Assignment details | Learning from Data: R programming

Individual Web portfolio

Mon, 12 Apr 2021 00:00:00 +0000

Overview

Besides having a Facebook, Instagram, LinkedIn, etc. accounts, this project is about building your own presence in the web where you tell the world who you are and show them examples of your work.

Building your own online presence is useful for a number of reasons, such as

It is useful for recruiting purposes, as you can easily point someone to your portfolio website
You participate in the wider data science/ R community
It builds discipline of working on your code and making sure it is reproducible, easy to understand, and share
You learn how to use Github along the way

You do not have to know much about HTML, you just have to invest some time on learning how to set up an account/repo at and working with Github, learning how the blogdown package works with Hugo, and link everything to Netlify

Sign up for `GitHub` and `Netlify` accounts

First, you need to have a Github account and set up Github to work with R.

Make sure you work through the first 13 chapters of Happy Git with R. It seems like a lot, but it goes fairly fast.

Netlify uses Hugo, an engine to render, or transform, your HTML webpage. If you connect your Github account to Netlify, then every time you push changes to Github, Netlify will automatically update your website to the latest version. You also have the option of manually deploying your site.

Set up a new repository in Github, named my_website

Make sure you check to make your repository Public, and Initialize this repository with a README.

Once you do these, you will have an empty repo, which should look like this

If you click on the green Code Button, you can copy the repo’s address so you can paste it in the next step to clone your repo inside RStudio.

Clone the new GitHub repository to your computer via RStudio

In RStudio, start a new Project:

Choose File > New Project > Version Control > Git. In Repository URL, paste the URL of your new GitHub repository. It will be something like this https://github.com/YOUR_USERNAME/my_website.git.

Accept the default project directory name, e.g. my_website, which coincides with the GitHub repo name.
Notice where (i.e., which folder/directory) the Project is saved locally. A common mistake is to have no idea where you are saving files or what your working directory is.
Click Create Project.

Install `blogdown` and `hugo` in RStudio

You can install the blogdown package through the Packages pane, or by using

install.packages("blogdown")

Once installed, you need to load the library and then install_hugo() to actually get the hugo engine.

library(blogdown)
blogdown::install_hugo()

Create a new site by choosing a hugo theme

While inside our RStudio project, we can now download the theme and a sample website. Hugo comes with many different themes. We have seen a portfolio website by Isabella Benabaye that uses the Academic Theme for Hugo; so does the one from Benjamin Wolfe.

You do not need to limit yourself to the Academic Theme for Hugo; there are many Hugo themes you can choose from, e.g., choose the Personal Web Theme or theme Forty: A portfolio/blog website built for companies or personal freelancers.

We will use the Forty theme instead, and we build the example site with the following command

blogdown::new_site(theme = "MarcusVirg/forty",
          sample = TRUE,
          theme_example = TRUE,
          empty_dirs = TRUE,
          to_yaml = TRUE)

Once everything loads, we have our website which we can see at the right bottom part, in the Viewer tab. You can even click the rightmost of the three little buttons and open up the website within a new browser.

Before we proceed, let us have a look at the right part of RStudio:

Select the Git tab in the upper window and
Select the Files tab in the lower window

You should be able to see the following if you used Forty

The Git tab at the top contains all the changes that Git is tracking, whereas at the bottom, we can see under Files all the files and directories that were downloaded and set up for our website. By default, hugo generates a public directory which is what you publish as your completed website.

You should never change anything within the public directory.

Customise your site

Most of the changes we make in setting up the website will be with YAML and TOML (Tom’s Obvious, Minimal Language) files. If you notice at the root directory there are two toml files - config.yaml and netlify.toml

Change `config.yaml`

You can change pretty much everything with this file, but let us start with the basics:

