tomsing1

library(glue)
library(RSQLite)

con <- dbConnect(RSQLite::SQLite(), ":memory:")
var = "test"
glue_sql("{var}", .con = con)            # <SQL> 'test'
glue_sql("{`var`}", .con = con)          # <SQL> `test`
glue_sql("{DBI::SQL(var)}", .con = con)  # <SQL> test (unquoted)
glue_sql("`{var}`", .con = con)          # <SQL> `'test'` NOT USEFUL

tomsing1

# The following code is based on the shiny::conditionalPanel() help page. The `ui` and `server`
# components have been compartmentalized into the `mod_histogram` module. The `histogramApp`
# function shows an example of using this module.
# Note that the `ns = ns` argument needs to be passed ot the conditionalPanel() call.

library(shiny)

mod_histogram_ui <- function(id){
    ns <- NS(id)
    fluidPage(

tomsing1

library(glue)
library(httr)
library(jsonlite)
library(xml2)

#' Query ENA's REST API for information about records
#' 
#' @param accessions Character vector of one or more ENA record identifiers
#' @return An `xml_document` object
get_records <- function(accessions) {

tomsing1

library(palmerpenguins)
library(shiny)
library(shinyjqui)

categories <- colnames(penguins)[vapply(penguins, is.factor, logical(1))]

server <- function(input, output) {
  lapply(categories, \(category) {
    output[[category]] <- renderPrint({ print(input[[category]]) })
  })

tomsing1

#' Create an image tag with an example image
#'
#' @param width Scalar integer, the width of the image
#' @param height Scalar integer, the height of the image
#' @param title Scalar character, the title of the image
#' @return A `shiny.tag` with the URL to a random image from
#' [Lorem Picsum](https://picsum.photos/)
#' @export
#' @importFrom htmltool tags
#' @importFrom checkmate assert_count assert_character

tomsing1

There is a lot of confusing information about virtual environments in python out there, in part because the tool chain has evolved over many years.

I decided to follow the advice of Real python and The hitchhiker's guide to python and manage both multiple python versions and multiple virtual environments with pyenv

tomsing1

---
title: "Embedding R into Quarto documents with quarto-webr"
subtitle: "Example: intersecting differential expression results"
author: "Thomas Sandmann"
date: '2023/11/18'
format: html
engine: knitr
webr: 
  show-startup-message: true
  packages: ['ggvenn', 'huxtable']

tomsing1

The fgsea::fgsea function returns a data.frame with the following columns:

• pathway – name of the pathway as in ‘names(pathway)‘;
• pval – an enrichment p-value;
• padj – a BH-adjusted p-value;
• ES – enrichment score

  GSEA calculates the ES by walking down the ranked list of genes, increasing a running-sum statistic when a gene is in the gene set and decreasing it when it is not. The magnitude of the increment depends on the correlation of the gene with the phenotype. The ES is the maximum deviation from zero encountered in walking the list.

tomsing1

library(duckdb)
library(jsonlite)
library(purrr)

con <- dbConnect(duckdb())

# duckdb: install and load postgres extension to connect
# to a Postgres database via duckdb
dbExecute(con, "INSTALL postgres;")
dbExecute(con, "LOAD postgres;")

tomsing1