NY-Mount Marcy

The three of us at the summit marker just below the actual summit.

Summit Date

August 12, 2017 (around 11:00 am)

Party

Ryan Cragun, Mark Woolley, Tom Triplett

Trip Report

In my big swing across the US that allowed me to complete most of the highpoints in the Northeast in 2013, I didn’t manage to fit in Mount Marcy. It’s a solid day hike, and I just didn’t have the time. I ended up arranging a trip to Lake Placid, NY specifically to hike Mount Marcy, with my two hiking buddies.

We all flew into Newark on Friday, August 11th, picked up a rental car, then headed to Lake Placid, stopping in Albany for dinner and food to take up on our hike the next day. We arrived kind of late (close to 11:00 pm) and planned an early start the next morning (on the mountain at 7:00) in order to hopefully avoid the impending rain storm that was forecast for the next day.

The trip reports we read about the hike varied quite a bit. Some suggested it was really challenging, with a lot of uphill and rugged terrain. Others suggested it wasn’t that challenging and was a pleasant hike. We also got variable times and distances for the hike. Some trip reports suggested it would take as short as 4 hours while others suggested as many as 15 (that’s a pretty big range). Mileage estimates were also varied, though with a smaller range, hovering between 12 and 17 miles. Because of all the varied estimates, we planned for a 10 to 12 hour, 17-mile hike, just to be safe. As it turns out, using my GPS enabled watch, I now have much more accurate information on the hike.

We stayed at a B&B in Lake Placid, got up at 6:00 am, and drove straight to the Adirondack Loj. There is a parking fee there ($5.00), and by the time we arrived just before 7:00 am, the lot was getting pretty full. This is obviously a popular destination for hikers. We got our boots and gear on, did some stretching (a requirement once you hit 40), signed the register, and hit the trail.

We made good time for the first three miles or so, covering them in about an hour. The first three miles of the trail are fairly level and it is mostly a well-maintained dirt trail, with a few roots, rocks, and other small objects in the way. But around the 3-mile mark, there was a noticeable shift in the trail and terrain. Not only was there substantially more uphill terrain, but it became rocky to the point that at times you are literally boulder hopping.

Me on a nice patch of the more rugged terrain.
Me on a nice patch of the more rugged terrain.

I’ve climbed a lot of mountains and was impressed with how rugged this trail got. This is not a trail you’d want to attempt in light tennis shoes (unless you’re an experienced trail runner); sturdy boots are a very good idea for this hike, ideally with good ankle support. We didn’t make as good of time on the remaining 4 miles to the summit but still did fairly well.

We arrived at the summit at just under 4 hours. When we arrived, the summit was completely enshrouded with clouds. We had no view whatsoever. We spent about 40 minutes on the summit, eating a little food and chatting with the forest ranger on the top who was reminding people to avoid the vegetation, which they are trying to get to grow back.

The three of us at the summit marker just below the actual summit.
The three of us at the summit marker just below the actual summit.

Alas, about 20 minutes after we dropped off the summit, the clouds broke and we finally had some nice views. It was at this point I took a photosphere:

We got better photos at this point, but we were still worried about the impending rain storm. The top of the mountain is largely exposed rock that wouldn’t be all that fun to ascend or descend in the rain. As a result, we opted not to return to the summit and instead to continue our descent. We stopped a few times on the way down to take advantage of some of the toilets that are along the trail and took a quick detour to the waterfall that is also fairly close to the trail. With our detours and stops, we returned to the parking lot in just under 8 hours. The distance on my watch indicated exactly 15 miles. So, there you have it – it is a 15-mile hike. Our average moving pace was 26 minutes per mile. If you know how quickly you can move on fairly rugged terrain, you should be able to estimate how long the hike will take you. We were passed by a couple who were clearly trail runners. They were the only ones moving more quickly than we were and they probably did the entire hike in 6 1/2 hours. I can see how this hike would easily take 12 hours if you’re not an avid hiker and in good shape. It is genuinely rugged terrain, particularly after the 3-mile mark, and you should be prepared for it.

Obviously, if you can, try to go on a nice day. The views from the top are supposed to be quite nice. But even hiking in cloudy conditions, the terrain was pretty. We passed through multiple types of forest – pine and maple – and really enjoyed ourselves.

