library(tidyverse)
library(skimr)Raichu’s 3 topics
Proposal
Data 1
Problem or question
Identify the problem you will solve or the question you will answer
What are the factors that might influence the price of airbnb in New York city?
Explain why you think this topic is important.
Airbnb has revolutionized the travel landscape, offering travelers unique accommodation options while enabling homeowners to monetize their properties. But with a plethora of listings and diverse pricing, how can a potential host identify the best property investments tailored for Airbnb? Similarly, for a traveler looking for budget accommodations but with specific amenities like ‘cooking basics,’ how can they navigate the vast array of choices? Our objective is to pinpoint the crucial elements influencing listing prices, especially those prevalent in premium listings. By understanding these, potential hosts can optimize their offerings to command higher rates without deterring guests. Conversely, travelers can strategize to secure the best deals while still meeting their desired amenities.
Identify the types of data/variables you will use.
Categorical and Quantitative data types:
- name : chr
- description : chr
- neighborhood_overview : chr
- host_id : num
- neighbourhood_group_cleansed : chr
- neighbourhood_cleansed : chr
- property_type: chr
- amenities : chr
- host_since: date
- bedroom: num
- accommodates : num
- price : num
- maximum_nights : num
- availability_365 : num
State the major deliverable(s) you will create to solve this problem/answer this question.
- Analytics Reports
We will produce a final report focusing on three primary aspects:
Descriptive Analysis: This section will detail price-related statistics including the average, median, standard deviation, and range of prices for listings, analyzed across different parameters.
Correlation Analysis: Here, we will evaluate how various factors, especially amenities, correlate with pricing.
Comparative Analysis: Our report will delve into pricing strategies across different neighborhoods. For example, we’ll contrast strategies in Brooklyn and Manhattan.
- Interactive Web Application
We plan to develop an interactive web application targeting a diverse audience, including Airbnb hosts, travelers, and property investors. This application will be built using Shiny, an R package designed to streamline the creation of interactive web applications.
In terms of user interface, the application will feature:
Interactive Dashboards: These will visualize pricing trends to offer users a clear understanding of the current market dynamics.
Filter Options: Users will have the convenience of sliders or dropdown menus, enabling them to select specific amenities and see how these influence prices.
Data Visualization Tools: The platform will also include various graphical representations, such as geographic heatmaps, to further illuminate pricing trends and variations across different regions.
Introduction and data
If you are using a dataset:
Identify the source of the data.
The datasets for this project were obtained from sideairbnb: http://insideairbnb.com/get-the-data/
We used the Airbnb dataset for New York City and analyzed the listings in New York.
State when and how it was originally collected (by the original data curator, not necessarily how you found the data).
1. host submissions(primary source of data): Hosts create and manage their property listings on the Airbnb platform. They will provide information : property descriptions, photos, availability, pricing, and other details.
2. User Interactions: Guest interactions with Airbnb’s website or mobile app generate data on user searches, reviews, and ratings.
3. Third-Party Data Providers: Airbnb may also use data from third-party sources to enhance its listing information, such as neighborhood data, geographic data, or external reviews.
Write a brief description of the observations.
As we could see from the data, it includes details information for each listing, like host information, type of property. Also, the Information on the amenities and features provided in the listing, such as the number of bedrooms, bathrooms, kitchen facilities, Wi-Fi availability, etc. We could also see the availability and reviews for each listing. The information provided by this data set would be support for our future data analysis. During data exploration, we would clean the data and delete the unrelated variables and make data good for visualization.
Address ethical concerns about the data, if any.
We do not have any ethical concerns. Our data was extracted from an transparent, legal environment and did not required consent.
Glimpse of data
# add code here
airbnb_raw <- read_csv("data/listings 2.csv")Rows: 38792 Columns: 75
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (26): listing_url, source, name, description, neighborhood_overview, pi...
dbl (37): id, scrape_id, host_id, host_listings_count, host_total_listings_...
lgl (7): host_is_superhost, host_has_profile_pic, host_identity_verified, ...
