Wide and narrow data

Wide and narrow (sometimes un-stacked and stacked) are terms used to describe two different presentations for tabular data.^[1]^[2]

Wide

Wide, or unstacked data is presented with each different data variable in a separate column.

Person	Age	Weight
Bob	32	128
Alice	24	86
Steve	64	95

Narrow

See also: Entity–attribute–value model

Narrow, or stacked data is presented with one column containing all the values and another column listing the context of the value

Person	Variable	Value
Bob	Age	32
Bob	Weight	128
Alice	Age	24
Alice	Weight	86
Steve	Age	64
Steve	Weight	95

This is often easier to implement, addition of a new field does not require any changes to the structure of the table, however it can be harder for people to understand.

Implementations

Many statistical and data processing systems have functions to convert between these two presentations, for instance the R programming language has several packages such as the reshape package

References

↑ Thompson, M. E. (1997), Theory of sample surveys, Chapman & Hall, London. ISBN 0-412-31780-X
↑ Chantala, K. (2006) "Using STATA to Analyze data from a Sample Survey". 1-10-2001. UNC Chapel Hill, Carolina Population Center. 10-1-2006.

External links

Look up table in Wiktionary, the free dictionary.

http://cran.r-project.org/web/packages/reshape

Visualization of technical information

Fields	Biological data visualization Chemical imaging Crime mapping Data visualization Educational visualization Flow visualization Geovisualization Information visualization Mathematical visualization Medical imaging Molecular graphics Product visualization Scientific visualization Software visualization Technical drawing User interface design Visual culture Volume visualization

Image types	Chart Diagram Engineering drawing Graph of a function Ideogram Map Photograph Pictogram Plot Schematic Statistical graphics Table Technical drawings Technical illustration User interface

People	Jacques Bertin Stuart Card Thomas A. DeFanti Michael Friendly George Furnas Nigel Holmes Alan MacEachren Jock D. Mackinlay Michael Maltz Bruce H. McCormick Charles Joseph Minard Gaspard Monge Otto Neurath Florence Nightingale Clifford A. Pickover William Playfair Adolphe Quetelet George G. Robertson Arthur H. Robinson Lawrence J. Rosenblum Ben Shneiderman Edward Tufte Fernanda Viégas Howard Wainer

Related topics	Cartography Chartjunk Computer graphics in computer science Graph drawing Graphic design Graphic organizer Imaging science Information graphics Information science Mental visualisation Misleading graph Neuroimaging Patent drawing Scientific modelling Spatial analysis Visual analytics Visual perception

Statistics

Descriptive statistics

Continuous data

Location	Mean arithmetic geometric harmonic Median Mode

Dispersion	Range Standard deviation Coefficient of variation Percentile Interquartile range

Shape	Variance Skewness Kurtosis Moments L-moments

Count data

Index of dispersion

Summary tables

Dependence

Statistical graphics

Data collection

Study design	Effect size Standard error Statistical power Sample size determination

Survey methodology	Sampling stratified cluster Opinion poll Questionnaire

Controlled experiments	Design control optimal Controlled trial Randomized Random assignment Replication Blocking Factorial experiment

Uncontrolled studies	Observational study Natural experiment Quasi-experiment

Statistical inference

Statistical theory

Frequentist inference

Confidence interval Testing hypotheses Power

Unbiased estimators	Mean unbiased minimum-variance Median unbiased

Biased estimators	Maximum likelihood Method of moments Minimum distance Density estimation

Parametric tests	Likelihood-ratio Wald Score

Specific tests

Z (normal) Student's t-test F Shapiro–Wilk Kolmogorov–Smirnov

Goodness of fit	Chi-squared G Sample source (Anderson–Darling) Sample normality (Shapiro–Wilk) Skewness / kurtosis normality (Jarque-Bera) Model comparison (Likelihood-ratio) Model quality (Akaike criterion)

Signed-rank	1-sample (Wilcoxon) 2-sample (Mann–Whitney U) 1-way anova (Kruskal–Wallis)

Bayesian inference

Correlation	Pearson product–moment Partial correlation Confounding variable Coefficient of determination

Regression analysis	Errors and residuals Regression model validation Mixed effects models Simultaneous equations models Multivariate adaptive regression splines (MARS)

Linear regression	Simple linear regression Ordinary least squares General linear model Bayesian regression

Non-standard predictors	Nonlinear regression Nonparametric Semiparametric Isotonic Robust Heteroscedasticity Homoscedasticity

Generalized linear model	Exponential families Logistic (Bernoulli) / Binomial / Poisson regressions

Partition of variance	Analysis of variance (ANOVA, anova) Analysis of covariance Multivariate ANOVA Degrees of freedom

Categorical / Multivariate / Time-series / Survival analysis

Categorical

Multivariate

Time-series

General	Decomposition Trend Stationarity Seasonal adjustment Exponential smoothing Cointegration Structural break Granger causality

Specific tests	Dickey–Fuller Johansen Q-statistic (Ljung–Box) Durbin–Watson Breusch–Godfrey

Time domain	Autocorrelation (ACF) partial (PACF) Cross-correlation (XCF) ARMA model ARIMA model (Box–Jenkins) Autoregressive conditional heteroskedasticity (ARCH) Vector autoregression (VAR)

Frequency domain	Spectral density estimation Fourier analysis Wavelet

Survival

Survival function	Kaplan–Meier estimator (product limit) Proportional hazards models Accelerated failure time (AFT) model First hitting time

Hazard function	Nelson–Aalen estimator

Test	Log-rank test

Applications

Biostatistics	Bioinformatics Clinical trials / studies Epidemiology Medical statistics

Engineering statistics	Chemometrics Methods engineering Probabilistic design Process / quality control Reliability System identification

Social statistics	Actuarial science Census Crime statistics Demography Econometrics National accounts Official statistics Population Psychometrics

Spatial statistics	Cartography Environmental statistics Geographic information system Geostatistics Kriging

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