webbedfeet

import re, os

regex = "```\{r (.+)\}"

def name_chunks(f):
    """
    This function takes a quarto (.qmd) file, checks for rmarkdown-style
    labels, and converts to quarto-style labels. If a quarto-style label
    exists, it preserves it (it checks the first line of the chunk metadata).
    If no label exists, it creates one based on the file name, in sequential order

webbedfeet

from pathlib import Path
import tarfile

with tarfile.open('data.tar','w') as tar:
  for name in Path('.').glob('*/filtered_feature_bc_matrix.h5'):
    tar.add(name)
 

webbedfeet

syntax on
filetype plugin indent on
set number
set path+=**
set noswapfile
set undofile
set incsearch
set smartindent
set ic
set expandtab

webbedfeet

def df_diff(d1, d2):
    """
    df_diff Create a DataFrame containing rows of d1 not in d2

    Arguments:
        d1 -- A data frame
        d2 -- Another DataFrame which is a subset of d1

    Returns:
        A pandas DataFrame containing rows of d1 that are not in d2

webbedfeet

# For a description of this file, please see:
# https://www.innoq.com/en/blog/markdown-with-zotero-workflow
#------------------------------------------------------------

# what's the name of the generated output file(s)
OUTPUT=out

# what's the name of the markdown source file
SOURCE=principles-content.md

webbedfeet

---
creation date: <% tp.file.creation_date() %>
tags: DailyNote <% tp.file.title.split('-')[0] %>
---

modification date: <%+ tp.file.last_modified_date("dddd Do MMMM YYYY HH:mm:ss") %> // This doesn't currently work in front matter, hoping that gets fixed.

# <% tp.file.title %>

<< [[<% tp.date.now("YYYY-MM-DD", -1, tp.file.title, "YYYY-MM-DD") %>]] | [[<% tp.date.now("YYYY-MM-DD", 1, tp.file.title, "YYYY-MM-DD") %>]]>>

webbedfeet

---
title: "Example dashboard"
output: 
    flexdashboard::flex_dashboard:
        orientation: columns
        vertical_layout: scroll
    html_document: default
---

```{r setup, include=FALSE}

webbedfeet

# remotes::install_github('ramikrispin/coronavirus')
library(coronavirus)
update_datasets()
pacman::p_load(char=c('tidyverse','janitor', 'ggrepel'))

deaths <- coronavirus %>% 
  clean_names() %>% 
  filter(type=='death') %>% 
  group_by(country_region, date) %>% 
  summarise(cases = sum(cases)) %>% 

webbedfeet

# https://stackoverflow.com/questions/25191620/
#   creating-lowpass-filter-in-scipy-understanding-methods-and-units

import numpy as np
from scipy.signal import butter, lfilter, freqz
from matplotlib import pyplot as plt


def butter_lowpass(cutoff, fs, order=5):
    nyq = 0.5 * fs

webbedfeet

#' ---
#' title: An annotated R script for Homework 6 using lists and maps
#' output: html_document
#' ---
#' 
#+, include=FALSE
knitr::opts_chunk$set(error=F, warning=F, message=F)
#' First, read the data in. We've stored just the data files in the folder `data/HW6`.

library(tidyverse)