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This is the R script/materials repository of the "Mastering R Skills" course in the 2022/2023 Spring term, part of the MSc in Business Analytics at CEU. For the previous edition, see 2018/2019 Spring, 2019/2020 Spring, and 2020/2021 Spring.

Table of Contents

Schedule

2 x 150 mins on May 22, 31:

  • 13:30 - 15:00 session 1
  • 15:00 - 15:15 break
  • 15:15 - 16:15 session 2

1 x 300 mins on June 5:

  • 13:30 - 15:10 session 1
  • 15:10 - 15:40 break
  • 15:40 - 17:20 session 2
  • 17:20 - 17:40 break
  • 17:40 - 19:20 session 3

Location

In-person at the Vienna campus (QS B-421).

Syllabus

Please find in the syllabus folder of this repository.

Technical Prerequisites

  1. Bookmark, watch or star this repository so that you can easily find it later.

  2. Please bring your own laptop and make sure to install R and RStudio before attending the first class!

    💪 R packages to be installed from CRAN via install.packages:

    • data.table
    • httr
    • jsonlite
    • lubridate
    • ggplot2
    • scales
    • zoo
    • RMySQL
    • RSQLite
    • openxlsx
    • googlesheets4
    • devtools
    • roxygen2
    • pander
    • logger
    • botor (requires Python and the boto3 Python module)
    • purrr
    • memoise

    💪 R packages to be installed from GitHub via remotes::install_github:

    • daroczig/binancer
    • daroczig/logger
    • daroczig/dbr

    If you get stuck, feel free to use the preconfigured, shared RStudio Server at http://mr.ceudata.net/rstudio (I will share the usernames and passwords at the start of the class). In such case, you can skip all the steps prefixed with "💪" as the server already have that configured.

  3. Join the #ba-mr-2022 Slack channel in the ceu-bizanalytics Slack group.

  4. If you do not already have a GitHub account, create one

  5. Optionally create a new GitHub repository called mastering-r (or similar), but can be done later as well for th e R package (see below).

  6. 💪 Install git from https://git-scm.com/

  7. 💪 Verify that in RStudio, you can see the path of the git executable binary in the Tools/Global Options menu's "Git/Svn" tab -- if not, then you might have to restart RStudio (if you installed git after starting RStudio) or installed git by not adding that to the PATH on Windows. Either way, browse the "git executable" manually (in some bin folder look for thee git executable file).

  8. Create an RSA key via Tools/Global options/Git/Create RSA Key button (optionally with a passphrase for increased security -- that you have to enter every time you push and pull to and from GitHub), then copy the public key (from ~/.ssh/id_rsa.pub) and add that to you SSH keys on your GitHub profile.

  9. Create a new project in RStudio choosing "version control", then "git" and paste the SSH version of the repo URL copied from GitHub (from point 4) in the pop-up -- now RStudio should be able to download the repo. If it asks you to accept GitHub's fingerprint, say "Yes".

  10. If RStudio/git is complaining that you have to set your identity, click on the "Git" tab in the top-right panel, then click on the Gear icon and then "Shell" -- here you can set your username and e-mail address in the command line, so that RStudio/git integration can work. Use the following commands:

    $ git config --global user.name "Your Name"
$ git config --global user.email "Your e-mail address"

    Close this window, commit, push changes, all set.

Find more resources in Jenny Bryan's "Happy Git and GitHub for the useR" tutorial if in doubt or contact me.

Class materials

Report on the current price of 0.42 BTC

We have 0.42 Bitcoin. Let's write an R script reporting on the current value of this asset in USD.

Click here for a hint ...

We installed the binancer package for a reason! Look up the related functions via help(package = binancer).

Click here for a potential solution ... 
## library(devtools)
## install_github('daroczig/binancer')

library(binancer)
coin_prices <- binance_coins_prices()
coin_prices[symbol == 'BTC', usd]

## don't forget that we need to report on the price of 0.42 BTC instead of 1 BTC
coin_prices[symbol == 'BTC', usd * 0.42]

Report on the current price of 0.42 BTC in EUR

Let's do the same report as above, but instead of USD, now let's report in Euros.

Click here for a potential solution ... 
## How to get EUR/HUF rate?
## See eg https://exchangerate.host for free API access

## Loading data without any dependencies
## https://api.exchangerate.host/latest
## https://api.exchangerate.host/latest?base=USD

readLines('https://api.exchangerate.host/latest?base=USD')

## Parse JSON
library(jsonlite)
fromJSON(readLines('https://api.exchangerate.host/latest?base=USD'))
fromJSON('https://api.exchangerate.host/latest?base=USD')

## Extract the USD/HUF exchange rate from the list
usdeur <- fromJSON('https://api.exchangerate.host/latest?base=USD&symbols=EUR')$rates$EUR
coin_prices[symbol == 'BTC', 0.42 * usd * usdeur]
Click here for a potential solution ... after cleaning up 
## loading requires packages on the top of the script
library(binancer)
library(httr)

