{"id":11694,"date":"2018-12-31T07:36:32","date_gmt":"2018-12-31T12:36:32","guid":{"rendered":"https:\/\/rud.is\/b\/?p=11694"},"modified":"2018-12-31T07:36:32","modified_gmt":"2018-12-31T12:36:32","slug":"exploring-2018-r-bloggers-r-weekly-posts-with-feedly-the-seymour-package","status":"publish","type":"post","link":"https:\/\/rud.is\/b\/2018\/12\/31\/exploring-2018-r-bloggers-r-weekly-posts-with-feedly-the-seymour-package\/","title":{"rendered":"Exploring 2018 R-bloggers & R Weekly Posts with Feedly & the ‘seymour’ package"},"content":{"rendered":"

Well, 2018 has flown by and today seems like an appropriate time to take a look at the landscape of R bloggerdom as seen through the eyes of readers of R-bloggers<\/a> and R Weekly<\/a>. We’ll do this via a new package designed to make it easier to treat Feedly<\/a> as a data source: seymour<\/code> [GL<\/a> | GH<\/a>] (which is a pun-ified name based on a well-known phrase from Little Shop of Horrors<\/em>).<\/p>\n

The seymour<\/code> package builds upon an introductory Feedly API blog post from back in April 2018<\/a> and covers most<\/em> of the “getters” in the API (i.e. you won’t be adding anything to or modifying anything in Feedly through this package unless you PR into it with said functions). An impetus for finally creating the package came about when I realized that you don’t need a Feedly account to use the search or stream endpoints. You do<\/em> get more data back if you have a developer token<\/a> and can also access your own custom Feedly components if you have one. If you are a “knowledge worker” and do not have a Feedly account (and, really, a Feedly Pro account) you are missing out. But, this isn’t a rah-rah post about Feedly, it’s a rah-rah post about R! Onwards!<\/p>\n

Feeling Out The Feeds<\/h3>\n

There are a bunch of different ways to get Feedly metadata about an RSS feed. One easy way is to just use the RSS feed URL itself:<\/p>\n

library(seymour) # git[la|hu]b\/hrbrmstr\/seymour\nlibrary(hrbrthemes) # git[la|hu]b\/hrbrmstr\/hrbrthemes\nlibrary(lubridate)\nlibrary(tidyverse)\n<\/code><\/pre>\n
r_bloggers <- feedly_feed_meta(\"http:\/\/feeds.feedburner.com\/RBloggers\")\nr_weekly <- feedly_feed_meta(\"https:\/\/rweekly.org\/atom.xml\")\nr_weekly_live <- feedly_feed_meta(\"https:\/\/feeds.feedburner.com\/rweeklylive\")\n\nglimpse(r_bloggers)\n## Observations: 1\n## Variables: 14\n## $ feedId      <chr> \"feed\/http:\/\/feeds.feedburner.com\/RBloggers\"\n## $ id          <chr> \"feed\/http:\/\/feeds.feedburner.com\/RBloggers\"\n## $ title       <chr> \"R-bloggers\"\n## $ subscribers <int> 24518\n## $ updated     <dbl> 1.546227e+12\n## $ velocity    <dbl> 44.3\n## $ website     <chr> \"https:\/\/www.r-bloggers.com\"\n## $ topics      <I(list)> data sci....\n## $ partial     <lgl> FALSE\n## $ iconUrl     <chr> \"https:\/\/storage.googleapis.com\/test-site-assets\/X...\n## $ visualUrl   <chr> \"https:\/\/storage.googleapis.com\/test-site-assets\/X...\n## $ language    <chr> \"en\"\n## $ contentType <chr> \"longform\"\n## $ description <chr> \"Daily news and tutorials about R, contributed by ...\n\nglimpse(r_weekly)\n## Observations: 1\n## Variables: 13\n## $ feedId      <chr> \"feed\/https:\/\/rweekly.org\/atom.xml\"\n## $ id          <chr> \"feed\/https:\/\/rweekly.org\/atom.xml\"\n## $ title       <chr> \"RWeekly.org - Blogs to Learn R from the Community\"\n## $ subscribers <int> 876\n## $ updated     <dbl> 1.546235e+12\n## $ velocity    <dbl> 1.1\n## $ website     <chr> \"https:\/\/rweekly.org\/\"\n## $ topics      <I(list)> data sci....