As said, SPSS is easier to learn than R. But SPSS, SAS and Stata come
-with major downsides when comparing it with R:
--
-
R is highly modular.
-The official R network
-(CRAN) features more than 16,000 packages at the time of writing,
-our AMR package being one of them. All these packages were
-peer-reviewed before publication. Aside from this official channel,
-there are also developers who choose not to submit to CRAN, but rather
-keep it on their own public repository, like GitHub. So there may even
-be a lot more than 14,000 packages out there.
-Bottom line is, you can really extend it yourself or ask somebody to
-do this for you. Take for example our AMR package. Among
-other things, it adds reliable reference data to R to help you with the
-data cleaning and analysis. SPSS, SAS and Stata will never know what a
-valid MIC value is or what the Gram stain of E. coli is. Or
-that all species of Klebiella are resistant to amoxicillin and
-that Floxapen® is a trade name of flucloxacillin. These facts
-and properties are often needed to clean existing data, which would be
-very inconvenient in a software package without reliable reference data.
-See below for a demonstration.
-
--
-
R is extremely flexible.
-Because you write the syntax yourself, you can do anything you want.
-The flexibility in transforming, arranging, grouping and summarising
-data, or drawing plots, is endless - with SPSS, SAS or Stata you are
-bound to their algorithms and format styles. They may be a bit flexible,
-but you can probably never create that very specific publication-ready
-plot without using other (paid) software. If you sometimes write
-syntaxes in SPSS to run a complete analysis or to ‘automate’ some of
-your work, you could do this a lot less time in R. You will notice that
-writing syntaxes in R is a lot more nifty and clever than in SPSS.
-Still, as working with any statistical package, you will have to have
-knowledge about what you are doing (statistically) and what you are
-willing to accomplish.
-
--
-
R can be easily automated.
-Over the last years, R
-Markdown has really made an interesting development. With R
-Markdown, you can very easily produce reports, whether the format has to
-be Word, PowerPoint, a website, a PDF document or just the raw data to
-Excel. It even allows the use of a reference file containing the layout
-style (e.g. fonts and colours) of your organisation. I use this a lot to
-generate weekly and monthly reports automatically. Just write the code
-once and enjoy the automatically updated reports at any interval you
-like.
-For an even more professional environment, you could create Shiny apps: live manipulation of
-data using a custom made website. The webdesign knowledge needed
-(JavaScript, CSS, HTML) is almost zero.
-
--
-
R has a huge community.
-Many R users just ask questions on websites like StackOverflow.com, the largest
-online community for programmers. At the time of writing, 489 583
-R-related questions have already been asked on this platform (that
-covers questions and answers for any programming language). In my own
-experience, most questions are answered within a couple of
-minutes.
-
--
-
R understands any data type, including
-SPSS/SAS/Stata.
-And that’s not vice versa I’m afraid. You can import data from any
-source into R. For example from SPSS, SAS and Stata (link), from Minitab, Epi Info
-and EpiData (link), from Excel
-(link), from flat files like
-CSV, TXT or TSV (link), or
-directly from databases and datawarehouses from anywhere on the world
-(link). You can even scrape
-websites to download tables that are live on the internet (link) or get the results of
-an API call and transform it into data in only one command (link).
-And the best part - you can export from R to most data formats as
-well. So you can import an SPSS file, do your analysis neatly in R and
-export the resulting tables to Excel files for sharing.
-
--
-
R is completely free and open-source.
-No strings attached. It was created and is being maintained by
-volunteers who believe that (data) science should be open and publicly
-available to everybody. SPSS, SAS and Stata are quite expensive. IBM
-SPSS Staticstics only comes with subscriptions nowadays, varying between USD
-1,300 and USD 8,500 per user per year. SAS Analytics Pro
-costs around
-USD 10,000 per computer. Stata also has a business model with
-subscription fees, varying between
-USD 600 and USD 2,800 per computer per year, but lower prices come
-with a limitation of the number of variables you can work with. And
-still they do not offer the above benefits of R.
-If you are working at a midsized or small company, you can save it
-tens of thousands of dollars by using R instead of e.g. SPSS - gaining
-even more functions and flexibility. And all R enthousiasts can do as
-much PR as they want (like I do here), because nobody is officially
-associated with or affiliated by R. It is really free.
-
--
-
R is (nowadays) the preferred analysis software in
-academic papers.
-At present, R is among the world most powerful statistical languages,
-and it is generally very popular in science (Bollmann et al.,
-2017). For all the above reasons, the number of references to R as an
-analysis method in academic papers is
-rising continuously and has even surpassed SPSS for academic use
-(Muenchen, 2014).
-I believe that the thing with SPSS is, that it has always had a great
-user interface which is very easy to learn and use. Back when they
-developed it, they had very little competition, let alone from R. R
-didn’t even had a professional user interface until the last decade
-(called RStudio, see below). How people used R between the nineties and
-2010 is almost completely incomparable to how R is being used now. The
-language itself has been
-restyled completely by volunteers who are dedicated professionals in
-the field of data science. SPSS was great when there was nothing else
-that could compete. But now in 2023, I don’t see any reason why SPSS
-would be of any better use than R.
-
-
-# not all values are valid MIC values:
-as.mic(0.125)
-#> Class 'mic'
-#> [1] 0.125
-as.mic("testvalue")
-#> Class 'mic'
-#> [1] <NA>
-
-# the Gram stain is available for all bacteria:
-mo_gramstain("E. coli")
-#> [1] "Gram-negative"
-
-# Klebsiella is intrinsic resistant to amoxicillin, according to EUCAST:
-klebsiella_test <- data.frame(
- mo = "klebsiella",
- amox = "S",
- stringsAsFactors = FALSE
-)
-klebsiella_test # (our original data)
-#> mo amox
-#> 1 klebsiella S
-eucast_rules(klebsiella_test, info = FALSE) # (the edited data by EUCAST rules)
-#> mo amox
-#> 1 klebsiella R
-
-# hundreds of trade names can be translated to a name, trade name or an ATC code:
-ab_name("floxapen")
-#> [1] "Flucloxacillin"
-ab_tradenames("floxapen")
-#> [1] "culpen" "floxacillin" "floxacillin sodium"
-#> [4] "floxapen" "floxapen sodium salt" "fluclox"
-#> [7] "flucloxacilina" "flucloxacillin" "flucloxacilline"
-#> [10] "flucloxacillinum" "fluorochloroxacillin" "staphylex"
-ab_atc("floxapen")
-#> [1] "J01CF05"