Fixed RSS feed.

Author: sjmgarnier

_config.yml

@@ -68,7 +68,7 @@ people:
       url: https://www.linkedin.com/pub/courtney-rockenbach/1a/8a8/794
 
 - name: Megan Wilson
-  pic: default
+  pic: megan
   position: Graduate Student (rotation)
 
 - name: Maggie Wisniewska

_includes/rss_footer.html

@@ -1,5 +1,5 @@
 <footer class="bg-darkest-gray">
     <div class="container">
-        <span style="color: white">Subscribe <a href="{{ "/feed.xml" | prepend: site.baseurl }}">via RSS</a></span>
+        <span style="color: white">Subscribe <a href="{{ "/feed.xml" | prepend: site.url }}">via RSS</a></span>
     </div>
 </footer>

_posts/2014-01-05-r-vs-python-round-1.md

@@ -4,9 +4,10 @@ date: 2014-01-05
 author: Simon Garnier
 layout: post
 type: post
-category: 
+category:
     - blog
     - rvspython
+    - r
 published: true
 
 ---
@@ -31,11 +32,11 @@ ___
 
 ##### 1 - Introduction #####
 
-For this first challenge, we will use data collected by Randy for his recent post on the ["Top 25 most violence packed 
-films" in the history of the movie industry](www.randalolson.com/2013/12/31/most-violence-packed-films/). For his post, 
-Randy generated a simple horizontal barchart showing the top 25 more violent films ordered by number of on screen deaths 
-per minute. In the rest of this document, we will show you how to reproduce this graph using Python and how to achieve a 
-similar result with R. We will detail the different steps of the process and provide for each step the corresponding 
+For this first challenge, we will use data collected by Randy for his recent post on the ["Top 25 most violence packed
+films" in the history of the movie industry](www.randalolson.com/2013/12/31/most-violence-packed-films/). For his post,
+Randy generated a simple horizontal barchart showing the top 25 more violent films ordered by number of on screen deaths
+per minute. In the rest of this document, we will show you how to reproduce this graph using Python and how to achieve a
+similar result with R. We will detail the different steps of the process and provide for each step the corresponding
 code. You will also find the entire codes at the end of this document.
 
 And now without further ado, let's get started!
@@ -47,7 +48,7 @@ First thing first, let's set up our working environment by loading some necessar
 {% highlight r %}
 # Load libraries
 library(lattice)        # Very versatile graphics package
-library(latticeExtra)   # Addition to lattice that makes layering graphs a breathe 
+library(latticeExtra)   # Addition to lattice that makes layering graphs a breathe
 {% endhighlight %}
 
 {% highlight python %}
@@ -58,8 +59,8 @@ from pandas import *
 {% endhighlight %}
 
 
-Now let's load the data for today's job. The raw data were scraped by Randy (using Python) from 
-[www.MovieBodyCounts.com](http://www.MovieBodyCounts.com) and he generously provided the result of his hard work on 
+Now let's load the data for today's job. The raw data were scraped by Randy (using Python) from
+[www.MovieBodyCounts.com](http://www.MovieBodyCounts.com) and he generously provided the result of his hard work on
 FigShare at this address: [http://dx.doi.org/10.6084/m9.figshare.889719](http://dx.doi.org/10.6084/m9.figshare.889719).
 
 {% highlight r %}
@@ -73,13 +74,13 @@ body_count_data = read_csv("http://files.figshare.com/1332945/film_death_counts.
 {% endhighlight %}
 
 
-For each movie, the data frame contains a column for the total number of on screen deaths ("Body_Count") and a column for 
-the duration ("Length_Minutes"). We will now create an extra column for the number of on screen deaths per minute of each 
+For each movie, the data frame contains a column for the total number of on screen deaths ("Body_Count") and a column for
+the duration ("Length_Minutes"). We will now create an extra column for the number of on screen deaths per minute of each
 movie ("Deaths_Per_Minute")
 
 {% highlight r %}
-# Compute on screen deaths per minute for each movie. 
-body.count.data <- within(body.count.data, { 
+# Compute on screen deaths per minute for each movie.
+body.count.data <- within(body.count.data, {
   Deaths_Per_Minute <- Body_Count / Length_Minutes
   ord <- order(Deaths_Per_Minute, decreasing = TRUE)  # useful later
 })
@@ -92,7 +93,7 @@ body_count_data["Deaths_Per_Minute"] = (body_count_data["Body_Count"].apply(floa
 {% endhighlight %}
 
 
-Now we will reorder the data frame by (descending) number of on screen deaths per minute, and select the top 25 most 
+Now we will reorder the data frame by (descending) number of on screen deaths per minute, and select the top 25 most
 violent movies according to this criterion.
 
 {% highlight r %}
@@ -112,7 +113,7 @@ body_count_data = body_count_data.sort("Deaths_Per_Minute", ascending=True)
 {% endhighlight %}
 
 
-In Randy's graph, the "y" axis shows the film title with the release date. We will now generate the full title for each 
+In Randy's graph, the "y" axis shows the film title with the release date. We will now generate the full title for each
 movie following a "Movie name (year)" format, and append it to the data frame.
 
