Code Review Stack Exchange is a question and answer site for peer programmer code reviews. It only takes a minute to sign up. Sign up to join this community. Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Home; Questions; Tags; Users; Unanswered; best way to apply across an xts object. Ask Question Asked 6 years, 4 months ago. Active 6.
Package: xts Type: Package Version: 0.8-0 Date: 2011-02-22 License: GPL-2 Easily convert one of R’s many time-series (and non-time-series) classes to a true time-based object which inherits all of zoo’s methods, while allowing for new time-based tools where appropriate. Additionally, one may use xts to create new objects which can contain arbitrary attributes named during creation as name.
Data Frame Row Slice. We retrieve rows from a data frame with the single square bracket operator, just like what we did with columns. However, in additional to an index vector of row positions, we append an extra comma character. This is important, as the extra comma signals a wildcard match for the second coordinate for column positions. Numeric Indexing. For example, the following retrieves.
Converting data between wide and long format. Problem; Solution. Sample data; tidyr. From wide to long; From long to wide; reshape2. From wide to long; From long to wide; Problem. You want to do convert data from a wide format to a long format. Many functions in R expect data to be in a long format rather than a wide format. Programs like SPSS, however, often use wide-formatted data. Solution.
Handling date-times in R Cole Beck August 30, 2012 1 Introduction Date-time variables are a pain to work with in any language. We’ll discuss some of the common issues and how to overcome them. Before we examine the combination of dates and times, let’s focus on dates. Even by themselves dates can be a pain. One idea is to refuse to use them. Generally dates are interally stored as integers.
Manipulating, analyzing and exporting data with tidyverse Data Carpentry contributors. Manipulating and analyzing data with dplyr. Learning Objectives. Describe the purpose of the dplyr and tidyr packages. Select certain columns in a data frame with the dplyr function select. Select certain rows in a data frame according to filtering conditions with the dplyr function filter. Link the output.
This tutorial describes how to reorder (i.e., sort) rows, in your data table, by the value of one or more columns (i.e., variables). You will learn how to easily: Sort a data frame rows in ascending order (from low to high) using the R function arrange() (dplyr package); Sort rows in descending order (from high to low) using arrange() in combination with the function desc() (dplyr package).
Introduction to Volatility 2017-07-12. by Jonathan Regenstein. This is the beginning of a series on portfolio volatility, variance, and standard deviation. I realize that it’s a lot more fun to fantasize about analyzing stock returns, which is why television shows and websites constantly update the daily market returns and give them snazzy green and red colors. But good ol’ volatility is.
Presently it is possible to convert all the major time-series like classes in R to xts. This list includes objects of class: matrix, data.frame, ts, zoo, irts, and.
Introduction 1. Application 1.1. editR 1.2. flexdashboard 1.3. htmlwidgets 1.4. listviewer 1.5. miniUI 1.6. shiny 1.7. shinygadgets 1.8. shinyLP 1.9. shinystan 1.10.
By default, R will sort the vector in ascending order. However, you can add the decreasing argument to the function, which will explicitly specify the sort order as in the example above. Sorting Data Frames. In R, a data frame is an object with multiple rows and multiple columns.
Welcome to the second installment of my three-part series dedicated to portfolio standard deviation, also known as volatility. In this series, you will learn to build a Shiny application to visualize total portfolio volatility over time, as well as how each asset has contributed to that volatility.
Prices can be for any time scale, such as daily, weekly, monthly or annual, as long as the data consists of regular observations. Irregular observations require time period scaling to be comparable. Fortunately, to.period in the xts package, or the aggregate.zoo in the zoo package supports supports management and conversion of irregular time.
Re: (R) as.xts convert all my numeric data to character This message: ( Message body ) ( More options ) Related messages: ( Previous message ) ( In reply to ).
R Character Matrix to Numeric When read data containing characters, R will readin them as character matrix in default. For example, the following file will be readed as a character matrix in default: However we can convert the 3rd row to the last row into numeric matrix because there are all numbers, following is the code: R Tutorials: Data Type and Structures. Loop, Condition Statements.
Reorder the correlation matrix. This section describes how to reorder the correlation matrix according to the correlation coefficient. This is useful to identify the hidden pattern in the matrix. hclust for hierarchical clustering order is used in the example below. Helper function to reorder the correlation matrix.
A simple and reliable way to convert many different objects into a uniform format for use within R. It is possible with a call to as.xts to convert objects of class timeSeries, ts, irts, fts, matrix, data.frame, and zoo. xtsible safely checks whether an object can be converted to an xts object; returning TRUE on success and FALSE otherwise.
Forecast Stock Prices Example with r and STL. Given a time series set of data with numerical values, we often immediately lean towards using forecasting to predict the future. In this forecasting example, we will look at how to interpret the results from a forecast model and make modifications as needed. The forecast model we will use is stl(). Natural gas companies usually display a seasonal.
Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up. Sign up to join this community. Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Home; Questions; Tags; Users; Unanswered; Column-wise matrix normalization.