Playing around with #rstats twitter data

As a bit of weekend fun, I decided to briefly look into the #rstats twitter data that Stephen Turner collected and made available (thanks!). Essentially, this data set contains some basic information about over 100,000 tweets that contain the hashtag "#rstats" that denotes that a tweeter is tweeting about R.

As a warning, I don't know much about how these data were collected, whether it was collected and random times during the day or whether it was biased toward particular times and, therefore, locations. I wouldn't really read too much into this.

Most common co-occuring hashtags
When a tweet uses a hashtag at all, it very often uses more than one. To extract the co-occuring hashtags, I used the following perl script:


    $_ = lc($_);
    $_ =~ s/#rstats//g;
    my @matches;
    push @matches, /(#\w+)/;
    print join "\n" => @matches if @matches;

which uses the regular expression "(#\w+)" to search for hashtags after removing "#rstats" from every tweet.

On the unix command-line, I put these other hashtags into a file and sorted via these commands:

cat data/R-hashtag-data.txt | ./ | tee other-hashtags.txt

sort other-hashtags.txt | uniq -c | sort -n -r > sorted-other-hashtags.txt

After running these commands, I get a numbered list of co-occuring hashtags, sorted in descending order. The top 10 co-occuring hashtags were as follows (you can see the rest here :

5258 #datascience
1665 #python
1625 #bigdata
1542 #r
1451 #dataviz
1360 #ggplot2
 852 #statistics
 783 #dplyr
 749 #machinelearning
 743 #analytics

Neat-o. The presence of "#python" and "#ggplot2" in the top 10 made me wonder what the top 10 programming language and R package related hashtags were. Here they are, respectively:

1665 #python
 423 #d3js (plus 72 for #d3) (plus 2 for #js)
 343 #sas
 312 #julialang (plus 43 for #julia)
 240 #fsharp
 140 #spss  (plus 7 for #ibmspss)
 102 #stata
  75 #matlab
  55 #sql
  38 #java

1360 #ggplot2  (plus 298 for ggplot)  (plus for 6 #gglot2) (plus 4 for #ggpot)
 783 #dplyr
 663 #shiny
 557 #rcpp (plus 22 for rcpp11)
 251 #knitr
 156 #magrittr
 105 #lme4
  93 #ggvis   (plus 11 for #ggivs)
  65 #datatable
  46 #rneo4j

You can view the full list here and here.

I was happy to see my favorite languages (python, perl, clojure, lisp, haskell, c) besides R being represented in the first list. Additionally, most of my favorite packages were fairly well tweeted about--at least as far as hashtags-applied-to-a-package go.

Before moving on to the next section, I wanted to share my favorite co-occuring hashtags that I found while sifting through the data: #rcatladies, #rdogfella, #bayesianbootycall, #dontbeaplyrhater, #overlyhonestmethods, #rickshaw (??), #statafail, and #monkeysinfrontoftypewriters.

Most prolific #rstats tweeters
One of the first things I did with these data is a simple aggregation and sort to find the tweeters that used the hashtag most often:

  group_by(User) %>%
  summarise(count = n()) %>%
  arrange(desc(count)) -> prolific.rstats.tweeters

Here is the top 10 (you can see the rest here.)

@Rbloggers	1081
@hadleywickham	498
@timelyportfolio	427
@recology_	419
@revodavid	210
@chlalanne	209
@adolfoalvarez	199
@RLangTip	175
@jmgomez	160

Nothing terribly surprising here.

Normalizing by total tweets
In a twitter discussion about these data, a twitter friend Tim Hopper posited that though he had fewer #rstats tweets than another mutual friend, Trey Causey, he would have a higher number of #rstats tweets if you control for total tweet volume. I wondered how this sorting would look.

Answering this question gave me an excuse to use Hadley Wickham's new package, rvest (I literally just got why the package is named as much while typing this out) which makes web scraping easier--in part by leveraging the expressive power of the magrittr package.

To get the total number of tweets for a particular tweeter, I wrote the following function:

get.num.tweets <- function(handle){
    unraw <- function(raw_str){
      raw_str <- sub(",", "", raw_str)    # remove commas if any
      if(grepl("K", raw_str)){
        return(as.numeric(sub("K", "", raw_str))*1000)   # in thousands
    html(paste0("", sub("@", "", handle))) %>%
      html_nodes(".is-active .ProfileNav-value") %>%
      html_text() %>%

The real logic (and beauty) of which is contained only in the last few lines:

    html(paste0("", sub("@", "", TWITTER_HANDLE))) %>%
      html_nodes(".is-active .ProfileNav-value") %>%

The CSS element that houses the number of total tweets from a useR's twitter page was found easily using SelectorGadget.

