David Curran cavedave

## slope.html
<!DOCTYPE html>

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<!--[if (gt IE 9)|!(IE)]><!--> <html lang="en" class="no-js"> <!--<![endif]-->
<head>
  <meta charset="utf-8">

## dictionary_bat.txt
abk, ?, grey
ara, day blind, lightyellow
aze, night bird, orange
bel, leather one, turquoise
bos, blind mouse, lightgreen
bre, blind mouse, lightblue
bul, sticking one, blue
cat, winged rat, yellow
cau, ?, grey
ces, night flyer, lightgreen

## heapslaw.py
import matplotlib.pyplot as plt
unique = []
check = []
k = 50 #These need to be estimated for particular languages but this is a base estimate
b=0.4 #

for x in range(1, 100000, 100):
  unique.append(k*(x**b))
  check.append(x)

## parseCroidhePdf.py
import pdfplumber

def is_bold_font(char):
    return "bold" in char['fontname'].lower()

def parse_pdf_to_dictionary(pdf_path):
    term_definition_dict = {}
    term = ""
    next_term = ""
    definition = ""

## EuclidTrans.csv

          
            Year
             Language
            taxis
            iso

            
              1505
              Latin
              1
              va

            
              1543
              Italian
              1
              it

            
              1557
              Latin
              1
              va

            
              1558
              German
              1
              de

            
              1562
              German
              1
              de

            
              1562
              German
              1
              de

            
              1564
              French
              1
              fr

            
              1570
              English
              1
              gb

            
              1572
              Latin
              1
              va

## PresidentialOrders.r

objects <-c('Truman','Eisenhower','Kennedy','Johnson','Nixon','Ford','Carter','Reagan','Bush','Clinton','G. W. Bush','Obama','Trump','Biden')
#'FDR', 0.796,

performance <- c(0.319,0.165,0.206,0.172,0.170,0.188,0.219,0.130,0.113,0.105,0.099,0.094,0.15,0.165)
color<-c('#0015BC', '#E9141D', '#0015BC', '#0015BC', '#E9141D', '#E9141D', '#0015BC', '#E9141D', '#E9141D', '#0015BC', '#E9141D', '#0015BC','#E9141D','#0015BC')

df<- data.frame(objects, performance, color)

#lock in order

## TourdeFrance.r
library(ggplot2)
library("ggthemes")

data <- read.csv("winners.csv",header=TRUE)

#Year   AverageSpeed
#	1903	25.679
#	1904	25.265

ggplot(data, aes(x=Year, y=AverageSpeed, fill=type)) +

## coalandtotal.r
library(dplyr)
library(lubridate)
library(tidyverse)

gridwatch <-
  read_csv(
    file = "gridwatch.csv")


#get date int data format

## IrelandTemp2021.r
get data from https://www.met.ie/climate/available-data/historical-data
for Dublin airport


```{r}
dub<-read_csv('dly532.csv', skip=25, col_names = TRUE)
head(dub)
```
date looks like 01-jan-1942
separate it out into 3 columns

## age15to64.r

library(eurostat)      # eurostat data
library(rnaturalearth) # worldwide map data
library(tidyverse)     # tidy data transformation
library(lubridate)     # date and time support
library(sf)            # simple features GIS
library(RColorBrewer)

euro_pop <-
  get_eurostat('demo_r_pjanaggr3', stringsAsFactors = FALSE) %>%
	<!DOCTYPE html>

	<!-- paulirish.com/2008/conditional-stylesheets-vs-css-hacks-answer-neither/ -->
	<!--[if lt IE 7 ]> <html lang="en" class="no-js ie6"> <![endif]-->
	<!--[if IE 7 ]> <html lang="en" class="no-js ie7"> <![endif]-->
	<!--[if IE 8 ]> <html lang="en" class="no-js ie8"> <![endif]-->
	<!--[if IE 9 ]> <html lang="en" class="no-js ie9"> <![endif]-->
	<!--[if (gt IE 9)\|!(IE)]><!--> <html lang="en" class="no-js"> <!--<![endif]-->
	<head>
	<meta charset="utf-8">
	abk, ?, grey
	ara, day blind, lightyellow
	aze, night bird, orange
	bel, leather one, turquoise
	bos, blind mouse, lightgreen
	bre, blind mouse, lightblue
	bul, sticking one, blue
	cat, winged rat, yellow
	cau, ?, grey
	ces, night flyer, lightgreen
	import matplotlib.pyplot as plt
	unique = []
	check = []
	k = 50 #These need to be estimated for particular languages but this is a base estimate
	b=0.4 #

	for x in range(1, 100000, 100):
	unique.append(k(x*b))
	check.append(x)
	import pdfplumber

	def is_bold_font(char):
	return "bold" in char['fontname'].lower()

	def parse_pdf_to_dictionary(pdf_path):
	term_definition_dict = {}
	term = ""
	next_term = ""
	definition = ""
Year	Language	taxis	iso
1505	Latin	1	va
1543	Italian	1	it
1557	Latin	1	va
1558	German	1	de
1562	German	1	de
1562	German	1	de
1564	French	1	fr
1570	English	1	gb
1572	Latin	1	va

	objects <-c('Truman','Eisenhower','Kennedy','Johnson','Nixon','Ford','Carter','Reagan','Bush','Clinton','G. W. Bush','Obama','Trump','Biden')
	#'FDR', 0.796,

	performance <- c(0.319,0.165,0.206,0.172,0.170,0.188,0.219,0.130,0.113,0.105,0.099,0.094,0.15,0.165)
	color<-c('#0015BC', '#E9141D', '#0015BC', '#0015BC', '#E9141D', '#E9141D', '#0015BC', '#E9141D', '#E9141D', '#0015BC', '#E9141D', '#0015BC','#E9141D','#0015BC')

	df<- data.frame(objects, performance, color)

	#lock in order
	library(ggplot2)
	library("ggthemes")

	data <- read.csv("winners.csv",header=TRUE)

	#Year AverageSpeed
	# 1903 25.679
	# 1904 25.265

	ggplot(data, aes(x=Year, y=AverageSpeed, fill=type)) +
	library(dplyr)
	library(lubridate)
	library(tidyverse)

	gridwatch <-
	read_csv(
	file = "gridwatch.csv")


	#get date int data format
	get data from https://www.met.ie/climate/available-data/historical-data
	for Dublin airport


	```{r}
	dub<-read_csv('dly532.csv', skip=25, col_names = TRUE)
	head(dub)
	```
	date looks like 01-jan-1942
	separate it out into 3 columns

	library(eurostat) # eurostat data
	library(rnaturalearth) # worldwide map data
	library(tidyverse) # tidy data transformation
	library(lubridate) # date and time support
	library(sf) # simple features GIS
	library(RColorBrewer)

	euro_pop <-
	get_eurostat('demo_r_pjanaggr3', stringsAsFactors = FALSE) %>%