Tag Archives: R

R {swirls} – Learning R by doing

16 Thursday Apr 2015

Posted by in Data Science, Programming

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A swirl is an interactive way of learning R by installing a package called {swirl} into R and then installing a course.

I have used swirls in the Data Science Courses on Coursera and found them a useful way of learning and testing your knowledge.

swirl is installed as a package into R using the following command in R (internet connection required).

> install.packages("swirl")

Then launching the swirl library and run it.

> library("swirl")
> swirl()

To locate a swirl course use the following command.

?InstallCourses

Sources: Swirlstats

There are a list of courses available in the swirl repository on GitHub. There are 3 levels of courses available.

Beginner

  • R Programming: The basics of programming in R
  • R Programming Alt: Same as the original, but modified slightly for in-class use
  • Data Analysis: Basic ideas in statistics and data visualization
  • Mathematical Biostatistics Boot Camp: One- and two-sample t-tests, power, and sample size
  • Open Intro: A very basic introduction to statistics, data analysis, and data visualisation

Intermediate

  • Regression Models: The basics of regression modeling in R
  • Getting and Cleaning Data: dplyr, tidyr, lubridate, oh my!

Advanced

  • Statistical Inference: This intermediate to advanced level course closely follows the Statistical Inference course of the Johns Hopkins Data Science Specialization on Coursera.

To install a course you can use the following commands in R

library(swirl)
install_from_swirl("Course Name Here")
swirl()

Datacamp have recently released a free browser based R learning tool. This is a browser based  version to learn R based on a flipcard version of swirl teaching you in bite sized chunks.

Sources:

R

16 Ordnance Survey tools – Open Maps

24 Tuesday Mar 2015

Posted by in Data Science, Programming

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The Ordnance Survey (OS) have released some more tools as part of their Open Mapping products which are free to use. This takes the products up to 16 available for the UK geographical areas

The new products are:

The opportunities for using the data with results from R projects and Data Science are vast.  Time to start downloading to see if I can use my R skills to good effect.

R Cheat Sheets

13 Friday Mar 2015

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There is a good collection of R Cheat Sheets at RStudio that cover:

  • Package Development Cheat Sheet
  • Data Wrangling Cheat Sheet (using dplyr and tidyr)
  • R Markdown Cheat Sheet
  • R Markdown Reference Guide (using Markdown, Knitr and Pandoc)

Course on Data Analysis and Statistical Inference

06 Friday Mar 2015

Posted by in Data Science

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Scanning my daily feeds from feedly I came across this post about a new Data Analysis and Statistical Inference course on Coursera that has just started this week. looks to be a good grounding on the subject.

The course is split into 7 modules

  • Unit 1 – Introduction to data
  • Unit 2 – Probability and distributions
  • Unit 3 – Foundations for inference
  • Unit 4 – Inference for numerical variables
  • Unit 5 – Inference for categorical variables
  • Unit 6 – Introduction to linear regression
  • Unit 7 – Multiple linear regression

The timetable of work over 10 weeks

Week 1: Introduction to Data, March 2 – 9

Review the START HERE! pages
Review Learning Objectives for Unit 1
Watch the videos for Unit 1 Introduction to Data
Start Quiz 1 — due at 13:00 EST (-5:00), Monday, March 16
Begin Lab 0 — due at 13:00 EST (-5:00), Monday, March 16, this lab is not graded (for practice)
Begin Lab 1 — due at 13:00 EST (-5:00), Monday, March 16
Explore the Discussion Forums and contribute
Week 2: Probability and Distributions, March 9 – 16

Review Learning Objectives for Unit 2
View videos for Unit 2 Probability and Distributions
Start Quiz 2 — due at 13:00 EST (-5:00), Monday, March 23
Begin Lab 2 — due at 13:00 EST (-5:00), Monday, March 23
Explore the Discussion Forums and contribute
Begin your Project Proposal — due at 13:00 EST (-5:00), Monday, March 23
Complete Quiz 1 — due at 13:00 EST (-5:00), Monday, March 16
Complete Lab 0 and Lab 1 — due at 13:00 EST (-5:00), March 16
Week 3: Foundations for Inference, March 16 – 23

