Last observation carried forward (LOCF)

Metode imputasi penting lainnya adalah Last observation carried forward atau LOCF. Metode ini bekerja dengan mengisi nilai yang hilang menggunakan nilai terakhir sebelumya yang bukan NA.

Pada latihan ini, Anda akan mengimputasi deret waktu nilai ujian bulanan seorang siswa, bernama monthly_test_scores. Himpunan data ini, bersama paket ggplot2 dan zoo, tersedia untuk Anda.

Latihan ini adalah bagian dari kursus

Memanipulasi Data Deret Waktu di R

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Petunjuk latihan

Gunakan last observation carried forward untuk mengimputasi nilai yang hilang pada monthly_test_scores; simpan sebagai monthly_scores_locf.
Lengkapi autoplot() pertama untuk membuat plot deret waktu asli monthly_test_scores.
Autoplot monthly_scores_locf dengan tema 'minimal', serta label sumbu y "Examination Score (LOCF)".
Bandingkan plot monthly_scores_locf dengan plot monthly_test_scores dengan beralih di antara plot tersebut.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Impute using last observation carried forward
___ <- ___

# Create a plot of the original time series 
autoplot(___) + 
  theme_minimal() + 
  labs(y = "Examination Score")

# Autoplot with theme and axis title and compare to the above plot
autoplot(___) + 
  ___

Edit dan Jalankan Kode

Memanipulasi Data Deret Waktu di R

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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!