RamiKrispin

from datetime import datetime, timedelta
from airflow.utils.dates import days_ago

from airflow import DAG
from airflow.providers.docker.operators.docker import DockerOperator
from airflow.operators.python import PythonOperator
from docker.types import Mount

import os

RamiKrispin

version: "3.9"
services:
  rstudio:
    image: "rkrispin/atsaf:dev.0.0.0.9000"
    ports:
      - "8787:8787"
    volumes:
      - type: "bind"
        source: "$ATSAF_DIR"
        target: "/home/rstudio"

RamiKrispin

# Forecasting Sandbox ----
# This is an example for a Shinylive R app
# The app provides a forecasting sandbox for the AirPassengers dataset
# It supports 3 stats forecasting models - Linear Regression, ARIMA, and Holt-Winters

library(shiny)
data(AirPassengers)

# UI ----
ui <- fluidPage(

RamiKrispin

#!/bin/bash

# Launching RStuido server on a docker with different settings

# Basic run command to launce rocker container with RStudio Server
docker run -d -p 8787:8787 \
            -e USER=rstudio -e PASSWORD=rstudio \
            rocker/rstudio:4.2

# Disabling the authentication 

RamiKrispin

# Print messages with print and cat
print("This is the message I want to print")
cat("This is the message I want to print\n") # Using the \n operator to jump for the next line

# The print command can also print code output
print(data.frame(x = 1:3, y = 4:6))
# Where cat cannot print non text or multiple vector objects
cat(data.frame(x = 1:3, y = 4:6))

RamiKrispin

#generate random numbers
set.seed(1234)
x1 <- rnorm(n = 1000, mean = 3, sd = 3)
x2 <- rnorm(n = 1000, mean = 0, sd = 1)
noise <- runif(n = 1000, min = 0, max = 1)

# Set formula
y <- 5 * x1 - 3 * x2 + noise

# Run regression

RamiKrispin

# Adding regression line to a plot
set.seed(1234)


# Creating a random dataset
x <- rnorm(n = 1000, mean = 10, sd = 3)
y <- 2 * x + 10 + rnorm(n = 1000, mean = 0, sd = 4)

d <- data.frame(y = y,
                x = x)

RamiKrispin

library(plotly)

df <- data.frame(y = c(21, 44, 27, 8),
                 x = c(100, 200, 150, 250))

df$x_cum <- cumsum(df$x)
df$y_cum <- cumsum(df$y)

df

RamiKrispin

# Plotting COVID19 daily new cases in the Ghana
# Using the following libraries:
# coronavirus for data
# plotly for dataviz
# dplyr for data manipulation
library(coronavirus)
library(plotly)
library(dplyr)

df <- refresh_coronavirus_jhu()

RamiKrispin

# Plotting COVID19 daily new cases in the US
# Using the following libraries:
# coronavirus for data
# plotly for dataviz
# dplyr for data manipulation
library(coronavirus)
library(plotly)
library(dplyr)

df <- refresh_coronavirus_jhu()