Hitung dan plot rata-rata bergulir

Indikator baseball terakhir yang ingin Anda buat adalah L10, yaitu rata-rata menang/kalah bergerak dari sepuluh pertandingan sebelumnya. Sementara rata-rata kumulatif menang/kalah memberi tahu Anda performa tim secara keseluruhan, indikator L10 memberikan gambaran yang lebih spesifik tentang performa terbaru tim. Di luar dunia olahraga, ukuran ini sebanding dengan indikator keuangan yang berfokus pada kinerja portofolio terkini.

Untuk menghasilkan rata-rata menang/kalah bergulir, kembali ke perintah rollapply() yang digunakan di bab sebelumnya. Dalam hal ini, Anda akan menerapkan fungsi mean pada 10 pertandingan terakhir yang dimainkan Red Sox pada waktu tertentu selama musim 2013.

Objek redsox_xts, termasuk kolom win_loss, tersedia di ruang kerja Anda.

Latihan ini adalah bagian dari kursus

Studi Kasus: Menganalisis Data Deret Waktu Kota di R

Petunjuk latihan

Buat xts baru yang hanya mencakup musim 2013. Beri nama objek ini redsox_2013.
Gunakan rollapply() untuk menghitung indikator lastten_2013 berdasarkan kolom win_loss dalam redsox_2013. Tetapkan width sama dengan 10 untuk memasukkan sepuluh pertandingan terakhir yang dimainkan Red Sox dan tetapkan argumen FUN ke mean untuk menghasilkan rata-rata dari kolom win_loss.
Gunakan plot.xts() untuk melihat indikator baru Anda selama musim 2013. Biarkan argumen ylim sebagaimana adanya pada kode yang sudah disediakan.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Select only the 2013 season
redsox_2013 <- ___["___"]

# Use rollapply to generate the last ten average
lastten_2013 <- rollapply(___$___, width = ___, FUN = ___)

# Plot the last ten average during the 2013 season
plot.xts(___, ylim = c(0, 1))

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: Handling missingness Exercise 2: Exploring economic data Exercise 3: Replace missing data - I Exercise 4: Replace missing data - II Exercise 5: Estimating missing GDP Exercise 6: Lagging and differencing Exercise 7: Exploring unemployment data Exercise 8: Lagging unemployment Exercise 9: Differencing unemployment Exercise 10: Rolling functions Exercise 11: Add a discrete rolling sum to GDP data Exercise 12: Add a continuous rolling average to unemployment data Exercise 13: Manipulating MA unemployment data

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: Fitur lanjutan xts Exercise 2: Menyandi dan memplot data Red Sox Exercise 3: Hitung rata-rata penutupan Exercise 4: Hitung dan plot rerata musiman Exercise 5: Hitung dan plot rata-rata bergulir

Latihan Saat Ini

Exercise 6: Perintah pengindeksan di xts Exercise 7: Ekstrak pertandingan akhir pekan Exercise 8: Hitung rata-rata bergulir di semua cabang olahraga Exercise 9: Melihat tren olahraga Exercise 10: Selamat