Intermediate Tidyverse Toolbox

Missing data is an issue in any data analysis. Data missing can occur in unexpected places, making it hard to understand analyses. This course will show you how to use tidyverse, the naniar package, and visualize missing values. You will clean up missing values and then use them to analyze data. Other patterns of missing data will also be revealed. You will also learn how to fill out missing values with imputation models. These imputed datasets will also help you visualise, evaluate and make decisions.

Lists can be difficult to understand and manipulate but they can offer a lot of information that can be very useful. This course will show you how to extract, summarize and manage lists. This course will teach you how to export data to any object, including vectors, lists, and other objects. The purrr package and a variety of datasets from repurrrsive will be used, including data from Star Wars movies and Wes Anderson movies and data about GitHub repos. After this course, your list skills will be flawless!

You might be interested in purrr description (functional program toolkit for R). You are in the right spot. This course will show you how to use purrr's functional programming component. It will also show you how map(.x.f) can be used to create data, vectors, or other lists. Frame with simple, robust, and easy to maintain code. This course will show you how to create mappers and lambda functions from scratch, as well as how to use predicates and adverbs. The knowledge can then be applied to a specific case. This knowledge will be applied to a specific use case.

This course will teach you how to use tools from the "tidyverse" to create, evaluate, and analyze machine-learning models. To deal with complex models, you will be able to use purrr and tidyr packages. You will also learn how to use the Broom package to explore your models. You will learn the test-train-validate process. This will enable you to evaluate both classification and regression models. It also provides information that can be used to optimize model performance via hyperparameter tuning.