After scraping the number of tweets for almost 10,000 #rstats tweeters (waiting a few seconds between each request because I'm considerate) I divided number of #rstats tweets by the total number of tweets to come up with a normalized value.

The top 10 tweeteRs were as follows:

              User count num.of.tweets     ratio 
1     @medzihorsky     9            28 0.3214286 
2        @statworx     5            16 0.3125000 
3    @LearnRinaDay   114           404 0.2821782 
4  @RforExcelUsers     4            15 0.2666667 
5     @showmeshiny    27           102 0.2647059 
6           @tcrug     6            25 0.2400000 
7   @DailyRpackage   155           666 0.2327327 
8   @R_Programming    49           250 0.1960000 
9        @hexadata     8            41 0.1951220 
10     @Deep_RHelp    11            58 0.1896552 

In case you were wondering, Trey Causey still "won" by a long shot:

> tweeters[which(tweeters$User=="@tdhopper"),]   
Source: local data frame [1 x 4]                 
       User count num.of.tweets        ratio     
1 @tdhopper     8         26700 0.0002996255     
> tweeters[which(tweeters$User=="@treycausey"),] 
Source: local data frame [1 x 4]                 
         User count num.of.tweets      ratio     
1 @treycausey    50         28700 0.00174216

Before ending this post, I feel compelled to issue an almost certainly unnecessary but customary warning against using number of #rstats tweets as a proxy for who likes R the most or who are the biggest R "thought leaders" (whatever that is). Most tweets about R don't use the #rstats hashtag, anyway.

Again, I would't read too much into this :)

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What does Flatland have to do with Haskell?

Edwin Abbott's 1884 novella, Flatland, recounts the misadventures of a square that lives in a two-dimensional world called "Flatland". In this story, the square has a dream where he visits a one-dimensional world (Lineland) and unsuccessfully tries to educate the populace about Flatland's existence. Shortly thereafter, a sphere visits Flatland to introduce our protagonist to his own home, Spaceland. The sphere looks like a circle to the square, because the square can only see the part of the sphere that intersects Flatland's plane. The square can't fathom Spaceland until the sphere actually brings him into the third dimension.

Having his view of reality sufficiently rocked, the square postulates the existence of still higher dimensional lands. The sphere denies this possibility and returns the square to Flatland on bad terms.

The irony here is that, in spite of being aware of the square's previous insistence that Flatland is the only reality there is, the square's corresponding limitation of thought, and the square's subsequent enlightenment; the sphere arrogantly asserted that his own land represented the limits of dimensionality and that there can be no more dimensions.

I begin with this summary not as an impromptu book report, but because this is a perfect analogy for why I've decided to learn Haskell.
It isn't hard to imagine the inveterate C programmer being hesitant to embrace higher-level languages like Python and Perl. "Imagine the whole world that opens up to you when you don't have to worry about memory management and resolving pointers," requests the proselytizing Python programmer.

But the C programmer got on fine without knowing Python all this time. Besides the (standard) Python interpreter is written in C.

"Why? So I can change types on a whim?" retorts the C programmer. " I can pass functions as arguments to other functions? Big deal! I can do that with function pointers in C."

While it is technically true that, in C, functions can be passed as arguments to other functions, and can be returned by functions, this is only a small part of the functional programming techniques that Python allows. But it is the only part that most only trained in C can understand. This is like when the square thought that he saw the sphere because he saw a circle where the sphere intersected Flatland. There's a bigger picture (lambda functions, currying, closures) that only appears when the constraints imposed by a programming paradigm are pushed back.

Shifting tides in industry eventually compel the C programmer to give Python a shot. Once she is fluent in Python and its idioms (becomes a "pythonista", as they say) things begin to change. The OOP of Python allows the programmer to gain a new perspective on programming that had been, up to this point, unavailable to her simply because the concept doesn't exist in C.

Resolved to follow the road to higher abstraction, the (former) C programmer asks the Python programmer if there are other languages that will provoke a similar shift in thought relative to even Python. "Haskell, perhaps?" she offers.

