AdamSpannbauer

import numpy as np
from scipy import stats


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

AdamSpannbauer

import warnings
import statsmodels.api as sm
from statsmodels.stats.outliers_influence import variance_inflation_factor


def print_vif(x):
    """Utility for checking multicollinearity assumption
    
    :param x: input features to check using VIF. This is assumed to be a pandas.DataFrame
    :return: nothing is returned the VIFs are printed as a pandas series

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

# Barnsley fern in R
# reference: https://en.wikipedia.org/wiki/Barnsley_fern

library(plotly)

# FUNCTIONS FOR GENERATING BARNSLEY FERN POINTS ################################
transform_1 = function(x, y) {
  x = 0
  y = 0.16 * y
  return(c(x, y))

AdamSpannbauer

library(R6)
library(ggplot2)
library(uuid)
options(stringsAsFactors = FALSE)

branch_base = R6Class('branch_base',
                      public = list(
                          start_x = NA_integer_,
                          start_y = NA_integer_,
                          end_x = NA_integer_,

AdamSpannbauer

library(ggplot2)
library(gganimate)
library(data.table)

gen_heart_y = function(x, a) {
  # source: https://i.imgur.com/avE8cxJ.gifv
  (x^2)^(1 / 3) + 0.9 * (3.3 - x^2)^(1 / 2) * sin(a * pi * x)
}

heart_dt_list = lapply(seq(1, 15, by = 0.1), function(a) {

AdamSpannbauer

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

data("mtcars")

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

# single column

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

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

# 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.