## constants
BITCOINS <- 0.42

## get Bitcoin price in USD
coin_prices <- binance_coins_prices()
btcusdt <- coin_prices[symbol == 'BTC', usd]

## get USD/HUF exchange rate
usdeur <- fromJSON('https://api.exchangerate.host/lat?base=USD&symbols=EUR')$rates$EUR

## report
BITCOINS * btcusdt * usdeur
Click here for a potential solution ... with logging 
library(binancer)
library(httr)
library(data.table)
library(logger)

BITCOINS <- 0.42

coin_prices <- binance_coins_prices()
log_info('Found {coin_prices[, .N]} coins on Binance')
btcusdt <- coin_prices[symbol == 'BTC', usd]
log_info('The current Bitcoin price is ${btcusdt}')

usdeur <- fromJSON('https://api.exchangerate.host/latest?base=USD&symbols=EUR')$rates$EUR
log_info('1 USD currently costs {usdeur} EUR')

log_eval(BITCOINS * btcusdt * usdeur, level = INFO)
log_info('{BITCOINS} Bitcoins now worth {round(btcusdt * usdeur * BITCOINS)} EUR')
Click here for a potential solution ... with validating values received from the API 
library(binancer)
library(httr)
library(data.table)
library(logger)
library(checkmate)

BITCOINS <- 0.42

coin_prices <- binance_coins_prices()
log_info('Found {coin_prices[, .N]} coins on Binance')
btcusdt <- coin_prices[symbol == 'BTC', usd]
log_info('The current Bitcoin price is ${btcusdt}')
assert_number(btcusdt, lower = 1000)

usdeur <- fromJSON('https://api.exchangerate.host/latest?base=USD&symbols=EUR')$rates$EUR
log_info('1 USD currently costs {usdeur} EUR')
assert_number(usdeur, lower = 0.9, upper = 1.1)

log_info('{BITCOINS} Bitcoins now worth {round(btcusdt * usdeur * BITCOINS)} EUR')
Click here for a potential solution ... with auto-retries for API errors 
library(binancer)
library(httr)
library(data.table)
library(logger)
library(checkmate)

BITCOINS <- 0.42

get_usdeur <- function() {
  tryCatch({
    usdeur <- fromJSON('https://api.exchangerate.host/latest?base=USD&symbols=EUR')$rates$EUR
    assert_number(usdeur, lower = 0.9, upper = 1.1)
  }, error = function(e) {
    ## str(e)
    log_error(e$message)
    Sys.sleep(1)
    get_usdeur()
  })
  log_info('1 USD={usdeur} EUR')
  usdeur
}

get_bitcoin_price <- function() {
  tryCatch({
      btcusdt <- binance_coins_prices()[symbol == 'BTC', usd]
      assert_number(btcusdt, lower = 1000)
      log_info('The current Bitcoin price is ${btcusdt}')
      btcusdt
  },
  error = function(e) {
    log_error(e$message)
    Sys.sleep(1)
    get_bitcoin_price()
  })
}

log_info('{BITCOINS} Bitcoins now worth {round(get_bitcoin_price() * get_usdeur() * BITCOINS)} EUR')
Click here for a potential solution ... with auto-retries for API errors with exponential backoff 
get_usdeur <- function(retried = 0) {
  tryCatch({
    ## httr
    usdeur <- fromJSON('https://api.exchangerate.host/latest?base=USD&symbols=EUR')$rates$EUR
    assert_number(usdeur, lower = 0.9, upper = 1.1)
  }, error = function(e) {
    ## str(e)
    log_error(e$message)
    if (retried > 3) {
      stop('Gave up')
    }
    Sys.sleep(1 + retried ^ 2)
    get_usdeur(retried = retried + 1)
  })
  log_info('1 USD={usdeur} EUR')
  usdeur
}
Click here for a potential solution ... with better currency formatter 
round(btcusdt * usdeur * BITCOINS)
format(btcusdt * usdeur * BITCOINS, big.mark = ',', digits = 10)
format(btcusdt * usdeur * BITCOINS, big.mark = ',', digits = 6)

library(scales)
dollar(btcusdt * usdeur * BITCOINS)
dollar(btcusdt * usdeur * BITCOINS, prefix = '€')
dollar(btcusdt * usdeur * BITCOINS, prefix = '', suffix = ' EUR')

euro <- function(x) {
  dollar(x, prefix = '€')
}
euro(get_bitcoin_price() * get_usdeur() * BITCOINS)

Move helpers to a new R package

  1. Click File / New Project / New folder and create a new R package (maybe call it mr, also create a git repo for it) -- that will fill in your newly created folder with a package skeleton delivering the hello function in the hello.R file.