 {% highlight r %}
@@ -138,7 +139,7 @@ ax.xaxis.tick_bottom()data["Full_Title"] = array(full_title)
 {% endhighlight %}
 
 
-Now we are ready to generate the barchart. We're going to start with the default options and then we will make this thing 
+Now we are ready to generate the barchart. We're going to start with the default options and then we will make this thing
 look pretty.
 
 {% highlight r %}
@@ -167,12 +168,12 @@ yticks(range(len(body_count_data["Full_Title"])), body_count_data["Full_Title"].
 
 ![Base Python graph](/img/posts/2014-01-05-r-vs-python-round-1/Py/basePy.png){: .full }
 
-Ok, now let's make this pretty. 
+Ok, now let's make this pretty.
 
 {% highlight r %}
 # Create theme
 my.bloody.theme <- within(trellis.par.get(), {    # Initialize theme with default value
-  axis.line$col <- NA                             # Remove axes 
+  axis.line$col <- NA                             # Remove axes
   plot.polygon <- within(plot.polygon, {
     col <- "#8A0606"                              # Set bar colors to a nice bloody red
     border <- NA                                  # Remove bars' outline
@@ -187,7 +188,7 @@ my.bloody.theme <- within(trellis.par.get(), {    # Initialize theme with defaul
 
 # Update figure with new theme + other improvements (like a title for instance)
 graph <- update(
-  graph, 
+  graph,
   main='25 most violence packed films by deaths per minute',    # Title of the barchart
   par.settings = my.bloody.theme,                               # Use custom theme
   xlab = NULL,                                                  # Remove label of x axis
@@ -222,7 +223,7 @@ ax.xaxis.grid(color="white", linestyle="-")
 
 ![Pretty Python graph](/img/posts/2014-01-05-r-vs-python-round-1/Py/prettyPy.png){: .full }
 
-Finally, the last thing we want to add to our graph is the number of deaths per minute and the duration of each movie on 
+Finally, the last thing we want to add to our graph is the number of deaths per minute and the duration of each movie on
 the right of the graph.
 
 {% highlight r %}
@@ -231,8 +232,8 @@ body.count.data <- within(body.count.data, {
   Deaths_Per_Minute_With_Length = paste0(round(body.count.data$Deaths_Per_Minute, digits=2), " (", body.count.data$Length_Minutes, " mins)")
 })
 
-# Add number of on screen deaths per minute and duration of movies at the end of each bar 
-graph <- graph + layer(with(body.count.data, 
+# Add number of on screen deaths per minute and duration of movies at the end of each bar
+graph <- graph + layer(with(body.count.data,
   panel.text(
     Deaths_Per_Minute,                  # x position of the text
     25:1,                               # y position of the text
@@ -279,7 +280,7 @@ library(grid)  # Graphics library with better image plotting capabilities
 
 # Download a pretty background image; mode is set to "wb" because it seems that
 # Windows needs it. I don't use Windows, I can't confirm
-download.file(url = "http://www.theswarmlab.com/wp-content/uploads/2014/01/bloody_gun.jpg", 
+download.file(url = "http://www.theswarmlab.com/wp-content/uploads/2014/01/bloody_gun.jpg",
               destfile = "bloody_gun.jpg", quiet = TRUE, mode = "wb")
 
 # Load gun image using "readJPEG" from the "jpeg" package
@@ -306,11 +307,10 @@ ___
 
 R and Python source codes are available [here](https://github.com/morpionZ/R-vs-Python/tree/master/Deadliest%20movies/code).
 
-For F# fan, [Terje Tyldum](http://terjetyl.ghost.io/) has written his version of the code in F# 
+For F# fan, [Terje Tyldum](http://terjetyl.ghost.io/) has written his version of the code in F#
 [here](http://terjetyl.ghost.io/f-charting-challenge/).
 
-Randy and I also recommend that you check out 
+Randy and I also recommend that you check out
 [this post](http://nbviewer.ipython.org/github/yaph/ipython-notebooks/blob/master/Exploring%20Movie%20Body%20Counts.ipynb)
-by [Ramiro Gómez](http://ramiro.org/) ([@yaph](https://twitter.com/yaph)) where he does a more in-depth analysis of the 
+by [Ramiro Gómez](http://ramiro.org/) ([@yaph](https://twitter.com/yaph)) where he does a more in-depth analysis of the
 data set we used for today’s challenge.
-

_posts/2014-01-12-r-vs-python-round-2-1.md

@@ -4,9 +4,10 @@ date: 2014-01-12
 author: Simon Garnier
 layout: post
 type: post
-category: 
+category:
     - blog
     - rvspython
+    - r
 published: true
 