Line 3: change the title to, e.g., title: Portfolio website for Kostis Christodoulou
Line 13: Add your real name and short description
Lines 11-12. If you don’t want a subtitle, comment out that line with a hashtag # – Do not delete stuff, just comment it out
Line 15-30: Change your contact details: address (lines 17-19), email (line 24), phone (line 28)
Line 37 onwards: Change what appears at the footer (bottom) of each webpage. If there is something you do not want to add, just comment it out with hashtags #
Line 44 onward: Change the social media links to correspond to your own accounts. If you don’t want something, comment it out with a hashtag #– Do not delete stuff, just comment it out.
Line 62 onwards: navigation
- When a user clicks on the menu, there are four choices, defined in lines 70, 72, 74, 76. If you only want to keep, e.g, two choices, comment out with # everything from lines 74-77
- BY default, we also get two buttons in lines 63-68.
Line 81 onwards: This is where you define six tiles that appear on your website– these could be six blog posts, six projects, or whatever you like. Let us look at the first one in detail:
- Line 86: title: Aliquam – this is the title of your tile shown in larger font size
- Line 85: subtitle: Ipsum Dolor Sit Amet – a subtitle, with smaller font size
- Line 84: image: pic01.jpg - what image do you wish to use as background– you can find them and change them, in the themes\forty\static\img directory
- Line 86: url: blogs/aliquam - address with directory that that particular tile links to

Change `netlify.toml`

The netlify.toml file is what automates the rendering of your website anytime you commit and push changes to GitHub.

First, you have to check the version of hugo you are running– you can find this by typing in the console blogdown::hugo_version(). As of the writing of this in March 2021, the latest version of hugo is 0.81.0. Copy/paste the code below so your netlify.toml file contains just the following

[build] 
  publish = "public"
  command = "hugo"

[build.environment]
  HUGO_VERSION = "0.81.0"
  HUGO_ENABLEGITINFO = "true"

[context.production.environment]
  HUGO_ENV = "production"
  
[context.branch-deploy.environment]
  HUGO_VERSION = "0.81.0" 

[context.deploy-preview.environment]
  HUGO_VERSION = "0.81.0"

Publishing your website

Manual deployment

You could deploy your website manually, without connecting to GitHub, by dragging and dropping the folder public that contains your rendered website in the lower part.

Once you drag and drop your public folder, Netlify will build the website and come back with a unique name/ address– when I tried it, my website was named compassionate-galileo-051845

Click on Domain Settings> Options>Edit site name

Continuous deployment, when you link Netlify and GitHub

Once you sign up with Netlify, you can connect your Github account and enjoy continuous deployment, namely every time you commit and push changes to Github, Netlify renders and publishes the new version of your website within seconds.

Final changes to config.toml

Once you change your domain name to something that makes more sense, then you need to revisit the config.toml file and change line 1 to the correct link, with a slash / at the end. In my case, I used

baseURL: https://kostis-portfolio.netlify.app/

Adding your own projects, as knitted Rmd Files

Any content you create and want to upload on your website, has to be added under the content folder. Let us say you have worked on an assignment or a dataset, have created a brilliant analysis you are proud of and want to show the world.

Create `my_website/content/projects`

The first thing would be to create a new folder called projects, e.g., my_website/content/projects. Under this folder, you can add multiple folders, e.g., project1, project2, etc. and within each of them you need an index.Rmd file. Your Rmd can as usual contain text and R code and you do not need to knit it, as Hugo will automatically knit it, create the HTML page, etc.

Change images for tiles

You can change the images that appear in the tiles of the forty theme and use whatever you like. You need to change e.g., line 97 of the config.toml file, to refer to the folder where you have the image. You save images under static/img/projects, and then your line 97, or wherever you define images for tiles, would become

title = "My Project 1 title"
subtitle = "What is the question I address?"
image = "projects/project1_image.png"
url = "projects/project1"

What kind of projects/posts should I add to my portfolio website?

This is entirely up to you: work on something that you will enjoy sharing with others. Besides the articles contained in the sample portfolio projects, here is a nice post on visualising Tour De France data in R

Deliverables

There are a number of steps involved in getting your website up and running and as such there are two deliverables; one in setting everything up (github, netlify, hugo) and then the actual website with your own content.

Setting everything up

Create your own GitHub account, and please use a name appropriate for a professional profile that you can share with job applications, etc.
Once you have a Github account, sign up for an account at https://app.netlify.com/signup using your Github account
Create a new public repo for your portfolio website & initialize it with a “readme” file.
Turn your repo into a default website, using the steps outlined earlier - please change your name, email address, etc. Modify content if you like, but for now it’s fine to use the theme content provided

Now that you managed to set up your website with the default content, please share with the class its address, etc.

