Menjelajahi data pengangguran

Sekarang setelah Anda meninjau langkah-langkah dasar untuk menangani data hilang, Anda dapat lebih mudah menelaah dan membersihkan data deret waktu baru secara langsung.

Dalam latihan ini, Anda akan berlatih lebih lanjut dengan mengekplorasi, membersihkan, dan memplot data pengangguran, baik di Amerika Serikat secara umum maupun khusus di Massachusetts (MA). Sebuah objek xts yang memuat data ini, unemployment, tersedia di ruang kerja Anda.

Latihan ini adalah bagian dari kursus

Studi Kasus: Menganalisis Data Deret Waktu Kota di R

Petunjuk latihan

Lihat informasi ringkasan tentang data unemployment Anda menggunakan summary(). Perhatikan dengan saksama jumlah NA's yang teridentifikasi pada keluaran Anda. Perhatikan juga bahwa nilai min dan max dari indeks waktu memberi tahu periode yang dicakup oleh data Anda.
Gunakan na.approx() untuk menghapus nilai hilang dari data pengangguran Anda melalui interpolasi linear. Simpan nilai yang sudah diisi kembali ke dalam objek unemployment Anda.
Gunakan plot.zoo() untuk memplot data unemployment Anda. Tetapkan plot.type sebagai "single" agar data tingkat nasional AS dan khusus Massachusetts berada pada plot yang sama. Biarkan argumen lty dan pemanggilan legend() sebagaimana adanya.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# View a summary of your unemployment data


# Use na.approx to remove missing values in unemployment data
unemployment <- 

# Plot new unemployment data
plot.zoo(___, plot.type = "___", lty = lty)
legend("topright", lty = lty, legend = labels, bg = "white")

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

Latihan Saat Ini

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