Menyandi dan memplot data Red Sox

Setelah mengeksplorasi dan memanipulasi data penerbangan, cuaca, dan ekonomi, klien Anda ingin menutup semua kemungkinan. Secara alami, mereka ingin Anda mengumpulkan data tentang tim olahraga besar Boston: Boston Red Sox (bisbol), New England Patriots (sepak bola Amerika), Boston Bruins (hoki), dan Boston Celtics (bola basket). Pada bab ini, Anda akan menyiapkan data tentang jadwal dan hasil semua pertandingan yang melibatkan tim-tim tersebut dari 2010 hingga 2015. Ini kesempatan yang tepat untuk berlatih lebih lanjut memanipulasi data deret waktu!

Sebagai awal, Anda telah mengompilasi data pertandingan yang dimainkan oleh Boston Red Sox dari 2010 hingga 2015. Pada latihan ini, Anda akan mengeksplorasi data, menyandikannya ke xts, dan memplot beberapa tren dari waktu ke waktu. Data frame redsox tersedia di ruang kerja Anda.

Latihan ini adalah bagian dari kursus

Studi Kasus: Menganalisis Data Deret Waktu Kota di R

Petunjuk latihan

Gunakan summary() untuk melihat beberapa statistik ringkas tentang data redsox Anda. Perhatikan kolom tanggal dan nilai apakah ada data hilang (NA) yang perlu ditangani.
Setelah Anda yakin bahwa data redsox dapat dikonversi ke xts, mulailah proses ini dengan menyandikan kolom date menjadi objek berbasis waktu menggunakan as.Date().
Gunakan as.xts() untuk mengonversi data redsox ke xts, pastikan untuk menyetel order.by pada kolom date. Hapus juga kolom tanggal (gunakan notasi [, -1]) agar objek xts Anda bersifat numerik.
Gunakan plot.zoo() untuk memplot skor Red Sox (boston_score) dan skor lawan (opponent_score) dari waktu ke waktu. Tren apa yang dapat Anda identifikasi dari plot ini?

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# View summary information about your redsox data


# Convert the date column to a time-based format
redsox$date <- as.Date(___$___)

# Convert your red sox data to xts
redsox_xts <- as.xts(___[,-1], order.by = ___$___)

# Plot the Red Sox score and the opponent score over time
plot.zoo(___[, c("___", "___")])

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

Latihan Saat Ini

Exercise 3: Hitung rata-rata penutupan Exercise 4: Hitung dan plot rerata musiman Exercise 5: Hitung dan plot rata-rata bergulir 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