Add master program to programmaticaly knit R notebooks

master_program.R

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+library(pacman)
+library(rmarkdown)
+
+# use this function before knitting each notebook to 'unload' packages (except base R packages)
+unload_pkgs <- function() p_unload(setdiff(p_loaded(),c("pacman","rmarkdown")), character.only = TRUE)#
+
+# function for automated knitting of a single notebook
+# name: character; the name of the notebook to be knitted (without .Rmd extension)
+
+# input_data: character; the name of .Rds file generated by 'data_prep_XXX.R' script
+
+# suffix: character; suffix used in notebook output file (html and rda) names
+
+# load_outputdata: boolean, if TRUE, then intermediate data sets used in plotting, DEA will not be created, but loaded
+
+# save_outputdata: boolean, if TRUE, then intermediate data sets used in plotting, DEA will be saved as .rda file for later use. 
+#                  The name of the file goes, e.g., as follows "datadriven_unit_outdata<SUFFIX>.rda"
+
+# subsample: numeric; 0 means all proteins, e.g. 50 means that 50 proteins will be randomly sampled.
+#            subsample should be an integer >=0; if a very small number is specified, some errors may occurs (e.g. in normalization or DEA)
+
+knit_notebook <- function(name, input_data, suffix='', load_outputdata=LOAD, save_outputdata=SAVE, subsample=0){
+  notebook.params=list(input_data_p=input_data, suffix_p=suffix, load_outputdata_p=load_outputdata, save_outputdata_p=save_outputdata, subsample_p=subsample)
+  if (suffix=='') tmp=name else tmp=paste0(name, '_', suffix)
+  render(input = paste0(name,".Rmd"), 
+         output_file = file.path(dirname(paste0(name,".Rmd")), tmp),
+         params=notebook.params)
+}
+
+# first you need to run 'data_prep_XXX.R' script to process your raw data and save the resulting data
+# as 'input_data_XXX.Rds' used in knitting the notebooks
+
+# suffix to be added to when creating the html report and .rda file with output data. 
+SUFFIX = ''
+# e.g. SUFFIX = 'msstatstmt' lead to "datadriven_unit_msstatstmt.html" and "datadriven_unit_outdata_msstatstmt.rda"
+
+# path to the processed input data that should be created by running earlier the data/data_prep.R script
+INPUT_DATA = 'data/input_data.rds'
+
+SAVE=TRUE
+
+LOAD=FALSE
+
+SUBSAMPLE=0
+
+VARIABLES_TO_KEEP = c('VARIABLES_TO_KEEP', 'unload_pkgs', 'knit_notebook', 'INPUT_DATA', 'SUFFIX', 'SAVE', 'LOAD', 'SUBSAMPLE')
+
+# knit all the notebooks specified below
+rm(list=setdiff(ls(),VARIABLES_TO_KEEP)); unload_pkgs(); 
+knit_notebook('intro', input_data=INPUT_DATA)
+
+#### data-driven notebooks ####
+rm(list=setdiff(ls(),VARIABLES_TO_KEEP)); unload_pkgs(); 
+knit_notebook("datadriven_unit", input_data=INPUT_DATA, suffix=SUFFIX, load_outputdata=LOAD, save_outputdata=SAVE, subsample=SUBSAMPLE) 
+
+rm(list=setdiff(ls(),VARIABLES_TO_KEEP)); unload_pkgs(); 
+knit_notebook("datadriven_summarization", input_data=INPUT_DATA, suffix=SUFFIX, load_outputdata=LOAD, save_outputdata=SAVE, subsample=SUBSAMPLE) 
+
+rm(list=setdiff(ls(),VARIABLES_TO_KEEP)); unload_pkgs(); 
+knit_notebook("datadriven_normalization", input_data=INPUT_DATA, suffix=SUFFIX, load_outputdata=LOAD, save_outputdata=SAVE, subsample=SUBSAMPLE) 
+
+rm(list=setdiff(ls(),VARIABLES_TO_KEEP)); unload_pkgs(); 
+knit_notebook("datadriven_DEA", input_data=INPUT_DATA, suffix=SUFFIX, load_outputdata=LOAD, save_outputdata=SAVE, subsample=SUBSAMPLE) 
+
+#### model-based notebooks ####
+rm(list=setdiff(ls(),VARIABLES_TO_KEEP)); unload_pkgs(); 
+knit_notebook("modelbased_unit", input_data=INPUT_DATA, suffix=SUFFIX, load_outputdata=LOAD, save_outputdata=SAVE, subsample=SUBSAMPLE) 
+
+rm(list=setdiff(ls(),VARIABLES_TO_KEEP)); unload_pkgs(); 
+knit_notebook("modelbased_summarization", input_data=INPUT_DATA, suffix=SUFFIX, load_outputdata=LOAD, save_outputdata=SAVE, subsample=SUBSAMPLE) 
+
+rm(list=setdiff(ls(),VARIABLES_TO_KEEP)); unload_pkgs(); 
+knit_notebook("modelbased_normalization", input_data=INPUT_DATA, suffix=SUFFIX, load_outputdata=LOAD, save_outputdata=SAVE, subsample=SUBSAMPLE)  
+
+rm(list=setdiff(ls(),VARIABLES_TO_KEEP)); unload_pkgs(); 
+knit_notebook("modelbased_DEA", input_data=INPUT_DATA, suffix=SUFFIX, load_outputdata=LOAD, save_outputdata=SAVE, subsample=SUBSAMPLE) 
+
+#### other notebooks ####
+rm(list=setdiff(ls(),VARIABLES_TO_KEEP)); unload_pkgs(); 
+knit_notebook("compare_defaults", input_data=INPUT_DATA, suffix=SUFFIX, load_outputdata=LOAD, save_outputdata=SAVE, subsample=SUBSAMPLE) 
+
+rm(list=setdiff(ls(),VARIABLES_TO_KEEP)); unload_pkgs(); 
+knit_notebook("CONSTANd_vs_medianSweeping", input_data=INPUT_DATA, suffix=SUFFIX, load_outputdata=LOAD, save_outputdata=SAVE, subsample=SUBSAMPLE)  
+
+rm(list=setdiff(ls(),VARIABLES_TO_KEEP)); unload_pkgs(); 
+knit_notebook("datadriven_unit_rawratio", input_data=INPUT_DATA, suffix=SUFFIX, load_outputdata=LOAD, save_outputdata=SAVE, subsample=SUBSAMPLE)