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This seminar covers Stata commands and methods to prepare data for statistical analysis. Under the Syntax section will Dating married Vars be a listing and description of options available for marridd command. Below that will typically be examples of using the command, including video examples for some commonly used commands.

In the Syntax section of the help file for describethe first letter d is underlined like so: The underlined part of the Dating married Vars marrird is the minimal abbreviation of the command required for Stata to understand it.

We will use abbreviations, though not usually the minimal abbreviation, throughout this seminar. We highly recommend writing and running code in do-files, text files where Stata commands can be saved.

The do-file editor can be Dating married Vars by issuing the command doeditor clicking on the pencil-and-paper Dafing. Stata allows only one dataset to be loaded at a time.

Because of this, Stata requires that no dataset be already loaded when loading Dating married Vars a dataset from file. We can clear marred dataset from memory with the clear command:. As a convenience, Stata usually allows the data to be cleared in commands that load in data through a clear option e.

Throughout the seminar, we load datasets over the internet. Dating married Vars files and comma-separated values.

Both storage types are read in using a variant of the import command. We read in Excel files using import excel. Below, we load in an Excel dataset pulled from our website. We add the option clear to clear any data in memory first before importing. Stata allows data to be entered directly through the keyboard with the input command, even when another dataset is already in memory.

This can be useful to add data that may not be used in the ensuing statistical analysis, such as graphing data. If we Dating married Vars inputting string marrued variables, precede the string variable Dating married Vars with str nwhere n is Jarried maximum length of any string for that variable. Datasets stored in the native Stata. This dataset contains fake cancer patient data. Each patient mardied also linked to a doctor in the dataset.

Another No strings head 4 you serious only containing doctor variables will be merged into this dataset later in the seminar.

Instead, list followed by variable names will display only those variables. Ranges of variables are allowed. Data can also be viewed in a spreadsheet-style window by either issuing the command browse or clicking on the Browse icon in the toolbar, the spreadsheet and magnifying glass.

The describe command provides the following information about how variables are stored:. For more detailed information about the values mwrried each variable, use codebookwhich provides the following:. The codebook command can be followed by specific variable names, or specified by itself to process all variables. The simplest method of selection is by observation Dating married Vars, such as the first 10 observations, or observations 30 through In Stata, the in operator can be used to specify a range of consecutive observations to select.

Often datasets Single housewives wants sex Salem Oregon split into multiple files, Dating married Vars because data are collected in several waves or by different researchers. In Stata mmarried append data files when Dating married Vars need to add more rows of observations of the same variables. Now we append another patient dataset from a different hospital, with Dating married Vars the same variables except that the new hospital did not assess Dating married Vars variable nmorphine.

We see 25 variables, so no new variables were added. A tabulate of nmorphine with the missing option shows that it is missing the new observations:.

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When we append datasets we add more rows of observations, whereas when we merge datasets, we add more columns of variables. Datasets to be merged should generally be matched on some id variable, so that the correct variable values are grouped together. Dating married Vars

The doctor dataset will be our Dating married Vars dataset. We can take a quick look at the doctor dataset without loading into memory using describe:. We see that there are 40 doctors in the dataset.

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Each doctor sees multiple patients in the patient dataset. We see this with a tabulate of docid:. With each docid repeated in the master patient dataset, and each docid unique in the using doctor dataset, we will be doing a Dating married Vars merge on the merge variable docid:. In output we see that of our original patient observations were successfully matched to doctors. However, seven patients in the master data were not matched, and one doctor in the using data was also not matched.

Inadvertently Dating married Vars observations can Dating married Vars hard to spot in a visual inspection of the data, particularly if there is no unique ID for each observation or the dataset is large. Seven observations are duplicated once eachcreating a total of 14 observations, 7 of which are surplus.

The remaining observations are unduplicated. We can also check for duplicates along a limited set of Looking for a sexy freak, rather than all variables. Missing data can be Dating married Vars vexing problem, particularly when data are not self-collected and missing data codes Dating married Vars.

Stata provides a number of commands to count and report missing values, and to convert missing data codes to true Stata missing values.

See help missing for an overview of missing values in Stata. Stata represents missing values for numeric variables with a dot. Additional missing data values are available, starting with. When reading in data from a text or Excel file, missing data for both numeric and string variables can be represented by an empty field.

Often we work Dating married Vars datasets where certain extreme Wife want casual sex Ganado values are used to represent missing values, such as or Undetected, these missing data codes can be included as real data in statistical analysis, greatly distorting results.

We can use numeric reports and Dating married Vars to detect these missing data codes if we are not sure where they are used. The summarize command estimates means, variances, min and max values for variables.

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Missing data values are often found in the min or max columns:. We see suspicious codes and in the variables co2, lungcapacity, test1, and test2.

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We also see the suspicious value Notice that we do not get Dating married Vars for string variables. We will need to use another command to detect missing data codes for string variables. Boxplots highlight outliers, which missing data codes tend to be. Here we use graph box marfied detect missing data codes in the variables co2, lungcapacity, test1, and test For discrete variables, we can use tabulate abbreviated as tab to print tables of unique values where missing data codes can be easily spotted.

The missing option will print any true missing values to the table as well. We can quickly convert all user-defined missing codes to true missing values for numeric variables with mvdecode. Notice that although we specified all variables, mafried string variables were Dating married Vars.

Unfortunately, mvdecode will not work at all on string variables. Remember to add the option missing to tabulate to report missing values in the table. By default mvdecode will convert the missing data codes to. We can also specify other missing values, such as. Here we convert the code to missing values. Finally, we noticed a likely data error for age, Imagine we know that all patients in this dataset marrid adults, such that any ages below 18 Vrs above a realistic upper age, sayshould be declared as errors.

Now that all of our missing data codes have been converted to missing values, we can count the number of Dating married Vars and non-missing values with misstable summarize. Often, examining how Dating married Vars are missing together can help the researcher Dating married Vars the marrie for missing.

For example, variables Dating married Vars at the same time are likely to be missing together. With no variables specified, misstable patterns will report patterns across all variables with missing values:.

The original dataset variables may not necessarily be the variable we need for analysis. Instead, we often need variables that are transformations or combinations of matried variables. The basic variable generation commands generate Dating married Vars gen or even g and replace can be used Women looking real sex Hampton Virginia create variables that are formed by performing arithmetic or logical operations on existing variables.

Below we create a variable Dating married Vars is the average of test1 marrried test2. When performing arithmetic operations with generateif any input variable is missing, the resulting Daging will be missing as well.

We use misstable patterns to check Married ladies wants hot sex Orlando Florida values on all three variables.

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We see above that average variable 3 is always missing if either or both of test1 and test2 are missing as well. Care must be taken when using logical operations on variables with missing. For example, the following set of commands incorrectly create a dummy indicator variable coding for age over Here we show how to create the dummy indicator for age over 50 mraried Dating married Vars for missing on Dating married Vars original age variable Staffordshire sex contacts. Functions accept an input and return some sort of output, so naturally can be used to transform variables with generate and Dating married Vars.

Consult help functions for links to several help pages for functions split by category.

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Almost all of the functions that work with generate accept only one variable or none as an argument. Below we use functions with generate to create a variable representing the running sum of married and to create a random number variable based on the standard uniform distribution, which could later be used a random selection variable:.

First Dating married Vars create variables representing the mean and Dating married Vars of Datung and test2, using the egen -specific functions, rowtotal and rowmean.