Version Beta.1 upload to github

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+Package: ggPlantmap
+Type: Package
+Title: ggPlantmap
+Version: 0.1.0
+Authors@R:  
+    person("Leonardo", "jo", ,"[email protected]", role = c("aut", "cre"))
+Maintainer: Leonardo Jo <[email protected]>
+Description: gPlantmap is a R tidyverse based package with a series of plant images to be mapped in a ggplot enviroment. 
+    We also provide a series of functions and instructions to easily create your own personal ggPlantmap. 
+    These ggPlantmap objects can be added into R pipelines for the visual representation of quantitative data in plant distinct cells/structures. 
+License: `use_gpl3_license()`
+Encoding: UTF-8
+LazyData: true
+Imports:
+    dplyr,
+    tibble,
+    ggplot2,
+    XML
+Depends: 
+    R (>= 2.10)
+RoxygenNote: 7.2.3

.Rproj.user/02275A6F/sources/s-77013F18/1E5380C1-contents

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+#' XML.to.ggPlantmap
+#'
+#' @description Open an Icy generated XML file and extract x and y coordinates of ROIs
+#'
+#' @param data XML file name (character string; e.g. "arabidopsis.root.crosssection.xml").
+#'
+#' @return A tibble table with x,y coordinates of individual ROIs (ROI.id) and the name of the ROI (ROI.name)
+#' @import dplyr XML
+#' @importFrom dplyr tibble
+#' @export
+#'
+#' @examples
+#' XML.to.ggPlantmap("arabidopsis.root.crossection.xml")
+XML.to.ggPlantmap <- function(data) {
+  xml.ROI <- XML::xmlToList(data)
+  final.ROI <- NULL
+  for (x in 1:length(xml.ROI)) {
+    ROI <- xml.ROI[x]
+    for (k in 1:length(ROI$roi$points)) {
+      x <- as.numeric(ROI$roi$points[k]$point$pos_x)
+      y <- as.numeric(ROI$roi$points[k]$point$pos_y)
+      final.ROI2 <- dplyr::tibble(point=k,x=x,y=y*-1,ROI.name=ROI$roi$name,id=ROI$roi$id)
+      final.ROI <- rbind(final.ROI,final.ROI2)
+    }
+  }
+
+  final.ROI3 <-  dplyr::tibble(id = as.numeric(unique(final.ROI$id))) %>%
+    dplyr::arrange(id) %>%
+    dplyr::mutate(ROI.id = seq(1,length(unique(final.ROI$id)))) %>%
+    merge(final.ROI,by="id") %>%
+    dplyr::arrange(ROI.id,point) %>%
+    dplyr::select(ROI.name,ROI.id,point,x,y)
+ dplyr::tibble(final.ROI3)
+}
+
+#' ggPlantmap.plot
+#'
+#' @description create a ggplot sample from a ggPlantmap table object
+#'
+#' @param data The ggPlantmap tibble to be displayed
+#' @param layer The level in which you want to distinguish the different ROIs. For example ROI.name,ROI.id or any other classifier you determined (Default: ROI.name).
+#' @param linewdith The line width of the tracing lines of your ggplot (Default = 0.5).
+#' @param show.legend logical. Should a legend for the levels to be included in the plot? If there are too many levels, legends can overwhelm the image. In this case, change to FALSE (Default: TRUE).
+#' @return A ggplot generated map of your ggPlantmap object with colors separated by layers.
+#' @import ggplot2
+#' @export
+#'
+#' @examples
+#' ggPlantmap.plot(data=ggPm.At.3weekrosette.topview,layer=ROI.name,linewdith=1,show.legend=T)
+ggPlantmap.plot <- function(data,layer=ROI.name,linewidth=0.5,show.legend=T) {
+  ggPlantmap <- data
+  ##ar <- (max(ggPlantmap$y) - min(ggPlantmap$y))/(max(ggPlantmap$x) - min(ggPlantmap$x)) ## recording the aspect ratio of the whole image to adjust it in the final plot.
+  ggplot2::ggplot(ggPlantmap, aes(x = x, y = y)) +
+    geom_polygon(aes(group=ROI.id,fill=factor({{layer}})),colour="black",size=linewidth,show.legend={{show.legend}}) +
+    theme_void() +
+    theme(panel.grid = element_blank(),legend.position="right") +
+    coord_fixed()
+    ##theme(aspect.ratio = ar)
+}
+
+#' ggPlantmap.merge
+#'
+#' @description combine a ggPlantmap with a table that contain quantitative data for specific ROIs of your ggPlantmap.
