CRAN feasibility (#139)

* remove unknown global bindings, fix the package description and repair urls

* urls in <>

* dataset links fixes

---------

Co-authored-by: Fersoil <Fersoil>

DESCRIPTION

@@ -1,15 +1,15 @@
 Package: PvSTATEM
 Type: Package
-Title: Reading, quality control and preprocessing of MBA assay data
-Description: The package speeds up the process of loading raw data from MBA 
+Title: Reading, Quality Control and Preprocessing of MBA Assay Data
+Description: Speeds up the process of loading raw data from MBA 
     assays, performs quality control checks, and automatically normalizes the data, 
     preparing it for more advanced downstream tasks. The main objective of the 
     package is to create a simple environment for a user, who does not 
     necessarily have experience with R language. The package is developed 
     within the project of the same name - `PvSTATEM` - an international 
     project aiming for malaria elimination.
 BugReports: https://github.com/mini-pw/PvSTATEM/issues
-Version: 0.0.3
+Version: 0.0.4
 License: BSD_3_clause + file LICENSE
 Encoding: UTF-8
 Authors@R: c(
@@ -39,5 +39,4 @@ Suggests:
 Config/testfhat/edition: 3
 Roxygen: list(markdown = TRUE, r6 = TRUE)
 VignetteBuilder: knitr
-biocViews: DataImport, QualityControl, Preprocessing
-URL: https://github.com/mini-pw/PvSTATEM
+URL: <https://github.com/mini-pw/PvSTATEM>

R/plots-mfi.R

@@ -32,23 +32,24 @@ plot_mfi_for_analyte <- function(plate, analyte_name,
 
   df <- plate$data[["Median"]] %>%
     dplyr::select(analyte_name) %>%
-    dplyr::rename("MFI" = analyte_name) %>%
-    dplyr::mutate(
-      SampleId = paste0("SampleId: ", seq_len(nrow(.))),
+    dplyr::rename("MFI" = analyte_name)
+
+  df <-  dplyr::mutate(df,
+      SampleId = paste0("SampleId: ", seq_len(nrow(df))),
       SampleType = plate$sample_types,
     )
 
-  blanks_df <- df %>% dplyr::filter(SampleType == "BLANK")
+  blanks_df <- df %>% dplyr::filter(.data$SampleType == "BLANK")
   blank_mean <- mean(blanks_df$MFI)
-  sc_df <- df %>% dplyr::filter(SampleType == "STANDARD CURVE")
+  sc_df <- df %>% dplyr::filter(.data$SampleType == "STANDARD CURVE")
   test_df <- df %>%
-    dplyr::filter(SampleType == "TEST") %>%
+    dplyr::filter(.data$SampleType == "TEST") %>%
     dplyr::mutate(
-      outlier = ifelse(is_outlier(MFI), SampleId, as.character(NA))
+      outlier = ifelse(is_outlier(.data$MFI), .data$SampleId, as.character(NA))
     )
 
   p <- test_df %>%
-    ggplot2::ggplot(aes(x = SampleType, y = MFI)) +
+    ggplot2::ggplot(aes(x = .data$SampleType, y = .data$MFI)) +
     main_geom(color = "blue") +
     ggplot2::geom_hline(
       aes(yintercept = blank_mean, linetype = "BLANK MEAN"),
@@ -74,7 +75,7 @@ plot_mfi_for_analyte <- function(plate, analyte_name,
     is_out <- !is.na(test_df$outlier)
     hjust[is_out] <- ifelse(seq_len(sum(is_out)) %% 2 == 0, -0.18, 1.18)
 
-    p <- p + ggrepel::geom_text_repel(aes(label = outlier), na.rm = TRUE, hjust = hjust, color = "grey", min.segment.length = 0.3)
+    p <- p + ggrepel::geom_text_repel(aes(label = .data$outlier), na.rm = TRUE, hjust = hjust, color = "grey", min.segment.length = 0.3)
   }
 
   p

R/plots-plate.R

@@ -68,7 +68,7 @@ plot_plate <- function(colors, plot_numbers = FALSE, numbers = NULL, plot_title
   well_positions$category <- factor(well_positions$color, levels = legend_mapping, labels = categories)
 