  2. Get familiar with:

  3. Install the package (in the Build menu), load it and try hello(), then ?hello

  4. Create a git repo (if not done that already) and add/commit this package skeleton

  5. Add a new function called euro in the R subfolder:

    euro.R 
    euro <- function(x) {
  dollar(x, prefix = '€')
}

  6. Install the package, re-load it, and try running euro eg calling on 42 -- realize it's failing

  7. After loading the scales package (that delivers the dollar function), it works ... we need to prepare our package to load scales::dollar without user interventation

  8. Also, look at the docs of euro -- realize it's missing, so let's learn about roxygen2 and update the euro.R file to explicitely list the function to be exported and note that dollar is to be imported from the scales package:

    euro.R 
    #' Formats number in EUR currency
#' @param x number
#' @return string
#' @export
#' @importFrom scales dollar
#' @examples
#' euro(1000)
#' euro(10.3241245125125)
euro <- function(x) {
  dollar(x, prefix = '€')
}

  9. Run roxygen2 on the package by enabling it in the "Build" menu's "Configure Build Tools", then "Document" it (if there's no such option, probably you need to install the roxygen2 package first), and make sure to check what changes happened in the man, NAMESPACE (note that you might need to delete the original one) and DESCRIPTION files. It's also a good idea to automatically run roxygen2 before each install, so I'd suggests marking that option as well. The resulting files should look something like:

    DESCRIPTION 
    Package: mr
Type: Package
Title: Demo R package for the Mastering R class
Version: 0.1.0
Author: Gergely <***@***.***>
Maintainer: Gergely <***@***.***>
Description: Demo R package for the Mastering R class
License: AGPL
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.0
Imports: scales
    NAMESPACE 
    # Generated by roxygen2: do not edit by hand

export(euro)
importFrom(scales,dollar)

  10. Keep committing to the git repo

  11. Delete hello.R and rerun roxygen2 / reinstall the package

  12. Add a new function that gets the most exchange rate for USD/EUR:

    converter.R 
    #' Look up the value of a US Dollar in EURs
#' @param retried number of times the function already failed
#' @return number
#' @export
#' @importFrom jsonlite fromJSON
#' @importFrom logger log_error log_info
#' @importFrom checkmate assert_number
get_usdeur <- function(retried = 0) {
  tryCatch({
    ## httr
    usdeur <- fromJSON('https://api.exchangerate.host/latest?base=USD&symbols=EUR')$rates$EUR
    assert_number(usdeur, lower = 0.9, upper = 1.1)
  }, error = function(e) {
    ## str(e)
    log_error(e$message)
    if (retried > 3) {
      stop('Gave up')
    }
    Sys.sleep(1 + retried ^ 2)
    get_usdeur(retried = retried + 1)
  })
  log_info('1 USD={usdeur} EUR')
  usdeur
}

  13. Now you can run the original R script hitting the Binance and exchangerate.host APIs by using these helper functions:

library(binancer)
library(logger)
log_threshold(TRACE)
library(scales)
library(mr)

BITCOINS <- 0.42
log_info('Number of Bitcoins: {BITCOINS}')

usdeur <- get_usdeur()

btcusd <- binance_coins_prices()[symbol == 'BTC', usd]
log_info('1 BTC={dollar(btcusd)}')

log_info('My crypto fortune is {euro(BITCOINS * btcusd * usdeur)}')
  1. Make sure that the R package works as intended, and then push to Github.

Recap of week 1

  • writing helper functions
  • API integrations
  • documenting helper functions
  • creating an R package from helper functions

Homework for week 1 gotchas

  • easy to mess up copy/paste
  • make sure to test your function in a clean environment
  • import data.table if a package needs i!

The homework has been published at https://github.com/daroczig/CEU-R-mastering-demo-pkg/tree/76b283914380f05e0ddfdb44b98fe6560d86dc02

Let's fork the above repository and continue working on that from now on, so that later we can also prepare a pull request for the main repo!

You can also install the above version of mr via:

devtools::install_github('daroczig/CEU-R-mastering-demo-pkg')

Replace the home-brew retry with something better maintained

Check out how purrr::insistently works!

  1. Import the insistently function purrr with a roxygen tag
  2. Add purrr to the Imports of your DESCRIPTION file
  3. Drop the tryCatch handler and let the function fail on error
  4. Wrap your function with insistently
  5. Optionally enable reporting on errors via setting the quiet flag to FALSE
#' Look up the current price of a Bitcoin in USD
#' @param retried number of times the function already failed
#' @return number
#' @export
#' @importFrom binancer binance_coins_prices
#' @importFrom logger log_error log_info
#' @importFrom checkmate assert_number
#' @import data.table
#' @importFrom purrr insistently
get_bitcoin_price <- insistently(function() {
    if (runif(1) > 0.5) stop('oh nooo') # TODO drop
    btcusdt <- binance_coins_prices()[symbol == 'BTC', usd]
    assert_number(btcusdt, lower = 1000)
    log_info('The current Bitcoin price is ${btcusdt}')
    btcusdt
}, quiet = FALSE)

Speed up flaky API calls with caching

Check out how memoise::memoise works! Make sure to set a TTL (time to live) for the cached value .. crypto markets are changing rapidly :)

