代写INFO180、代做Python，Java程序

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INFO180 Lab: Describe data
October 10, 2023
Instructions
This lab is again! similar to the previous one, just now we ask you to actually do some analysis of it. It
broadly covers the topics discussed in the INFO180 Notes Ch 5.1-5.3 (pipelines, dplyr functions).
• Please submit your results, and any code you may have written in the process. You can use any
program to write the text. If you can, please submit it either as pdf, or you can also copy your
answer to the canvas’ text box there.
You can copy-paste the code at the end of your answers as an appendix.
• Working together is fun and useful but you have to submit your own work. Discussing the solutions
and problems with your classmates is all right but do not copy-paste their solution! First understand,
and thereafter create your own solution. Please list all your collaborators on the solution.
1 Analyze dataset with a pipeline
Let’s analyze UAH (University of Alabama, Huntsville) global temperature data (UAH-lower-tropospherewide.csv). See my data repo for documentation–scroll a little down to find “UAH lower troposhphere
temperature”.
1. Load the dataset
2. Compute the number of rows and the number of columns in there.
3. Now think about a task you might do with the data. Start with something very-very simple,
something you can actually code later. Explain the task and design a pipeline for completing this
task.
Write it in human language, like a cooking recipe. You should attempt to broadly use the tools we
have discussed in class, but you may not know enough to be able to do it well.
4. Now try to write the pipeline down as computer code that actually does this stuff.
5. Now repeat with a more complex and interesting task. First write the task down as a humanlanguage recipe, thereafter attempt to mold it into computer code.