Tijdelijke attributen vergelijken

De begin- en eindpunten van een tijdreeks worden in R opgeslagen zoals elke andere datum- of tijdobject, waardoor je ze kunt vergelijken en rekenkundig kunt bewerken om het tijdsinterval ertussen te berekenen.

Zet je R-skills in om de tijdsgebonden attributen van card_prices te manipuleren – een tijdreeks met de dagprijzen van een kaart uit een populair ruilkaartspel.

De tijdreeks card_prices, en de pakketten zoo en lubridate, zijn voor je beschikbaar.

Deze oefening maakt deel uit van de cursus

Tijdreeksgegevens bewerken in R

Cursus bekijken

Oefeninstructies

Ken het beginpunt van card_prices toe aan de variabele card_start.
Ken het eindpunt van card_prices toe aan de variabele card_end.
Trek card_start af van card_end en bekijk het resultaat.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Assign the start point to card_start
___

# Assign the end point to card_end
___

# Subtract the start point from the end point
___

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Tijdreeksgegevens bewerken in R

SkillTag.level.intermediateSkillTag.label

4.9+

Begin de cursus gratis

You'll learn the foundations of what a time series represents, how to retrieve summary statistics about the data in a time series, and how to visually interpret a time series plot as part of the exploration step of your analysis. You’ll also cover how to manage date and time information within R objects and ways of incorporating consistent formatting for dates.

Exercise 1: What is time series data?Exercise 2: Plotting time series with autoplot Exercise 3: Time series summary Exercise 4: Interpreting time series plots Exercise 5: Temporal data classes in R Exercise 6: Finding the class Exercise 7: is-dot functions Exercise 8: lubridate's as_date Exercise 9: Formatting dates in R Exercise 10: Conversion specifications Exercise 11: Parsing and formatting dates

Here, you’ll learn to retrieve key attributes of time series information, such as the range in time of the data and how often the data were sampled, to understand your data better. You'll also be introduced to the zoo package, which contains tools and functions for creating and manipulating time series objects. Many data science applications in R use the data frame paradigm; you'll learn how to convert between a data frame and a time series.

Exercise 1: Attributen van tijdreeksen Exercise 2: Temporale attributen en decimale datums Exercise 3: Tijdelijke attributen vergelijken

Huidige oefening

Exercise 4: Tijdreeksobjecten met het pakket zoo Exercise 5: Een zoo-object maken Exercise 6: zoo en ggplot2 Exercise 7: Zoo-objecten bewerken Exercise 8: Indices bijwerken en vervangen Exercise 9: Overlappende indexen vinden Exercise 10: Converteren tussen zoo en data frame Exercise 11: Wisselen tussen data.frame en zoo

You’ll cover how to subset a window from a time series to focus on a particular period of interest. You’ll see that when working with real-world time series data, the timespan of your dataset may cover more information than you need, which can clutter your visualizations. You’ll sample time series data at various rates, such as every minute, hour, month, or year. Further, by aggregating your data, you’ll learn to see the overall trends in the data using the xts package. You'll also learn methods of 'imputing' your data – filling in missing values with constant fill, LOCF, or linear interpolation methods.

Exercise 1: Subsetting a window of observations Exercise 2: Selecting a window from a time series Exercise 3: Finding an observation from a specific date Exercise 4: Logical expressions and subsets Exercise 5: Monthly and quarterly data Exercise 6: zoo::yearmon Exercise 7: zoo::yearqtr Exercise 8: Resampling and aggregating observations Exercise 9: Aggregating data Exercise 10: Custom aggregation level Exercise 11: Plotting an aggregated time series with ggplot2 Exercise 12: Imputing missing values Exercise 13: Imputation methods Exercise 14: Constant Fill Exercise 15: Last observation carried forward (LOCF)Exercise 16: Linear interpolation

You’ll learn to create “rolling” windows of a time series that move, or "roll" along with data, making it possible to summarize trends in the data across time, such as the average over success months of observations or the sum over several weeks of sales. Overall summary statistics, like mean, median, sum, maximum, and so on, do not always provide insight into how data changes over time, and rolling windows will allow you to compute statistics dynamically. In addition to rolling windows, you will also learn how to create expanding windows, which show how these summary statistics approach their final value.

Exercise 1: What is a rolling window?Exercise 2: Rolling window functions Exercise 3: Rolling windows versus aggregation Exercise 4: Applying functions to rolling windows Exercise 5: Rolling minimum Exercise 6: Rolling apply with a custom function Exercise 7: Expanding windows Exercise 8: Rolling versus expanding windows Exercise 9: Expanding sum Exercise 10: Expanding mean Exercise 11: Congratulations!