Datums parsen en opmaken

Een essentiële vroege stap bij het werken met "ruwe" tijdreeksgegevens (meestal een kolom in een data frame of een vector met observaties) is controleren of al je datums in hetzelfde "datumformaat" staan. Dat geldt zeker wanneer je gegevens uit meerdere bronnen komen.

In deze oefening ga je het opmaken van dates op twee manieren oefenen met lubridate. Eerst zet je het object dates, een character-vector, om naar een vector van de klasse Date. Daarna formatteer je dates_order opnieuw, een object met inconsistente datumopmaak.

De objecten dates en dates_order, en het pakket lubridate, zijn voor je ingeladen.

Deze oefening maakt deel uit van de cursus

Tijdreeksgegevens bewerken in R

Cursus bekijken

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Print the class of the dates object
___

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Tijdreeksgegevens bewerken in R

SkillTag.level.intermediateSkillTag.label

4.9+

244 reviews

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: Wat is tijdreeksdata?Exercise 2: Tijdreeksen plotten met autoplot Exercise 3: Samenvatting van tijdreeks Exercise 4: Tijdreeksplots interpreteren Exercise 5: Temporele dataklassen in R Exercise 6: De klasse bepalen Exercise 7: is-dot-functies Exercise 8: as_date van lubridate Exercise 9: Datums opmaken in R Exercise 10: Conversiespecificaties Exercise 11: Datums parsen en opmaken

Huidige oefening

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: Time series attributes Exercise 2: Temporal attributes and decimal dates Exercise 3: Comparing temporal attributes Exercise 4: Time series objects with the zoo package Exercise 5: Creating a zoo Exercise 6: zoo and ggplot2 Exercise 7: Manipulating zoo objects Exercise 8: Updating and replacing indices Exercise 9: Finding overlapping indices Exercise 10: Converting between zoo and data frame Exercise 11: Moving between data.frame and 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!