Find kostis-christodoulou in GitHub and locate the github-practice-bit2021 class repo
Click on README.md and then edit the README by clicking on the pencil icon in the upper right
Add a row in the table below with your information, using the first row as a template: Add your Name, Study Group, Github ID, url for Github Repo that contains portfolio website, and portfolio website address. Also, please ensure the URL link works (e.g., wrap url in < and > to activate links to website)
commit your changes and submit a pull request

write a descriptive commit message (e.g. added Kostis Christodoulou to class table)
click green button “Propose file change” & start pull request

Important: Please note that your entry will NOT appear on the class table right away. Once you submit the pull request the owner of the repo (me) needs to approve and merge it into the “master” before your entry will appear in the class table on the website.

Finalised website with your own content

Once you set everything up, you need to add some of your own work as blog posts. You can use any of the examples/exercises you have worked on. Make sure your code is easy to read, your graphs look nicer than the standard ggplot default graphs, you have used titles, correct labelling, provide links to sources, etc.

Further resources

Summer of blogdown A brilliant set of four workshops By Alison Hill, all using the academic theme.
blogdown: Creating Websites with R Markdown
Hugo Theme Forty

Homework 1

Sun, 14 Mar 2021 00:00:00 +0000

`homework1.Rmd`

You will be working on homework1.Rmd. This Rmd is included in the session2-workshop1.zip file that contains all necessary session files (data, code, etc.)

You can download the zip file from Canvas, or by pulling from the course Github repo.
Alternatively, please install package usethis. Once you have it, you can download, unzip, and open everything within an RStudio project by typing the following in the RStudio console.

usethis::use_course("https://github.com/kostis-christodoulou/bit2021/raw/master/session2-workshop1.zip")

Instructions

Please finish the assignment and submit your work as a knitted HTML or PDF file on Canvas.

Also, please submit your work without any of the text I have written– just your code, plots, and explanations/stories.

Rubric

Check minus (1/5): Displays minimal effort. Doesn’t complete all components. Code is poorly written and not documented. Uses the same type of plot for each graph, or doesn’t use plots appropriate for the variables being analysed.
Check (3/5): Solid effort. Hits all the elements. No clear mistakes. Easy to follow, both the code and the output.
Check plus (5/5): Finished all components of the assignment correctly and addressed both challenges. Code is well-documented. Used tidyverse, instead of base R. Graphs and tables are properly labelled. Analysis is clear and easy to follow, either because graphs are labeled clearly or you’ve written additional text to describe how you interpret the output.

Final Group Project: AirBnB analytics

Sun, 11 Apr 2021 00:00:00 +0000

In your final group assignment, you have to analyse data about Airbnb listings and fit a model to predict the total cost for two people staying 4 nights in an AirBnB in a city. You can download AirBnB data from insideairbnb.com; it was originally scraped from airbnb.com.

All of the listings are a GZ file, namely they are archive files compressed by the standard GNU zip (gzip) compression algorithm. You can download, save and extract the file if you wanted, but vroom::vroom() or readr::read_csv() can immediately read and extract this kind of a file. You should prefer vroom() as it is faster, but if vroom() is limited by a firewall, please use read_csv() instead.

listings <- vroom("http://data.insideairbnb.com/germany/bv/munich/2020-06-20/data/listings.csv.gz")

vroom will download the *.gz zipped file, unzip, and provide you with the dataframe.

Even though there are many variables in the dataframe, here is a quick description of some of the variables collected, with cost data typically expressed in US$

price = cost per night
cleaning_fee: cleaning fee
extra_people: charge for having more than 1 person
property_type: type of accommodation (House, Apartment, etc.)
room_type:
- Entire home/apt (guests have entire place to themselves)
- Private room (Guests have private room to sleep, all other rooms shared)
- Shared room (Guests sleep in room shared with others)
number_of_reviews: Total number of reviews for the listing
review_scores_rating: Average review score (0 - 100)
longitude , latitude: geographical coordinates to help us locate the listing
neighbourhood*: three variables on a few major neighbourhoods in each city

Exploratory Data Analysis (EDA)

In the R4DS Exploratory Data Analysis chapter, the authors state:

“Your goal during EDA is to develop an understanding of your data. The easiest way to do this is to use questions as tools to guide your investigation… EDA is fundamentally a creative process. And like most creative processes, the key to asking quality questions is to generate a large quantity of questions.”