\n## $ partial     <lgl> FALSE\n## $ iconUrl     <chr> \"https:\/\/storage.googleapis.com\/test-site-assets\/2...\n## $ visualUrl   <chr> \"https:\/\/storage.googleapis.com\/test-site-assets\/2...\n## $ contentType <chr> \"longform\"\n## $ language    <chr> \"en\"\n\nglimpse(r_weekly_live)\n## Observations: 1\n## Variables: 9\n## $ id          <chr> \"feed\/https:\/\/feeds.feedburner.com\/rweeklylive\"\n## $ feedId      <chr> \"feed\/https:\/\/feeds.feedburner.com\/rweeklylive\"\n## $ title       <chr> \"R Weekly Live: R Focus\"\n## $ subscribers <int> 1\n## $ updated     <dbl> 1.5461e+12\n## $ velocity    <dbl> 14.7\n## $ website     <chr> \"https:\/\/rweekly.org\/live\"\n## $ language    <chr> \"en\"\n## $ description <chr> \"Live Updates from R Weekly\"\n<\/code><\/pre>\n
Feedly uses some special terms, one of which (above) is velocity<\/code>. “Velocity” is simply the average number of articles published weekly (Feedly’s platform updates that every few weeks for each feed). R-bloggers has over 24,000 Feedly subscribers so any post-rankings we do here should be fairly representative. I included both the “live” and the week-based R Weekly feeds as I wanted to compare post coverage between R-bloggers and R Weekly in terms of raw content.<\/p>\n
On the other hand, R Weekly’s “weekly” RSS feed has less than 1,000 subscribers. WAT?! While I have mostly nothing against R-bloggers-proper I heartily encourage ardent readers to also subscribe to R Weekly and perhaps even consider switching to it<\/em> (or at least adding the individual blog feeds they monitor<\/a> to your own Feedly). It wasn’t until the Feedly API that I had any idea of how many folks were really viewing my R blog posts since we must provide a full post RSS feed to R-bloggers and get very little in return (at least in terms of data). R Weekly uses a link counter but redirects all clicks to the blog author’s site where we can use logs or analytics platforms to measure engagement. R Weekly is also run by a group of volunteers (more eyes == more posts they catch!) and has a Patreon<\/a> where the current combined weekly net is likely not enough to buy each volunteer a latte. No ads, a great team and direct engagement stats for the community of R bloggers seems like a great deal for $1.00 USD. If you weren’t persuaded by the above rant, then perhaps at least consider installing this<\/a> (from source that you control).<\/p>\n
Lastly, I believe I’m that “1<\/code>” subscriber to R Weekly Live O_o. But, I digress.<\/p>\n
We’ve got the feedId<\/code>s (which can be used as “stream” ids) so let’s get cracking!<\/p>\n
Binding Up The Posts<\/h3>\n
We need to use the feedId<\/code> in calls to feedly_stream()<\/code> to get the individual posts. The API claims<\/em> there’s a temporal parameter that allows one to get posts only after a certain date but I couldn’t get it to work (PRs are welcome on any community source code portal you’re most comfortable in if you’re craftier than I am). As a result, we need to make a guess as to how many calls we need to make for two of the three feeds. Basic maths of 44 * 52 \/ 1000<\/code> suggests ~3 should suffice for R Weekly (live) and R-bloggers but let’s do 5 to be safe. We should be able to get R Weekly (weekly) in one go.<\/p>\n