Panorama

Directions

R (Linux) – creating a wordcloud from PDF

On my professional website, I use wordclouds from the text of my publications as the featured images for the posts where I share the publications. I have used a website to generate those wordclouds for quite a while, but I’m trying to learn how to use the R statistical environment and knew that R can generate wordclouds. So, I thought I’d give it a try.

Here are the steps to generating a wordcloud from the text of a PDF using R.

First, in R, install the following four packages: “tm”, “SnowballC”, “wordcloud”, and “readtext”. This is done by typing the following into the R terminal:

install.packages(“tm”)
install.packages(“SnowballC”)
install.packages(“wordcloud”)
install.packages(“readtext”)

Next, you need to load those packages into the R environment. This is done by typing the following in the R terminal:

library(tm)
library(SnowballC)
library(wordcloud)
library(readtext)

Before we begin creating the wordcloud, we have to get the text out of the PDF file. To do this, first find out where your “working directory” is. The working directory is where the R environment will be looking for and storing files as it runs. To determine your “working directory,” use the following function:

getwd()

There are no arguments for this function. It will simply return where the R environment is currently looking for and storing files.

You’ll need to put the PDF from which you want to extract data into your working directory or change your working directory to the location of your PDF (technically, you could just include the path, but putting it in your working directory is easier). To change the working directory, use the “setwd()” function. Like this:

setwd(“/home/ryan/RWD”)

Once you have your PDF in your working directory, you can use the readtext package to extract the text and put it into a list. You can do that using the following command:

wordbase <- readtext(“paper.pdf”)

“wordbase” is a variable I’m creating to hold the text from the PDF. “readtext” is the package that extracts the text from the PDF. The readtext package is robust enough to be able to extract text from numerous documents (see here) and is even able to determine what kind of document it is from the file extension; in this case, it recognize that it’s a PDF.

The list can now be converted into a corpus, which is a vector (see here for the different data types in R). To do this, we use the following function:

corp <- Corpus(VectorSource(wordbase))

In essence, we’re creating a new variable, “corp,” by using the Corpus function that calls the VectorSource function and applies it to the list of words in the variable “wordbase.”

We’re close to having the words ready to create the wordcloud, but it’s a good idea to clean up the corpus with several commands from the “tm” package. First, we want to make sure the corpus is a plain text:

corp <- tm_map(corp, PlainTextDocument)

Next, since we don’t want any of the punctuation included in the wordcloud, we remove the punctuation with this function from “tm”:

corp <- tm_map(corp, removePunctuation)

For my wordclouds, I don’t want numbers included. So, use this function to remove the numbers from the corpus:

corp <- tm_map(corp, removeNumbers)

Finally, I’m not interested in words like “the” or “a”, so I removed all of those words using this function:

corp <- tm_map(corp, removeWords, stopwords(‘english’))

At this point, you’re ready to generate the wordcloud. What follows is a wordcloud command, but it will generate the wordcloud in a window and you’ll then have to do a screen capture to turn the wordcloud into an image. Even so, here is the basic command:

wordcloud(corp, max.words = 100, random.order = FALSE)

To explain the command, “wordcloud” is the package and function. “corp” is the corpus containing all the words. The other components of the command are parameters that can, of course, be adjusted. “max.words” can be increased or decreased to reflect the number of words you want to include in your wordcloud. “random.order” should be set to FALSE if you want the more frequently occurring words to be in the center with the less frequently occurring words surrounding them. If you set that parameter to TRUE, the words will be in random order, like this:

There are additional parameters that can be added to the wordcloud command, including a scale parameter (scale) that adjusts the relative sizes of the more and less frequently occurring words, a minimum frequency parameter (min.freq) that will limit the plotted words to only those that occur a certain number of times, a parameter for what proportion of words should be rotated 90 degrees (rot.per). Other parameters are detailed in the wordcloud documentation here.

