Changes requested for CRAN-release

DESCRIPTION

@@ -2,34 +2,15 @@ Package: MetaNLP
 Type: Package
 Title: Natural Language Processing for Meta Analysis
 Version: 0.1.1
-Authors@R: c(
-        person("Nico", "Bruder", 
-                role = c("aut"), 
-                email = "[email protected]"
-        ),
-        person("Samuel", "Zimmermann", 
-                role = c("aut"), 
-                email = "[email protected]",
-                comment = c(ORCID = "0009-0000-4828-9294"),
-        ),
-        person("Johannes", "Vey", 
-                role = c("aut"), 
-                email = "[email protected]",
-                comment = c(ORCID = "0000-0002-2610-9667"),
-        ),
-        person("Maximilian", "Pilz", 
-                role = c("aut", "cre"), 
-                email = "[email protected]",
-                comment = c(ORCID = "0000-0002-9685-1613")
-        ),
-        person(given = "Institute of Medical Biometry - University of Heidelberg",
-               role = c("cph")
-        )
-    )
-Description: Given a CSV file with titles and abstracts, the package
-    creates a word count matrix that is lemmatized and stemmed and can directly
-    be used to train machine learning methods for automatic title-abstract 
-    screening in the preparation of a meta analysis.
+Authors@R: c(person("Nico", "Bruder", role = c("aut"), email = "[email protected]"),
+        person("Samuel", "Zimmermann", role = c("aut"), email = "[email protected]", comment = c(ORCID = "0009-0000-4828-9294")),
+        person("Johannes", "Vey", role = c("aut"), email = "[email protected]", comment = c(ORCID = "0000-0002-2610-9667")),
+        person("Maximilian", "Pilz", role = c("aut", "cre"), email = "[email protected]", comment = c(ORCID = "0000-0002-9685-1613")),
+        person(given = "Institute of Medical Biometry - University of Heidelberg", role = c("cph")))
+Description: Given a CSV file with titles and abstracts, the package creates a
+    word count matrix that is lemmatized and stemmed and can directly be used to
+    train machine learning methods for automatic title-abstract screening in the
+    preparation of a meta analysis.
 License: MIT + file LICENSE
 Suggests: 
     knitr,

R/MetaNLP.R

@@ -14,14 +14,15 @@
 #'
 #' A \code{MetaNLP} object is the base class of the package \pkg{MetaNLP}.
 #' It is initialized by passing the path to a CSV file and constructs
-#' a data frame which column names are the words that occur in the titles
-#' and abstracts and which cells contain the word counts for each
+#' a data frame whose column names are the words that occur in the titles
+#' and abstracts and whose cells contain the word counts for each
 #' paper.
 #'
 #' @rdname MetaNLP
 setClass("MetaNLP", representation(data_frame = "data.frame"))
 
-#' @param file Either the path to the CSV file or data frame contain
+#' @param file Either the path to the CSV file or a data frame containing the
+#' abstracts
 #' @param bounds An integer vector of length 2. The first value specifies
 #' the minimum number of appearances of a word to become a column of the word
 #' count matrix, the second value specifies the maximum number.
@@ -50,17 +51,16 @@ setClass("MetaNLP", representation(data_frame = "data.frame"))
 #' The value "maybe" is handled as a "yes"/"include".
 #'
 #' @examples
-#' \dontrun{
-#' obj <- MetaNLP("test_data.csv")}
+#' path <- system.file("extdata", "test_data.csv", package = "MetaNLP", mustWork = TRUE)
+#' obj <- MetaNLP(path)
 #'
 #' @note
 #' To ensure correct processing of the data when there are special characters
 #' (e.g. "é" or "ü"), make sure that the csv-file is correctly encoded
 #' as \code{UTF-8}.
 #' The stemming algorithm makes use of the C libstemmer library generated by
 #' Snowball. When german texts are stemmed, umlauts are replaced by their
-#' non-umlaut equivalent, so "ä" becomes "a" etc. This behaviour cannot be
-#' changed.
+#' non-umlaut equivalent, so "ä" becomes "a" etc.
 #'
 #' @rdname MetaNLP
 #' @export
@@ -169,9 +169,10 @@ setMethod("show", signature("MetaNLP"),
 #' @param colors Character vector with the colors in
 #' @param ... Additional parameters for \link[wordcloud]{wordcloud}
 #'
-#' @examples \dontrun{
-#' obj <- MetaNLP("test_data.csv")
-#' plot(obj)}
+#' @examples
+#' path <- system.file("extdata", "test_data.csv", package = "MetaNLP", mustWork = TRUE)
+#' obj <- MetaNLP(path)
+#' plt <- plot(obj)
 #'
 #' @return nothing
 #' @export

R/delete_functions.R

@@ -23,11 +23,11 @@ setGeneric("delete_words", function(object, delete_list) {
 
 
 #' @examples
-#' \dontrun{
-#' obj<- MetaNLP("test_data.csv")
+#' path <- system.file("extdata", "test_data.csv", package = "MetaNLP", mustWork = TRUE)
+#' obj <- MetaNLP(path)
 #' del_words <- c("beautiful", "considering", "found")
 #' obj <- delete_words(obj, del_words)
-#' }
+#'
 #'
 #' @rdname delete_words
 #' @export
@@ -72,10 +72,9 @@ setGeneric("delete_stop_words", function(object, ...) {
 })
 
