A blog of thoughts

Exploring the Book of Esther in the Tidy Verse

library(httr) library(tidytext) ## Warning: package 'tidytext' was built under R version 3.6.2 library(jsonlite) library(tidyverse) ## ── Attaching packages ─────────────────────────────── tidyverse 1.3.0 ── ## ✓ ggplot2 3.3.0 ✓ purrr 0.3.4 ## ✓ tibble 3.0.1 ✓ dplyr 0.8.5 ## ✓ tidyr 1.0.2 ✓ stringr 1.4.0 ## ✓ readr 1.3.1 ✓ forcats 0.5.0 ## Warning: package 'tibble' was built under R version 3.6.2 ## Warning: package 'purrr' was built under R version 3.

Word With Friends Sidekick in R

The following post is discovering what else I can do with words. One thing that I have recently picked up is playing Scrabble or (5_2_2)

Timeseries Analysis Using R and Advantager

Threw error due to the API In the past, the best way to get stock prices was to use either Google Finance or Yahoo Finance data streams. These have since become difficult to keep up to date and thus another outlet to get this information is AlphaVantager. Following is a simple R implementation to get up to date data. You will be able to thus use this to find the information.

Text Generation

IN PROGRESS Text Generation (Generative Texr) is a very interesting field of study. There are a number of different packages that help generate the lists of words to give the user a better understanding of the technology. Tracery The following Node library gives a structure which is used to generate random words that are assigned to lexical structure that is defined by the user. centar : { animal : ["wolf","bear","tiger","lion","snake","anteater"], fruit : ["banana","tomato","cherry","strawberry","starfruit"], said : ["purring", "whispering", "saying", "murmurring", "growling"], timeofday : ["morning","evening","dusk","dawn","afternoon","breakfast","breakfast"], lastSyl : "a ia ea u y en am is on an o io i el ios ax ox ix ex izz ius ian ean ekang anth".

Gantt Charts in R

In Progress Using timevis library(timevis) data <- data.frame( id = 1:4, content = c("Item one" , "Item two" ,"Ranged item", "Item four"), start = c("2016-01-10", "2016-01-11", "2016-01-20", "2016-02-14 15:00:00"), end = c(NA , NA, "2016-02-04", NA) ) timevis(data) + - {"x":{"items":[{"id":"1","content":"Item one","start":"2016-01-10"},{"id":"2","content":"Item two","start":"2016-01-11"},{"id":"3","content":"Ranged item","start":"2016-01-20","end":"2016-02-04"},{"id":"4","content":"Item four","start":"2016-02-14 15:00:00"}],"groups":null,"showZoom":true,"zoomFactor":0.5,"fit":true,"options":[],"height":null,"api":[]},"evals":[],"jsHooks":[]} Using DiagrammerR library(tidyr) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(DiagrammeR) mermaid(" gantt dateFormat YYYY-MM-DD title A Very Nice Gantt Diagram section Basic Tasks This is completed :done, first_1, 2014-01-06, 2014-01-08 This is active :active, first_2, 2014-01-09, 3d Do this later : first_3, after first_2, 5d Do this after that : first_4, after first_3, 5d section Important Things Completed, critical task :crit, done, import_1, 2014-01-06,24h Also done, also critical :crit, done, import_2, after import_1, 2d Doing this important task now :crit, active, import_3, after import_2, 3d Next critical task :crit, import_4, after import_3, 5d section The Extras First extras :active, extras_1, after import_4, 3d Second helping : extras_2, after extras_1, 20h More of the extras : extras_3, after extras_1, 48h ") {"

Tips on Feature Engineering

Tips on Feature Engineering to fit how classifiers work; giving a geometry problem to a tree, oversized dimension to a kNN and interval data to an SVM are not a good ideas remove as much nonlinearities as possible; expecting that some classifier will do Fourier analysis inside is rather naive (even if, it will waste a lot of complexity there) make features generic to all objects so that some sampling in the chain won’t knock them out check previous works – often transformation used for visualisation or testing similar types of data is already tuned to uncover interesting aspects avoid unstable, optimizing transformations like PCA which may lead to overfitting experiment a lot

Great Statistics Books to Read

Following you will find a number of the best books to learn more about statistics and its philosophy. Opinionated Lessons on Statistics Introduction to Statistical Learning The Elements of Statistical Learning Applied Predicitive Modeling Statistical Inference Statistical Rethinking Data Analysis Using Regression and Multilevel/Hierarchical Models Mostly Harmless Econometrics Mastering Metrics: The Path from Cause to Effect All of Statistics Statistics Statistics for Experimenters Think Bayes Computer Age Statistical Inference Think Stats Machine Learning for Hackers Probability and Statistics Statistical Evidence: A likelihood paradigm

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