Bonus: Character Functions Exercises

We just added this week’s set of bonus exercises! Bonus exercises are weekly exercises sets, available to subscribers to our weekly newsletter. Please sign up using the form on the right, and receive further details by email how to get access to the bonus exercises (and solutions, of course).

This week’s bonus exercises focus on Character Functions.

As a subscriber, you also have access to all previous bonus sets:

  • Character Functions
  • Tidy the data Up!
  • ROC curves
  • Working with tm package and wordclouds
  • Working with and visualizing a confidence interval
  • Evaluating a linear time series model
  • Peparing data for time series model
  • Rebuilding a Figure
  • Simplifying For loops



Udemy Discounts: All Courses $15 Until Thursday

ShortTermSitewide-15usd300x250Did you ever consider taking an R course? It might be a good complement to the exercises here on R-exercises. Udemy is a platform that currently offers the largest number of R courses (more than 40). Until this Thur (19 january) you can sign up for any course for just $15. This is a huge discount, given that the average Udemy R course is priced at ~$60, and prices vary from $20 to $200 (besides some free courses).

For a full overview of all Udemy R courses, go to the Udemy selection in R Course Finder, or go to Udemy directly to search through all their courses.

Courses with the steepest discount (regular price $200, now for $15):

If you’d sign up for these five courses now, you’d have lifetime access and save $900! Tens of thousands of students have enrolled in these courses already (Machine Learning A-Z has close to 25,000 students enrolled), and ratings are 4.5 / 5 or higher.

Disclaimer: If you sign up for a course using these links, R-exercises earns a commission. It does not impact what you pay for a course, and helps us to keep R-exercises free.

Did you take a Udemy R course already? Please tell us about your experiences, and add a comment below! What did you learn? How did it help you to advance your career or studies? Would you recommend it to others?




January Update R Course Finder: Shiny, Quantitative Trading, and Much More

eye-15699__180A few months ago we launched R Course Finder, an online directory that helps you to find the right R course quickly. With so many R courses available online, we thought it was a good idea to offer a tool that helps people to compare these courses, before they decide where to spend their valuable time and (sometimes) money.

If you haven’t looked at it yet, go to the R Course Finder now by clicking here.

Last month we added 9 courses to the Course Finder. Currently we are at 149 courses on 14 different online platforms, and 2 offline Learning Institutes.

Highlighted Courses

 

R Shiny Interactive Web Apps – Next Level Data Visualization

Are you a business analyst, data scientist, entrepeneur, or student, looking for modern data visualization tools? Then you have to check out the Shiny package! This is the first course in our directory that teaches how to create Shiny apps. It starts with creating the basic structure of a Shiny app, adding interactive input controls and widgets, and styling. After this, you’ll learn more advanced functionality, such as embedding videos, tables, and multi-page apps. Finally, it offers a real-world project, where you’ll build a financial app.

Quantitative Trading Analysis with R

Finance professionals, DIY investors and students who want to learn about quantitative trading analysis, should check out Quantitative Trading Analysis with R. In 53 lectures (7 hrs) you learn how to use R to analyse mean-reversion and trend-following trading strategies, calculate risk management and trading statistics such as the Kelly criterion, simulate historical returns, and use walk-forward testing (cross-validation) to avoid over-optimization.

Reproducible Research

This is a 4-week course, part of Coursera’s Data Science Specialization, where you’ll learn about concepts and tools behind reporting modern data analyses in a reproducible manner (including Markdown and knitr).

Besides these courses, we also added these other 6 courses:

R Machine Learning solutions
R for Data Science Solutions
Mastering R Programming
Building R Packages
The Data Scientist’s Toolbox
Statistical Inference

How you can help to make R Course Finder better

  • If you miss a course that is not included yet, please post a reminder in the comments and we’ll add it.
  • If you miss an important filter or search functionality, please let us know in the comments below.
  • If you already took one of the courses, please let all of us know about your experiences in the review section, an example is available here.

And, last but not least: If you like R Course Finder, please share this announcement with friends and colleagues using the buttons below.




Bonus: Tidy the data up!

We just added this week’s set of bonus exercises! Bonus exercises are weekly exercises sets, available to subscribers to our weekly newsletter. Please sign up using the form on the right, and receive further details by email how to get access to the bonus exercises (and solutions, of course).

This week’s bonus exercises focus on the Tidyr package.

As a subscriber, you also have access to all previous bonus sets:

  • ROC curves
  • Working with tm package and wordclouds
  • Working with and visualizing a confidence interval
  • Evaluating a linear time series model
  • Peparing data for time series model
  • Rebuilding a Figure
  • Simplifying For loops



Bonus: ROC curves

We just added this week’s set of bonus exercises! Bonus exercises are weekly exercises sets, available to subscribers to our weekly newsletter. Please sign up using the form on the right, and receive further details by email how to get access to the bonus exercises (and solutions, of course).

This week’s bonus exercises focus on ROC (receiver operating characteristic) curves.

As a subscriber, you also have access to all previous bonus sets:

  • ROC curves
  • Working with tm package and wordclouds
  • Working with and visualizing a confidence interval
  • Evaluating a linear time series model
  • Peparing data for time series model
  • Rebuilding a Figure
  • Simplifying For loops



Bonus: Text mining and wordclouds

We just added this week’s set of bonus exercises! Bonus exercises are weekly exercises sets, available to subscribers to our weekly newsletter. Please sign up using the form on the right, and receive further details by email how to download the bonus exercises (and solutions, of course).

This week’s bonus exercises focus on the tm package and word clouds.

As a subscriber, you also have access to all previous bonus sets:

  • Working with and visualizing a confidence interval
  • Evaluating a linear time series model
  • Peparing data for time series model
  • Rebuilding a Figure
  • Simplifying For loops



Bonus: Working with and visualizing a confidence interval

Bonus iconWe just added this week’s set of bonus exercises! Bonus exercises are weekly exercises sets, available to subscribers to our weekly newsletter. Please sign up using the form on the right, and receive further details by email how to download the bonus exercises (and solutions, of course).

This week’s bonus exercises focus on confidence intervals (after running a regression model). Feel free to post any comments on these exercises (and solutions) below.




Bonus: Timeseries modelling (part 2)

Bonus iconWe just added this week’s set of bonus exercises! Bonus exercises are weekly exercises sets, available to subscribers to our weekly newsletter. Please sign up using the form on the right, and receive further details by email how to download the bonus exercises (and solutions, of course).

This and last week’s bonus exercises aim to practise time series modelling. In part 2, we’re going to evaluate a simple model with one dependent and two explanatory variables. Feel free to post any comments on these exercises (and solutions) below.




Bonus: Timeseries modelling (part 1)

Bonus iconWe just added this week’s set of bonus exercises! Bonus exercises are weekly exercises sets, available to subscribers to our weekly newsletter. Please sign up using the form on the right, and receive further details by email how to download the bonus exercises (and solutions, of course).

This and next week’s bonus exercises aim to practise time series modelling. In part 1, we’re going to prepare the data and run a simple model with one dependent and two explanatory variables. Feel free to post any comments on these exercises (and solutions) below.




Bonus: Rebuild a figure (step-by-step)

rebuild-a-figureWe just added this week’s set of bonus exercises! Bonus exercises are weekly exercises sets, available to subscribers to our weekly newsletter. Please sign up using the form on the right, and receive further details by email how to download the bonus exercises (and solutions, of course).

This week’s bonus exercises aim to practise building a figure step-by-step, using the ggplot package. In 8 exercises you will learn to build the figure shown, starting with a small dataset and adding more detail in each exercises. Feel free to post any comments on these exercises (and solutions) below.