mkiang

## A quick R script for cleaning up your Twitter timeline. This assumes you
## have Twitter API creds but it probably isn't necessary. This is based on
## Chris Albon's Python script: 
##      https://gist.github.com/chrisalbon/b9bd4a6309c9f5f5eeab41377f27a670
##      
## In this example, I remove old tweets in two stages: (1) if tweets are 
## older than two years and have fewer than 100 likes and then (2) if tweets
## are older than 90 days and have fewer than 25. Retweets are removed after
## 90 days. Importantly, tweets (and retweets) that I liked are *always* 
## protected from deletion.

mkiang

library(tidyverse)
library(tidyquant)
source(
    "https://raw.githubusercontent.com/mkiang/disproportionate_prescribing/master/code/mk_nytimes.R"
)

getSymbols("^DJI")

DJI <- as_tibble(DJI) %>%
    mutate(

mkiang

## Visualizing authorship networks: code for this blog post:
## https://mathewkiang.com/2019/12/07/collaboration-network-from-2010-to-2019/

## Imports ----
library(scholar) # remotes::install_github("jkeirstead/scholar")
library(tidyverse)
library(fs)
library(here)

## Constants ----

mkiang

## Import packages
library(deSolve)

## Basic SIR using number of people (not proportion of pop)
SIR <- function(t, x, parms){
    with(as.list(c(parms, x)), {
        N = S + I + R
        dS <- -beta*S*I/N
        dI <- beta*S*I/N - r*I
        dR <- r*I

mkiang

install.packages("rgdal", dep = TRUE)
install.packages("reshape2", dep = TRUE)
install.packages("viridis", dep = TRUE)
install.packages("rgeos", dep = TRUE)
install.packages("Imap", dep = TRUE)
install.packages("network", dep = TRUE)
install.packages("DT", dep = TRUE)
install.packages("tidyverse", dep = TRUE)
install.packages("deSolve", dep = TRUE)
install.packages("manipulate", dep = TRUE)

mkiang

## Visualizing authorship networks: code for this blog post:
## https://mathewkiang.com/2018/06/17/my-collaboration-network/
library(scholar) # devtools::install_github("jkeirstead/scholar")
library(visNetwork)
library(tidyverse)

## Constants
MIN_TIME <- 60 * 5
MAX_TIME <- 60 * 30
PROJ_WEIGHT <- .8

mkiang

## For blog post here:  https://mathewkiang.com/2017/12/02/tldr-san-diego-weather-is-better-than-boston-weather/

## Imports ----
library(tidyverse)
library(GSODR)
library(ggmap)


## Define places we are interested in and google maps query ----
city_dict <- list(sandiego   = "san diego airport", 

mkiang

Keybase proof

I hereby claim:

• I am mkiang on github.
• I am mkiang (https://keybase.io/mkiang) on keybase.
• I have a public key ASBcR0WeyXKq1QlGo0lxZxRG2VRNJKIJ8-h6NNFJLDFSYAo

To claim this, I am signing this object:

mkiang

## Code for the blog post here: 
## https://mathewkiang.com/2017/10/08/using-r-wikipedia-sherparomeo-show-new-england-journal-medicines-pre-print-statement-empirically-false
## You can download the original files from the blogpost.
## If downloading new files, you probably want to change the date in the file names.

## Imports
library(rvest)
library(tidyverse)

## Wiki data

mkiang

#SIR practical from Debarre and Bonhoeffer ("SIR models of epidemics")
library(deSolve)

parms <- c(beta = 0.333, k = 3 , r = 0.333)
inits <- c(S = 499, I = 1, R = 0)
dt <- seq(0, 300, 1)

SIR <- function(t, x, parms){
    with(as.list(c(parms, x)), {