wjhopper

library(rstudio.prefs)

use_rstudio_prefs(
  
  save_workspace = "never", # General --> Basic --> Save Workspace
  load_workspace = FALSE, # General --> Basic --> Restore .RData
  restore_last_project = FALSE, # General --> Basic --> Restore most recent project
  restore_source_documents = FALSE, # General --> Basic --> Restore previously open source documents
  check_for_updates = FALSE,  # General --> Basic --> Check for updates
  

wjhopper

if (.Platform$OS.type == "windows") {
  folder <- file.path(Sys.getenv("AppData"), "RStudio", "templates")
} else {
  folder <- "~/.config/rstudio/templates"
}

if (!dir.exists(folder)) { dir.create(folder, recursive = TRUE) }

f <- file(file.path(folder, "default.Rmd") ,"wt")

wjhopper

#! \bin\bash

## This script is to help clean up the experiment manager backups
cd /media/PatricksHD/CC9_Backups

touch removal_list.txt

# Compressed tarballs of the pydio user data directory are taken twice a day with a cron job
# The filenames follow the naming convention pydio_userdata_backup_YYYY-MM-DD_HH-MM.tar.bz
# Thus, this script assumes that the date of a backup can be extacted by splitting

wjhopper

library(pryr)

sample_df <- data.frame(a = 1:5, b = 5:1, c = c(5, 3, 1, 4, 1))

scramble <- function(x) x[sample(nrow(x)), ]

subset1 <- function(x, cond) {
  condition_call <- substitute(cond)
  r <- eval(condition_call, x, parent.frame())
  x[r, ]

wjhopper

#The task: sum 1000 different 10x10 matrices stored in a list

matrices <- vector(mode="list", length=1000)
for (i in seq_along(matrices)) {
  matrices[[i]] <- matrix(rnorm(1000), 10, 10)  
}

f1 <- function () {
  S <- matrix(0, 10, 10)
  for (M in matrices) {

wjhopper

library(ggplot2)
library(scales)
library(gridExtra)

set.seed(20)
n <- 10
y <- rexp(n, 2)
dat <- data.frame(
  xval = rep(c("A","B"), n/2),
  yval = y,

wjhopper

Keybase proof

I hereby claim:

• I am wjhopper on github.
• I am whopper (https://keybase.io/whopper) on keybase.
• I have a public key whose fingerprint is B703 3199 97F5 7774 B0C9 4FAC 7048 A20E F958 73DF

To claim this, I am signing this object:

wjhopper

# Create a function programmatically by creating its constituents: 
# an argument list, a function body of expressions, and an enclosing environment

args <- alist(x=,y=)
exps <- expression(z <- x^2 + y^2, z <- sqrt(z), return(z))
body <- as.call(c(as.name("{"), exps))
f <- as.function(x = c(args,body), envir = parent.frame())
f(x=1,y=1)

wjhopper

%--------------------------------------------------------%
% Onscreen script to record race/ethnic/sex demographics %
% for Matlab                                             %
% Updated 09/21/2015                                     %
%--------------------------------------------------------%

%{
Purpose:
A Matlab script which will generate a set of dialog boxes that ask 
participants an assortment of questions regarding demographics (in 

wjhopper

## Some sort of list comprehension using only functional programming 
## techniques that returns a list holding the coefficients of variation
## (i.e. the mean divided by the standard deviation) for each column in the 
## mtcars data set. 

## This isn't the clearest code (i.e. its really hard to tell its dividing
## the mean by the standard deviation) but it uses absolutely no imperative control 
## flow structures and no vectorized R functions, so if you can read and understand
## this you can do you some functional programming for great good =). I can't believe I
## actually figured this out, much less that I figured it out in like 5 minutes,