Alison Presmanes Hill apreshill

## h2olead.csv

          
            county
            sample
            state
            name
            exceedances

            
              Mobile
              20
              al
              Dauphin Island Water & Sewer
              1

            
              Monroe
              23
              al
              Excel Water System
              1

            
              Washington
              95
              al
              Tate & Lyle Sucralose, Inc. Water System
              1

            
              Coosa
              18.3
              al
              Lake Mitchell Water, Incorporated
              1

            
              Juneau City and Borough
              15.5
              ak
              Thunder Mountain Mobile Park
              1

            
              Juneau City and Borough
              31
              ak
              Hecla Greens Creek Hawk Inlet
              1

            
              Ketchikan Gateway Borough
              27.7
              ak
              Boyer Towing
              1

            
              Ketchikan Gateway Borough
              32.9
              ak
              Thorne Bay, City Of
              1

            
              Ketchikan Gateway Borough
              16.2
              ak
              Clover Pass Christian School
              2

## analysis.R
download.file("https://ndownloader.figshare.com/articles/5170705/versions/1", "kd.zip")
unzip("kd.zip")

library(readr)

words <- readr::read_csv("KSP-Word.csv")
users <- readr::read_csv("KSP-User.csv")
entries <- readr::read_csv("KSP-Entry.csv")
keypress <- readr::read_csv("KSP-KeyPress.csv")
pressure <- readr::read_csv("KSP-Pressure.csv")

## games_howell.R
games.howell <- function(grp, obs) {

  #Create combinations
  combs <- combn(unique(grp), 2)

  # Statistics that will be used throughout the calculations:
  # n = sample size of each group
  # groups = number of groups in data
  # Mean = means of each group sample
  # std = variance of each group sample

## pmap-and-map2-also-useful.R
library(tidyverse)

## approximates big_df in the post
big_df <- mtcars %>%
  select(cyl, mpg, disp) %>%
  arrange(cyl) %>%
  slice(17:22) %>%
  rename(ID = cyl)

## dummy function that needs access to ID and data

## five-rounds.csv

          
            sample_id
            B1
            B2
            B3
            reading_date
            ward
            service_line_material

            
              2
              8.1
              10.8
              2.8
              aug-15
              9
              Unknown

            
              4
              1.1
              BD
              BD
              aug-15
              1
              Copper

            
              7
              7.2
              1.4
              BD
              aug-15
              9
              Copper

            
              8
              40.6
              9.7
              6.1
              aug-15
              9
              Lead

            
              12
              10.6
              1.0
              1.3
              aug-15
              9
              Unknown

            
              15
              4.4
              BD
              BD
              aug-15
              9
              Copper

            
              16
              24.4
              8.8
              4.3
              aug-15
              5
              Galvanized

            
              17
              6.6
              5.8
              1.4
              aug-15
              2
              Unknown

            
              18
              4.1
              1.1
              1.1
              aug-15
              7
              Copper

## scrape.R
library(tidyverse)
library(rvest)

"https://en.wikipedia.org/wiki/List_of_superhero_debuts" %>%
  read_html() %>%
  html_nodes(xpath = "//*[@id='mw-content-text']/div/table") %>%
  map(html_table, fill = TRUE) %>%
  map_df( ~ mutate(., `Year Debuted` = as.character(`Year Debuted`))) %>%
  mutate(`Char_Team` = coalesce(`Character / Team / Series`, `Character / Team`)) %>%
  select(Char_Team, Year_Debut = `Year Debuted`) %>%

## snippet.txt
snippet bdfig
	Figure \@ref(fig:${1:chunk-name}) ${0}

	(ref:${1:chunk-name}) Text reference for caption

	```{r ${1:chunk-name}, fig.cap = "(ref:${1:chunk-name})"}