date (5): last_scraped, host_since, calendar_last_scraped, first_review, la...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#glimpse of the dataset
skim(airbnb_raw) | Name | airbnb_raw |
| Number of rows | 38792 |
| Number of columns | 75 |
| _______________________ | |
| Column type frequency: | |
| character | 26 |
| Date | 5 |
| logical | 7 |
| numeric | 37 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| listing_url | 0 | 1.00 | 33 | 47 | 0 | 38792 | 0 |
| source | 0 | 1.00 | 11 | 15 | 0 | 2 | 0 |
| name | 0 | 1.00 | 26 | 83 | 0 | 12050 | 0 |
| description | 568 | 0.99 | 1 | 1000 | 0 | 34051 | 0 |
| neighborhood_overview | 16235 | 0.58 | 1 | 1000 | 0 | 16918 | 0 |
| picture_url | 0 | 1.00 | 60 | 126 | 0 | 37938 | 0 |
| host_url | 0 | 1.00 | 38 | 43 | 0 | 23811 | 0 |
| host_name | 5 | 1.00 | 1 | 34 | 0 | 8828 | 0 |
| host_location | 8277 | 0.79 | 5 | 35 | 0 | 1022 | 0 |
| host_about | 17152 | 0.56 | 1 | 7309 | 0 | 12372 | 22 |
| host_response_time | 5 | 1.00 | 3 | 18 | 0 | 5 | 0 |
| host_response_rate | 5 | 1.00 | 2 | 4 | 0 | 81 | 0 |
| host_acceptance_rate | 5 | 1.00 | 2 | 4 | 0 | 100 | 0 |
| host_thumbnail_url | 5 | 1.00 | 55 | 131 | 0 | 23307 | 0 |
| host_picture_url | 5 | 1.00 | 57 | 134 | 0 | 23307 | 0 |
| host_neighbourhood | 7752 | 0.80 | 4 | 44 | 0 | 525 | 0 |
| host_verifications | 0 | 1.00 | 2 | 32 | 0 | 8 | 0 |
| neighbourhood | 16235 | 0.58 | 23 | 55 | 0 | 170 | 0 |
| neighbourhood_cleansed | 0 | 1.00 | 4 | 25 | 0 | 223 | 0 |
| neighbourhood_group_cleansed | 0 | 1.00 | 5 | 13 | 0 | 5 | 0 |
| property_type | 0 | 1.00 | 4 | 34 | 0 | 81 | 0 |
| room_type | 0 | 1.00 | 10 | 15 | 0 | 4 | 0 |
| bathrooms_text | 42 | 1.00 | 6 | 17 | 0 | 32 | 0 |
| amenities | 0 | 1.00 | 2 | 2099 | 0 | 32035 | 0 |
| price | 0 | 1.00 | 5 | 10 | 0 | 1184 | 0 |
| license | 35853 | 0.08 | 6 | 18 | 0 | 278 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| last_scraped | 0 | 1.00 | 2023-10-01 | 2023-10-02 | 2023-10-02 | 2 |
| host_since | 5 | 1.00 | 2008-08-11 | 2023-09-29 | 2016-06-08 | 4842 |
| calendar_last_scraped | 0 | 1.00 | 2023-10-01 | 2023-10-02 | 2023-10-02 | 2 |
| first_review | 10352 | 0.73 | 2009-05-25 | 2023-10-01 | 2020-03-18 | 3962 |
| last_review | 10352 | 0.73 | 2011-05-12 | 2023-10-01 | 2023-06-01 | 2924 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| host_is_superhost | 255 | 0.99 | 0.21 | FAL: 30587, TRU: 7950 |
| host_has_profile_pic | 5 | 1.00 | 0.98 | TRU: 38142, FAL: 645 |
| host_identity_verified | 5 | 1.00 | 0.88 | TRU: 34036, FAL: 4751 |
| bathrooms | 38792 | 0.00 | NaN | : |
| calendar_updated | 38792 | 0.00 | NaN | : |
| has_availability | 0 | 1.00 | 0.84 | TRU: 32742, FAL: 6050 |
| instant_bookable | 0 | 1.00 | 0.20 | FAL: 31166, TRU: 7626 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| id | 0 | 1.00 | 2.820216e+17 | 3.854980e+17 | 2.5950e+03 | 1.976673e+07 | 4.542122e+07 | 7.208503e+17 | 9.927295e+17 | ▇▁▁▂▂ |
| scrape_id | 0 | 1.00 | 2.023100e+13 | 0.000000e+00 | 2.0231e+13 | 2.023100e+13 | 2.023100e+13 | 2.023100e+13 | 2.023100e+13 | ▁▁▇▁▁ |
| host_id | 0 | 1.00 | 1.558350e+08 | 1.676485e+08 | 1.6780e+03 | 1.643725e+07 | 7.616643e+07 | 2.754027e+08 | 5.395985e+08 | ▇▂▂▁▂ |
| host_listings_count | 5 | 1.00 | 1.416400e+02 | 6.255200e+02 | 1.0000e+00 | 1.000000e+00 | 2.000000e+00 | 7.000000e+00 | 4.559000e+03 | ▇▁▁▁▁ |
| host_total_listings_count | 5 | 1.00 | 2.284300e+02 | 9.778100e+02 | 1.0000e+00 | 1.000000e+00 | 3.000000e+00 | 1.000000e+01 | 8.820000e+03 | ▇▁▁▁▁ |
| latitude | 0 | 1.00 | 4.073000e+01 | 6.000000e-02 | 4.0500e+01 | 4.069000e+01 | 4.073000e+01 | 4.076000e+01 | 4.091000e+01 | ▁▂▇▅▁ |
