R has extensive facilities for analyzing time series data. This section describes the creation of a time series, seasonal decompostion, modeling with exponential and ARIMA models, and forecasting with the forecast package.
See the post on statsmethods for time series here.
Or my personal favourite with excellent data examples from successive Kings of England, to Australian Souvenir Shops, and Hem Sizes on Skirts see A little book of r for Time Series.
R has a lot of apis and plugin libraries which can make it impossible to remember what everything does. The following is a list of Cheat Sheets for R, Python, numpy, scipy, pandas to do regression analysis, machine learning, predictive analytics, and whatever else that might be relevant and interesting:
R Cheat Sheets for Everything
Ref Card in R for Regression Analysis
Ricci Ref Card for Time Series Data
R Cheat Sheets with Quandl File
R ggplot2 Cheat Sheet
Geoffrey Beall published this famous paper in Biometrika in 1942 to highlight ‘The Transformation of Data from Entomological Field Experiments so that the Analysis of Variance Becomes Applicable’.
For any data scientist Python is a must, but Python alone will not go very far on its own. Pandas is the data analytics library that allows Python to deliver the functionality which comes out of the box in R.
Setting up Python & Pandas is now made very easy with Anaconda, and the running of Python can be made very intuitive with Jupyter Notebook.
- Download Python & Install. – python 2.7 was used here
- Download Anaconda & Install
- Open Command Prompt after installation
- set PATH=%PATH%;c:\Python27;
- conda –version
- conda install pandas
- conda install ipython
- conda install pip
- jupyter notebook
For more details, please see the full tutorial to install Pandas here.
A simple, intuitive, and powerful introduction to Pandas can be found here.
The graphics matplotlib library is discussed here.
Statistical analysis made easy in Python with SciPy and Pandas DataFrames.
5 Questions which can teach you Multiple Regressions (with R and Python).
Data files useful to run analysis on:
Files required for this lecture: