Maintenance edits and minor upgrades, v1.1

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: tidyqpcr
 Type: Package
 Title: Quantitative PCR Analysis with the Tidyverse
-Version: 1.0
+Version: 1.1
 Authors@R: c(person("Edward", "Wallace", email = "[email protected]", role = c("aut", "cre")),
              person("Sam", "Haynes", email = "[email protected]", role = c("aut")))
 Description: For reproducible quantitative PCR (qPCR) analysis building on packages from the ’tidyverse’, notably ’dplyr’ and ’ggplot2’. It normalizes (by ddCq), summarizes, and plots pre-calculated Cq data, and plots raw amplification and melt curves from Roche Lightcycler (tm) machines. It does NOT (yet) calculate Cq data from amplification curves.
@@ -25,7 +25,7 @@ Suggests:
 VignetteBuilder: knitr
 License: Apache License (>= 2)
 LazyData: true
-RoxygenNote: 7.1.2
+RoxygenNote: 7.3.1
 Encoding: UTF-8
 URL: https://docs.ropensci.org/tidyqpcr, https://github.com/ropensci/tidyqpcr
 BugReports: https://github.com/ropensci/tidyqpcr/issues

NAMESPACE

@@ -18,6 +18,8 @@ export(create_rowkey_8_in_16_plain)
 export(debaseline)
 export(display_plate)
 export(display_plate_qpcr)
+export(display_plate_sample_id)
+export(display_plate_target_id)
 export(display_plate_value)
 export(label_plate_rowcol)
 export(make_row_names_echo1536)
@@ -29,4 +31,3 @@ importFrom(rlang,.data)
 importFrom(stats,median)
 importFrom(tibble,as_tibble)
 importFrom(tibble,tibble)
-importFrom(tidyr,"%>%")

NEWS.md

@@ -1,5 +1,11 @@
 ## News
 
+* May 2024, maintenance upgrades for R 4.4.0, including
+  - base pipe `|>` rather than magrittr pipe `%>%`
+  - `display_plate_helpers` new functions to make it easier to display plate plans with varied information
+  - remove superseded `.data$` syntax
+  - checking all tests pass, etc.
+  - update to v1.1
 * June 2022, removed plot helper functions `scale_..._nice` and `scale_loglog` from tidyqpcr, because those capabilities are now available in the [scales package](https://scales.r-lib.org) using `label_log` and similar functions. Older code may need to change `scale_y_log10nice` to `scale_y_log10(labels = scales::label_log())`, for example.
 * May 2022, Improvements in documentation and testing. Reorganized `display_plate` function to be more flexible, so older code will need to use `display_plate_qpcr` to ensure that `sample_id` and `target_id` info displays. Updated to v0.5.
 * January 2022, Improvements in documentation and argument-checking for v0.4.

R/amp_melt_curve_functions.R

@@ -25,7 +25,6 @@
 #'   }
 #'
 #' @export
-#' @importFrom tidyr %>%
 #' @importFrom rlang .data
 #'
 #' @examples
@@ -41,7 +40,7 @@
 #'                           program_no = 2)
 #'
 #' # remove base fluorescence from dataset
-#' raw_fluor_tibble %>%
+#' raw_fluor_tibble |>
 #'     debaseline()
 #'     
 debaseline <- function(plateamp, maxcycle = 10) {
@@ -55,13 +54,13 @@ debaseline <- function(plateamp, maxcycle = 10) {
         msg = "plateamp contains a variable called fluor_base, which breaks debaseline"
     )
     baseline <-
-        plateamp %>%
-        dplyr::group_by(.data$well) %>%
+        plateamp |>
+        dplyr::group_by(.data$well) |>
         dplyr::filter(.data$program_no == 2,
-                      .data$cycle <= maxcycle) %>%
+                      .data$cycle <= maxcycle) |>
         dplyr::summarize(fluor_base = stats::median(.data$fluor_raw))
-    plateamp %>%
-        dplyr::left_join(baseline, by = "well") %>%
+    plateamp |>
+        dplyr::left_join(baseline, by = "well") |>
         dplyr::mutate(fluor_signal = .data$fluor_raw - .data$fluor_base)
 }
 
