Typical ame will not work with the data.table, so check out Slicing and dicing operators without requiring all of the same $ and Which improves speed by vectorizing many typical dataframe operationsĪnd automatically parallelizing when possible, and has more streamlined Note: we are loading our per-barcode counts in as a data.table object, Sorted into a bin, and add some other useful information to our counts Illumina read counts to estimates of the number of cells that were Number of reads of each barcode in each of the sort bins, convert from # nnet_7.3-12 nlme_3.1-140 compiler_3.6.1įirst, we will read in metadata on our sort samples and the table giving # loaded via a namespace (and not attached): # stats graphics grDevices utils datasets methods base # LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C # LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
# LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
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