenborg.

What you can currently do

First, let’s install the version we used for this post:

devtools::install_github('hms-dbmi/[email protected]')

Our ultimate goal is to submit a pull request that enables UpSetR users to specify a color by row for the dots instead of the actual rows. We had already identified an example that we could work with.

library('UpSetR')
movies <- read.csv( system.file("extdata", "movies.csv", package = "UpSetR"), 
                    header=T, sep=";" )

require(ggplot2); require(plyr); require(gridExtra); require(grid);
## Loading required package: ggplot2
## Loading required package: plyr
## Loading required package: gridExtra
## Loading required package: grid
upset(movies, 
      sets = c("Action", "Comedy", "Drama"), 
      order.by="degree", matrix.color="blue", point.size=5,
      sets.bar.color=c("maroon","blue","orange"))

We also explored the set metadata vignette that includes examples such as the following one.

set.seed(20180727)

## Create the metadata object first
sets <- names(movies[3:19])
avgRottenTomatoesScore <- round(runif(17, min = 0, max = 90))
metadata <- as.data.frame(cbind(sets, avgRottenTomatoesScore))
names(metadata) <- c("sets", "avgRottenTomatoesScore")
metadata$avgRottenTomatoesScore <- as.numeric(as.character(metadata$avgRottenTomatoesScore))
Cities <- sample(c("Boston", "NYC", "LA"), 17, replace = T)
metadata <- cbind(metadata, Cities)
metadata$Cities <- as.character(metadata$Cities)
metadata[which(metadata$sets %in% c("Drama", "Comedy", "Action", "Thriller", 
    "Romance")), ]
##        sets avgRottenTomatoesScore Cities
## 1    Action                     68 Boston
## 4    Comedy                     40    NYC
## 7     Drama                     48     LA
## 13  Romance                     77 Boston
## 15 Thriller                     19    NYC
accepted <- round(runif(17, min = 0, max = 1))
metadata <- cbind(metadata, accepted)
metadata[which(metadata$sets %in% c("Drama", "Comedy", "Action", "Thriller", 
    "Romance")), ]
##        sets avgRottenTomatoesScore Cities accepted
## 1    Action                     68 Boston        0
## 4    Comedy                     40    NYC        1
## 7     Drama                     48     LA        0
## 13  Romance                     77 Boston        1
## 15 Thriller                     19    NYC        0
## Now make the plot
upset(movies, set.metadata = list(data = metadata, plots = list(list(type = "hist", 
    column = "avgRottenTomatoesScore", assign = 20), list(type = "matrix_rows", 
    column = "Cities", colors = c(Boston = "green", NYC = "navy", LA = "purple"), 
    alpha = 0.5))))

Hacking our way

Using the metadata looked complicated to us and hopefully not necessary for what we are trying to accomplish. That is, we really wanted to change the colors of the circles in each row, not the rows themselves. So we found the GitHub repo with the code, plugged a laptop to a TV and started exploring as a group. We went the rabbit hole to see how the matrix.color argument got used. To actually hack our way through, we downloaded the latest version of the code using git.

git clone [email protected]:hms-dbmi/UpSetR.git
cd UpSetR

Next, we created the objects that match the default arguments of upset() by finding and replacing commas by semi-colons. Well, not all of the commas. Also, for inputs that specified a vector (mostly 2 options), we chose the first one to match the default R behavior. This way we could execute them and have them in our session.