The Python programmer scoffs, "Nah, Python is as good as it gets. Besides, purely functional programmers are a bunch of weirdos."


Haskell is a relatively obscure programming language (20th place or lower on most popularity indices), but accounts for a disproportionate number of the "this-language-will-change-the-way-you-look-at-programming" claims. By the accounts of Haskell aficionados, finally understanding Haskell is a transformative experience.

Up until recently, I found myself mirroring the thoughts and behavior of the Python programer in this story; I keep hearing about how great Haskell is, and how it'll facilitate an enormous shift in how I'll view computer programming, but I largely dismissed these claims as the rantings of eccentrics that are more concerned with mathematical elegance than practical concerns.

Is it any wonder that I didn't believe the Haskell programmers? I can't see the benefits of Haskell within the constraints of how I view computer programming—constraints subtly imposed by my language of choice. (Lisp programmer Paul Graham describes this as the "Blub Paradox" in this essay Beating The Averages)

But just like the sphere and the arrogant Python programmer should, I have to remain open to the idea that there's a whole world out there that I'm not availing myself of just because I’m too obstinate to try it.

Any language that is Turing-complete will let you do anything. It's trivially true that there is nothing that you can do in one language that you can't do in another. What sets languages apart (from most trivial to least) is the elegance of the syntax, it's community and third party packages, the ease in which you can perform certain tasks, and whether you’ll achieve enlightenment as a result of using it. I don't know if Haskell will do this for me but I think I ought to give it a shot.

If the ideas of relativity when applied to the domain of programming languages interests you, you might also be interested in the Sapir–Whorf hypothesis, which applies similar ideas mentioned in this post to the realm of natural languages.

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Why is my OS X Yosemite install taking so long?: an analysis

Since the latest Mac OS X update, 10.10 "Yosemite", was released last Thursday, there have been complaints springing up online of the progress bar woefully underestimating the actual time to complete installation. More specifically, it appeared as if, for a certain group of people (myself included), the installer would stall out at "two minutes remaining" or "less than a minute remaining"–sometimes for hours.

In the vast majority of these cases, though, the installation process didn't hang, it was just performing a bunch of unexpected tasks that it couldn't predict.

During the install, striking "Command" + "L" would bring up the install logs. In my case, the logs indicated that the installer was busy right until the very last minute.

Not knowing very much about OS X's installation process and wanting to learn more, and wanting to answer why the installation was taking longer than the progress bar expected, I saved the log to a file on my disk with the intention of analyzing it before the installer automatically restarted my computer.

The log file from the Yosemite installer wasn't in a format that R (or any program) could handle natively so before we can use it, we have to clean/munge it. To do this, we'll write a program in the queen of all text-processing languages: perl.

This script will read the log file, line-by-line from standard input (for easy shell piping), and spit out nicely formatted tab-delimited lines.


use strict;
use warnings;

# read from stdin
    my $line = $_;
    my ($not_message, $message) = split ': ', $line, 2;

    # skip lines with blank messages
    next if $message =~ m/^\s*$/;

    my ($month, $day, $time, $machine, $service) = split " ", $not_message;

    print join("\t", $month, $day, $time, $machine, $service, $message) . "\n";

We can output the cleaned log file with this shell command:

echo "Month\tDay\tTime\tMachine\tService\tMessage" > cleaned.log
grep '^Oct' ./YosemiteInstall.log | grep -v ']:  ' | grep -v ': }' |  ./ >> cleaned.log

This cleaned log contains 6 fields: 'Month', 'Day', 'Time', 'Machine (host)', 'Service', and 'Message'. The installation didn't span days (it didn't even span an hour) so technically I didn't need the 'Month' and 'Day' fields, but I left them in for completeness' sake.


