Photo by Peiheng Yang on Unsplash
Photo by Peiheng Yang on Unsplash
This assignment will use the following packages:
tidyverse: a collection of packages for doing data analysis in a “tidy” wayggplot2: a package that contains the function for the grammer of graphicslubridate: a package for formatting dates in R (in case if you need)We use the library() function to load packages.
In your R Markdown document you should see an R chunk labelled load-packages which has the necessary code for loading both packages.
Note that these packages are also get loaded in your R Markdown environment when you Knit your R Markdown document.
Airbnb is an online marketplace where people can book short-term stays in hotels or in other people’s houses. Recent developments in Edinburgh regarding the growth of Airbnb and its impact on the housing market means a better understanding of the Airbnb listings is needed. Using data provided by Airbnb, we can explore how Airbnb availability and prices vary by neighbourhood.
A copy of the Edinburgh Airbnb data set has been available (because of dsbox package version problem in Rstudio). To read this data, ensure that you have the following code near the beginning of your R Markdown file, from your directory having the data file (ie. data/).
You can view the dataset as a spreadsheet using the View() function.
Note that you should not put this function in your R Markdown document, but instead type it directly in the Console, as it pops open a new window.
When you run this in the console, you will see the following data viewer window pop up.
You can find out more about the dataset by inspecting its documentation, which you can access by running ?edibnb in the Console or using the Help menu in RStudio to search for edibnb (If you can install and call dsbox package correctly).
Otherwise, you can also find this information here.
The edibnb data set contain 13245 listings in Edinburgh, with values about 10 different variables that includes the property neighborhood location, number of rooms and review ratings.
Edinburgh council collects the second data set from their assessments of a randomly selected sample of listings. The variables in this data set are:
- id, the ID number of the listing (according to edibnb)
- rating, the council’s rating of the listing of the property either being good, adequate or poor.
- assessment_date, the date the council performed their assessment of the property.
The data is contained in the file council_assessments.csv within the data/ directory. It can be loaded into your workflow using the following code (or any other directory name that you have instead of /data).
In the edibnb data set, what is the ID of the listing that has the highest number of reviews with a perfect review score of 100%?
Which variables have the missing observations in the edibnb data set.
Visualize the missing observations using some functions from visdat or naniar packages (Notes from Week 4 might be useful)
✏️️🧶 ✅ ⬆️ Write your answer in your R Markdown document , knit the document, make sure that your added R code chunk works well and you did not face with any knitting problem
perfect_reviews <- edibnb %>%
filter(review_scores_rating == 100)
listing_review_counts <- perfect_reviews %>%
group_by(id) %>%
summarize(num_reviews = n())
max_reviews_listing <- listing_review_counts %>%
top_n(1, num_reviews)
max_reviews_listing_id <- max_reviews_listing$id
(perfect_reviews)## # A tibble: 2,699 × 10
## id price neighbourhood accommodates bathrooms bedrooms beds
## <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 51505 175 New Town 5 1 2 3
## 2 145675 60 <NA> 3 1 2 2
## 3 159769 200 Southside 4 1.5 2 2
## 4 197220 85 Marchmont 2 NA 1 1
## 5 327168 120 Leith 3 1 1 1
## 6 449075 140 Newington 4 1 2 2
## 7 517409 200 Old Town 4 1 2 2
## 8 603443 435 Old Town 6 1 1 5
## 9 603493 200 Old Town 3 1 1 2
## 10 603497 229 Old Town 3 1 1 2
## # ℹ 2,689 more rows
## # ℹ 3 more variables: review_scores_rating <dbl>, number_of_reviews <dbl>,
## # listing_url <chr>## # A tibble: 2,699 × 2
## id num_reviews
## <dbl> <int>
## 1 51505 1
## 2 145675 1
## 3 159769 1
## 4 197220 1
## 5 327168 1
## 6 449075 1
## 7 517409 1
## 8 603443 1
## 9 603493 1
## 10 603497 1
## # ℹ 2,689 more rows## [1] 51505 145675 159769 197220 327168 449075 517409 603443
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## [2697] 35837628 35840304 35905471variables_with_missing_obs <- colnames(edibnb)[colSums(is.na(edibnb)) > 0]
(variables_with_missing_obs)## [1] "price" "neighbourhood" "bathrooms"
## [4] "bedrooms" "beds" "review_scores_rating"##
## The downloaded binary packages are in
## /var/folders/0l/0n3rw26n0p53c1lcs4jqddsr0000gn/T//RtmpuCeW0b/downloaded_packages
Calculate the minimum, maximum and average price from the Airbnb properties in Southside for a single night stay for four people (Try to use the summarise function).
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southside_properties <- edibnb %>%
filter(neighbourhood == "Southside" & accommodates == 4)
View(southside_properties)
price_summary <- southside_properties %>%
summarise(min_price = min(price),
max_price = max(price),
avg_price = mean(price))
print(price_summary)## # A tibble: 1 × 3
## min_price max_price avg_price
## <dbl> <dbl> <dbl>
## 1 39 450 120.When looking at the data you will notice that some of the listings have a value for the number of bathrooms that is not a whole number, e.g. 1.5 or 2.5.
Mutate the bathrooms variable to round the number of bathrooms up to the nearest whole number (hint, look at ceiling()).
Using this mutated variable, how many listings have more bathrooms than bedrooms?
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## # A tibble: 6 × 10
## id price neighbourhood accommodates bathrooms bedrooms beds
## <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 15420 80 New Town 2 1 1 1
## 2 24288 115 Southside 4 2 2 2
## 3 38628 46 <NA> 2 1 0 2
## 4 44552 32 Leith 2 1 1 1
## 5 47616 100 Southside 2 1 1 1
## 6 48645 71 Old Town 3 1 1 2
## # ℹ 3 more variables: review_scores_rating <dbl>, number_of_reviews <dbl>,
## # listing_url <chr>View(edibnb)
listings_more_bathrooms <- edibnb %>%
filter(bathrooms > bedrooms)
num_listings_more_bathrooms <- nrow(listings_more_bathrooms)
cat("Number of listings with more bathrooms than bedrooms:", num_listings_more_bathrooms, "\n")## Number of listings with more bathrooms than bedrooms: 963Join the edibnb to the council data frames. Create a bar plot of the neighbourhood variable for the properties that have been assessed by the council (remember to iterate the data visualization to make it as informative as possible).
What does the data visualization suggest?
Is the council targeting all neighborhoods within Edinburgh equally?
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library(ggplot2)
council <- read.csv('council_assessments.csv')
joined_data <- inner_join(edibnb, council, by = "id")
print(colnames(joined_data))## [1] "id" "price" "neighbourhood"
## [4] "accommodates" "bathrooms" "bedrooms"
## [7] "beds" "review_scores_rating" "number_of_reviews"
## [10] "listing_url" "rating" "assessment_date"assessed_properties <- joined_data %>%
filter(!is.na(assessment_date))
View(assessed_properties)
assessed_properties <- joined_data %>%
filter(!is.na(assessment_date))
ggplot(assessed_properties, aes(x = neighbourhood)) +
geom_bar(fill = "skyblue", color = "black") +
labs(title = "Distribution of Assessed Properties by Neighbourhood",
x = "Neighbourhood",
y = "Count") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
#The visualization illustrates the distribution of assessed properties across Edinburgh’s neighborhoods. Disparities in bar heights may suggest unequal targeting by the council among neighborhoods.