Membuat rata-rata bulanan

Meskipun perintah to.period() berguna dalam banyak konteks, untuk tujuan Anda mungkin kurang tepat jika memilih satu baris sebagai perwakilan seluruh bulan.

Sebagai gantinya, lebih masuk akal untuk menghasilkan nilai suhu rata-rata per bulan. Untuk melakukannya, Anda perlu menghitung rata-rata bulanan secara manual menggunakan split() dan lapply(), lalu membuat objek xts baru menggunakan as.xts(). Ini mungkin terlihat rumit, tetapi Anda sudah memiliki keterampilan untuk melakukannya!

Objek xts hasil subset dari latihan sebelumnya, temps_xts_2, sudah dimuat di ruang kerja Anda. Juga telah dimuat sebuah objek index yang berisi vektor tanggal untuk hari pertama setiap bulan yang tercakup dalam data.

Latihan ini adalah bagian dari kursus

Studi Kasus: Menganalisis Data Deret Waktu Kota di R

Petunjuk latihan

Gunakan split() untuk membuat daftar bulanan dari kolom mean pada objek temps_xts_2 Anda. Pastikan menentukan "months" sebagai periodenya (argumen f).
Gunakan lapply() untuk menghitung "mean of means", atau rata-rata nilai mean suhu per bulan.
Gunakan as.xts() untuk membuat objek xts baru yang berisi suhu rata-rata bulanan di Boston dari 2010 hingga 2015. Untuk melakukan ini, Anda perlu menggabungkan data mean_of_means bulanan dengan objek index bulanan Anda.
Terakhir, konfirmasikan bahwa objek temps_monthly baru Anda memiliki durasi dan periodisitas yang sama dengan flights_xts.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Split temps_xts_2 into separate lists per month
monthly_split <- split(___$___ , f = "___")

# Use lapply to generate the monthly mean of mean temperatures
mean_of_means <- lapply(___, FUN = ___)

# Use as.xts to generate an xts object of average monthly temperature data
temps_monthly <- as.xts(as.numeric(___), order.by = ___)
 
# Compare the periodicity and duration of your new temps_monthly and flights_xts 
periodicity(___)
periodicity(___)

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: Menggabungkan data deret waktu berdasarkan baris Exercise 2: Menjelajahi data suhu Exercise 3: Langkah selanjutnya - I Exercise 4: Menggabungkan dengan rbind()Exercise 5: Memvisualisasikan musim dingin di Boston Exercise 6: Menggabungkan data deret waktu berdasarkan kolom Exercise 7: Subset dan penyesuaian periodisitas Exercise 8: Membuat rata-rata bulanan

Latihan Saat Ini

Exercise 9: Menggunakan merge() dan membuat plot dari waktu ke waktu Exercise 10: Apakah penundaan penerbangan terkait dengan suhu?Exercise 11: Alur kerja data deret waktu Exercise 12: Langkah berikutnya - II Exercise 13: Memperluas data Anda Exercise 14: Apakah keterlambatan penerbangan terkait dengan jarak pandang atau angin?

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: 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