Conduct a thorough EDA. Recall that an EDA involves three things:

Looking at the raw values.
- dplyr::glimpse()
Computing summary statistics of the variables of interest, or finding NAs
- mosaic::favstats()
- skimr::skim()
Creating informative visualizations.
- ggplot2::ggplot()
  - geom_histogram() or geom_density() for numeric continuous variables
  - geom_bar() or geom_col() for categorical variables
- GGally::ggpairs() for scaterrlot/correlation matrix
  - Note that you can add transparency to points/density plots in the aes call, for example: aes(colour = gender, alpha = 0.4)

You may wish to have a level 1 header (#) for your EDA, then use level 2 sub-headers (##) to make sure you cover all three EDA bases. At a minimum you should address these questions:

How many variables/columns? How many rows/observations?
Which variables are numbers?
Which are categorical or factor variables (numeric or character variables with variables that have a fixed and known set of possible values?
What are the correlations between variables? Does each scatterplot support a linear relationship between variables? Do any of the correlations appear to be conditional on the value of a categorical variable?

At this stage, you may also find you want to use filter, mutate, arrange, select, or count. Let your questions lead you!

In all cases, please think about the message your plot is conveying. Don’t just say “This is my X-axis, this is my Y-axis”, but rather what’s the so what of the plot. Tell some sort of story and speculate about the differences in the patterns in no more than a paragraph.

Data wrangling

Once you load the data, it’s always a good idea to use glimpse to see what kind of variables you have and what data type (chr, num, logical, date, etc) they are.

Notice that some of the price data (price, cleaning_fee, extra_people) is given as a character string, e.g., “$176.00”

Since price is a quantitative variable, we need to make sure it is stored as numeric data num in the dataframe. To do so, we will first use readr::parse_number() which drops any non-numeric characters before or after the frst number

listings <- listings %>% 
  mutate(price = parse_number(price))

Use typeof(listing$price) to confirm that price is now stored as a number.

Handling missing values (NAs)

Use skimr::skim() function to view a summary of the cleaning_fee data. This is also stored as a character, so you have to turn it into a number, as discussed earlier.

How many observations have missing values for cleaning_fee?
What do you think is the most likely reason for the missing observations of cleaning_fee? In other words, what does a missing value of cleaning_fee indicate?

cleaning_fee an example of data that is missing not at random, since there is a specific pattern/explanation to the missing data.

Fill in the code below to impute the missing values of cleaning_fee with an appropriate numeric value. Then use skimr::skim() function to confirm that there are no longer any missing values of cleaning_fee.

listings <- listings %>%
  mutate(cleaning_fee = case_when(
    is.na(cleaning_fee) ~ ______, 
    TRUE ~ cleaning_fee
  ))

Next, we look at the variable property_type. We can use the count function to determine how many categories there are their frequency. What are the top 4 most common property types? What proportion of the total listings do they make up?

Since the vast majority of the observations in the data are one of the top four or five property types, we would like to create a simplified version of property_type variable that has 5 categories: the top four categories and Other. Fill in the code below to create prop_type_simplified.

listings <- listings %>%
  mutate(prop_type_simplified = case_when(
    property_type %in% c("Apartment","______", "______","______") ~ property_type, 
    TRUE ~ "Other"
  ))

Use the code below to check that prop_type_simplified was correctly made.

listings %>%
  count(property_type, prop_type_simplified) %>%
  arrange(desc(n))

Airbnb is most commonly used for travel purposes, i.e., as an alternative to traditional hotels. We only want to include listings in our regression analysis that are intended for travel purposes:

What are the most common values for the variable minimum_nights?
Is ther any value among the common values that stands out?
What is the likely intended purpose for Airbnb listings with this seemingly unusual value for minimum_nights?