## Default upset() arguments
nsets = 5; nintersects = 40; sets = NULL; keep.order = F; set.metadata = NULL; intersections = NULL;
matrix.color = "gray23"; main.bar.color = "gray23"; mainbar.y.label = "Intersection Size"; mainbar.y.max = NULL;
sets.bar.color = "gray23"; sets.x.label = "Set Size"; point.size = 2.2; line.size = 0.7;
mb.ratio = c(0.70,0.30); expression = NULL; att.pos = NULL; att.color = main.bar.color; order.by = 'freq';
decreasing = T; show.numbers = "yes"; number.angles = 0; group.by = "degree";cutoff = NULL;
queries = NULL; query.legend = "none"; shade.color = "gray88"; shade.alpha = 0.25; matrix.dot.alpha =0.5;
empty.intersections = NULL; color.pal = 1; boxplot.summary = NULL; attribute.plots = NULL; scale.intersections = "identity";
scale.sets = "identity"; text.scale = 1; set_size.angles = 0 ; set_size.show = FALSE 

Next, we did the same (commas to semicolons) for the inputs of the first example.

## Initial inputs on the first example
movies <- read.csv( system.file("extdata", "movies.csv", package = "UpSetR"), 
                    header=T, sep=";" )

## comma -> semicolon
data = movies; sets = c("Action", "Comedy", "Drama"); 
      order.by="degree"; matrix.color="blue"; point.size=5;
      sets.bar.color=c("maroon","blue","orange")

Now we were ready to start modifying some of the internal UpSetR (Gehlenborg, 2016) code.

Hacking internals

The function upset() is pretty long and uses many un-exported functions from the package itself. In order to test thing quickly we added UpSetR::: calls before the un-exported functions. Here’s our modified version where we added a piece of code to modify the Matrix_layout object and add some colors.

## Piece of code we introduced
for(i in 1:3) {
      j <- which(Matrix_layout$y == i & Matrix_layout$value == 1)
      if(length(j) > 0) Matrix_layout$color[j] <- c("maroon","blue","orange")[i]
  }

Ok, here’s the full modified upset() function.

## Modified internal upset() code

startend <- UpSetR:::FindStartEnd(data)
  first.col <- startend[1]
  last.col <- startend[2]

  if(color.pal == 1){
    palette <- c("#1F77B4", "#FF7F0E", "#2CA02C", "#D62728", "#9467BD", "#8C564B", "#E377C2",
                 "#7F7F7F", "#BCBD22", "#17BECF")
  } else{
    palette <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00",
                 "#CC79A7")
  }

  if(is.null(intersections) == F){
    Set_names <- unique((unlist(intersections)))
    Sets_to_remove <- UpSetR:::Remove(data, first.col, last.col, Set_names)
    New_data <- UpSetR:::Wanted(data, Sets_to_remove)
    Num_of_set <- UpSetR:::Number_of_sets(Set_names)
    if(keep.order == F){
      Set_names <- UpSetR:::order_sets(New_data, Set_names)
    }
    All_Freqs <- UpSetR:::specific_intersections(data, first.col, last.col, intersections, order.by, group.by, decreasing,
                                        cutoff, main.bar.color, Set_names)
  } else if(is.null(intersections) == T){
    Set_names <- sets
    if(is.null(Set_names) == T || length(Set_names) == 0 ){
      Set_names <- UpSetR:::FindMostFreq(data, first.col, last.col, nsets)
    }
    Sets_to_remove <- UpSetR:::Remove(data, first.col, last.col, Set_names)
    New_data <- UpSetR:::Wanted(data, Sets_to_remove)
    Num_of_set <- UpSetR:::Number_of_sets(Set_names)
    if(keep.order == F){
    Set_names <- UpSetR:::order_sets(New_data, Set_names)
    }
    All_Freqs <- UpSetR:::Counter(New_data, Num_of_set, first.col, Set_names, nintersects, main.bar.color,
                         order.by, group.by, cutoff, empty.intersections, decreasing)
  }
  Matrix_setup <- UpSetR:::Create_matrix(All_Freqs)
  labels <- UpSetR:::Make_labels(Matrix_setup)
  #Chose NA to represent NULL case as result of NA being inserted when at least one contained both x and y
  #i.e. if one custom plot had both x and y, and others had only x, the y's for the other plots were NA
  #if I decided to make the NULL case (all x and no y, or vice versa), there would have been alot more if/else statements
  #NA can be indexed so that we still get the non NA y aesthetics on correct plot. NULL cant be indexed.
  att.x <- c(); att.y <- c();
  if(is.null(attribute.plots) == F){
    for(i in seq_along(attribute.plots$plots)){
      if(length(attribute.plots$plots[[i]]$x) != 0){
        att.x[i] <- attribute.plots$plots[[i]]$x
      }
      else if(length(attribute.plots$plots[[i]]$x) == 0){
        att.x[i] <- NA
      }
      if(length(attribute.plots$plots[[i]]$y) != 0){
        att.y[i] <- attribute.plots$plots[[i]]$y
      }
      else if(length(attribute.plots$plots[[i]]$y) == 0){
        att.y[i] <- NA
      }
    }
  }