 #' @examples
-#' \dontrun{
-#' obj <- MetaNLP("test_data.csv")
+#' path <- system.file("extdata", "test_data.csv", package = "MetaNLP", mustWork = TRUE)
+#' obj <- MetaNLP(path)
 #' obj <- delete_stop_words(obj, "english")
-#' }
 #'
 #' @rdname delete_stop_words
 #' @export
@@ -106,10 +105,9 @@ setMethod("delete_stop_words", signature("MetaNLP"),
 #' special characters anymore.
 #'
 #' @examples
-#' \dontrun{
-#' obj <- MetaNLP("test_data.csv", language = "french")
+#' path <- system.file("extdata", "test_data.csv", package = "MetaNLP", mustWork = TRUE)
+#' obj <- MetaNLP(path, language = "french")
 #' obj <- replace_special_characters(obj)
-#' }
 #'
 #' @rdname replace_special_characters
 #' @export

R/feature_selection.R

@@ -18,10 +18,6 @@
 #' parameter \eqn{\lambda}, which determines the weight of the penalty, can
 #' either be chosen via cross validation (using \link[glmnet]{cv.glmnet} or by
 #' giving a numeric value.
-#' For further documentation and background information on \code{\link{glmnet}},
-#' have a look at the
-#' \href{https://glmnet.stanford.edu/articles/glmnet.html#logistic-regression-family-binomial}{online documentation}.
-#' @importFrom glmnet glmnet
 #'
 #' @importFrom glmnet glmnet
 #'
@@ -68,12 +64,12 @@ setGeneric("select_features", function(object, ...) {
 #' columns will be chosen.
 #'
 #' @examples
-#' \dontrun{
-#' obj <- MetaNLP("test_data.csv")
+#' path <- system.file("extdata", "test_data.csv", package = "MetaNLP", mustWork = TRUE)
+#' obj <- MetaNLP(path)
+#'
+#' obj2 <- select_features(obj, alpha = 0.7)
+#' obj2 <- select_features(obj, lambda = "1se")
 #'
-#' obj <- select_features(obj, alpha = 0.7)
-#' obj <- select_features(obj, lambda = "1se", type.measure = "auc")
-#' }
 #'
 #' @rdname select_features
 #' @export

R/useful_functions.R

@@ -13,10 +13,9 @@
 #' @return A list of most frequent words.
 #'
 #' @examples
-#' \dontrun{
-#' obj <- MetaNLP("test_data.csv")
+#' path <- system.file("extdata", "test_data.csv", package = "MetaNLP", mustWork = TRUE)
+#' obj <- MetaNLP(path)
 #' summary(obj, n = 8)
-#' }
 #'
 #' @rdname summary
 #' @export
@@ -89,8 +88,7 @@ setGeneric("write_csv", function(object, ...) {
 #' as a csv-file.
 #'
 #' @param object An object of class MetaNLP.
-#' @param path Path where to save the csv. If no path is set, the csv is saved
-#' in the current working directory
+#' @param path Path where to save the csv.
 #' @param type Specifies if the word count matrix should be saved as
 #' "train_wcm.csv" or "test_wcm.csv". If the user wants to use another file name,
 #' the whole path including the file name should be given as the \code{path}
@@ -99,28 +97,25 @@ setGeneric("write_csv", function(object, ...) {
 #' as \code{UTF-8}.
 #'
 #' @details
-#' Overall, there are three options to specify the path. By
-#' default, no path is set, so the csv is saved as "train_wcm.csv" or
-#' "test_wcm.csv" in the current working directory. If a path to a specific
-#' folder is given (but the path name does not end with ".csv"), the file is
-#' saved in this folder as "train_wcm.csv" or "test_wcm.csv".
+#' If a path to a specific folder is given (but the path name does not end with
+#' ".csv"), the file is saved in this folder as "train_wcm.csv" or "test_wcm.csv".
 #' By providing a path ending with ".csv", the user can override the default
 #' naming convention and the file is saved according to this path.
 #'
 #' @examples
-#' \dontrun{
-#' obj <- MetaNLP("test_data.csv")
+#' path <- system.file("extdata", "test_data.csv", package = "MetaNLP", mustWork = TRUE)
+#' obj <- MetaNLP(path)
 #' obj2 <- delete_stop_words(obj)
-#' write_csv(obj2)
-#' write_csv(obj2, path = "foo.csv")
-#' }
+#' write_path <- tempdir()
+#' write_csv(obj2, path = write_path)
+#' file.remove(file.path(write_path, "train_wcm.csv"))
 #'
 #' @return nothing
 #'
 #' @rdname write_csv
 #' @export
 setMethod("write_csv", signature("MetaNLP"),
-          function(object, path = "", type = c("train", "test"), ...) {
+          function(object, path, type = c("train", "test"), ...) {
             lastchar <- 0
 
             # extract data
@@ -134,13 +129,9 @@ setMethod("write_csv", signature("MetaNLP"),
             # create file path
             if(lastchar == ".csv") {
               path_to_save <- path
-            } else if(path == ""){
-              type <- match.arg(type)
-              path_to_save <- paste0(type, "_wcm.csv")
             } else {
               type <- match.arg(type)
               path_to_save <- file.path(path, paste0(type, "_wcm.csv"))
-
             }
 
             utils::write.csv2(data, file = path_to_save, row.names = FALSE, ...)
@@ -163,10 +154,11 @@ setMethod("write_csv", signature("MetaNLP"),
 #' @param ... Further arguments to \code{MetaNLP}.
 #'
 #' @examples
-#' \dontrun{
-#' obj <- MetaNLP("test_data.csv")
-#' to_test_obj <- read_test_data(obj, "path/to_test.csv")
-#' }
+#' path_train <- system.file("extdata", "test_data.csv", package = "MetaNLP", mustWork = TRUE)
+#' path_test <- system.file("extdata", "test_data_changed.csv", package = "MetaNLP", mustWork = TRUE)
+#' obj_train <- MetaNLP(path_train)
+#' obj_test <- MetaNLP(path_test)
+#' to_test_obj <- read_test_data(obj_train, obj_test)
 #'
 #' @return An object of class MetaNLP
 #'