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Taking a sabbatical

Marcin Kosiński MarcinKosinski

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MarcinKosinski / RTCGA_info_gain.R
Last active Apr 11, 2016
information gain for RTCGA BRCA
View RTCGA_info_gain.R
library(devtools)
devtools::install_github('RTCGA/RTCGA')
library(RTCGA)
installTCGA('RTCGA.clinical')
installTCGA('RTCGA.rnaseq')
library(RTCGA.rnaseq)
library(RTCGA.clinical)
library(dplyr)
survivalTCGA(BRCA.clinical) %>%
View venn_diagram
library(VennDiagram)
venn.diagram(list(first = c("a", "b", "c"),
second = c("a", "c", "d"),
third = c("a", "d"))
imagetype = "png",
col = "transparent",
filename = "example.png",
height = 2000,
width = 2000,
View data z gumtree
library(stringi)
library(stringr)
library(rvest)
aktualne_oferty <- function(link) {
linki<-read_html(link) %>%
html_nodes('.href-link') %>%
View disqus_shiny_example
library(shiny)
library(shinydashboard)
ui <- dashboardPage(
dashboardHeader(),
dashboardSidebar(
sidebarMenu(
menuItem(
"Comments",
tabName = "disqus_here",
icon = icon("info")
View survminer_playing_with_fonts_and_text.R
devtools::install_github("kassambara/survminer", build_vignettes = TRUE)
library("survminer")
library("survival")
fit<- survfit(Surv(time, status) ~ sex, data = lung)
ggsurvplot(
fit, # survfit object with calculated statistics.
risk.table = TRUE, # show risk table.
pval = TRUE, # show p-value of log-rank test.
conf.int = TRUE, # show confidence intervals for
# point estimaes of survival curves.
View FSelectorRcpp_1
# install.packages(c('magrittr', 'FSelectorRcpp'))
library(magrittr)
library(FSelectorRcpp)
information_gain( # Calculate the score for each attribute
formula = Species ~ ., # that is on the right side of the formula.
data = iris, # Attributes must exist in the passed data.
type = "infogain", # Choose the type of a score to be calculated.
threads = 2 # Set number of threads in a parallel backend.
) %>%
cut_attrs( # Then take attributes with the highest rank.
View provide_genomic_analysis_on_tcga_mutations_data.R
extractSurvival <- function(cohorts){
survivalData <- list()
for(i in cohorts){
get(paste0(i, ".clinical"), envir = .GlobalEnv) %>%
select(patient.bcr_patient_barcode,
patient.vital_status,
patient.days_to_last_followup,
patient.days_to_death ) %>%
mutate(bcr_patient_barcode = toupper(patient.bcr_patient_barcode),
View america_is_on_the_move.R
library(rgdal) # read and store spatial data
library(dplyr) # manipulate data frames for data wrangling
library(leaflet) # connect to LeafletJS library from R
library(magrittr) # pipe workflows
library(openxlsx) # read xlsx files
library(readxl) # read xls files
# prepare the data ------------------------------------------------------------
# read MSA layer
View ggsurvplot_example.R
ggsurv <- ggsurvplot(
fit, # survfit object with calculated statistics.
data = BRCAOV.survInfo, # data used to fit survival curves.
risk.table = TRUE, # show risk table.
pval = TRUE, # show p-value of log-rank test.
conf.int = TRUE, # show confidence intervals
xlim = c(0,2000), # present narrower X axis, but not affect
# survival estimates.
xlab = "Time in days", # customize X axis label.
break.time.by = 500, # break X axis in time intervals by 500.
View plot_coxphSGD_estimates
library(coxphSGD)
library(survival)
library(reshape2)
set.seed(456)
x <- matrix(sample(0:1, size = 20000, replace = TRUE), ncol = 2)
head(x)
dCox <- dataCox(10^4, lambda = 3, rho = 2, x,
beta = c(2,2), cens.rate = 5)
head(dCox)