Cph Exam Quick Reference Review Pdf' title='Cph Exam Quick Reference Review Pdf' />Why R is Hard to Learnby Robert A. Muenchen. R has a reputation of being hard to learn. Some of that is due to the fact that it is radically different from other analytics software. Some is an unavoidable byproduct of its extreme power and flexibility. And, as with any software, some is due to design decisions that, in hindsight, could have been better. Cph Exam Quick Reference Review Pdf' title='Cph Exam Quick Reference Review Pdf' />If you have experience with other data science tools, you may at first find R very alien. Training and documentation that leverages your existing knowledge and which points out where your previous knowledge is likely to mislead you can save much frustration. This is the approach I use in my books, R for SAS and SPSS Users and R for Stata Users, as well as in the workshops that are based on those books. Below is a list of complaints about R that I commonly hear from people taking my R workshops. By listing these, I hope R beginners will be forewarned, will become aware that many of these problems come with benefits, and may consider the solutions offered by the add on packages that I suggest. We would like to show you a description here but the site wont allow us. BibMe Free Bibliography Citation Maker MLA, APA, Chicago, Harvard. Tour Start here for a quick overview of the site Help Center Detailed answers to any. As many have said, R makes easy things hard, and hard things easy. However, add on packages help make the easy things easy as well. Lack of Graphical User Interface GUILike most other packages, Rs full power is only accessible through programming. However unlike many others, it does not offer a standard GUI to help non programmers do analyses. The two which are most like SAS Studio, SPSS or Stata are R Commander and Deducer. While they offer enough analytic methods to make it through an undergraduate degree in statistics, they lack control when compared to a powerful GUI. Worse, beginners must initially see a programming environment and then figure out how to find, install, and activate either GUI. Given that GUIs are aimed at people with fewer computer skills, this is a problem. Unhelpful Help. Rs help files are often thorough and usually contain many working examples. However, theyre definitely not written for beginners My favorite example of this is the help file for one of the first commands that beginners learn print. The SAS help file for its print procedure says that it Prints observations in a SAS data set using some or all of the variables. Clear enough. The R help file for its print function says, print prints its argument and returns it invisibly via invisiblex. It is a generic function which means that new printing methods can be easily added for new classes. The reader is left to wonder what invisible output looks like and what methods and classes are. The help files will tell you more about methods but not classes. You have to know to look for help on class to find that. Another confusing aspect to Rs help files stems from Rs ability to add new features to existing functions as you load add on packages. This means you cant simply read a help file, understand it, and youre done learning that function forever. However, it does mean that you have fewer commands to learn. For example, once you learn to use the predict function, when you load a new package, that function may gain new abilities to deal with model objects that are computed specifically within the new package. So an R beginner has to learn much more than a SAS or SPSS beginner before he or she will find the help files very useful. However, there is a vast array of tutorials, workshops and books available, many of them free, to get beginners over this hump. Too Many Commands. Other data science packages have relatively few analysis commands but each of them have many options to control their output. Rs approach is quite the opposite, which takes some getting used to. For example, when doing a linear regression in SAS or SPSS you usually specify everything in advance and then see all the output at once equation coefficients, analysis of variance ANOVA table, and so on. However, when you create a model in R, one command summary will provide the parameter estimates while another anova provides the ANOVA table. There are other commands such as coefficients that display only that part of the model. It is relatively easy to recognize the correct answer on a multiple choice test, but much harder to recall it from scratch for an essay exam. While SASSPSS output may include much more than you wanted to see, it allows you to recognize the pieces of output you wanted, without having to recall the command to get it. Bomba Patch 11. Rs piecemeal approach to commands means you are more dependent upon recall, making it inherently harder to learn. However, Rs focus is always on enabling each output to be used as input to further analysis. Its get just what you ask for approach makes this easy, and that attracts developers. People looking only to use methods programmed by others the great majority will then benefit from the vast array of packages that developers make available. Misleading Function or Parameter Names. The most difficult time people have learning R is when functions dont do the obvious thing. For example when sorting data, SAS, SPSS and Stata all use commands appropriately named sort. Turning to R, they look for such a command and, sure enough, there is one named exactly that. However, it does not sort data sets Instead it sorts individual variables, which is often a very dangerous thing to do. In R, the order function sorts data sets and it does so in a somewhat convoluted way. However, the dplyr package has an arrange function that sorts data sets and it is quite easy to use. Perhaps the biggest shock comes when the new R user discovers that sorting is often not even needed by R. Other packages require sorting before they can do three common tasks 1 summarizing aggregating data, 2 repeating an analysis for each group by or split file processing and 3 merging files by key variables. R does not need the user to explicitly sort datasets before performing any of these tasks Another command that commonly confuses beginners is the simple if function. While it is used to recode variables among other tasks in other software, in R if controls the flow of commands, while ifelse performs tasks such as recoding. Inconsistent Function Names. All languages have their inconsistencies. For example, it took SPSS developers decades before they finally offered a syntax checking text editor. I was told by an SPSS insider that they would have done it sooner if the language hadnt contained so many inconsistencies. SAS has its share of inconsistencies as well, with OUTPUT statements for some procedures and OUT options on others. However, I suspect that R probably has far more inconsistencies than most since it lacks a consistent naming convention. You see names in alllowercase, period. Camel. Case and Upper. Camel. Case. Some of the built in examples include names, colnames. Sums, rowsum. row. Means, no parallel rowmean exists. URL, contrib. url, fixup. URLs. package. contents, package. Status. get. Method, get. S3method. read. csv and write. RDS and save. RDS. Sys. time, system. When you include add on packages, you can come across some real whoppers For example, R has a built in reshape function, the Hmisc package has a re. Shape function case matters, and there are both reshape and reshape. Left 4 Dead 2 Blackbox Repack. The most popular R package for reshaping data is tidyr, with its gather and spread commands. Inconsistent Syntax. Since everyone is free to add new capabilities to R, the resulting code for different R packages is often a bit of a mess.