diff --git a/.github/workflows/check-standard.yaml b/.github/workflows/check-standard.yaml
index 271d466..e23739c 100644
--- a/.github/workflows/check-standard.yaml
+++ b/.github/workflows/check-standard.yaml
@@ -31,16 +31,16 @@ jobs:
steps:
- uses: actions/checkout@v2
- - uses: r-lib/actions/setup-pandoc@v1
+ - uses: r-lib/actions/setup-pandoc@v2
- - uses: r-lib/actions/setup-r@v1
+ - uses: r-lib/actions/setup-r@v2
with:
r-version: ${{ matrix.config.r }}
http-user-agent: ${{ matrix.config.http-user-agent }}
use-public-rspm: true
- - uses: r-lib/actions/setup-r-dependencies@v1
+ - uses: r-lib/actions/setup-r-dependencies@v2
with:
extra-packages: rcmdcheck
- - uses: r-lib/actions/check-r-package@v1
+ - uses: r-lib/actions/check-r-package@v2
diff --git a/DESCRIPTION b/DESCRIPTION
index 11b30b8..5e805f6 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -28,7 +28,7 @@ Suggests:
ggplot2,
HistDat
Config/testthat/edition: 3
-RoxygenNote: 7.1.2
+RoxygenNote: 7.3.1
Roxygen: list(markdown = TRUE)
VignetteBuilder: knitr
URL: https://multimeric.github.io/TidyMultiqc/, https://github.com/multimeric/TidyMultiqc, https://cran.r-project.org/package=TidyMultiqc
diff --git a/R/internal_utils.R b/R/internal_utils.R
index 1fa58e8..b814330 100644
--- a/R/internal_utils.R
+++ b/R/internal_utils.R
@@ -30,7 +30,7 @@ kv_map <- function(l, func, map_keys = FALSE) {
sanitise_column_name <- function(name) {
name %>%
stringr::str_replace_all(pattern = "[- ]", replacement = "_") %>% # Any dividing characters become underscores
- stringr::str_remove_all(pattern = "[^\\w%_]") %>% # Any special characters bar underscore and % get deleted
+ stringr::str_remove_all(pattern = "[^\\w%_]") %>% # Any special characters except underscore and % get deleted
stringr::str_to_lower()
}
diff --git a/R/multiqc.R b/R/multiqc.R
index b91cf27..a114369 100644
--- a/R/multiqc.R
+++ b/R/multiqc.R
@@ -6,9 +6,8 @@
#' instead refer to the documentation website at , which
#' provides more accessible documentation.
#' @importFrom magrittr `%>%`
-#' @docType package
#' @name TidyMultiqc-package
-NULL
+"_PACKAGE"
# Make R CMD Check hush
utils::globalVariables(c(".", "metadata.sample_id"))
diff --git a/R/plot_parsers.R b/R/plot_parsers.R
index b8e1e42..12e721d 100644
--- a/R/plot_parsers.R
+++ b/R/plot_parsers.R
@@ -1,3 +1,32 @@
+#' Parses a list of xyline plot datasets
+#' @param dataset A list which has the names "name" and "data", which
+#' originate from a MultiQC plot
+#' @return A list whose names are sample names. Each value is a data frame with one column.
+#' @keywords internal
+map_xy_line_datasets <- function(datasets, plot_name){
+ kv_map(datasets, function(dataset){
+ list(
+ key = dataset$name,
+ value = dataset$data %>%
+ purrr::map(~ tibble::tibble_row(x = .[[1]], y = .[[2]])) %>%
+ purrr::list_rbind() %>%
+ # Chop the multi-row data frame into one row
+ tidyr::nest(.key = stringr::str_c("plot", plot_name, sep = "."))
+ )
+ })
+}
+
+#' Determine if a plot is Plotly
+#'
+#' Plotly plots are generated by MultiQC 1.2 and above.
+#' This matters because the plot data formats differ between these two formats.
+#' @keywords internal
+#' @param plot_data A list containing the top level data for a single plot.
+#' @return A logical scalar. TRUE if the plot is a plotly plot, or FALSE if it's a HighCharts one.
+is_plotly <- function(plot_data){
+ !is.null(plot_data$layout)
+}
+
#' Takes the JSON dictionary for an xyline plot, and returns a named list of
#' data frames, one for each sample.
#' @keywords internal
@@ -12,22 +41,23 @@ parse_xyline_plot <- function(plot_data, name) {
# This only works on xyline plots
assertthat::assert_that(plot_data$plot_type == "xy_line")
- plot_data$datasets %>%
- purrr::map(function(dataset) {
+ # MultiQC >=1.2 plotly parser
+ if (is_plotly(plot_data)){
+ plot_data$datasets %>%
+ purrr::map(function(dataset){
+ dataset$lines %>%
+ map_xy_line_datasets(plot_name = name)
+ }) %>%
+ purrr::list_flatten()
+ }
+
+ # MultiQC <=1.1 highcharts parser
+ else {
+ plot_data$datasets %>%
# For some reason there are two levels of nesting here
- dataset %>%
- kv_map(function(subdataset) {
- name <- stringr::str_c("plot", name, sep = ".")
