Merge pull request #53 from thiloshon/master

Final CRAN version

.Rbuildignore

@@ -1,7 +1,13 @@
+^codecov\.yml$
+^appveyor\.yml$
 ^cran-comments\.md$
 ^CRAN-RELEASE$
 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^\.travis\.yml$
 ^\.idea$
 ^img$
+^\.gitignore$
+^\.Rhistory$
+^\.RData$
+^\.gitattributes$

.gitattributes

@@ -0,0 +1,4 @@
+* text=auto
+data/* binary
+src/* text=lf
+R/* text=lf

.travis.yml

@@ -2,4 +2,17 @@
 
 language: R
 sudo: false
-cache: packages
+cache: packages
+
+r:
+  - oldrel
+  - release
+  - devel
+
+r_check_args: "--as-cran"
+
+r_packages:
+  - covr
+
+after_success:
+  - Rscript -e 'library(covr); codecov()'

CRAN-RELEASE

@@ -0,0 +1,2 @@
+This package was submitted to CRAN on 2019-04-10.
+Once it is accepted, delete this file and tag the release (commit 1faa2f9a78).

DESCRIPTION

@@ -1,9 +1,9 @@
 Package: bdclean
 Type: Package
 Title: A User-Friendly Biodiversity Data Cleaning App for the Inexperienced R User
-Description: Provides features to manage complete work flow for biodiversity data cleaning, from uploading the data; gathering input from the user, in order to adjust cleaning procedures; perform the cleaning; and finally, generating various reports and several versions of the data. Facilitates user-level data cleaning, designed for the inexperienced R user. T Gueta et al (2018) <doi:10.3897/biss.2.25564>. T Gueta et al (2017) <doi:10.3897/tdwgproceedings.1.20311>.
-Version: 0.1.12
-Date: 2019-03-02
+Description: Provides features to manage the complete workflow for biodiversity data cleaning. Uploading data, gathering input from users (in order to adjust cleaning procedures), cleaning data and finally, generating various reports and several versions of the data. Facilitates user-level data cleaning, designed for the inexperienced R user. T Gueta et al (2018) <doi:10.3897/biss.2.25564>. T Gueta et al (2017) <doi:10.3897/tdwgproceedings.1.20311>.
+Version: 0.1.15
+Date: 2019-04-10
 License: GPL-3
 URL: https://github.com/bd-R/bdclean, https://bd-r.github.io/The-bdverse/index.html
 BugReports: https://github.com/bd-R/bdclean/issues
@@ -42,5 +42,7 @@ Imports: rmarkdown, knitr, shiny, shinydashboard, shinyjs, leaflet, DT, data.tab
 Depends: R (>= 2.10)
 RoxygenNote: 6.1.1
 Suggests: 
-    testthat
+    testthat,
+    roxygen2,
+    covr
 LazyData: true

NAMESPACE

@@ -6,13 +6,13 @@ export(clean_data)
 export(cleaning_function)
 export(create_default_questionnaire)
 export(create_report_data)
-export(earliestDate)
+export(earliest_date)
 export(get_checks_list)
 export(run_bdclean)
 export(run_questionnaire)
-export(spatialResolution)
-export(taxoLevel)
-export(temporalResolution)
+export(spatial_resolution)
+export(taxo_level)
+export(temporal_resolution)
 import(bdDwC)
 import(bdchecks)
 import(data.table)

R/bdclean.R

@@ -8,17 +8,17 @@
 #' in order to achieve minimum quality to use the data further for any
 #' analysis or modelling.
 #'
-#'@section Data cleaning:
-#'\itemize{
-#'\item \link{run_bdclean}
-#'\item \link{clean_data}
-#'}
+#' @section Data cleaning:
+#' \itemize{
+#' \item \link{run_bdclean}
+#' \item \link{clean_data}
+#' }
 #'
 #'
-#'@section Citation:
-#'\itemize{
-#'\item Gueta, T., Barve, V., Nagarajah, T., Agrawal, A. & Carmel, Y. (2018). bdclean: Biodiversity data cleaning workflows (R package V 1.0.0). Retrieved from https://github.com/bd-R/bdclean/
-#'}
+#' @section Citation:
+#' \itemize{
+#' \item Gueta, T., Barve, V., Nagarajah, T., Agrawal, A. & Carmel, Y. (2019). bdclean: Biodiversity data cleaning workflows (R package V 0.1.13). Retrieved from https://github.com/bd-R/bdclean/
+#' }
 #'
 #' @docType package
 #' @name bdclean

