| Type: | Package |
| Title: | Converts 'MultiQC' Reports into Tidy Data Frames |
| Version: | 1.0.3 |
| Author: | Michael Milton |
| Maintainer: | Michael Milton <michael.r.milton@gmail.com> |
| Description: | Provides the means to convert 'multiqc_data.json' files, produced by the wonderful 'MultiQC' tool, into tidy data frames for downstream analysis in R. This analysis might involve cohort analysis, quality control visualisation, change-point detection, statistical process control, clustering, or any other type of quality analysis. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| Imports: | assertthat, dplyr, jsonlite, magrittr, purrr, rlang, stringr, tibble |
| Suggests: | tidyr, testthat (≥ 3.0.0), knitr, rmarkdown, ggplot2, HistDat |
| Config/testthat/edition: | 3 |
| RoxygenNote: | 7.1.2 |
| VignetteBuilder: | knitr |
| URL: | https://multimeric.github.io/TidyMultiqc/, https://github.com/multimeric/TidyMultiqc, https://cran.r-project.org/package=TidyMultiqc |
| BugReports: | https://github.com/multimeric/TidyMultiqc/issues |
| NeedsCompilation: | no |
| Packaged: | 2022-09-25 01:30:10 UTC; migwell |
| Repository: | CRAN |
| Date/Publication: | 2022-09-25 02:10:02 UTC |
TidyMultiqc: Converting MultiQC reports into tidy data frames
Description
This package provides the means to convert multiqc_data.json
files, produced by the wonderful MultiQC tool,
into tidy data.frames for downstream analysis in R.
If you are reading this manual, you should immediately stop reading this and
instead refer to the documentation website at https://multimeric.github.io/TidyMultiqc/, which
provides more accessible documentation.
List the plot identifiers of all the plots in a given multiqc report
Description
List the plot identifiers of all the plots in a given multiqc report
Usage
list_plots(path)
Arguments
path |
The file path to the multiqc report. This should be a length 1 character vector |
Details
The main use for this function is finding the plot identifiers
that you will then pass into the plots argument of the load_multiqc()
function.
Refer to the section on "Extracting Plot Data" in the main vignette for more information.
Value
A data frame containing n rows, where n is the number
of plots in the report you have provided, and two columns:
- id
The identifier for the plot. This is the one you should use as a name in
plot_opts.- name
The plot title. This is likely what you see in the multiqc report when you open it with your browser.
Examples
# Ignore this, choose your own filepath as the `filepath` variable
filepath <- system.file("extdata", "HG00096/multiqc_data.json", package = "TidyMultiqc")
# This is the actual invocation
list_plots(filepath)
Loads one or more MultiQC reports into a data frame
Description
Loads one or more MultiQC reports into a data frame
Usage
load_multiqc(
paths,
plots = NULL,
find_metadata = function(...) { list() },
plot_parsers = list(),
sections = "general"
)
Arguments
paths |
A string vector of filepaths to multiqc_data.json files |
plots |
A string vector, each of which contains the ID of a plot you
want to include in the output. You can use |
find_metadata |
A single function that will be called with a sample name and the parsed JSON for the entire report and returns a named list of metadata fields for the sample. Refer to the vignette for an example. |
plot_parsers |
Advanced. A named list of custom parser functions. The names of the list should correspond to plotly plot types, such as "xy_line", and the values should be functions that return a named list of named lists. For the return value, the outer list is named by the sample ID, and the inner list is named by the name of the column. Refer to the source code for some examples. |
sections |
A string vector of zero or more sections to include in the output. Each section can be:
This defaults to 'general', which tends to contain the most useful statistics |
Value
A tibble (data.frame subclass) with QC data and metadata as columns, and samples as rows. Columns are named according to the respective section they belong to, and will always be listed in the following order:
metadata.X |
This column contains metadata for this sample.
By default this is only the sample ID, but if you have provided the
|
general.X |
This column contains a generally useful summary statistic for each sample |
plot.X |
This column contains a data frame of plot data for each sample.
Refer to the plot parsers documentation (ie the |
raw.X |
This column contains a raw summary statistic or value relating to each sample |
See Also
parse_xyline_plot() parse_bar_graph()
Examples
load_multiqc(system.file("extdata", "wgs/multiqc_data.json", package = "TidyMultiqc"))
Takes the JSON dictionary for a bar graph, and returns a named list of data frames, one for each sample.
Description
Takes the JSON dictionary for a bar graph, and returns a named list of data frames, one for each sample.
Usage
parse_bar_graph(plot_data, name)
Value
A list of data frames, one for each sample. Each data frame will have one column corresponding to each category in the bar chart. For example, for the plot "SnpEff: Counts by Genomic Region", we will have 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.
Takes the JSON dictionary for an xyline plot, and returns a named list of data frames, one for each sample.
Description
Takes the JSON dictionary for an xyline plot, and returns a named list of data frames, one for each sample.
Usage
parse_xyline_plot(plot_data, name)
Value
A list of data frames, one for each sample. Each data frame will have two columns: x, and y. These correspond to the x and y coordinates in the plot. For example, for histogram data, the x values are values of the random variable, and the y values are the number of counts for that value.