Stata: Data Manipulation Commands

• Stata is a powerful statistical software widely used for data analysis and manipulation.
• Stata's command-driven interface allows for precise and reproducible research.
• Stata's syntax is generally consistent, making it easier to learn and use.
• Stata commands are case-sensitive.

Data Manipulation

• Data manipulation commands allow users to modify, create, and manage datasets effectively.

Creating Variables

• generate: Creates new variables.
  • generate new_variable = expression
  • Example: generate age_squared = age^2
• egen: Extends generate with additional functions.
  • egen new_variable = function(arguments)
  • Example: egen mean_income = mean(income)

Modifying Variables

• replace: Modifies the values of existing variables.
  • replace variable = expression if condition
  • Example: replace age = 25 if age == .
• recode: Changes variable values based on specified rules.
  • recode variable (old_value = new_value) (old_value = new_value)
  • Example: recode educ (1=0) (2=1) (3=1), generate(high_educ)

Labelling Variables

• label variable: Assigns descriptive labels to variables.
  • label variable variable_name "Descriptive label"
  • Example: label variable age "Age of respondent"
• label define: Creates value labels for categorical variables.
  • label define label_name value "Label" value "Label"
  • Example: label define educ_labels 0 "Low" 1 "High"
• label values: Applies value labels to a variable.
  • label values variable_name label_name
  • Example: label values educ educ_labels

Working with Observations

• sort: Sorts the dataset based on the values of one or more variables.
  • sort variable_name
  • Example: sort age
• bysort: Sorts data within groups defined by one or more variables.
  • bysort group_variable: command
  • Example: bysort gender: summarize income
• keep: Keeps specified variables or observations, dropping the rest.
  • keep variable_list or keep if condition
  • Example: keep age income gender
• drop: Drops specified variables or observations.
  • drop variable_list or drop if condition
  • Example: drop var1 var2
• if: Conditionally executes commands based on a specified condition.
  • command if condition
  • Example: summarize income if age > 30

Combining Datasets

• merge: Merges two datasets based on common variables.
  • merge 1:1 var using "filename" or merge m:1 var using "filename"
  • Specifies one-to-one or many-to-one merging.
• append: Appends one dataset to the end of another.
  • append using "filename"

Reshaping Data

• reshape: Converts data between wide and long formats.
  • reshape wide variable, i(id_variable) j(time_variable)
  • reshape long variable, i(id_variable) j(time_variable)
  • Essential for panel data analysis.

Statistical Analysis

• Stata offers a wide array of statistical commands for various types of data analysis.

Descriptive Statistics

• summarize: Provides summary statistics for variables.
  • summarize variable_list, detail
  • Includes mean, standard deviation, min, max, and percentiles.
• tabulate: Creates frequency tables.
  • tabulate variable or tabulate var1 var2 for cross-tabulations.
• tabstat: Offers customizable summary statistics.
  • tabstat variable_list, statistics(mean sd min max n) by(group_variable)

Regression Analysis

• regress: Performs ordinary least squares (OLS) regression.
  • regress dependent_variable independent_variables
  • Example: regress wage educ exper tenure
• logistic: Performs logistic regression for binary outcomes.
  • logistic dependent_variable independent_variables
  • Example: logistic employed educ age gender
• poisson: Performs Poisson regression for count data.
  • poisson dependent_variable independent_variables
  • Example: poisson num_visits income age
• xtreg: Performs panel data regression.
  • xtreg dependent_variable independent_variables, fe (fixed effects)
  • xtreg dependent_variable independent_variables, re (random effects)
• ivregress: Performs instrumental variables regression.
  • ivregress 2sls depvar [indepvars] (endogenous = instruments)

Hypothesis Testing

• ttest: Performs t-tests for comparing means.
  • ttest variable == value
  • ttest var1 == var2 (paired t-test)
  • ttest variable, by(group_variable) (independent samples t-test)
• anova: Performs analysis of variance (ANOVA).
  • anova dependent_variable group_variable
• chi2: Performs chi-squared tests for independence.
  • tabulate var1 var2, chi2

Time Series Analysis

• tsset: Declares the time variable for time series data.
  • tsset time_variable
• arima: Estimates Autoregressive Integrated Moving Average (ARIMA) models.
  • arima variable, ar(p) ma(q)
• regress with time series operators (L., F., D.) can analyze lagged, future, and differenced values.

Survival Analysis

• stset: Declares survival time and event variables.
  • stset time_variable, failure(event_variable)
• sts graph: Plots Kaplan-Meier survival curves.
• stcox: Estimates Cox proportional hazards models.

Graphing Commands

• Stata provides powerful graphing capabilities for visualizing data.

Basic Graphs

• scatter: Creates a scatter plot.
  • scatter y_variable x_variable
• line: Creates a line plot.
  • line y_variable x_variable
• histogram: Creates a histogram.
  • histogram variable
• bar: Creates a bar chart.
  • graph bar (count), over(variable)
• pie: Creates a pie chart.
  • graph pie, over(variable)
• boxplot: Creates a box plot.
  • graph box variable

Customizing Graphs

• title(): Adds a title to the graph.
  • graph command, title("Graph Title")
• xlabel() and ylabel(): Add labels to the x and y axes.
  • graph command, xlabel(, angle(45))
• legend(): Modifies the legend.
  • graph command, legend(off)
• graph export: Exports the graph to various formats (e.g., PNG, PDF).
  • graph export "filename.png", as(png)

Combining Graphs

• graph combine: Combines multiple graphs into a single figure.
  • graph combine graph1.gph graph2.gph

Advanced Graphing

• twoway: Combines multiple plot types in a single graph.
  • twoway (scatter y1 x) (line y2 x)
• graph matrix: Creates a matrix of scatter plots for multiple variables.
  • graph matrix var1 var2 var3

Best Practices

• Always label axes clearly.
• Use informative titles.
• Customize legends for clarity.
• Export graphs in high resolution for publication.