| longitude | 0 | 1.00 | -7.395000e+01 | 6.000000e-02 | -7.4250e+01 | -7.398000e+01 | -7.395000e+01 | -7.393000e+01 | -7.371000e+01 | ▁▁▇▂▁ |
| accommodates | 0 | 1.00 | 2.910000e+00 | 2.100000e+00 | 1.0000e+00 | 2.000000e+00 | 2.000000e+00 | 4.000000e+00 | 1.600000e+01 | ▇▁▁▁▁ |
| bedrooms | 16900 | 0.56 | 1.590000e+00 | 9.800000e-01 | 1.0000e+00 | 1.000000e+00 | 1.000000e+00 | 2.000000e+00 | 5.000000e+01 | ▇▁▁▁▁ |
| beds | 663 | 0.98 | 1.650000e+00 | 1.170000e+00 | 1.0000e+00 | 1.000000e+00 | 1.000000e+00 | 2.000000e+00 | 4.200000e+01 | ▇▁▁▁▁ |
| minimum_nights | 0 | 1.00 | 3.064000e+01 | 2.663000e+01 | 1.0000e+00 | 3.000000e+01 | 3.000000e+01 | 3.000000e+01 | 1.250000e+03 | ▇▁▁▁▁ |
| maximum_nights | 0 | 1.00 | 5.701389e+04 | 1.090426e+07 | 1.0000e+00 | 1.800000e+02 | 3.650000e+02 | 1.125000e+03 | 2.147484e+09 | ▇▁▁▁▁ |
| minimum_minimum_nights | 0 | 1.00 | 3.115000e+01 | 2.980000e+01 | 1.0000e+00 | 3.000000e+01 | 3.000000e+01 | 3.000000e+01 | 1.250000e+03 | ▇▁▁▁▁ |
| maximum_minimum_nights | 0 | 1.00 | 3.648000e+01 | 5.101000e+01 | 1.0000e+00 | 3.000000e+01 | 3.000000e+01 | 3.000000e+01 | 1.250000e+03 | ▇▁▁▁▁ |
| minimum_maximum_nights | 0 | 1.00 | 3.892761e+05 | 2.884558e+07 | 1.0000e+00 | 3.650000e+02 | 1.125000e+03 | 1.125000e+03 | 2.147484e+09 | ▇▁▁▁▁ |
| maximum_maximum_nights | 0 | 1.00 | 1.496482e+06 | 5.663646e+07 | 1.0000e+00 | 3.650000e+02 | 1.125000e+03 | 1.125000e+03 | 2.147484e+09 | ▇▁▁▁▁ |
| minimum_nights_avg_ntm | 0 | 1.00 | 3.588000e+01 | 4.819000e+01 | 1.0000e+00 | 3.000000e+01 | 3.000000e+01 | 3.000000e+01 | 1.250000e+03 | ▇▁▁▁▁ |
| maximum_nights_avg_ntm | 0 | 1.00 | 1.194086e+06 | 4.840311e+07 | 1.0000e+00 | 3.650000e+02 | 1.125000e+03 | 1.125000e+03 | 2.147484e+09 | ▇▁▁▁▁ |
| availability_30 | 0 | 1.00 | 7.440000e+00 | 1.094000e+01 | 0.0000e+00 | 0.000000e+00 | 0.000000e+00 | 1.300000e+01 | 3.000000e+01 | ▇▁▁▁▂ |
| availability_60 | 0 | 1.00 | 1.922000e+01 | 2.270000e+01 | 0.0000e+00 | 0.000000e+00 | 5.000000e+00 | 3.800000e+01 | 6.000000e+01 | ▇▁▂▁▃ |
| availability_90 | 0 | 1.00 | 3.285000e+01 | 3.485000e+01 | 0.0000e+00 | 0.000000e+00 | 2.000000e+01 | 6.400000e+01 | 9.000000e+01 | ▇▁▂▂▃ |
| availability_365 | 0 | 1.00 | 1.487500e+02 | 1.422600e+02 | 0.0000e+00 | 0.000000e+00 | 1.200000e+02 | 3.000000e+02 | 3.650000e+02 | ▇▂▂▂▅ |
| number_of_reviews | 0 | 1.00 | 2.536000e+01 | 5.585000e+01 | 0.0000e+00 | 0.000000e+00 | 4.000000e+00 | 2.400000e+01 | 1.843000e+03 | ▇▁▁▁▁ |
| number_of_reviews_ltm | 0 | 1.00 | 6.950000e+00 | 1.663000e+01 | 0.0000e+00 | 0.000000e+00 | 0.000000e+00 | 5.000000e+00 | 8.140000e+02 | ▇▁▁▁▁ |
| number_of_reviews_l30d | 0 | 1.00 | 4.900000e-01 | 1.390000e+00 | 0.0000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 9.400000e+01 | ▇▁▁▁▁ |
| review_scores_rating | 10347 | 0.73 | 4.630000e+00 | 7.400000e-01 | 0.0000e+00 | 4.600000e+00 | 4.830000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_accuracy | 10762 | 0.72 | 4.740000e+00 | 4.900000e-01 | 0.0000e+00 | 4.690000e+00 | 4.890000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_cleanliness | 10752 | 0.72 | 4.620000e+00 | 5.600000e-01 | 0.0000e+00 | 4.500000e+00 | 4.800000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_checkin | 10766 | 0.72 | 4.810000e+00 | 4.200000e-01 | 0.0000e+00 | 4.800000e+00 | 4.940000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_communication | 10758 | 0.72 | 4.810000e+00 | 4.500000e-01 | 0.0000e+00 | 4.800000e+00 | 4.960000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_location | 10769 | 0.72 | 4.720000e+00 | 4.200000e-01 | 0.0000e+00 | 4.620000e+00 | 4.840000e+00 | 5.000000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| review_scores_value | 10768 | 0.72 | 4.620000e+00 | 5.200000e-01 | 0.0000e+00 | 4.510000e+00 | 4.750000e+00 | 4.930000e+00 | 5.000000e+00 | ▁▁▁▁▇ |
