chainsawriot

use_eupl <- function() {
    EUPL_BZIP2 <- as.raw(c(0x42, 0x5a, 0x68, 0x39, 0x31, 0x41, 0x59, 0x26, 0x53, 
0x59, 0xfb, 0x2b, 0x04, 0x38, 0x00, 0x04, 0xb0, 0x5f, 0xf5, 0x00, 
0x10, 0x48, 0xff, 0xff, 0xf9, 0x3f, 0xf7, 0xfe, 0xa0, 0x3f, 0xff, 
0xff, 0xf0, 0x40, 0x00, 0x04, 0x60, 0x00, 0x20, 0x18, 0x00, 0x10, 
0x00, 0x60, 0x16, 0xfd, 0x35, 0xaf, 0xb7, 0x68, 0x28, 0x03, 0xd0, 
0x00, 0x1e, 0x95, 0x7b, 0xeb, 0x2f, 0xbe, 0xe3, 0x59, 0x73, 0xaa, 
0xa4, 0xfa, 0x5e, 0x87, 0xb4, 0x29, 0x5e, 0xde, 0x62, 0xed, 0xb1, 
0xec, 0x68, 0x96, 0xdb, 0xde, 0xd5, 0xd9, 0xb5, 0xb6, 0xd6, 0x16, 
0x3a, 0xee, 0xef, 0x4f, 0x5a, 0xeb, 0xdd, 0xb9, 0xe9, 0x4f, 0x0c, 

chainsawriot

require(rang)
require(purrr)
require(igraph)

convert_edgelist <- function(x) {
    output <- data.frame(x = x$pkg, y = rang:::.extract_queryable_dependencies(x$original, x$no_enhances, x$no_suggests))
    for (dep in x$deps) {
        if (!rang:::.is_terminal_node(dep, x$no_enhances)) {
            el <- data.frame(x = unique(dep$x_pkgref), y = rang:::.extract_queryable_dependencies(dep, x$no_enhances, x$no_suggests))
            output <- rbind(output, el)

chainsawriot

require(tidyverse)
require(tidyr)
require(moments)

nrespondants <- c(223, 41, 63, 86, 64, 251, 82, 73, 74, 17, 64, 168, 43, 47, 64, 35, 135, 28, 15, 43, 10, 43, 366, 45, 73, 50, 26, 121, 25, 38, 44, 5, 46, 260, 36, 54, 49, 31, 63, 26, 30, 29, 3, 44)

score <- rep(0:10, 4)

wave <- sort(rep(1:4, 11))

chainsawriot

require(R6)

dog <- list(name = 'hundno', say = 'whhooo!')
class(dog) <- "animal"

cat <- list(name = 'kitty', say = 'meow!')
class(cat) <- "animal"

ipad <- list(name = "iPad mini")
class(ipad) <- "computer"

chainsawriot

var click_score_pos = function(rev_id, pos) {
    var reviews = document.querySelector("div[role='presentation'].x-grid3-body").querySelectorAll('tr');
    tds = reviews[rev_id].querySelectorAll('td')
    for (var key of tds.keys()) {
        if (tds[key].getAttribute('class').includes('wrong')) {
            var exId = key;
        }
    }
    tds[exId + pos].click()
//    sleep(2000)                                                                                                

chainsawriot

gendf <- function() {
    a <- data.frame(x = rnorm(200, 1, 1), y = rnorm(200, 3, 1))
    b <- data.frame(x = rnorm(200, 9, 1), y = rnorm(200, 10, 1))
    z <- c(rep(1, 200), rep(2, 200))
    cbind(rbind(a, b), z)[sample(1:400),]
}

df1 <- gendf()
df2 <- gendf()

chainsawriot

## prereq:
## 1) How to define a function

## Security level
## Warning: Does not stop execution
## Error: Stop execution

## example of warning

log(-1)

chainsawriot

# github.com/chainsawriot

import os, sys
from selenium import webdriver
import selenium.webdriver.support.ui as ui
from selenium.webdriver.common.keys import Keys

# your facebook username and password

USERNAME = ""

chainsawriot

require(dplyr)
require(magrittr)

# 95% CI: assume normally distributed

read.table("rainstorm.fwf") %>% mutate(hr = V6 + (V7/60)) %>% group_by(V1) %>% summarise(meanhr = mean(hr), sehr = sqrt(var(hr)/(length(hr)-1)), lowerCI = meanhr - (1.96*sehr), upperCI = meanhr + (1.96*sehr))

# Median and 2.5 and 97.5 percentile. Mean > Median, the data is positive skew and  mean will overestimate the central tendency. Use median for robustness

read.table("rainstorm.fwf") %>% mutate(hr = V6 + (V7/60)) %>% group_by(V1) %>% summarise(medianhr = median(hr), lowerqhr = quantile(hr, probs = 0.025), upperqhr = quantile(hr, probs = 0.975))

chainsawriot

iris[c(3,2,1),] # index the dataframe by an "indices vector", will only pick the first three rows in reversed order
order(iris$Sepal.Length) # generate an "indices vector" based on the ranked value of Sepal Length

# therefore
iris[order(iris$Sepal.Length, decreasing=TRUE),] 

# is ordered by row based on the value of Sepal Length, you still need to specify the column required.

# try these also