jmclawson

library(tidyverse)

# make a pinwheel: first set up directions. The blades are drawn in different orders for clockwise and counterclockwise
clockwise_t <- c(2, 1, 3, 4)
clockwise_f <- c(4, 3, 1, 2)
direction <- list(clockwise_t, clockwise_f)

# create a 4-color pinwheel with 4 blades facing the same direction
get_pinwheel <- 
  function(

jmclawson

# import_bib.R
# To convert from Bibtex to a data frame for working with the data in R.

library(dplyr)
library(stringr)
library(tidyr)

# 0. Set filename for the bibfile
the_bibfile <- "~/path/to/my.bib"

jmclawson

##### Load libraries #####
library(dplyr)
library(stringi)
library(stringr)
library(tidyr)
library(stringdist)
library(tokenizers)

##### Set up replicable workflow #####
# Set the directories to be used. Both directories should exist in project directory, and dir_start should include text files needing to be processed.

jmclawson

get_if_needed <- function(
    # Url to be downloaded, necessary
  url, 
  
  # destination filename (optional)
  filename = NULL, 
  
  # destination directory (optional)
  destdir = "data"
) {

jmclawson

##### stylo_log #####
# Pipe from stylo() directly into stylo_log() 
# or wrap stylo() in stylo_log()
# Examples:
#    stylo() |> stylo_log()
#    stylo_log(stylo())

stylo_log <- function(
    stylo_object,
    log_label = NULL,

jmclawson

# library(tidyverse)
# library(tidytext)

##### Use the following function for reading a folder of prose text files. #####
## Put all the text files you want to read in the same folder. If that folder's
## called, for example, "project2", here's the function in practice:
##
##      my_table <- tidy_prose_texts(folder = "project2")
##

jmclawson

unnest_without_caps <- function(
    df, 
    column = "text") {
  
  full <- df |> 
    tidytext::unnest_tokens(word, {{column}}, to_lower = FALSE) 
  
  big <- full |> 
    dplyr::filter(str_detect(word, "^[A-Z]")) |> 
    dplyr::pull(word)

jmclawson

library(wordcloud)
library(topicmodels)
library(plotly)

# Moves a table of texts through the necessary
# steps of preparation before building a topic
# model. The function applies these steps:
#  1. identifies text divisions by the `doc_id`
#     column
#  2. divides each of the texts into same-sized 

jmclawson

---
title: "Importing SAS data into R"
---

## Get the data

```{r}
# Just download it once
if(!file.exists("medical.sas7bdat")) {
    download.file("http://www.principlesofeconometrics.com/sas/medical.sas7bdat", 

jmclawson

---
title: "Mapping with R"
output: 
  html_document:
    df_print: paged
    toc: true
    toc_float: true
date: "2023-06-30"
---