diff --git a/R-package/NAMESPACE b/R-package/NAMESPACE
index 7f6fa5817..a9ae672a3 100644
--- a/R-package/NAMESPACE
+++ b/R-package/NAMESPACE
@@ -12,6 +12,7 @@ export(xgb.load)
 export(xgb.model.dt.tree)
 export(xgb.plot.deepness)
 export(xgb.plot.importance)
+export(xgb.plot.multi.trees)
 export(xgb.plot.tree)
 export(xgb.save)
 export(xgb.save.raw)
@@ -36,6 +37,7 @@ importFrom(data.table,setnames)
 importFrom(magrittr,"%>%")
 importFrom(magrittr,add)
 importFrom(magrittr,not)
+importFrom(stringr,str_detect)
 importFrom(stringr,str_extract)
 importFrom(stringr,str_extract_all)
 importFrom(stringr,str_match)
diff --git a/R-package/R/xgb.model.dt.tree.R b/R-package/R/xgb.model.dt.tree.R
index 5833389e2..13d3ecc5b 100644
--- a/R-package/R/xgb.model.dt.tree.R
+++ b/R-package/R/xgb.model.dt.tree.R
@@ -12,7 +12,6 @@
 #' @importFrom magrittr add
 #' @importFrom stringr str_extract
 #' @importFrom stringr str_split
-#' @importFrom stringr str_extract
 #' @importFrom stringr str_trim
 #' @param feature_names names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.
 #' @param filename_dump the path to the text file storing the model. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}).
diff --git a/R-package/R/xgb.plot.multi.trees.R b/R-package/R/xgb.plot.multi.trees.R
new file mode 100644
index 000000000..f53d1a13f
--- /dev/null
+++ b/R-package/R/xgb.plot.multi.trees.R
@@ -0,0 +1,112 @@
+#' Project all trees on one tree and plot it
+#' 
+#' Visualization of the ensemble of trees as a single collective unit.
+#'
+#' @importFrom data.table data.table
+#' @importFrom data.table rbindlist
+#' @importFrom data.table setnames
+#' @importFrom data.table :=
+#' @importFrom magrittr %>%
+#' @importFrom stringr str_detect
+#' @importFrom stringr str_extract
+#' 
+#' @param filename_dump the path to the text file storing the model. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}).
+#' @param model dump generated by the \code{xgb.train} function. Avoid the creation of a dump file.
+#' @param features.keep number of features to keep in each position of the multi tree.
+#' @param plot.width width in pixels of the graph to produce
+#' @param plot.height height in pixels of the graph to produce
+#' 
+#' @return Two graphs showing the distribution of the model deepness.
+#' 
+#' @details
+#' 
+#' This function tries to capture the complexity of gradient boosted tree ensembles 
+#' in a cohesive way. 
+#' The goal is to improve the interpretability of the model generally seen as black box.
+#' The function is dedicated to boosting applied to decision trees only.
+#' 
+#' The purpose is to move from an ensemble of trees to a single tree only.
+#' It takes advantage of the fact that the shape of a binary tree is only defined by 
+#' its deepness.
+#' Therefore in a boosting model, all trees have the same shape. 
+#' Moreover, the trees tend to reuse the same features.
+#' 
+#' The function will project each trees on one, and keep for each position the 
+#' \code{features.keep} first features (based on Gain per feature).
+#' 
+#' This function is inspired from this blog post:
+#' \url{https://wellecks.wordpress.com/2015/02/21/peering-into-the-black-box-visualizing-lambdamart/}
+#'
+#' @examples
+#' data(agaricus.train, package='xgboost')
+#'
+#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 15,
+#'                  eta = 1, nthread = 2, nround = 30, objective = "binary:logistic",
+#'                  min_child_weight = 50)
+#'
+#' p <- xgb.plot.multi.trees(bst, agaricus.train$data@Dimnames[[2]], 3)
+#' print(p)
+#'
+#' @export
+xgb.plot.multi.trees <- function(model, names, features.keep = 5, plot.width = NULL, plot.height = NULL){
+  tree.matrix <- xgb.model.dt.tree(names, model = model)
+  
+  # first number of the path represents the tree, then the following numbers are related to the path to follow
+  # root init
+  root.nodes <- tree.matrix[str_detect(ID, "\\d+-0"), ID]
+  tree.matrix[ID %in% root.nodes, abs.node.position:=root.nodes]
+  
+  precedent.nodes <- root.nodes
