Author: R-Bloggers

Feed: R-bloggers. Author: . Topics and Categories in the Russian Troll Tweets I decided to return to the analysis I conducted for the IRA tweets dataset. (You can read up on that analysis and R code here.) Specifically, I returned to the LDA results, which looked like they lined up pretty well with the account categories identified by Darren Linvill and Patrick Warren. But with slightly altered code, we can confirm that or see if there’s more to the topics data than meets the eye. (Spoiler alert: There is more than meets the eye.) I reran much of the original ... Read More

Feed: R-bloggers. Author: . IRA Tweet Data You may have heard that two researchers at Clemson University analyzed almost 3 millions tweets from the Internet Research Agency (IRA) – a “Russian troll factory”. In partnership with FiveThirtyEight, they made all of their data available on GitHub. So of course, I had to read the files into R, which I was able to do with this code: files c("IRAhandle_tweets_1.csv","IRAhandle_tweets_2.csv","IRAhandle_tweets_3.csv","IRAhandle_tweets_4.csv","IRAhandle_tweets_5.csv","IRAhandle_tweets_6.csv","IRAhandle_tweets_7.csv","IRAhandle_tweets_8.csv","IRAhandle_tweets_9.csv")my_files paste0("~/Downloads/russian-troll-tweets-master/",files)each_file function(file) {tweet read_csv(file) }library(tidyverse) tweet_data NULLfor (file in my_files) {temp each_file(file)temp$id sub(".csv", "", file)tweet_data rbind(tweet_data, temp)} Note that this is a large file, with 2,973,371 observations of 16 variables. Let’s do ... Read More

Feed: R-bloggers. Author: . Highlighting Specific Cases in ggplot2Here’s my belated Statistics Sunday post, using a cool technique I just learned about: gghighlight. This R package works with ggplot2 to highlight a subset of data. To demonstrate, I’ll use a dataset I analyzed for a previous post about my 2017 reading habits. [Side note: My reading goal for this year is 60 books, and I’m already at 43! I may have to increase my goal at some point.] setwd("~/R")library(tidyverse) booksread_csv("2017_books.csv", col_names = TRUE) ## Warning: Duplicated column names deduplicated: 'Author' => 'Author_1' [13] ## Parsed with column specification:## cols(## .default ... Read More

Feed: R-bloggers. Author: . Using Text Mining on Open Ended ItemsGood survey design is both art and science. You have to think about how people will read and process your questions, and what sorts of responses might result from different question forms and wording. One of the big rules I follow in survey design is that you don’t assess any of your most important topics with an open-ended item. Most people skip them, because they’re more work than selecting options from a list, and people who do complete may give you terse, unhelpful, or gibberish answers. When I was working ... Read More

Feed: R-bloggers. Author: . Statistics Sunday: Term Frequency and Inverse Document Frequency As a mixed methods researcher, I love working with qualitative data, but I also love the idea of using quantitative methods to add some meaning and context to the words. This is the main reason I’ve started digging into using R for text mining, and these skills have paid off in not only fun blog posts about Taylor Swift, Lorde, and “Hotel California”, but also in analyzing data for my job (blog post about that to be posted soon). So today, I thought I’d keep moving forward to ... Read More

Feed: R-bloggers. Author: . Converting Between Effect Sizes I’m currently working on my promised video on mixed effects meta-analysis, and was planning on covering this particular topic in that video – converting between effect sizes. But I decided to do this as a separate post that I can reference in the video, which I hope to post next week. As a brief refresher, meta-analysis is aimed at estimating the true effect (or effects) in an area of study by combining findings from multiple studies on that topic. Effect sizes, the most frequently used being Cohen’s d, Pearson’s r, and log ... Read More

Feed: R-bloggers. Author: . As I’ve been blogging more about statistics, R, and research in general, I’ve been trying to increase my online presence, sharing my blog posts in groups of like-minded people. Those efforts seem to have paid off, based on my view counts over the past year: And based on read counts, here are my top 10 blog posts, most of which are stats-related: Beautiful Asymmetry – none of us is symmetrical, and that’s okay  Statistical Sins: Stepwise Regression – just step away from stepwise regression Statistics Sunday: What Are Degrees of Freedom? (Part 1) – and read Part ... Read More

Feed: R-bloggers. Author: . I haven’t had a lot of time to play with this but yesterday, I discovered the tuber R package, which allows you to interact with the YouTube API. To use the tuber package, not only do you need to install it in R – you’ll need a Google account and will have to authorize 4 APIs through Developer Console: all 3 YouTube APIs (though the Data API will be doing the heavy lifting) and the Freebase API. Before you authorize the first API, Google will have you create a project to tie the APIs to. Then, ... Read More

Feed: R-bloggers. Author: . How to Read in and Clean Your Facebook Data – I recently learned that you can download all of your Facebook data, so I decided to check it out and bring it into R. To access your data, go to Facebook, and click on the white down arrow in the upper-right corner. From there, select Settings, then, from the column on the left, “Your Facebook Information.” When you get the Facebook Information screen, select “View” next to “Download Your Information.” On this screen, you’ll be able to select the kind of data you want, a date ... Read More

Feed: R-bloggers. Author: . I recently discovered the R Graph Gallery, where users can share the beautiful visualizations they’ve created using R and its various libraries (especially ggplot2). One of my favorite parts about this gallery is a section called Art From Data, in which users create works of art, sometimes with real data, and sometimes with a random number generator and a little imagination. Last night, I completed a DataCamp project to learn how to draw flowers in R and ggplot2. Based on that, I created this little yellow flower: Not only was the flower fun to create, it ... Read More