  BoxPlots <- NULL
  if(is.null(boxplot.summary) == F){
    BoxData <- UpSetR:::IntersectionBoxPlot(All_Freqs, New_data, first.col, Set_names)
    BoxPlots <- list()
    for(i in seq_along(boxplot.summary)){
      BoxPlots[[i]] <- UpSetR:::BoxPlotsPlot(BoxData, boxplot.summary[i], att.color)
    }
  }

  customAttDat <- NULL
  customQBar <- NULL
  Intersection <- NULL
  Element <- NULL
  legend <- NULL
  EBar_data <- NULL
  if(is.null(queries) == F){
    custom.queries <- UpSetR:::SeperateQueries(queries, 2, palette)
    customDat <- UpSetR:::customQueries(New_data, custom.queries, Set_names)
    legend <- UpSetR:::GuideGenerator(queries, palette)
    legend <- UpSetR:::Make_legend(legend)
    if(is.null(att.x) == F && is.null(customDat) == F){
      customAttDat <- UpSetR:::CustomAttData(customDat, Set_names)
    }
    customQBar <- UpSetR:::customQueriesBar(customDat, Set_names, All_Freqs, custom.queries)
  }
  if(is.null(queries) == F){
    Intersection <- UpSetR:::SeperateQueries(queries, 1, palette)
    Matrix_col <- UpSetR:::intersects(QuerieInterData, Intersection, New_data, first.col, Num_of_set,
                             All_Freqs, expression, Set_names, palette)
    Element <- UpSetR:::SeperateQueries(queries, 1, palette)
    EBar_data <-UpSetR:::ElemBarDat(Element, New_data, first.col, expression, Set_names,palette, All_Freqs)
  } else{
    Matrix_col <- NULL
  }
  
  Matrix_layout <- UpSetR:::Create_layout(Matrix_setup, matrix.color, Matrix_col, matrix.dot.alpha)

As a little pause in upset(), let’s check what actually Matrix_layout looks.

Matrix_layout
##    y x value  color alpha Intersection
## 1  1 1     1   blue   1.0         1yes
## 2  2 1     1   blue   1.0         1yes
## 3  3 1     1   blue   1.0         1yes
## 4  1 2     0 gray83   0.5          4No
## 5  2 2     1   blue   1.0         2yes
## 6  3 2     1   blue   1.0         2yes
## 7  1 3     1   blue   1.0         3yes
## 8  2 3     0 gray83   0.5          8No
## 9  3 3     1   blue   1.0         3yes
## 10 1 4     1   blue   1.0         4yes
## 11 2 4     1   blue   1.0         4yes
## 12 3 4     0 gray83   0.5         12No
## 13 1 5     0 gray83   0.5         13No
## 14 2 5     0 gray83   0.5         14No
## 15 3 5     1   blue   1.0         5yes
## 16 1 6     0 gray83   0.5         16No
## 17 2 6     1   blue   1.0         6yes
## 18 3 6     0 gray83   0.5         18No
## 19 1 7     1   blue   1.0         7yes
## 20 2 7     0 gray83   0.5         20No
## 21 3 7     0 gray83   0.5         21No

We figured out that we had to change the colors only the rows with value = 1 and that y was the row grouping variable.