Let's set some options and load the libraries we are going to use:

# options

# libraries

Now we read the log file that I cleaned and add a few columns with correctly parsed timestamps using lubridate’s "parse_date_time()" function

yos.log <- read.delim("./cleaned.log", sep="\t") %>%
  mutate(, Day, "2014", Time)) %>%
                                  "%b %d! %Y! %H!:%M!:%S!", 

And remove the rows of dates that didn't parse correctly

yos.log <- yos.log[!$lub.time),]


##   Month Day     Time   Machine        Service
## 1   Oct  18 11:28:23 localhost opendirectoryd
## 2   Oct  18 11:28:23 localhost opendirectoryd
## 3   Oct  18 11:28:23 localhost opendirectoryd
## 4   Oct  18 11:28:23 localhost opendirectoryd
## 5   Oct  18 11:28:23 localhost opendirectoryd
## 6   Oct  18 11:28:23 localhost opendirectoryd
##                                                                    Message
## 1                   opendirectoryd (build 382.0) launched - installer mode
## 2                                  Logging level limit changed to 'notice'
## 3                                               Initialize trigger support
## 4 created endpoint for mach service ''
## 5                                set default handler for RPC 'reset_cache'
## 6                           set default handler for RPC 'reset_statistics'
##                lub.time
## 1 Oct 18 2014 11:28:23 2014-10-18 11:28:23
## 2 Oct 18 2014 11:28:23 2014-10-18 11:28:23
## 3 Oct 18 2014 11:28:23 2014-10-18 11:28:23
## 4 Oct 18 2014 11:28:23 2014-10-18 11:28:23
## 5 Oct 18 2014 11:28:23 2014-10-18 11:28:23
## 6 Oct 18 2014 11:28:23 2014-10-18 11:28:23

The first question I had was how long the installation process took

install.time <- yos.log[nrow(yos.log), "lub.time"] - yos.log[1, "lub.time"]
## [1] "1848s (~30.8 minutes)"

Ok, about a half-hour.

Let's make a column for cumulative time by subtracting each row's time by the start time

yos.log$cumulative <- yos.log$lub.time - min(yos.log$lub.time, na.rm=TRUE)

In order to see what processes were taking the longest, we have to make a column for elapsed time. To do this, we can subtract each row's time from the time of the subsequent row.

yos.log$elapsed <- lead(yos.log$lub.time) - yos.log$lub.time

# remove last row
yos.log <- yos.log[-nrow(yos.log),]

Which services were responsible for the most writes to the log and what services took the longest? We can find out with the following elegant dplyr construct. While we're at it, we should add columns for percentange of the whole for easy plotting.

counts <- yos.log %>%
  group_by(Service) %>%
  summarise(n=n(), totalTime=sum(elapsed)) %>%
  arrange(desc(n)) %>%
  top_n(8, n) %>%
  mutate(percent.n = n/sum(n)) %>%
  mutate(percent.totalTime = as.numeric(totalTime)/sum(as.numeric(totalTime)))

## Source: local data frame [8 x 5]
##           Service     n totalTime percent.n percent.totalTime
## 1     OSInstaller 42400 1586 secs 0.9197197          0.867615
## 2  opendirectoryd  3263   43 secs 0.0707794          0.023523
## 3         Unknown   236  157 secs 0.0051192          0.085886
## 4  _mdnsresponder    52   17 secs 0.0011280          0.009300
## 5              OS    49    1 secs 0.0010629          0.000547
## 6 diskmanagementd    47    7 secs 0.0010195          0.003829
## 7     storagekitd    29    2 secs 0.0006291          0.001094
## 8         configd    25   15 secs 0.0005423          0.008206

Ok, the "OSInstaller" is responsible for the vast majority of the writes to the log and to the total time of the installation. "opendirectoryd" was the next most verbose process, but its processes were relatively quick compared to the "Unknown" process' as evidenced by "Unknown" taking almost 4 times longer, in aggregate, in spite of having only 7% of "opendirectoryd"'s log entries.

We can more intuitively view the number-of-entries/time-taken mismatch thusly:

melted <- melt([,c("Service",

ggplot(melted, aes(x=Service, y=as.numeric(value), fill=factor(variable))) +
  geom_bar(width=.8, stat="identity", position = "dodge",) +
  ggtitle("Breakdown of services during installation by writes to log") +
  ylab("percent") + xlab("service") +
  scale_fill_discrete(name="Percent of",
                      breaks=c("percent.n", "percent.totalTime"),
                      labels=c("writes to logfile", "time elapsed"))


As you can see, the "Unknown" process took a disproportionately long time for its relatively few log entries; the opposite behavior is observed with "opendirectoryd". The other processes contribute very little to both the number of log entries and the total time in the installation process.