+  
+  while(tree.matrix[,sum(is.na(abs.node.position))] > 0) {
+    yes.row.nodes <- tree.matrix[abs.node.position %in% precedent.nodes & !is.na(Yes)]
+    no.row.nodes <- tree.matrix[abs.node.position %in% precedent.nodes & !is.na(No)]
+    yes.nodes.abs.pos <- yes.row.nodes[, abs.node.position] %>% paste0("_0")
+    no.nodes.abs.pos <- no.row.nodes[, abs.node.position] %>% paste0("_1")
+    
+    tree.matrix[ID %in% yes.row.nodes[, Yes], abs.node.position := yes.nodes.abs.pos]
+    tree.matrix[ID %in% no.row.nodes[, No], abs.node.position := no.nodes.abs.pos]
+    precedent.nodes <- c(yes.nodes.abs.pos, no.nodes.abs.pos)
+  }
+  
+  tree.matrix[!is.na(Yes),Yes:= paste0(abs.node.position, "_0")]
+  tree.matrix[!is.na(No),No:= paste0(abs.node.position, "_1")]
+  
+  
+  
+  remove.tree <- . %>% str_replace(pattern = "^\\d+-", replacement = "")
+  
+  tree.matrix[,`:=`(abs.node.position=remove.tree(abs.node.position), Yes=remove.tree(Yes), No=remove.tree(No))]
+  
+  nodes.dt <- tree.matrix[,.(Quality = sum(Quality)),by = .(abs.node.position, Feature)][,.(Text =paste0(Feature[1:min(length(Feature), features.keep)], " (", Quality[1:min(length(Quality), features.keep)], ")") %>% paste0(collapse = "\n")), by=abs.node.position]
+  edges.dt <- tree.matrix[Feature != "Leaf",.(abs.node.position, Yes)] %>% list(tree.matrix[Feature != "Leaf",.(abs.node.position, No)]) %>% rbindlist() %>% setnames(c("From", "To")) %>% .[,.N,.(From, To)] %>% .[,N:=NULL]
+  
+  nodes <- DiagrammeR::create_nodes(nodes = nodes.dt[,abs.node.position],
+                                    label = nodes.dt[,Text],
+                                    style = "filled",
+                                    color = "DimGray",
+                                    fillcolor= "Beige",
+                                    shape = "oval",
+                                    fontname = "Helvetica"
+  )
+  
+  edges <- DiagrammeR::create_edges(from = edges.dt[,From],
+                                    to = edges.dt[,To],
+                                    color = "DimGray", 
+                                    arrowsize = "1.5", 
+                                    arrowhead = "vee",
+                                    fontname = "Helvetica",
+                                    rel = "leading_to")
+  
+  graph <- DiagrammeR::create_graph(nodes_df = nodes,
+                                    edges_df = edges,
+                                    graph_attrs = "rankdir = LR")
+  
+  DiagrammeR::render_graph(graph, width = plot.width, height = plot.height)  
+}
+
+globalVariables(
+  c(
+    "Feature", "no.nodes.abs.pos", "ID", "Yes", "No", "Tree", "yes.nodes.abs.pos", "abs.node.position"
+  )
+)
\ No newline at end of file
diff --git a/R-package/R/xgb.plot.tree.R b/R-package/R/xgb.plot.tree.R
index 63bebf6cf..2976f1b07 100644
--- a/R-package/R/xgb.plot.tree.R
+++ b/R-package/R/xgb.plot.tree.R
@@ -10,8 +10,8 @@
 #' @param filename_dump the path to the text file storing the model. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}). Possible to provide a model directly (see \code{model} argument).
 #' @param model generated by the \code{xgb.train} function. Avoid the creation of a dump file.
 #' @param n_first_tree limit the plot to the n first trees. If \code{NULL}, all trees of the model are plotted. Performance can be low for huge models.
-#' @param width  the width of the diagram in pixels.
-#' @param height	the height of the diagram in pixels.
+#' @param plot.width  the width of the diagram in pixels.
+#' @param plot.height	the height of the diagram in pixels.
 #'
 #' @return A \code{DiagrammeR} of the model.
 #'
@@ -43,7 +43,7 @@
 #' xgb.plot.tree(agaricus.train$data@@Dimnames[[2]], model = bst)
 #' 
 #' @export
-xgb.plot.tree <- function(feature_names = NULL, filename_dump = NULL, model = NULL, n_first_tree = NULL, width = NULL, height = NULL){
+xgb.plot.tree <- function(feature_names = NULL, filename_dump = NULL, model = NULL, n_first_tree = NULL, plot.width = NULL, plot.height = NULL){
 
