Module 1, Assignment 3: Getting to Know Your Home

Author

Ellen Bledsoe, Lily McMullen

Assignment Description

Purpose

The goal of this assignment is to get comfortable using the tidyverse with 2-dimensional data sets (data frames).

Task

Write R code using the tidyverse to successfully answer each question below.

Criteria for Success

  • Code is within the provided code chunks
  • Code chunks run without errors
  • Code produces the correct result
    • Code that produces the correct answer will receive full credit
    • Code attempts with logical direction will receive partial credit
  • Written answers address the questions in sufficient detail

Assignment Questions

For this final assignment for Module 1, you’ll be working with another real-world data set: a collection of data from climate stations scattered across Antarctica.

  1. In your own words, describe what the tidyverse is. Your answer should be between 1-3 sentences.

    Answer:

  2. Load in the tidyverse package.

# Write your code here
  1. In your data folder, look for the data file called aggregated_station_data.csv and load it in using the read_csv() function. Save the data as an object called weather.
# Note: use the read_csv function, not the read.csv function!
# Write your code here

  1. Take a look at the data in whichever way you would like (looking in the environment, viewing the data, typing a function into the console below, etc.). How many rows and columns are in the data? Type your answers below:

    rows:
    columns:

# using code is optional for Question 4
  1. Write some code to choose rows that only include temperatures which are above freezing (AKA greater than 0 because the temperature is currently in Celsius)
# Write your code here
  1. Write some code to choose only columns with the following information: the year, day, month, temperature, and the station ID. Save these columns as a new object called station_temp.
# Write your code here

  1. Using the data frame you created in Q6 above (station_temp), add a new column to that data frame that converts the temperature column (currently in Celsius) to Fahrenheit. Call the new column tempF.

    Hint: we did this in class, use that same equation!

# Write your code here

  1. In your own words (either bullet points or sentence form is fine), explain two benefits of using the pipe (%>%).

    Answer:

  2. Using the original data frame (weather), find the minimum (min()) temperature recorded for each month (in Celsius, the original column).

    Hint: think about months first (split) and then temperature (apply). You will also want to remove all the NA values.

# Write your code here

  1. Again, using the original data frame, produce a data frame with the average temperature in the month of January for each station.

    Some hints:

    • take note of how months are represented in the data

    • think about using the pipe, how we choose which rows we want, and how we split-apply-combine

    • remember to remove the NA values!

# Write your code here

Bonus! (up to 2 points)

Write code to determine how many unique stations are in the weather data set. (Hint: look up the help files for the distinct() and the count() functions).

# Write your code here

Turning in Your Assignment

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