ctesta01

Recommendations

This is a list of writings about mathematics that I think are accessible to a mathematically interested audience, as well as quite worth the read. I have read many of these multiple times, and will be quite happy if somebody finds something they enjoy on this list.

General Mathematics

• A Mathematician's Apology by G. H. Hardy
• Mathematical Knowledge: Internal, Social, and Cultural Aspects by Yuri Manin
• A Mathematician's Lament by Paul Lockhart
• On Proof and Progress in Mathematics by William Thurston
• List of Important Publications in Mathematics from Wikipedia

ctesta01

Quickstart Guide to undertsanding the Qualtrics Survey File

This information is likely to quickly become outdated when Qualtrics next changes the formatting of the QSF file. This guide was started February 2017. I hope that it is a useful introduction to understanding the contents of the QSF file that one can download from Qualtrics.

This document includes:

• An introduction to JSON
• The Anatomy of a Qualtrics Survey File (QSF)
• A Breakdown of Question Structure
• A Guide to Recognizing Question Types

ctesta01

# Let's try and find out what parameters are undocumented in my package QualtricsTools.
# July 2017
# Christian Testa

# This uses some internals (such as parse_rd and .Rd_get_argument_names) from the tools package
# with the .Rd files generated by Roxygen with the args function to get arguments of functions
# loaded from the QualtricsTools in order to approximately tell which parameters are undocumented
# in the package.

# The script below seems to be prone to false-positives where the documentation for a parameter does

ctesta01

coerce_df_coltypes <- function(df, ...) {
  types <- list(...)
  # check for arguments
  if (length(types) > 0) {
    # if types is named, use names to coerce columns
    typenames <- names(types)
    if (!is.null(typenames) && length(typenames) == length(types)) {
      if (!all(typenames %in% colnames(df)))
        stop("the names of types must be colnames of df.")
      for (i in seq(types)) {

ctesta01

library(tidyverse)
library(tidycensus)
library(magrittr)

variables_dict <-
  tibble::tribble(
  ~var,            ~desc,
  "B03002_001",  'total_race',    "total pop for race/non-hispanic estimate",
  "B03002_003",  'white_nh',      "white non-hispanic pop",
  "B25014_001",  'total_crowd',   "total for occupants by room by owner/renter",

ctesta01

library(tidyverse)
library(magrittr)

# 1. here's some made up data 

df <- data.frame(
  value = rgamma(n=1000, 1, .1))

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install.packages("tigris")

library(tigris)
library(sp)

fulton20 <- tracts(state = 'GA', county = c('Fulton'), year = 2020)
fulton10 <- tracts(state = 'GA', county = c('Fulton'), year = 2010)
fulton00 <- tracts(state = 'GA', county = c('Fulton'), year = 2000)

library(leaflet)

ctesta01

library(tidyverse)
library(magrittr)

# New York Times style 7-day moving average 
# 
# Use each value from the given vector and the prior 6 values
# to compute a moving average where values less than 0 are dropped 
# both from the sum in the numerator and from the count of days 
# in the denominator.
# 

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library(plotly)
library(sf)
library(dplyr)

nc <- sf::st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)

N <- 10

# make random data
df <- lapply(1:N, function(x) nc) %>% bind_rows %>% 

ctesta01

# animate a saddle differential equation system 
# 
#     dX = -x - y
#     dY = x - y
#     dZ = .25 * z
# 
# 25 trajectories are simulated and animated -- rendered using rgl in R
#
# Read more about rgl: https://dmurdoch.github.io/rgl/