Click to see R set-up code

# Libraries
if(!require("pacman")) {
  install.packages("pacman")
}
pacman::p_load(
  data.table,
  re2,
  scales,
  ggplot2,
  plotly, 
  DT,
  patchwork,
  survival,
  ggfortify,
  scales)

# Set knitr params
knitr::opts_chunk$set(
  comment = NA,
  fig.width = 12,
  fig.height = 8,
  out.width = '100%'
)

NOTE: The read time for this post is overstated because of the formatting of the Plotly code. There are ~2,500 words, so read time should be ~10 minutes. Click to see R code generating plot

# Load function to plot dual y-axis plot
source("train_sec.R")

# Get data series from FRED
symbols <- c("CP", "GDP", "WASCUR")
start_date <- '1947-01-01'
end_date <- '2021-07-30'
quantmod::getSymbols(
  Symbols = symbols,
  src = "FRED",
  start_date = start_date,
  end_date = end_date
)

[1] "CP"     "GDP"    "WASCUR"

# Merge series and convert to dt
d <- as.data.table(merge(WASCUR/GDP, CP/GDP, join = "inner"))

# Build superimposed dual y-axis line plot
sec <- with(d, train_sec(CP, WASCUR))
p <- 
  ggplot(d, aes(index)) +
    geom_line(aes(y = CP),
              colour = "blue", 
              size = 1) +
    geom_line(aes(y = sec$fwd(WASCUR)),
              colour = "red", 
              size = 1) +
    scale_y_continuous(
      "Corporate Profits to GDP",
      labels = scales::percent,
      sec.axis = sec_axis(
        ~ sec$rev(.),
        name = "Compensation of Employees to GDP",
        labels = scales::percent)
    ) +
    scale_x_date(date_breaks = "10 years",
                 date_labels = "%Y") + 
    labs(title = "Labor vs Capital",
         x = "Year",
         caption = "Source: Lots of places") +
    theme_bw(base_size = 22)

Introduction The rise in monopoly power particularly ...

Click to see R set-up code

# Libraries
if(!require("pacman")) {
  install.packages("pacman")
}
pacman::p_load(
  data.table,
  scales,
  ggplot2,
  plotly, 
  DT)

# Set knitr params
knitr::opts_chunk$set(
  comment = NA,
  fig.width = 12,
  fig.height = 8,
  out.width = '100%'
)

# Load annual data only
path <- 
  "~/Desktop/David/Projects/new_constructs_targets/_targets/objects/"
red_flags <- 
  readRDS(paste0(path, "nc_annual_red_flags"))
annual_data <- 
  readRDS(paste0(path, "nc_annual_final"))

Key Findings 1999-2000 was an exceptional period for both “Red Flag” prevalence and return differentiation, though apparent benefits of the strategy appear in most periods. Approximately 2.0% of filings we checked had 5 or more “Red Flags” among annual and quarterly filings, so sparsity is ...

Introduction The IRS SOI Tax Statistics is a fabulous seemingly undiscovered data set, which should be frequently referenced in any discussion about income and taxes. We often see median income represented by Census data, but have always been ske...

The Government Finance Database Viewed in RStudio Connections Pane Introduction We have been exploring Willamette University Government Finance Database, a cleaned up and aggregated version of 50 years of annual Census of Governments. For those interested in learning more, please see Willamette’s paper The Government Finance Database: A Common Resource ...

Introduction The IRS SOI Tax Statistics is a fabulous seemingly undiscovered data set, which should be frequently referenced in any discussion about income and taxes. We often see median income represented by Census data, but have always been ske...

Figure 1: Average Single Family Homes in Towns of Connecticut 1999-2018 Introduction Though losing ground in recent years, Connecticut has long had some of the highest average incomes and home prices in the country. Within the State, some towns have had significantly higher selling prices than others (shown in chart above). ...

Click to see package details

# Libraries
packages <- 
  c("tidyverse",
    "sergeant",
    "tictoc"
    )

if (length(setdiff(packages,rownames(installed.packages()))) > 0) {
  install.packages(setdiff(packages, rownames(installed.packages())))  
}

invisible(lapply(packages, library, character.only = TRUE))

knitr::opts_chunk$set(
  comment = NA,
  fig.width = 12,
  fig.height = 8,
  out.width = '100%'
)

Introduction At Redwall, we have been in nonstop exploration of new data sets over the last couple of years. As our data grows and the targets of interest get bigger, we have been finding the old method of loading csv’s from disc, and ...

Source: xbrl.org Introduction This is part 2 of a 3-part series on extracting XBRL data from secdatabase.com. In Learning SQL and Exploring XBRL with secdatabase.com - Part 1, we showed how to set up the database connection from RStudio. In this post, we will discuss the basics of XBRL ...

# Libraries
packages <- 
  c("data.table",
    "ggplot2",
    "stringr",
    "skimr",
    "janitor",
    "glue"
    )

if (length(setdiff(packages,rownames(installed.packages()))) > 0) {
  install.packages(setdiff(packages, rownames(installed.packages())))  
}

invisible(lapply(packages, library, character.only = TRUE))

knitr::opts_chunk$set(
  comment = NA,
  fig.width = 12,
  fig.height = 8,
  out.width = '100%',
  cache = TRUE
)

EPA AirData Air Quality Monitors Introduction As our journey with open source software continues, there is a growing list of things we have tried, but were unable to or took too long to figure out, so moved on. Sometimes its a blog or twitter post, others a new package ...

# Libraries
packages <- 
  c("data.table",
    "DBI",
    "reticulate",
    "keyring",
    "RAthena"
    )

if (length(setdiff(packages,rownames(installed.packages()))) > 0) {
  install.packages(setdiff(packages, rownames(installed.packages())))  
}

invisible(lapply(packages, library, character.only = TRUE))

knitr::opts_chunk$set(
  comment = NA,
  fig.width = 12,
  fig.height = 8,
  out.width = '100%'
)

Introduction In A Walk Though of Accessing Financial Statements with XBRL in R - Part 1, we showed how to use R to extract Apple financial statement data from the SEC Edgar website. This would be a cumbersome process to scale across sectors, but works well for a single company. ...

# R Libraries for this blogdown post
# See Github for libraries used in drake project
library(data.table)
library(DT)

knitr::opts_chunk$set(
  fig.width = 15,
  fig.height = 8,
  out.width = '100%')

Introduction The State of Connecticut requires each of its 169 municipalities to report real estate sales used in the assessment process. All reported transactions by towns are published on the Office of Policy and Management (OPM) website. In the past, annual databases were disclosed with differing storage formats each year (...

Introduction Active funds have done poorly over the last ten years, and in most cases, struggled to justify their fees. A growing list of commentators appropriately advocate for index funds, although sometimes go a little beyond what we believe to be fairly representing the facts. The inspiration for this article ...

# R Libraries
library("reticulate")

knitr::opts_chunk$set(
  fig.width = 15,
  fig.height = 8,
  out.width = '100%')

# Choose Python 3.7 miniconda
reticulate::use_condaenv(
  condaenv = "r-reticulate",
  required = TRUE
  )

# Install Python packages
lapply(c("plotnine"), function(package) {
       conda_install("r-reticulate", package, pip = TRUE)
})

# Python libraries
from datatable import *
import numpy as np
import plotnine as p9 
import re

Introduction In this post, we start out where we left off in Exploring Big MT Cars with Python datatable and plotnine-Part 1. In the chunk below, we load our cleaned up big MT Cars data set in order to be able to refer directly to the variable ...