thoughtfulbloke

co2 = read.table("ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_annmean_mlo.txt")

temp = read.table("https://climate.nasa.gov/system/internal_resources/details/original/647_Global_Temperature_Data_File.txt")

names(co2) = c("year", "co2", "unc")
names(temp) = c("year", "raw_temp", "smoothed_temp")

dta <- merge(co2,temp, by="year")

thoughtfulbloke

# inspired by 
# http://blog.plover.com/2017/02/21/#anagram-scoring
download.file("http://pic.blog.plover.com/lang/anagram-scoring/Web2.txt.gz", destfile = "compressWords.txt.gz")
w <- tolower(readLines("compressWords.txt.gz"))
ord <- as.character(lapply(lapply(strsplit(w,NULL),sort),paste,collapse=""))

# what I haven't tested is if it is faster to find the pairs and test all or test and find the best pair
# the first is much simpler code and computers are fast enough. I am a data guy not a CS guy.

# avoid self matches by picking only one starter from each group there is an anagram for

thoughtfulbloke

library(tidypvals)
library(dplyr)
library(fulltext)
library(tidytext)
library(tidyr)
library(ggplot)
library(parallel)

hasDOI <- allp %>% filter(!is.na(doi), operator == "equals")
plosDOI <- hasDOI[grep("pone", hasDOI$doi),]

thoughtfulbloke

library(readxl)
library(lubridate)
library(dplyr)
houses <- read.csv("~/Downloads/raw_data/AU.csv", stringsAsFactors = FALSE)
houses$Date <- ymd(houses$Date)
hs <- houses %>% arrange(CODE, Date) %>% group_by(CODE) %>%
  mutate(new_houses = Stock - lag(Stock)) %>% ungroup() %>%
  mutate(new_value = new_houses * Median_SP) %>% group_by(Date) %>%
  summarise(amount_spent = sum(new_value, na.rm=TRUE))
# amount_spent is an esitmated value of the amount of new housing for

thoughtfulbloke

# my graph for when people say Climate Change is due to natural processes
# modify CO2_label and natural_label to suit their individual arguement wording

co2_label <- "Trend caused by CO2 added by people (what climate change is about)"
natural_label <- "Variation caused by natural events (what you are talking about)"
co2 = read.table("ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_annmean_mlo.txt")

temp = read.table("https://climate.nasa.gov/system/internal_resources/details/original/647_Global_Temperature_Data_File.txt",
                  header=FALSE)

thoughtfulbloke

library(dplyr)
library(tidyr)
library(ggplot2)
# from http://www.elections.org.nz/events/2017-general-election/2017-general-election-party-lists

listText <- "number	person
NA	ACT NEW ZEALAND
1	SEYMOUR, David
2	HOULBROOKE, Beth

thoughtfulbloke

#2017 NZ electorate candidates 
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggridges)
# from 

listText <- "party	firstname	surname
National	Kanwaljit	Singh Bakshi
National	Tim	van de Molen

thoughtfulbloke

library(lubridate)
library(dplyr)
library(ggplot2)

#some example data
set.seed(20180101)
example_data <- data.frame(
dt = sample(seq.Date(from=as.Date("2018-01-01"), to=as.Date("2018-12-31"), by="day"), size=400, replace=TRUE),
ampm = sample(c("AM","PM"), size=400, replace=TRUE),
tutor = sample(c("alpha","beta", "gamma"), size=400, replace=TRUE),

thoughtfulbloke

library(OECD)
library(feather)
library(dplyr)
library(countrycode)
library(tidyr)
library(purrr)
library(broom)
library(ggplot2)
library(viridis)

thoughtfulbloke

library(OECD)
library(feather)
library(dplyr)
library(tidyr)
library(purrr)
library(broom)
library(ggplot2)
library(viridis)