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TL;DR Sometimes for loops are useful, and sometimes they shouldn’t really be used, because they don’t really help you understand your data, and even if you try, they might still be slow(er) than other ways of doing things. Comparing Groups I have some code where I am trying to determine duplicates of a group of things. This data looks something like this: create_random_sets = function(n_sets = 1000){ set.seed(1234) sets = purrr::map(seq(5, n_sets), ~ sample(seq(1, .

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TL;DR If you are getting crappy looking png images from rmarkdown html or word documents, try using type='cairo' or dev='CairoPNG' in your chunk options. PNG Graphics?? So, I write a lot of reports using rmarkdown and knitr, and have been using knitr for quite a while. My job involves doing analyses for collaborators and communicating results. Most of the time, I will generate a pdf report, and I get beautiful graphics, thanks to the eps graphics device.

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TL;DR If you do a statistical test before a dimensional reduction method like PCA, the highest source of variance is likely to be whatever you tested statistically. Wait, Why?? Let me describe the situation. You’ve done an -omics level analysis on your system of interest. You run a t-test (or ANOVA, etc) on each of the features in your data (gene, protein, metabolite, etc). Filter down to those things that were statistically significant, and then finally, you decide to look at the data using a dimensionality reduction method such as principal components analysis (PCA) so you can see what is going on.

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TL; DR If you have a unimodal distribution of values, you can use R’s density or Scipy’s gaussian_kde to create density estimates of the data, and then take the maxima of the density estimate to get the mode. See below for actual examples in R and Python. Mode in R First, lets do this in R. Need some values to work with. library(ggplot2) set.seed(1234) n_point <- 1000 data_df <- data.

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TL;DR If you notice yourself using split -> unsplit / rbind on two object to match items up, maybe you should be using dplyr::join_ instead. Read below for concrete examples. Motivation I have had a lot of calculations lately that involve some sort of normalization or scaling a group of related values, each group by a different factor. Lets setup an example where we will have 1e5 values in 10 groups, each group of values being normalized by their own value.

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TL;DR The IRanges package implements interval algebra, and is very fast for finding overlaps of two ranges. If you have non-integer data, multiply values by a large constant factor and round them. The constant depends on how much accuracy you need. IRanges?? IRanges is a bioconductor package for interval algebra of integer ranges. It is used extensively in the GenomicRanges package for finding overlaps between various genomic features. For genomic features, integers make sense, because one cannot have fractional base locations.

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TL;DR If I raise $100 by August 25ths The Walk to End Alzheimer’s, I will have my beard dyed purple in support of Alzheimer’s awareness. If you are in another country, donate to your local Alzheimer’s charity and email me with the subject walk so I count it towards my total. Links: My donation page (Charity report on Alzheimer’s Association) Facebook Fundraising Page (if you want to share it!

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TL;DR If you like dplyr progress bars, and wished you could use them everywhere, including from within Rmd documents, non-interactive shells, etc, then you should check out knitrProgressBar (cran github). Why Yet Another Progress Bar?? I didn’t set out to create another progress bar package. But I really liked dplyrs style of progress bar, and how they worked under the hood (thanks to the examples from Bob Rudis).

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TL;DR If you include others code in your own R package, list them as contributors with comments about what they contributed, and add a license statement in the file that includes their code. Motivation I recently created the knitrProgressBar package. It is a really simple package, that takes the dplyr progress bars and makes it possible for them to write progress to a supplied file connection. The dplyr package itself is licensed under MIT, so I felt fine taking the code directly from dplyr itself.

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TL;DR If you use the docopt package to create command line R executables that take options, there is something to know about numeric command line options: they should have as.double before using them in your script. Setup Lets set up a new docopt string, that includes both string and numeric arguments. " Usage: test_numeric.R [--string=<string_value>] [--numeric=<numeric_value>] test_numeric.R (-h | --help) test_numeric.R Description: Testing how values are passed using docopt.

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