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# File: voom_mode_r.py
# Last Modified: 2023-05-11
# Description: VOoM -- two-pane outliner plugin for Python-enabled Vim
# Website: http://www.vim.org/scripts/script.php?script_id=2657
# Author: Iago Gine-Vazquez (iago.gin-vaz AT protonmail DOT com)
# License: CC0, see http://creativecommons.org/publicdomain/zero/1.0/

"""
VOoM markup mode for R headline markup.

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#! /bin/bash

tmp="/tmp/zotero"
platform="linux-$(uname -m)"



mkdir -p $tmp

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# date_sql_firefox

# when looking at the history of firefox, only the last entry of a web is shown
# we may be interested in looking at other dates when we entered in such a web

url_of_int <- "https://www.r-bloggers.com/" # the webpage of interest
title_of_int <- "R-bloggers | R news and tutorials contributed by hundreds of R bloggers"

library("RSQLite") # or DBI? # load the library to open and using an SQLite database

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library(curl)
url = "ftp://.../tas_201711.nc"
curl_download(url = url, destfile = "/tmp/tas_201711.nc")


# Write a simple R script that 
## 1. reads the netcdf file

as.mon <- function(posixlt){
  posixlt <- unclass(posixlt)

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# Get the Data

# Read in with tidytuesdayR package 
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest

# Either ISO-8601 date or year/week works!

tuesdata <- tidytuesdayR::tt_load('2022-01-25')
tuesdata <- tidytuesdayR::tt_load(2022, week = 4)

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# Get the Data

# Read in with tidytuesdayR package 
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest

# Either ISO-8601 date or year/week works!

tuesdata <- tidytuesdayR::tt_load('2022-01-18')
tuesdata <- tidytuesdayR::tt_load(2022, week = 3)

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# Get the Data

# Read in with tidytuesdayR package 
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest

# Either ISO-8601 date or year/week works!

tuesdata <- tidytuesdayR::tt_load('2022-01-11')
tuesdata <- tidytuesdayR::tt_load(2022, week = 2)

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# Get the Data

# Read in with tidytuesdayR package 
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest

# Either ISO-8601 date or year/week works!

tuesdata <- tidytuesdayR::tt_load('2021-12-21')
tuesdata <- tidytuesdayR::tt_load(2021, week = 52)

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# Get the Data

# Read in with tidytuesdayR package 
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest

# Either ISO-8601 date or year/week works!

tuesdata <- tidytuesdayR::tt_load('2021-12-14')
tuesdata <- tidytuesdayR::tt_load(2021, week = 51)

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# Get the Data

# Read in with tidytuesdayR package 
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest

# Either ISO-8601 date or year/week works!

tuesdata <- tidytuesdayR::tt_load('2021-12-07')
tuesdata <- tidytuesdayR::tt_load(2021, week = 50)