sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
ce3ee8c121cf6bcd0d2b92f21aca5fe7a6b4f0bb5ac22dc4470b92656c7b2809 | R | 1,722 | 65 | context("ng_colours")
test_that("ng_colour works", {
expect_equal(ng_colour("night"), "#323D5A")
expect_equal(ng_colour("giraffe"), NA_character_)
expect_equal(ng_colour(), ng_colour_list)
})
test_that("ng_palette works", {
expect_is(ng_palette_d(), "function")
expect_is(ng_palette_c(), "function")
expect... |
01612e66996ae45144870460b447c1407f55aa03c03976e69cb2bf41eb5a17ec | R | 1,726 | 74 | /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(EnsDb.Hsapiens.v86)
library(ggplot2)
library(patchwork)
set.seed(1234)
library(Signac)
library(GenomicRanges)
pbmc=readRDS('/home/disk/database/data/scHIC/GSE130711_analysis/scATAC_pbmc.rds')
pbmc$type[which(pbmc$seurat... |
6f1d4e0c69375169ac5aa11aa870149130241895ea050f8a186a5a096484b32a | R | 1,746 | 40 |
#' Gene expression data distribution plot
#'
#' This function plots the expression data distributions of the two single cell datasets (e.g. query and reference) to be compared.
#'
#' @param sce.E1,sce.E2 Data objects, namely E1 and E2.
#' @param name.E1,name.E2 Customized names for E1 and E2. Default: \code{"E1"} and ... |
bcfe8554e9aa85c0e52a1063cae57cd1600011a202006052ac5aafb3402d9697 | R | 1,757 | 40 | ## Norms the data within specified groups in a data frame; it normalizes each
## subject (identified by idvar) so that they have the same mean, within each group
## specified by betweenvars.
## data: a data frame.
## idvar: the name of a column that identifies each subject (or matched subjects)
## measurevar: the... |
e5b05729e03e8d0dc2e62f523cce0ad6d7a11513ae73eed15c863595ee877183 | R | 1,757 | 82 | # /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(Seurat)
library(GenomeInfoDb)
#library(EnsDb.Hsapiens.v86) #hg38
library(EnsDb.Mmusculus.v79)
library(ggplot2)
library(patchwork)
set.seed(1234)
#############################################################i
library(monocle)
library(cicero)
pbmc=readRDS(... |
1c6bf32c3bc149496a85db4268f5c0702ea48786c61cbc64b875fdcf086a3f26 | R | 1,760 | 38 | #' @title
#' CBDA Spectrum plot function for Compressive Big Data Analytics
#'
#' @description
#' This CBDA function generates histograms of the feature counts/densities as returned
#' by the Accuracy and MSE metrics after the Learning/Training step.
#'
#' @param top Top ranked predictive models from the Learn... |
b0dcead2df3a2b534fadd40c7b1d69ea347025e5750390d21d23b4f2752c09e2 | R | 1,776 | 36 |
#' Prediction function for FR-Match results
#'
#' This function takes in the \code{\link[FRmatch]{FRmatch}} output and predicts the most similar reference cluster for each query cluster.
#'
#' @param rst.FRmatch The \code{\link[FRmatch]{FRmatch}} output.
#' @param p.adj.method P-value adjustment method for multiple hy... |
3cfde57a96121061b8e9b2b8a41eb5bca5eca486e02e6facafc842a6b47c987d | R | 1,784 | 74 |
library(Seurat)
library(hdf5r)
library(tidyverse)
library(qs)
library(future)
library(furrr)
# Directory setup ---------------------------------------------------------
birds = c(
"pu37bk12",
"wh32br40",
"bk27bk53",
"or74pu64",
"bu21wh41",
"bu22wh42",
"pk90gr56",
"pk87gr53",
"or37pu44",
"or38pu... |
6a4bd1541b941ab4da26e00550f6a6f9c608fd9b0610e98b0df8abd08de4e694 | R | 1,795 | 48 | # This script takes the fsaverage space noddi surfaces for each participant,
# extracts LH and RH V1 according to the Benson retinotopic atlas, and collates
# ficvf and odi measures together. It also filters out diffusivity values,
# keeping 1.1e-9, and averages ficvf and odi values across dir98 and dir99
# scans. The ... |
7c7c228dc6d86c605fc8863869bc1a214287fe04c0ff8ccadea1ed5ae91cdc32 | R | 1,801 | 45 | #!/usr/bin/env Rscript
# libraries
suppressPackageStartupMessages(library("argparse"))
suppressPackageStartupMessages(library("data.table"))
# create parser object
parser <- ArgumentParser()
# specifying options
parser$add_argument("tableX", nargs=1, help="First table to join on (tab-delim with header)")
parser$add_... |
74507e051350cb5f7f66270d694a7d9a1946d885e1722cafe7958a74d80cea59 | R | 1,811 | 55 | #!/usr/bin/env Rscript
library(getopt)
options(digits=3)
Args <- commandArgs()
self<-Args[4]
self<-substring(self,8,nchar(as.character(self)))
spec = c(
'mask' , 'm', "0", "character" ," name of brain mask image ",
'input' , 'i', "1", "character"," the input prefix ",
'dim' , 'd', "3", "character"," the i... |
dda3b211718c8d7ed60736b8597213c0318ea7964be5a60ae63b9506a6bb545f | R | 1,813 | 74 | #!/usr/bin/Rscript
library(caret)
library(ROCR)
library(xgboost)
library(doMC)
registerDoMC(48)
args <- commandArgs(trailingOnly=TRUE)
