Loading data from a very large dataframe directly impacts the app initialization time and in order to identify the best file reading method we've made a quick benchmark. Comparison between utils::read.csv, readr::read_csv, base::load, base::readRDS, feather::read_feather and data.table::fread.
Even most sophisticated machine learning methods, most beautiful visualisations and perfect datasets can be useless if you do your research carelessly. Let's see some examples where things can go wrong and how to avoid these situations.
There are a numerous data science challenges in the Maritime industry. Today, I will tell you about a Shiny app, which we use to benchmark and identify bottle-necks in the ports. Interactive demo included.
What if you need to find a specific element in a dataset? There are a lot of options to do that in R, but in datasets with few million rows or more, it may be extremely slow. Brief comparison of different lookup methods, that can be used to improve the...
Viridis is colorful palette and at the same time perceptually uniform, robust and last but not least pretty. We show how to make a plotly chart and leaflet map using viridis palette.