r_weekly_wk <- feedly_stream(r_weekly$feedId)\n\nrange(r_weekly_wk$items$published) # my preview of this said it got back to 2016!\n## [1] \"2016-05-20 20:00:00 EDT\" \"2018-12-30 19:00:00 EST\"\n\n# NOTE: If this were more than 3 I'd use a loop\/iterator\n# In reality, I should make a helper function to do this for you (PRs welcome)\n\nr_blog_1 <- feedly_stream(r_bloggers$feedId)\nr_blog_2 <- feedly_stream(r_bloggers$feedId, continuation = r_blog_1$continuation)\nr_blog_3 <- feedly_stream(r_bloggers$feedId, continuation = r_blog_2$continuation)\n\nr_weekly_live_1 <- feedly_stream(r_weekly_live$feedId)\nr_weekly_live_2 <- feedly_stream(r_weekly_live$feedId, continuation = r_weekly_live_1$continuation)\nr_weekly_live_3 <- feedly_stream(r_weekly_live$feedId, continuation = r_weekly_live_2$continuation)\n\nbind_rows(r_blog_1$items, r_blog_2$items, r_blog_3$items) %>% \n  filter(published >= as.Date(\"2018-01-01\")) -> r_blog_stream\n\nbind_rows(r_weekly_live_1$items, r_weekly_live_2$items, r_weekly_live_3$items) %>% \n  filter(published >= as.Date(\"2018-01-01\")) -> r_weekly_live_stream\n\nr_weekly_wk_stream <- filter(r_weekly_wk$items, published >= as.Date(\"2018-01-01\"))\n<\/code><\/pre>\n
Let’s take a look:<\/p>\n
glimpse(r_weekly_wk_stream)\n## Observations: 54\n## Variables: 27\n## $ id                  <chr> \"2nIALmjjlFcpPJKakm2k8hjka0FzpApixM7HHu8B0...\n## $ originid            <chr> \"https:\/\/rweekly.org\/2018-53\", \"https:\/\/rw...\n## $ fingerprint         <chr> \"114357f1\", \"199f78d0\", \"9adc236e\", \"63f99...\n## $ title               <chr> \"R Weekly 2018-53 vroom, Classification\", ...\n## $ updated             <dttm> 2018-12-30 19:00:00, 2018-12-23 19:00:00,...\n## $ crawled             <dttm> 2018-12-31 00:51:39, 2018-12-23 23:46:49,...\n## $ published           <dttm> 2018-12-30 19:00:00, 2018-12-23 19:00:00,...\n## $ alternate           <list> [<https:\/\/rweekly.org\/2018-53.html, text\/...\n## $ canonicalurl        <chr> \"https:\/\/rweekly.org\/2018-53.html\", \"https...\n## $ unread              <lgl> TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, F...\n## $ categories          <list> [<user\/c45e5b02-5a96-464c-bf77-4eea75409c...\n## $ engagement          <int> 1, 5, 5, 3, 2, 3, 1, 2, 3, 2, 4, 3, 2, 2, ...\n## $ engagementrate      <dbl> 0.33, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...\n## $ recrawled           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...\n## $ tags                <list> [NULL, NULL, NULL, NULL, NULL, NULL, NULL...\n## $ content_content     <chr> \"<p>Hello and welcome to this new issue!<\/...\n## $ content_direction   <chr> \"ltr\", \"ltr\", \"ltr\", \"ltr\", \"ltr\", \"ltr\", ...\n## $ origin_streamid     <chr> \"feed\/https:\/\/rweekly.org\/atom.xml\", \"feed...\n## $ origin_title        <chr> \"RWeekly.org - Blogs to Learn R from the C...\n## $ origin_htmlurl      <chr> \"https:\/\/rweekly.org\/\", \"https:\/\/rweekly.o...\n## $ visual_processor    <chr> \"feedly-nikon-v3.1\", \"feedly-nikon-v3.1\", ...\n## $ visual_url          <chr> \"https:\/\/github.com\/rweekly\/image\/raw\/mast...\n## $ visual_width        <int> 372, 672, 1000, 1000, 1000, 1001, 1000, 10...\n## $ visual_height       <int> 479, 480, 480, 556, 714, 624, 237, 381, 36...\n## $ visual_contenttype  <chr> \"image\/png\", \"image\/png\", \"image\/gif\", \"im...