# datasets
positive <- rownames(read.csv("./data/Dang.tsv", sep="\t", head=TRUE, row.names=1))
negative <- as.character(read.csv("./data/1kg_LoFT.tsv", head=FALSE)$... |
eba0def9c92bdb544e3924d553bd522a25a89d6ed16ad5e2ad937bb06b6e0630 | R | 1,818 | 77 | ---
title: "SiT - Supplementary Figure 02"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression i... |
44dbbc4526b96c778827bd1dd37434db68382360e414eaef18e941755bd88ae7 | R | 1,821 | 44 | context("Test .convert_common_score_to_mat")
## .convert_common_score_to_mat is correct
test_that(".convert_common_score_to_mat returns reasonable values", {
# load("tests/assets/test_data1.RData")
load("../assets/test_data1.RData")
common_score <- test_data$common_score
score_1 <- test_data$score_1
score_2 ... |
f4b6aacbad5dddec3f83eab739ccaaaef3fd368d6bd6086d14d55865fa495c22 | R | 1,823 | 62 | library(tensorflow)
library(SingleCellExperiment)
library(cellassign)
library(scran)
input_dir = '/home/kd/Desktop/cellassign'
path = input_dir
#dirs = dir(input_dir)
learn_rate_vec = c(1:4)
#for (dir in dirs){
#print(dir)
#path = paste(input_dir, dir, sep="/")
C_gs = read.csv(paste(path, "C_gs.csv", s... |
27d0f7cae00b17e0d8660e039c56ec085ce2289d0c9ef6e06f717aa6f2464d70 | R | 1,824 | 67 | # /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
#args <- commandArgs(TRUE)
#opath=args[1]
pbmc.isloop=readRDS('/home/disk/database/data/scHIC/GSE130711_analysis/scATAC_pbmc.rds')
pbmc.isloop$type[which(pbmc.isloop$se... |
e56d8974098fe4a755db94df9681b6c8adf1f6922c11cf5ed870c1f08b1c35b6 | R | 1,831 | 76 | ---
title: "SiT - Supplementary Figure 03"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression i... |
6ea8c9bd40849179bb0e50558c97c3d498d033e0a118d06b630d43dbae5528f7 | R | 1,842 | 91 |
# /home/toolkit/tools/R4.2.0/bin/R
p
p
p
p
p
library(Signac)
library(GenomicRanges)
library(Seurat)
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
.getRNA<-function(pred.ref){
#pred.ref=readRDS('./ics_out/COM_pred.ref_r0.rds')
LOOP2GENE=read.table("./RNA/LoopAndTSS.bed",header=F,sep='\t',row.names=NU... |
e5cebfd8e2224c9706c37ff379701437abce0f24e62cfbe22dfd1fa20ab3f906 | R | 1,892 | 66 | # /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
#args <- commandArgs(TRUE)
#opath=args[1]
pbmc.isloop=readRDS('/home/disk/database/data/scHIC/GSE130711_analysis/scATAC_pbmc.rds')
pbmc.isloop$type[which(pbmc.isloop$se... |
a88b02975061a43ad7e98b9d84b7eb86a2474da49de5a7736a9a79b4082d62a7 | R | 1,900 | 41 | library(Seurat)
library(ggplot2)
library(miloR)
library(readxl)
library(gridExtra)
library(cowplot)
setwd("/data/aronow/Kang/single_cell_projects/10X-Pak/integration/patient_derived_engineered/v2_filtered/")
organoids = readRDS("./combined_all_organoids_filtered_v2.rds")
df_cells = read.table("ctype_v6.txt", sep = "\... |
7b906708781b2ef2f28231c86cef9caa3ead2fed6855b9889489b96bcf9d8717 | R | 1,909 | 83 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
warning = FALSE,
fig.path = "man/figures/"
)
```
```{r echo=FALSE, results="hide", message=FALSE}
library("badger")
```
# MetNor... |
845440498e57f7c5a7384a3890a59190de60f50a4db3bf01e4e0acc6c797b7c5 | R | 1,912 | 57 | context("Test .compute_distinct_score")
## .compute_distinct_score is correct
test_that("(Basic) .compute_distinct_score works", {
# load("tests/assets/test_data1.RData")
load("../assets/test_data1.RData")
score_1 <- test_data$score_1
score_2 <- test_data$score_2
common_score <- test_data$common_score
... |
d130762e59c685aaf2066df8a5a2c4f562a68f1833648a4810e60c74c2bf9b0e | R | 1,914 | 86 | # /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
pbmc=readRDS('/home/disk/database/data/scHIC/GSE130711_analysis/scATAC_pbmc.rds')
pbmc$type[which(pbmc$seurat_clusters %in% c... |
1bdb27c2849849ce87e0be3f0b4de45e4da43dacfd78f003a8519cebc4dea664 | R | 1,916 | 79 | ---
title: "SiT - Supplementary Figure 06"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression i... |
c2131a368bb18bc758dc34f077be8370a5a0282cf4db87ff69ab257b704031fa | R | 1,919 | 79 | /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(EnsDb.Hsapiens.v86)
library(ggplot2)
library(patchwork)
set.seed(1234)
library(Signac)
library(GenomicRanges)
pbmc=readRDS('/home/disk/database/data/scHIC/GSE130711_analysis/scATAC_pbmc.rds')
pbmc$type[which(pbmc$seurat... |
4f6cd40e995c82e658288c77659b803642079adfa6e172a1af13c89d03f872e7 | R | 1,924 | 52 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
alpha=as.numeric(args[3])
dataset_file = as.array(args[4])
workspace_directory=as.array(args[5])
label=as.array(args[6])
i_exp=as.numeric(args[7])
print(label)
print(worksp... |
18158e067150dd82e1bc9b2261b5a7f8cf80c398e17b6aab02b4258ca39900e0 | R | 1,926 | 46 | ## Summarizes data.
## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
## data: a data frame.
## measurevar: the name of a column that contains the variable to be summariezed
## groupvars: a vector containing names of columns that contain grouping variable... |
f38a4b782e7268a9f20dd5e32e0da4875c655f8c86c505b1bbdedc314e62122c | R | 1,941 | 46 | context("Test compute synchrony")
## tiltedCCA_decomposition is correct
test_that("(Basic) compute_synchrony works", {
# load("tests/assets/test_data4.RData")
load("../assets/test_data4.RData")
mat_1 <- test_data$mat_1
mat_2 <- test_data$mat_2
n <- nrow(mat_1)
large_clustering_1 <- test_data$clustering_1
... |
6c89ae04afceb4353b8cff65f8c6fe9b97a9e7a655246c146cbea50e2fd50a8c | R | 1,949 | 84 | context("Geom testing")
library(ggplot2)
library(vdiffr)
library(magrittr)
# Define input df
df <-
# Use built-in demo dataset
df_linear_associations %>%
# Arrange by name in order to filter the first few biomarkers for more
# than one studies
dplyr::arrange(name) %>%
# Estimate confidence intervals
dpl... |
dfe571d313bb7826af45568d44a80e416aa4a7a6cf6c7f94993ccb78f64e8de4 | R | 1,953 | 69 | #' @title MetNormalizer_logo
#' @description The MetNormalizer logo, using ASCII or Unicode characters
#' @author Xiaotao Shen
#' \email{shenxt@@stanford.edu}
#' @param unicode Whether to use Unicode symbols. Default is `TRUE`
#' on UTF-8 platforms.
#' @return A ASCII log of MetNormalizer
#' @export
#' @import ggplot2
... |
764d11133caeed83f11eba8fef9dc5cd32ce4eda586e66227620827d9949835b | R | 1,955 | 80 | ---
title: "Figure S7"
output: html_notebook
---
# Libraries
```{r message=FALSE, warning=FALSE, echo=FALSE,include=FALSE}
rm(list = ls())
library(dplyr)
library(ggpubr)
library(ggplot2)
library(rstatix)
library(magrittr)
library(knitr)
library(openxlsx)
```
# Data
```{r}
df.HRV <- readRDS(... |
cdd554ec618629f7b9f01129d19bc02da91db9592d9f3444c2f7c7104dfd35b1 | R | 1,992 | 62 | library(rstatix)
# for visualization
library(ggpubr)
library(ggplot2)
dat <- read.table('../Output/Logratio_fraction_in_behavior.txt', header = TRUE, sep = ",")
# preprocess data
dat$group <- as.factor(dat$group)
dat$mouse <- as.factor(dat$mouse)
dat$behavior <- as.factor(dat$behavior)
# visualize the data
bp <- gg... |
d20ac0a8630230e27a20b37b1fe0be6c1fa0c4d62a0c435d84a0fb893ac6b643 | R | 1,996 | 46 | library(data.table)
pheno <- fread("~/muscle_targets/regenie/UKBB_maxHGS_WBLM.pheno")
cov <- fread("~/muscle_targets/regenie/UKBB_maxHGS_WBLM.cov")
dat <- merge(pheno,cov,by=c("FID","IID"))
loco <- fread("~/muscle_targets/regenie/UKBB_maxHGS_WBLM_step1_1.loco")
loco_t <- as.data.frame(t(loco[,-1]))
colnames(loco_t) <... |
dce8c0f81aed69a5bc0de859c24320033061df70ec9873e16981571bac119682 | R | 2,007 | 58 |
########################################################################################################
## filter_cluster()
########################################################################################################
filter_cluster <- function(sce.object, filter.size, filter.fscore=NULL, filter.nomarker=... |
aac58f78d2b2fe010635afd7cbfbcce78e9dd79179eaf2fe4d43577e67492160 | R | 2,008 | 90 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
.getRNA<-function(pred.ref){
#pred.ref=readRDS('./ics_out/COM_pred.ref_r0.rds')
LOOP2GENE=read.table("./RNA/LoopAndTSS.bed",header=F,sep='\t',row.names=NULL,check.n... |
8bf5db2204477e41f1a030371137a959ae2fbdc0e93a29c4aa04b03bd28cad1e | R | 2,016 | 89 |
#/home/toolkit/tools/R4.2.0/bin/R
library(Seurat)
library(stringr)
LIST=read.table('LIST.txt',sep='\t')
pbmc=readRDS('./eQTL/scAtlas_pbmc.rds')
#ORGAN=t(matrix(unlist(str_split(colnames(pbmc),'_')),nrow=2))[,1]
#TYPE=colnames(pbmc)#t(matrix(unlist(str_split(colnames(pbmc),'_')),nrow=2))[,2]
LOOP=read.table('./e... |
26ece447b92992ddd68a7a0a5ff69a806d7d6c90028eb2958a5413cbf29d5f0a | R | 2,028 | 66 | ## Attach the R image that contains the relevant functions and load packages.
attach("FLICFunctions",pos=2)
require(ggplot2)
require(stats)