- list(
- key = subdataset$name,
- value = subdataset$data %>%
- purrr::map_dfr(~ list(x = .[[1]], y = .[[2]])) %>%
- # Chop the multi-row data frame into one row
- tidyr::nest({{ name }} := tidyr::everything()) # %>%
- )
- })
- }) %>%
- purrr::reduce(~ purrr::list_merge(.x, !!!.y))
+ purrr::map(map_xy_line_datasets, plot_name = name) %>%
+ purrr::list_flatten()
+ }
}
#' Takes the JSON dictionary for a bar graph, and returns a named list of
@@ -41,30 +71,60 @@ parse_xyline_plot <- function(plot_data, name) {
#' one column for the number of intron variants, one column for the number of exon variants, etc.
#' This means that the number of columns will be fairly variable for different plots.
parse_bar_graph <- function(plot_data, name) {
- # This only works on bar_graphs
assertthat::assert_that(plot_data$plot_type == "bar_graph")
-
- # Make a list of samples
- samples <- plot_data$samples[[1]] %>% purrr::flatten_chr()
+ plot_data$datasets %>% length() %>% `==`(1) %>% assertthat::assert_that(msg = "Only bar graphs with 1 dataset are understood by this parser!")
colname <- stringr::str_c("plot", sanitise_column_name(name), sep = ".")
- plot_data$datasets[[1]] %>%
- # First, build up a dictionary of samples -> dictionary of quality metrics
- purrr::map(function(dataset) {
- segment_name <- dataset$name
- dataset$data %>%
- # For this segment, each sample has a value
- kv_map(function(value, idx) {
- list(
- key = samples[[idx]],
- value = list(value) %>% purrr::set_names(sanitise_column_name(segment_name))
- )
- }, map_keys = TRUE)
- }) %>%
- purrr::reduce(utils::modifyList) %>%
- # Then, convert each inner dictionary to a tibble row
- purrr::map(tibble::as_tibble_row) %>%
- # And nest each df so that we only have 1 cell of output per sample
- purrr::map(~ tidyr::nest(., {{ colname }} := tidyr::everything()))
+ if (is_plotly(plot_data)){
+ # MultiQC 1.2+
+ dataset <- plot_data$datasets[[1]]
+ samples <- dataset$samples %>% purrr::flatten_chr()
+ # We make a data frame whose rows are samples and whose columns are categories
+ # Ideally this would be the final output, but currently the other code
+ # expects a list of samples
+ df <- dataset$cats %>%
+ purrr::map(function(cat){
+ tibble::as_tibble_col(
+ purrr::flatten_dbl(cat$data),
+ column_name = sanitise_column_name(cat$name)
+ )
+ }) %>%
+ purrr::list_cbind()
+
+ # For compatibility with the old format
+ if ("unknown" %in% colnames(df)){
+ df <- dplyr::rename(df, none = unknown)
+ }
+
+ # And then we slice out each row to become its own list
+ seq_along(samples) %>%
+ purrr::map(function(sample_idx){
+ df[sample_idx, ] %>% tidyr::nest(.key = colname)
+ }) %>%
+ purrr::set_names(samples) %>%
+ `[`(sort(samples))
+ }
+ else {
+ # Make a list of samples
+ samples <- plot_data$samples[[1]] %>% purrr::flatten_chr()
+ plot_data$datasets[[1]] %>%
+ # First, build up a dictionary of samples -> dictionary of quality metrics
+ purrr::map(function(dataset) {
+ segment_name <- dataset$name
+ dataset$data %>%
+ # For this segment, each sample has a value
+ kv_map(function(value, idx) {
+ list(
+ key = samples[[idx]],
+ value = list(value) %>% purrr::set_names(sanitise_column_name(segment_name))
+ )
+ }, map_keys = TRUE)
+ }) %>%
+ purrr::reduce(utils::modifyList) %>%
+ # Then, convert each inner dictionary to a tibble row
+ purrr::map(tibble::as_tibble_row) %>%
+ # And nest each df so that we only have 1 cell of output per sample
+ purrr::map(~ tidyr::nest(., .key = colname))
+ }
}
diff --git a/inst/extdata/1.2_wgs/multiqc_data.json b/inst/extdata/1.2_wgs/multiqc_data.json
new file mode 100644
index 0000000..32566a6
--- /dev/null
+++ b/inst/extdata/1.2_wgs/multiqc_data.json
@@ -0,0 +1,51833 @@
+{
+ "report_data_sources": {
+ "QualiMap": {
+ "genome_results": {
+ "P4107_1003": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1003.clean.dedup.qc/genome_results.txt",
+ "P4107_1004": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1004.clean.dedup.qc/genome_results.txt",
+ "P4107_1005": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1005.clean.dedup.qc/genome_results.txt",
