Loading and Manipulating Data Thomas J. Leeper Department of - - PowerPoint PPT Presentation

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Loading and Manipulating Data Thomas J. Leeper Department of - - PowerPoint PPT Presentation

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Loading Data Basic Data Summaries Data Manipulation Loading and Manipulating Data Thomas J. Leeper Department of Political Science and Government Aarhus University November 14, 2013 Loading Data Basic Data Summaries Data Manipulation

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Loading Data Basic Data Summaries Data Manipulation

Loading and Manipulating Data

Thomas J. Leeper

Department of Political Science and Government Aarhus University

November 14, 2013

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Loading Data Basic Data Summaries Data Manipulation

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Loading Data

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Basic Data Summaries

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Data Manipulation

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Basic Data Summaries

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Data Manipulation

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Data as Dataframe

Typically use data in a “dataframe” Not restricted to one dataframe at a time Functions to load data all create a dataframe Statistical functions accept vectors or dataframes

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Loading Data Basic Data Summaries Data Manipulation

Try on your own

Understand dataframe objects: “Dataframe Structure” “Rearranging Dataframes”

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Loading Data Basic Data Summaries Data Manipulation

Questions so far?

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Loading Data

There’s no “open” button A functions for each file format:

CSV: read.csv TSV: read.delim Stata: read.dta (from foreign) SPSS: read.spss (from foreign)

Almost anything can be loaded

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Loading Data Basic Data Summaries Data Manipulation

Try on your own

Understand dataframe objects: Do the “Loading Data” Tutorial

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Questions so far?

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Summary Statistics

Lots of built-in functions to summarize data One important function: summary

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Loading Data Basic Data Summaries Data Manipulation

Try on your own

Understand basic data summaries: “Univariate data summaries” “Correlations”

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Summary Tables

Tabulation is easy with table Creates univariate tables and cross-tables Tables are objects (of class “table”), so we can work with them like any other object

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Loading Data Basic Data Summaries Data Manipulation

Try on your own

Understand tabulation and cross-tabulation: “Tabulation”

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Summary Plots

Visualization is one of R’s greatest strengths One important function: plot Many other functions for specific types of plots Basic plots look okay Plots can be made beautiful with a little work

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Loading Data Basic Data Summaries Data Manipulation

Try on your own

Understand basic plots: “Summary plots” “Plotting colors”

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Questions so far?

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Basic Data Summaries

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Data Manipulation

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Recoding vectors

Recoding is all about indexing Several different ways to do it. The car package has a nice function: recode:

  • utvec <- recode(invec, "old1=new1;
  • ld2=new2; else=NA")
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Loading Data Basic Data Summaries Data Manipulation

Try on your own

Understand recoding: “Vector recoding”

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Questions so far?

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Scale Construction

Vectorization makes scaling easy Use the usual operators: + - * / ˆ Convenience functions for sums and means

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Loading Data Basic Data Summaries Data Manipulation

Try on your own

Understand scale construction: “Basic Scale Construction”

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Questions so far?

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Missing Data

R has one missing data value: NA Best to handle missing data during preprocessing

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Loading Data Basic Data Summaries Data Manipulation

Try on your own

Understand Missing Data: “Missing Data” “Handling Missing Data” “Multiple Imputation”

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Loading Data Basic Data Summaries Data Manipulation

Questions so far?

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