## Create the parameters used for the analysis.
## For a single well, start with the default Single Well Chamber
## set up. (but adjust as necessary)
p<-ParametersClass.SingleWell... |
6a38d3e08677269e00aa7a5053d0ddcb168c347f743bd5aeaebe4dbb4a362133 | R | 2,034 | 53 | prepare_matrix_B <- function(ST_data, address_candidate, cell_types, lead_genes, tresh_tau, tresh_rho ){
nTypes <- length(cell_types)
list_marker_genes_all_types <- vector("list", length = nTypes)
for(i in 1:nTypes){
print(i)
candidate_markers <- unique(unlist(read.table(paste0(address_candidate, cell_typ... |
40c8fb7022670972377ff7078beaec4c358602ee4594ef986854f410e6c31b89 | R | 2,045 | 67 | ---
title: "Test3 overview"
author: "Pierre Ekelmans"
output:
html_document:
toc: no
toc_float: yes
theme: cerulean
pdf_document:
toc: no
word_document:
toc: no
---
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(ggplot2)
library(dplyr)
library(tidyr)
source("Load... |
40ac6eab2c77813cb1249c640aa36807dd4566258bc48265fec9b3694db4a0d3 | R | 2,051 | 63 | library(Seurat)
library(ggplot2)
library(ggpubr)
library(tidyverse)
library(plyr)
library(EnhancedVolcano)
source('../functions/_global.R')
source('../functions/_visualize.R')
# try enhancedvolcano on a specific plot
path = 'single_cell_projects/10X-Pak/integration/patient_derived_engineered/v2_filtered/DEGs/time_sam... |
eae226928be5df0d3a57890a287cdcceb2d3386e4351d25acba19994c1b87c8a | R | 2,056 | 46 | load("~/Info_for_consolidation.RData")
label = c("ADNI_MN2_balanced_INFERENCE")
for(j_global in 1:100)
{
eval(parse(text=paste0("load(file= \"",workspace_directory,"CBDA_Inference_",j_global,"_",label,".RData\")")))
}
eval(parse(text=paste0("save.image(\"",workspace_directory,"CBDA_Inference_",label,".RData\")")))
ev... |
028136d1c1e3735a16166951816e64b1ef46a0067485e69620474771ed13cbb9 | R | 2,058 | 117 | # /home/toolkit/tools/R4.2.0/bin/R
setwd('/home/disk/database/data/ICS_scHIC/f01_frontalCortex/tracks/')
library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(EnsDb.Hsapiens.v86)
library(ggplot2)
library(patchwork)
set.seed(1234)
library(Signac)
library(GenomicRanges)
pbmc=readRDS('/home/disk/database/data... |
1ff21325a138112483fab5a6f539fc3f6a73b86e775ce176a12261204f6e4f4a | R | 2,065 | 74 | ---
title: "SiT - Supplementary Figure 15"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression i... |
4d6e93a51fc89e0a94a45e4d9d430c108901c44eb9c9025b3aea81c24125eea8 | R | 2,071 | 68 | ---
title: "melas_wnn_analysis"
author: "Nate Mullin"
date: "1/11/2023"
output: html_document
---
```{r}
library(Signac)
library(Seurat)
library("Signac")
library(tidyverse)
library(ggpubr)
library(EnsDb.Hsapiens.v86)
library(BSgenome.Hsapiens.UCSC.hg38)
library(ggrepel)
library(RColorBrewer)
library(scCustomize)
```
... |
5423a8ba1e961be1ba02d93cfa3ba31906daeb752417412572692cc42dbeaec0 | R | 2,098 | 63 | test_that("pmQueryBuild creates simple query", {
q <- pmQueryBuild(terms = "bibliometrics", language = "english")
expect_true(grepl("bibliometrics\\[Title/Abstract\\]", q))
expect_true(grepl("english\\[LA\\]", q))
expect_true(grepl("AND", q))
})
test_that("pmQueryBuild handles multiple terms with OR", {
q <-... |
861da393503aae36c4542bf61d3980df7fd1d176c7a5357b408fb694dd212f2e | R | 2,098 | 95 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(Seurat)
library(GenomeInfoDb)
#library(EnsDb.Hsapiens.v86) #hg38
library(EnsDb.Mmusculus.v79)
library(ggplot2)
library(patchwork)
set.seed(1234)
#############################################################i
library(monocle)
library(cicero)
pbmc=readRDS... |
18cac19c0eebd1a36f30b66e112d4a193685aa886932e297f8fa58a6d5d01c7c | R | 2,100 | 65 | ## Create asymmetry value for each Davi130 region ##
## Conduct one-sample t-test for hemispheric asymmetry ##
library("reshape2")
library("broom")
library("readr")
library("dplyr")
library("ggplot2")
library("lsr")
library("stringr")
# read in Davi ROI data #
# data is a csv file with cols - Name, Age, ... |
1706c2d395d36302531e58cdd3212c296ce4d9378e75f7242eb6e8b8b2156fee | R | 2,108 | 104 |
.getSub<-function(TAG){
###################
LOOP=fread(paste0("./ics_out/this.out.",TAG),header=F,sep='\t',select=c(1))$V1
SSN=fread(paste0("./ics_out/this.out.",TAG),header=F,sep='\t',select=c(2:(ncol(pbmc)+1)))
SSN=as.matrix(SSN)
rownames(SSN)=LOOP
colnames(SSN)=colnames(pbmc)
##########
#CICERO_LOOP=fread(paste... |
30f58a7ab652389077de4b4c67874d94017606c68336731b88bb714c018d1239 | R | 2,128 | 43 | ##' Create curve info object
#'
#' @param curvedata a dataframe or tibble with columns "lm1", "lm2" and "ptswanted".