\n## $ webfeeds_icon       <chr> \"https:\/\/storage.googleapis.com\/test-site-...\n## $ decorations_dropbox <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...\n\nglimpse(r_weekly_live_stream)\n## Observations: 1,333\n## Variables: 27\n## $ id                  <chr> \"rhkRVQ8KjjGRDQxeehIj6RRIBGntdni0ZHwPTR8B3...\n## $ originid            <chr> \"https:\/\/link.rweekly.org\/ckb\", \"https:\/\/l...\n## $ fingerprint         <chr> \"c11a0782\", \"c1897fc3\", \"c0b36206\", \"7049e...\n## $ title               <chr> \"Top Tweets of 2018\", \"My #Best9of2018 twe...\n## $ crawled             <dttm> 2018-12-29 11:11:52, 2018-12-28 11:24:22,...\n## $ published           <dttm> 2018-12-28 19:00:00, 2018-12-27 19:00:00,...\n## $ canonical           <list> [<https:\/\/link.rweekly.org\/ckb, text\/html...\n## $ alternate           <list> [<http:\/\/feedproxy.google.com\/~r\/RWeeklyL...\n## $ unread              <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, ...\n## $ categories          <list> [<user\/c45e5b02-5a96-464c-bf77-4eea75409c...\n## $ tags                <list> [<user\/c45e5b02-5a96-464c-bf77-4eea75409c...\n## $ canonicalurl        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...\n## $ ampurl              <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...\n## $ cdnampurl           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...\n## $ engagement          <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...\n## $ summary_content     <chr> \"<p>maraaverick.rbind.io<\/p><img width=\\\"1...\n## $ summary_direction   <chr> \"ltr\", \"ltr\", \"ltr\", \"ltr\", \"ltr\", \"ltr\", ...\n## $ origin_streamid     <chr> \"feed\/https:\/\/feeds.feedburner.com\/rweekly...\n## $ origin_title        <chr> \"R Weekly Live: R Focus\", \"R Weekly Live: ...\n## $ origin_htmlurl      <chr> \"https:\/\/rweekly.org\/live\", \"https:\/\/rweek...\n## $ visual_url          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...\n## $ visual_processor    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...\n## $ visual_width        <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...\n## $ visual_height       <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...\n## $ visual_contenttype  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...\n## $ decorations_dropbox <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...\n## $ decorations_pocket  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...\n\nglimpse(r_blog_stream)\n## Observations: 2,332\n## Variables: 34\n## $ id                  <chr> \"XGq6cYRY3hH9\/vdZr0WOJiPdAe0u6dQ2ddUFEsTqP...\n## $ keywords            <list> [\"R bloggers\", \"R bloggers\", \"R bloggers\"...\n## $ originid            <chr> \"https:\/\/datascienceplus.com\/?p=19513\", \"h...\n## $ fingerprint         <chr> \"2f32071a\", \"332f9548\", \"2e6f8adb\", \"3d7ed...\n## $ title               <chr> \"Leaf Plant Classification: Statistical Le...\n## $ crawled             <dttm> 2018-12-30 22:35:22, 2018-12-30 19:01:25,...\n## $ published           <dttm> 2018-12-30 19:26:20, 2018-12-30 13:18:00,...\n## $ canonical           <list> [<https:\/\/www.r-bloggers.com\/leaf-plant-c...\n## $ author              <chr> \"Giorgio Garziano\", \"Sascha W.\", \"Economet...\n## $ alternate           <list> [<http:\/\/feedproxy.google.com\/~r\/RBlogger...