audhiaprilliant

Hi there, I'm [Audhi Aprilliant][website] 👋

I'm a Man, Friend, and Researcher!

• 🔭 I’m currently working on a [personal project][website]!
• 🌱 I’m currently learning Data Science and Analytics 🤣
• 👯 I’m looking to collaborate with other content creators
• 🥅 2020 Goals: Give impact to society
• ⚡ Fun fact: I love blogging, reading books, and drawing

Connect with me:

audhiaprilliant

name: Latest Medium story workflow
on:
  schedule:
    # Runs every hour
    - cron: '0 * * * *'
  workflow_dispatch:

jobs:
  update-readme-with-blog:
    name: Update this repo's README with latest Medium post

audhiaprilliant

# Function to conduct simulation
generate_beta = function(ti, rho, repetition) {
  # Vector intercept and slope
  vector_intercept_auto = rep(x = 0, len = repetition)
  vector_slope_auto = rep(x = 0, len = repetition)
  vector_intercept_non = rep(x = 0, len = repetition)
  vector_slope_non = rep(x = 0, len = repetition)
  
  j = 1
  while (j < (repetition + 1)) {

audhiaprilliant

# Install package
install.packages('tidyr')
# Load the package
library(tidyr)

# Data melting
long = df %>% 
  gather(Coefficient, Value, -c(Rho))
# Create a new column indicating whether it is intercept or slope
long$Status = ifelse(substring(long$Coefficient, 1, 9) == 'intercept', 'Intercept', 'Slope')

audhiaprilliant

# Install package
install.packages('dplyr')
# Load the package
library(dplyr)

# Create a data aggregation
statDf = df %>%
  group_by(Rho) %>%
  summarize(
    # Without autocorrelation

audhiaprilliant

# Install package
install.packages('ggplot2')
# Load the package
library(ggplot2)

# Without autocorrelation - intercept
df_InterceptNonAutocorrelation = long %>%
  filter(Coefficient == 'interceptNonAutocorrelation')
unique(df_InterceptNonAutocorrelation$Rho)
ggplot(data = df_InterceptNonAutocorrelation)+

audhiaprilliant

# Modules for web scraping
import requests
from bs4 import BeautifulSoup
# Module for data manipulation
import pandas as pd
# Module for regular expression
import re
# Module for file management
import os
# Module for timing

audhiaprilliant

def get_url():
    # URL
    url = 'https://www.kompas.com/covid-19'
    page = requests.get(url)
    # Wrangling HTML with BeautifulSoup
    soup = BeautifulSoup(page.content,'html.parser')
    return(soup)

audhiaprilliant

# Create dictionary for date manipulation
dict_month = {'Januari':'01','Februari':'02','Maret':'03','April':'04','Mei':'05','Juni':'06','Juli':'07','Agustus':'08','September':'09','Oktober':'10','November':'11','Desember':'12'}
# Function to get the current date
def get_current_date(**kwargs):
    date_scrape = soup.find('span',class_='covid__date').text
    date_scrape = re.findall(r'Update terakhir: (\S+.+WIB)',date_scrape)[0].replace(', ',',')
    date = date_scrape.split(',')[0]
    time = date_scrape.split(',')[1]
    # Date manipulation
    date_format = re.findall(r'\w+',date)[0]

audhiaprilliant

# Function to get the daily aggregated data
def get_daily_summary(**kwargs):
    soup = get_url()
    date,time = get_current_date()
    # Get summary
    # Regular expression pattern
    pattern_summary = re.compile(r'\d[^\s]+')
    for job_elem in soup.find_all('div',class_='covid__box'):
        # Each job_elem is a new BeautifulSoup object.
        terkonfirmasi_elem = job_elem.find('div',class_='covid__box2 -cases')