chris-prener

# batch edit column names based on a particular phrase

# store data in global environment, modify
mpg <- ggplot2::mpg
colnames(mpg) <- paste("depression", colnames(mpg), sep = "_")

# as a function
rename_by_phrase <- function(.data, pattern, replacement){
  
  # modify variable names based on input

chris-prener

# find where to locate Anchorage, AK using the albersusa package

# dependencies
library(albersusa)   # composite projection
library(dplyr)       # data wrangling
library(ggplot2)     # mapping
library(mapview)     # preview
library(sf)          # spatial data

# load composite map

chris-prener

---
title: "SOC 1120-01"
subtitle: "Fall 2019 Attendance"
author: "Christopher Prener, Ph.D."
institute: "Saint Louis University"
date: "2019/08/19 (updated: `r Sys.Date()`)"
output:
  xaringan::moon_reader:
    lib_dir: libs
    nature:

chris-prener

# dependencies
library(dplyr)
library(googledrive)
library(lubridate)
library(readr)
library(stringr)

# download form data
forms <- drive_find(pattern = "FA19-01")

chris-prener

# Bivariate Choropleth Map
## this is based on Timo Grossenbacher and Angelo Zehr's tutorial - 
## https://timogrossenbacher.ch/2019/04/bivariate-maps-with-ggplot2-and-sf/

# dependencies
library(dplyr)      # data wrangling
library(ggplot2)    # plotting
library(purrr)      # iteration
library(tidyr)      # data wrangling

chris-prener

# Parse Census Bureau address range data

# Problem:
# We need a way to match incidents (that have address-level data) to the blockface they occur on.
# By blockface, I mean the houses on either side of a street between two cross streets (i.e.
# the 100-block of Main Street between 1st and 2nd Avenues).
#
# Typically, census block and city block shapefiles do not represent a blockface. Instead, 
# they have parts of up to four different streets, typically representing half of a blockface 
# for each of the included streets. So a block bounded by Main Street on the south, 1st Avenue

chris-prener

# These two functions address a common problem in my work - files named "monthYYYY.CSV.html" - 
# e.g. "August2018.CSV.html". The prep_dir() function creates a vector of filenames in a given directory
# and then iterates over each using purrr::map(). The map() function calls our second function, edit_filename().
# For each time it is called, edit_filename() uses the magic of stringr and fs (okay, it isn't really magic)
# to rename the file appropriately.

# dependencies:
# install.packages(c("fs", "magrittr", "purrr", "stringr"))

# include in script:

chris-prener

# dependencies
library(dplyr)     # data wrangling
library(classInt)  # calculate breaks
library(leaflet)   # leaflet maps
library(sf)        # spatial data tools
library(stringr)   # work with strings

# load data and re-project to WGS 1984
st_read("STL_DEMOS_Nhoods.shp", stringsAsFactors = FALSE) %>%
    st_transform(crs = 4326) -> popChange

chris-prener

# Workflow for Creating Multiple Choice Exams

# This process creates a pseudo-random sample of correct answers, and then generates
# an exam based on a specified number of questions assigned to different sections of
# the course.

# Install Dependencies
# install.packages(c("dplyr", "purrr", "readr", "rlang"))

# Create 

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
)