1. Aprende
    2. /
  2. Proyectos
    3. /
  3. Case Study: Analyzing City Time Series Data in R
  • 1

    Flight Data

    Gratuito

    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.

  • 2

    Weather Data

    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!

  • 3

    Economic Data

    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.

  • 4

    Sports 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.

Initializing

Ejercicio

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).

Instrucciones

100 XP
  • 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.