MarcinKosinski

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) %>% 

MarcinKosinski

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,

MarcinKosinski

library(shiny)
library(shinydashboard)
ui <- dashboardPage(
  dashboardHeader(),
  dashboardSidebar(
   sidebarMenu(
    menuItem(
     "Comments",
     tabName = "disqus_here",
     icon = icon("info")

MarcinKosinski

library(stringi)
library(stringr)
library(rvest)



aktualne_oferty <- function(link) {
  linki<-read_html(link) %>%
    html_nodes('.href-link') %>%

MarcinKosinski

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.

MarcinKosinski

# 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.

MarcinKosinski

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)

MarcinKosinski

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),

MarcinKosinski

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 

MarcinKosinski

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.