## our modification
  for(i in 1:3) {
      j <- which(Matrix_layout$y == i & Matrix_layout$value == 1)
      if(length(j) > 0) Matrix_layout$color[j] <- c("maroon","blue","orange")[i]
  }

Here’s our modified Matrix_layout:

Matrix_layout
##    y x value  color alpha Intersection
## 1  1 1     1 maroon   1.0         1yes
## 2  2 1     1   blue   1.0         1yes
## 3  3 1     1 orange   1.0         1yes
## 4  1 2     0 gray83   0.5          4No
## 5  2 2     1   blue   1.0         2yes
## 6  3 2     1 orange   1.0         2yes
## 7  1 3     1 maroon   1.0         3yes
## 8  2 3     0 gray83   0.5          8No
## 9  3 3     1 orange   1.0         3yes
## 10 1 4     1 maroon   1.0         4yes
## 11 2 4     1   blue   1.0         4yes
## 12 3 4     0 gray83   0.5         12No
## 13 1 5     0 gray83   0.5         13No
## 14 2 5     0 gray83   0.5         14No
## 15 3 5     1 orange   1.0         5yes
## 16 1 6     0 gray83   0.5         16No
## 17 2 6     1   blue   1.0         6yes
## 18 3 6     0 gray83   0.5         18No
## 19 1 7     1 maroon   1.0         7yes
## 20 2 7     0 gray83   0.5         20No
## 21 3 7     0 gray83   0.5         21No

Ok, let’s continue with the rest of upset().

## continuing with upset()
  
  Set_sizes <- UpSetR:::FindSetFreqs(New_data, first.col, Num_of_set, Set_names, keep.order)
  Bar_Q <- NULL
  if(is.null(queries) == F){
    Bar_Q <- UpSetR:::intersects(QuerieInterBar, Intersection, New_data, first.col, Num_of_set, All_Freqs, expression, Set_names, palette)
  }
  QInter_att_data <- NULL
  QElem_att_data <- NULL
  if((is.null(queries) == F) & (is.null(att.x) == F)){
    QInter_att_data <- UpSetR:::intersects(QuerieInterAtt, Intersection, New_data, first.col, Num_of_set, att.x, att.y,
                                  expression, Set_names, palette)
    QElem_att_data <- UpSetR:::elements(QuerieElemAtt, Element, New_data, first.col, expression, Set_names, att.x, att.y,
                               palette)
  }
  AllQueryData <- UpSetR:::combineQueriesData(QInter_att_data, QElem_att_data, customAttDat, att.x, att.y)

  ShadingData <- NULL

  if(is.null(set.metadata) == F){
    ShadingData <- get_shade_groups(set.metadata, Set_names, Matrix_layout, shade.alpha)
    output <- Make_set_metadata_plot(set.metadata, Set_names)
    set.metadata.plots <- output[[1]]
    set.metadata <- output[[2]]

    if(is.null(ShadingData) == FALSE){
    shade.alpha <- unique(ShadingData$alpha)
    }
  } else {
    set.metadata.plots <- NULL
  }
  if(is.null(ShadingData) == TRUE){
  ShadingData <- UpSetR:::MakeShading(Matrix_layout, shade.color)
  }
  Main_bar <- suppressMessages(UpSetR:::Make_main_bar(All_Freqs, Bar_Q, show.numbers, mb.ratio, customQBar, number.angles, EBar_data, mainbar.y.label,
                            mainbar.y.max, scale.intersections, text.scale, attribute.plots))
  Matrix <- UpSetR:::Make_matrix_plot(Matrix_layout, Set_sizes, All_Freqs, point.size, line.size,
                             text.scale, labels, ShadingData, shade.alpha)
  Sizes <- UpSetR:::Make_size_plot(Set_sizes, sets.bar.color, mb.ratio, sets.x.label, scale.sets, text.scale, set_size.angles,set_size.show)