One of the more important parameters that can be added is color (colors). By default, wordclouds are black letters on a white background. If you want the word color to vary with the frequency, you need to create a variable that details to the wordcloud function how many colors you want and from what color palette. A number of color palettes are pre-defined in R (see here). Here’s a sample command to create a color variable that can be used with the wordcloud package:

color <- brewer.pal(8,”Spectral”)

The parameters in the parentheses indicate first, the number of colors desired (8 in the example above), and second, the palette title from the list noted above. Generating the wordcloud with the color palette applied involves adding one more variable to the command:

wordcloud(corp, max.words = 100, min.freq=15, random.order = FALSE, colors = color, scale=c(8, .3))

Finally, if you want to output the wordcloud as an image file, you can adjust the command to generate the wordcloud as, for instance, a PNG file. First, tell R to create the PNG file:

png(“wordcloud.png”, width=1280,height=800)

The text in quotes is the name of the PNG file to be created. The other two commands indicate the size of the PNG. Then create the wordcloud with the parameters you want:

wordcloud(corp, max.words = 100, random.order = FALSE, colors = color, scale=c(8, .3))

And, finally, pass the wordcloud just created on to the PNG file with this function:

dev.off()

If all goes according to plan, you will have created a PNG file with a wordcloud of your cleaned up corpus of text:

 

 

Source Information:

Reading PDF files into R for text mining

Building Wordclouds in R

Word cloud in R

R (Linux) – basic installation

To install the R programming environment on Linux is pretty straightforward, but it does require a little bit of know how in order to find the correct packages. As is typically the case with Linux, there are multiple ways to get things done. I like to use Synaptic for installing and removing software, but you can also use the software manager that comes with your Linux distribution (in Linux Mint it’s called Software Manager) or the command line (in KDE based distributions, Konsole).

For the most up-to-date installation of R, it’s actually best to install directly from the R repository. A list of Linux repositories for the R environment is located here. In order to install from the repository, you need to update your list of repositories in Synaptic. To access your repository list in Synaptic, click on Settings -> Repositories.

In the new Software Sources window, click on “Additional repositories” and you’ll get this window:

Click on Add a new repository. You’ll get this window:

The exact information you put into that window will vary based on which mirror you chose. Here is what I added in mine:

deb https://cran.cnr.berkeley.edu/bin/linux/ubuntu xenial/

In order to ensure you have the right files and to follow best security practices, you should install the signing key as well. Directions for installing the signing key are found here, but it can be done with a simple command from a terminal:

sudo apt-key adv –keyserver keyserver.ubuntu.com –recv-keys E084DAB9

Once you have done all of that, you can install R from Synaptic.

First, open Synaptic, which will require your password. You’ll get the basic Synaptic Package Manager window:

Next, in the search box, search for “r-base”. Right-click it and select “Mark for installation” to install “r-base”:

In the above screenshot, I have already installed r-base, so the option “Mark for installation” is greyed out. But, obviously, that’s what I already did. When you select this, Synaptic will automatically select all the other necessary packages (there are about 10 to 15 additional packages necessary for R to run: r-cran-class, r-cran-lattice, r-cran-spatial, r-cran-survival, r-cran-codetools, r-cran-nnet, r-cran-mass, r-cran-boot, r-cran-nlme, r-cran-rpart, r-cran-cluster, r-cran-kernsmooth, r-cran-foreign, r-cran-mgcv, r-cran-matrix, r-recommended, r-base-core).

If you plan on installing any other R packages, it’s not a bad idea to also install “r-base-dev,” as it helps fill in dependencies for other packages.

Once you’ve selected r-base, hit Apply in Synaptic and all the software will be installed.

You now have the base software for R installed.

To open the R environment in a terminal, launch a terminal and simply type “R” at the prompt, like this:

Here’s where things can get a little complicated. To do different things in R requires various libraries or packages. Some of these can be installed using the R terminal while others need to be installed from your Linux distribution’s repositories. To install a library or package using the R terminal, you use the following command once you have opened the R environment:

install.packages(“PACKAGENAME”)

The first time you run this, the R environment will ask you to select a mirror.

Choose one close to your location. R will then install the package, assuming you type everything correctly.