+ "P4107_1002": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1002.clean.dedup.qc/genome_results.txt",
+ "P4107_1006": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1006.clean.dedup.qc/genome_results.txt",
+ "P4107_1001": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1001.clean.dedup.qc/genome_results.txt"
+ },
+ "coverage_histogram": {
+ "P4107_1003": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1003.clean.dedup.qc/raw_data_qualimapReport/coverage_histogram.txt",
+ "P4107_1004": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1004.clean.dedup.qc/raw_data_qualimapReport/coverage_histogram.txt",
+ "P4107_1005": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1005.clean.dedup.qc/raw_data_qualimapReport/coverage_histogram.txt",
+ "P4107_1002": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1002.clean.dedup.qc/raw_data_qualimapReport/coverage_histogram.txt",
+ "P4107_1006": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1006.clean.dedup.qc/raw_data_qualimapReport/coverage_histogram.txt",
+ "P4107_1001": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1001.clean.dedup.qc/raw_data_qualimapReport/coverage_histogram.txt"
+ },
+ "insert_size_histogram": {
+ "P4107_1003": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1003.clean.dedup.qc/raw_data_qualimapReport/insert_size_histogram.txt",
+ "P4107_1004": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1004.clean.dedup.qc/raw_data_qualimapReport/insert_size_histogram.txt",
+ "P4107_1005": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1005.clean.dedup.qc/raw_data_qualimapReport/insert_size_histogram.txt",
+ "P4107_1002": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1002.clean.dedup.qc/raw_data_qualimapReport/insert_size_histogram.txt",
+ "P4107_1006": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1006.clean.dedup.qc/raw_data_qualimapReport/insert_size_histogram.txt",
+ "P4107_1001": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1001.clean.dedup.qc/raw_data_qualimapReport/insert_size_histogram.txt"
+ },
+ "mapped_gc_distribution": {
+ "P4107_1003": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1003.clean.dedup.qc/raw_data_qualimapReport/mapped_reads_gc-content_distribution.txt",
+ "P4107_1004": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1004.clean.dedup.qc/raw_data_qualimapReport/mapped_reads_gc-content_distribution.txt",
+ "P4107_1005": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1005.clean.dedup.qc/raw_data_qualimapReport/mapped_reads_gc-content_distribution.txt",
+ "P4107_1002": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1002.clean.dedup.qc/raw_data_qualimapReport/mapped_reads_gc-content_distribution.txt",
+ "P4107_1006": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1006.clean.dedup.qc/raw_data_qualimapReport/mapped_reads_gc-content_distribution.txt",
+ "P4107_1001": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/06_final_alignment_qc/P4107_1001.clean.dedup.qc/raw_data_qualimapReport/mapped_reads_gc-content_distribution.txt"
+ }
+ },
+ "SnpEff": {
+ "all_sections": {
+ "P4107_1004": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/07_variant_calls/P4107_1004.clean.dedup.recal.bam.recalibrated.snp.annotated.vcf.gz.snpEff.summary.csv",
+ "P4107_1003": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/07_variant_calls/P4107_1003.clean.dedup.recal.bam.recalibrated.snp.annotated.vcf.gz.snpEff.summary.csv",
+ "P4107_1006": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/07_variant_calls/P4107_1006.clean.dedup.recal.bam.recalibrated.snp.annotated.vcf.gz.snpEff.summary.csv",
+ "P4107_1002": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/07_variant_calls/P4107_1002.clean.dedup.recal.bam.recalibrated.snp.annotated.vcf.gz.snpEff.summary.csv",
+ "P4107_1005": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/07_variant_calls/P4107_1005.clean.dedup.recal.bam.recalibrated.snp.annotated.vcf.gz.snpEff.summary.csv",
+ "P4107_1001": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/07_variant_calls/P4107_1001.clean.dedup.recal.bam.recalibrated.snp.annotated.vcf.gz.snpEff.summary.csv"
