I just started a new R package called ‘SnakebiteTools’. I would like to collect there small helper functions to better analyse and monitor Snakemake runs. The output can later be used for resource optimization, checking the status of an ongoing Snakemake run (which might be messy for runs with plenty of jobs) etc.
Our Fluidigm R-package was just released on Cran. The package is designed to streamline the process of analyzing genotyping data from Fluidigm machines. It offers a suite of tools for data handling and analysis, making it easier for researchers to work with their data. Here are the key functions provided by the package:
fluidigm2PLINK(...): Converts Fluidigm data to the format used by PLINK, creating a ped/map-file pair from the CSV output received from the Fluidigm machine.estimateErrors(...): Estimates errors in the genotyping data.calculatePairwiseSimilarities(...): Calculates pairwise similarities between samples.getPairwiseSimilarityLoci(...): Determines pairwise similarity loci.similarityMatrix(...): Generates a similarity matrix.Users can choose to run these functions individually or execute them all at once using the convenient fluidigmAnalysisWrapper(...) wrapper function.
With the upcoming autumn holiday ahead, my autumn holiday read just arrived in time! Looking forward to having a light read, as my Python become a little rusty lately it is a nice kick-start again to read about it from an introductory perspective. First glance looks really nice!
The normal table command table() calculates the frequency of each element of a vector like this:
R> df <- data.frame(var = c("A", "A", "B", "B", "C", "C", "C"))
R> table(df)
df
A B C
2 2 3 So, it tells us, we have two times A and B and three times C, accordingly.
However, if we have now the situation like this:
df <- data.frame(var = c("A", "A", "B", "B", "C", "C", "C"), value = c(10, 20, 20, 40, 15, 25, 35))Meaning, we have a categorical variable var and a numeric variable value and for each categorical variable we would like to get the sum over the numerical variable, we can simply use the base-R command aggregate like this
R> aggregate(value ~ var, data = df, FUN = sum)
var wt
1 A 40
2 B 60
3 C 70As I often use also the data.table package, here is also a simple solution using this package, assuming we do (respective have a data table from some other source, like fread)
library("data.table")
dt <- data.table(df)Then we can just sume over a column name with respect to another column like this (and assign the value into a new variable tot) :
setDT(dt)[, .(n = sum(value)), var]
Today I faced a strange thing, while I tried to install an update to one of my R-packages. As usually, I installed the latest version from it like this
library("devtools")
install_github("fischuu/GenomicTools")But the installation failed, and when I tried to reinstall it, I got this error message:
Installing package into ‘/homeappl/home/<user>/R/x86_64-redhat-linux-gnu-library/4.1’
(as ‘lib’ is unspecified)
ERROR: failed to lock directory ‘/homeappl/home/<user>/R/x86_64-redhat-linux-gnu-library/4.1’ for modifying
Try removing ‘/homeappl/home/<user>/R/x86_64-redhat-linux-gnu-library/4.1/00LOCK-GenomicTools’
Warning message:
In i.p(...) :
installation of package ‘/tmp/Rtmp<...>Lv/file3c36<...>34/GenomicTools_0.2.11.tar.gz’ had non-zero exit statusSo, I tried to go in said directory and delete the folder manually and there I received another error:
rm: cannot remove '00LOCK-GenomicTools/GenomicTools/libs/.nfs00000001002e2<...>d': Device or resource busyI tried this and this, but nothing helped to delete that folder, it kept mocking my that the device is busy. Eventually, it helped just to rename the folder list this
mv 00LOCK-GenomicTools/ 00LOCK-GenomicTools-deleteThis/It is now still hanging there in the folder, but I was able to reinstall the R-package and now I need to revisit the folder in a few days and check, if the device is still busy or if I can delete it then…