An example analysis, which follows my project template.
Some data redacted.
makefile controls all code (except the Rmd notebooks)
all: clean data process pool
absl-py==0.10.0 | |
cachetools==4.1.1 | |
certifi==2020.6.20 | |
chardet==3.0.4 | |
fsspec==0.8.3 | |
future==0.18.2 | |
google-auth==1.22.1 | |
google-auth-oauthlib==0.4.1 | |
grpcio==1.32.0 | |
idna==2.10 |
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An example analysis, which follows my project template.
Some data redacted.
makefile controls all code (except the Rmd notebooks)
all: clean data process pool
--- | |
title: "A new analysis workflow" | |
output: github_document | |
--- | |
# Organize your data processing program with MECE pieces | |
*MECE = Mutually exclusive, collectively exhaustive. From McKinsey* | |
## Summary |
Quickly deploy a Python-Flask app to Heroku from scratch with this tutorial:
Bonus: nice styling with Bootstrap.
The above is one of the best tutorials I've seen - gives you exactly what you want and nothing you don't need. However, it may be just a tad outdated, so you may have to look at updated docs on heroku.
Here is one small Flask app that I built:
library(tibble) | |
library(dplyr) | |
## sample data | |
df <- frame_data( | |
~x, ~y, ~z, ~x1, ~x2, ~x3, ~y1, ~y2, ~y99, ~z99, ~zz, | |
99, 2, 3.6, 99, 2, 3.6, 99, 2, 3.6, 99, 2, | |
99, 2, 3.6, 99, 2, 3.6, 99, 2, 3.6, 99, 2, | |
99, 2, 3.6, 99, 2, 3.6, 99, 2, 3.6, 99, 2 | |
) |
/* macro to convert variables to indicators*/ | |
/* outputs dataset "df" with patid and the indicator only for easy later merge*/ | |
/* v = var for indicator // n = name of new var(use same prefix with consecutive #s!!) */ | |
%MACRO INDX(v=, n=); | |
data df; | |
set bmd; | |
if &V > . then tt = 1; else tt =0; | |
rename tt=&N; | |
label | |
&N = "&V 0/1" |