Filter the airbnb data so that it only includes observations with minimum_nights <= 4

Mapping

Visualisations of feature distributions and their relations are key to understanding a data set, and they can open up new lines of exploration. While we do not have time to go into all the wonderful geospatial visualisations one can do with R, you can use the following code to start with a map of your city, and overlay all AirBnB coordinates to get an overview of the spatial distribution of AirBnB rentals. For this visualisation we use the leaflet package, which includes a variety of tools for interactive maps, so you can easily zoom in-out, click on a point to get the actual AirBnB listing for that specific point, etc.

The following code, having created a dataframe listings with all AirbnB listings in Bordeaux, will plot on the map all AirBnBs where minimum_nights is less than equal to four (4). You could learn more about leaflet, by following the relevant Datacamp course on mapping with leaflet

leaflet(data = filter(listings, minimum_nights <= 4)) %>% 
  addProviderTiles("OpenStreetMap.Mapnik") %>% 
  addCircleMarkers(lng = ~longitude, 
                   lat = ~latitude, 
                   radius = 1, 
                   fillColor = "blue", 
                   fillOpacity = 0.4, 
                   popup = ~listing_url,
                   label = ~property_type)

Regression Analysis

For the target variable $Y$, we will use the cost for two people to stay at an Airbnb location for four (4) nights.

Create a new variable called price_4_nights that uses price, cleaning_fee, guests_included, and extra_people to calculate the total cost for two people to stay at the Airbnb property for 4 nights. This is the variable $Y$ we want to explain.

Use histograms or density plots to examine the distributions of price_4_nights and log(price_4_nights). Which variable should you use for the regression model? Why?

Fit a regression model called model1 with the following explanatory variables: prop_type_simplified, number_of_reviews, and review_scores_rating.

Interpret the coefficient review_scores_rating in terms of price_4_nights.
Interpret the coefficient of prop_type_simplified in terms of price_4_nights.

We want to determine if room_type is a significant predictor of the cost for 4 nights, given everything else in the model. Fit a regression model called model2 that includes all of the explanantory variables in model1 plus room_type.

Further variables/questions to explore on our own

Our dataset has many more variables, so here are some ideas on how you can extend your analysis

Are the number of bathrooms, bedrooms, beds, or size of the house (accomodates) significant predictors of price_4_nights?
Do superhosts (host_is_superhost) command a pricing premium, after controlling for other variables?
Most owners advertise the exact location of their listing (is_location_exact == TRUE), while a non-trivial proportion don’t. After controlling for other variables, is a listing’s exact location a significant predictor of price_4_nights?
For all cities, there are 3 variables that relate to neighbourhoods: neighbourhood, neighbourhood_cleansed, and neighbourhood_group_cleansed. There are typically more than 20 neighbourhoods in each city, and it wouldn’t make sense to include them all in your model. Use your city knowledge, or ask someone with city knowledge, and see whether you can group neighbourhoods together so the majority of listings falls in fewer (5-6 max) geographical areas. You would thus need to create a new categorical variabale neighbourhood_simplified and determine whether location is a predictor of price_4_nights
What is the effect of cancellation_policy on price_4_nights, after we control for other variables?

Diagnostics, collinearity, summary tables

As you keep building your models, it makes sense to:

Check the residuals, using autoplot(model_x)
As you start building models with more explanatory variables, make sure you use `car::vif(model_x)`` to calculate the Variance Inflation Factor (VIF) for your predictors and determine whether you have colinear variables. A general guideline is that a VIF larger than 5 or 10 is large, and your model may suffer from collinearity. Remove the variable in question and run your model again without it.
Create a summary table, using huxtable (https://bit-2021.netlify.app/example/modelling_side_by_side_tables/) that shows which models you worked on, which predictors are significant, the adjusted $R^2$, and the Residual Standard Error.
Finally, you must use the best model you came up with for prediction. Suppose you are planning to visit the city you have been assigned to over reading week, and you want to stay in an Airbnb. Find Airbnb’s that are apartment with a private room, have at least 10 reviews, and an average rating of at least 90. Use your best model to predict the total cost to stay at this Airbnb for 4 nights. Include the appropriate 95% interval with your prediction. Report the point prediction and interval in terms of price_4_nights.
If you used a log(price_4_nights) model, make sure you anti-log to convert the value in $. To interpret variables that are log-transformed, please have a look at FAQ HOW DO I INTERPRET A REGRESSION MODEL WHEN SOME VARIABLES ARE LOG TRANSFORMED?

