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Optimizing Data Analysis: Web application with Shiny

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Olga Scrivner
Olga Scrivner

In the format of hands-on session, this workshop will introduce participants to the Language Variation Suite (LVS), a user-friendly interactive web application built in R. LVS provides access to advanced statistical methods and visualization techniques, such as mixed-effects modeling, conditional and random tree analyses, cluster analysis. These advanced methods enable researchers to handle imbalanced data, measure individual and group variation, estimate significance, and rank variables according to their significance. Workshop files: Categorical data csv – Use of R in New York (Labov 1966) - http://cl.indiana.edu/~obscrivn/docs/categoricaldata.csv Continuous data csv – Intervocalic /d/ (Díaz-Campos et al. 2016) - http://cl.indiana.edu/~obscrivn/docs/continuousdata.csv Language Variation Suite - https://languagevariationsuite.shinyapps.io/Pages/

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Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Optimizing (Socio-)linguistic Analysis: Language Variation Suite Toolkit Dr. Olga Scrivner Research Scientist, CNS, SICE, IU Corporate Faculty, Data Analytics Graduate Program, HU CEWIT Faculty Fellow April 12, 2018 1 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Goal Provide researchers with a variety of quantitative methods to advance language variation studies. PositionSentence p < 0.001 1 ind, pre post Heaviness p = 0.003 2 ≤ 1 > 1 Period p < 0.001 3 ≤ 1 > 1 Node 4 (n = 81) VOOV 0 0.2 0.4 0.6 0.8 1 Node 5 (n = 119) VOOV 0 0.2 0.4 0.6 0.8 1 Node 6 (n = 181) VOOV 0 0.2 0.4 0.6 0.8 1 Period p < 0.001 7 ≤ 2 > 2 Node 8 (n = 221) VOOV 0 0.2 0.4 0.6 0.8 1 Focus p < 0.001 9 cf nf Node 10 (n = 66) VOOV 0 0.2 0.4 0.6 0.8 1 Main_Verb_Structure p < 0.001 11 ACIOther, Restructuring Node 12 (n = 43) VOOV 0 0.2 0.4 0.6 0.8 1 Node 13 (n = 265) VOOV 0 0.2 0.4 0.6 0.8 1 2 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Objectives 1 Introduce a novel (socio)linguistic toolkit 2 Develop practical skills 3 Understand and interpret advanced statistical models 3 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References What is LVS? Language Variation Suite It is a Shiny web application designed for data analysis in sociolinguistic research. It can be used for: Processing spreadsheet data Reporting in tables and graphs Analyzing means, regression, conditional trees ... (and much more) 4 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Background LVS is built in R using Shiny package: 1 R - a free programming language for statistical computing and graphics 2 Shiny App - a web application framework for R Computational power of R + Web interactivity 5 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Background http://littleactuary.github.io/blog/Web-application-framework-with-Shiny/ 6 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References 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 7 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Workspace Browser Chrome, Firefox, Safari - recommendable Explorer may cause instability issues Accessibility PC, Mac, Linux Data files will be uploaded from any location on your computer Smart Phone Data files must be on a cloud platform connected to your phone account (e.g. dropbox) 8 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Server Since LVS is hosted on a server, Shiny idle time-out settings may stop application when it is left inactive (it will grey out). Solution: Click reload and re-upload your csv file 9 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Terminology Review a. Categorical - non-numerical data with two values yes - no; deletion - retention; perfective - imperfective b. Continuous - numerical data duration, age, chronological period c. Multinomial - non-numerical data with three or more values deletion - aspiration - retention d. Ordinal - scale: currently not supported 10 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Terminology Review a. Categorical - non-numerical data with two values yes - no; deletion - retention; perfective - imperfective b. Continuous - numerical data duration, age, chronological period c. Multinomial - non-numerical data with three or more values deletion - aspiration - retention d. Ordinal - scale: currently not supported 10 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Workshop Files https://languagevariationsuite.wordpress.com/ 1 categoricaldata.csv: categorical dependent - Labov New York 1966 study 2 continuousdata.csv: continuous dependent - Intervocalic /d/ in Caracas corpus (D´ıaz-Campos et al.) 3 LVS web site: www.languagevariationsuite.com 11 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References 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 12 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References 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 12 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Upload File Upload movie metadata.csv 13 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Excel Format 1 Slow processing 2 Requires the name of your excel sheet 14 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Save Excel as CSV Format To optimize speed - Save as CSV prior upload 15 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Upload File Upload categoricaldata.csv 16 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Uploaded Dataset The data content is imported as a table and allows for sorting columns. 