update to 1.3.0

alexkychen · Mar 13, 2024 · 050909b · 050909b
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diff --git a/.Rbuildignore b/.Rbuildignore
@@ -1,4 +1,5 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
 .travis.yml
-LICENSE.md
+LICENSE.md
+README.md
diff --git a/.gitignore b/.gitignore
@@ -2,3 +2,5 @@
 .Rhistory
 .RData
 .Ruserdata
+.Rbuildignore
+.travis.yml
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -2,7 +2,7 @@ Package: assignPOP
 Type: Package
 Title: Population Assignment using Genetic, Non-Genetic or Integrated Data in a
     Machine Learning Framework
-Version: 1.2.4
+Version: 1.3.0
 Author: Kuan-Yu (Alex) Chen [aut, cre], Elizabeth A. Marschall [aut], Michael
     G. Sovic [aut], Anthony C. Fries [aut], H. Lisle Gibbs [aut], Stuart A. Ludsin
     [aut]
@@ -27,6 +27,7 @@ Imports:
     reshape2,
     stringr,
     tree,
+    rlang,
 Suggests:
     gtable,
     iterators,
@@ -36,5 +37,5 @@ Suggests:
     rmarkdown,
     testthat
 License: GPL (>= 2)
-RoxygenNote: 7.1.1
+RoxygenNote: 7.3.1
 Encoding: UTF-8
diff --git a/NAMESPACE b/NAMESPACE
@@ -27,6 +27,7 @@ importFrom(parallel,stopCluster)
 importFrom(randomForest,importance)
 importFrom(randomForest,randomForest)
 importFrom(reshape2,melt)
+importFrom(rlang,.data)
 importFrom(stats,model.matrix)
 importFrom(stats,prcomp)
 importFrom(stats,predict)

diff --git a/R/accuracy.MC.R b/R/accuracy.MC.R
@@ -1,4 +1,4 @@
-#' Estimate assignment accuracies of Monte-Carlo cross-validation results
+#' Estimate assignment accuracy of Monte-Carlo cross-validation results
 #'
 #' This function allows you to estimate assignment accuracies of Monte-Carlo cross-validation results. The output results can be used to make assignment accuracy plots (use function accuracy.plot).
 #' @param dir A character string to specify the folder that has your Monte-Carlo cross-validation results. A slash should be included at the end (e.g., dir="YourFolderName/").

diff --git a/R/accuracy.plot.R b/R/accuracy.plot.R
@@ -6,6 +6,7 @@
 #' @return This function returns a boxplot plot using the ggplot2 library. Users can modified the plot (e.g., change color, text, etc.) using functions provided by ggplot2 library.
 #' @import ggplot2
 #' @importFrom reshape2 melt
+#' @importFrom rlang .data
 #' @examples
 #' Your_df <- read.table(system.file("extdata/Rate.txt", package="assignPOP"), header=TRUE)
 #' accuracy.plot(Your_df, pop="all")
@@ -14,6 +15,8 @@
 accuracy.plot <- function(df, pop="all"){
   #claim variables
   train.inds <- NULL; train.loci <- NULL; value <- NULL; KF <- NULL 
+  #claim variables for aes use
+  train_inds <- "train.inds"; train_loci <- "train.loci"; KF_ <- "KF"
   #validate specified pop names
   df_popName <- substring(colnames(df)[4:ncol(df)], 13, 1000L)
   if(!all(pop %in% df_popName)){ #if specified pop name not in df 
@@ -43,7 +46,8 @@ accuracy.plot <- function(df, pop="all"){
       col <- paste0("assign.rate.",pop)
       #see if multiple levels of train loci used.(e.g.,10%, 20%...of loci)
       if(length(unique(df$train.loci)) > 1 ){
-        boxplot <- ggplot(df, aes_string(y=col, x="train.inds", fill="train.loci"))+
+        ## boxplot <- ggplot(df, aes_string(y=col, x="train.inds", fill="train.loci"))+ # aes_string is deprecated in ggplot2 3.0
+        boxplot <- ggplot(df, aes(y=.data[[col]], x=.data[[train_inds]], fill=.data[[train_loci]]))+
           geom_boxplot()+
           xlab(x_label) + ylab("Assignment accuracy")+
           scale_fill_discrete(name="Prop. of\ntrain loci",guide=guide_legend(reverse=TRUE))+
@@ -58,7 +62,7 @@ accuracy.plot <- function(df, pop="all"){
         return(boxplot)
       #see if only one level of train loci used (e.g.,used all loci)
       }else if(length(unique(df$train.loci))==1){
-        boxplot <- ggplot(df, aes_string(y=col, x="train.inds"))+
+        boxplot <- ggplot(df, aes(y=.data[[col]], x=.data[[train_inds]]))+
           geom_boxplot()+
           xlab(x_label) + ylab("Assignment accuracy")+
           theme_bw()+
@@ -133,7 +137,7 @@ accuracy.plot <- function(df, pop="all"){
       col <- paste0("assign.rate.",pop)
       #
       if(length(unique(df$train.loci)) > 1 ){ #see if multiple levels of train loci used.(e.g.,10%, 20%...of loci)
-        boxplot <- ggplot(df, aes_string(y=col, x="KF" ,fill="train.loci"))+
+        boxplot <- ggplot(df, aes(y=.data[[col]], x=.data[[KF_]] ,fill=.data[[train_loci]]))+
           geom_boxplot()+
           #geom_point(size=5, position=dodge)+
           xlab("K") + ylab("Assignment accuracy")+
@@ -149,7 +153,7 @@ accuracy.plot <- function(df, pop="all"){
         return(boxplot)
 