\n## $ unread              <lgl> TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, F...\n## $ categories          <list> [<user\/c45e5b02-5a96-464c-bf77-4eea75409c...\n## $ entities            <list> [<c(\"nlp\/f\/entity\/en\/-\/leaf plant classif...\n## $ engagement          <int> 50, 39, 482, 135, 33, 12, 13, 41, 50, 31, ...\n## $ engagementrate      <dbl> 1.43, 0.98, 8.76, 2.45, 0.59, 0.21, 0.22, ...\n## $ enclosure           <list> [NULL, NULL, NULL, NULL, <c(\"https:\/\/0.gr...\n## $ tags                <list> [NULL, NULL, NULL, NULL, NULL, NULL, NULL...\n## $ recrawled           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...\n## $ updatecount         <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...\n## $ content_content     <chr> \"<p><div><div><div><div data-show-faces=\\\"...\n## $ content_direction   <chr> \"ltr\", \"ltr\", \"ltr\", \"ltr\", \"ltr\", \"ltr\", ...\n## $ summary_content     <chr> \"CategoriesAdvanced Modeling\\nTags\\nLinear...\n## $ summary_direction   <chr> \"ltr\", \"ltr\", \"ltr\", \"ltr\", \"ltr\", \"ltr\", ...\n## $ origin_streamid     <chr> \"feed\/http:\/\/feeds.feedburner.com\/RBlogger...\n## $ origin_title        <chr> \"R-bloggers\", \"R-bloggers\", \"R-bloggers\", ...\n## $ origin_htmlurl      <chr> \"https:\/\/www.r-bloggers.com\", \"https:\/\/www...\n## $ visual_processor    <chr> \"feedly-nikon-v3.1\", \"feedly-nikon-v3.1\", ...\n## $ visual_url          <chr> \"https:\/\/i0.wp.com\/datascienceplus.com\/wp-...\n## $ visual_width        <int> 383, 400, NA, 286, 456, 250, 450, 456, 397...\n## $ visual_height       <int> 309, 300, NA, 490, 253, 247, 450, 253, 333...\n## $ visual_contenttype  <chr> \"image\/png\", \"image\/png\", NA, \"image\/png\",...\n## $ webfeeds_icon       <chr> \"https:\/\/storage.googleapis.com\/test-site-...\n## $ decorations_dropbox <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...\n## $ decorations_pocket  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...\n<\/code><\/pre>\n
And also check how far into December for each did I get as of this post? (I’ll check again after the 31 and update if needed).<\/p>\n
range(r_weekly_wk_stream$published)\n## [1] \"2018-01-07 19:00:00 EST\" \"2018-12-30 19:00:00 EST\"\n\nrange(r_blog_stream$published)\n## [1] \"2018-01-01 11:00:27 EST\" \"2018-12-30 19:26:20 EST\"\n\nrange(r_weekly_live_stream$published)\n## [1] \"2018-01-01 19:00:00 EST\" \"2018-12-28 19:00:00 EST\"\n<\/code><\/pre>\n
Digging Into The Weeds<\/strike> Feeds<\/h3>\n
In the above glimpses there’s another special term, engagement<\/code>. Feedly defines this as an “indicator of how popular this entry is. The higher the number, the more readers have read, saved or shared this particular entry”<\/em>.  We’ll use this to look at the most “engaged” content in a bit. What’s noticeable from the start is that R Weekly Live has 1,333 entries and R-bloggers has 2,330 entries (so, nearly double the number of entries). Those counts are a bit of “fake news” when it comes to overall unique posts as can be seen by:<\/p>\n
bind_rows(\n  mutate(r_weekly_live_stream, src = \"R Weekly (Live)\"),\n  mutate(r_blog_stream, src = \"R-bloggers\")\n) %>% \n  mutate(wk = lubridate::week(published)) -> y2018\n\nfilter(y2018, title == \"RcppArmadillo 0.9.100.5.0\") %>% \n  select(src, title, originid, published) %>% \n  gt::gt()\n<\/code><\/pre>\n
<\/p>\n