update examples to satisfy dontrun and add SysReq to desc

.Rhistory

@@ -1,70 +1,3 @@
-Sys.time()
-py(model)
-reticulate::py_call(model)
-reticulate::py_call("model")
-py_run_string("print(model)")
-py_run_string("model")
-py_run_string('print(model)')
-?r_to_py
-set.seed(89)
-## Read data for analysis
-cat_vars <- c("CC18_401","CC18_413d","educ","race","marstat","votereg","OScode","CompRating",
-"region","pid7","immstat","employ","sexuality","trans","industryclass","pew_churatd")
-bin_vars <- c("CC18_415a","CC18_417_a","gender")
-cont_vars <- c("citylength_1","numchildren","birthyr")
-data_0_test <- fread("data/cces_jss_format.csv", select = c(cat_vars, bin_vars, cont_vars))
-r_to_py(data_0_test)
-py_run_string("print(data_0_test")
-py_run_string("print(data_0_test)")
-py_load_object(data_0_test)
-py$data_0_test <- data_0_test
-py_run_string("print(data_0_test")
-py_run_string("print(data_0_test)")
-cces <- read_csv("../linux/JSS/replication_materials/data/cces_jss_format.csv")
-library(tidyverse)
-cces <- read_csv("../linux/JSS/replication_materials/data/cces_jss_format.csv")
-print(object.size(cces), "Mb")
-print(object.size(cces),units =  "Mb")
-library(devtools)
-check()
-check_rhub()
-build()
-# Imputation workflow
-library("rMIDAS")
-install.packages("rMIDAS")
-library(rMIDAS)
-version(rMIDAS)
-data(quakes)
-force(quakes)
-data(airquality)
-x_conv <- convert(add_missingness(quakes, prop=0.1),
-minmax_scale = TRUE)
-x_train <- train(x_conv,
-training_epochs = 20,
-layer_structure = c(128,128),
-input_drop = 0.75,
-seed = 89)
-force(airquality)
-x_complete <- complete(x_train, m = 1)
-?complete
-complete2 <- function(mid_obj,
-m=10L,
-unscale = TRUE,
-bin_label = TRUE,
-cat_coalesce = TRUE,
-fast = FALSE,
-file = NULL,
-file_root = NULL) {
-if (!inherits(mid_obj,"midas_base.Midas")) {
-stop("Trained midas object not supplied to 'mid_obj' argument")
-}
-if (is.null(options("python_initialised")$python_initialised)) {
-python_init()
-}
-if (!("preproc" %in% names(mid_obj))) {
-unscale = FALSE
-bin_label = FALSE
-cat_coalesce = FALSE
 }
 draws <- mid_obj$generate_samples(m = as.integer(m))$output_list
 if ((unscale || bin_label || cat_coalesce)) {
@@ -510,3 +443,70 @@ library(devtools)
 install_github("MIDASverse/rMIDAS")
 library(rMIDAS)
 vignette(package = "rMIDAS")
+library(devtools)
+check()
+release()
+library(Amelia)
+data(quakes)
+quakes_missing <- add_missingess(quakes)
+library(rMIDAS)
+quakes_missing <- add_missingess(quakes)
+quakes_missing <- add_missingess(quakes)
+quakes_missing <- add_missingness(quakes)
+quakes_missing <- add_missingness(quakes, 0.1)
+amelia(quakes_missing, m = 1)
+quakes_complete <- amelia(quakes_missing, m = 1)
+quakes_complete$imputations[[1]]
+quakes$cat <- sample(c("a","b","c"), length(quakes), replace = TRUE)
+quakes
+quakes_missing <- add_missingness(quakes, 0.1)
+quakes_complete$imputations[[1]]
+quakes_complete <- amelia(quakes_missing, m = 1)
+quakes_complete <- amelia(quakes_missing, m = 1, noms = c("cat"))
+quakes_complete$imputations$imp1
+library(Amelia)
+data(quakes)
+quakes
+quakes$cat <- sample(c("a","b","c"), nrow(quakes), replace = TRUE)
+quakes
+quakes <- add_missingness(quakes, 0.3)
+quakes
+quakes$ord <- sample(c("1","2","3","4","5"), nrow(quakes), replace = TRUE)
+quakes
+data(quakes)
+quakes$cat <- sample(c("a","b","c"), nrow(quakes), replace = TRUE)
+quakes$ord <- sample(c("1","2","3","4","5"), nrow(quakes), replace = TRUE)
+quakes
+quakes <- add_missingness(quakes, 0.3)
+quakes
+quakes_complete <- amelia(quakes, m = 1,
+noms = c("cat"),
+ords = c("ord"))
+quakes_complete <- amelia(quakes, m = 1,
+noms = c("cat"))
+data(quakes)
+quakes$cat <- sample(c("a","b","c"), nrow(quakes), replace = TRUE)
+quakes$ord <- as.factor(sample(c("1","2","3","4","5"), nrow(quakes), replace = TRUE))
+quakes
+quakes <- add_missingness(quakes, 0.3)
+quakes
+quakes_complete <- amelia(quakes, m = 1,
+noms = c("cat"),
+ords = c("ord"))
+levels(quakes$ord)
+quakes$ord <- factor(sample(c("1","2","3","4","5"), nrow(quakes), replace = TRUE),
+levels = c("1","2","3","4","5"))
+levels(quakes$ord)
+data(quakes)
+quakes$cat <- sample(c("a","b","c"), nrow(quakes), replace = TRUE)
+quakes$ord <- factor(sample(c("1","2","3","4","5"), nrow(quakes), replace = TRUE),
+levels = c("1","2","3","4","5"))
+quakes
+quakes <- add_missingness(quakes, 0.3)
+quakes
+quakes
+quakes_complete <- amelia(quakes, m = 1,
+noms = c("cat"),
+ords = c("ord"))
+quakes_data <- quakes_complete$imputations$imp1
+quakes_data