 ---
@@ -48,7 +49,7 @@ library(RCurl)      # Everything necessary to grab webpages on the Web
 library(XML)        # Everything necessary to parse XML and HTML code
 library(pbapply)    # Progress bars!!! Just because why not :-)
 
-# Create curl handle which can be used for multiple HHTP requests. 
+# Create curl handle which can be used for multiple HHTP requests.
 # followlocation = TRUE in case one of the URLs we want to grab is a redirection
 # link.
 curl <- getCurlHandle(useragent = "R", followlocation = TRUE)
@@ -77,7 +78,7 @@ With all this information in mind, our first task is to create a list of all the
 # Prepare URLs of the movie lists alphabetically ordered by first letter of
 # movie title (capital A to Z, except for v and y) + "numbers" list (for movies
 # which title starts with a number)
-urls.by.letter <- paste0("http://www.moviebodycounts.com/movies-", 
+urls.by.letter <- paste0("http://www.moviebodycounts.com/movies-",
                          c("numbers", LETTERS[1:21], "v", "W" , "x", "Y", "Z"), ".htm")
 
 {% endhighlight %}
@@ -100,15 +101,15 @@ For each movie list, we will...
 {% highlight r %}
 # For each movie list... For loops are frowned upon in R, let's use the classier
 # apply functions instead. Here I use the pblapply from the pbapply package.
-# It's equivalent to the regular lapply function, but it provides a neat 
-# progress bar. Unlist to get a vector. 
+# It's equivalent to the regular lapply function, but it provides a neat
+# progress bar. Unlist to get a vector.
 urls.by.movie <- unlist(pblapply(urls.by.letter, FUN = function(URL) {
 {% endhighlight %}
 
 
 {% highlight python %}
 list_of_films = []
-  
+
 # Go through each movie list page and gather all of the movie web page URLs
 for letter in letters:
   try:
@@ -149,9 +150,9 @@ for letter in letters:
 
 
 {% highlight r %}
-  # Extract desired links from HTML content using XPath. 
+  # Extract desired links from HTML content using XPath.
   # The desired links are all the URLs ("a/@href") directly following
-  # ("/following::") the image which source file is called "graphic-movies.jpg" 
+  # ("/following::") the image which source file is called "graphic-movies.jpg"
   # ("//img[@src='graphic-movies.jpg']").
   links <- as.vector(xpathSApply(parsed.html, "//img[@src='graphic-movies.jpg']/following::a/@href"))
 
@@ -181,7 +182,7 @@ urls.by.movie <- urls.by.movie[-ix]
           # The URL is in between parentheses (), so we can simply split the string on those
           # Some URLs are full URLs, e.g. www.moviebodycounts.com/movie_name.html, so splitting on the / gives us only the page name
           list_of_films.append(line.split("(")[-1].strip(")").split("/")[-1])
-      
+
     # If the movie list page doesn't exist, keep going
     except:
       print "\nerror with " + letter + "\n"
@@ -194,7 +195,7 @@ For each movie, we will...
 
 
 {% highlight r %}
-# For each movie... 
+# For each movie...
 # do.call(rbind, ...) to reorganize the results in a nice data frame
 data <- do.call(rbind, pblapply(urls.by.movie, FUN = function(URL) {
 {% endhighlight %}
@@ -205,7 +206,7 @@ data <- do.call(rbind, pblapply(urls.by.movie, FUN = function(URL) {
 # extract the movie name, kill counts, etc.
 out_file = open("film-death-counts.csv", "wb")
 out_file.write("Film,Year,Kill_Count,IMDB_url\n")
-  
+
 for film_page in list_of_films:
   try:
       # The information we're looking for on the page:
@@ -320,15 +321,15 @@ for film_page in list_of_films:
   # Using gsub, remove everything in parenthesis and all non number characters
   Body_Count <- gsub("\\(.*?\\)", " ", Body_Count)
   Body_Count <- gsub("[^0-9]+", " ", Body_Count)
-  
+
   # In case the total count has been split, we want to separate these numbers
   # from each other so that we can add them up later. Using strsplit, split the
   # character string at spaces
   Body_Count <- unlist(strsplit(Body_Count, " "))
-  
+
   # For now, we have extracted characters. Transform them into numbers.
   Body_Count <- as.numeric(Body_Count)
-  
+
   # Sum up the numbers (in case they have been split into separate categories.
   Body_Count <- sum(Body_Count, na.rm = TRUE)
 {% endhighlight %}
@@ -380,6 +381,6 @@ ___
 
 #### 4 - Bonus for the braves ####
 
-Today's challenge was code and text heavy. No pretty pictures to please the eye. So, for all the brave people who made it to the end, here is a cat picture :-) 
+Today's challenge was code and text heavy. No pretty pictures to please the eye. So, for all the brave people who made it to the end, here is a cat picture :-)
 
-![Programming cat](/img/posts/2014-01-12-r-vs-python-round-2-1/programming_cat.jpg){: .full }
+![Programming cat](/img/posts/2014-01-12-r-vs-python-round-2-1/programming_cat.jpg){: .full }