diff --git a/R-package/R/xgb.model.dt.tree.R b/R-package/R/xgb.model.dt.tree.R
index 4d8e10e3b..0083dae93 100644
--- a/R-package/R/xgb.model.dt.tree.R
+++ b/R-package/R/xgb.model.dt.tree.R
@@ -1,6 +1,6 @@
-#' Convert tree model dump to data.table
+#' Parse boosted tree model text dump
 #' 
-#' Read a tree model text dump and return a data.table.
+#' Parse a boosted tree model text dump and return a \code{data.table}.
 #' 
 #' @importFrom data.table data.table
 #' @importFrom data.table set
@@ -13,17 +13,19 @@
 #' @importFrom stringr str_extract
 #' @importFrom stringr str_split
 #' @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 model dump generated by the \code{xgb.train} function. Avoid the creation of a dump file.
-#' @param text dump generated by the \code{xgb.dump} function. Avoid the creation of a dump file. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}).
-#' @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 feature_names names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If the model already contains feature names, this argument should be \code{NULL} (default value).
+#' @param model object created by the \code{xgb.train} function.
+#' @param text \code{character} vector generated by the \code{xgb.dump} function. Model dump must include the gain per feature and per tree (parameter \code{with.stats = TRUE} in function \code{xgb.dump}).
+#' @param n_first_tree limit the plot to the \code{n} first trees. If set to \code{NULL}, all trees of the model are plotted. Performance can be low depending of the size of the model.
 #'
-#' @return A \code{data.table} of the features used in the model with their gain, cover and few other thing.
+#' @return A \code{data.table} of the features used in the model with their gain, cover and few other information.
 #'
 #' @details 
-#' General function to convert a text dump of tree model to a Matrix. The purpose is to help user to explore the model and get a better understanding of it.
+#' General function to convert a text dump of tree model to a \code{data.table}. 
 #' 
-#' The content of the \code{data.table} is organised that way:
+#' The purpose is to help user to explore the model and get a better understanding of it.
+#' 
+#' The columns of the \code{data.table} are:
 #' 
 #' \itemize{
 #' \item \code{ID}: unique identifier of a node ;
@@ -35,21 +37,16 @@
 #'  \item \code{Quality}: it's the gain related to the split in this specific node ;
 #'  \item \code{Cover}: metric to measure the number of observation affected by the split ;
 #'  \item \code{Tree}: ID of the tree. It is included in the main ID ;
-#'  \item \code{Yes.X} or \code{No.X}: data related to the pointer in \code{Yes} or \code{No} column ;
+#'  \item \code{Yes.Feature}, \code{No.Feature}, \code{Yes.Cover}, \code{No.Cover}, \code{Yes.Quality} and \code{No.Quality}: data related to the pointer in \code{Yes} or \code{No} column ;
 #' } 
 #'   
 #' @examples
 #' data(agaricus.train, package='xgboost')
 #' 
-#' #Both dataset are list with two items, a sparse matrix and labels 
-#' #(labels = outcome column which will be learned). 
-#' #Each column of the sparse Matrix is a feature in one hot encoding format.
-#' train <- agaricus.train
-#' 
-#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
+#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2, 
 #'                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
 #' 
-#' #agaricus.test$data@@Dimnames[[2]] represents the column names of the sparse matrix.
+#' # agaricus.train$data@@Dimnames[[2]] represents the column names of the sparse matrix.
 #' xgb.model.dt.tree(feature_names = agaricus.train$data@@Dimnames[[2]], model = bst)
 #' 
 #' @export
diff --git a/R-package/man/xgb.model.dt.tree.Rd b/R-package/man/xgb.model.dt.tree.Rd
index 7dadb20aa..c82ba3cf4 100644
--- a/R-package/man/xgb.model.dt.tree.Rd
+++ b/R-package/man/xgb.model.dt.tree.Rd
@@ -2,30 +2,32 @@
 % Please edit documentation in R/xgb.model.dt.tree.R
 \name{xgb.model.dt.tree}
 \alias{xgb.model.dt.tree}
-\title{Convert tree model dump to data.table}
+\title{Parse boosted tree model text dump}
 \usage{
 xgb.model.dt.tree(feature_names = NULL, model = NULL, text = NULL,
   n_first_tree = 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}.}
+\item{feature_names}{names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If the model already contains feature names, this argument should be \code{NULL} (default value).}
 
-\item{model}{dump generated by the \code{xgb.train} function. Avoid the creation of a dump file.}
+\item{model}{object created by the \code{xgb.train} function.}
 
-\item{text}{dump generated by the \code{xgb.dump} function. Avoid the creation of a dump file. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}).}
+\item{text}{\code{character} vector generated by the \code{xgb.dump} function. Model dump must include the gain per feature and per tree (parameter \code{with.stats = TRUE} in function \code{xgb.dump}).}
 
-\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{n_first_tree}{limit the plot to the \code{n} first trees. If set to \code{NULL}, all trees of the model are plotted. Performance can be low depending of the size of the model.}
 }
 \value{
-A \code{data.table} of the features used in the model with their gain, cover and few other thing.
+A \code{data.table} of the features used in the model with their gain, cover and few other information.
 }
 \description{
-Read a tree model text dump and return a data.table.
+Parse a boosted tree model text dump and return a \code{data.table}.
 }
 \details{
-General function to convert a text dump of tree model to a Matrix. The purpose is to help user to explore the model and get a better understanding of it.
+General function to convert a text dump of tree model to a \code{data.table}. 
 
-The content of the \code{data.table} is organised that way:
+The purpose is to help user to explore the model and get a better understanding of it.
+
+The columns of the \code{data.table} are:
 
 \itemize{
 \item \code{ID}: unique identifier of a node ;
@@ -37,21 +39,16 @@ The content of the \code{data.table} is organised that way:
  \item \code{Quality}: it's the gain related to the split in this specific node ;
  \item \code{Cover}: metric to measure the number of observation affected by the split ;
  \item \code{Tree}: ID of the tree. It is included in the main ID ;
- \item \code{Yes.X} or \code{No.X}: data related to the pointer in \code{Yes} or \code{No} column ;
+ \item \code{Yes.Feature}, \code{No.Feature}, \code{Yes.Cover}, \code{No.Cover}, \code{Yes.Quality} and \code{No.Quality}: data related to the pointer in \code{Yes} or \code{No} column ;
 }
 }
 \examples{
 data(agaricus.train, package='xgboost')
 
-#Both dataset are list with two items, a sparse matrix and labels 
-#(labels = outcome column which will be learned). 
-#Each column of the sparse Matrix is a feature in one hot encoding format.
-train <- agaricus.train
-
-bst <- xgboost(data = train$data, label = train$label, max.depth = 2, 
+bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2, 
                eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
 
-#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.
+# agaricus.train$data@Dimnames[[2]] represents the column names of the sparse matrix.
 xgb.model.dt.tree(feature_names = agaricus.train$data@Dimnames[[2]], model = bst)
 
 }