[R-bloggers] Recreating Michael Betancourt’s Bayesian modeling course from his online materials (and 1 more aRticles)

Recreating Michael Betancourt’s Bayesian modeling course from his online materials

Posted: 23 Sep 2018 01:27 AM PDT

(This article was first published on Shravan Vasishth's Slog (Statistics blog), and kindly contributed to R-bloggers)

Several people wanted to have the slides from Betancourt's lectures at SMLP2018. It is possible to recreate most of the course from his writings:

1. Intro to probability:
https://betanalpha.github.io/assets/case_studies/probability_theory.html

2. Workflow:
https://betanalpha.github.io/assets/case_studies/principled_bayesian_workflow.html

3. Diagnosis:
https://betanalpha.github.io/assets/case_studies/divergences_and_bias.html

4. HMC: https://www.youtube.com/watch?v=jUSZboSq1zg

5. Validating inference: https://arxiv.org/abs/1804.06788

6. Calibrating inference: https://arxiv.org/abs/1803.08393

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sjrdata: all SCImago Journal & Country Rank data, ready for R

Posted: 22 Sep 2018 05:00 PM PDT

(This article was first published on Ilya Kashnitsky, and kindly contributed to R-bloggers)

SCImago Journal & Country Rank provides valuable estimates of academic journals' prestige. The data is freely available at the project website and is distributed for deeper analysis in forms of .csv and .xlsx files. I downloaded all the files and pooled them together, ready to be used in R.

Basically, all the package gives you three easily accessible data frames: sjr_journals (Journal Rank), sjr_countries (Country Rank, year-by-year), and sjr_countries_1996_2017 (Country Rank, all years together).

The whole process of data acquisition can be foun in the github repo (dev directory) or this gist.

How to use `sjrdata`

Install the package from github, load it and use the data.

The instalation will take a while since the main dataset sjr_journals is pretty heavy (15.7MB compressed).

  # install  devtools::install_github("ikashnitsky/sjrdata")    # load  library(sjrdata)    # use  View(sjr_countries)    

A couple of examples

Let's compare Nature and Science.

  library(tidyverse)  library(sjrdata)    sjr_journals %>%      filter(title %in% c("Nature", "Science")) %>%      ggplot(aes(cites_doc_2years, sjr, color = title))+      geom_path(size = 1, alpha = .5)+      geom_label(aes(label = year %>% str_sub(3, 4)),                size = 3, label.padding = unit(.15, "line"))    

Several demographic journals.

  sjr_journals %>%      filter(title %in% c(          "Demography",          "Population and Development Review",          "European Journal of Population",          "Population Studies",          "Demographic Research",          "Genus"      )) %>%      ggplot(aes(cites_doc_2years, sjr, color = title))+      geom_point()+      stat_ellipse()+      scale_color_brewer(palette = "Dark2")+      coord_cartesian(expand = F)    

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