+ }
+ },
+ "GATK": {
+ "varianteval": {
+ "P4107_1004": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/07_variant_calls/P4107_1004.clean.dedup.recal.bam.snp.eval",
+ "P4107_1002": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/07_variant_calls/P4107_1002.clean.dedup.recal.bam.snp.eval",
+ "P4107_1003": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/07_variant_calls/P4107_1003.clean.dedup.recal.bam.snp.eval",
+ "P4107_1005": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/07_variant_calls/P4107_1005.clean.dedup.recal.bam.snp.eval",
+ "P4107_1006": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/07_variant_calls/P4107_1006.clean.dedup.recal.bam.snp.eval",
+ "P4107_1001": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/07_variant_calls/P4107_1001.clean.dedup.recal.bam.snp.eval"
+ }
+ },
+ "Picard": {
+ "DuplicationMetrics": {
+ "P4107_1001": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/05_processed_alignments/P4107_1001.metrics",
+ "P4107_1002": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/05_processed_alignments/P4107_1002.metrics",
+ "P4107_1003": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/05_processed_alignments/P4107_1003.metrics",
+ "P4107_1006": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/05_processed_alignments/P4107_1006.metrics",
+ "P4107_1005": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/05_processed_alignments/P4107_1005.metrics",
+ "P4107_1004": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/piper_ngi/05_processed_alignments/P4107_1004.metrics"
+ }
+ },
+ "FastQ Screen": {
+ "all_sections": {
+ "P4107_1003": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/qc_ngi/P4107_1003/fastq_screen/P4107_1003_S3_L004_R2_001_screen.txt",
+ "P4107_1004": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/qc_ngi/P4107_1004/fastq_screen/P4107_1004_S4_L005_R2_001_screen.txt",
+ "P4107_1005": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/qc_ngi/P4107_1005/fastq_screen/P4107_1005_S5_L006_R1_001_screen.txt",
+ "P4107_1002": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/qc_ngi/P4107_1002/fastq_screen/P4107_1002_S2_L003_R1_001_screen.txt",
+ "P4107_1001": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/qc_ngi/P4107_1001/fastq_screen/P4107_1001_S1_L002_R1_001_screen.txt",
+ "P4107_1006": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/qc_ngi/P4107_1006/fastq_screen/P4107_1006_S6_L007_R1_001_screen.txt"
+ }
+ },
+ "FastQC": {
+ "all_sections": {
+ "P4107_1003": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/qc_ngi/P4107_1003/fastqc/P4107_1003_S3_L004_R1_001_fastqc.zip",
+ "P4107_1004": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/qc_ngi/P4107_1004/fastqc/P4107_1004_S4_L005_R1_001_fastqc.zip",
+ "P4107_1005": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/qc_ngi/P4107_1005/fastqc/P4107_1005_S5_L006_R2_001_fastqc.zip",
+ "P4107_1002": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/qc_ngi/P4107_1002/fastqc/P4107_1002_S2_L003_R2_001_fastqc.zip",
+ "P4107_1001": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/qc_ngi/P4107_1001/fastqc/P4107_1001_S1_L002_R2_001_fastqc.zip",
+ "P4107_1006": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs/data/qc_ngi/P4107_1006/fastqc/P4107_1006_S6_L007_R2_001_fastqc.zip"
+ }
+ }
+ },
+ "report_general_stats_data": [
+ {
+ "P4107_1003": {
+ "total_reads": 868204107,
+ "mapped_reads": 847562410,
+ "mean_coverage": 40.46,
+ "percentage_aligned": 97.62248337302556,
+ "median_coverage": 40,
+ "median_insert_size": 365,
+ "avg_gc": 41.35698208795284,
+ "1_x_pc": 92.29751163907807,
+ "5_x_pc": 92.22753318515709,
+ "10_x_pc": 92.07815353718176,
+ "30_x_pc": 82.37846792457299,
+ "50_x_pc": 16.220900002951474
+ },
+ "P4107_1004": {
+ "total_reads": 1002828927,
+ "mapped_reads": 985115356,
+ "mean_coverage": 47.04,
+ "percentage_aligned": 98.23363980405004,
+ "median_coverage": 46,
+ "median_insert_size": 363,
+ "avg_gc": 40.99306639893834,
+ "1_x_pc": 92.29608730699667,
+ "5_x_pc": 92.2323784611382,
+ "10_x_pc": 92.0764160970611,
