puterleat

. clear all

. set seed 123

. 
. * recruit subjects (if only this easy...)

. set obs 2000
number of observations (_N) was 0, now 2,000

puterleat

import difflib
import requests
import itertools 
from bs4 import BeautifulSoup
from envelopes import Envelope, GMailSMTP

indexpage = requests.get("https://www.plymouth.ac.uk/schools/school-of-psychology/academics")
indexsoup = BeautifulSoup(indexpage.text, 'html.parser')

staffpages = (i.get("href") for i in list(indexsoup.find_all("a")) if i.get("href").find("/staff/") > -1)

puterleat

Perhaps more likely to make a mistake when marked with @
Less likely to fluff places marked $


                      @ @                         $     $ 
COUNT 8   1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 
          – – – – – – – X – – – – – – – X – – – – – – – X – – – – – – – X – – – – – – – X
          – – – – – – X – – – – – – X – – – – – – X – – – – – – X – – – – – – X – – – – – 
COUNT 7   1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 

puterleat

• Information [a single page split into]

  • Biography
  • Teaching at Plymouth
  • Other work roles
  • Tai Chi and Shiatsu
  • Directory content

• Writing [a single page for each of the below... think of this a bit like a blog with a categorised archive]

  • Reviews

• Articles

puterleat

t tests - Means: Difference between two independent means (two groups)

Analysis:	Post hoc: Compute achieved power 
Input:		Tail(s)                       	=	One
			Effect size d                 	=	0.52
			α err prob                    	=	0.05
			Sample size group 1           	=	49
			Sample size group 2           	=	48
Output:		Noncentrality parameter δ     	=	2.5605669
			Critical t                    	=	1.6610518

puterleat

import os
import speech_recognition as sr

r = sr.Recognizer()

def gettextfromaudio(file):
	
	try:
		with sr.WavFile(file) as source:    	
			audio = r.record(source)    

puterleat

    "witnesses": "dudes I know",
    "allegedly": "kinda probably",
    "new study": "Tumblr post",
    "rebuild": "avenge",
    "space": "spaaace",
    "Google Glass": "Virtual Boy",
    "smartphone": "Pokédex",
    "electric": "atomic",
    "senator": "elf-lord",
    "car": "cat",

puterleat

. use for_analysis.dta, replace

. keep if after == 1 & tetris == 1
(1,867 observations deleted)

. count
  603

. statsby, by(person) clear: count 

puterleat

. use for_analysis.dta, replace


. // frequency of measurement occasions
. tab cravingcats

    Secondary coding |      Freq.     Percent        Cum.
---------------------+-----------------------------------
         not craving |        867       70.32       70.32

puterleat

. preserve

. keep if after == 1 & tetris == 1
(1,863 observations deleted)

. count
  603

. statsby, by(person cravingcats tetris) clear: count 
(running count on estimation sample)