chris-prener

\documentclass{article}
\usepackage[utf8]{inputenc}

\title{}
\author{}
\date{}

\begin{document}

\maketitle

chris-prener

@book{fisher1937design,
  title="The design of experiments",
  author="Fisher, Ronald Aylmer",
  year="1937",
  address="London, UK",
  publisher= "Oliver and Boyd"
}
@article{mcgill1978variations,
  title={Variations of box plots},
  author={McGill, Robert and Tukey, John W and Larsen, Wayne A},

chris-prener

library(ggplot2)
library(dplyr)

# load data
autoData <- mpg

# subset to create data frame with only variables of interest for scale
autoSubset <- select(autoData, cyl, cty, hwy)

# visually inspect cronbach's alpha results

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# DEPENDENCIES:
# leaflet and devtools, installed from CRAN
# stlData, installed from GitHub

# LOAD PACKAGES:
library(leaflet)
library(stlData)

# MAP 1A: MORRISSEY HALL
# use OpenStreetMap map tiles by default

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# dependencies
library(dplyr)          # data wrangling
library(gender)         # assess gender
library(randomNames)    # generate names
library(stringr)        # work with strings

# create data frame of random names
names <- randomNames(30, which.names = "both", name.order = "first.last", name.sep = " ")
names <- as.data.frame(names, stringsAsFactors = FALSE)

chris-prener

library(dplyr)
library(ggplot2)
library(latex2exp)

abline <- as_tibble(data.frame(
  xmin = c(0),
  xmax = c(10),
  ymin = c(5),
  ymax = c(5)
))

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#' Two-tailed Probabilities Under the t Distribution
#' 
#' @description This function calculates the probability of observing a t score
#'     at least as extreme as the given t value for a one sample t test
#'     
#' @param t A given t score
#' @param n The sample size associated with t
#'
#' @return A probability value     
#'      

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#' Two-tailed Probabilities Under the t Distribution
#' 
#' @description This function calculates the probability of observing a t score
#'     at least as extreme as the given t value for any t test
#'     
#' @param t A given t score
#' @param df The degrees of freedom associated with t
#'
#' @details Depending on the type of t test, degrees of freedom (v) is calculated
#'     in a different manner. For one sample and dependent t tests, v is n-1.

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library(dplyr)
library(stringr)

# create test data
orders <- data.frame(
  id = c(1,2,3,4),
  items = c("Apples and Turkey", "Bacon and Crackers", "Bananas and Juice", "Pecan Pie and Ice Cream"),
  stringsAsFactors = FALSE
)

chris-prener

library(dplyr)
library(stringr)

# create test data
orders <- data.frame(
  id = c(1,2,3,4),
  items = c("Apples and Turkey", "Bacon and Crackers", "Bananas and Juice", "Pecan Pie and Ice Cream"),
  stringsAsFactors = FALSE
)