Data Analysis using R script R Tutorial, March 2018

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1 Data Analysis using R script R Tutorial, March 2018 Dr. John Xie Statistics Support Officer, Quantitative Consulting Unit, Research Office, Charles Sturt University, NSW, Australia gxie@csu.edu.au

Learning resources: For minimizing the learning obstacles, we use The R Book (2 nd edition, 2013) as the primary learning resource for this R Tutorial series.

2 Learning resources: For minimizing the learning obstacles, we use The R Book (2 nd edition, 2013) as the primary learning resource for this R Tutorial series. The electronic copy of The R Book along with the data sets are available in the resource folder. One of the resource folder is in CSU shared S drive: S:\Common\ Temp Folder Less Than 30 Days\QCU_RTutorial_ /03/2018 2

3 Learning Objectives: Writing your own functions (i.e., user defined functions) in R: focus on for(), if(), optim(), and function() A couple of useful functions on dates and times in R: Sys.time(), Sys.Date(), Sys.sleep(), strptime(), difftime() The simplest function for simulation: sample() Some simple simulation applications in R 30/03/2018 3

4 Major R functions used for writing your own R functions R is essentially a computer programming language that was developed by following the object-oriented programming (OOP) paradigm. This provides the maximum flexibility for R users to construct/write their own ad hoc R functions for (theoretically) solving any specific problems by calling any available R functions as needed. The most often used R functions for writing your own R functions are: for(), if(), optim(), and function() 30/03/2018 4

5 Major R functions used for writing your own R functions More precisely, for() and if() are two of the five control-flow constructs of the R (you may type?control for more details) : for() enables us to do the classic, Fortran-like loop for calculation; if() can be used alone or in the combination of if() { } else() { } for making a conditional treatment on data. The function optim() is a general-purpose optimization based on Nelder-mead, quasi-newton and conjugate-gradient algorithms. It could be indispensable if you intend to write your own R function for performing optimization with regard to any self-defined targe function in terms of the vector of parameters as the first argument in optim(). However, for one dimensional optimization you may use function optimize() instead of optim(). 30/03/2018 5

6 Major R functions used for writing your own R functions The syntax for the usage of function() is function (argument list) { body } For example, we may write a function to convert the temperature measures from Fahrenheit to Celsius using the formula C = (F - 32)*5/9 Another example, we may want to combine a few plots in one graph and we would like to do this with one single line of R code. In this case, function() is applicable. Of course, function() becomes the only reasonable solution when we want to implement some very complex functions. 30/03/2018 6

7 Examples of writing your own R functions 30/03/2018 7

8 Examples of writing your own R functions 30/03/2018 8

9 Examples of writing your own R functions 30/03/2018 9

10 Dates and times in R Sys.time() and Sys.Date() are functions which return the system s idea of the current date with and without time, respectively. strptime() is a function for converting between character representations of and objects of classes POSIXlt and POSIXct representing calendar dates and times. difftime() calculates the time intervals between two datetime or date objects. Sys.sleep() is a function that suspend execution of R expressions for a specified time interval. 30/03/

11 Some examples for dates and times in R 30/03/

12 Some examples for dates and times in R 30/03/

13 The simplest function for simulation: sample() simulation = to obtain numeric results through generation of a group of random numbers which follow a probability distribution. The simplest function for performing simulation: sample(), takes the usage format sample(x, size, replace=false, prob=null) to generate a random sample (out of the population x) of the specified size and the specified probability distribution (default setting is a uniform distribution, e.g., equally likely) with or without replacement. 30/03/

14 Some simple simulation applications in R 30/03/

15 Some simple simulation applications in R 30/03/

process that proceeds without external help.

16 Simulation and bootstrap bootstrap = boot+strap Bootstrap Shoelaces = Shoe strings metaphor: a self-sustaining process that proceeds without external help. Both pictures are copied from online Wikipedia on 29/07/ /03/

17 Simulation and bootstrap Bootstrap and Statistical Analysis: Bootstrapping = resampling with replacement Therefore, essentially, bootstrap is a simulation approach for data analysis. Standard error of a summary statistic of a random sample or a parameter estimate from a statistical model can be obtained using bootstrapping. Alternatively, we can construct 95% quantile confidence band using bootstrapping to get the interval estimation. 30/03/

18 Simulation and bootstrap There are two types of bootstrapping methods: parametric bootstrap and nonparametric bootstrap. One of the most original /often-cited references on bootstrap is: Efron, B. & Tibshirani, R.J. (1993). An Introduction to the Bootstrap. Chap-man & Hall. The R package bootstrap was developed for implementing the functions in the above book; the boot is another special R package that contains various bootstrap functions. You may type help(package=bootstrap) or help(package=boot) in R for more details. The bootstrap approach can help us learn about the sample characteristics by resampling (we retake samples from the original observed sample with replacement) and use this information to infer to the population of our interest. 30/03/

19 Simulation and bootstrap 30/03/

20 Simulation and bootstrap 30/03/

21 Mid-month R tutorial online Q & A session A three-hour mid-month R tutorial online Q & A session will be run between the monthly crow meetings to help R beginner users who participated or are boing to participate the R tutorials. Our next mid-month R tutorial online Q & A session will be on TBD April 2018 (Tuesday) afternoon 2pm to 5pm. Participants are able to attend an Adobe Connect online meeting hosted by John by clicking the following link: 30/03/

22 R functions / topics for next R tutorial (next crow meeting) Special R topics on: time series analysis survival analysis Meta analysis and inter-rater reliability analysis 30/03/

23 Thank You for Attending This R Tutorial Series Welcome for Questions and Comments John Xie contact details: gxie@csu.edu.au Phone: /03/

Lecture 12. August 23, Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Johns Hopkins University.

Lecture 12. August 23, Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Johns Hopkins University. Lecture 12 Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Johns Hopkins University August 23, 2007 1 2 3 4 5 1 2 Introduce the bootstrap 3 the bootstrap algorithm 4 Example