R/clean_data.R

@@ -7,156 +7,152 @@
 #' You can add your custom questions to this questionnaire and then pass it to this
 #' function to process the data.
 #'
-#'@param data Biodiversity data in a data frame
-#'@param customQuestionnaire Custom user created questionnaire responses if to pypass answering questions each time.
-#'@param clean Whether to clean after flagging. If false only flagging will be done.
-#'@param missing How to treat data with missing values. Default: false - will be treated as bad data.
-#'@param report Whether to print report of cleaning done.
-#'@param format Formats of the cleaning report required. Options are: Markdown, HTML or / and PDF
+#' @param data Biodiversity data in a data frame
+#' @param custom_questionnaire Custom user created questionnaire responses if to pypass answering questions each time.
+#' @param clean Whether to clean after flagging. If false only flagging will be done.
+#' @param missing How to treat data with missing values. Default: false - will be treated as bad data.
+#' @param report Whether to print report of cleaning done.
+#' @param format Formats of the cleaning report required. Options are: Markdown, HTML or / and PDF
 #'
-#'@return data frame with clean data
+#' @return data frame with clean data
 #'
-#'@examples \dontrun{
-#'library(rgbif)
-#'occdat <- occ_data(
-#'  country = "AU",     # Country code for australia
-#'  classKey= 359,      # Class code for mammalia
-#'  limit=5000          # Get only 5000 records
-#'  )
-#'  myData<-occdat$data
-#'
-#'  cleanedData <- clean_data(myData)
-#'
-#'  responses <- run_questionnaire()
-#'  cleanedData <- clean_data(myData, responses)
-#'
-#'  customQuestionnaire <- create_default_questionnaire()
-#'  customResponses <- run_questionnaire(customQuestionnaire)
-#'  cleanedData <- clean_data(myData, customResponses)
-#'  }
+#' @examples
+#' 
+#' custom_questionnaire <- create_default_questionnaire()
+#' 
+#' if(interactive()){
+#' 
+#' library(rgbif)
+#' occdat <- occ_data(
+#'   country = 'AU', # Country code for australia
+#'   classKey = 359, # Class code for mammalia
+#'   limit = 50 # Get only 50 records
+#' )
+#' myData <- occdat$data
+#' 
+#' responses <- run_questionnaire()
+#' cleaned_data <- clean_data(myData, responses)
+#' 
+#' cleaned_data2 <- clean_data(myData)
+#' 
+#' } 
 #'
-#'@export
+#' @export
 clean_data <-
     function(data,
-             customQuestionnaire = NULL,
+             custom_questionnaire = NULL,
              clean = TRUE,
              missing = FALSE,
              report = TRUE,
              format = c("html_document", "pdf_document")) {
         responses <- list()
-        inputData <- data
-        flaggedData <- data
-        cleanedData <- data
-
+        input_data <- data
+        flagged_data <- data
+        cleaned_data <- data
 
         # Questionnaire
-        if (is.null(customQuestionnaire)) {
+        if (is.null(custom_questionnaire)) {
             responses <- run_questionnaire()
         } else {
-            responses <- customQuestionnaire
+            responses <- custom_questionnaire
         }
 
         # Flagging
-        flaggedData <- responses$flagData(inputData, missing)
-
+        flagged_data <- responses$flag_data(input_data, missing)
 
         # Decision Making
         if (clean) {
-            cleanedData <- cleaning_function(flaggedData)
+            cleaned_data <- cleaning_function(flagged_data)
         }
 