	```

## SuzanmapPlots.Rmd
---
title: "Untitled"
author: "Brandon Hurr"
date: "4/18/2018"
output:
  beamer_presentation:
    latex_engine: xelatex
---

```{r setup, include=FALSE}

## beanplots.R
## reproduce the figures from http://www.jstatsoft.org/v28/c01/paper using ggplot2

library(ggplot2)

## parameters
set.seed(2710)

## Figure 1
d <- rnorm(50)

## Brewfile
tap 'homebrew/fuse'
tap 'homebrew/dupes'
tap 'homebrew/cask'
tap 'homebrew/cask-fonts'
tap 'homebrew/cask-versions'
tap 'homebrew/cask-drivers'

cask_args appdir: '/Applications'

brew 'ack'
county	sample	state	name	exceedances
Mobile	20	al	Dauphin Island Water & Sewer	1
Monroe	23	al	Excel Water System	1
Washington	95	al	Tate & Lyle Sucralose, Inc. Water System	1
Coosa	18.3	al	Lake Mitchell Water, Incorporated	1
Juneau City and Borough	15.5	ak	Thunder Mountain Mobile Park	1
Juneau City and Borough	31	ak	Hecla Greens Creek Hawk Inlet	1
Ketchikan Gateway Borough	27.7	ak	Boyer Towing	1
Ketchikan Gateway Borough	32.9	ak	Thorne Bay, City Of	1
Ketchikan Gateway Borough	16.2	ak	Clover Pass Christian School	2
	download.file("https://ndownloader.figshare.com/articles/5170705/versions/1", "kd.zip")
	unzip("kd.zip")

	library(readr)

	words <- readr::read_csv("KSP-Word.csv")
	users <- readr::read_csv("KSP-User.csv")
	entries <- readr::read_csv("KSP-Entry.csv")
	keypress <- readr::read_csv("KSP-KeyPress.csv")
	pressure <- readr::read_csv("KSP-Pressure.csv")
	games.howell <- function(grp, obs) {

	#Create combinations
	combs <- combn(unique(grp), 2)

	# Statistics that will be used throughout the calculations:
	# n = sample size of each group
	# groups = number of groups in data
	# Mean = means of each group sample
	# std = variance of each group sample
	library(tidyverse)

	## approximates big_df in the post
	big_df <- mtcars %>%
	select(cyl, mpg, disp) %>%
	arrange(cyl) %>%
	slice(17:22) %>%
	rename(ID = cyl)

	## dummy function that needs access to ID and data
sample_id	B1	B2	B3	reading_date	ward	service_line_material
2	8.1	10.8	2.8	aug-15	9	Unknown
4	1.1	BD	BD	aug-15	1	Copper
7	7.2	1.4	BD	aug-15	9	Copper
8	40.6	9.7	6.1	aug-15	9	Lead
12	10.6	1.0	1.3	aug-15	9	Unknown
15	4.4	BD	BD	aug-15	9	Copper
16	24.4	8.8	4.3	aug-15	5	Galvanized
17	6.6	5.8	1.4	aug-15	2	Unknown
18	4.1	1.1	1.1	aug-15	7	Copper
	library(tidyverse)
	library(rvest)

	"https://en.wikipedia.org/wiki/List_of_superhero_debuts" %>%
	read_html() %>%
	html_nodes(xpath = "//*[@id='mw-content-text']/div/table") %>%
	map(html_table, fill = TRUE) %>%
	map_df( ~ mutate(., `Year Debuted` = as.character(`Year Debuted`))) %>%
	mutate(`Char_Team` = coalesce(`Character / Team / Series`, `Character / Team`)) %>%
	select(Char_Team, Year_Debut = `Year Debuted`) %>%
	snippet bdfig
	Figure \@ref(fig:${1:chunk-name}) ${0}

	(ref:${1:chunk-name}) Text reference for caption

	```{r ${1:chunk-name}, fig.cap = "(ref:${1:chunk-name})"}

	```
	---
	title: "Untitled"
	author: "Brandon Hurr"
	date: "4/18/2018"
	output:
	beamer_presentation:
	latex_engine: xelatex
	---

	```{r setup, include=FALSE}
	## reproduce the figures from http://www.jstatsoft.org/v28/c01/paper using ggplot2

	library(ggplot2)

	## parameters
	set.seed(2710)

	## Figure 1
	d <- rnorm(50)
	tap 'homebrew/fuse'
	tap 'homebrew/dupes'
	tap 'homebrew/cask'
	tap 'homebrew/cask-fonts'
	tap 'homebrew/cask-versions'
	tap 'homebrew/cask-drivers'

	cask_args appdir: '/Applications'

	brew 'ack'