  1. Import the memoise function memoise with a roxygen tag
  2. Add memoise to the Imports of your DESCRIPTION file
  3. Wrap your function with memoise
  4. Look up the cache_mem function of the cachem package mentioned in the memoise docs
  5. Set up a custom cache with a 5 seconds TTL by calling cache_mem(max_age = 5) as the cache argument of memoise, and make sure to do the related imports properly: add a roxygen tag to import cache_mem from cachem and add cachem in the DESCRIPTION file
  6. Indent your code so that it is clear which argument belongs to which function
#' Look up the current price of a Bitcoin in USD
#' @param retried number of times the function already failed
#' @return number
#' @export
#' @importFrom binancer binance_coins_prices
#' @importFrom logger log_error log_info
#' @importFrom checkmate assert_number
#' @import data.table
#' @importFrom purrr insistently
#' @importFrom memoise memoise
#' @importFrom cachem cache_mem
get_bitcoin_price <- memoise(
    insistently(
        function() {
            btcusdt <- binance_coins_prices()[symbol == 'BTC', usd]
            assert_number(btcusdt, lower = 1000)
            log_info('The current Bitcoin price is ${btcusdt}')
            btcusdt
        },
        quiet = FALSE),
    cache = cache_mem(max_age = 5)
)

Report on the price of 0.42 BTC in the past 30 days

Let's do the same report as above, but instead of reporting the most recent value of the asset, let's report on the daily values from the past 30 days, e.g. on a line plot.

Click here for a potential solution ... with fixed USD/HUF exchange rate 
library(binancer)
library(httr)
library(data.table)
library(logger)
library(ggplot2)
library(mr)

## ########################################################
## CONSTANTS

BITCOINS <- 0.42

## ########################################################
## Loading data

usdeur <- get_usdeur()

btcusdt <- binance_klines('BTCUSDT', interval = '1d', limit = 30)
str(btcusdt)

balance <- btcusdt[, .(date = as.Date(close_time), btcusd = close)]
str(balance)

balance[, btceur := btcusd * usdeur]
balance[, btc := BITCOINS]
balance[, value := btc * btceur]
str(balance)

## ########################################################
## Report

ggplot(balance, aes(date, value)) +
  geom_line() +
  xlab('') +
  ylab('') +
  scale_y_continuous(labels = euro) +
  theme_bw() +
  ggtitle('My crypto fortune',
          subtitle = paste(BITCOINS, 'BTC'))
Click here for a potential solution ... with daily corrected USD/HUF exchange rate 
library(binancer)
library(httr)
library(data.table)
library(logger)
library(scales)
library(ggplot2)
library(mr)

## ########################################################
## CONSTANTS

BITCOINS <- 0.42

## ########################################################
## Loading data

usdeur <- get_usdeur()

## try with a single date?
fromJSON('https://api.exchangerate.host/2023-05-01?base=USD&symbols=HUF')
## no, it's just a single day
# fromJSON('https://api.exchangerate.host/timeseries?start_date=2023-05-01&base=USD&symbols=HUF')
## need end
fromJSON('https://api.exchangerate.host/timeseries?start_date=2023-05-01&end_date=2023-05-05&base=USD&symbols=HUF')
## we can do a much better job!

library(httr)
response <- GET(
  'https://api.exchangerate.host/timeseries',
  query = list(
    start_date = Sys.Date() - 30,
    end_date   = Sys.Date(),
    base       = 'USD',
    symbols    = 'EUR'
  ))
exchange_rates <- content(response)
str(exchange_rates)
exchange_rates <- exchange_rates$rates

library(data.table)
usdeur <- data.table(
  date = as.Date(names(exchange_rates)),
  usdeur = as.numeric(unlist(exchange_rates)))
str(usdeur)
## NOTE last element might be an empty list if early in the day ...
##      query yesterday or drop last row when this occurs

## Bitcoin price in USD
btcusdt <- binance_klines('BTCUSDT', interval = '1d', limit = 30)
str(btcusdt)

balance <- btcusdt[, .(date = as.Date(close_time), btcusd = close)]
str(balance)
str(usdeur)

balance <- merge(balance, usdeur, by = 'date')
balance[, btceur := btcusd * usdeur]
balance[, btc := 0.42]
balance[, value := btc * btceur]

## ########################################################
## Report

ggplot(balance, aes(date, value)) +
  geom_line() +
  xlab('') +
  ylab('') +
  scale_y_continuous(labels = euro) +
  theme_bw() +
  ggtitle('My crypto fortune',
          subtitle = paste(BITCOINS, 'BTC'))

Now let's create the get_usdeurs function (similar to get_usdeur) to take start and end dates! Although we can set the start and end date default to today, so would return the same value as get_usdeur and could be the latter deprecated, not that this new function will return a data.frame or data.table object, so thus there's value in keeping the previous one as well.

exchange_rates.R 
#' Look up the value of a US Dollar in Euro
#' @param start_date date
#' @param end_date date
#' @return \code{data.table} object with dates and values
#' @export
#' @importFrom httr GET content
#' @importFrom logger log_error log_info
#' @importFrom checkmate assert_numeric
#' @importFrom data.table data.table
#' @importFrom purrr insistently
#' @importFrom memoise memoise
get_usdeurs <- memoise(
    insistently(
        function(start_date = Sys.Date(), end_date = Sys.Date()) {
            response <- GET(
                'https://api.exchangerate.host/timeseries',
                query = list(
                    start_date = start_date,
                    end_date   = end_date,
                    base       = 'USD',
                    symbols    = 'EUR'
                )
            )
            exchange_rates <- content(response)$rates
            usdeur <- data.table(
                date = as.Date(names(exchange_rates)),
                usdeur = as.numeric(unlist(exchange_rates)))
            assert_numeric(usdeur$usdeur, lower = 0.9, upper = 1.1)
            usdeur
        }
    )
)
Cleaned up R script using the above helper function 
library(binancer)
library(data.table)
library(ggplot2)
library(mr)