Deliverables

By midnight on Monday 19 Oct 2020, you must upload on Canvas a short presentation (max 4-5 slides) with your findings, as some groups will be asked to present in class. You should present your Exploratory Data Analysis, as well as your best model. In addition, you must upload on Canvas your final report, written using R Markdown that incoprorates code and text to introduce, frame, and describe your story and findings.

Remember to follow R Markdown etiquette rules and style; don’t have the Rmd output extraneous messages or warnings, include summary tables in nice tables (use kableExtra), and remove any placeholder texts from past Rmd templates.

Rubric

We will use a basic checklist (adapted from the book Elements of Data Analytic Style) when reviewing data analyses. It can be used as a guide during the process of a data analysis, as a rubric for grading data analysis projects, or as a way to evaluate the quality of a reported data analysis. You don’t have to answer every one of these questions for every data analysis, but they are a useful set of ideas to keep in the back of your mind when reviewing a data analysis.

Answering the question

Did you specify the type of data analytic question (e.g. exploration, association causality) before touching the data?
Did you define the metric for success before beginning?
Did you understand the context for the question and the business application?
Did you consider whether the question could be answered with the available data?

Checking the data

Did you plot univariate and multivariate summaries of the data?
Did you check for outliers? How did you handle outliers?
Did you identify the missing data code?

Tidying the data

Is each variable one column?
Is each observation one row?
Did you record the steps for moving from raw to tidy data?
Did you record all parameters, units, and functions applied to the data?

Exploratory analysis

Did you identify missing values?
Did you make univariate plots (histograms, density plots, boxplots)?
Did you consider correlations between variables (scatterplots)?
Did you check the units of all data points to make sure they are in the right range?
Did you try to identify any errors or miscoding of variables?
Did you consider plotting on a log scale?
Would a scatterplot be more informative?

Inference

Did you identify what large population you are trying to describe?
Did you clearly identify the quantities of interest in your model?
Did you consider potential confounders?
Did you identify and model potential sources of correlation such as measurements over time or space?
Did you calculate a measure of uncertainty for each estimate?

Prediction

Did you identify in advance your error measure?
Did you chck for colinearity in your model(s)?

Written analyses

Did you describe the question of interest?
Did you describe the data set, experimental design, and question you are answering?
Did you specify the type of data analytic question you are answering?
Did you specify in clear notation the exact model you are fitting?
Did you explain on the scale of interest what each estimate and measure of uncertainty means?
Did you report a measure of uncertainty for each estimate?

Figures

Does each figure communicate an important piece of information or address a question of interest?
Do all your figures include plain language axis labels?
Is the font size large enough to read?
Does every figure have a detailed caption that explains all axes, legends, and trends in the figure?

Presentations

Did you lead with a brief, understandable to everyone statement of your problem?
Did you explain the data, measurement technology, and experimental design before you explained your model?
Did you explain the features you will use to model data before you explain the model?
Did you make sure all legends and axes were legible from the back of the room?

Reproducibility

Did you avoid doing calculations manually?
Did you create a script/Rmd that reproduces all your analyses?
Did you save the raw and processed versions of your data?
Did you record all versions of the software you used to process the data?
Did you try to have someone else run your analysis code to confirm they got the same answers?

Acknowledgements

The data from this lab is from insideairbnb.com
The rubric/data analysis guidelines is derived in part from Elements of Data Analytic Style

Homework 2

Sun, 21 Mar 2021 00:00:00 +0000

`homework2.Rmd`

You will be working on homework2.Rmd. This Rmd is included in the session4-workshop2.zip file that contains all necessary session files (data, code, etc.)

You can download the zip file from Canvas, or by pulling from the course Github repo.
Alternatively, please install package usethis. Once you have it, you can download, unzip, and open everything within an RStudio project by typing the following in the RStudio console.

usethis::use_course("https://github.com/kostis-christodoulou/bit2021/raw/master/session4-workshop2.zip")

Instructions

Please finish the assignment and submit your work as a knitted HTML or PDF file on Canvas.

