Tambahkan rerata rolling kontinu ke data pengangguran

Selain ukuran diskret seperti jumlah year-to-date, Anda mungkin ingin menambahkan jumlah atau rerata rolling ke data runtun waktu Anda.

Untuk itu, mari kembali ke data unemployment bulanan Anda. Meskipun Anda mungkin tertarik pada tingkat pengangguran statis pada bulan tertentu, gambaran ekonomi yang lebih luas dapat memerlukan indikator rolling selama beberapa bulan.

Untuk melakukannya, Anda akan menggunakan perintah rollapply(), yang menerima objek runtun waktu, ukuran jendela width, dan argumen FUN untuk diterapkan pada setiap jendela rolling.

Latihan ini adalah bagian dari kursus

Studi Kasus: Menganalisis Data Deret Waktu Kota di R

Petunjuk latihan

Gunakan rollapply() untuk menghitung rerata rolling tahunan pengangguran AS. Pastikan Anda menentukan kolom us dari data unemployment Anda, atur argumen width ke jumlah periode bulanan yang sesuai, dan atur argumen FUN ke mean. Simpan rerata rolling Anda ke dalam objek unemployment sebagai year_avg.
Plot kedua indikator pengangguran AS (us dan year_avg) menggunakan plot.zoo(). Atur argumen plot.type ke "single" untuk menempatkan kedua ukuran dalam panel yang sama.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Use rollapply to calculate the rolling yearly average US unemployment
unemployment$year_avg <- rollapply(___$___, width = ___, FUN = ___)

# Plot all columns of US unemployment data
plot.zoo(unemployment[, c("___", "___")], plot.type = "___", lty = lty, lwd = lwd)

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 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

Latihan Saat Ini

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