Review Learning Objectives for Unit 3
View videos for Unit 3 Foundations for Inference
Start Quiz 3 — due at 13:00 EST (-5:00), Monday, March 30
Begin Labs 3A and 3B — due at 13:00 EST (-5:00), Monday, March 30
Explore the Discussion Forums and contribute
Submit your Project Proposal before 13:00 EST (-5:00), Monday, March 23
Complete Quiz 2 — due at 13:00 EST (-5:00), Monday, March 23
Complete Lab 2 — due at 13:00 EST (-5:00), Monday, March 23
Week 4: Foundations for Inference and Midterm, March 23 – 30

No new materials
Review Learning Objectives for Unit 3
Complete videos for Unit 3 Foundations for Inference
Complete Quiz 3 — due at 13:00 EST (-5:00), Monday, March 30
Complete Lab 3A and 3B — due at 13:00 EST (-5:00), Monday, March 30
Begin assessing Project Proposals — due 13:00 EST (-5:00), Monday, April 6
Begin Midterm — due 13:00 EST (-5:00), Monday, April 6
Week 5: Statistical Inference for Numerical Variables, March 30 – April 6

Review Learning Objectives for Unit 4
View videos for Unit 4 Statistical Inference for Numerical Variables
Start Quiz 4 — due at 13:00 EST (-5:00), Monday, April 13
Begin Lab 4 — due at 13:00 EST (-5:00), Monday, April 13
Explore the Discussion Forums and contribute
Complete Project Proposal assessments — due 13:00 EST (-5:00), Monday, April 6
Complete Midterm — due 13:00 EST (-5:00), Monday, April 6
Please submit at least 3 hours before the deadline
Week 6: Statistical Inference for Categorical Variables, April 6 – 13

Review Learning Objectives for Unit 5
View videos for Unit 5 Statistical Inference for Categorical Variables
Start Quiz 5 — due at 13:00 EST (-5:00), Monday, April 20
Begin Lab 5 — due at 13:00 EST (-5:00), Monday, April 20
Begin Data Analysis Project — due at 13:00 EST (-5:00), Monday, April 20
Explore the Discussion Forums and contribute
Complete Quiz 4 — due at 13:00 EST (-5:00), Monday, April 13
Complete Lab 4 — due at 13:00 EST (-5:00), Monday, April 13
Week 7: Simple Linear Regression, April 13 – 20

Review Learning Objectives for Unit 6
View videos for Unit 6 Simple Linear Regression
Start Quiz 6 — due at 13:00 EST (-5:00), Monday, April 27
Begin Lab 6 — due at 13:00 EST (-5:00), Monday, April 27
Explore the Discussion Forums and contribute
Complete Quiz 5 — due at 13:00 EST (-5:00), Monday, April 20
Complete Lab 5 — due at 13:00 EST (-5:00), Monday, April 20
Complete Data Analysis Project — due at 13:00 EST (-5:00), Monday, April 20
Week 8: Multiple Linear Regression, April 20 – 27

Review Learning Objectives for Unit 7
View videos for Unit 7 Multiple Linear Regression
Start Quiz 7 — due at 13:00 EST (-5:00), Monday, May 4
Begin Lab 7 — due at 13:00 EST (-5:00), Monday, May 4
Explore the Discussion Forums and contribute
Begin review of Data Analysis Project due at 13:00 EST (-5:00), Monday, May 4
Complete Quiz 6 — due at 13:00 EST (-5:00), Monday, April 27
Complete Lab 6 — due at 13:00 EST (-5:00), Monday, April 27
Week 9: Review and catch up, April 27 – May 4

Note that all assignment due times are now in Eastern Standard Time (EST)
View review videos
Complete Quiz 7 — due at 13:00 EST (-5:00), Monday, May 4
Complete Lab 7 — due at 13:00 EST (-5:00), Monday, May 4
Explore the Discussion Forums and contribute
Complete review of Data Analysis Project — due at 13:00 EST (-5:00), Monday, May 4
Final exam is available this week — due 13:00 EST (-5:00), Monday, May 11
Week 10: Final Exam, May 4 – 11

(Source: https://www.coursera.org/course/statistics)