17 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Summary Summary provides a quantitative summary for each variable, e.g. frequency count, mean, median. 18 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References 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) Int - integer values (1, 2, 3) 19 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Cross Tabulation Cross-tabulation examines the relationship between variables. 20 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Cross-Tabulation: One Dependent and One Independent Variables 21 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Cross-Tabulation Output Raw frequency / Proportion by column / Proportion across row 22 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References 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 23 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Adjusting Browser - Layout Shiny pages are fluid and reactive. 24 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References One Variable Plot 25 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Two Variables Plot 26 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Saving Plot 27 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Three Variables Plot 28 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Cluster Plot Classification of data into sub-groups is based on pairwise similarities Groups are clustered in the form of a tree-like dendrogram 29 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Cluster Plot 30 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Cluster Plot Saks (upper middle-class store), Macy’s (middle-class store), Kleins (working-class) 31 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Inferential Statistics 32 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References 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 33 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References How to Create a Regression Model Step 1 Modeling - create a model with dependent and independent 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 tests to the model 34 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Modeling 35 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Modeling 35 / 72 We are interested in RETENTION = Application
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Regression Types Model a.) Fixed effect b.) Mixed effect - individual speaker/token variation (within group) Type of Dependent Variable a.) Binary/categorical (only two values) b.) Continuous (numeric) c.) Multinomial - categorical with more than two values 36 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Regression 37 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Model Output 38 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Model Output 38 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Interpretation Deletion is the reference value Positive coefficient - positive effect Negative coefficient - negative effect 39 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Interpretation Lexical item Fourth has a negative effect on retention compared to Floor and is significant Normal style has a slightly negative effect on retention but its coefficient is not significant Macy’s and Saks have a positive and significant effect on retention. Saks (upper middle class store) is more significant than Macy’s (middle class store) http://www.free-online-calculator-use.com/scientific-notation-converter.html40 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Interpretation Lexical item Fourth has a negative effect on retention compared to Floor and is significant Normal style has a slightly negative effect on retention but its coefficient is not significant Macy’s and Saks have a positive and significant effect on retention. Saks (upper middle class store) is more significant than Macy’s (middle class store) http://www.free-online-calculator-use.com/scientific-notation-converter.html40 / 72 exponential notation: 1.48e-8 0.0000000148
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References 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 capture interaction between variables Recursive splitting (tree branches) 41 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Conditional Tree - Tagliamonte and Baayen 2012 1 The distribution of was/were is split in two groups by individuals. 2 The variant were occurs significantly more frequently with the first group. 42 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Conditional Tree - Tagliamonte and Baayen (2012) 1 Polarity is relevant to the second group of individuals. 2 The variant were occurs significantly more often with negative polarity 43 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Conditional Tree - Tagliamonte and Baayen (2012) 1 Affirmative Polarity is conditioned by Age. 2 The variant was is produced significantly more often by Individuals of 46 and younger. 44 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Conditional Tree 45 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Conditional Tree 1 Store is the most significant factor for R-use Kleins (working class store) - more R-deletion 2 R-use in Macy’s and Saks is conditioned by lexical item: Floor shows more R-retention than Fourth 3 Style is not significant 46 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Random Forest 1 Variable importance for predictors 2 Robust technique with small n large p data 3 All predictors considered jointly (allows for inclusion of correlated factors) 47 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Random Forest 48 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Random Forest 1 Store is the most important predictor 2 Lexical Item is the second predictor 3 Style is irrelevant: close to zero and red dotted line (cut-off value). 