  # Make_base_plot(Main_bar, Matrix, Sizes, labels, mb.ratio, att.x, att.y, New_data,
  #                expression, att.pos, first.col, att.color, AllQueryData, attribute.plots,
  #                legend, query.legend, BoxPlots, Set_names, set.metadata, set.metadata.plots)

  structure(class = "upset",
    .Data=list(
      Main_bar = Main_bar,
      Matrix = Matrix,
      Sizes = Sizes,
      labels = labels,
      mb.ratio = mb.ratio,
      att.x = att.x,
      att.y = att.y,
      New_data = New_data,
      expression = expression,
      att.pos = att.pos,
      first.col = first.col,
      att.color = att.color,
      AllQueryData = AllQueryData,
      attribute.plots = attribute.plots,
      legend = legend,
      query.legend = query.legend,
      BoxPlots = BoxPlots,
      Set_names = Set_names,
      set.metadata = set.metadata,
      set.metadata.plots = set.metadata.plots)
  )

Line colors

Ok, that’s great but we have a problem with the lines. The color is no longer black, so we went deeper into the rabbit hole and found that the internal Make_matrix_plot() function is where the lines are made. We made some edits but got a plot where the lines were on top of the circles as shown in this screenshot.

Our club session was out of time, so we decided to continue our project another day and ask for help on twitter. And yay, we got help super fast!

So here’s our modified version of Make_matrix_plot() that keeps the lines black.

Make_matrix_plot <- function(Mat_data,Set_size_data, Main_bar_data, point_size, line_size, text_scale, labels,
                             shading_data, shade_alpha){

  if(length(text_scale) == 1){
    name_size_scale <- text_scale
  }
  if(length(text_scale) > 1 && length(text_scale) <= 6){
    name_size_scale <- text_scale[5]
  }
  
  Mat_data$line_col <- 'black'

  Matrix_plot <- (ggplot()
                  + theme(panel.background = element_rect(fill = "white"),
                          plot.margin=unit(c(-0.2,0.5,0.5,0.5), "lines"),
                          axis.text.x = element_blank(),
                          axis.ticks.x = element_blank(),
                          axis.ticks.y = element_blank(),
                          axis.text.y = element_text(colour = "gray0",
                                                     size = 7*name_size_scale, hjust = 0.4),
                          panel.grid.major = element_blank(),
                          panel.grid.minor = element_blank())
                  + xlab(NULL) + ylab("   ")
                  + scale_y_continuous(breaks = c(1:nrow(Set_size_data)),
                                       limits = c(0.5,(nrow(Set_size_data) +0.5)),
                                       labels = labels, expand = c(0,0))
                  + scale_x_continuous(limits = c(0,(nrow(Main_bar_data)+1 )), expand = c(0,0))
                  + geom_rect(data = shading_data, aes_string(xmin = "min", xmax = "max",
                                                              ymin = "y_min", ymax = "y_max"),
                              fill = shading_data$shade_color, alpha = shade_alpha)
                  + geom_line(data= Mat_data, aes_string(group = "Intersection", x="x", y="y",
                                                         colour = "line_col"), size = line_size)
                 + geom_point(data= Mat_data, aes_string(x= "x", y= "y"), colour = Mat_data$color,
                     size= point_size, alpha = Mat_data$alpha, shape=16)
                  + scale_color_identity())
  Matrix_plot <- ggplot_gtable(ggplot_build(Matrix_plot))
  return(Matrix_plot)
}

Using that modified version we can then run the code again (note that we are not using UpSetR::: before Make_matrix_plot) and get the plot we wanted.

