update tests and dependencies bugs

Author: stemangiola

NAMESPACE

@@ -6,22 +6,31 @@ export(format_input)
 export(ppc_seq)
 import(Rcpp)
 import(dplyr)
+import(ggplot2)
 import(methods)
 import(rstan)
 import(tidybayes)
+importFrom(dplyr,enquo)
 importFrom(foreach,"%do%")
 importFrom(foreach,foreach)
 importFrom(magrittr,"%$%")
 importFrom(magrittr,divide_by)
 importFrom(magrittr,multiply_by)
 importFrom(magrittr,set_rownames)
+importFrom(purrr,as_mapper)
+importFrom(purrr,map)
 importFrom(purrr,map2)
 importFrom(purrr,map_int)
+importFrom(purrr,pmap)
 importFrom(rstan,sampling)
 importFrom(rstan,vb)
 importFrom(tibble,as_tibble)
+importFrom(tibble,rowid_to_column)
 importFrom(tibble,tibble)
+importFrom(tidyr,drop_na)
 importFrom(tidyr,gather)
+importFrom(tidyr,nest)
+importFrom(tidyr,separate)
 importFrom(tidyr,spread)
 importFrom(ttBulk,add_normalised_counts_bulk)
 useDynLib(ppcSeq, .registration = TRUE)

R/ppcSeq.R

@@ -12,6 +12,7 @@ add_attr = function(var, attribute, name){
 #' This is a generalisation of ifelse that acceots an object and return an objects
 #'
 #' @import dplyr
+#' @importFrom purrr as_mapper
 #'
 #' @param .x A tibble
 #' @param .p A boolean
@@ -230,6 +231,10 @@ find_optimal_number_of_chains = function(how_many_posterior_draws,
 
 #' Identify the optimal number of chain
 #' based on how many draws we need from the posterior
+#'
+#' @importFrom tibble rowid_to_column
+#' @importFrom purrr map
+#'
 #' @param counts_MPI A matrix of read count information
 #' @param to_exclude A vector of oulier data points to exclude
 #' @param shards An integer
@@ -297,6 +302,10 @@ inits_fx =
 
 #' Produce generated quantities plots with marked uotliers
 #'
+#' @importFrom purrr pmap
+#' @importFrom purrr map_int
+#' @import ggplot2
+#'
 #' @param .x A tibble
 #' @param value_column A symbol object
 #' @param sample_column A symbol object
@@ -394,6 +403,9 @@ add_deleterious_if_covariate_exists = function(input.df, X){
 		)
 }
 
+#' merge_results
+#'
+#' @importFrom tidyr nest
 merge_results = function(res_discovery, res_test, formula, gene_column, value_column, sample_column, do_check_only_on_detrimental){
 
 	res_discovery %>%
@@ -463,9 +475,10 @@ merge_results = function(res_discovery, res_test, formula, gene_column, value_co
 		)
 }
 
-# Select only significant genes plus background for efficient normalisation
-# Input: tibble
-# Ouyput: tibble
+#' Select only significant genes plus background for efficient normalisation
+#'
+#' @importFrom rstan sampling
+#' @importFrom rstan vb
 select_to_check_and_house_keeping = function(input.df, do_check_column, significance_column, gene_column, how_many_negative_controls){
 	input.df %>%
 		{
@@ -530,11 +543,13 @@ run_model = function(model, full_bayes, chains, how_many_posterior_draws, inits_
 	)
 }
 
+#' add_exposure_rate
+#'
+#' @importFrom tidyr separate
 add_exposure_rate = function(input.df, fit){
 
 	writeLines(sprintf("executing %s", "add_exposure_rate"))
 
-
 	input.df %>%
 		left_join(
 			fit %>%
@@ -568,6 +583,11 @@ check_if_within_posterior = function(input.df, my_df, do_check_column, value_col
 		ungroup
 }
 
+#' parse_fit
+#'
+#' @importFrom tidyr separate
+#' @importFrom tidyr nest
+#'
 parse_fit = function(fit, adj_prob_theshold){
 
 	writeLines(sprintf("executing %s", "parse_fit"))
@@ -624,6 +644,11 @@ check_columns_exist = function(input.df, sample_column, gene_column, value_colum
 		)
 }
 
+
+#' Check if NA
+#'
+#' @importFrom tidyr drop_na
+#' @importFrom dplyr enquo
 check_if_any_NA = function(input.df, sample_column, gene_column, value_column, significance_column, do_check_column, formula_columns){
 
 	# Prepare column same enquo

man/add_exposure_rate.Rd

@@ -1,38 +1,23 @@
 context('ppcSeq')
 
-test_that("dummy",expect_equal(1,1))
+test_that("Quick test",{
 
-#
-#
-# test_that("Quick test",{
-#
   FDR_threshold = 0.01
 
   res =
-    ppcSeq::counts %>%
-    mutate(is_significant = FDR < FDR_threshold) %>%
     ppc_seq(
+      dplyr::mutate(ppcSeq::counts, is_significant = FDR < FDR_threshold),
       formula = ~ Label,
       significance_column = PValue,
       do_check_column  = is_significant,
       value_column = value,
       percent_false_positive_genes = "5%", tol_rel_obj = 0.01
     )
-#
-#   expect_equal(
-#
-#     length(
-#       attr(
-#         create_ttBulk(
-#           ttBulk::counts_mini,
-#           sample_column = sample,
-#           transcript_column = transcript,
-#           counts_column = `read count`
-#         ) ,
-#         "parameters"
-#       )
-#     ),
-#     3
-#   )
-#
-# })
+
+  expect_equal(
+
+    as.integer(unlist(res[c(1, 2, 4, 5, 6),5])),
+    c(0,1,0,0,0)
+  )
+
+})