data science

Introduction In this second part of the “journey through tyecon” series, I want to showcase the package’s facilities in simplifying tasks common to the predictive modelling part of data analysis. I will be using the Food.com dataset as before. Setup and Data Import library(tidyverse) library(tidyselect) library(magrittr) library(vroom) library(tyecon) library(rsample) library(yardstick) library(glmnet) library(earth) library(pls) knitr::opts_chunk$set(fig.path = "") knitr::opts_chunk$set(dev = 'svg') theme_set(theme_light()) set.seed(123) recipes <- vroom("~/Workspace/foodrecipes/RAW_recipes.csv") interacts <- vroom("~/Workspace/foodrecipes/RAW_interactions.csv") Nutrition values, except for calories, are all percent of daily value, a daily quota filled by the amount of the percentage by the consumption of the specific food....

Introduction I’ve been working on my R package, tyecon for some time now. I think it can be a useful tool in performing day to day data analysis tasks. The idea is very simple: More higher order macros. There’s no reason to focus on the handful of existing ones, like the magrittr pipes or the dplyr syntax. We have a language here that supports lazy evaluation and first class functions. This is all we need to make programming life easier....