Matrix <- Make_matrix_plot(Matrix_layout, Set_sizes, All_Freqs, point.size, line.size,
                             text.scale, labels, ShadingData, shade.alpha)
  Sizes <- UpSetR:::Make_size_plot(Set_sizes, sets.bar.color, mb.ratio, sets.x.label, scale.sets, text.scale, set_size.angles,set_size.show)

  # Make_base_plot(Main_bar, Matrix, Sizes, labels, mb.ratio, att.x, att.y, New_data,
  #                expression, att.pos, first.col, att.color, AllQueryData, attribute.plots,
  #                legend, query.legend, BoxPlots, Set_names, set.metadata, set.metadata.plots)

  structure(class = "upset",
    .Data=list(
      Main_bar = Main_bar,
      Matrix = Matrix,
      Sizes = Sizes,
      labels = labels,
      mb.ratio = mb.ratio,
      att.x = att.x,
      att.y = att.y,
      New_data = New_data,
      expression = expression,
      att.pos = att.pos,
      first.col = first.col,
      att.color = att.color,
      AllQueryData = AllQueryData,
      attribute.plots = attribute.plots,
      legend = legend,
      query.legend = query.legend,
      BoxPlots = BoxPlots,
      Set_names = Set_names,
      set.metadata = set.metadata,
      set.metadata.plots = set.metadata.plots)
  )

We have quite a bit more to do in order to complete our pull request. We are also curious if you would have used a different approach to hack your way through UpSetR (Gehlenborg, 2016). For example, maybe some functions from devtools (Wickham, Hester, and Chang, 2018) would have enabled to do this equally fast without having to introduce UpSetR::: calls.

Thanks for reading!

References

[1] C. Boettiger. knitcitations: Citations for ‘Knitr’ Markdown Files. R package version 1.0.8. 2017. URL: https://CRAN.R-project.org/package=knitcitations.

[2] N. Gehlenborg. UpSetR: A More Scalable Alternative to Venn and Euler Diagrams for Visualizing Intersecting Sets. R package version 1.4.0. 2016. URL: http://github.com/hms-dbmi/UpSetR.

[3] A. Oleś, M. Morgan and W. Huber. BiocStyle: Standard styles for vignettes and other Bioconductor documents. R package version 2.8.2. 2018. URL: https://github.com/Bioconductor/BiocStyle.

[4] H. Wickham, J. Hester and W. Chang. devtools: Tools to Make Developing R Packages Easier. R package version 1.13.6. 2018. URL: https://CRAN.R-project.org/package=devtools.

[5] Y. Xie, A. P. Hill and A. Thomas. blogdown: Creating Websites with R Markdown. ISBN 978-0815363729. Boca Raton, Florida: Chapman and Hall/CRC, 2017. URL: https://github.com/rstudio/blogdown.

