dplyr 0.8.1 grouping functions update

Changes to group_modify() and group_map()

RStudio has just released a minor update to dplyr. They had a rethink of new grouping purrr-style functions used to iterate on grouped tibbles. The changes include: group_map() is now used for iterating on grouped tibbles. It however makes no assumptions about the return type of each operation, combining results in a list - similar to purrr::map(). The previous behaviour was renamed to group_modify(), always returning and combining grouped tibbles by evaluating each operation with a reconstructed grouping structure - similar to purrr::modify(). [Read More]


Supercharge data import in R

I’m very excited the learn about vroom, RStudio’s latest tidyverse offering. It imports data a lot faster compared with existing R solutions. Check out the following benchmark that provides a comparison across a handful of similar functions and interactions between various libraries. Benchmark The speed is already a game-changer, but the following features sweeten the deal: Similar to readr vroom shares many features with readr, including nearly all of the parsing features of readr for delimited and fixed width files. [Read More]

Planning weekly food

using R to menu plan and create a food budget

Introduction A few years ago I woke up to an epiphany, realising that I was becoming my dad. I had started a campaign of dealing with wastefulness, switching off lights and eating leftovers to name but a few examples. I set out to transform our menu planning and the weekly food shop as part of this crusade. Menu planning is a chore which comes easily to some. For others like me, though, it is just another thing to think about on top of an already busy life. [Read More]

Summarising tables

Approach to streamline workflow when summarising tables

Introduction The result of the data science process is to communicate findings, typically to an audience that doesn’t talk technical. It is the most important deliverable of the process, even if not the first thing that springs to mind when considering data science. Fantastic insights are of no use if the intended audience doesn’t understand or trust it. It is therefore vital to take care when presenting findings. There are typical and often repeated actions when summarising data in tables. [Read More]