R语言编程代写 - assignment代写 - 数据集代写

Assignment

r语言编程代写 N.B. Please make sure to carefully read this document before beginning work on the assignment. The assignment is worth 70% of the final grade···

Due Friday 15th November – 23:55 R语言编程代写

N.B. Please make sure to carefully read this document before beginning work on the assignment. The assignment is worth 70% of the final grade for the first part of the module

i.e. 35% of the entire module. If you have any further questions, jusk ask.

Reports should not be difficult to write, as they simply represent an honest account of the work done. Ideally, a report has a voice, is interesting to read and tells the story of the work. For this assignment 10 pages would be an indicator of the expected length. The deadline for submission of the word processed document and accompanying files is Week 9, Friday 15^th November – 23:55. (submissions will not be accepted under any circumstances after 9/12/2019).

Notes: R语言编程代写

1.) Plots should only be included in the report when they serve the purpose to illustrate a key point of the analysis i.e. not fill up the pages.

2.) Large sections of R code should not be included in the report, a simple referencing mechanism at the end of the paragraph is fine e.g. (thisFile.R lines 75-90).

3.) Plots not included in the report can be submitted in a separate folder or appendix along with the code used to generate them e.g. as part of this section of the analysis the plots in folder x were generated etc..

4.) Plain English is fine for the style of writing as your audience is the lecturer (n.b. please use the spell-check as marks cannot be awarded unless the text is clearly understood).

5.) The submitted code will be carefully examined against the following criteria:

Complexityof operations being
Whetherit is easy to understand g. variable names are informative (not temp161), indentation is used and comments are placed before different sections (line-by-line commenting obscures the structure of the code and is not necessary

i.e. the variable names and indentation should suffice). R语言编程代写

Codesourced from elsewhere is appropriately cited (bear in mind your R skills are being examined).
Thecode works correctly and is not reliant on hardcoded

6.) Students are encouraged to present their reports in a professional manner, however bear in mind that the majority of the marks for written sections will be based on the structure of the paragraphs e.g. points made (what was done), flow of reasoning (motivation for steps), depth of analysis and conclusions reached. It is therefore better to focus attention on these areas.

Deliverable:

Zip File containing:

Word Processed Document e.g. .doc or .pdf called “report” followed by an underscore “_” and the student number.

R files: Q1.R, Q2.R, Q3.R, Q4.R and Q5.R

Any other files necessary to run the code. R语言编程代写

Note: Statements made in the report will only be taken into account when there is runnable code available for confirmation.

Grade breakdown

The breakdown of the grade for all the tasks except 5.2, is half the marks for the R code and half the marks for the text in the report. These marks are based on the criteria outlined in the notes above.

Task 1

In the News

20% (Submission File – Q1.R) 1 Page

Climate change has generated much press coverage in recent years, download a dataset from either Kaggle or data.world that contains data related to this topic. Use qplot to create a graph of some of the variables in the dataset (original or derived). Discuss what you find interesting about the patterns displayed in the plot and why this visualisation was chosen instead of other options that were considered (it is not necessary to include the plot in the main report).

Task 2 APIs R语言编程代写

20% (Submission File – Q2.R) 2 Pages

APIs are an abundant source of data online, sign up or register to use an API of choice (not Dublin Bikes). Use R to download a reasonable chunk of data from the API e.g. a guide to the amount of data would be something like 500 rows and 10 columns.

(10/20)Summarise the data available in the different variables g. suppose that you are having a 5 minute conversation with somebody who is interestred in the data and they are looking for a short but fairly complete overview of key characteristics of the dataset.
(10/20)Think of a particular use-case where you or someone else (possibly a time-pressured individual) wishes to obtain relevant information from this Use qplot to create a clear and simple graph (3 variables) that facilitates an at-a- glance interpretation of the data for them.

Task 3 R语言编程代写

(Scale) Dublin Bus

20% (Submission File – Q3.R) 3 Pages

Use R to import the DublinBusGTFS.zip file. Info on the GTFS format may be found here.

(10/20)Shape the data and conduct an initial exploratory analysis of the entire dataset, focussing on all the
(10/20) Pick a bus route (forward and reverse directions) and drill down into thedetails of it extracting statistics to answer a series of questions e.g. suppose that you were moving to a house along the route and relying on this bus to get to and from work on time everyday, what questions would need to be answered to make you an expert on the route?. Create a plot for the most interesting piece of this

Task 4 R语言编程代写

(Retail) Footfall

20% (Submission File – Q4.R) 2 Pages

Use R to import the Footfall2013.ods dataset. More information about this dataset can be found here.

(10/20) Pick two cameras (e.g. one on the Northside and the other on theSouthside) and analyse the similarities and / or differences between the footfall patterns over the course of the 52 weeks in the
(10/20) Move outside the dataset and scrape some data from the web. Use thisdata to create an interesting comparative plot against the footfall data from one of the

Task 5 Reflection R语言编程代写

20% (Submission File – Q5.R)

Key parts of any data analysis workflow include: 1.) Scheduling sessions to work on parts of the assignment. 2.) Keeping track of outcomes. 3.) Setting aside time at the end of the assignment to reflect on the results achieved. As such, the student is advised to maintain a personal log or diary of their activities.

Analytic (10/20)(1 Page)

The previous tasks have covered data generated elsewhere. This part of the assignment looks at the data generated during the completion of the assignment and applies data wrangling to the points outlined above in a bid to retrospectively analyse performance.

RStudio maintains a log of console activities in a file called history_database in a folder named RStudio-Desktop (Windows) or rstudio-desktop (Ubuntu). The format of the file is the time in mili-seconds followed by the command entered. Before beginning the assignment, the student is advised to confirm the existence of this file on their system and back it up at appropriate intervals to avoid data loss.

Task: Import a copy of this data (i.e. not the original file) into R and conduct an analysis of general patterns / evident trends over the course of completing the assignment.

PersonalReflection (10/20) (1 Page) R语言编程代写

A reflection looks back on the experience of completing a project with a view to getting a sense of perspective and absorbing relevant lessons (positive or negative) to take forward to future projects. Topics to include might be 1.) challenges overcome (work-arounds), 2.) efficient work practices 3.) areas of frustration 4.) time management.

A useful way to structure a reflection is by using the four I’s e.g. Identify an element during the assignment (past) – results, quality, emotions, productivity. Investigate – determine the source of this (present). Incorporate – based on the investigation, is there a lesson to absorb (future)? Improve – the goal of reflection.

urls https://developers.google.com/transit/gtfs/reference/

https://data.smartdublin.ie/dataset/pedestrian-footfall-index-in-dublin-city-centre

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