Also, please submit your work without any of the text I have written– just your code, plots, and explanations/stories.

Rubric

Check minus (1/5): Displays minimal effort. Doesn’t complete all components. Code is poorly written and not documented. Uses the same type of plot for each graph, or doesn’t use plots appropriate for the variables being analysed.
Check (3/5): Solid effort. Hits all the elements. No clear mistakes. Easy to follow, both the code and the output.
Check plus (5/5): Finished all components of the assignment correctly and addressed challenges. Code is well-documented. Used tidyverse, instead of base R. Graphs and tables are properly labelled. Analysis is clear and easy to follow, either because graphs are labeled clearly or you’ve written additional text to describe how you interpret the output.

Final Exam

Mon, 19 Apr 2021 00:00:00 +0000

The final exam will cover:

Programming in R and the tidyverse (about a third of total marks)
the core statistical tools of inferential statistics and linear models.

You final exam will be released at 2021-04-19 09:00 UTC and even though you can take it anytime before midnight, this will be a timed, 3-hour exam. You can use any notes and readings, but you must take the exam on your own and not talk to anyone about it.

Homework 3

Mon, 29 Mar 2021 00:00:00 +0000

`homework3.Rmd`

You will be working on homework3.Rmd. This Rmd is included in the session6-workshop3.zip file that contains all necessary session files (data, code, etc.)

You can download the zip file from Canvas, or by pulling from the course Github repo.
Alternatively, please install package usethis. Once you have it, you can download, unzip, and open everything within an RStudio project by typing the following in the RStudio console.

usethis::use_course("https://github.com/kostis-christodoulou/bit2021/raw/master/session6-workshop3.zip")

Instructions

Please finish the assignment and submit your work as a knitted HTML or PDF file on Canvas.

Also, please submit your work without any of the text I have written– just your code, plots, and explanations/stories.

Rubric

Check minus (1/5): Displays minimal effort. Doesn’t complete all components. Code is poorly written and not documented. Uses the same type of plot for each graph, or doesn’t use plots appropriate for the variables being analysed.
Check (3/5): Solid effort. Hits all the elements. No clear mistakes. Easy to follow, both the code and the output.
Check plus (5/5): Finished all components of the assignment correctly and addressed challenges. Code is well-documented. Used tidyverse, instead of base R. Graphs and tables are properly labelled. Analysis is clear and easy to follow, either because graphs are labeled clearly or you’ve written additional text to describe how you interpret the output.

Assignment details | Learning from Data: R programming

Individual Web portfolio

Overview

Sign up for GitHub and Netlify accounts

Set up a new repository in Github, named my_website

Clone the new GitHub repository to your computer via RStudio

Install blogdown and hugo in RStudio

Create a new site by choosing a hugo theme

Customise your site

Change config.yaml

Change netlify.toml

Publishing your website

Manual deployment

Continuous deployment, when you link Netlify and GitHub

Final changes to config.toml

Adding your own projects, as knitted Rmd Files

Create my_website/content/projects

Change images for tiles

What kind of projects/posts should I add to my portfolio website?

Deliverables

Setting everything up

Finalised website with your own content

Further resources

Homework 1

homework1.Rmd

Instructions

Rubric

Final Group Project: AirBnB analytics

Exploratory Data Analysis (EDA)

Data wrangling

Handling missing values (NAs)

Mapping

Regression Analysis

Further variables/questions to explore on our own

Diagnostics, collinearity, summary tables

Deliverables

Rubric

Answering the question

Checking the data

Tidying the data

Exploratory analysis

Inference

Prediction

Written analyses

Figures

Presentations

Reproducibility

Acknowledgements

Homework 2

homework2.Rmd

Instructions

Rubric

Final Exam

Homework 3

homework3.Rmd

Instructions

Rubric

Sign up for `GitHub` and `Netlify` accounts

Install `blogdown` and `hugo` in RStudio

Change `config.yaml`

Change `netlify.toml`

Create `my_website/content/projects`

`homework1.Rmd`

`homework2.Rmd`

`homework3.Rmd`