Using Explicit Subscripts with Variables in Stata

When Stata has data in memory, it also defines certain system variables that describe those data. For example, _N represents the total number of observations. _n represents the observation number: _n = 1 for the first observation, _n = 2 for the second, and so on to the last observation ( _n = _N

Importing Data from Other Programs to Stata

Previous sections illustrated how to enter and edit data using the Data Editor. If our original data reside in an appropriately formatted spreadsheet, we just can copy and paste blocks of data from the spreadsheet into the empty Data Editor. Alternatively, Stata can import data from Excel spreadsheets directly through menu selections File >

Combining Two or More Stata Files

We can combine Stata datasets in two general ways: append a second dataset that contains additional observations, or merge with other datasets that contain new variables or values. For example, file lakewinl.dta contains the ice-out dates for New Hampshire’s largest lake, recorded by local observers over 121 years from 1887 through 2007. In 2007,

Collapsing Data in Stata

Long after a dataset has been created, we might discover that for some purposes it has the wrong organization. Fortunately, several commands facilitate drastic restructuring of datasets. The simplest of these, collapse, aggregates data into means, medians or other statistics for groups defined by one or more variables. For illustration, we return to the

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Reshaping Data in Stata

A different sort of restructuring is possible through the reshape command. This command switches datasets between two basic configurations termed wide and long. Earlier in this chapter we built a dataset with the Multivariae ENSO Index (MEIO.dta). The data are in wide format: years define the rows, but each month is a separate column.

Using Weights in Stata

Stata understands four types of weighting: aweight   Analytical weights, used in weighted least squares (WLS) regression and similar procedures. fweight    Frequency weights, counting the number of duplicated observations. Frequency weights must be integers. iweight    Importance weights, however you define importance. pweight   Probability or sampling weights, proportional to the inverse of the probability

Creating Random Data and Random Samples in Stata

The pseudo-random number function runiform() lies at the heart of Stata’s ability to generate random data or to sample randomly from the data at hand. The Base Reference Manual (Functions) provides a technical description of this 32-bit pseudo-random generator. If we presently have data in memory, then a command such as the following creates

Writing Programs for Data Management in Stata

Data management on larger projects often involves repetitive or error-prone tasks that are best handled by writing specialized Stata programs. Advanced programming can become very technical, but we could also begin by writing simple programs that consist of nothing more than a sequence of Stata commands, typed and saved as a text file. Text

Histograms by using Stata

Histograms, displaying the distribution of measurement variables, are most easily produced with their own command histogram. For examples, we return to the data on 194 nations seen earlier in Chapter 2, containing human-development indicators gathered by the United Nations. Figure 3.1 shows a simple histogram of adfert, the adolescent fertility rate. It was produced

Box Plots by using Stata

Box plots convey information about center, spread, symmetry and outliers at a glance. For example, Figure 3.4 is a simple box plot of adfert (adolescent fertility rate) obtained by typing Figure 3.4 confirms the positive skew of this distribution, and shows five high outliers. The box in a box plot extends from approximate first

Scatterplots and Overlays by using Stata

Scatterplots belong to a broad family called twoway graphs. Stata’s basic scatterplot command has the form . graph twoway scatter y x where y is the vertical or y-axis variable, and x is the horizontal or x-axis one. (The initial graph twoway part of this command is optional, but kept here to emphasize a

Line Plots and Connected-Line Plots by using Stata

Mechanically, connected-line plots (graph twoway connect) are just scatterplots in which the points are connected by line segments. Line plots (graph twoway line) show the line segments without markers for the scatterplot points. Both belong to Stata’s versatile graph twoway family, which can be overlaid in any combinations. The scatterplot options that control axis

Other Twoway Plot Types by using Stata

In addition to basic line plots and scatterplots, the graph twoway command can draw a wide variety of other types. This section illustrates several more; type help graph twoway for a complete list. Earlier, in Figures 3.10 and 3.11, we used graph twoway lfit (linear fit) to draw a simple regression line. A similar

Bar Charts and Pie Charts by using Stata

The graph bar command, unlike graph twoway bar, works well to display relationships involving one or more categorical variables. Such graphs prove particularly useful with survey data, as will be shown in Chapter 4. This section serves just to introduce the command, with an example using variables from the cross-national dataset Nations_2.dta. . use

Symmetry and Quantile Plots by using Stata

Box plots, bar charts and histograms summarize measurement variable distributions, hiding individual data points to clarify overall patterns. Symmetry and quantile plots, on the other hand, include points for every observation. They take more effort to read than summary graphs, because they convey more detailed information. A histogram of the ratio of females to

Adding Text to Graphs by using Stata

Titles, captions and notes can be added to make graphs more self-explanatory. The default versions of titles and subtitles appear above the data region; notes (which might document the data source, for instance) and captions appear below. See Figure 3.7 for an example using title, caption and note. These defaults can be overridden, of

Graphing with Do-Files by using Stata

Complicated graphics like Figure 3.26 require graph commands that are many physical lines long (although Stata views the whole command as one logical line). Do-files, introduced in Chapter 2, help in writing such multi-line commands. They also make it easy to save the command for future re-use, in case we later want to modify

Retrieving and Combining Graphs by using Stata

Any graph saved in Stata’s “live” .gph format can subsequently be retrieved into memory by the graph use command. For example, we could retrieve Figure 3.26 by typing . graph use fig03_26.gph Once the graph is in memory, it is displayed onscreen and can be printed or saved again with a different name or

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Graph Editor in Stata

The Graph Editor allows us to alter the appearance of a graph currently in memory, whether just drawn or previously saved and retrieved by graph use. It is easier to learn about this useful feature by experimenting yourself, rather than reading from a book. As an example to get started, however, we can show

Creative Graphing by using Stata

Edward Tufte, in his elegant and influential books about graphing data (1990, 1997, 2001, 2006), calls for more effort at designing clear, information-packed graphics. Presenting a rich collection of impressively good or humorously awful examples, Tufte shows how successful graphics allow viewers to draw their own comparisons and examine details of relationships between variables.