+ "30_x_pc": 84.74393690711297,
+ "50_x_pc": 40.45506563396865
+ },
+ "P4107_1005": {
+ "total_reads": 974955793,
+ "mapped_reads": 955921317,
+ "mean_coverage": 45.58,
+ "percentage_aligned": 98.04765753107331,
+ "median_coverage": 45,
+ "median_insert_size": 368,
+ "avg_gc": 41.17477503107992,
+ "1_x_pc": 92.29828148121867,
+ "5_x_pc": 92.2346776709854,
+ "10_x_pc": 92.11250283024343,
+ "30_x_pc": 85.26307080995146,
+ "50_x_pc": 35.61445297024546
+ },
+ "P4107_1002": {
+ "total_reads": 865975844,
+ "mapped_reads": 847067526,
+ "mean_coverage": 40.38,
+ "percentage_aligned": 97.81653054978287,
+ "median_coverage": 40,
+ "median_insert_size": 367,
+ "avg_gc": 41.29061839482755,
+ "1_x_pc": 92.29794180799979,
+ "5_x_pc": 92.22694426994362,
+ "10_x_pc": 92.07669477353262,
+ "30_x_pc": 82.33885866147037,
+ "50_x_pc": 15.934603451516617
+ },
+ "P4107_1006": {
+ "total_reads": 912383669,
+ "mapped_reads": 894970438,
+ "mean_coverage": 42.59,
+ "percentage_aligned": 98.09145739981456,
+ "median_coverage": 43,
+ "median_insert_size": 362,
+ "avg_gc": 41.269984866552605,
+ "1_x_pc": 92.29880346765438,
+ "5_x_pc": 92.23074228464515,
+ "10_x_pc": 92.10216755684698,
+ "30_x_pc": 84.09635794292338,
+ "50_x_pc": 23.664833468422913
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+ "Mouse percentage": 9.940000000000001,
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+ "total_reads": 200018,
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+ "Human percentage": 97.46000000000001,
+ "Mouse counts": 19868.0,
+ "Mouse percentage": 9.93,
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+ "Xtropicalis percentage": 9.209999999999999,
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+ }
+ },
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+ "Filename": "P4107_1003_S3_L004_R1_001.fastq.gz",
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+ "sequence_duplication_levels": "pass",
+ "overrepresented_sequences": "warn",
+ "adapter_content": "pass",
+ "kmer_content": "fail"
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+ "Filename": "P4107_1004_S4_L005_R1_001.fastq.gz",
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+ "overrepresented_sequences": "fail",
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+ "sequence_length_distribution": "pass",
+ "sequence_duplication_levels": "pass",
+ "overrepresented_sequences": "warn",
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+ "kmer_content": "fail"
+ }
+ },
+ "multiqc_general_stats": {
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+ "FastQC_mqc-generalstats-fastqc-avg_sequence_length": 151.0,
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+ "FastQC_mqc-generalstats-fastqc-percent_fails": 8.333333333333332,
+ "FastQC_mqc-generalstats-fastqc-total_sequences": 431379000.0
+ },
+ "P4107_1004": {
+ "QualiMap_mqc-generalstats-qualimap-avg_gc": 40.99306639893834,
+ "QualiMap_mqc-generalstats-qualimap-median_insert_size": 363,
+ "QualiMap_mqc-generalstats-qualimap-1_x_pc": 92.29608730699667,
+ "QualiMap_mqc-generalstats-qualimap-5_x_pc": 92.2323784611382,
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+ "QualiMap_mqc-generalstats-qualimap-30_x_pc": 84.74393690711297,
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+ "QualiMap_mqc-generalstats-qualimap-mean_coverage": 47.04,
+ "QualiMap_mqc-generalstats-qualimap-percentage_aligned": 98.23363980405004,
+ "QualiMap_mqc-generalstats-qualimap-mapped_reads": 985115356,
+ "QualiMap_mqc-generalstats-qualimap-total_reads": 1002828927,
+ "SnpEff_mqc-generalstats-snpeff-Change_rate": 765.0,
+ "SnpEff_mqc-generalstats-snpeff-Ts_Tv_ratio": 1.996252,
+ "SnpEff_mqc-generalstats-snpeff-Number_of_variants_before_filter": 4052117.0,
+ "GATK: GATK VariantEval_mqc-generalstats-gatk_gatk_varianteval-known_titv": 2.07,
+ "GATK: GATK VariantEval_mqc-generalstats-gatk_gatk_varianteval-novel_titv": 1.49,
+ "Picard: Mark Duplicates_mqc-generalstats-picard_mark_duplicates-PERCENT_DUPLICATION": 0.393866,
+ "FastQC_mqc-generalstats-fastqc-percent_duplicates": 3.940427809644561,
+ "FastQC_mqc-generalstats-fastqc-percent_gc": 40.0,
