Practical Financial Investment Analysis in R and tidyquant

Learn to read csv and txt data into R and RStudio, handle standard and nonstandard separators, and import Excel data with readxl for analysis.

read data from online html tables in r using xml and rcurl, extract the 2016 summer olympics medal table from wikipedia, and convert it into a dataframe.

Learn to read data from online HTML tables using the rvest package, specifying XPath to extract Wikipedia tables, handle null names, and fill data frames from multiple pages.

Edit data in R using the data editor package with Excel-like capabilities in a shiny app. Install via devtools or CRAN, edit the empty cars dataset, and save to CSV.

Learn practical data cleaning in R by handling missing data with na.omit and complete.cases, imputing with means, and performing predictive mean mapping imputation with mice on inbuilt datasets.

Clean a CSV dataset by removing dollar signs and commas from GDP per capita, convert to numeric, and store the cleaned data as CSV with population and biocapacity.

Explore dplyr for data summarizing and intuitive selection on air quality data, using select and filter. Group by month and compute mean wind, and create a new temp in c.

learn to use dplyr for data summarization with the pipe operator to select, group by month, and compute means, filters, and summaries on air quality data.

Learn to fetch Tesla stock prices in R using tidyquant and tidyverse, install and load required packages, and retrieve open, high, low, close, and adjusted prices with tq_get.

Learn how to obtain additional stock data, including dividends, using tidyquant functions like tq_get, retrieving Apple and other stocks online and handling retries.

Explore Quandl, a marketplace for financial, economic, and alternative data, and learn to sign up for academic or business use, access free or paid data, and use quandl_api_key in R.

Use Quandl to obtain financial data in R with quantmod, tidyverse, and tidyquant, set your api key, and fetch fb stock data with date, open, high, close, and adjusted values.

Install and load the crypto package to identify active coins, fetch their information, and obtain market history with opening, high, low, and closing prices for top ten cryptocurrencies from 2020-01-02.

Explore data visualization as an exploratory data analysis tool that reveals patterns, trends, and correlations through bar plots, pie charts, histograms, box plots, line charts, and scatter plots.

Develop basic financial data visualizations in R using tidyquant, including line charts and candlestick charts with Tesla stock data from 2018 onward.

Explore basic visualisations in tidyquant: compare bar charts and candlesticks for stock data, using open, high, low, and close prices with ggplot and proper labels.

Explore data pre-processing with tidyquant in R by loading sp500 data, computing monthly and annual returns, and integrating functions from quantmod and zoo for robust financial analysis.

Visualize fang stock annual returns in tidyquant by computing yearly arithmetic returns from the inbuilt fang dataset and plotting them with ggplot, with percent scaling and facetting by symbol.

Explore stock prices across daily and monthly scales using the Fang dataset, converting daily series to monthly, and visualizing adjusted values with ggplot and facetwrap by symbol.

Explore simple moving averages to identify trend direction with 50- and 200-day SMAs, including bullish cross signals, and learn to compute and plot them in R using tidyquant.

Analyze MACD as a trend-following momentum indicator by comparing 12 period and 26 period EMAs, with the 9 period signal line guiding buy or sell signals, shown across four stocks.

Visualize JPMorgan's yearly log returns from 2007 to 2016 in R using tidyquant, with an annual returns function and a linear regression trend line.

Compare Ford and Coca-Cola prices from 1990–2020, compute monthly returns, and use line and density plots to identify which stock yields more frequent large positive returns.

Learn to compute and evaluate the Sharpe ratio in tidyquant to assess risk-adjusted portfolio returns using a 90-day treasury bill as the risk-free proxy.

Identify risk-adjusted returns by calculating and visualizing the Sharpe ratio for Coca-Cola and Ford using monthly stock returns in R and tidyquant, and compare downside risk and drawdowns.

Compare a three-stock portfolio to a baseline index in R using tidyquant, compute the Capm table, and visualize monthly returns versus the XLK baseline over a ten-year period.

Scale a single portfolio to multiple assets, apply weights, and merge returns to compute Capm using a performance function in R with tidyquant.

Choose the right database by comparing SQL and NoSQL options. Apply the quick rule for SQL data to guide database selection in practical investment analysis.