| #' Source specific lines in an R file | |
| #' | |
| #' @param file character string with the path to the file to source. | |
| #' @param lines numeric vector of lines to source in \code{file}. | |
| source_lines <- function(file, lines){ | |
| source(textConnection(readLines(file)[lines])) | |
| } |
To run the script from the command line use:
curl -s https://gist.githubusercontent.com/christophergandrud/8f467beaf72a0ae1f417c3e8364c0039/raw/774b35818c72575e42aa703f472ee43cbc9d4c79/streamlit-build.sh -o script.sh && chmod +x script.sh && ./script.sh && rm script.sh
| #' An R function for filling in missing values of a variable from one data frame with the values from another variable. | |
| #' | |
| #' \code{FillIn} uses values of a variable from one data set to fill in missing values in another. | |
| #' | |
| #' @param D1 the data frame with the variable you would like to fill in. | |
| #' @param D2 the data frame with the variable you would like to use to fill in \code{D1}. | |
| #' @param Var1 a character string of the name of the variable in \code{D1} you want to fill in. | |
| #' @param Var2 an optional character string of variable name in \code{D2} that you would like to use to fill in. | |
| #' @param KeyVar a character vector of variable names that are shared by \code{D1} and \code{D2} that can be used to join the data frames. | |
| #' |
| """ | |
| Create the card game "War" | |
| where the player with the most cards at the end of 1 round wins | |
| (written using GitHub Copilot) | |
| """ | |
| # Create a range with max from len(x) | |
| def range_len(x) : | |
| """ | |
| Return the range of the length of x |
| #' Multi-core replicate. From the rethinking package: | |
| #' <https://github.com/rmcelreath/rethinking/blob/3b48ec8dfda4840b9dce096d0cb9406589ef7923/R/utilities.r#L206> | |
| #' | |
| #' @param n integer: the number of replications. | |
| #' @param expr the expression (a language object, usually a call) to evaluate repeatedly. | |
| #' @param refresh status update refresh interval | |
| #' @param mc.cores number of cores to use | |
| #' | |
| #' @importFrom parallel mclapply | |
| #' @export |
| #' Convert the output of a topicmodels Latent Dirichlet Allocation to JSON | |
| #' for use with LDAvis | |
| #' | |
| #' @param fitted Output from a topicmodels \code{LDA} model. | |
| #' @param corpus Corpus object used to create the document term | |
| #' matrix for the \code{LDA} model. This should have been create with | |
| #' the tm package's \code{Corpus} function. | |
| #' @param doc_term The document term matrix used in the \code{LDA} | |
| #' model. This should have been created with the tm package's | |
| #' \code{DocumentTermMatrix} function. |
| """ | |
| Functions for recursively calculating mean and variance and comparing | |
| comparing to full sample calculated mean and variance. | |
| Functions based on: | |
| - Chou (2021, 5) <https://arxiv.org/pdf/2102.03316.pdf> | |
| - Chan et al (1983) <http://www.cs.yale.edu/publications/techreports/tr222.pdf> | |
| """ |
| """ | |
| ebt_sampler(total_questions::Int = 310) | |
| Create question set to practice for the Deutscher Einbürgerungstest. | |
| The official question set can be found at: <http://oet.bamf.de/pls/oetut/f?p=514:1:329473569276328:::::>. | |
| The question set has 310 questions, but who knows, maybe this could change. | |
| Adjust the total question set with `total_questions::Int`. | |
| """ | |
| function ebt_sampler(total_questions::Int = 310) | |
| i::Int = -9; u::Int = 0 | |
| out = zeros(Int, 0) |
| # --- | |
| # title: "Covid-19 Vaccination Program Growth Rates" | |
| # author: "Christopher Gandrud" | |
| # date: "2021-02-21" | |
| # --- | |
| # For hrbragg installation instructions see: | |
| # https://git.rud.is/hrbrmstr/hrbragg#installation | |
| xfun::pkg_attach2("hrbragg", "tsibble", "tidyverse") |
| """ | |
| robustness_value(;fit::StatsModels.TableRegressionModel, treatment::String = "D", q::Int64 = 1) | |
| Find the Cinelli and Hazlett (2020) robustness value for a generalised linear model. | |
| The response will be a in the range 0 and 1. Values closer to 0 indicate that | |
| the conclusions from the original model are highly subject to omitted variable | |
| bias. Values closer to 1 indicate that the original model is less likely to be | |
| caused by omitted variable bias. | |
| """ | |
| function robustness_value(;fit::StatsModels.TableRegressionModel, |