   # Plot the plate with colored wells
-  p <- ggplot(well_positions, aes(x = x, y = y, fill = category)) +
+  p <- ggplot(well_positions, aes(x = .data$x, y = .data$y, fill = .data$category)) +
     geom_tile(key_glyph = "point") +
     annotation_custom(
       rasterGrob(plate_img, width = unit(1, "npc"), height = unit(1, "npc")),

R/plots-standard_curve.R

@@ -23,7 +23,7 @@ plot_standard_curve_analyte <- function(plate,
                                         analyte_name,
                                         data_type = "Median",
                                         decreasing_dilution_order = TRUE,
-                                        log_scale = c("dilutions"),
+                                        log_scale = c("all"),
                                         plot_line = TRUE,
                                         plot_blank_mean = TRUE,
                                         plot_dilution_bounds = TRUE,
@@ -79,7 +79,7 @@ plot_standard_curve_analyte <- function(plate,
   }
 
   options(scipen = 30)
-  p <- ggplot2::ggplot(plot_data, aes(x = dilution_values, y = MFI)) +
+  p <- ggplot2::ggplot(plot_data, aes(x = .data$dilution_values, y = .data$MFI)) +
     ggplot2::geom_point(aes(color = "Standard curve samples"), size = 3)
   if (plot_line) {
     p <- p + ggplot2::geom_line(aes(color = "Standard curve samples"), linewidth = 1.2)
@@ -92,10 +92,10 @@ plot_standard_curve_analyte <- function(plate,
   }
   if (plot_dilution_bounds) {
     p <- p + ggplot2::geom_vline(
-      ggplot2::aes(color = "Min-max dilution bounds", xintercept = min(dilution_values)),
+      ggplot2::aes(color = "Min-max dilution bounds", xintercept = min(.data$dilution_values)),
       linetype = "dashed"
     ) + ggplot2::geom_vline(
-      ggplot2::aes(color = "Min-max dilution bounds", xintercept = max(dilution_values)),
+      ggplot2::aes(color = "Min-max dilution bounds", xintercept = max(.data$dilution_values)),
       linetype = "dashed"
     )
   }
@@ -179,12 +179,12 @@ plot_standard_curve_analyte_with_model <- function(plate,
   test_sample_estimates <- predict(model, test_samples_mfi)
 
   p <- p + ggplot2::geom_line(
-    ggplot2::aes(x = dilution, y = MFI, color = "Fitted model predictions"),
+    ggplot2::aes(x = .data$dilution, y = .data$MFI, color = "Fitted model predictions"),
     data = model$get_plot_data(), linewidth = 1
   )
   if (plot_test_predictions) {
     p <- p + ggplot2::geom_point(
-      ggplot2::aes(x = dilution, y = MFI, color = "Test sample predictions"),
+      ggplot2::aes(x = .data$dilution, y = .data$MFI, color = "Test sample predictions"),
       data = test_sample_estimates, shape = 4,
       size = 3
     )

vignettes/our_datasets.Rmd

@@ -80,8 +80,8 @@ To check the package functionalities on the data from different sources, we gath
 
 -   `Chul_TotalIgG_2.csv` - GitHub repo RTSS_Kisumu_Schisto [source](https://github.com/IDEELResearch/RTSS_Kisumu_Schisto/tree/main/data/raw/luminex)
 
--   `pone.0187901.s001.csv` - data shipped with drLumi package [source](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5685631/)
+-   `pone.0187901.s001.csv` - data shipped with drLumi package [source](https://doi.org/10.1371/journal.pone.0187901)
 
--   `New_Batch_6_20160309_174224.csv` - dataset posted on ReaserchGate [source](https://www.researchgate.net/publication/309410897_Raw_data_generated_by_Luminex)
+-   `New_Batch_6_20160309_174224.csv` - dataset included in the paper *A single-nucleotide-polymorphism-based genotyping assay for simultaneous detection of different carbendazim-resistant genotypes in the Fusarium graminearum species complex*, H. Zhang et. al. 
 
--   `New_Batch_14_20140513_082522.csv` - dataset posted on ReaserchGate [source](https://www.researchgate.net/publication/309411021_The_second_run_of_raw_data_exported_from_Luminex_200_applied_forcarbendazim_resistance_species_and_chemotype_detection_of_156_Fusarium_isolates)
+-   `New_Batch_14_20140513_082522.csv` - dataset included in the paper *A single-nucleotide-polymorphism-based genotyping assay for simultaneous detection of different carbendazim-resistant genotypes in the Fusarium graminearum species complex*, H. Zhang et. al.