+ "FastQC_mqc-generalstats-fastqc-avg_sequence_length": 151.0,
+ "FastQC_mqc-generalstats-fastqc-median_sequence_length": 151,
+ "FastQC_mqc-generalstats-fastqc-percent_fails": 16.666666666666664,
+ "FastQC_mqc-generalstats-fastqc-total_sequences": 498165399.0
+ },
+ "P4107_1005": {
+ "QualiMap_mqc-generalstats-qualimap-avg_gc": 41.17477503107992,
+ "QualiMap_mqc-generalstats-qualimap-median_insert_size": 368,
+ "QualiMap_mqc-generalstats-qualimap-1_x_pc": 92.29828148121867,
+ "QualiMap_mqc-generalstats-qualimap-5_x_pc": 92.2346776709854,
+ "QualiMap_mqc-generalstats-qualimap-10_x_pc": 92.11250283024343,
+ "QualiMap_mqc-generalstats-qualimap-30_x_pc": 85.26307080995146,
+ "QualiMap_mqc-generalstats-qualimap-50_x_pc": 35.61445297024546,
+ "QualiMap_mqc-generalstats-qualimap-median_coverage": 45,
+ "QualiMap_mqc-generalstats-qualimap-mean_coverage": 45.58,
+ "QualiMap_mqc-generalstats-qualimap-percentage_aligned": 98.04765753107331,
+ "QualiMap_mqc-generalstats-qualimap-mapped_reads": 955921317,
+ "QualiMap_mqc-generalstats-qualimap-total_reads": 974955793,
+ "SnpEff_mqc-generalstats-snpeff-Change_rate": 762.0,
+ "SnpEff_mqc-generalstats-snpeff-Ts_Tv_ratio": 1.993718,
+ "SnpEff_mqc-generalstats-snpeff-Number_of_variants_before_filter": 4069620.0,
+ "GATK: GATK VariantEval_mqc-generalstats-gatk_gatk_varianteval-known_titv": 2.07,
+ "GATK: GATK VariantEval_mqc-generalstats-gatk_gatk_varianteval-novel_titv": 1.46,
+ "Picard: Mark Duplicates_mqc-generalstats-picard_mark_duplicates-PERCENT_DUPLICATION": 0.244861,
+ "FastQC_mqc-generalstats-fastqc-percent_duplicates": 6.603658645191075,
+ "FastQC_mqc-generalstats-fastqc-percent_gc": 41.0,
+ "FastQC_mqc-generalstats-fastqc-avg_sequence_length": 151.0,
+ "FastQC_mqc-generalstats-fastqc-median_sequence_length": 151,
+ "FastQC_mqc-generalstats-fastqc-percent_fails": 8.333333333333332,
+ "FastQC_mqc-generalstats-fastqc-total_sequences": 484233426.0
+ },
+ "P4107_1002": {
+ "QualiMap_mqc-generalstats-qualimap-avg_gc": 41.29061839482755,
+ "QualiMap_mqc-generalstats-qualimap-median_insert_size": 367,
+ "QualiMap_mqc-generalstats-qualimap-1_x_pc": 92.29794180799979,
+ "QualiMap_mqc-generalstats-qualimap-5_x_pc": 92.22694426994362,
+ "QualiMap_mqc-generalstats-qualimap-10_x_pc": 92.07669477353262,
+ "QualiMap_mqc-generalstats-qualimap-30_x_pc": 82.33885866147037,
+ "QualiMap_mqc-generalstats-qualimap-50_x_pc": 15.934603451516617,
+ "QualiMap_mqc-generalstats-qualimap-median_coverage": 40,
+ "QualiMap_mqc-generalstats-qualimap-mean_coverage": 40.38,
+ "QualiMap_mqc-generalstats-qualimap-percentage_aligned": 97.81653054978287,
+ "QualiMap_mqc-generalstats-qualimap-mapped_reads": 847067526,
+ "QualiMap_mqc-generalstats-qualimap-total_reads": 865975844,
+ "SnpEff_mqc-generalstats-snpeff-Change_rate": 762.0,
+ "SnpEff_mqc-generalstats-snpeff-Ts_Tv_ratio": 1.994323,
+ "SnpEff_mqc-generalstats-snpeff-Number_of_variants_before_filter": 4067653.0,
+ "GATK: GATK VariantEval_mqc-generalstats-gatk_gatk_varianteval-known_titv": 2.07,
+ "GATK: GATK VariantEval_mqc-generalstats-gatk_gatk_varianteval-novel_titv": 1.48,
+ "Picard: Mark Duplicates_mqc-generalstats-picard_mark_duplicates-PERCENT_DUPLICATION": 0.098979,
+ "FastQC_mqc-generalstats-fastqc-percent_duplicates": 2.1580757020873165,
+ "FastQC_mqc-generalstats-fastqc-percent_gc": 41.0,
+ "FastQC_mqc-generalstats-fastqc-avg_sequence_length": 151.0,
+ "FastQC_mqc-generalstats-fastqc-median_sequence_length": 151,
+ "FastQC_mqc-generalstats-fastqc-percent_fails": 16.666666666666664,
+ "FastQC_mqc-generalstats-fastqc-total_sequences": 430185524.0
+ },
+ "P4107_1006": {
+ "QualiMap_mqc-generalstats-qualimap-avg_gc": 41.269984866552605,
+ "QualiMap_mqc-generalstats-qualimap-median_insert_size": 362,
+ "QualiMap_mqc-generalstats-qualimap-1_x_pc": 92.29880346765438,
+ "QualiMap_mqc-generalstats-qualimap-5_x_pc": 92.23074228464515,
+ "QualiMap_mqc-generalstats-qualimap-10_x_pc": 92.10216755684698,
+ "QualiMap_mqc-generalstats-qualimap-30_x_pc": 84.09635794292338,
+ "QualiMap_mqc-generalstats-qualimap-50_x_pc": 23.664833468422913,
