nassimhaddad

result = tryCatch({
    expr
}, warning = function(w) {
    warning-handler-code
}, error = function(e) {
    error-handler-code
}, finally {
    cleanup-code
}

nassimhaddad

# remove non-ascii characters
df$text <- gsub("[^\x20-\x7E]", "", df$text)

nassimhaddad

require(rCharts)
require(shiny)
smpl<-data.frame(gene = c("gene1","gene2","gene3","gene4","gene5",
                          "gene6","gene7","gene8","gene9","gene10"),
                 direction = c("up","down","up","up","up",
                               "up","up","down","down","down"),
                 type = c("norm","norm","tum","tum","norm",
                          "tum","tum","norm","tum","tum"),
                 foldChange = c(1.3, 0.4, 1.3, 3.0, 1.6,
                                2.9, 1.3, 0.5, 0.5, 0.6))

nassimhaddad

# notes: it's all about fine-tuning the details:
# good color palette (-> google 'color palette')
# white borders and font
# gray edges (even better when weighted)
#
library(qgraph)
adj=matrix(sample(0:1,10^2,TRUE,prob=c(0.8,0.2)),nrow=10,ncol=10)
groups <- list(group1 = 1:4,
               group2 = 5:7,
               group3 = 8:10)

nassimhaddad

#' Turn a function into a new function that helps debugging upon exception
#' 
#' @param fn the function to transform
#' @param saveFile an optional path to save RDS to, if NULL output will be in global variable '.problem'
#' @return new function that behaves like fn(...) normally, but if fn(...) throws an exception saves to variable or saveFile RDS of list .problem such that do.call(.problem$fn_name,.problem$args) repeats the call to fn with args.
#'
#' @examples 
#' sum_of_log <- function(x, y){
#'   stopifnot(x>=0)

nassimhaddad

library(openxlsx)
Sys.setenv(R_ZIPCMD= "/usr/bin/zip")
openxlsx::write.xlsx(res, "mytable.xlsx", row.names = FALSE)

nassimhaddad

Dplyr is a well known R package to work on structured data, either in memory or in DB and, more recently, in cluster. The in memory implementations have in general capabilities that are not found in the others, so the notion of backend is used with a bit of a poetic license. Even the different DB and cluster backends differ in subtle ways. But it sure is better than writing SQL directly! Here I provide a list of backends with links to the packages that implement them when necessary. I've done my best to provide links to active projects, but I am not endorsing any of them. Do your own testing. Enjoy and please contribute any corrections or additions, in the comments.

Backend	Package
data.frame	builtin
data.table	builtin
arrays	builtin
SQLite	builtin
PostgreSQL/Redshift	builtin

nassimhaddad

cols_to_paste <- c("col1", "col2")
concatenation <- paste0("paste(", paste(cols_to_paste, collapse = ","), ")")
text_table <- the_data %>% mutate_(verbatim = concatenation)
  

nassimhaddad

nonNa <- function(x)Filter(Negate(is.na), x)

nassimhaddad

cut_equal <- function(x, n = 3){
  q <- quantile(x, prob= seq(0,1,length.out = n+1 ))

  cut(x,
      breaks = as.numeric(q), 
      include.lowest=TRUE, labels = 1:n)
}