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In STATA, tabulate displays tables of frequency counts.

The last argument, na.rm = TRUE, is passed to the mean() function and makes sure that missing values are removed from the variable when attempting to calculate the mean. If we wanted to apply one of these functions to the entire data, we could use the apply() function (again, we only display the first five elements for the sake of space): head(apply(mri, 2, mean, na.rm = TRUE), n = 5) # ptid mridate age male race The R functions give us flexible options in case we ever need only a subset of these summary statistics. # The following object is masked from 'package:base':

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R Code and Output: # Load the uwIntroStats package Note that normally this will print all variables, but we have not included them all */ * This will throw ''errors'' - this is due to the headers in the file * Infile ptid mridate age male race weight height packyrs yrsquit alcoh physact chf chd stroke diabetes genhlth ldl alb crt plt sbp aai fev dsst atrophy whgrd numinf volinf obstime death using '' STATA Code and Output * First read in the mri data * Goal: Calculate summary statistics - mean, standard deviation, minimum, and maximum - for variables in the mri data. We can also do this in R (see РІР‚СљAn Introduction to RРІР‚Сњ for more information), but it is much easier and faster to load it in from the uwIntroStats package.

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In STATA, we need to download from the internet and read in the file. For our example, letРІР‚в„ўs use the mri data set. Many of the other simple functions ( sum - compute a sum, dim - return the dimensions of an object) operate in a similar way. To find the mean or the median, we simply type mean(data) or median(data). The STATA command conveniently displays all of these summary statistics with one command. In STATA, the summarize command calculates the number of observations, mean, standard deviation, minimum, and maximum value for each variable in the data set. We leave introduction of this package to a separate document entitled РІР‚СљUsing the uwIntroStats PackageРІР‚Сњ. In particular, one of the goals of the uwIntroStats package is to facilitate easy adoption of R for STATA users.

#T test stata install

However, some functions are only available in other packages which you have to install and load manually. Many of these functions are available in the base R package, which is automatically installed when R is installed and automatically loaded each time you boot up R. We now present a detailed walkthrough of functions in R that perform the same task as functions in STATA, with syntax provided. In this document, we assume that the reader has read РІР‚СљAn Introduction to RРІР‚Сњ or is at least familiar with the basic R data types, data manipulation, basic functions for descriptive statistics, installing and loading packages. We have written another document, РІР‚СљAn Introduction to RРІР‚Сњ, which serves as an introduction to the R philosophy of programming and lays out some of the basic data manipulation strategies. While STATA is an important tool to know, we argue that learning the fundamentals of R opens up many possibilities and a lot of power. Simulations are especially difficult to run. Only one data set can be read in at a time, and there is only one data type. While STATA is a powerful software, with relatively user-friendly syntax, it is not very flexible. At the University of Washington, STATA has been taught for many years.

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Each statistics and biostatistics department around the world, in conjunction with their collaborators (for us at the University of Washington this includes departments like Epidemiology and Global Health), must choose a statistical software to teach in introductory courses.