chainsawriot

tokens freq
的 366981
一 232555
年 229119
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chainsawriot

# combining list of data frames
irisBroken <- list(iris[1:10,], iris[11:30,])
do.call(rbind, irisBroken)

chainsawriot

testarray <- array(1:6, c(2,3))
dimnames(testarray) <- list(c("a", "b"), c("d", "e","f"))
testarray
dimnames(testarray) <- list(c("H", "K"), c("R", "U","G"))

chainsawriot

### assuming the test.tab have this structure
# hello
# ! This is comment
# # This is also a comment
# 1
# 2
# 3


read.table(text=sub(paste0("[!#]", ".*"), "", readLines("test.tab")), header=TRUE)

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

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

# 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

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

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

## example of warning

log(-1)

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

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)