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![Look Out! Real Code!
# Read U.S. States shape data from census GIS data set
usShape <- readShapeSpatial("gz_2010_us_040_00_500k.shp")
# Attach the delta CPI data to the states
usShape@data$delta <- stateCPIdelta # Consumer price indices in this table
# This sets up break points for color designations.
# We want 20 gradations of color across all choropleths.
bfloor <- floor(min(usShape@data[,"delta"],na.rm=TRUE)*10)/10
bceil <- (ceiling(max(usShape@data[,"delta"],na.rm=TRUE)*10)/10) + 20
breaks <- seq(bfloor, bceil, 20)
# Attach the color cut points to the shape data
usShape@data$zCat <- cut(usShape@data[,"delta"],breaks,include.lowest=TRUE)
cutpoints <- levels(usShape@data$zCat) # For later use with the legend](https://image.slidesharecdn.com/whatisr-140108125310-phpapp02/75/Why-R-A-Brief-Introduction-to-the-Open-Source-Statistics-Platform-9-2048.jpg)
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Why R? A Brief Introduction to the Open Source Statistics Platform
- 1. Why R? Jeffrey Stanton SyracuseUniversity
- 2. What is R? •R is a statistics, data management, and graphics platform • R is open source, maintained and developed by a community of developers. • The R code repository, as well as compiled binaries (ready-to-install software) available at: http://cran.r-project.org • R comprises a core program plus 1000s of freely available add-in packages.
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- 4. So Why orWhy Not R? • Most popular statistics software (other than R) and some of their audiences: – SPSS: Social Scientists – Stata: Social Scientists – Mathematica/Matlab: Engineers, mathematicians, computer scientists, and physicists – Python/NumPy: Computer scientists, web developers – SAS: Data intensive industries (e.g., financial services) – Excel: All types of organizations • R is more popular and used by a larger number of analysts than each of these
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- 6. But. . . •Statistics users like point and click • R is command line oriented; there are GUIs that can be loaded as add-on packages; • R-Studio is a Integrated Development Environment (IDE) for R, but more for code development than statistical analysis • R is free, but this also means that there is no formal support mechanism; large organizations often like to contract with a commercial provider
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- 8. Command Line? Advantages? •In social sciences there has been a lot of talk lately about replication, the necessity of having results that are reproducible • In the world of “big data,” analysts want to produce systems that are transparent, reliable, and that maintain a chain of provenance for each transformation that affects the data • Looking at statistical analysis as a kind of “programming” task (like the old days!) has immense advantages
- 9. Look Out! RealCode! # Read U.S. States shape data from census GIS data set usShape <- readShapeSpatial("gz_2010_us_040_00_500k.shp") # Attach the delta CPI data to the states usShape@data$delta <- stateCPIdelta # Consumer price indices in this table # This sets up break points for color designations. # We want 20 gradations of color across all choropleths. bfloor <- floor(min(usShape@data[,"delta"],na.rm=TRUE)*10)/10 bceil <- (ceiling(max(usShape@data[,"delta"],na.rm=TRUE)*10)/10) + 20 breaks <- seq(bfloor, bceil, 20) # Attach the color cut points to the shape data usShape@data$zCat <- cut(usShape@data[,"delta"],breaks,include.lowest=TRUE) cutpoints <- levels(usShape@data$zCat) # For later use with the legend
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- 11. Many Packages -CRAN Task View ChemPhys Econometrics Environmetrics ExperimentalDesign Finance Genetics Graphics HighPerformanceComputing MachineLearning MedicalImaging MetaAnalysis Multivariate NaturalLanguageProcessing Optimization Pharmacokinetics Phylogenetics Psychometrics ReproducibleResearch SocialSciences Spatial Survival TimeSeries WebTechnologies Chemometrics and Computational Physics Computational Econometrics Analysis of Ecological and Environmental Data Design of Experiments (DoE) & Analysis of Experimental Data Empirical Finance Statistical Genetics Graphic Displays & Dynamic Graphics & Graphic Devices & Visualization High-Performance and Parallel Computing with R Machine Learning & Statistical Learning Medical Image Analysis Meta-Analysis Multivariate Statistics Natural Language Processing Optimization and Mathematical Programming Analysis of Pharmacokinetic Data Phylogenetics, Especially Comparative Methods Psychometric Models and Methods Reproducible Research Statistics for the Social Sciences Analysis of Spatial Data Survival Analysis Time Series Analysis Web Technologies and Services
- 12. Why R? • Freeand open source • Huge community of users, enormous repository of working code examples, many sources of online expertise/support • Dizzying array of add-on packages for almost any imaginable data application • Encourages good data practice: coding a reproducible chain of data transformations
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