## ########################################################
## CONSTANTS

BITCOINS <- 0.42

## ########################################################
## Loading data

usdeurs <- get_usdeurs(Sys.Date() - 30, Sys.Date())
btcusdt <- binance_klines('BTCUSDT', interval = '1d', limit = 30)
balance <- btcusdt[, .(date = as.Date(close_time), btcusd = close)]

balance <- merge(balance, usdeurs, by = 'date')
balance[, btceur := btcusd * usdeur]
balance[, btc := BITCOINS]
balance[, value := btc * btceur]

## ########################################################
## Report

ggplot(balance, aes(date, value)) +
  geom_line() +
  xlab('') +
  ylab('') +
  scale_y_continuous(labels = euro) +
  theme_bw() +
  ggtitle('My crypto fortune',
          subtitle = paste(BITCOINS, 'BTC'))

Make sure our helper functions work!

Make sure to consult the related chapter of Hadley Wickham's "R packages" book at http://r-pkgs.had.co.nz, but in short:

  1. Load the usethis package to scaffold the boring parts of setting up unit tests.

  2. Run use_testthat to configure the package for unit testing with testthat. This will update the DESCRIPTION file, create the tests/testthat folder and the tests/testthat.R file.

  3. Run use_test('euro') to generate tests/testthat/test-euro.R.

  4. Edit the test-euro.R file to write an actual test:

    test_that("euro sign added", {
  expect_equal(euro(2), '€2')
})

  5. Run the test via devtools::test()

  6. Check test coverage via devtools::test_coverage()

Check out some of the relevant advanced topics, e.g.

Recap of week 2

  • revisit retries and caching
  • further API integrations
  • unit testing

It is recommended to install the current version of mr:

devtools::install_github('daroczig/CEU-R-mastering-demo-pkg@week2')

Homework for week 2 gotchas

A QA engineer walks into a bar. Orders a beer. Orders 0 beers. Orders 99999999999 beers. Orders a lizard. Orders -1 beers. Orders a ueicbksjdhd. First real customer walks in and asks where the bathroom is. The bar bursts into flames, killing everyone.

Report on the price of 0.42 BTC and 1.2 ETH in the past 30 days

Let's do the same report as above, but now we not only have 0.42 Bitcoin, but 1.2 Ethereum as well.

Click here for a potential solution ... 
library(binancer)
library(data.table)
library(ggplot2)
library(mr)

## ########################################################
## CONSTANTS

BITCOINS  <- 0.42
ETHEREUMS <- 1.2

## ########################################################
## Loading data

usdeurs <- get_usdeurs(start_date = Sys.Date() - 30, end_date = Sys.Date())

## Cryptocurrency prices in USD
btcusdt <- binance_klines('BTCUSDT', interval = '1d', limit = 30)
ethusdt <- binance_klines('ETHUSDT', interval = '1d', limit = 30)
coinusdt <- rbind(btcusdt, ethusdt)
str(coinusdt)
## oh no, how to keep the symbol??
coinusdt[, .(date = as.Date(close_time), btcusd = close, symbol = ???)]

## DRY (don't repeat yourself)
balance <- rbindlist(lapply(c('BTC', 'ETH'), function(s) {
  binance_klines(paste0(s, 'USDT'), interval = '1d', limit = 30)[, .(
    date = as.Date(close_time),
    usdt = close,
    symbol = s
  )]
}))

balance <- balance[, amount := switch(
  symbol,
  'BTC' = BITCOINS,
  'ETH' = ETHEREUMS,
  stop('Unsupported coin')),
  by = symbol]
str(balance)

balance <- merge(balance, usdeurs, by = 'date')
balance[, value := amount * usdt * usdeur]
str(balance)

## ########################################################
## Report

ggplot(balance, aes(date, value, fill = symbol)) +
  geom_col() +
  xlab('') +
  ylab('') +
  scale_y_continuous(labels = euro) +
  theme_bw() +
  ggtitle(
    'My crypto fortune',
    subtitle = balance[date == max(date), paste(paste(amount, symbol), collapse = ' + ')])

Report on the price of cryptocurrency assets read from a database

  1. 💪 Create a new MySQL database at Amazon AWS and don't forget to set an "inital database name" and make it publicly accessible.

  2. Log in and give a try with MySQL client:

    mysql -h mr.cf27iwlo5bzr.eu-west-1.rds.amazonaws.com -u admin -p

    Look around:

    show databases;
use crypto;
show tables;
desc coins;
select * FROM coins;

  3. 💪 Install dbr from GitHub:

    library(devtools)
install_github('daroczig/logger')
install_github('daroczig/dbr')

  4. 💪 Install botor as well to be able to use encrypted credentials (note that this requires you to install Python first and then pip install boto3 as well):

    install_github('daroczig/botor')

  5. Set up a YAML file (menu: new file/text file, save as databases.yml) for the database connection, something like:

    remotemysql:
  host: ...
  port: 3306
  dbname: ...
  user: ...
  drv: !expr RMySQL::MySQL()
  password: ...