Reproducibility

## Session info ----------------------------------------------------------------------------------------------------------
##  setting  value                       
##  version  R version 3.5.1 (2018-07-02)
##  system   x86_64, darwin15.6.0        
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  tz       America/New_York            
##  date     2018-07-27
## Packages --------------------------------------------------------------------------------------------------------------
##  package       * version date       source                            
##  assertthat      0.2.0   2017-04-11 cran (@0.2.0)                     
##  backports       1.1.2   2017-12-13 cran (@1.1.2)                     
##  base          * 3.5.1   2018-07-05 local                             
##  bibtex          0.4.2   2017-06-30 CRAN (R 3.5.0)                    
##  bindr           0.1.1   2018-03-13 cran (@0.1.1)                     
##  bindrcpp        0.2.2   2018-03-29 cran (@0.2.2)                     
##  BiocStyle     * 2.8.2   2018-05-30 Bioconductor                      
##  blogdown        0.8     2018-07-15 CRAN (R 3.5.0)                    
##  bookdown        0.7     2018-02-18 CRAN (R 3.5.0)                    
##  colorout      * 1.2-0   2018-05-03 Github (jalvesaq/[email protected])
##  colorspace      1.3-2   2016-12-14 cran (@1.3-2)                     
##  compiler        3.5.1   2018-07-05 local                             
##  crayon          1.3.4   2017-09-16 cran (@1.3.4)                     
##  datasets      * 3.5.1   2018-07-05 local                             
##  devtools      * 1.13.6  2018-06-27 cran (@1.13.6)                    
##  digest          0.6.15  2018-01-28 CRAN (R 3.5.0)                    
##  dplyr           0.7.6   2018-06-29 CRAN (R 3.5.1)                    
##  evaluate        0.11    2018-07-17 CRAN (R 3.5.0)                    
##  ggplot2       * 3.0.0   2018-07-03 CRAN (R 3.5.0)                    
##  glue            1.3.0   2018-07-17 CRAN (R 3.5.0)                    
##  graphics      * 3.5.1   2018-07-05 local                             
##  grDevices     * 3.5.1   2018-07-05 local                             
##  grid          * 3.5.1   2018-07-05 local                             
##  gridExtra     * 2.3     2017-09-09 CRAN (R 3.5.0)                    
##  gtable          0.2.0   2016-02-26 CRAN (R 3.5.0)                    
##  htmltools       0.3.6   2017-04-28 cran (@0.3.6)                     
##  httr            1.3.1   2017-08-20 CRAN (R 3.5.0)                    
##  jsonlite        1.5     2017-06-01 CRAN (R 3.5.0)                    
##  knitcitations * 1.0.8   2017-07-04 CRAN (R 3.5.0)                    
##  knitr           1.20    2018-02-20 cran (@1.20)                      
##  labeling        0.3     2014-08-23 cran (@0.3)                       
##  lazyeval        0.2.1   2017-10-29 CRAN (R 3.5.0)                    
##  lubridate       1.7.4   2018-04-11 CRAN (R 3.5.0)                    
##  magrittr        1.5     2014-11-22 cran (@1.5)                       
##  memoise         1.1.0   2017-04-21 CRAN (R 3.5.0)                    
##  methods       * 3.5.1   2018-07-05 local                             
##  munsell         0.5.0   2018-06-12 CRAN (R 3.5.0)                    
##  pillar          1.3.0   2018-07-14 CRAN (R 3.5.0)                    
##  pkgconfig       2.0.1   2017-03-21 cran (@2.0.1)                     
##  plyr          * 1.8.4   2016-06-08 cran (@1.8.4)                     
##  purrr           0.2.5   2018-05-29 cran (@0.2.5)                     
##  R6              2.2.2   2017-06-17 CRAN (R 3.5.0)                    
##  Rcpp            0.12.18 2018-07-23 CRAN (R 3.5.1)                    
##  RefManageR      1.2.0   2018-04-25 CRAN (R 3.5.0)                    
##  rlang           0.2.1   2018-05-30 cran (@0.2.1)                     
##  rmarkdown       1.10    2018-06-11 CRAN (R 3.5.0)                    
##  rprojroot       1.3-2   2018-01-03 cran (@1.3-2)                     
##  scales          0.5.0   2017-08-24 cran (@0.5.0)                     
##  stats         * 3.5.1   2018-07-05 local                             
##  stringi         1.2.4   2018-07-20 CRAN (R 3.5.0)                    
##  stringr         1.3.1   2018-05-10 CRAN (R 3.5.0)                    
##  tibble          1.4.2   2018-01-22 cran (@1.4.2)                     
##  tidyselect      0.2.4   2018-02-26 cran (@0.2.4)                     
##  tools           3.5.1   2018-07-05 local                             
##  UpSetR        * 1.4.0   2018-07-27 Github (hms-dbmi/[email protected])  
##  utils         * 3.5.1   2018-07-05 local                             
##  withr           2.1.2   2018-03-15 CRAN (R 3.5.0)                    
##  xfun            0.3     2018-07-06 CRAN (R 3.5.0)                    
##  xml2            1.2.0   2018-01-24 CRAN (R 3.5.0)                    
##  yaml            2.1.19  2018-05-01 CRAN (R 3.5.0)

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