David Curran cavedave

## Mail2013article.r
#choose or open the file directly
f <- file.choose()
library(dplyr)
trust <- read.csv(f)
#trust <- read.csv("HadCRUT.csv",header=TRUE)
head(trust)
#names(trust)[names(trust) == 'Anomaly..deg.C.'] <- "Anomaly..deg.C"
#if graphing all not filtering by year
vac<-trust
vac<-trust %>%

## PiTurtle.py
import turtle
import random
random.seed(42)
i=0
#file = open("emil.txt", "r")
#file = open("pi1000000.txt", "r")
file = open("sqrt2.txt", "r")
text=file.read()
pi = list(text)
pi = list(map(int, pi))

## README.md

      
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                cavedave
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              Last active
              January 3, 2022 20:40
            
              
                When will the Carnage end?
              
          
    "This American carnage stops right here and stops right now." Donald Trump
Draw for yourself the pattern of how many people are murdered in the US each year
This code was made by Adam Pearce and the data comes from Homicide Trends in United States

  
## polio.csv

          
            Year
            Recorded

            
              1975
              49293

            
              1980
              52552

            
              1985
              38637

            
              1988
              35251

            
              1990
              23484

            
              1993
              10487

            
              1995
              7035

            
              2000
              719

            
              2005
              1979

## vaccine.csv

          
            Country
            vaccinated
            long
            lat

            
              Denmark
              77.4
              10.445
              55.728

            
              Belgium
              75.2
              4.39
              50.86

            
              Austria
              66.5
              15.3
              47.97

            
              Croatia
              48.4
              16.173
              45.567

            
              Albania
              36.1
              19.8
              41.28

            
              Belarus
              29.2
              27.56
              53.9

            
              Bosnia
              24.4
              18.41
              43.86

            
              Iceland
              82.5
              -21.95
              64.15

            
              Ireland
              76.8
              -7.063
              53.208

## Martin-Quinn.r

```{r}
library(dplyr)
library(readr)
library(ggplot2)  # devtools::install_github("hadley/ggplot2")
#library(hrbrmisc) # devtools::install_github("hrbrmstr/hrbrmisc")
library(grid)
library(scales)

library(grid)

## TrustVaccines
trust <- read.csv("self-reported-trust-attitudes.csv",header=TRUE)

library(dplyr)
trust <- trust %>%
             group_by(Entity) %>%
             filter(Year == max(Year))

vac <- read.csv("owid-covid-data (1).csv",header=TRUE)
vac<-vac %>%
  dplyr::select(people_vaccinated_per_hundred, location, date)

## vaccinewaffle.r
library(ggplot2)
library(waffle)

people <- c(`Vaccinated `=91,`Vaccinated in ICU`=1, `Unvaccinated`=7,
               `Unvaccinated in ICU `=1)

waffle(people, rows=10, size=0.6,
       colors=c("#44D2AC", "#E48B8B", "#B67093",
                "#3A9ABD"),
       title="Irish Adults",

## Oregon
df <-
  readr::read_csv("https://projects.oregonlive.com/weather/pdx_temps.csv")


  #Jun$Temp <- cut(Jun$tmax, breaks=c(50,104, Inf), labels=c("50","100+"))
Jun$Temp <- cut(Jun$tmax, breaks=c(50,60,70,80,90,100, Inf), labels=c("50","60","70","80","90","100+"))


hot <- c("#6BBCD1","#fed976","#feb24c","#fd8d3c","#fc4e2a","#e31a1c","#b10026","#FF2A24")
#hot <- c("#000000","#FF2A24")

## cherry.r
#data from https://datagraver.com/case/kyoto-cherry-blossom-full-flower-dates#google_vignette
df <- read.csv(file ="kyoto_dates_cherryblossom2021.csv")
library(tidyverse)
df<-df %>% drop_na()
library(lubridate)
df<-df %>% select(Year, Month, Day) %>%
        mutate(date2 = make_date(Year, Month, Day))
df <- df %>%
  mutate(dated = yday(date2))
	#choose or open the file directly
	f <- file.choose()
	library(dplyr)
	trust <- read.csv(f)
	#trust <- read.csv("HadCRUT.csv",header=TRUE)
	head(trust)
	#names(trust)[names(trust) == 'Anomaly..deg.C.'] <- "Anomaly..deg.C"
	#if graphing all not filtering by year
	vac<-trust
	vac<-trust %>%
	import turtle
	import random
	random.seed(42)
	i=0
	#file = open("emil.txt", "r")
	#file = open("pi1000000.txt", "r")
	file = open("sqrt2.txt", "r")
	text=file.read()
	pi = list(text)
	pi = list(map(int, pi))
	Year	Recorded
	1975	49293
	1980	52552
	1985	38637
	1988	35251
	1990	23484
	1993	10487
	1995	7035
	2000	719
	2005	1979
Country	vaccinated	long	lat
Denmark	77.4	10.445	55.728
Belgium	75.2	4.39	50.86
Austria	66.5	15.3	47.97
Croatia	48.4	16.173	45.567
Albania	36.1	19.8	41.28
Belarus	29.2	27.56	53.9
Bosnia	24.4	18.41	43.86
Iceland	82.5	-21.95	64.15
Ireland	76.8	-7.063	53.208

	```{r}
	library(dplyr)
	library(readr)
	library(ggplot2) # devtools::install_github("hadley/ggplot2")
	#library(hrbrmisc) # devtools::install_github("hrbrmstr/hrbrmisc")
	library(grid)
	library(scales)

	library(grid)
	trust <- read.csv("self-reported-trust-attitudes.csv",header=TRUE)

	library(dplyr)
	trust <- trust %>%
	group_by(Entity) %>%
	filter(Year == max(Year))

	vac <- read.csv("owid-covid-data (1).csv",header=TRUE)
	vac<-vac %>%
	dplyr::select(people_vaccinated_per_hundred, location, date)
	library(ggplot2)
	library(waffle)

	people <- c(`Vaccinated `=91,`Vaccinated in ICU`=1, `Unvaccinated`=7,
	`Unvaccinated in ICU `=1)

	waffle(people, rows=10, size=0.6,
	colors=c("#44D2AC", "#E48B8B", "#B67093",
	"#3A9ABD"),
	title="Irish Adults",
	df <-
	readr::read_csv("https://projects.oregonlive.com/weather/pdx_temps.csv")


	#Jun$Temp <- cut(Jun$tmax, breaks=c(50,104, Inf), labels=c("50","100+"))
	Jun$Temp <- cut(Jun$tmax, breaks=c(50,60,70,80,90,100, Inf), labels=c("50","60","70","80","90","100+"))


	hot <- c("#6BBCD1","#fed976","#feb24c","#fd8d3c","#fc4e2a","#e31a1c","#b10026","#FF2A24")
	#hot <- c("#000000","#FF2A24")
	#data from https://datagraver.com/case/kyoto-cherry-blossom-full-flower-dates#google_vignette
	df <- read.csv(file ="kyoto_dates_cherryblossom2021.csv")
	library(tidyverse)
	df<-df %>% drop_na()
	library(lubridate)
	df<-df %>% select(Year, Month, Day) %>%
	mutate(date2 = make_date(Year, Month, Day))
	df <- df %>%
	mutate(dated = yday(date2))