learning R interactivly


Swirl is a Responsive imagecollection of courses to learn and practice R.

I like this way of learning the code but in some way,
it is restricted. This means that due to the coding sometimes only one solution is the “right” one.  But Just try it and make your own decision.

Sean Kross, Nick Carchedi, Bill Bauer and Gina Grdina (2016). swirl:
Learn R, in R. R package version 2.4.2.

# Tutorial for Swirl by Mementonature

#sometimes you have to install this package as well

#Now to install different courses
# Here are more informations
# https://github.com/swirldev/swirl_courses

install_from_swirl("Open Intro")
install_from_swirl("R Programming")
install_from_swirl("Data Analysis")
install_from_swirl("Mathematical Biostatistics Boot Camp")

install_from_swirl("Regression Models")
install_from_swirl("Getting and Cleaning Data")

install_from_swirl("Statistical Inference")
uninstall_course("Data Analysis")



# To start swirl just type
"When you are at the R prompt (>):
Typing skip() allows you to skip the current question.
Typing play() lets you experiment with R on your own;
swirl will ignore what you do...
UNTIL you type nxt() which will regain swirl's attention.
Typing bye() causes swirl to exit.
Your progress will be saved.
Typing main() returns you to swirl's main menu.
Typing info() displays these options again."

### Information on the differnt courses
"Open Intro"
1: Overview of Statistics

"R Programming alt"
1: Basic Building Blocks
2: Sequences of Numbers
3: Vectors
4: Missing Values
5: Subsetting Vectors
6: Matrices and Data Frames
7: Logic
8: lapply and sapply
9: vapply and tapply
10: Looking at Data
11: Simulation
12: Dates and Times 

"Data Analysis"
1: Central Tendency
2: Dispersion
3: Data Visualization1 

"Mathematical Biostatistics Boot Camp"
1: One Sample t-test
2: Two Sample t-test
3: Errors Power and Sample Size

1: Principles of Analytic Graphs
2: Exploratory Graphs
3: Graphics Devices in R
4: Plotting Systems
5: Base Plotting System
6: Lattice Plotting System
7: Working with Colors
8: GGPlot2 Part1
9: GGPlot2 Part2
10: GGPlot2 Extras
11: Hierarchical Clustering
12: K Means Clustering
13: Dimension Reduction
14: Clustering Example 

"Regression Models"
1: Introduction
2: Residuals
3: Least Squares Estimation
4: Residual Variation
5: Introduction to Multivariable Regression
6: MultiVar Examples
7: MultiVar Examples2
8: MultiVar Examples3
9: Residuals Diagnostics and Variation
10: Variance Inflation Factors
11: Overfitting and Underfitting
12: Binary Outcomes
13: Count Outcomes 

"Getting and Cleaning Data"
1: Manipulating Data with dplyr
2: Grouping and Chaining with dplyr
3: Tidying Data with tidyr
4: Dates and Times with lubridate

"Statistical Inference"
1: Introduction
2: Probability1
3: Probability2
4: ConditionalProbability
5: Expectations
6: Variance
7: CommonDistros
8: Asymptotics
9: T Confidence Intervals
10: Hypothesis Testing
11: P Values
12: Power
13: Multiple Testing
14: Resampling 

1: ErrTrack

Sean Kross, Nick Carchedi, Bill Bauer and Gina Grdina (2016). swirl:
Learn R, in R. R package version 2.4.2.

One thought on “learning R interactivly

  1. Pingback: Finally made it | mementonature

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s