Applied Econometrics with R (Use R!)
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If the package argument is omitted, all packages currently in the search path are checked whether they provide a data set Journals. Most of the commands are designed to be similar to read. For example, for Stata files, both read. In the preceding paragraphs, some interaction with the file system wasnecessary to read and write data files. R possesses a rich functionality for interacting with external files and communicating with the operating system. This is far beyond the scope of this book, but we would like to provide the interested reader with a few pointers that may serve as a basis for further reading.
Files available in a directory or folder can be queried via dir and also copied using file. For example, the Stata file created above can be deleted again from within R via. Other potentially system-dependent commands can be sent as strings to the operating system using system.
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See the respective manual pages for more information and worked-out examples. Above, we discussed how data objects especially data frames can be written to files in various formats. Beyond that, one often wants to save commands or their output to text files. One possibility to achieve this is to use sink , which can direct output to a file connection to which the strings could be written with cat.
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In some situations, writeLines is more convenient for this. Furthermore, dump can create text representations of R objects and write them to a file connection. Sometimes, one needs to manipulate the strings before creating output. R alsoprovidesrichand flexiblefunctionalityforthis. For pattern matching and replacing, grep and gsub are available,which also support regular expressions. For combining text and variable values, sprintf is helpful.
Factors are an extension of vectors designed for storing categorical information. Typical econometric examples of categorical variables include gender, union membership, or ethnicity. In many software packages, these are created using a numerical encoding e. In R, categorical variables should be specified as factors.
As an example, we first create a dummy-coded vector with a certain pattern and subsequently transform it into a factor using factor :. The terminology is that a factor has a set of levels, say k levels.
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Internally, a k-level factor consists of two items: a vector of integers between 1 and k and a character vector, of length k , containing strings with the corresponding labels. Above,wecreatedthefactorfromanintegervector;alternatively,itcouldhave been constructed from other numerical, character, or logical vectors. The advantage of this approach is that R knows when a certain variable is categorical and can choose appropriate methods automatically. For example, the labels can be used in printed output, different summary and plotting methods can be chosen, and contrast codings e.
Note that for these actions the ordering of the levels can be important. Many data sets contain observations for which certain variables are unavailable. Econometric software needs ways to deal with this. All standard computations on NA become NA. Special care is needed when reading data that use a different encoding. For example, when preparing the package AER , we encountered several data sets that employed for missing values.
If a file mydata. To query whether certain observations are NA or not, the function is.
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A simple example in R is the function summary , which is a generic function choosing, depending on the class of its argument, the summary method defined for this class. For example, for the numerical vector x and the factor g used above,. For the numerical vector x, a five-number summary i.
This shows that R has different summary methods available for different types of classes in particular, it knows that a five-number summary is not sensible for categorical variables.
In R, every object has a class that can be queried using the function class. In fact, R offers several paradigms of object orientation. The S3 system is much simpler, using a dispatch mechanism based on a naming convention for methods. The S4 system is more sophisticated and closer to other OOP concepts used in computer science, but it also requires more discipline and experience. For most tasks, S3 is sufficient, and therefore it is the only OOP system briefly discussed here.
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In S3 , a generic function is defined as a function with a certain list of arguments and then a UseMethod call with the name of the generic function. For example, printing the function summary reveals its definition:. It takes a first required argument object plus an arbitrary number of further arguments passed through … to its methods. What happens if this function is applied to an object, say of class "foo" , is that R tries to apply the function summary.
If not, it will call summary. Furthermore, R objects can have a vector of classes e. In this case, R first tries to apply summary.
All methods that are currently defined for a generic function can be queried using methods ; e. Among them is a method summary. However, there is no summary. As it is not recommended to call methods directly, some methods are marked as being non-visible to the user, and these cannot easily be called directly. However, even for visible methods, we stress that in most situations it is clearly preferred to use, for example, summary g instead of summary.
To illustrate how easy it is to define a class and some methods for it, let us consider a simple example. We create an object of class "normsample" that contains a sample from a normal distribution and then define a summary method that reports the empirical mean and standard deviation for this sample.
First, we write a simple class creator. In principle, it could have any name, but it is often called like the class itself:. This function takes a required argument n the sample size and further arguments …,which are passed on to rnorm ,the function for generating normal random numbers. In addition to the sample size, it takes further arguments- the mean and the standard deviation; see? After generation of the vector of normal random numbers, it is assigned the class "normsample" and then returned. To define a summary method, we create a function summary.
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Other generic functions with methods for most standard R classes are print , plot , and str , which print, plot, and summarize the structure of an object, respectively. R indeed has powerful graphics. An excellent overview of R graphics is given in Murrell The basic function is the default plot method. It is a generic function and has methods for many objects, including data frames, time series, and fitted linear models. Below, we describe the default plot method, which can create various types of scatterplots, but many of the explanations extend to other methods as well as to other high-level plotting functions.
The scatterplot is probably the most common graphical display in statistics. A scatterplot of y vs. As noted in Section 1. The file contains observations the journals on 10 variables, among them the number of library subscriptions subs , the library subscription price price , and the total number of citations for the journal citations. These data will reappear in Chapter 3. Here, we are interested in the relationship between the number of subscriptions and the price per citation.