AdamSpannbauer

library(ggplot2)
library(dplyr)
library(tidyr)

plot_centroids_table <- function(kmeans_object) {
  n_clusters <- nrow(kmeans_object$centers)

  plot_df <- data.frame(t(kmeans_object$centers))
  names(plot_df) <- paste("Cluster", 1:n_clusters)
  plot_df$feature_name <- rownames(plot_df)

AdamSpannbauer

library(arules)

transaction2df <- function(transation_obj) {
  if (!inherits(transation_obj, "transactions")) {
    error_msg <- paste0(
      "\ntransactions objects only pls\n",
      "\ncheck your object with: class(",
      substitute(transation_obj),
      ")"
    )

AdamSpannbauer

# From: https://otexts.com/fpp3/diagnostics.html#portmanteau-tests-for-autocorrelation
#
# We suggest using l = 10 for non-seasonal data and l = 2 * m for seasonal data, where m is the period of
# seasonality. However, the test is not good when l is large, so
# if these values are larger than T / 5 then use l = T / 5.

# Translating into R function:
#  * n_obs - number of observations in the series (referred to as T in the text)
#  * n_seasonal_periods - NA if non-seasonal; 4 if quarterly; 12 if monthly; etc.

AdamSpannbauer

prettify_discretize_labs <- function(x, sep = " to ") {
  all_labs <- levels(x)
  split_labs <- strsplit(all_labs, ",")

  old_options <- options(scipen = 999)
  pretty_labs <- vapply(split_labs, function(el) {
    num_chrs <- gsub("\\[|\\]|\\(|\\)", "", el)
    nums <- as.numeric(num_chrs)
    pretty_lab <- paste(nums[1], nums[2], sep = sep)
  }, character(1))

AdamSpannbauer

import webview
from dash import Dash, html


def run_native_dash_app(dash_app: Dash, window_title: str = None) -> None:
    """Run dash app as native web app

    Use PyWebView to run a dash app as a native web app
      * install with `pip install pywebview`
      * project home page: https://pywebview.flowrl.com/

AdamSpannbauer

# install.packages('sqldf')
library(sqldf)

data("mtcars")

# Select all
sqldf("SELECT *
      FROM mtcars")

# single column

AdamSpannbauer

/*
A base class to create p5js sketches as React components.

Requires p5js: `npm install p5`

## Usage

### Extend Sketch and create a p5js sketch

```

AdamSpannbauer

import numpy as np
from scipy import stats


# ---------------------------
# Independent samples -------
# ---------------------------
def cles_ind(x1, x2):
    """Calc common language effect size

AdamSpannbauer

# An extension of help / `?` to work with roxygen documentation in a
# Pythonic / docstring style for non-packaged functions.


.fun_body = function(fun) {
  #' Get body of function (including comments) as a character vector
  #'
  #' @param fun name of function obj to return body of
  #'
  #' @return character vector of function body (1 element per line)

AdamSpannbauer

# Modified from https://www.science-emergence.com/Articles/How-to-plot-a-normal-distribution-with-matplotlib-in-python-/
import numpy as np
import scipy.stats
import matplotlib.pyplot as plt


def plot_area(mean, std, pt1, pt2, fill):
  plt.plot([pt1, pt1], [0.0, scipy.stats.norm.pdf(pt1, mean, std)], color='black')
  plt.plot([pt2, pt2], [0.0, scipy.stats.norm.pdf(pt2, mean, std)], color='black')