Attributi temporali e date decimali

Gli attributi temporali di una serie temporale – inizio, fine e frequenza – sono fondamentali per capire come è stata campionata.

Per esempio, i punti di inizio e fine aiutano a determinare la durata dei tuoi dati. La frequenza indica ogni quanto sono stati campionati i dati. Una serie temporale campionata regolarmente ogni mese avrà una frequenza pari a 12; ci sono 12 mesi in un anno.

Spesso, le date sono memorizzate come date decimali (ad es., 2019.125), utili quando la serie temporale è stata campionata, per esempio, esattamente 100 volte in un anno.

Convertire le date decimali in date leggibili è un passaggio importante nell’analisi delle serie temporali, soprattutto quando presenti i dati ad altre persone.

La serie temporale maunaloa e i pacchetti zoo e lubridate sono a tua disposizione.

Questo esercizio fa parte del corso

Manipolare dati di serie temporali in R

Visualizza il corso

Esercizio pratico interattivo

Passa dalla teoria alla pratica con uno dei nostri esercizi interattivi

Inizia esercizio

Questo esercizio fa parte del corso

Manipolare dati di serie temporali in R

IntermediárioNível de habilidade

4.9+

Inizia il corso 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: Attributi delle serie temporali Exercise 2: Attributi temporali e date decimali

Esercizio in corso

Exercise 3: Confrontare gli attributi temporali Exercise 4: Oggetti di serie temporali con il pacchetto zoo Exercise 5: Creare uno zoo Exercise 6: zoo e ggplot2 Exercise 7: Manipolare gli oggetti zoo Exercise 8: Aggiornare e sostituire gli indici Exercise 9: Trovare indici sovrapposti Exercise 10: Conversione tra zoo e data frame Exercise 11: Passare tra data.frame e 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!