49 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Fixed and Mixed Models Fixed Effects Model : All predictors are treated independent. Underlying assumption - no group-internal variation between speakers or tokens Mixed Effects Model : Allows for evaluation of individual- and group-level variation 50 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Fixed and Mixed Models Fixed Regression Model - ignoring individual variations (speakers or words) may lead to Type I Error: “a chance effect is mistaken for a real difference between the populations” Mixed Regression Model - prone to Type II Error: “if speaker variation is at a high level, we cannot discern small population effects without a large number of speakers” (Johnson 2009, 2015) 51 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Mixed Effect Regression Mixed Model = fixed effects + random effects Fixed-effect factor - “repeatable and a small number of levels” Random-effect factor - “a non-repeatable random sample from a larger population” (Wieling 2012) walk, sleep, study, finish, eat, etc event verb, stative verb speaker1, speaker3, speaker3, etc male, female 52 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Mixed Effect Regression Mixed Model = fixed effects + random effects Fixed-effect factor - “repeatable and a small number of levels” Random-effect factor - “a non-repeatable random sample from a larger population” (Wieling 2012) walk, sleep, study, finish, eat, etc event verb, stative verb speaker1, speaker3, speaker3, etc male, female 52 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Preparing for Mixed Model 1 Download continuousdata.csv 2 Upload this file on LVS 53 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Data - Uploaded Dataset 54 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Mixed Effect Modeling 55 / 72 NULL when the dependent variable is continuous Fixed Effects - independent variables
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Mixed Effect Modeling 56 / 72 Mixed Effects - group-internal variation
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Regression Results 57 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Regression Results 57 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Interpretation - Random Effects 1 Standard Deviation: a measure of the variability for each random effect (speakers and tokens) 2 Residual: random variation that is not due to speakers or tokens (residual error) 58 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Interpretation - Fixed Effects 1 Estimate/coefficient: reported in log-odds (negative or positive) 2 P-value: tells you if the level is significant 59 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Bonus - Word Clouds 60 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Frequency Plot 61 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Frequency Plot 62 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Appendix 1: Density 63 / 72 Number of bins can have a disproportionate effect on visualization
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Histogram Density: a non-parametric model of the distribution of points based on a smooth density estimate http://scikit-learn.org/stable/modules/density.html 64 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Appenix 2 - Data Modification 65 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Adjust Data Retain: Select data subset Exclude: Exclude variables from a factor group Recode: Combine and rename variables Change class: Numeric → factor; factor → numeric Transform: Apply log transformation to a specific column ADJUSTED DATASET: Run - to apply all above changes Reset - to reset to the original dataset 66 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Exclude: Emphatic Style 67 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Adjusted Dataset 68 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Adjusting Dataset To revert to the original data, select RESET: 69 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Appendix 3 - Model Comparison 70 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References Thank you! obscrivn@indiana.edu What features/analyses would you like to see in LVS? 71 / 72
Introduction Data Preparation Language Variation Suite Working with Data Visual Analytics Inferential Analysis Mixed Effects Appendix References References I [1] Baayen, Harald. 2008. Analyzing linguistic data: A practical introduction to statistics. Cambridge: Cambridge University Press [2] Bentivoglio, Paola and Mercedes Sedano. 1993. Investigaci´on socioling¨u´ıstica: sus m´etodos aplicados a una experiencia venezolana. Bolet´ın de Ling¨u´ıstica 8. 3-35 [3] Gries, Stefan Th. 2015. Quantitative designs and statistical techniques. In Douglas Biber Randi Reppen (eds.), The Cambridge Handbook of English Corpus Linguistics. Cambridge: Cambridge University Press [4] Labov, W. 1966. The Social Stratification of English in New York City. Washington: Center for Applied Linguistics [5] Schnapp, Jeffrey, and Peter Presner. 2009. Digital Humanities Manifesto 2.0. [6] http://gifsanimados.espaciolatino.com/x bob esponja 8.gif [7] https://daniellestolt.files.wordpress.com/2013/01/are-you-ready1.gif [8] http://www.martijnwieling.nl/R/sheets.pdf 72 / 72

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