If you run into an error message, there are several possibilities. First, check to make sure you typed everything correctly. R is not forgiving on spelling mistakes. Second, if the error is something like:

installation of package ‘PACKAGENAME’ had non-zero exit status

Or

dependency ‘PACKAGENAME’ is not available

There is a good chance that you need to install a package or library using Synaptic (or from a terminal using apt). For instance, to install the “tm” package, there is an unsatisfied dependency (meaning, a library or package that needs to be installed but cannot be installed using the R installer). The dependency is the ‘slam’ package. This can be installed using Synaptic (or, from a terminal, using the command “sudo apt-get install r-cran-slam”). Once you’ve installed the dependency, try re-installing the package and the error messages should go away.

Iceland – final post – drone footage

I took my drone to Iceland with us. I knew that there were lots of places where I could fly the drone and it seemed like the ideal opportunity to take advantage of the drone to get shots we couldn’t otherwise get. Here’s my Iceland drone compilation:

Iceland – Day 7 – The Golden Circle: Gulffoss, Geysir, Strokkur, and Þingvellir National Park

Debi, Toren, and Ryan at Þingvellir National Park

We saved some of the most visited sites for our last day in Iceland. Lots of buses take tourists to visit three sights in a single day: Gullfoss, Geysir, and Þingvellir National Park. This is often referred to as The Golden Circle as you can include Seljalandfoss and actually make it into a circle. Since we had already visited Seljalandfoss, we headed straight to Gullfoss.

Gullfoss is a very powerful waterfall with two levels.

To get a good view of how tall the lower falls are, you need to hike up a bit so you can see down into the trench it has carved.

Toren, Debi, and Ryan at Gulfoss
Toren, Debi, and Ryan at Gulfoss

Just down the road from Gulfoss are two geysers, Geysir and Strokkur. Geysir was the first geyser to be documented by modern Europeans and is the source of the English word “geyser.” Geysir no longer regularly erupts, but Strokkur does every few minutes.

We walked around the geysers for a bit and watched several eruptions, then jumped back in the car and headed to our final destination for the day, Þingvellir National Park. Þingvellir is cool for a lot of reasons. First, it was the original seat of Iceland’s Parliament and an important meeting place for the various tribes of Iceland for a long time. Second, it is the location where two continental plates are separating by about 2 centimeters per year, and you can literally see the result as the area is being pulled apart. There is a large canyon you can walk down that is the result of tectonic plates moving. You can see the canyon in this photosphere:

Here’s another photosphere from the Parliament rock, where the laws used to be read:

We spent a couple of hours here walking around the lake, streams, the church, and the canyon.

Ryan, Debi, and Rosemary at Þingvellir National Park
Ryan, Debi, and Rosemary at Þingvellir National Park

Here’s a short clip of a waterfall that drops right into the canyon:

And a photo of us in front of the waterfall:

Debi, Toren, and Ryan at Þingvellir National Park
Debi, Toren, and Ryan at Þingvellir National Park

We actually had big plans for this evening – it was time to try Icelandic cuisine. We made a reservation for a nice restaurant in Reykjavik, Þrír frakkar, where they serve traditional Icelandic fare. We ordered three appetizers and two entrees to split between the four of us. First up, fermented shark:

fermented shark
fermented shark

Everyone but Debi was able to get their piece of frozen, fermented shark down. Debi gagged on hers. Imagine the most fishy tasting fish you’ve ever had, then leave it to spoil for, let’s say, a week. Then freeze it. That’s what fermented shark tastes like. Not a winner.

Next up was, sadly, puffin breast:

puffin
puffin

We asked on our whale and puffin viewing trip if puffins were endangered and they said no, so I didn’t feel bad ordering this. It’s basically thin strips of puffin breast, perhaps lightly cooked, served with a mustard sauce. It tasted kind of like chicken, but more oily and stringy. Everyone tried it, but I ended up eating most of it.

We also ordered fish stew as an appetizer, which wasn’t particularly exotic, and most everyone liked it. For the entrees, it was a lamb steak (split between Debi and Rosemary) and a horse steak (split between Toren and me). The steaks were all good; horse tastes a lot like cow.

Dinner was crazy-expensive, but we got to sample the local cuisine.

After dinner, we headed back to our B&B to pack up and get ready for our early flight the next day. We did stop briefly at the park near our B&B to let Toren run around a bit, but otherwise that pretty much wraps up our trip to Iceland. Though, see my next post where I highlight one other thing we did while we were there…