  6. Set up dbr to use that YAML file:

    options('dbr.db_config_path' = '/path/to/databases.yml')

  7. 💪 Create a table for the balances and insert some records:

    library(dbr)
db_config('remotemysql')
db_query('CREATE TABLE coins (symbol VARCHAR(3) NOT NULL, amount DOUBLE NOT NULL DEFAULT 0)', 'remotemysql')
db_query('TRUNCATE TABLE coins', 'remotemysql')
db_query('INSERT INTO coins VALUES ("BTC", 0.42)', 'remotemysql')
db_query('INSERT INTO coins VALUES ("ETH", 1.2)', 'remotemysql')

  8. Write the reporting script, something like:

    Click here for a potential solution ... 
    library(binancer)
library(data.table)
library(logger)
library(ggplot2)
library(mr)

library(dbr)
options('dbr.db_config_path' = '/path/to/databases.yml')
options('dbr.output_format' = 'data.table')

## ########################################################
## Loading data

## Read actual balances from the DB
balance <- db_query('SELECT * FROM coins', 'remotemysql')

## Look up cryptocurrency prices in USD and merge balances
balance <- rbindlist(lapply(balance$symbol, function(s) {
  binance_klines(paste0(s, 'USDT'), interval = '1d', limit = 30)[, .(
    date = as.Date(close_time),
    usdt = close,
    symbol = s,
    amount = balance[symbol == s, amount]
  )]
}))

## USD in EUR
usdeurs <- get_usdeurs(start_date = Sys.Date() - 30, end_date = Sys.Date())

## join USD/HUF exchange rate to balances
balance <- merge(balance, usdeurs, by = 'date')
balance[, value := amount * usdt * usdeur]

## ########################################################
## Report

ggplot(balance, aes(date, value, fill = symbol)) +
  geom_col() +
  xlab('') +
  ylab('') +
  #scale_y_continuous(labels = forint) +
  theme_bw() +
  ggtitle(
    'My crypto fortune',
    subtitle = balance[date == max(date), paste(paste(amount, symbol), collapse = ' + ')])

  9. Rerun the above report after inserting two new records to the table:

    db_query("INSERT INTO coins VALUES ('NEO', 100)", 'remotemysql')
db_query("INSERT INTO coins VALUES ('LTC', 25)", 'remotemysql')

Report on the price of cryptocurrency assets based on the transaction history read from a database

💪 Let's prepare the transactions table:

library(dbr)
options('dbr.db_config_path' = '/path/to/database.yml')
options('dbr.output_format' = 'data.table')

db_query('
  CREATE TABLE transactions (
    date TIMESTAMP NOT NULL,
    symbol VARCHAR(3) NOT NULL,
    amount DOUBLE NOT NULL DEFAULT 0)',
  db = 'remotemysql')

db_query('TRUNCATE TABLE transactions', 'remotemysql')
db_query('INSERT INTO transactions VALUES ("2023-05-11 10:42:02", "BTC", 1.42)', 'remotemysql')
db_query('INSERT INTO transactions VALUES ("2023-05-11 10:45:20", "ETH", 1.2)', 'remotemysql')
db_query('INSERT INTO transactions VALUES ("2023-05-18", "BTC", -1)', 'remotemysql')
db_query('INSERT INTO transactions VALUES ("2023-05-23", "NEO", 100)', 'remotemysql')
db_query('INSERT INTO transactions VALUES ("2023-05-30 12:12:21", "LTC", 25)', 'remotemysql')
Click here for a potential solution for the report ... 
library(binancer)
library(data.table)
library(logger)
library(ggplot2)
library(zoo)
library(mr)

## ########################################################
## Loading data

## Read transactions from the DB
transactions <- db_query('SELECT * FROM transactions', 'remotemysql')

## Prepare daily balance sheets
balance <- transactions[, .(date = as.Date(date), amount = cumsum(amount)), by = symbol]
balance

## Transform long table into wide
balance <- dcast(balance, date ~ symbol)
balance

## Add missing dates
dates <- data.table(date = seq(from = Sys.Date() - 30, to = Sys.Date(), by = '1 day'))
balance <- merge(balance, dates, by = 'date', all.x = TRUE, all.y = TRUE)
balance

## Fill in missing values between actual balances
balance <- na.locf(balance, na.rm = FALSE)

## Fill in remaining missing values with zero
balance[is.na(balance)] <- 0

## Transform wide table back to long format
balance <- melt(balance, id.vars = 'date', variable.name = 'symbol', value.name = 'amount')
balance

## Get crypto prices
prices <- rbindlist(lapply(as.character(unique(balance$symbol)), function(s) {
    binance_klines(paste0(s, 'USDT'), interval = '1d', limit = 30)[
      , .(date = as.Date(close_time), symbol = s, usdt = close)]
}))
balance <- merge(balance, prices, by = c('date', 'symbol'), all.x = TRUE, all.y = FALSE)