What were the 5 most lengthy processes?

yos.log %>%
  arrange(desc(elapsed)) %>%
  select(Service, Message, elapsed) %>%

##       Service
## 1 OSInstaller
## 2 OSInstaller
## 3     Unknown
## 4 OSInstaller
## 5 OSInstaller
##                                                                                                                                            Message
## 1 PackageKit: Extracting file:///System/Installation/Packages/Essentials.pkg (destination=/Volumes/Macintosh HD/.OSInstallSandboxPath/Root, uid=0)
## 2                                    System Reaper: Archiving previous system logs to /Volumes/Macintosh HD/private/var/db/PreviousSystemLogs.cpgz
## 3                       kext file:///Volumes/Macintosh%20HD/System/Library/Extensions/JMicronATA.kext/ is in hash exception list, allowing to load
## 4                                                                   Folder Manager is being asked to create a folder (down) while running as uid 0
## 5                                                                                                                      Checking catalog hierarchy.
##    elapsed
## 1 169 secs
## 2 149 secs
## 3  70 secs
## 4  46 secs
## 5  44 secs

The top processes were:

  • Unpacking and moving the contents of "Essentials.pkg" into what is to become the newsystem directory structure. This ostensibly contains items like all the updated applications (Safari, Mail, etc..). (almost three minutes)
  • Archiving the old system logs (two and a half minutes)
  • Loading the kernel module that allows the onboard serial ATA controller to work (a little over a minute)

Let's view a density plot of the number of writes to the log file during installation.

ggplot(yos.log, aes(x=lub.time)) +
  geom_density(adjust=3, fill="#0072B2") +
  ggtitle("Density plot of number of writes to log file during installation") +
  xlab("time") + ylab("")


This graph is very illuminating; the vast majority of log file writes were the result of very quick processes that took place in the last 15 minutes of the install, which is when the progress bar read that only two minutes were remaining.

In particular, there were a very large number of log file writes between 11:47 and 11:48; what was going on here?

# if the first time is in between the second two, this returns TRUE <- function(time, start, end){
  if(time > start && time < end)

the.start <- ymd_hms("14-10-18 11:47:00", tz="EST")
the.end <- ymd_hms("14-10-18 11:48:00", tz="EST")

# logical vector containing indices of writes in time interval <- sapply(yos.log$lub.time,,

# extract only these rows
in.interval <- yos.log[, ]

# what do they look like?
silence <- in.interval %>%
  select(Message) %>%
  sample_n(7) %>%
  apply(1, function (x){cat("\n");cat(x);cat("\n")})

## (NodeOp) Move /Volumes/Macintosh HD/Recovered Items/usr/local/texlive/2013/tlpkg/tlpobj/featpost.tlpobj -> /Volumes/Macintosh HD/usr/local/texlive/2013/tlpkg/tlpobj Final name: featpost.tlpobj (Flags used: kFSFileOperationDefaultOptions,kFSFileOperationSkipSourcePermissionErrors,kFSFileOperationCopyExactPermissions,kFSFileOperationSkipPreflight,k_FSFileOperationSuppressConversionCopy)
## (NodeOp) Move /Volumes/Macintosh HD/Recovered Items/usr/local/texlive/2013/texmf-dist/tex/generic/pst-eucl/pst-eucl.tex -> /Volumes/Macintosh HD/usr/local/texlive/2013/texmf-dist/tex/generic/pst-eucl Final name: pst-eucl.tex (Flags used: kFSFileOperationDefaultOptions,kFSFileOperationSkipSourcePermissionErrors,kFSFileOperationCopyExactPermissions,kFSFileOperationSkipPreflight,k_FSFileOperationSuppressConversionCopy)
## (NodeOp) Move /Volumes/Macintosh HD/Recovered Items/Library/Python/2.7/site-packages/pandas-0.12.0_943_gaef5061-py2.7-macosx-10.9-intel.egg/pandas/tests/ -> /Volumes/Macintosh HD/Library/Python/2.7/site-packages/pandas-0.12.0_943_gaef5061-py2.7-macosx-10.9-intel.egg/pandas/tests Final name: (Flags used: kFSFileOperationDefaultOptions,kFSFileOperationSkipSourcePermissionErrors,kFSFileOperationCopyExactPermissions,kFSFileOperationSkipPreflight,k_FSFileOperationSuppressConversionCopy)
## (NodeOp) Move /Volumes/Macintosh HD/Recovered Items/usr/local/texlive/2013/texmf-dist/tex/latex/ucthesis/uct10.clo -> /Volumes/Macintosh HD/usr/local/texlive/2013/texmf-dist/tex/latex/ucthesis Final name: uct10.clo (Flags used: kFSFileOperationDefaultOptions,kFSFileOperationSkipSourcePermissionErrors,kFSFileOperationCopyExactPermissions,kFSFileOperationSkipPreflight,k_FSFileOperationSuppressConversionCopy)
## (NodeOp) Move /Volumes/Macintosh HD/Recovered Items/usr/local/texlive/2013/texmf-dist/doc/latex/przechlewski-book/wkmgr1.tex -> /Volumes/Macintosh HD/usr/local/texlive/2013/texmf-dist/doc/latex/przechlewski-book Final name: wkmgr1.tex (Flags used: kFSFileOperationDefaultOptions,kFSFileOperationSkipSourcePermissionErrors,kFSFileOperationCopyExactPermissions,kFSFileOperationSkipPreflight,k_FSFileOperationSuppressConversionCopy)
## WARNING : ensureParentPathExists: Created  `/Volumes/Macintosh HD/usr/local/texlive/2013/texmf-dist/doc/latex/moderntimeline' w/ {
## (NodeOp) Move /Volumes/Macintosh HD/Recovered Items/usr/local/texlive/2013/texmf-dist/fonts/type1/wadalab/mrj/mrjkx.pfb -> /Volumes/Macintosh HD/usr/local/texlive/2013/texmf-dist/fonts/type1/wadalab/mrj Final name: mrjkx.pfb (Flags used: kFSFileOperationDefaultOptions,kFSFileOperationSkipSourcePermissionErrors,kFSFileOperationCopyExactPermissions,kFSFileOperationSkipPreflight,k_FSFileOperationSuppressConversionCopy)

