here. Obviously for many users RStudio offersa superior way to work, and as far as I know works with all of the material presentedin these tutorials and with all the packages included. (However, some of themiscellaneous scripts at the bottom of the LabDSV R page will cause RStudio tocrash.) Personally, I'm an OldSchool guy, and I'm not completely sold on RStudio. As a linux user, it solvesproblems I don't have and it encourages (if not enforces) a way of working Idon't appreciate. Nonetheless, since so many people find it useful I'llcontribute some thoughts on best practices using RStudio with LabDSV.

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System details RStudio Edition: RStudio Version: 1.1.442 OS Version: Ubuntu 16.04 R Version: 3.4.4 (2018-03-15) Steps to reproduce the problem Select menu from Tools - Global Options - Appearance Describe the proble. Strings for quartz font in r studio for mac. Core Specifications: - Plucked and Tapped Multi-Samples (x6-8RR) - Bowed Sustains and Swells Multi-Samples - Deep-Phrase.

If you just want advice on best practices in RStudio, skip down to here and avoid my rant.

The Good

Projects

One of the excellent things RStudio does is encourage users to define 'projects'and work within the projects. This keeps all the related data and work together in asingle workspace and separates it from other projects you might be working on.Windows users, in particular, that start R by clicking on the icon ontheir desktop often end up with much (or all) of their work in a singleworkspace, commingling work from many potentially different projects. Rof course has facilities to work with separate projects natively, but usersenamored of the desktop often do not use them to full extent. So, RStudioprojects are a good idea. You will certainly want a 'project' for the labspresented in LabDSV, and you might want to separate the labs into separateprojects like 'species modeling', 'ordination', and 'clustering.'

Script Editor

RStudio encourages users to work from a script file rather than directly at theconsole. In addition, it provides a text editor for those script files that ismuch superior to generic Wordpad. (Word obviously should never be used to writeR scripts.) As a bonus, the editor can be made to mimic vi, but that's perhapsmoot to most Windows and even Mac users.

Tool Tips

When you use the console or script editor window RStudio pops up the command-line version oftool-tips, providing the list of arguments for any function you start to enter.This can be really helpful for commands you're less familiar with and makes itunnecessary to start up the help file just to get the command arguments. Bravofor this innovation.

The Bad

Panel Format

By default, RStudio starts up with a four-panel configuration packed into asingle frame. This packaging is very Windowsesque and in my mind impractical.The distribution (and number) of window types within the frame is configurable, and each ofthe windows may have tabs allowing you to select exactly what will appear in thewindow. However, even if you have a large, high resolution monitor the four panel arrangementtries to pack too much into too small a space and results in undesirablecompression of information.

Command Redirection

Even if you work in the console, RStudio traps and redirects many simple Rcommands. In particular, I dislike the way it crams R help files into a tinylittle window. Normally,?command will produce a formatted output to theconsole that is easily negotiated with the arrow keys. If you want to stretchout, help.start() pops up the help file system in your browser with fullhot-link capability. It's the same browser with the same capability you'realready familiar with, and you can shrink it or pop it behind your R session to get it out ofthe way when you don't need it. It's a vastly superior solution to the RStudiobox, but RStudio captures the Strings for quartz font in r studio for mac 10 11

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help.start() command and redirects it tothe little box. Bummer.

Wasted Space

Some of the little windows are of limited utility and waste space, e.g. theEnvironment, History, Connections window. The information it providesis normally easily printed to the console (if and when you want it) except thatnow RStudio traps those function calls. For example, history(100) Font

willcall up the last 100 commands entered at the console which you can browse andmanage with your arrow keys. You can easily cut-and-paste commands from yourhistory into the console. Instead, in RStudio it redirects the output to thelittle Environment, History, Connections box. Bummer.

The Environment, History, Connections window provides theEnvironment tab which gives a summary of objects in yourworkspace. It's more detail than the ls() command provides, but notnearly as much information as str() provides. Clicking on the bluearrow button will provide the str() output, however. Maybe it'shelpful, but ls() and str() in the console provide the sameinformation without taking up real estate on your screen unless you specificallywant to see something.

Data File Import

Importing data into R seems to be a significant problem for many users.Admittedly the plethora of optional arguments for read.table() and theprofusion of read.whatever() functions makes things a littlefunky. You might think that this is one area where a GUI could improve things.Unfortunately, this appears not to be the case.

In RStudio there are at least two ways to import data: (1) using the File menu andselecting 'Importing Dataset', or (2) clicking on a file in the File tab in the'Files, Plots, Etc' panel. Unfortunately (maybe deliberately?) they do thingsdifferently. The File menu approach first pops up a list of import formatoptions. Notably, it offers a choice between 'From text (base) ..' and 'FromText (readr) ..' This is important because base and readrdiffer significantly in the data formatting they support. If you choose 'Fromtext (base)' you get a file chooser. Selecting a file opens the import GUI withoptions to the left, a file previewer to the top right, and a data.framepreviewer to the bottom right. This part is nice. Unfortunately, it doesn'toffer a code preview of the R function that will ultimately be called to do theimport. After clicking on 'Import' you can see in the console that it usesread.table. Importantly, it offers options for choosing or settingrow.names() and a box to click for 'stringsAsFactors'. It follows theread.table() protocol that if the first row has one fewer entries thanthe rest of the lines then the first line is assumed to be a header with columnnames. It offers a fairly limited set of separators (e.g not including |), butis otherwise fairy flexible and functional.

The second option (clicking on a file name in the File panel) is quitedifferent. First, it only offers the option to import files with a limitednumber of file extensions (maybe just .csv?). Files with .dat or .txt extensionscannot be imported. Second, even if package readr is not loaded, it'sfirst choice is to load readr and then use read_csv(). Forexample, for a file called test.csv, it provides a 'Code Preview' that lookslike

library(readr)test <- read_csv('test.csv')View(test)

There doesn't seem to be an option to choose (base) read.table(), butyou can edit the Code Preview and change whatever you like. To the lower left isa panel with options to select. This time, the Delimiter dropdown menu offers'other' and you can specify '|' for example, which automatically changes theCode Preview to use read_delim() instead of read_csv.Unfortunately, read_delim() seems incapable of reading files where thefirst line has fewer entries than the rest of the lines. The previewer gives nowarning (although you can see the headings are misaligned if you look). Whenyou click 'Impo

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