+ "QualiMap_mqc-generalstats-qualimap-median_coverage": 43,
+ "QualiMap_mqc-generalstats-qualimap-mean_coverage": 42.59,
+ "QualiMap_mqc-generalstats-qualimap-percentage_aligned": 98.09145739981456,
+ "QualiMap_mqc-generalstats-qualimap-mapped_reads": 894970438,
+ "QualiMap_mqc-generalstats-qualimap-total_reads": 912383669,
+ "SnpEff_mqc-generalstats-snpeff-Change_rate": 761.0,
+ "SnpEff_mqc-generalstats-snpeff-Ts_Tv_ratio": 1.993421,
+ "SnpEff_mqc-generalstats-snpeff-Number_of_variants_before_filter": 4072161.0,
+ "GATK: GATK VariantEval_mqc-generalstats-gatk_gatk_varianteval-known_titv": 2.07,
+ "GATK: GATK VariantEval_mqc-generalstats-gatk_gatk_varianteval-novel_titv": 1.48,
+ "Picard: Mark Duplicates_mqc-generalstats-picard_mark_duplicates-PERCENT_DUPLICATION": 0.124364,
+ "FastQC_mqc-generalstats-fastqc-percent_duplicates": 1.7758820535142377,
+ "FastQC_mqc-generalstats-fastqc-percent_gc": 41.0,
+ "FastQC_mqc-generalstats-fastqc-avg_sequence_length": 151.0,
+ "FastQC_mqc-generalstats-fastqc-median_sequence_length": 151,
+ "FastQC_mqc-generalstats-fastqc-percent_fails": 8.333333333333332,
+ "FastQC_mqc-generalstats-fastqc-total_sequences": 453238610.0
+ },
+ "P4107_1001": {
+ "QualiMap_mqc-generalstats-qualimap-avg_gc": 41.36746349676451,
+ "QualiMap_mqc-generalstats-qualimap-median_insert_size": 358,
+ "QualiMap_mqc-generalstats-qualimap-1_x_pc": 92.29432810535144,
+ "QualiMap_mqc-generalstats-qualimap-5_x_pc": 92.21025634005906,
+ "QualiMap_mqc-generalstats-qualimap-10_x_pc": 91.9610844336904,
+ "QualiMap_mqc-generalstats-qualimap-30_x_pc": 74.66018701572433,
+ "QualiMap_mqc-generalstats-qualimap-50_x_pc": 5.240211027996628,
+ "QualiMap_mqc-generalstats-qualimap-median_coverage": 36,
+ "QualiMap_mqc-generalstats-qualimap-mean_coverage": 35.78,
+ "QualiMap_mqc-generalstats-qualimap-percentage_aligned": 97.28985936468062,
+ "QualiMap_mqc-generalstats-qualimap-mapped_reads": 751147332,
+ "QualiMap_mqc-generalstats-qualimap-total_reads": 772071557,
+ "SnpEff_mqc-generalstats-snpeff-Change_rate": 764.0,
+ "SnpEff_mqc-generalstats-snpeff-Ts_Tv_ratio": 1.995169,
+ "SnpEff_mqc-generalstats-snpeff-Number_of_variants_before_filter": 4059325.0,
+ "GATK: GATK VariantEval_mqc-generalstats-gatk_gatk_varianteval-known_titv": 2.07,
+ "GATK: GATK VariantEval_mqc-generalstats-gatk_gatk_varianteval-novel_titv": 1.49,
+ "Picard: Mark Duplicates_mqc-generalstats-picard_mark_duplicates-PERCENT_DUPLICATION": 0.063884,
+ "FastQC_mqc-generalstats-fastqc-percent_duplicates": 3.693292003886029,
+ "FastQC_mqc-generalstats-fastqc-percent_gc": 41.0,
+ "FastQC_mqc-generalstats-fastqc-avg_sequence_length": 151.0,
+ "FastQC_mqc-generalstats-fastqc-median_sequence_length": 151,
+ "FastQC_mqc-generalstats-fastqc-percent_fails": 16.666666666666664,
+ "FastQC_mqc-generalstats-fastqc-total_sequences": 383592756.0
+ }
+ }
+ },
+ "config_analysis_dir_abs": [
+ "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs"
+ ],
+ "config_analysis_dir": [
+ "."
+ ],
+ "config_creation_date": "2024-02-28, 14:39 CET",
+ "config_git_hash": null,
+ "config_intro_text": null,
+ "config_report_comment": null,
+ "config_report_header_info": null,
+ "config_script_path": "/Users/ewels/.miniconda3/miniconda3/envs/py3.12/lib/python3.12/site-packages/multiqc/utils",
+ "config_short_version": "1.21",
+ "config_subtitle": null,
+ "config_title": null,
+ "config_version": "1.21",
+ "config_output_dir": "/Users/ewels/GitHub/MultiQC/website/public/examples/wgs"
+}
diff --git a/man/TidyMultiqc-package.Rd b/man/TidyMultiqc-package.Rd
index 53d73d2..e345196 100644
--- a/man/TidyMultiqc-package.Rd
+++ b/man/TidyMultiqc-package.Rd
@@ -2,6 +2,7 @@
% Please edit documentation in R/multiqc.R
\docType{package}
\name{TidyMultiqc-package}
+\alias{TidyMultiqc}
\alias{TidyMultiqc-package}
\title{TidyMultiqc: Converting MultiQC reports into tidy data frames}
\description{
@@ -12,3 +13,13 @@ If you are reading this manual, you should immediately stop reading this and
instead refer to the documentation website at \url{https://multimeric.github.io/TidyMultiqc/}, which
provides more accessible documentation.