Ah, so these processes are the result of the installer having to move files back into the new installation directory structure. In particular, the vast majority of these move operations are moving files related to a program called "texlive". I'll explain why this is to blame for the inaccurate projected time to completion in the next section.

But lastly, let's view a faceted density plot of the number of log files writes by process. This might give us a sense of what steps go on as the installation progresses by showing us with processes are most active.

# reduce number of service to a select few of the most active
smaller <- yos.log %>%
  filter(Service %in% c("OSInstaller", "opendirectoryd",
                        "Unknown", "OS"))

ggplot(smaller, aes(x=lub.time, color=Service)) +
  geom_density(aes( y = ..scaled..)) +
  ggtitle("Faceted density of log file writes by process (scaled)") +
  xlab("time") + ylab("")


This shows that no one process runs consistently throughout the entire installation process, but rather that the process run in spurts.

the answer
The vast majority of Mac users don't place strange files in certain special system-critical locations like '/usr/local/' and '/Library/'. Among those who do, though, these directories are littered with hundreds and hundreds of custom files that the installer doesn't and can't have prior knowledge of.

In my case, and probably many others, the estimated time-to-completion was inaccurate because the installer couldn't anticipate needing to copy back so many files to certain special directories after unpacking the contents of the new OS. Additionally, for each of these copied files, the installer had to make sure the subdirectories had the exact same meta-data (permissions, owner, reference count, creation date, etc…) as before the installation began. This entire process added many minutes to the procedure at a point when the installer thought it was pretty much done.

What were some of the files that the installer needed to copy back? The answer will be different for each system but, as mentioned above, anything placed '/usr/local' and '/Library' directories that wasn't Apple-supplied needed to be moved and moved back.

/usr/local/ is used chiefly for user-installed software that isn't part of the OS distribution. In my case, my /usr/local contained a custom compliled Vim; ClamXAV, a lightweight virus scanner that I use only for the benefit of my Windows-using friends; and texlive, software for the TeX typesetting system. texlive was, by far, the biggest time-sink since it had over 123,491 files.

In addition to these programs, many users might find that the Homebrew package manager is to blame for their long installation process, since this software also uses the /usr/local prefix (although it probably should not).

Among other things, this directory holds (subdirectories that hold) modules and packages that the Apple-supplied Python, Ruby, and Perl uses. If you use these Apple-supplied versions of these languages and you install your own packages/modules using super-user privileges, the new packages will go into this directory and it will appear foreign to the Yosemite installer.

To get around this issue, either install packages/modules in a local (non-system) library, or use alternate versions of these programming languages that you either download and install yourself, or use MacPorts to install.


You can find all the code and logs that I used for this analysis in this git repository

This post is also available as a RMarkdown report here

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