Ganti data hilang - II

Seperti sebagian besar aspek manipulasi data deret waktu, ada banyak cara untuk menangani missingness. Seperti yang Anda temukan pada latihan sebelumnya, baik pendekatan locf maupun nocb mengharuskan Anda membuat asumsi tertentu tentang pola pertumbuhan dalam data Anda. Sementara locf lebih konservatif dan nocb lebih agresif, keduanya menghasilkan pertumbuhan bertahap (step-wise) dari data yang hilang.

Namun bagaimana jika Anda memiliki alasan untuk mengharapkan pertumbuhan linear dalam data? Dalam kasus ini, mungkin lebih bermanfaat menggunakan interpolasi linear, yang menghasilkan nilai baru di antara data pada kedua sisi nilai yang hilang dengan bobot berdasarkan waktu.

Dalam latihan ini, Anda akan mengisi nilai yang hilang dalam data gdp_xts menggunakan perintah na.approx(), yang menggunakan interpolasi untuk memperkirakan nilai linear terhadap waktu.

Latihan ini adalah bagian dari kursus

Studi Kasus: Menganalisis Data Deret Waktu Kota di R

Petunjuk latihan

Gunakan na.approx() untuk mengisi nilai yang hilang di gdp_xts dengan interpolasi linear. Simpan objek xts baru ini sebagai gdp_approx.
Plot objek xts baru Anda menggunakan plot.xts().
Ambil nilai PDB untuk tahun 1993 dari objek xts baru Anda.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Fill NAs in gdp_xts using linear approximation
gdp_approx <- 

# Plot your new xts object
plot.xts(___, major.format = "%Y")
  
# Query for GDP in 1993 in gdp_approx

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Studi Kasus: Menganalisis Data Deret Waktu Kota di R

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

You've been hired to understand the travel needs of tourists visiting the Boston area. As your first assignment on the job, you'll practice the skills you've learned for time series data manipulation in R by exploring data on flights arriving at Boston's Logan International Airport (BOS) using xts & zoo.

Exercise 1: Review xts fundamentals Exercise 2: Identify the time series Exercise 3: Flight data Exercise 4: Pick out the xts object Exercise 5: Encoding your flight data Exercise 6: Manipulating and visualizing your data Exercise 7: Exploring your flight data Exercise 8: Visualize flight data Exercise 9: Calculate time series trends Exercise 10: Saving and exporting xts objects Exercise 11: Assessing flight trends Exercise 12: Saving time - I Exercise 13: Saving time - II

In this chapter, you'll expand your time series data library to include weather data in the Boston area. Before you can conduct any analysis, you'll need to do some data manipulation, including merging multiple xts objects and isolating certain periods of the data. It's a great opportunity for more practice!

Exercise 1: Merging time series data by row Exercise 2: Exploring temperature data Exercise 3: Next steps - I Exercise 4: Merging using rbind()Exercise 5: Visualizing Boston winters Exercise 6: Merging time series data by column Exercise 7: Subsetting and adjusting periodicity Exercise 8: Generating a monthly average Exercise 9: Using merge() and plotting over time Exercise 10: Are flight delays related to temperature?Exercise 11: Time series data workflow Exercise 12: Next steps - II Exercise 13: Expanding your data Exercise 14: Are flight delays related to visibility or wind?

Now it's time to go further afield. In addition to flight delays, your client is interested in how Boston's tourism industry is affected by economic trends. You'll need to manipulate some time series data on economic indicators, including GDP per capita and unemployment in the United States in general and Massachusetts (MA) in particular.

Exercise 1: Menangani nilai hilang Exercise 2: Menjelajahi data ekonomi Exercise 3: Ganti data hilang - I Exercise 4: Ganti data hilang - II

Latihan Saat Ini

Exercise 5: Mengestimasi PDB yang hilang Exercise 6: Lag dan diferensiasi Exercise 7: Menjelajahi data pengangguran Exercise 8: Lag pada tingkat pengangguran Exercise 9: Membedakan tingkat pengangguran Exercise 10: Fungsi rolling Exercise 11: Tambahkan jumlah rolling diskret ke data PDB Exercise 12: Tambahkan rerata rolling kontinu ke data pengangguran Exercise 13: Memanipulasi data pengangguran MA

Having exhausted other options, your client now believes Boston's tourism industry must be related to the success of local sports teams. In your final task on this project, your supervisor has asked you to assemble some time series data on Boston's sports teams over the past few years.

Exercise 1: Advanced features of xts Exercise 2: Encoding and plotting Red Sox data Exercise 3: Calculate a closing average Exercise 4: Calculate and plot a seasonal average Exercise 5: Calculate and plot a rolling average Exercise 6: Indexing commands in xts Exercise 7: Extract weekend games Exercise 8: Calculate a rolling average across all sports Exercise 9: Viewing sports trends Exercise 10: Congratulations