## USD in EUR
usdeurs <- get_usdeurs(start_date = Sys.Date() - 30, end_date = Sys.Date())

## join USD/HUF exchange rate to balances
balance <- merge(balance, usdeurs, by = 'date')
balance[, value := amount * usdt * usdeur]

## compute daily values in HUF
balance[, value := amount * usdt * usdhuf]

## ########################################################
## Report

ggplot(balance, aes(date, value, fill = symbol)) +
    geom_col() +
    ylab('') + scale_y_continuous(labels = euro) +
    xlab('') +
    theme_bw() +
    ggtitle(
        'My crypto fortune',
        subtitle = balance[date == max(date), paste(paste(amount, symbol), collapse = ' + ')])

Profiling, benchmarks

Breaking down the a single run of the get_usdhuf function to see which component is slow and taking up resources:

library(mr)

library(profvis)
profvis({
  get_usdeur()
})

profvis({
  get_usdhuf()
}, interval = 0.005)

A more realistic example: is ggplot2 indeed slow when generating scatter plots on a dataset with larger number of observations?

Note first run for the library call! Then run again.

profvis({
  library(ggplot2)
  x <- ggplot(diamonds, aes(price, carat)) + geom_point()
  print(x)
})

system.time(x <- ggplot(diamonds, aes(price, carat)) + geom_point())

Pipe VS Bracket:

library(data.table)
library(dplyr)
dt <- data.table(diamonds)
profvis({
  dt[, sum(carat), by = color][order(color)]
  group_by(dt, color) %>% summarise(price = sum(carat))
})
## run too quickly for profiling ...

library(microbenchmark)
results <- microbenchmark(
  aggregate(dt$carat, by = list(dt$color), FUN = sum),
  dt[, sum(carat), by = color][order(color)],
  group_by(dt, color) %>% summarise(price = sum(carat)),
  times = 100)

results
plot(results)
autoplot(results)

library(bench)
## needs to make sure that resulting objects are the same
results <- bench::mark(
  as.data.frame(dt[, .(price = sum(carat)), by = color][order(color)]),
  as.data.frame(group_by(dt, color) %>% summarize(price = sum(carat)))
)

results
autoplot(results)

## revisit benchmarking creating and printing ggplot
results <- microbenchmark(
  x <- ggplot(diamonds, aes(price, carat)) + geom_point(),
  print(x),
  times = 10)

Also check out dtplyr!

More examples at https://rstudio.github.io/profvis/examples.html

Reporting exercises

Connecting to and exploring the SQLite database

Download and extract the database file:

## download database file
download.file('http://bit.ly/CEU-R-ecommerce', 'ecommerce.zip', mode = 'wb')
unzip('ecommerce.zip')

Install the SQLite client on your operating system and then use the sqlite3 ecommerce.sqlite3 command to enter the command-line SQLite client to browse the database:

-- list tables in the database
.tables
-- show the structure of the sales table
.schema sales
-- show the first 5 rows of the table
select * from sales limit 5
-- tweak how the rows are shown
.headers on
.mode column
select * from sales limit 5

-- count number of rows in the table
SELECT COUNT(*) FROM sales;

-- count number of rows in January 2011 (lack of proper date/time handling in SQLite)
SELECT COUNT(*)
FROM sales
WHERE SUBSTR(InvoiceDate, 7, 4) || SUBSTR(InvoiceDate, 1, 2) || SUBSTR(InvoiceDate, 4, 2)
      BETWEEN '20110101' AND '20110131'

-- check on the date format
SELECT InvoiceDate FROM sales ORDER BY random() LIMIT 25;

-- count the number of rows per month
SELECT
  SUBSTR(InvoiceDate, 7, 4) || SUBSTR(InvoiceDate, 1, 2) AS month,
  COUNT(*)
FROM sales
GROUP BY month
ORDER BY month;

Let's switch to R!

Connect to SQLite from R

Create a database config file for the dbr package:

ecommerce:
  drv: !expr RSQLite::SQLite()
  dbname: /path/to/ecommerce.sqlite3

Update your dbr settings to use the config file:

library(dbr)
options('dbr.db_config_path' = '/path/to/database.yml')
options('dbr.output_format' = 'data.table')

sales <- db_query('SELECT * FROM sales', 'ecommerce')
str(sales)

## explore and fix the invoice date column
sales[, sample(InvoiceDate, 25)]
sales[, InvoiceDate := as.POSIXct(InvoiceDate, format = '%m/%d/%Y %H:%M')]
## see fasttime::fastPOSIXct

## number of sales per month like in SQL
library(lubridate)
sales[, .N, by = month(InvoiceDate)]
sales[, .N, by = year(InvoiceDate)]
sales[, .N, by = paste(year(InvoiceDate), month(InvoiceDate))]
# slow
sales[, .N, by = as.character(InvoiceDate, format = '%Y %m')]
# smart
sales[, .N, by = floor_date(InvoiceDate, 'month')]

system.time(sales[, .N, by = as.character(InvoiceDate, format = '%Y %m')])
system.time(sales[, .N, by = floor_date(InvoiceDate, 'month')])

library(microbenchmark)
microbenchmark(
  sales[, .N, by = as.character(InvoiceDate, format = '%Y %m')],
  sales[, .N, by = floor_date(InvoiceDate, 'month')],
  times = 10)

