Saving time - II

You've saved your flights_xts data to a rds file for future use. But what if you'd like to share your data with colleagues who don't use R?

A second option for saving xts objects is to convert them to shareable formats beyond the R environment, including comma-separated values (CSV) files. To do this, you'll use the write.zoo() command.

Once you've successfully exported your xts object to a csv file, you can load the data back into R using the read.zoo() command. Unlike readRDS, however, you will need to re-encode your data to an xts object (using as.xts).

This exercise is part of the course

Case Study: Analyzing City Time Series Data in R

Exercise instructions

Use write.zoo() to save the flights_xts data to "flights_xts.csv". Be sure to specify comma-separated values (",") using the sep argument.
Read your file back into R using read.zoo(). Specify the name of the file you exported as well as the method used to separate the data. Take a look at the other arguments but don't change the code. Save this new object as flights2.
Encode your flights2 object back into xts using as.xts(). Save your new xts object as flights_xts2.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Export your xts object to a csv file using write.zoo
write.zoo(flights_xts, file = "___", sep = "___")

# Open your saved object using read.zoo
flights2 <- read.zoo("___", sep = "___", FUN = as.Date, header = TRUE, index.column = 1)

# Encode your new object back into xts
flights_xts2 <- as.xts(___)

# Examine the first five rows of your new flights_xts2 object

Edit and Run Code

This exercise is part of the course

Case Study: Analyzing City Time Series Data in R

IntermediateSkill Level

4.8+

Start Course for Free

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

Current Exercise

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: 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