Big news, from the 13th till the 27th June Jumping Rivers will be running 6 courses on R in Hamburg!!. It should be noted that each course runs for one day, apart from the Predictive Analytics course, which runs for 2 days. The courses are as follows:
For this course, you need no prior programming knowledge of any type! You will learn how to efficiently manipulate, plot, import and export data using R.
A course on the suite of R packages known as the tidyverse. The tidyverse is essential for any statistician or data scientist who deals with data on a day-to-day basis. By focusing on small key tasks, the tidyverse suite of packages removes the pain of data manipulation. You will learn how to use packages at the forefront of cutting-edge data analytics such as dplyr and tidyr. Learn how to efficiently manage dates and times with lubridate and learn how to import and export data from anything from csv and excel files to SAS and SPSS files using the fantastic readr, readxl and foreign packages.
After mastering only half the potential of the tidyverse in Mastering the Tidyverse, this course will cover other tidyverse essentials. This includes the broom package, for tidying up statistical outputs. Tired of writing for loops? Look no further, the purrr package provides a complete and consistent set of tools for working with functions and vectors. Learn how to use stringr, regular expressions and tidytext, providing you with the tools for complex string and text analysis.
Tired of having to write a new report for every single data set? With R Markdown, and knitr you can build interactive reports, documents and dashboards that update when your data updates!
The R package Shiny allows you to create cutting-edge, interactive web graphics and applications that react to your data in a new and innovative way. Not only that, but shiny provides a platform where you can host your web applications for free!
You will learn about popular analytical techniques practised in industry today such as simple regression, clustering, discriminant analysis, random forests, splines and many more. Of course, by the end of the day, you will be able to use R to apply these methods to your data, often in one line of code!