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To see sample code for a particular function, try example(topic="functionName", package="packageName") or simply ?functionName for all available help about a function including any sample code (not all documentation includes samples).įor more on best R packages, see Great R Packages for data import, wrangling and visualization.įor specific information about U.S. For more information about a package, you can run help(package="packageName") in R to get info on functions included in the package and, if available, links to package vignettes (R-speak for additional documentation). Some of the sample code below comes from package documentation or blog posts by package authors. Once installed, you can load a package into your working session once each session using the format library("packageName").
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GitHub packages are best installed with the devtools package - install that once with install.packages("devtools") and then use that to install packages from GitHub using the format devtools::install_github("repositoryName/packageName"). Packages that are on CRAN can be installed on your system by using the R command install.packages("packageName") - you only need to run this once. The packages listed below make it easy to find economic, sports, weather, political and other publicly available data and import it directly into R - in a format that's ready for you to work your analytics magic. Amazon price is above $800, other stocks are under $200.There are lots of good reasons you might want to analyze public data, from detecting salary trends in government data to uncovering insights about a potential investment (or your favorite sports team).īut before you can run analyses and visualize trends, you need to have the data. This chart look weird, since the scale is not appropriate.
Ggplot(aes(x = date, y = adjusted, color = symbol)) +
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We can also chart the time series of all the prices. # symbol date open high low close volume adjusted To see the first row of each symbol, we need to slice the data. This data is in tidy format, where symbols are stacked on top of one another. We can also download multiple stock prices. For that we will use the very popular ggplot2 package. We can see that the object aapl is a tibble. To = "") # "AAPL" "NFLX" "AMZN" "K" "O" prices There are several steps to this tickers = c("AAPL", "NFLX", "AMZN", "K", "O") We can download prices for several stocks. We can even zoom into a certain period of the series. We just pass the command chart_Series chart_Series(AAPL) class(AAPL) # "xts" "zoo"Īs we mentioned before this is an xts zoo object. head(AAPL) # AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume You can change this by passing the argument auto.assign = FALSE. By default quantmod download and stores the symbols with their own names. library(tidyquant)įirst we will download Apple price using quantmod from January 2017 to February 2018. tidyquant includes quantmod so you can install just tidyquant and get the quantmod packages as well. You can download the tidyquant package by typing install.packages("tidyquant") in you R console. For our calculations we will use tidyquant package which downloads prices in a tidy format as a tibble. The prices downloaded in by using quantmod are xts zoo objects. You can install it by typing the command install.packages("quantmod") in your R console. The most popular method is the quantmod package. There are several ways to get financial data into R.