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
 Version: 0.4.0
-Date: 2022-02-02 18:01:53 UTC
-SHA: 6c541724f900f731cccf89e5d31a2f01c63582eb
+Date: 2022-02-03 10:00:51 UTC
+SHA: 5f3db7d61491ce824eda52b4080544898444d8db

DESCRIPTION

@@ -30,6 +30,7 @@ Suggests:
     testthat,
     knitr,
     rmarkdown
+SystemRequirements: Python (>= 2.7.0)
 VignetteBuilder: knitr
 License: Apache License (>= 2.0)
 URL: https://github.com/MIDASverse/rMIDAS

R/load_utils.R

@@ -9,3 +9,23 @@ skip_if_no_numpy <- function() {
   if (!have_numpy)
     testthat::skip("numpy not available for testing")
 }
+
+#' Check whether Python is capable of executing example code
+#'
+#' Checks if each Python dependency is available.
+#' This function is called within some examples to ensure code executes properly.
+#' @keywords setup
+#' @return `NULL`
+python_configured <- function() {
+
+  py_dep <- c("matplotlib","numpy","pandas","tensorflow","sklearn","os","random", "tensorflow_addons")
+  dep_avail <- sapply(py_dep, function (x) reticulate::py_module_available(x))
+
+  if (sum(dep_avail) == length(py_dep)) {
+    return(TRUE)
+  } else {
+    return(FALSE)
+  }
+}
+
+

inst/examples/basic_workflow.R

@@ -1,7 +1,7 @@
 # Generate raw data, with numeric, binary, and categorical variables
 \dontrun{
-# Run where Python available
-if (reticulate::py_module_available("numpy")) {
+# Run where Python available and configured correctly
+if (python_configured()) {
 set.seed(89)
 n_obs <- 10000
 raw_data <- data.table(a = sample(c("red","yellow","blue",NA),n_obs, replace = TRUE),

inst/examples/overimputation.R

@@ -1,6 +1,6 @@
 \dontrun{
-# Run where Python available
-if (reticulate::py_module_available("numpy")) {
+# Run where Python available and configured correctly
+if (python_configured()) {
 
 raw_data <- data.table(a = sample(c("red","yellow","blue",NA),1000, replace = TRUE),
                          b = 1:1000,