Introduction

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Interactive Visual Data AnalysisPart One

Language Variation Suite

Olga Scrivner

Indiana University

Workshop in Methods1 / 44

Introduction

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Outline

1 Introduce a web application for quantitativeanalysis: LVS

2 Develop practical skills

3 Understand and interpret advanced statistical modelsPositionSentence

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What is LVS?

Language Variation Suite

It is a Shiny web application originally designed for dataanalysis in sociolinguistic research.

It can be used for:

Processing spreadsheet data

Reporting in tables and graphs

Analyzing means, regression, conditional trees ...(and much more)

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Background

LVS is built in R using Shiny package:

1 R - a free programming language for statistical computingand graphics

2 Shiny App - a web application framework for R

Computational power of R + Web interactivity

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Background

http://littleactuary.github.io/blog/Web-application-framework-with-Shiny/

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Workspace

Browser

Chrome, Firefox, Safari - recommendable

Explorer may cause instability issues

Accessibility

PC, Mac, Linux

Data files will be uploaded from any location on yourcomputer

Smart Phone

Data files must be on a cloud platform connected to yourphone account (e.g. dropbox)

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Server

Since LVS is hosted on a server, Shiny idle time-out settingsmay stop application when it is left inactive (it will grey out).

Solution: Click reload and re-upload your csv file

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Data Preparation

Important things to consider before data entry:

File format:

Comma separated value (CSV) - faster processing

Excel format will slow processing

Column names should not contain spaces

Permitted: non-accented characters, numbers, underscore,hyphen, and period

One column must contain your dependent variable

The rest of the columns contain independent variables

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Terminology Review

a. Categorical - non-numerical data with two values

yes - no; male - female

b. Continuous - numerical data

duration, age, year

c. Multinomial - non-numerical data with three or morevalues

regions, nationalities

d. Ordinal - scale: currently not supported

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Terminology Review

a. Categorical - non-numerical data with two values

yes - no; male - female

b. Continuous - numerical data

duration, age, year

c. Multinomial - non-numerical data with three or morevalues

regions, nationalities

d. Ordinal - scale: currently not supported

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Workshop Files

http://ssrc.indiana.edu/seminars/wim.shtml

1 movie metadata.csvSimplified set from https://www.kaggle.com/deepmatrix/

imdb-5000-movie-dataset

2 LVS web site: https://languagevariationsuite.com

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Movie Data

Budget

Director

Actor 1

Director facebook likes

Actor 1 facebook likes

Genre

Year

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Language Variation Suite - Structure

1 Data

Upload file, data summary, adjust data, cross tabulation

2 Visual Analysis

Plotting, cluster classification

3 Inferential Statistics

Modeling, regression, conditional trees, random forest

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Language Variation Suite - Structure

1 Data

Upload file, data summary, adjust data, cross tabulation

2 Visual Analysis

Plotting, cluster classification

3 Inferential Statistics

Modeling, regression, conditional trees, random forest

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Upload File

Upload movie metadata.csv

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Uploaded Dataset

The data content is imported as a table and allows for sortingcolumns.

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Summary

Summary provides a quantitative summary for each variable,e.g. frequency count, mean, median.

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Data Structure

1 Total number of observations (rows)

2 Number of variables (columns)

3 Variable types

Factor - categorical values

Num - numeric values (0.95, 1.05)

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Cross Tabulation

Cross-tabulation examines the relationship between variables.

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Cross Tabulation Plot

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Cross Tabulation Plot

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Adjusting Browser - Layout

Shiny pages are fluid and reactive.

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Adjusting Browser - Layout

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Language Variation Suite - Structure

1 Data

Upload file, data summary, adjust data, cross tabulation

2 Visual Analysis

Plotting, cluster classification

3 Inferential statistics

Modeling, regression, varbrul analysis, conditional trees,random forest

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Visual Analytics

Visual Analytics: “The science of analytical reasoningfacilitated by visual interactive interfaces”

(Thomas et al. 2005)

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One Variable Plot

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One Variable Plot

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Customizing Plot

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Customizing Plot

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Saving Plot

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Cluster Plot

Classification of data into sub-groups is based onpairwise similarities

Groups are clustered in the form of a tree-likedendrogram

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Cluster Plot

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Cluster Plot

Group 1 Animation, Biography and Group 2 Action, Drama,Comedy

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Inferential Statistics

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Introduction

DataPreparation

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Working withData

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Language Variation Suite - Structure

1 Data

Upload file, data summary, adjust data, cross tabulation

2 Visual Analysis

Plotting, cluster classification

3 Inferential statistics

Modeling, regression, varbrul analysis, conditional trees,random forest

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How to Create a Regression Model

Step 1 Modeling - create a model with dependent andindependent variables

Step 2 Regression - specify the type of regression (fixed,mixed) and type of dependent variable (binary,continuous, multinomial)

Step 3 Stepwise Regression - compare models(Log-likelihood, AIC, BIC)

Step 4 Conditional Trees - apply non-parametric teststo the model

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Modeling

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Regression Types

Model

a.) Fixed effect

b.) Mixed effect - individual speaker/token variation (withingroup)

Type of Dependent Variable

a.) Binary/categorical (only two values)

b.) Continuous (numeric)

c.) Multinomial - categorical with more than two values

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Regression

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Model Output

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Model Output

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Interpretation: Budget and Genre

Genre Action is the reference value

Positive coefficient - positive effect

Negative coefficient - negative effect

http://www.free-online-calculator-use.com/scientific-notation-converter.html

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Interpretation: Budget and Genre

Genre Action is the reference value

Positive coefficient - positive effect

Negative coefficient - negative effect

http://www.free-online-calculator-use.com/scientific-notation-converter.html

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exponential notation:

1.46e-7

0.000000146

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Conditional Tree

Conditional tree: a simple non-parametric regression analysis,commonly used in social and psychological studies

Linear regression: all information is combined linearly

Conditional tree regression: visual splitting to captureinteraction between variables

Recursive splitting (tree branches)37 / 44

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Conditional Tree

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Conditional Tree

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Conditional Tree

1 Genre is the significant factor for budget

2 Budget distribution is split in two groups:

Action and Animation

Biography, Comedy and Drama

3 Budget is significantly higher for Animation and Action

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Random Forest

1 Variable importance for predictors

2 Robust technique with small n large p data

3 All predictors considered jointly (allows for inclusion ofcorrelated factors)

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Random Forest

Let’s add more factors!

Return to Modeling

Add independent factors: director facebook likes, actor1 facebook likes, title year

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Random Forest

Genre is the most important predictor for this model.

Close to zero or red-dotted line (cut off values) - irrelevantfor this model

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Random Forest

Genre is the most important predictor for this model.

Close to zero or red-dotted line (cut off values) - irrelevantfor this model

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Let’s Have a Short Break

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References I

[1] Baayen, Harald. 2008. Analyzing linguistic data: A practical introduction to statistics. Cambridge:Cambridge University Press

[2] Gries, Stefan Th. 2015. Quantitative designs and statistical techniques. In Douglas Biber RandiReppen (eds.), The Cambridge Handbook of English Corpus Linguistics. Cambridge: CambridgeUniversity Press

[3] Schnapp, Jeffrey, and Peter Presner. 2009. Digital Humanities Manifesto 2.0.

[4] http://gifsanimados.espaciolatino.com/x bob esponja 8.gif

[5] https://daniellestolt.files.wordpress.com/2013/01/are-you-ready1.gif

[6] http://www.martijnwieling.nl/R/sheets.pdf

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