   if (!class(model) %in% c("xgb.Booster", "NULL")) {
     stop("model: Has to be an object of class xgb.Booster model generaged by the xgb.train function.")
@@ -87,7 +87,7 @@ xgb.plot.tree <- function(feature_names = NULL, filename_dump = NULL, model = NU
                         edges_df = edges,
                         graph_attrs = "rankdir = LR")
   
-  DiagrammeR::render_graph(graph, width = width, height = height)
+  DiagrammeR::render_graph(graph, width = plot.width, height = plot.height)
 }
 
 # Avoid error messages during CRAN check.
diff --git a/R-package/man/xgb.plot.multi.trees.Rd b/R-package/man/xgb.plot.multi.trees.Rd
new file mode 100644
index 000000000..2bbe29ca5
--- /dev/null
+++ b/R-package/man/xgb.plot.multi.trees.Rd
@@ -0,0 +1,56 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/xgb.plot.multi.trees.R
+\name{xgb.plot.multi.trees}
+\alias{xgb.plot.multi.trees}
+\title{Project all trees on one tree and plot it}
+\usage{
+xgb.plot.multi.trees(model, names, features.keep = 5, plot.width = NULL,
+  plot.height = NULL)
+}
+\arguments{
+\item{model}{dump generated by the \code{xgb.train} function. Avoid the creation of a dump file.}
+
+\item{features.keep}{number of features to keep in each position of the multi tree.}
+
+\item{plot.width}{width in pixels of the graph to produce}
+
+\item{plot.height}{height in pixels of the graph to produce}
+
+\item{filename_dump}{the path to the text file storing the model. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}).}
+}
+\value{
+Two graphs showing the distribution of the model deepness.
+}
+\description{
+Visualization of the ensemble of trees as a single collective unit.
+}
+\details{
+This function tries to capture the complexity of gradient boosted tree ensembles 
+in a cohesive way. 
+The goal is to improve the interpretability of the model generally seen as black box.
+The function is dedicated to boosting applied to decision trees only.
+
+The purpose is to move from an ensemble of trees to a single tree only.
+It takes advantage of the fact that the shape of a binary tree is only defined by 
+its deepness.
+Therefore in a boosting model, all trees have the same shape. 
+Moreover, the trees tend to reuse the same features.
+
+The function will project each trees on one, and keep for each position the 
+\code{features.keep} first features (based on Gain per feature).
+
+This function is inspired from this blog post:
+\url{https://wellecks.wordpress.com/2015/02/21/peering-into-the-black-box-visualizing-lambdamart/}
+}
+\examples{
+data(agaricus.train, package='xgboost')
+
+bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 15,
+                 eta = 1, nthread = 2, nround = 30, objective = "binary:logistic",
+                 min_child_weight = 50)
+
+p <- xgb.plot.multi.trees(bst, agaricus.train$data@Dimnames[[2]], 3)
+print(p)
+
+}
+
diff --git a/R-package/man/xgb.plot.tree.Rd b/R-package/man/xgb.plot.tree.Rd
index f34e75bf9..2008014cf 100644
--- a/R-package/man/xgb.plot.tree.Rd
+++ b/R-package/man/xgb.plot.tree.Rd
@@ -5,7 +5,7 @@
 \title{Plot a boosted tree model}
 \usage{
 xgb.plot.tree(feature_names = NULL, filename_dump = NULL, model = NULL,
-  n_first_tree = NULL, width = NULL, height = NULL)
+  n_first_tree = NULL, plot.width = NULL, plot.height = NULL)
 }
 \arguments{
 \item{feature_names}{names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.}
@@ -16,9 +16,9 @@ xgb.plot.tree(feature_names = NULL, filename_dump = NULL, model = NULL,
 
 \item{n_first_tree}{limit the plot to the n first trees. If \code{NULL}, all trees of the model are plotted. Performance can be low for huge models.}
 
-\item{width}{the width of the diagram in pixels.}
+\item{plot.width}{the width of the diagram in pixels.}
 
-\item{height}{the height of the diagram in pixels.}
+\item{plot.height}{the height of the diagram in pixels.}
 }
 \value{
 A \code{DiagrammeR} of the model.