strengejacke

// save to windows-user directory

linters: with_defaults(object_name_linter = NULL,
                       object_length_linter(50),
                       commented_code_linter = NULL,
                       object_usage_linter = NULL,
                       line_length_linter(120),
                       cyclocomp_linter = cyclocomp_linter(50))

rohitfarmer

How to use Neovim or VIM Editor as an IDE for R

Note: This tutorial is written for Linux based systems.

Requirements

R >= 3.0.0

To install the latest version of R please flollow the download and install instructions at https://cloud.r-project.org/

Neovim >= 0.2.0

Neovim (nvim) is the continuation and extension of Vim editor with the aim to keep the good parts of Vim and add more features. In this tutorial I will be using Neovim (nvim), however, most of the steps are equally applicable to Vim also. Please follow download and installation instructions on nvim's GitHub wiki https://github.com/neovim/neovim/wiki/Installing-Neovim.

raryskin

# example of bar plot with individual subject points for Anya -04/24/2019
# added error bars - 04/25/2019

library(tidyverse) #will need to install this first (run: install.packages("tidyverse"))


## fake data
data = tibble( # creating a dataframe (aka "tibble") called data
  subject = rep(1:10,times = 2 ), # making a column/vector of subject numbers (1-10) x2
  condition = rep(c("hard", "easy"), each = 10), # making a column/vector of condition names

crazyhottommy

R solution

library(purrr)
library(tidyverse)
f_files<- list.files("results/superEnhancer/rna_expression/MSTC", pattern = "featureCount.txt", full.names = T)

read_in_feature_counts<- function(file){
        cnt<- read_tsv(file, col_names =T, comment = "#")
        cnt<- cnt %>% dplyr::select(-Chr, -Start, -End, -Strand, -Length)

tgirke

Nvim-R-Tmux: Neovim-based IDE for R

!!! This Gist tutorial is deprecated. Its new version is available here !!!

This is some red text.

• Author: Thomas Girke
• Last update: 18-Nov-2020

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conormm

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs:

bonzanini

# This code uses Biopython to retrieve lists of articles from pubmed
# you need to install Biopython first.

# If you use Anaconda:
# conda install biopython

# If you use pip/venv:
# pip install biopython

# Full discussion:

stephenturner

## RNA-seq analysis with DESeq2
## Stephen Turner, @genetics_blog

# RNA-seq data from GSE52202
# http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse52202. All patients with
# ALS, 4 with C9 expansion ("exp"), 4 controls without expansion ("ctl")

# Import & pre-process ----------------------------------------------------

# Import data from featureCounts

hieblmedia

#
# If all files excluded and you will include only specific sub-directories
# the parent path must matched before.
#

/**
!/.gitignore

###############################
# Un-ignore the affected subdirectory

sedm0784

g_LastCtrlKeyDownTime := 0
g_AbortSendEsc := false
g_ControlRepeatDetected := false

*CapsLock::
    if (g_ControlRepeatDetected)
    {
        return
    }