Nat  Grady

Nat Grady

1667480400

TSibble: Tidy Temporal Data Frames and Tools

tsibble 

The tsibble package provides a data infrastructure for tidy temporal data with wrangling tools. Adapting the tidy data principles, tsibble is a data- and model-oriented object. In tsibble:

  1. Index is a variable with inherent ordering from past to present.
  2. Key is a set of variables that define observational units over time.
  3. Each observation should be uniquely identified by index and key.
  4. Each observational unit should be measured at a common interval, if regularly spaced.

Installation

You could install the stable version on CRAN:

install.packages("tsibble")

You could install the development version from Github using

# install.packages("remotes")
remotes::install_github("tidyverts/tsibble")

Get started

Coerce to a tsibble with as_tsibble()

To coerce a data frame to tsibble, we need to declare key and index. For example, in the weather data from the package nycflights13, the time_hour containing the date-times should be declared as index, and the origin as key. Other columns can be considered as measured variables.

library(dplyr)
library(tsibble)
weather <- nycflights13::weather %>% 
  select(origin, time_hour, temp, humid, precip)
weather_tsbl <- as_tsibble(weather, key = origin, index = time_hour)
weather_tsbl
#> # A tsibble: 26,115 x 5 [1h] <America/New_York>
#> # Key:       origin [3]
#>   origin time_hour            temp humid precip
#>   <chr>  <dttm>              <dbl> <dbl>  <dbl>
#> 1 EWR    2013-01-01 01:00:00  39.0  59.4      0
#> 2 EWR    2013-01-01 02:00:00  39.0  61.6      0
#> 3 EWR    2013-01-01 03:00:00  39.0  64.4      0
#> 4 EWR    2013-01-01 04:00:00  39.9  62.2      0
#> 5 EWR    2013-01-01 05:00:00  39.0  64.4      0
#> # … with 26,110 more rows

The key can be comprised of empty, one, or more variables. See package?tsibble and vignette("intro-tsibble") for details.

The interval is computed from index based on the representation, ranging from year to nanosecond, from numerics to ordered factors. The table below shows how tsibble interprets some common time formats.

IntervalClass
Annualinteger/double
Quarterlyyearquarter
Monthlyyearmonth
Weeklyyearweek
DailyDate/difftime
SubdailyPOSIXt/difftime/hms

A full list of index classes supported by tsibble can be found in package?tsibble.

fill_gaps() to turn implicit missing values into explicit missing values

Often there are implicit missing cases in time series. If the observations are made at regular time interval, we could turn these implicit missingness to be explicit simply using fill_gaps(), filling gaps in precipitation (precip) with 0 in the meanwhile. It is quite common to replaces NAs with its previous observation for each origin in time series analysis, which is easily done using fill() from tidyr.

full_weather <- weather_tsbl %>%
  fill_gaps(precip = 0) %>% 
  group_by_key() %>% 
  tidyr::fill(temp, humid, .direction = "down")
full_weather
#> # A tsibble: 26,190 x 5 [1h] <America/New_York>
#> # Key:       origin [3]
#> # Groups:    origin [3]
#>   origin time_hour            temp humid precip
#>   <chr>  <dttm>              <dbl> <dbl>  <dbl>
#> 1 EWR    2013-01-01 01:00:00  39.0  59.4      0
#> 2 EWR    2013-01-01 02:00:00  39.0  61.6      0
#> 3 EWR    2013-01-01 03:00:00  39.0  64.4      0
#> 4 EWR    2013-01-01 04:00:00  39.9  62.2      0
#> 5 EWR    2013-01-01 05:00:00  39.0  64.4      0
#> # … with 26,185 more rows

fill_gaps() also handles filling in time gaps by values or functions, and respects time zones for date-times. Wanna a quick overview of implicit missing values? Check out vignette("implicit-na").

index_by() + summarise() to aggregate over calendar periods

index_by() is the counterpart of group_by() in temporal context, but it groups the index only. In conjunction with index_by(), summarise() aggregates interested variables over time periods. index_by() goes hand in hand with the index functions including as.Date(), yearweek(), yearmonth(), and yearquarter(), as well as other friends from lubridate. For example, it would be of interest in computing average temperature and total precipitation per month, by applying yearmonth() to the index variable (referred to as .).

full_weather %>%
  group_by_key() %>%
  index_by(year_month = ~ yearmonth(.)) %>% # monthly aggregates
  summarise(
    avg_temp = mean(temp, na.rm = TRUE),
    ttl_precip = sum(precip, na.rm = TRUE)
  )
#> # A tsibble: 36 x 4 [1M]
#> # Key:       origin [3]
#>   origin year_month avg_temp ttl_precip
#>   <chr>       <mth>    <dbl>      <dbl>
#> 1 EWR      2013 Jan     35.6       3.53
#> 2 EWR      2013 Feb     34.2       3.83
#> 3 EWR      2013 Mar     40.1       3   
#> 4 EWR      2013 Apr     53.0       1.47
#> 5 EWR      2013 May     63.3       5.44
#> # … with 31 more rows

While collapsing rows (like summarise()), group_by() and index_by() will take care of updating the key and index respectively. This index_by() + summarise() combo can help with regularising a tsibble of irregular time space too.