#' @param n_fixed a number indicating how many landmarks are fixed anatomical landmarks
#' @return output contains the necessary information to subsample curves, and the identity of each curve for slidin... |
f00d745fd59182fab05122f32bd3c31b83a8e035fcb49991e66f52869657500e | R | 2,135 | 35 | library(data.table)
library(dplyr)
gencode <- fread("/edgehpc/dept/compbio/human_genetics/users/yhuang5/ALS_progression/GTAC_ChiaYen/gencode.v39.annotation.gtf.gz",skip = 1,header = F)
gwas_catalog <- fread("/edgehpc/dept/compbio/human_genetics/users/yhuang5/muscle_targets/regenie/efotraits_EFO_0006941-associations-20... |
3738eface0e6f09458a83ed73fb6291d615ba1ba6bac213ab4cb45ce1dc2d924 | R | 2,142 | 64 | # Helper functions for analysis with Seurat
library(dplyr)
library(Seurat)
library(SeuratDisk)
library(pheatmap)
library(stringr)
library(grid)
# Integrated analysis: standard workflow
integrated.analysis <- function(combined.seurat, scale = FALSE){
if (scale){
combined.seurat <- ScaleData(combined.seurat, verb... |
62c63eeee019f872f72fee5fb30a2f5a9bbfb206b14ce5c3f356df1488ce96eb | R | 2,153 | 47 | library(tximport)
library(DESeq2)
library(stringr)
library(openxlsx)
setwd('/data/aronow/Kang/induced_neuron/ChangHui_RNAseq_fastq/isoform_RSEM/rsem_transcripts/differential_v2_DEG/')
source_folder = '/data/aronow/Kang/induced_neuron/ChangHui_RNAseq_fastq/isoform_RSEM/rsem_transcripts/transcripts/'
# load data
df_sam... |
538019c09d9f31aa78b80ea933a1655339e4a75c07fb88558cd5c3d541b7a8a3 | R | 2,154 | 36 | library(ggsci)
position_levels = c("ra", "arco", "hvc", "ncl", "lman", "nido", "x", "stri", "fieldl", "ov", "meso")
position_pretty_levels = c("RA", "Arco", "HVC", "NCL", "LMAN", "Nido", "Area X", "Striatum", "Field L", "Ovoid", "Meso")
position_colors = c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','... |
4dab8bc2629d22ee531407d2e0d9d12444d2161bb85bdb14a94d9b0d59415c52 | R | 2,159 | 46 | #!/usr/bin/env Rscript
# Writes webApp/cnlib.R: the verbatim source of every cancerCellNet function
# the PACNet web app calls, directly or indirectly. Web-specific patches are
# applied by hand afterwards and marked with "# PACNet-web:" comments.
#
# Usage (from repo root): Rscript tools/vendor_cnlib.R <cancerCellNet ... |
7a007ef923437a4059d4850588fa919f0e6e850eb9ec2dbc027a321626ee6f8a | R | 2,176 | 60 | library(Seurat)
library(SeuratWrappers)
library(monocle3)
library(ggplot2)
library(ggridges)
library(dplyr)
library(ggridges)
library(cowplot)
source('../Figures/functions/_global.R')
samples = c('Cre-d160', 'Flp-d160')
organoid.list = list()
for (i in 1:2)
{
sample = samples[i]
organoid.counts = Read10X_h5(past... |
026f69f7a2e6c4defe52f9ef865087fbf4d6981722b8ca94319199f9717b5e17 | R | 2,204 | 62 | #' Count the number of documents returned by a query
#'
#' It counts the number of documents that a query returns from the NCBI PubMed database.
#'
#' @param query is a character. It contains a search query formulated using the Entrez query language.
#' @param api_key is a character. It contains a valid API key for the... |
342980f177817dfaf985311ffc960cca7ccf7f9671dfdcc6e37ffa675b59c819 | R | 2,205 | 88 | context("plot_all_NG_biomarkers")
library(ggplot2)
library(vdiffr)
library(magrittr)
df <-
df_linear_associations %>%
left_join(
select(
df_NG_biomarker_metadata,
name,
machine_readable_name
),
by = "name"
)
test_that("plot_all_NG_biomarkers produces file ", {
tmp <- tempfile()... |
ab6d82a8078b59278e870ec70766c9b1c0199fd3ecc2cc6cfd6e1424ed415c07 | R | 2,210 | 92 | ---
title: "OverlapStats"
author: "Gian"
date: "2022-07-01"
output:
html_document:
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
set_factors <- function(df) {
df$Subject <- as.factor(df$Subject)
df$Hemi <- as.factor(df$Hemi)
df$Patch <- as.factor(df$Patch)
df$Cond <-... |
c6658e79099eebb50a483ce438e58aa381526996221a1dfe5824f81eccd67908 | R | 2,234 | 54 | #' Compute the differential expression across each pair of cell types
#'
#' @param seurat_obj seurat object
#' @param assay name of assay in the \code{seurat_obj}
#' @param idents variables name inside \code{seuart_obj} that contains the cell-type information (or clustering information) for each cell
#' @param slot slo... |
fd67aae6fa5c9b72471b11291d3105cee774b6056524cd68795437536f5dfc25 | R | 2,253 | 53 | load_ui <- function() {
local({
owd <- setwd(file.path(repo_root, "webApp")); on.exit(setwd(owd))
source("ui.R", local = TRUE)$value
})
}
ui_html <- function() paste(as.character(htmltools::renderTags(load_ui())$html), collapse = "\n")
count <- function(pattern, x) lengths(regmatches(x, gregexpr(pattern, x,... |
62ba9a9d390ec96ec528816102ae84b19ba81e1aee523ae1acbacf296001b0e2 | R | 2,282 | 49 | library(Seurat)
