Website: https://www.tidyverse.org/packages/
Comparison of dplyr and base functions: https://cran.r-project.org/web/packages/dplyr/vignettes/base.html
Piping:
library(dplyr)
gibran hemani explodecomputer
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| ($1 == 1) && ($4 >= 48227413) && ($4 <= 52227412) {print $2} | |
| ($1 == 2) && ($4 >= 86000000) && ($4 <= 100500000) {print $2} | |
| ($1 == 2) && ($4 >= 183291755) && ($4 <= 190291755) {print $2} | |
| ($1 == 3) && ($4 >= 47524996) && ($4 <= 50024996) {print $2} | |
| ($1 == 3) && ($4 >= 83417310) && ($4 <= 86917310) {print $2} | |
| ($1 == 5) && ($4 >= 128972101) && ($4 <= 131972101) {print $2} | |
| ($1 == 5) && ($4 >= 44500000) && ($4 <= 50500000) {print $2} | |
| ($1 == 6) && ($4 >= 57000000) && ($4 <= 64000000) {print $2} | |
| ($1 == 6) && ($4 >= 25392021) && ($4 <= 33392022) {print $2} | |
| ($1 == 6) && ($4 >= 139958307) && ($4 <= 142458307) {print $2} |
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| # sample size | |
| n <- 10000 | |
| # simulate x and y variables - no association | |
| x <- rnorm(n) | |
| y <- rnorm(n) | |
| # selected into the sample is influenced by x and y | |
| sel <- rbinom(n, 1, plogis(x + y)) |
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| #!/bin/bash | |
| path=$(readlink -f $*); | |
| cwd=$(pwd); | |
| winpath=$(wslpath -w $path); | |
| echo "Editing ${path}" | |
| echo "Windows path is ${winpath}" | |
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| library(dplyr) | |
| library(org.Hs.eg.db) | |
| # get KEGG pathways | |
| xx <- as.list(org.Hs.egPATH2EG) | |
| keggpathways <- lapply(names(xx), function(x) { | |
| tibble( | |
| symbol=select(org.Hs.eg.db, xx[[x]], c("SYMBOL"), "ENTREZID")$SYMBOL, | |
| pathway=x |
explodecomputer
/ ld.awk
Last active
September 16, 2020 12:11
— forked from samwalrus/ld.awk
Bash script and AWK script for creating genetic principle components including removing long range LD regions
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| ($1 == 1) && ($4 >= 48227413) && ($4 <= 52227412) {print $2} | |
| ($1 == 2) && ($4 >= 86000000) && ($4 <= 100500000) {print $2} | |
| ($1 == 2) && ($4 >= 183291755) && ($4 <= 190291755) {print $2} | |
| ($1 == 3) && ($4 >= 47524996) && ($4 <= 50024996) {print $2} | |
| ($1 == 3) && ($4 >= 83417310) && ($4 <= 86917310) {print $2} | |
| ($1 == 5) && ($4 >= 128972101) && ($4 <= 131972101) {print $2} | |
| ($1 == 5) && ($4 >= 44500000) && ($4 <= 50500000) {print $2} | |
| ($1 == 6) && ($4 >= 57000000) && ($4 <= 64000000) {print $2} | |
| ($1 == 6) && ($4 >= 25392021) && ($4 <= 33392022) {print $2} | |
| ($1 == 6) && ($4 >= 139958307) && ($4 <= 142458307) {print $2} |
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| # simulation | |
| n <- 10000 | |
| a <- rbinom(n, 2, 0.5) | |
| b <- rbinom(n, 2, 0.49) | |
| # perform fisher's exact test to obtain odds ratio for two allele frequencies being different | |
| cont <- matrix( | |
| c(sum(a==0) * 2 + sum(a==1), sum(a==2) * 2 + sum(a==1), sum(b==0) * 2 + sum(b==1), sum(b==2) * 2 + sum(b==1)), 2, 2) | |
| fisher.test(cont) |
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| # Number of individuals in the population | |
| npop <- 100000 | |
| # Distribution of variable in the population | |
| x <- rnorm(npop) | |
| # Individuals selected into sample with high x value | |
| s <- rbinom(npop, 1, plogis(x * 0.4)) | |
| # Population mean of x |
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| library(dplyr) | |
| library(data.table) | |
| library(TwoSampleMR) | |
| bmi_id <- "ukb-b-19953" | |
| chd_id <- "ukb-b-3983" | |
| datadir <- "/mnt/storage/private/mrcieu/research/mr-eve/UKBB_replication/replication/results" |