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#' 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

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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

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---
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']

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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.

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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;")

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# source: Matt Dray's blog: https://www.rostrum.blog/posts/2021-10-05-gorilla/

library(magick)

# download and read the image
img_file <- tempfile(fileext = ".jpg")
download.file(
  paste0(
    "https://classroomclipart.com/images/gallery/",
    "Clipart/Black_and_White_Clipart/Animals/",

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Installation on Mac OS X

brew install datalad wget

Creating a new dataset

First, we create a new, empty dataset. (Reminder: a dataset refers to a folder of files, not any single file.)

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Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations

Excerpted from Greenland et al, Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations, 2016

What P values, confidence intervals, and power calculations don't tell us

Common misinterpretations of single P values

1. The P value is the probability that the test hypothesis is true; for example, if a test of the null hypothesis gave P = 0.01, the null hypothesis has only a 1 % chance of being true; if instead it gave P = 0.40, the null hypothesis has a 40 % chance of being true.

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brew install duckdb

duckdb -c "INSTALL httpfs"

REMOTE_FILE="https://raw.githubusercontent.com/mwaskom/seaborn-data/master/penguins.csv"

# read a remote CSV file in to a duckdb database into an in-memory database
duckdb -c "SELECT * FROM '${REMOTE_FILE}';" 

# By default, the CLI will open a temporary in-memory database.