library(ggplot2)
source('../functions/_global.R')
source('../functions/_visualize.R')
# load data for integrated organoid samples
donor = readRDS('../../donor_reintegrate/donor_reintegrated.rds')
donor = donor[, donor$cellclass_draft_v2 %in% cellclass_ordered_v2]
donor$cellclass_draft_v2 = factor(dono... |
71b3aead9756873fba24990db2369cc8e82cef0ff3fc3b37c2c30d02cf85adf5 | R | 2,291 | 64 |
########################################################################################################
## FRtest_subsamp()
########################################################################################################
FRtest_subsamp <- function(samp1, samp2, subsamp.size, subsamp.iter, return.all=FALSE, .... |
ee7522199d8971ba88c56b09cb86a163cffd51b8bac8d8717e2be1bc8a0cdc1f | R | 2,312 | 71 | # CoDa analysis of fractions spent in each of 8 behaviors
require(data.table)
require(compositions)
require(npmv)
library(plyr)
library(rstatix)
# for visualization
require(ggplot2)
library(ggpubr)
# load data set
dat <- read.table("../Output/Mouse_behavior_data_set_controls.csv",header = TRUE, sep = ",")
dat <- wi... |
65e631215415591ecec4491177430ca727d8b9c19808c8f685db8280a9ce6a0f | R | 2,330 | 64 | library(Seurat)
library(SeuratWrappers)
library(SeuratDisk)
library(monocle3)
library(ggplot2)
library(ggridges)
library(dplyr)
library(ggridges)
library(cowplot)
# find trajectory of d101 only organoids
organoids = readRDS("../../v2_filtered/combined_all_organoids_filtered_v3.rds")
donor = organoids[, organoids$sourc... |
9dd05394aa0ba70f993758e534e1224bd25e4cc01bd15d0b7d01984f82705ff9 | R | 2,340 | 53 | ## multi-class classification
library(SuperLearner)
set.seed(843)
N <- 100
# outcome
Y <- sample(c("A", "B", "C"), size = N, replace = TRUE, prob = c(.1, .5, .4))
# variables
X1 <- rnorm(n = N, mean = (as.numeric(Y == "A") + .5*(as.numeric(Y == "C"))), sd = 1)
X2 <- rnorm(n = N, mean = (as.numeric(Y == "B")), sd = 1)... |
f22d436b4544504c185c8eeb315896f24e5f3f881c61564815138a1e98481eb5 | R | 2,392 | 46 | library(Seurat)
library(ggplot2)
source('../functions/_global.R')
source('../functions/_visualize.R')
# umap for combined organoids can be found in /data/aronow/Kang/single_cell_projects/10X-Pak/integration/patient_derived_engineered/v2_filtered/visualize/visualize.R
# umap for donor
donor = readRDS('../../donor_rein... |
54da836ee1ea020b2f50cd1b339de8ae3051d40b9f16eaef40d2aadd53f379f3 | R | 2,419 | 62 | #!/usr/bin/env Rscript
library(ggplot2)
library(readr)
library(dplyr)
library(purrr)
library(tidyr)
library(stringr)
args <- commandArgs(trailingOnly = TRUE)
df_crispro <- read_csv(args[1])
formula <- "position"
if(length(args) == 2)
formula <- args[2]
df_ctrl <- df_crispro %>%
filter(control == ... |
5d20395fcde539554b647454420fef659f839ee61b52ef0021652a443fc65f25 | R | 2,428 | 53 | make.slouch.simmap.AIC.table<-function(simmap.output,num.it){
#,model.type,pred.type,num.sp,prev.output=NULL){
#Code to create or update a table from slouch output
#Format for calling function: make.slouch.table(Slouch output,type of model,type of predictor variable,
#sample size,previous make.slouch output ... |
fcd883eb761b9aca67be6b7b7f2b4676add1c4d78960d67b694e1d7819174080 | R | 2,443 | 73 | #' Extract references from PubMed XML records
#'
#' Walks a result returned by \code{\link{pmApiRequest}} or
#' \code{\link{pmFetchById}} and pulls the \code{<ReferenceList>} block out of
#' every record. Returns one row per cited \code{<Reference>}, carrying the
#' source PMID, the free-text citation, and (when presen... |
6d6e6bdd944d36a4f3e15b05d80a72d453b24027880e6374eadb0e59822e4f04 | R | 2,449 | 53 | ##' Interactively Make Landmark Synonymy Table
#'
#' @param target_spec a p x 3 matrix of 3d landmark data for the template or specimen that has the correct landmarking scheme
#' @param original_spec a matrix of 3d landmarks representing the landmarking scheme to be changed
#' @param description a character vector givi... |
9016009062721ab8e4e791078afe8690964e062c9ef7143b3dd75236360e9dad | R | 2,471 | 50 | context("Test postprocess distinct variable selection")
## postprocess_distinct_variable_selection is correct
test_that("postprocess_distinct_variable_selection works", {
# load("tests/assets/test_data2.RData")
load("../assets/test_data2.RData")
mat_1 <- test_data$mat_1
mat_2 <- test_data$mat_2
n <- nrow(ma... |
5084a0e0a4d57be7a81a6cca5593dc6d7e982b11b6ae1e55cec9d729050da5c3 | R | 2,479 | 69 | ## STEP 1 - DATA CLEANING
## This is just an example with the MRI dataset "NeuroIm1.txt"
# Set the working directory for the R Code development
setwd("~/Desktop/R")