@@ -142,7 +141,6 @@ calculate_dydx <- function(x, y, method = "spline", ...) {
 #' @family melt_curve_functions
 #'
 #' @export
-#' @importFrom tidyr %>%
 #'
 #' @examples
 #' # create simple curve
@@ -157,15 +155,15 @@ calculate_dydx <- function(x, y, method = "spline", ...) {
 #'
 #' # calculate drdt of all melt curves
 #' #----- use case 1 : using splines
-#' temp_tibble %>%
+#' temp_tibble |>
 #'     calculate_drdt_plate()
 #' 
 #' # optional arguments are passed to smooth.splines function
-#' temp_tibble %>%
+#' temp_tibble |>
 #'     calculate_drdt_plate(spar = 0.5)
 #' 
 #' #----- use case 2 : using difference between adjacent points
-#' temp_tibble %>%
+#' temp_tibble |>
 #'     calculate_drdt_plate(method = "diff")
 #'
 calculate_drdt_plate <- function(platemelt, method = "spline", ...) {
@@ -177,14 +175,14 @@ calculate_drdt_plate <- function(platemelt, method = "spline", ...) {
     if (assertthat::has_name(platemelt,"plate") ) {
         warning("platemelt has a plate column, but calculate_drdt_plate works only for single plates.")
     }
-    platemelt %>%
-        dplyr::arrange(.data$well, .data$temperature) %>%
-        dplyr::group_by(.data$well) %>%
+    platemelt |>
+        dplyr::arrange(.data$well, .data$temperature) |>
+        dplyr::group_by(.data$well) |>
         dplyr::mutate(dRdT =
                           calculate_dydx(x = .data$temperature,
                                          y = .data$fluor_raw,
                                          method = method,
                                          ...)
-        ) %>%
+        ) |>
         dplyr::ungroup()
 }

R/calculate_deltacq.R

@@ -19,7 +19,6 @@
 #'   etc.
 #'
 #' @export
-#' @importFrom tidyr %>%
 #' @importFrom stats median
 #'
 #' @examples
@@ -38,13 +37,13 @@
 #' # normalise cq to reference target_id called 'T_norm'
 #' 
 #' #----- use case 1: median reference target_id value
-#' cq_tibble %>%
+#' cq_tibble |>
 #'     calculate_normvalue(ref_ids = "T_norm",
 #'                         value_name = "cq",
 #'                         id_name = "target_id")
 #' 
 #' #----- use case 2: mean reference target_id value 
-#' cq_tibble %>%
+#' cq_tibble |>
 #'     calculate_normvalue(ref_ids = "T_norm",
 #'                         value_name = "cq",
 #'                         id_name = "target_id",
@@ -62,8 +61,8 @@ calculate_normvalue <- function(value_df,
 
     # make subset of value_df where gene is one or more ref_ids
     value_to_norm_by <- dplyr::filter(value_df,
-                             .data[[id_name]] %in% ref_ids) %>%
-        dplyr::pull({{value_name}}) %>%
+                             .data[[id_name]] %in% ref_ids) |>
+        dplyr::pull({{value_name}}) |>
         norm_function(na.rm = TRUE)
 
     # assign summary (median) value to value_df$value_to_norm_by
@@ -98,7 +97,6 @@ calculate_normvalue <- function(value_df,
 #'   rel_abund \tab normalized ratio, \eqn{2^(-\Delta Cq)} }
 #'
 #' @export
-#' @importFrom tidyr %>%
 #' @importFrom stats median
 #' @importFrom rlang .data
 #' 
@@ -118,11 +116,11 @@ calculate_normvalue <- function(value_df,
 #' # calculate deltacq using reference target_id called 'T_norm'
 #' 
 #' #----- use case 1: median reference target_id value
-#' cq_tibble %>%
+#' cq_tibble |>
 #'     calculate_deltacq_bysampleid(ref_target_ids = "T_norm")
 #' 
 #' #----- use case 2: mean reference target_id value 
-#' cq_tibble %>%
+#' cq_tibble |>
 #'     calculate_deltacq_bysampleid(ref_target_ids = "T_norm",
 #'                                  norm_function = mean)
 #'
@@ -134,18 +132,18 @@ calculate_deltacq_bysampleid <- function(cq_df,
         assertthat::has_name(cq_df, 
                              c("target_id", "sample_id","cq")))
 
-    cq_df %>%
-        dplyr::group_by(.data$sample_id) %>%
+    cq_df |>
+        dplyr::group_by(sample_id) |>
         dplyr::do(calculate_normvalue(.data,
                                    ref_ids = ref_target_ids,
                                    value_name = "cq",
                                    id_name = "target_id",
-                                   norm_function = norm_function)) %>%
-        dplyr::rename(ref_cq = .data$value_to_norm_by) %>%
-        dplyr::ungroup() %>%
+                                   norm_function = norm_function)) |>
+        dplyr::rename(ref_cq = value_to_norm_by) |>
+        dplyr::ungroup() |>
         dplyr::mutate(
-               delta_cq    = .data$cq - .data$ref_cq,
-               rel_abund   = 2^-.data$delta_cq)
+               delta_cq    = cq - ref_cq,
+               rel_abund   = 2^ - delta_cq)
 }
 