         # Report
         if (report) {
             create_report_data(data,
-                               flaggedData,
-                               cleanedData,
+                               flagged_data,
+                               cleaned_data,
                                responses,
                                clean,
                                format)
         }
 
         # Cleaning
         if (clean) {
-            return(cleanedData)
+            return(cleaned_data)
         }
 
-        return(flaggedData)
+        return(flagged_data)
     }
 
 
 #' Execute the Questionnaire and save user responses.
 #'
 #'
-#'@param customQuestionnaire Custom User Created Questionnaire if already available.
+#' @param custom_questionnaire Custom User Created Questionnaire if already available.
 #'
-#'@return list with BdQuestionObjects containing user answers
+#' @return list with BdQuestionObjects containing user answers
 #'
-#'@examples \dontrun{
-#'library(rgbif)
-#'occdat1 <- occ_data(
-#'  country = "AU",     # Country code for australia
-#'  classKey= 359,      # Class code for mammalia
-#'  limit=5000,         # Get only 5000 records
-#'  )
-#'  myData<-occdat1$data
+#' @examples
+#' 
+#' if(interactive()){ 
 #'
-#'  responses <- run_questionnaire()
-#'  cleanedData <- clean_data_new(myData, responses)
-#'}
+#' responses <- run_questionnaire()
+#' 
+#' }
 #'
-#'@export
-run_questionnaire <- function(customQuestionnaire = NULL) {
+#' @export
+run_questionnaire <- function(custom_questionnaire = NULL) {
     responses <- list()
-
-    if (is.null(customQuestionnaire)) {
+    if (is.null(custom_questionnaire)) {
         message("Custom Questionnaire not given. Using package default Questionnaire...")
         responses <- create_default_questionnaire()
-
     } else {
-        if (class(customQuestionnaire) != "BdQuestionContainer") {
+        if (class(custom_questionnaire) != "BdQuestionContainer") {
             message(
                 "Provided Custom Questionnaire is not of class BdQuestionContainer.
                 Using package default Questionnaire"
             )
             responses <- create_default_questionnaire()
-
         } else {
             message("Custom Questionnaire detected.")
-            responses <- customQuestionnaire
+            responses <- custom_questionnaire
         }
     }
-
     message("Please answer the following questions to initiate cleaning process.")
-
-    for (question in responses$BdQuestions) {
+    for (question in responses$bdquestions) {
         if (question$question.type != "Child" &&
             question$question.type != "ChildRouter") {
-            getUserResponse(question)
+            get_user_response(question)
         }
     }
     message("Thank you! Cleaning can be started now based on your responses.")
     return(responses)
 }
 
 #' Internal function for getting user response
+#'
+#' @param bd_question The BDQuestion object to get users responses.
+#'
+#' @examples
 #' 
-#'@param bdQuestion The BDQuestion object to get users responses.
+#' if(interactive()){ 
+#'
+#' question <- BdQuestion()
+#' responses <- get_user_response(question)
 #' 
-getUserResponse <- function(bdQuestion) {
+#' }
+get_user_response <- function(bd_question) {
     # Child & ChildRouter already filtered in first loop above
-
-    if (bdQuestion$question.type == "Atomic") {
+    if (bd_question$question.type == "Atomic") {
         # Atomic is filtered
-        bdQuestion$printQuestion()
-        bdQuestion$getResponse()
-
+        bd_question$print_question()
+        bd_question$get_response()
     } else {
         # Router , Child as child & ChildRouter as child is filtered
-        bdQuestion$printQuestion()
-        bdQuestion$getResponse()
-        if (bdQuestion$users.answer %in% bdQuestion$router.condition) {
-            for (question in bdQuestion$child.questions) {
-                getUserResponse(question)
+        bd_question$print_question()
+        bd_question$get_response()
+        if (bd_question$users.answer %in% bd_question$router.condition) {
+            for (question in bd_question$child.questions) {
+                get_user_response(question)
             }
         }
     }