The purpose of this repo is to automate reports looking at a dataset of features about articles published by Mashable over a two year period. Each report is specific to one of six data channels and contains some summary statistics and plots, model-fitting for a linear regression model and a boosted tree, and a comparison of the predictive abilities of the two models. A list of required packages to run the reports in RStudio, links to the individual reports, and the code for rendering the reports are below.
tidyversecaretleapsrmarkdownknitrLifestyle report is available here.
Entertainment report is available here.
Business report is available here.
Social media report is available here.
Technology report is available here.
World report is available here.
channel_list <- c(“lifestyle”, “entertainment”, “bus”, “socmed”, “tech”, “world”)
output_file <- paste0(channel_list, “.md”)
params <- lapply(channel_list, FUN = function(x){list(channel = x)})
reports <- tibble(output_file, params)
apply(reports, MARGIN = 1, FUN = function(x){render(input = “Project3.Rmd”, output_file = x[[1]], params = x[[2]])})