 
@@ -189,7 +187,6 @@ calculate_deltacq_bysampleid <- function(cq_df,
 #'   normalized fold-change ratio, \eqn{2^(-\Delta \Delta Cq)} }
 #'
 #' @export
-#' @importFrom tidyr %>%
 #' @importFrom stats median
 #'
 #' @examples
@@ -208,11 +205,11 @@ calculate_deltacq_bysampleid <- function(cq_df,
 #' # calculate deltadeltacq using reference target_id called 'S_norm'
 #' 
 #' #----- use case 1: median reference sample_id value
-#' deltacq_tibble %>%
+#' deltacq_tibble |>
 #'     calculate_deltadeltacq_bytargetid(ref_sample_ids = "S_norm")
 #' 
 #' #----- use case 2: mean reference sample_id value 
-#' deltacq_tibble %>%
+#' deltacq_tibble |>
 #'     calculate_deltadeltacq_bytargetid(ref_sample_ids = "S_norm",
 #'                                  norm_function = mean)
 #'
@@ -227,17 +224,17 @@ calculate_deltadeltacq_bytargetid <- function(deltacq_df,
 
     ddcq_factor <- (-1) ^ ddcq_positive
 
-    deltacq_df %>%
-        dplyr::group_by(.data$target_id) %>%
+    deltacq_df |>
+        dplyr::group_by(target_id) |>
         dplyr::do(calculate_normvalue(.data,
                                    ref_ids = ref_sample_ids,
                                    value_name = "delta_cq",
                                    id_name = "sample_id",
-                                   norm_function = norm_function)) %>%
-        dplyr::rename(ref_delta_cq = .data$value_to_norm_by) %>%
-        dplyr::ungroup() %>%
+                                   norm_function = norm_function)) |>
+        dplyr::rename(ref_delta_cq = value_to_norm_by) |>
+        dplyr::ungroup() |>
         dplyr::mutate(
                deltadelta_cq = ddcq_factor *
-                   (.data$delta_cq - .data$ref_delta_cq),
-               fold_change   = 2 ^ .data$deltadelta_cq)
+                   (delta_cq - ref_delta_cq),
+               fold_change   = 2 ^ deltadelta_cq)
 }

R/calculate_efficiency.R

@@ -37,11 +37,11 @@
 #' # calculate primer efficiency
 #' 
 #' #----- use case 1: include difference across replicates in model
-#' dilution_tibble %>%
+#' dilution_tibble |>
 #'     calculate_efficiency()
 #' 
 #' #----- use case 2: ignore difference across replicates
-#' dilution_tibble %>%
+#' dilution_tibble |>
 #'     calculate_efficiency(formula = cq ~ log2(dilution))
 #'
 calculate_efficiency <- function(cq_df_1, formula = cq ~ log2(dilution) + biol_rep) {
@@ -90,7 +90,6 @@ calculate_efficiency <- function(cq_df_1, formula = cq ~ log2(dilution) + biol_r
 #' @seealso calculate_efficiency
 #'
 #' @export
-#' @importFrom tidyr %>%
 #' @importFrom rlang .data
 #'
 #' @examples
@@ -111,11 +110,11 @@ calculate_efficiency <- function(cq_df_1, formula = cq ~ log2(dilution) + biol_r
 #' # calculate primer efficiency for multiple targets
 #' 
 #' #----- use case 1: include difference across replicates in model
-#' dilution_tibble %>%
+#' dilution_tibble |>
 #'     calculate_efficiency_bytargetid()
 #' 
 #' #----- use case 2: ignore difference across replicates
-#' dilution_tibble %>%
+#' dilution_tibble |>
 #'     calculate_efficiency_bytargetid(formula = cq ~ log2(dilution))
 #'
 calculate_efficiency_bytargetid <- function(cq_df,
@@ -125,10 +124,10 @@ calculate_efficiency_bytargetid <- function(cq_df,
 
     if (!is.na(use_prep_types)) {
         assertthat::assert_that(assertthat::has_name(cq_df, "prep_type"))
-        cq_df <- dplyr::filter(cq_df, .data$prep_type %in% use_prep_types)
+        cq_df <- dplyr::filter(cq_df, prep_type %in% use_prep_types)
     }
-    cq_df %>%
-        dplyr::group_by(.data$target_id) %>%
-        dplyr::do(calculate_efficiency(.data, formula = formula)) %>%
+    cq_df |>
+        dplyr::group_by(target_id) |>
+        dplyr::do(calculate_efficiency(.data, formula = formula)) |>
         dplyr::ungroup()
 }