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View mail_through_mailR
library(mailR)
sender <- "hello@mail.com"
recipient <- "m.p.kosinski@gmail.com"
send.mail(from = sender,
to = recipient,
subject = "Subject",
body = "Hello World!",
smtp = list(host.name = "smtp.gmail.com", port = 465,
user.name = "hello@gmail.com",
passwd = pwd,
View polish stemming
download.file('https://github.com/MarcinKosinski/trigeR5/raw/master/dicts/polimorfologik-2.1.zip', destfile = 'poli.zip')
unzip('poli.zip', exdir = 'dicts')
library(data.table);library(dplyr)
polimorfologik <- fread('dicts/polimorfologik-2.1.txt', data.table = FALSE) %>% set_colnames(c('stem', 'words', 'random_stuff')) %>% select(1:2)
random_polish_text <-
'Wbrew wcześniejszym doniesieniom mediów, w bazie pod Latok I (szczyt w masywie w grupie Panmah Muztagh, części Karakorum), gdzie organizowana jest pomoc dla Aleksandra Gukowa (rosyjski himalaista nadał sygnał SOS i musi zostać ewakuowany, bo jest pozbawiony ekwipunku), nie ma też Andrzeja Bargiela. - Andrzej zgłosił chęć pomocy i wzięcia udziału w akcji ratunkowej. Jednak obecnie znajduje się w Skardu, gdzie dotarł wczoraj z bazy pod K2 - czytamy na twitterowym koncie pierwszego człowieka w historii, który zjechał z wierzchołka K2.'
(polish_stop_words <- readLines('https://raw.githubusercontent.com/MarcinKosinski/trigeR5/master/dicts/polish_stopwords.
View generate_ids_from_csv
\documentclass[15pt]{scrartcl}
\usepackage[a6paper,left=1cm,right=1cm,top=2cm,bottom=1cm,heightrounded]{geometry}
\usepackage[svgnames]{xcolor}
\usepackage{pdflscape}
\usepackage{setspace}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{graphicx}
\usepackage{wallpaper}
\usepackage[normalem]{ulem}
View add data.frame to CrunchDataset
library(dplyr);library(crunch)
iris_ds <-
newDataset(iris, name = 'iris')
# how to add the following data.frame as a CrunchCube or something similar to the Array?
iris_ds$some_statistics <-
iris %>%
group_by(Species) %>%
summarize_all(funs(mean))
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 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 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 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)
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 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.
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