+#'
+#' @param data The ggPlantmap tibble
+#' @param value The value table you want to combine with the ggPlantmap tibble. For simplicity, it would be better if this value table is pre-processed so the only information within it are the level ids (Ex: ROI.name) and the value column (Ex: A specific gene expression values in all the different level ids).
+#' @param id.x Character string. The name of the column in your ggPlantmap tibble where you want to match to the value table. IMPORTANT: It is important to write as a character string, so in between quotes #' @param id.x The name of the column in your ggPlantmap tibble where you want to match to the value table (Ex: "ROI.name").
+#' @param id.y Character string. The name of the column in your value table where you want to match to the ggPlantmap table (Ex: ROI.name). By default, this function will consider that the column names are exactly the same. In this case you don't need to specify id.y. In case of a different column name, you need to specify the column in the value table that correspond to the one in your ggPlantmap. Also as a character string (Ex: "Cell names").
+#' @return A merged tibble with values assigned to specific levels of your ggPlantmap
+#' @import dplyr
+#' @export
+#'
+#' @examples
+#' ggPlantmap.merge(map=ggPm.At.seed.developmentalseries,value=ggPm.At.seed.expressionsample,id.x="ROI.name")
+ggPlantmap.merge <- function(map,value,id.x,id.y=id.x) {
+  map %>%
+    merge(value,by.x={{id.x}},by.y={{id.y}},all.x=T) %>%
+    arrange(ROI.id,point) %>%
+    tibble()
+}
+
+#' ggPlantmap.heatmap
+#'
+#' @description produce a heatmap from a ggPlantmap that contains quantitative values for specific ROI levels.
+#'
+#' @param map.quant The ggPlantmap tibble with quantitative values.
+#' @param value.quant The name of the column that contains the quantitative values (Ex: Gene.expression).
+#' @param linewdith The line width of the tracing lines of your ggplot (Default = 0.5).
+#' @param show.legend logical. Should a legend for the levels to be included in the plot? If there are too many levels, legends can overwhelm the image. In this case, change to FALSE (Default: TRUE).
+#' @return A ggplot generated heatmap of your ggPlantmap with colors that depicts the continuous values of your quantitative data. You can integrate this with a ggplot gradient scale (Example: + scale_fill_gradient())
+#' @import ggplot2
+#' @export
+#'
+#' @examples
+#' quant.data <- ggPlantmap.merge(map=ggPm.At.seed.developmentalseries,value=ggPm.At.seed.expressionsample,id.x="ROI.name")
+#' ggPlantmap.heatmap(map.quant=quant.data,value.quant=gene.expression,linewdith=1,show.legend=T) +
+#' scale_fill_gradient(low="white",high="red")
+ggPlantmap.heatmap <- function(map.quant,value.quant=value.quant,show.legend=T,linewidth=0.5) {
+  ggPlantmap <- map.quant
+  ar <- (max(ggPlantmap$y) - min(ggPlantmap$y))/(max(ggPlantmap$x) - min(ggPlantmap$x)) ## recording the aspect ratio of the whole image to adjust it in the final plot.
+  ggplot2::ggplot(ggPlantmap, aes(x = x, y = y)) +
+    geom_polygon(aes(group=ROI.id,fill={{value.quant}}),colour="black",size=linewidth,show.legend={{show.legend}}) +
+    theme_void() +
+    theme(panel.grid = element_blank(),legend.position="right") +
+    theme(aspect.ratio = ar)
+}

.Rproj.user/02275A6F/sources/s-77013F18/32328C73-contents

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+library(ggplot2)
+ggplot2::ggplot(ggPm.At.3weekrosette.topview) +
+  geom_polygon(aes(x,y,group=ROI.id),fill="white",colour="black",linewidth=0.1)

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+## code to prepare `DATASET` dataset goes here
+
+usethis::use_data(DATASET, overwrite = TRUE)

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