Interpolazione lineare

Per dati continui e numerici – in cui i valori possono cadere ovunque entro un intervallo – l’interpolazione lineare è spesso la scelta migliore per l’imputazione. Dati come temperatura, altitudine e reddito pro capite sono esempi in cui si può usare l’interpolazione lineare.

In questo esercizio, determinerai il numero di valori mancanti nella serie temporale maunaloa_missing e userai l’interpolazione lineare per imputare questi valori.

Hai a disposizione maunaloa_missing, zoo e ggplot2.

Questo esercizio fa parte del corso

Manipolare dati di serie temporali in R

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Istruzioni dell'esercizio

Determina quante osservazioni di maunaloa_missing sono NA.
Usa l’interpolazione lineare per riempire i valori mancanti in maunaloa_missing; assegna il risultato a maunaloa_linear.
Genera un ggplot di maunaloa_linear, con una linea di colore rosso.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Count the number of missing values
___

# Fill in values with linear approximation
___

# Generate a full ggplot of maunaloa_linear
ggplot(___,
       aes(___)) + 
  scale_y_continuous() + 
  ___

Modifica ed esegui il codice

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: 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: Sottoinsiemi di una finestra di osservazioni Exercise 2: Selezionare una finestra da una serie temporale Exercise 3: Trovare un'osservazione da una data specifica Exercise 4: Espressioni logiche e sottoinsiemi Exercise 5: Dati mensili e trimestrali Exercise 6: zoo::yearmon Exercise 7: zoo::yearqtr Exercise 8: Ricampionare e aggregare le osservazioni Exercise 9: Aggregazione dei dati Exercise 10: Livello di aggregazione personalizzato Exercise 11: Rappresentare una serie temporale aggregata con ggplot2 Exercise 12: Imputare i valori mancanti Exercise 13: Metodi di imputazione Exercise 14: Riempimento costante Exercise 15: Last observation carried forward (LOCF)Exercise 16: Interpolazione lineare

Esercizio in corso

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!