       }else if(length(unique(df$train.loci))==1){ #see if only one level of train loci used (e.g.,used all loci)
-        boxplot <- ggplot(df, aes_string(y=col, x="KF"))+
+        boxplot <- ggplot(df, aes(y=.data[[col]], x=.data[[KF_]]))+
           geom_boxplot()+
           xlab("K") + ylab("Assignment accuracy")+
           theme_bw()+

diff --git a/README.md b/README.md
@@ -1,4 +1,3 @@
-[![Travis-CI Build Status](https://travis-ci.org/alexkychen/assignPOP.svg?branch=master)](https://travis-ci.org/alexkychen/assignPOP)
 [![CRAN status](http://www.r-pkg.org/badges/version/assignPOP)](https://cran.r-project.org/package=assignPOP)
 [![GitHub release](https://img.shields.io/github/release/alexkychen/assignPOP.svg)](https://github.com/alexkychen/assignPOP/releases)
 [![license](https://img.shields.io/github/license/alexkychen/assignPOP.svg)](https://github.com/alexkychen/assignPOP/blob/master/LICENSE.md)
@@ -36,12 +35,16 @@ Please visit our tutorial website for more infomration
 * [http://alexkychen.github.io/assignPOP/](http://alexkychen.github.io/assignPOP/)
 
 ## What's new
-Changes in ver. 1.2.4 (2021.10.27)
-- Update membership.plot - add argument 'plot.k' and 'plot.loci' to skip related question prompt.
+Changes in ver. 1.3.0 (2024.3.13)
+- Update accuracy.plot - adjust ggplot's aes_string() due to its deprecation. 
+- Update testthat test_accuracy and test_membership to pass CRAN evaluations.
 
 <details>
 <summary>History</summary>
 
+Changes in ver. 1.2.4 (2021.10.27)
+- Update membership.plot - add argument 'plot.k' and 'plot.loci' to skip related question prompt.
+
 Changes in ver. 1.2.3 (2021.8.17)
 - Update assign.X - (1)Add argument 'common' to specify whether stopping the analysis when inconsistent features between data sets were found. (2)Add argument 'skipQ' to skip data type checking on non-genetic data. (3)Modify argument 'mplot' to handle membership probability plot output.
 

diff --git a/assignPOP.Rproj b/assignPOP.Rproj
@@ -15,5 +15,5 @@ LaTeX: pdfLaTeX
 BuildType: Package
 PackageUseDevtools: Yes
 PackageInstallArgs: --no-multiarch --with-keep.source
-PackageCheckArgs: --as-cran
+PackageCheckArgs: --as-cran --no-manual
 PackageRoxygenize: rd,collate,namespace,vignette
diff --git a/tests/testthat/test_accuracy.R b/tests/testthat/test_accuracy.R
@@ -5,7 +5,7 @@ test_that("Calculate assignment accuracy for Monte-Carlo results",{
   expect_output(str(AccuMC),"data.frame")
   expect_true(file.exists("ResMCtest/Rate_of_3_tests_3_pops.txt"))
   plot <- accuracy.plot(AccuMC)
-  expect_output(str(plot), "List of 9")
+  expect_type(plot, "list")
 })
 
 unlink("ResMCtest/Rate_of_3_tests_3_pops.txt")
@@ -15,7 +15,7 @@ test_that("Calculate assignment accuracy for K-fold results",{
   expect_output(str(AccuKF),"data.frame")
   expect_true(file.exists("ResKFtest/Rate_of_3_tests_3_pops.txt"))
   plot <- accuracy.plot(AccuKF)
-  expect_output(str(plot), "List of 9")
+  expect_type(plot, "list")
 })
 
 unlink("ResKFtest/Rate_of_3_tests_3_pops.txt")

diff --git a/tests/testthat/test_membership.R b/tests/testthat/test_membership.R
@@ -2,5 +2,5 @@ context("Test membership.prob.plot")
 
 test_that("Plot membership probability",{
   plot <- membership.plot(dir="ResKFtest/", style=1, non.genetic=T)
-  expect_output(str(plot), "List of 10")
+  expect_type(plot, "list")
 })