## number of items per country
sales[, .N, by = Country]
sales[, .N, by = Country][order(-N)]

Aggregate transaction items into invoice summary

invoices <- sales[, .(date = min(as.Date(InvoiceDate)),
                      value  = sum(Quantity * UnitPrice)),
                  by = .(invoice = InvoiceNo, customer = CustomerID, country = Country)]

db_insert(invoices, 'invoices', 'ecommerce')

Check the structure of the newly (and automatically) created table using the command-line SQLite client:

.schema invoices

Check the date column after reading back from the database:

invoices <- db_query('SELECT * FROM invoices', 'ecommerce')
str(invoices)

invoices[, date := as.Date(date, origin = '1970-01-01')]

Report the daily revenue in Excel

revenue <- invoices[, .(revenue = sum(value)), by = date]

library(openxlsx)
wb <- createWorkbook()
sheet <- 'Revenue'
addWorksheet(wb, sheet)
writeData(wb, sheet, revenue)

## open for quick check
openXL(wb)

## write to a file to be sent in an e-mail, uploaded to Slack or as a Google Spreasheet etc
filename <- tempfile(fileext = '.xlsx')
saveWorkbook(wb, filename)
unlink(filename)

## static file name
filename <- 'report.xlsx'
saveWorkbook(wb, filename)

Tweak that spreadsheet:

freezePane(wb, sheet, firstRow = TRUE)

setColWidths(wb, sheet, 1:ncol(revenue), 'auto')

poundStyle <- createStyle(numFmt = '£0,000.00')
addStyle(wb, sheet = sheet, poundStyle,
         gridExpand = TRUE, cols = 2, rows = (1:nrow(revenue)) + 1, stack = TRUE)

greenStyle <- createStyle(fontColour = "#00FF00") # previously? fgFill = "#00FF00"
conditionalFormatting(wb, sheet, cols = 2,
                      rows = 2:(nrow(revenue) + 1),
                      rule = '$B2>66788.35', style = greenStyle)

standardStyle <- createStyle()
conditionalFormatting(wb, sheet, cols = 2,
                      rows = 2:(nrow(revenue) + 1),
                      rule = '$B2<=66788.35', style = standardStyle)

Add a plot:

addWorksheet(wb, 'Plot')

library(ggplot2)
library(ggthemes)
ggplot(revenue, aes(date, revenue)) + geom_line() + theme_excel()

insertPlot(wb, 'Plot')

saveWorkbook(wb, filename)
saveWorkbook(wb, filename,  overwrite = TRUE)

Report the monthly revenue and daily breakdowns in Excel

library(lubridate)
monthly <- invoices[, .(value = sum(value)), by = .(month = floor_date(date, 'month'))]

library(openxlsx)
wb <- createWorkbook()
sheet <- 'Summary'
addWorksheet(wb, sheet)
writeData(wb, sheet, monthly)

for (month in as.character(monthly$month)) {
  revenue <- invoices[floor_date(date, 'month') == month,
                      .(revenue = sum(value)), by = date]
  addWorksheet(wb, as.character(month))
  writeData(wb, month, revenue)
}

saveWorkbook(wb, 'monthly-report.xlsx')

Report on the top 10 customers in a Google Spreadsheet

top10 <- sales[!is.na(CustomerID),
               .(revenue = sum(UnitPrice * Quantity)), by = CustomerID][order(-revenue)][1:10]

library(openxlsx)
wb <- createWorkbook()
sheet <- 'Top Customers'
addWorksheet(wb, sheet)
writeData(wb, sheet, top10)
t <- tempfile(fileext = '.xlsx')
saveWorkbook(wb, t)

## upload file
library(googledrive)
drive_auth()
## NOTE you can clean up credentials in ~/.R/gargle/gargle-oauth
drive_upload(media = t, name = 'top customers', path = 'ceu')
drive_update(media = t, file = 'top customers')

## instead of top10, let's do top25 ... so appending a few rows to an already existing spreadsheet
top25 <- sales[
  !is.na(CustomerID),
  .(revenue = sum(UnitPrice * Quantity)), by = CustomerID][order(-revenue)][1:25]
library(googlesheets4)
gs4_auth()
for (i in 11:25) {
  sheet_append('your.spreadsheet.id', data = top25[i])
}

Homeworks

Week 1

Add the get_usdeur and get_bitcoin_price functions to your mr R package (including documentation and all required imports), and push to your GitHub repo, so that you can install the package on any computer via remotes::install_github. Submit the URL to your GitHub repo in Moodle.

Week 2

Write unit tests for the get_usdeurs function, e.g. what happens when end date is lower than the start date, when he dates are not valid dates. Create a pull request for the main repo, and share the URL on Moodle.

Week 3

Use GitHub Actions to either run the unit tests of the package after each push to GitHub, or to build documentation and publish using GitHub Pages.

References:

Share the URL of a successful GitHub Actions run on Moodle.

Contact

File a GitHub ticket.

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