## Retrieve the dataset (edit the directory where the dataset is located)
#NeuroIm1 = read.table("NeuroIm1.txt", header = TRUE)
NeuroIm1 = read.table("Neu... |
ece456b4fcdb8290e0fb38623782ee30c2a7b7ed5b66983fc5f0d21ed8ff96cf | R | 2,529 | 52 | context("Test fine tuning")
## fine_tuning is correct
test_that("(Basic) fine_tuning works", {
# load("tests/assets/test_data1.RData")
load("../assets/test_data1.RData")
mat_1 <- test_data$mat_1
mat_2 <- test_data$mat_2
n <- nrow(mat_1)
large_clustering_1 <- test_data$clustering_1
large_clustering_2 <- ... |
f590831ceb3ed9a4f018c79dbfc9f996550875da756074269f7fa2600de46aef | R | 2,531 | 40 | ---
title: "Variable Importance"
author: "Andrew Soncrant"
date: "April 18, 2017"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = "~/GitHub/CBDA/Pipeline/DISTRIBUTED/BINOMIALdataset/TEST")
library(party)
library(ggplot2)
```
# Variable Importance... |
17f9bfd76ec8006339f8cd3a29db6c8014351a45a75f7d98916c3be7f901aeee | R | 2,545 | 90 |
TAG='200k'
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
COL_LIST=list(DType=c('scATAC'='indianred1','scHIC'='royalblue1'),
CType=c('Astro'='red','MG'='g... |
5b8759ed97922b648a4948b5a81388e8d56b6c6328992037dd837a143d2adbd3 | R | 2,545 | 90 |
TAG='300k'
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
COL_LIST=list(DType=c('scATAC'='indianred1','scHIC'='royalblue1'),
CType=c('Astro'='red','MG'='g... |
69840820ec8abfcee39f5576567e6c848e22d680b224636375200c4f64e33376 | R | 2,545 | 90 |
TAG='400k'
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
COL_LIST=list(DType=c('scATAC'='indianred1','scHIC'='royalblue1'),
CType=c('Astro'='red','MG'='g... |
8201c2731a437c02e1e7b9ed59365a9302cafc150796fab265b4bb7796000de1 | R | 2,545 | 90 |
TAG='100k'
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
COL_LIST=list(DType=c('scATAC'='indianred1','scHIC'='royalblue1'),
CType=c('Astro'='red','MG'='g... |
3e838bc10db6fb8355a61fbdaed1efd8b5b8dd61ddf3c5d1903416d6f343ab98 | R | 2,591 | 72 | #############################
##### Load in Libraries #####
#############################
print("loading libraries")
library(crayon, lib.loc = "/Shared/IVR/apvoigt/programs/R-4.1.0/bin/PACKAGES/")
library(backports, lib.loc = "/Shared/IVR/apvoigt/programs/R-4.1.0/bin/PACKAGES/")
library(withr, lib.loc = "/Shared/IVR/a... |
7922431985a2a3298e2790a66c66eaef8ae3bdca060af680fc58d197e4723d89 | R | 2,616 | 89 | #' Generate orthogonal matrices correspond to an SBM
#'
#' @param B_mat symmetric matrix
#' @param membership_vec vector of positive integers
#' @param centered boolean, where if \code{TRUE}, one less dimension
#' is provided but the singular vectors are centered
#'
#' @return matrix
#' @export
generate_sbm_orthogonal ... |
6092bbd6e593166c1421fc59e7f586d11f4daf0ba3ffcda21644b1ce0fd0252e | R | 2,622 | 61 | #' Compute the synchrony based on Tilted-CCA
#'
#' @param input_obj \code{multiSVD} class, after applying \code{tiltedCCA_decomposition()}
#' @param anchor_modality numeric of \code{1} or \code{2}, denoting which modality the synchrony is computed on
#'
#' @returns a matrix with \code{n} cells and 2 columns, which one ... |
a550918640f0a8267f1eb841df743148ec82c4aec42f3c9f7b1cf3fd843ade81 | R | 2,622 | 117 | /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(ggplot2)
library(patchwork)
set.seed(1234)
library(Signac)
library(GenomicRanges)
setwd('/home/disk/database/data/ICS_scHIC/f04_multi')
pbmc=readRDS('scATAC_pbmc.rds')
Idents(pbmc)=pbmc$type
DimPlot(pbmc)
UMAP=pbmc@... |
6901322087c09f36894d808aae10ae64c99524b08f9edb5cab18a824b6d27a58 | R | 2,626 | 52 | setwd("~/Documents/NIH-grant/SOCR/PilotTest")
#load("CBDA_SL_M10000_miss0_n60_80_k15_30.RData")
#load("CBDA_SL_M10000_miss0_n60_80_k30_50.RData")
#load("CBDA_SL_M10000_miss10_n60_80_k15_30.RData")
load("CBDA_SL_M10000_miss10_n60_80_k30_50.RData")
#load("CBDA_SL_M10000_miss20_n60_80_k15_30.RData")
#load("CBDA... |
bb242f78be6f67f4f111b4dcd672cf81ca65eba19c335b1dfcc675989a893dbe | R | 2,637 | 59 | #### Trajectory Inference (TI) ####
#Convert Seurat object into sce
sce <- as.SingleCellExperiment(obj, assay = "RNA")
#You can set the root and/or endpoint, default is NULL
sce <- slingshot(sce, reducedDim = 'UMAP', clusterLabels = colData(sce)$seurat_clusters, start.clus = "5", end.clus = "1", approx_points = 100... |
9663b1d4b198a7027cab7588670a5a069de4a0523e74e5e25ccb65f57325aecc | R | 2,652 | 61 |
#' Cluster size plot
#'
#' This function plots the cluster sizes of the two single cell datasets (e.g. query and reference) to be compared.
#'
#' @param sce.E1,sce.E2 Data objects, namely E1 and E2.
#' @param decreasing Boolean variable if the cluster sizes should be sorted in decreasing order. Default: \code{TRUE}.