Learn more about tsibble

An ecosystem, the tidyverts, is built around the tsibble object for tidy time series analysis.

  • The tsibbledata package curates a range of tsibble data examples to poke around the tsibble object.
  • The feasts package provides support for visualising the data and extracting time series features.
  • The fable package provides common forecasting methods for tsibble, such as ARIMA and ETS. The fabletools package, which is fable built upon, lays the modelling infrastructure to ease the programming with tsibble.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

Download Details:

Author: tidyverts
Source Code: https://github.com/tidyverts/tsibble 
License: GPL-3.0 license

#r #data #frames #tools 

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TSibble: Tidy Temporal Data Frames and Tools
 iOS App Dev

iOS App Dev

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Ian  Robinson

Ian Robinson

1624399200

Top 10 Big Data Tools for Data Management and Analytics

Introduction to Big Data

What exactly is Big Data? Big Data is nothing but large and complex data sets, which can be both structured and unstructured. Its concept encompasses the infrastructures, technologies, and Big Data Tools created to manage this large amount of information.

To fulfill the need to achieve high-performance, Big Data Analytics tools play a vital role. Further, various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data.

List of Big Data Tools & Frameworks

The most important as well as popular Big Data Analytics Open Source Tools which are used in 2020 are as follows:

  1. Big Data Framework
  2. Data Storage Tools
  3. Data Visualization Tools
  4. Big Data Processing Tools
  5. Data Preprocessing Tools
  6. Data Wrangling Tools
  7. Big Data Testing Tools
  8. Data Governance Tools
  9. Security Management Tools
  10. Real-Time Data Streaming Tools

#big data engineering #top 10 big data tools for data management and analytics #big data tools for data management and analytics #tools for data management #analytics #top big data tools for data management and analytics

Gerhard  Brink

Gerhard Brink

1624692167

Top 10 Big Data Tools for 2021!

In today’s tech world, data is everything. As the focus on data grows, it keeps multiplying by leaps and bounds each day. If earlier mounds of data were talked about in kilobytes and megabytes, today terabytes have become the base unit for organizational data. This coming in of big data has transformed paradigms of data storage, processing, and analytics.

Instead of only gathering and storing information that can offer crucial insights to meet short-term goals, an increasing number of enterprises are storing much larger amounts of data gathered from multiple resources across business processes. However, all this data is meaningless on its own. It can add value only when it is processed and analyzed the right way to draw point insights that can improve decision-making.

Processing and analyzing big data is not an easy task. If not handled correctly, big data can turn into an obstacle rather than an effective solution for businesses. Effective handling of big data management  requires to use of tools that can steer you toward tangible, substantial results. For that, you need a set of great big data tools that will not only solve this problem but also help you in producing substantial results.

Data storage tools, warehouses, and data lakes all play a crucial role in helping companies store and sort vast amounts of information. However, the true power of big data lies in its analytics. There are a host of big data tools in the market today to aid a business’ journey from gathering data to storing, processing, analyzing, and reporting it. Let’s take a closer look at some of the top big data tools that can help you inch closer to your goal of establishing data-driven decision-making and workflow processes.

Apache Hadoop

Apache Spark

Flink

Apache Storm

Apache Cassandra

#big data #big data tools #big data management #big data tool #top 10 big data tools for 2021! #top-big-data-tool

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Nat  Grady

Nat Grady

1667480400

TSibble: Tidy Temporal Data Frames and Tools

tsibble 

The tsibble package provides a data infrastructure for tidy temporal data with wrangling tools. Adapting the tidy data principles, tsibble is a data- and model-oriented object. In tsibble:

  1. Index is a variable with inherent ordering from past to present.
  2. Key is a set of variables that define observational units over time.
  3. Each observation should be uniquely identified by index and key.
  4. Each observational unit should be measured at a common interval, if regularly spaced.

