vikjam

* Marginal means in Stata
* https://www.stata.com/features/overview/marginal-analysis/

* Probit
webuse nlsw88, clear
quietly probit union wage c.age c.age#c.age collgrad
margins, dydx(age)

vikjam

# https://ryan-holben.github.io/tex/latex/installation/macos/2016/08/21/installing-tex-on-mac/
brew cask install basictex
sudo tlmgr update --self
sudo tlmgr install texliveonfly
sudo tlmgr install latexmk
sudo tlmgr install biber
sudo tlmgr install standalone
sudo tlmgr install preview

vikjam

# Required packages
# dplyr
# stringr
# glue

lookfor <- function(df, pattern) {
  var_i <- names(df) %>% stringr::str_detect(stringr::regex(pattern, ignore_case = TRUE))
  return(names(df)[var_i])
}

vikjam

library(gapminder)
library(tidyverse)
library(broom)
library(huxtable)
library(lfe)

# Example dataset (via gapminder package)
glimpse(gapminder)

# Create a function to make looping easier

vikjam

Wrong mean: 5.84333333333333
Right mean: 5.936
The wrong mean didn't filter: 5.84333333333333

vikjam

require 'mini_exiftool'

photo = MiniExiftool.new('new.jpg')

photo.tags.sort.each do |tag|
  photo.copy_tags_from('original.jpg', tag)
end

photo.save

vikjam

brew tap caskroom/cask
brew install brew-cask
 
brew install Caskroom/cask/xquartz
brew cask install java
 
brew tap homebrew/science
brew install R
 
brew install Caskroom/cask/rstudio

vikjam

using JuMP, Ipopt

A = 1; δ = 0.1; θ = 0.3; β = 0.96; ɛ = 0.5; χ = 0.5;
r = (1 / β) - (1 - δ);

m = Model(solver = IpoptSolver())

@variable(m, 0 <= k <= Inf)
@variable(m, 0 <= n <= Inf)
@variable(m, 0 <= w <= Inf)

vikjam

Dir.glob('*.mp3') do | the_filename |
  track = the_filename[0..2].strip()
  File.rename(the_filename, "05-#{track}.mp3")
end

vikjam

#!/usr/bin/env python
from lxml import html
import requests
import nltk

page         = requests.get('http://nytimes.com')
tree         = html.fromstring(page.text)
top_stories  = tree.xpath('//*[@id="top-news"]/*//h2/a/text()')
more_stories = tree.xpath('//*[@id="main"]/div[10]/*//article/h2/a/text()')
all_stories  = top_stories + more_stories