Andy Teucher ateucher

library(dplyr)
library(sf)
#> Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUE
library(terra)
#> There appears to be a problem with the PROJ installation
#> terra 1.8.93
library(gdalraster)
#> GDAL 3.8.5 (released 2024-04-02), GEOS 3.13.0, PROJ 9.5.1
library(stars)

Replace knitr::include_graphics() with native markdown image syntax

You are a terse assistant designed to help Quarto users replace R computational code chunks that use knitr::include_graphics() with an image link using markdown syntax ![](){}.

Use the file path (the first argument in knitr::include_graphics()) as the image path in the () of the new markdown block. Preserve all of the metadata tags in the R chunk (those prefixed with #| in the body of the chunk, or within the curly braces in the chunk header), especially figure alignment (fig-align or fig.align), width (out-width or out.width), and alt text (fig-alt or fig.alt). These go in the {} of the new markdown block. Ignore the line with #| echo: false.

Respond with only the needed markdown text, no backticks or newlines around the response.

As example, given a code chunk like this:

library(duckdb)
#> Loading required package: DBI
library(glue)

url <- "http://www.env.gov.bc.ca/wsd/data_searches/water/Discharge.csv"

conn <- DBI::dbConnect(duckdb(), ":memory:")

# Install and load duckdb extensions to read from http

library(arrow)
#> 
#> Attaching package: 'arrow'
#> The following object is masked from 'package:utils':
#> 
#>     timestamp
library(duckdb)
#> Loading required package: DBI
library(dplyr)

author	date	output	title
ateucher	2021-06-08	reprex::reprex_document	ash-crane_reprex.R

library(osmdata)
#> Data (c) OpenStreetMap contributors, ODbL 1.0. https://www.openstreetmap.org/copyright

library(ggplot2)

iris$is_pretty <- sample(c(TRUE, FALSE), nrow(iris), replace = TRUE)

# dodge < width
ggplot(iris, aes(y = is_pretty, x = Petal.Length, fill = Species)) + 
  geom_bar(stat = "identity", position = position_dodge(0.5), width = 0.85)

	library(reticulate)
	library(terra)

	py_require(c("xarray", "netcdf4", "numpy"))

	xr <- import("xarray")
	np <- import("numpy")

	url <- "https://psl.noaa.gov/thredds/dodsC/Datasets/noaa.oisst.v2.highres/sst.day.mean.2022.nc"

	extract_pptx_notes <- function(pp_file, format = c("md", "docx")) {
	if (!requireNamespace("officer", quietly = TRUE))
	stop("pacakge 'officer' required.")
	if (!requireNamespace("xml2", quietly = TRUE))
	stop("pacakge 'xml2' required.")
	if (!requireNamespace("rmarkdown", quietly = TRUE))
	stop("pacakge 'rmarkdown' required.")
	if (!requireNamespace("glue", quietly = TRUE))
	stop("pacakge 'glue' required.")

	library(sf)
	library(jsonlite)
	library(purrr)
	library(mapview)

	f <- read_json("https://www.bchydro.com/power-outages/app/outages-map-data.json")

	polys <- lapply(f, function(x) {
	mat <- matrix(unlist(x$polygon), ncol = 2, byrow = TRUE)
	st_polygon(list(rbind(mat, mat[1,])))

	library(tidyverse)
	library(gghighlight)
	library(ggrepel)
	library(scales)

	data <- read_csv("https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")

	data_long <- pivot_longer(data, `1/22/20`:last_col(), names_to = "date", values_to = "cases") %>%
	mutate(date = as.Date(date, format = "%m/%d/%y")) %>%
	filter(!is.na(cases),