#... |
36059cae43e0657f54f9aa668fffe476276ecb9036f1441b645ae16e71d57858 | R | 2,653 | 91 | library(rstatix)
# for visualization
library(ggplot2)
library(ggpubr)
dat <- read.table('../Output/Classical_measures.txt', header = TRUE, sep = ",")
# fraction in inner region
dat <- as.data.frame(dat[,c('day','group','mouse','fraction_inner')])
dat$group <- as.factor(dat$group)
dat$day <- as.factor(dat$day)
dat$mo... |
c886f33c9641af752ddd6c4cb5597a67c01152d038391d1aa3a9c5fd7f2a6fa6 | R | 2,674 | 68 | # load environment
library(Seurat)
library(ggplot2)
library(ggpubr)
library(tidyverse)
library(plyr)
source('../../functions/_global.R')
source('../../functions/_visualize.R')
# load data
path = '../v2_filtered/trajectory/'
time_points = c('d22', 'd50', 'd101')
donors = list()
for (i in 1:length(time_points)){
time... |
960a9144bd93bed8ee67ab38cfdc33a575e9e09f0169289a5a7b127d1918ed8a | R | 2,675 | 67 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
label=as.array(args[1])
workspace_directory=as.array(args[2])
print(label)
print(workspace_directory)
filename_specs <- file.path(workspace_directory,paste0(label,"_info.RData"))
print(filename_specs)
load(filename_specs)
#workspace_directory <- "/ifs/loni/... |
366b4eaff48bb4274a8149fc8111bc226739df61ddecf5119e0f45b3762c22fb | R | 2,689 | 53 | library(ggplot2)
source('../functions/_global.R')
source('../functions/_visualize.R')
# load data
df_cells = read.table('../../../v2_filtered/ctype_v6.txt',
sep = '\t', header = 1, row.names = 1)
df_cells = df_cells[df_cells$cellclass_draft_v2 %in% cellclass_ordered_v2,]
df_cells$cellclass_draft... |
73b3494130370c83bb878309df50f078091a56945a5f761aebba84a555f134dd | R | 2,705 | 101 | # Observer Model (Alternation Frequency) - 2022
library(tidyverse)
# Add function to shift down vector n rows and replace with NA
shiftdown <- function(x, n){
c(rep(NA, n), head(x, -n))
}
# Main function
CountEventAlternation <- function(s, omega = Inf){
# Store input sequence in dataframe
df <- enframe(s, ... |
f160a3dc4cf2f1c10654745603fa9b915d0d0a6af3f76118f01551d19cc414b2 | R | 2,709 | 119 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(Seurat)
library(GenomeInfoDb)
#library(EnsDb.Hsapiens.v86) #hg38
library(EnsDb.Mmusculus.v79)
library(ggplot2)
library(patchwork)
set.seed(1234)
#############################################################i
library(monocle)
library(cicero)
pbmc=readR... |
b90cf916230680e45e4b364efd935844cef54d84ab62a6b13f379d6bda827901 | R | 2,715 | 60 | # Integrate bird and mouse samples
# Directories where to load and save data
datadir <- "./data/seurat/"
savedir_seurat <- "./data/seurat/"
savedir_anndata <- "./data/anndata/"
source("./src/seurat_utils.R") # integrated.analysis & packages
# Separately treat exc and inh neurons
for (selected_class in c('GABAergic', ... |
84f83872cd17903fb3c850561fa7632769c2b967cb6df281fe37dd133767af3e | R | 2,732 | 83 | library(lme4)
library(MuMIn)
library(lmerTest)
changeDirection <- function(df,form){
colnumber=which(colnames(df)=='DistanceBin')
for (sub in unique(df$ID)){
for (hemi in unique(df$Hemi)){
for (cond in c('C','I')){
mask=df$ID==sub & df$Cond==cond & df$Hemi==hemi
if (sum(mask)>0){
... |
12cb54959967d0420117a7fdd44a94ba879f47255d2fc1bf134ce602464ad1ff | R | 2,737 | 184 | # /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(Seurat)
library(GenomeInfoDb)
#library(EnsDb.Hsapiens.v86) #hg38
library(EnsDb.Mmusculus.v79)
library(ggplot2)
library(patchwork)
set.seed(1234)
opath='/home/disk/database/data/ICS_scHIC/fxx_spatial/'
fpath=paste0('/home/disk/database/data/ICS_scHIC/fxx_sp... |
4ab78e24b5caad3c4072e1ca823cd9d5e996d8b408e21fecaa326abeb890f0d3 | R | 2,764 | 145 | ---
title: ''
output: html_document
params:
address_input: "~/Celloscope/example/data/"
address_results: "~/Celloscope/example/"
---
```{r, echo=FALSE}
library(stringr)
library(data.table)
library(ggplot2)
library(grid)
library(gridExtra)
```
```{r, echo=FALSE}
source("~/Celloscope/code/functions.R")
... |
8f61b26c0e54d0d48157096bd8fcc23ae1f8ced9c31a66dbec8252e5b875c84e | R | 2,772 | 74 | # Combine the thickness and noddi information into single dataframes. for the
# vision ROIs, average data within each ROI. for full-V1, add the grouping for
# the bar ROIs. add demographic information such as diagnosis, disease onset,
# etc. and save to new files for analysis
library(janitor)
library(tidyverse)
# dem... |
4ec24636c5f9053fe29bb758a855deb314fb9a35aae09276ae4ca5ac30e4f403 | R | 2,777 | 68 |
#' Non-zero expression plot
#'
#' A function that calculates and plots "\% expressed per marker gene per cluster" for the input data object.
#' \% = number of cells that express the marker gene in the cluster / cluster size.
#'
#' @param sce.object A \code{FRmatch} input data object. See example in \code{\link[FRmatch... |
43b4ac239d3de6eb8495162371bf028234be3ddadd2094929faabc3ad94b52ee | R | 2,778 | 62 | #hicdc+ for normalization
#
# Usage
# screen
# bsub -n 2 -W 100:00 -R 'span[hosts=1] rusage[mem=64]' -Is /bin/bash
# /home/bin/R
# source("/epiphany/data_preparation/hicdcplus_norm_sig.R") # nolint
library(HiCDCPlus)
######################################
# Step 0. Define arguments #
#######################... |
60570d5599200a13f9e30fe45e7eb494af809d4616f925e6493276ac1a6460bd | R | 2,799 | 65 | # Set the directory path where the RDS files are located
dir_path <- "path/to/the/folder"
# List all the RDS files in the directory
file_list <- list.files(path = dir_path, pattern = "\\.rds$")
# Create an empty list to store the R objects
obj_list <- list()
# Loop through each file and read them into the l... |
00bf91486fc544a2d6aefe1071d4dd37f015e268c20bdceb511a68dff0793f0b | R | 2,839 | 70 | ## ----Installation of the CBDA package, eval = FALSE----------------------
# # Installation from the Windows binary (recommended for Windows systems)
# install.packages("/filepath/CBDA_1.0.0.zip", repos = NULL, type = "win.binary")
# # Installation from the source (recommended for Macs and Linux systems)
# ins... |
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