1.0 OBJECTIVES OF THIS COURSEWORK
This the assignment will help you to explore and analyze a set of data and reconstruct it into meaningful representations for decision-making.
2.0 INDIVIDUAL ASSIGNMENT DESCRIPTION
A DATA ANALYSIS PROJECT USING HOURLY WEATHER DATA
This assignment needs to explore hourly weather data set and categorize it by different techniques in such a way that it should retrieve the necessary information which helps to do a decision making. Your analysis should be deep and in detail, also it must go further than what has already been covered in this course.
You have to import the data then do the necessary pre-processing on the dataset, use the necessary commands to convert it into the desired format. You have to apply the data visualization, exploration, and manipulation techniques in your project. It is very important to explain and justify the techniques that have been chosen. Outline the findings, analyse them, and justify correctly with appropriate graphs. Also, a supporting document is needed to reflect the graph and code using R programming concepts. Additional features must explore further concepts that can improve retrieval effects.
The dataset provided for this assignment is related to the hourly meteorological data for LaGuardia Airport (LGA) and John F. Kennedy International Airport (JFK) in the United States. It contains 15 columns and 17,412 rows. The columns with the description are given in the table below.
Table 1. Dataset columns description.
|
Column(s) |
Description |
|
origin |
Weather station. |
|
year, month, day, hour |
Time of recording. |
|
temp, dewp |
Temperature and dewpoint in F. |
|
humid |
Relative humidity. |
|
wind_dir, wind_speed, wind_gust |
Wind direction (in degrees), speed and gust speed (in mph). |
|
precip |
Precipitation, in inches. |
|
pressure |
Sea level pressure in millibars. |
|
visib |
Visibility in miles. |
|
time_hour |
Date and hour of the recording as a POSIXct date. |
3.0 GENERAL REQUIREMENTS
· The program submitted should compile and be executed without errors.
· Validation should be done for each entry from the users to avoid logical errors.
· No duplication is allowed in dataset.
· To score Pass: you need to include 7 analysis examples covering data visualization, exploration, and manipulation topics. Please refer to the Marking Scheme for more details.
· To score Credit: you need to include 11 analysis examples covering data visualization, exploration, and manipulation. In addition, including at least 1 additional feature which can improve the results which is apart from the course. Please refer to the Marking Scheme for more details.
· To score Distinction: you need to include at least 14 analysis examples covering data visualization, exploration, and manipulation. In addition, including at least 2 additional features which can improve the results which is apart from the course. Please refer to the Marking Scheme for more details.
· You should include the good programming practice such as comments, variable naming conventions and indentation.
· You are required to use R programming language.
· In a situation where a student:
o Failed to attempt the assignment presentation, overall marks awarded for the assignment will be adjusted to 50% of the overall existing marks if it is more than 50%.
o Found to be involved plagiarism, the offence and will be dealt in accordance to APU and Staffordshire University regulations on plagiarism.
4.0 DELIVERABLES:
The complete code and report must be submitted to APU Learning Management System (Webspace).
Program coded in R.
o Name the file under your name and TP number.
o Start the first two lines in your program by typing your name and TP number. For example:
# NAME
#TP123456
o For each analysis example, give an id and explain the analysis that you did in a comment. For example:
# Analysis Example 1
# In this example, an analysis between X and Y is given to …….
o For each extra feature example, give an id and explain the analysis that you did in a comment. For example:
# Extra feature 1
# comments about the extra feature
A documentation of the system – submitted as NAME_TPNUMBER.pdf or .doc file - that incorporates basic documentation standards such as header and footer, page numbering and includes:
o Cover page
o Table of contents
o Introduction and assumptions
o Each analysis example must be in a separate page and contains:
ü Screenshot of source code with the explanation.
ü Screenshot of output/plot with the explanation.
o The extra feature example must be in a separate page and must include:
ü Screenshot of source code with the explanation.
ü Screenshot of output/plot with the explanation.
ü Explain how adding this extra feature can improve the results.
o Conclusion
o References (if any) using Harvard Name Convention
5.0 ASSIGNMENT ASSESSMENT CRITERIA
The assignment assessment consists of three components: Coding (50%), Documentation (30%) and Presentation (20%). Details of the division for each component are as follows:
i. Coding 50%
Running the submitted code to show the conducted analysis; Application of data analysis techniques covered in the course; good programming practices such as comments and indentation; how good the created graphs; and additional features and techniques which can improve the result which is apart from the course.
ii. Documentation 30%
Description and justification of the R concepts incorporated; program results’ screenshots and graphs with explanation; and adherence to document standard format and structure.
iii. Presentation 20%
Ability to answer questions addressed by the lecturer pertaining to the work done and presented; ability to explain and modify code upon request; and ability to explain all the implementation code and graphs incorporated.
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