Installation

You could install the stable version on CRAN:

install.packages("tsibble")

You could install the development version from Github using

# install.packages("remotes")
remotes::install_github("tidyverts/tsibble")

Get started

Coerce to a tsibble with as_tsibble()

To coerce a data frame to tsibble, we need to declare key and index. For example, in the weather data from the package nycflights13, the time_hour containing the date-times should be declared as index, and the origin as key. Other columns can be considered as measured variables.

library(dplyr)
library(tsibble)
weather <- nycflights13::weather %>% 
  select(origin, time_hour, temp, humid, precip)
weather_tsbl <- as_tsibble(weather, key = origin, index = time_hour)
weather_tsbl
#> # A tsibble: 26,115 x 5 [1h] <America/New_York>
#> # Key:       origin [3]
#>   origin time_hour            temp humid precip
#>   <chr>  <dttm>              <dbl> <dbl>  <dbl>
#> 1 EWR    2013-01-01 01:00:00  39.0  59.4      0
#> 2 EWR    2013-01-01 02:00:00  39.0  61.6      0
#> 3 EWR    2013-01-01 03:00:00  39.0  64.4      0
#> 4 EWR    2013-01-01 04:00:00  39.9  62.2      0
#> 5 EWR    2013-01-01 05:00:00  39.0  64.4      0
#> # … with 26,110 more rows

The key can be comprised of empty, one, or more variables. See package?tsibble and vignette("intro-tsibble") for details.

The interval is computed from index based on the representation, ranging from year to nanosecond, from numerics to ordered factors. The table below shows how tsibble interprets some common time formats.

IntervalClass
Annualinteger/double
Quarterlyyearquarter
Monthlyyearmonth
Weeklyyearweek
DailyDate/difftime
SubdailyPOSIXt/difftime/hms

A full list of index classes supported by tsibble can be found in package?tsibble.

fill_gaps() to turn implicit missing values into explicit missing values

Often there are implicit missing cases in time series. If the observations are made at regular time interval, we could turn these implicit missingness to be explicit simply using fill_gaps(), filling gaps in precipitation (precip) with 0 in the meanwhile. It is quite common to replaces NAs with its previous observation for each origin in time series analysis, which is easily done using fill() from tidyr.

full_weather <- weather_tsbl %>%
  fill_gaps(precip = 0) %>% 
  group_by_key() %>% 
  tidyr::fill(temp, humid, .direction = "down")
full_weather
#> # A tsibble: 26,190 x 5 [1h] <America/New_York>
#> # Key:       origin [3]
#> # Groups:    origin [3]
#>   origin time_hour            temp humid precip
#>   <chr>  <dttm>              <dbl> <dbl>  <dbl>
#> 1 EWR    2013-01-01 01:00:00  39.0  59.4      0
#> 2 EWR    2013-01-01 02:00:00  39.0  61.6      0
#> 3 EWR    2013-01-01 03:00:00  39.0  64.4      0
#> 4 EWR    2013-01-01 04:00:00  39.9  62.2      0
#> 5 EWR    2013-01-01 05:00:00  39.0  64.4      0
#> # … with 26,185 more rows

fill_gaps() also handles filling in time gaps by values or functions, and respects time zones for date-times. Wanna a quick overview of implicit missing values? Check out vignette("implicit-na").

index_by() + summarise() to aggregate over calendar periods

index_by() is the counterpart of group_by() in temporal context, but it groups the index only. In conjunction with index_by(), summarise() aggregates interested variables over time periods. index_by() goes hand in hand with the index functions including as.Date(), yearweek(), yearmonth(), and yearquarter(), as well as other friends from lubridate. For example, it would be of interest in computing average temperature and total precipitation per month, by applying yearmonth() to the index variable (referred to as .).

full_weather %>%
  group_by_key() %>%
  index_by(year_month = ~ yearmonth(.)) %>% # monthly aggregates
  summarise(
    avg_temp = mean(temp, na.rm = TRUE),
    ttl_precip = sum(precip, na.rm = TRUE)
  )
#> # A tsibble: 36 x 4 [1M]
#> # Key:       origin [3]
#>   origin year_month avg_temp ttl_precip
#>   <chr>       <mth>    <dbl>      <dbl>
#> 1 EWR      2013 Jan     35.6       3.53
#> 2 EWR      2013 Feb     34.2       3.83
#> 3 EWR      2013 Mar     40.1       3   
#> 4 EWR      2013 Apr     53.0       1.47
#> 5 EWR      2013 May     63.3       5.44
#> # … with 31 more rows

While collapsing rows (like summarise()), group_by() and index_by() will take care of updating the key and index respectively. This index_by() + summarise() combo can help with regularising a tsibble of irregular time space too.

Learn more about tsibble

An ecosystem, the tidyverts, is built around the tsibble object for tidy time series analysis.

  • The tsibbledata package curates a range of tsibble data examples to poke around the tsibble object.
  • The feasts package provides support for visualising the data and extracting time series features.
  • The fable package provides common forecasting methods for tsibble, such as ARIMA and ETS. The fabletools package, which is fable built upon, lays the modelling infrastructure to ease the programming with tsibble.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

Download Details:

Author: tidyverts
Source Code: https://github.com/tidyverts/tsibble 
License: GPL-3.0 license

#r #data #frames #tools