}
+\seealso{
+Useful links:
+\itemize{
+ \item \url{https://multimeric.github.io/TidyMultiqc/}
+ \item \url{https://github.com/multimeric/TidyMultiqc}
+ \item \url{https://cran.r-project.org/package=TidyMultiqc}
+ \item Report bugs at \url{https://github.com/multimeric/TidyMultiqc/issues}
+}
+
+}
diff --git a/man/is_plotly.Rd b/man/is_plotly.Rd
new file mode 100644
index 0000000..6015ad7
--- /dev/null
+++ b/man/is_plotly.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/plot_parsers.R
+\name{is_plotly}
+\alias{is_plotly}
+\title{Determine if a plot is Plotly}
+\usage{
+is_plotly(plot_data)
+}
+\arguments{
+\item{plot_data}{A list containing the top level data for a single plot.}
+}
+\value{
+A logical scalar. TRUE if the plot is a plotly plot, or FALSE if it's a HighCharts one.
+}
+\description{
+Plotly plots are generated by MultiQC 1.2 and above.
+This matters because the plot data formats differ between these two formats.
+}
+\keyword{internal}
diff --git a/man/load_multiqc.Rd b/man/load_multiqc.Rd
index f4c624b..e857a15 100644
--- a/man/load_multiqc.Rd
+++ b/man/load_multiqc.Rd
@@ -7,7 +7,9 @@
load_multiqc(
paths,
plots = NULL,
- find_metadata = function(...) { list() },
+ find_metadata = function(...) {
+ list()
+ },
plot_parsers = list(),
sections = "general"
)
diff --git a/man/map_xy_line_datasets.Rd b/man/map_xy_line_datasets.Rd
new file mode 100644
index 0000000..25a7459
--- /dev/null
+++ b/man/map_xy_line_datasets.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/plot_parsers.R
+\name{map_xy_line_datasets}
+\alias{map_xy_line_datasets}
+\title{Parses a list of xyline plot datasets}
+\usage{
+map_xy_line_datasets(datasets, plot_name)
+}
+\arguments{
+\item{dataset}{A list which has the names "name" and "data", which
+originate from a MultiQC plot}
+}
+\value{
+A list whose names are sample names. Each value is a data frame with one column.
+}
+\description{
+Parses a list of xyline plot datasets
+}
+\keyword{internal}
diff --git a/tests/testthat/test-multiqc-12.R b/tests/testthat/test-multiqc-12.R
new file mode 100644
index 0000000..7bc3ef1
--- /dev/null
+++ b/tests/testthat/test-multiqc-12.R
@@ -0,0 +1,27 @@
+test_that("The non-plot data are parsed identically", {
+ old <- system.file("extdata", "wgs", "multiqc_data.json", package = "TidyMultiqc") %>% TidyMultiqc::load_multiqc()
+ new <- system.file("extdata", "1.2_wgs", "multiqc_data.json", package = "TidyMultiqc") %>% TidyMultiqc::load_multiqc()
+ intersect(
+ colnames(old),
+ colnames(new)
+ ) %>%
+ purrr::walk(function(col){
+ testthat::expect_equal(old[, col], new[, col])
+ })
+})
+
+test_that("xy_line plots are parsed identically", {
+ old <- system.file("extdata", "wgs", "multiqc_data.json", package = "TidyMultiqc") %>% TidyMultiqc::load_multiqc(sections = "plot", plots = "qualimap_coverage_histogram")
+ new <- system.file("extdata", "1.2_wgs", "multiqc_data.json", package = "TidyMultiqc") %>% TidyMultiqc::load_multiqc(sections = "plot", plots = "qualimap_coverage_histogram")
+ testthat::expect_identical(old, new)
+})
+
+test_that("bar plots are parsed identically", {
+ old <- system.file("extdata", "wgs", "multiqc_data.json", package = "TidyMultiqc") %>% TidyMultiqc::load_multiqc(sections = "plot", plots = "snpeff_variant_effects_region")
+ new <- system.file("extdata", "1.2_wgs", "multiqc_data.json", package = "TidyMultiqc") %>% TidyMultiqc::load_multiqc(sections = "plot", plots = "snpeff_variant_effects_region")
+ # Check that the samples are identical and in the right order
+ testthat::expect_identical(old$metadata.sample_id, new$metadata.sample_id)
+ # Check that each tibble inside the plot column is mapequal. Namely, ignore differences in list order, since hopefully users
+ # are accessing these elements by name and not by position
+ purrr::map2(old$plot.snpeff_variant_effects_region, new$plot.snpeff_variant_effects_region, testthat::expect_mapequal)
+})