| calculated_host_listings_count | 0 | 1.00 | 3.858000e+01 | 1.133100e+02 | 1.0000e+00 | 1.000000e+00 | 1.000000e+00 | 6.000000e+00 | 6.020000e+02 | ▇▁▁▁▁ |
| calculated_host_listings_count_entire_homes | 0 | 1.00 | 1.772000e+01 | 7.857000e+01 | 0.0000e+00 | 0.000000e+00 | 1.000000e+00 | 2.000000e+00 | 6.020000e+02 | ▇▁▁▁▁ |
| calculated_host_listings_count_private_rooms | 0 | 1.00 | 2.078000e+01 | 7.997000e+01 | 0.0000e+00 | 0.000000e+00 | 0.000000e+00 | 2.000000e+00 | 5.290000e+02 | ▇▁▁▁▁ |
| calculated_host_listings_count_shared_rooms | 0 | 1.00 | 4.000000e-02 | 3.200000e-01 | 0.0000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 8.000000e+00 | ▇▁▁▁▁ |
| reviews_per_month | 10352 | 0.73 | 1.080000e+00 | 1.650000e+00 | 1.0000e-02 | 1.100000e-01 | 4.100000e-01 | 1.520000e+00 | 6.282000e+01 | ▇▁▁▁▁ |
Data 2
Problem or question
Identify the problem you will solve or the question you will answer
To analyze the historical performance and trends of national teams in FIFA World Cup matches from 1930 to 2018, with a focus on identifying factors influencing success.
Explain why you think this topic is important.
Understanding the historical context of World Cup matches is important for soccer enthusiasts, historians, and analysts. It provides insights into team strategies, the evolution of the sport, team strategies, fan engagement, the sports betting industry, and the cultural significance of the World Cup.
Identify the types of data/variables you will use.
Categorical and Quantitative data types:
- match_link : chr
- match_datetime : date
- edition : num
- phase_label : chr
- phase_type : chr
- home_team : chr
- score: chr
- away_team : chr
- winner_team: chr
- winner_home_away: chr
- aet : chr
- location : chr
- attendance_number : num
- referee_name : chr
- referee_country: chr
- is_wo : boolean
- home_score : num
- away_score : num
State the major deliverable(s) you will create to solve this problem/answer this question.
- Comprehensive Reports
We will produce a final report focusing on two primary aspects:
Historical Analysis: This section will delve into the historical performance of national teams in FIFA World Cup matches from 1930 to 2018, providing insights into trends, statistics, and memorable moments.
Predictive modeling: We will develop predictive models to forecast match outcomes and analyze the key factors that influence success in World Cup matches. This includes team statistics, and historical performance data.
- Interactive Web Application
We plan to create an interactive web application designed to engage a wide audience, including soccer enthusiasts, historians, and analysts. This application will be built using Shiny, an R package known for its capacity to create interactive web applications.
The web application will offer the following features:
Historical Insights: Interactive dashboards will visualize the historical performance of national teams, providing users with a clear understanding of World Cup trends and outcomes.
Predictive Analysis: Users can input team attributes and match scenarios to obtain predictions for future World Cup matches, gaining insights into the factors influencing match results.
Data Visualization Tools: Various graphical representations, including interactive maps and charts, will illuminate historical records, performance trends, and the geographical distribution of World Cup events.
Introduction and data
If you are using a dataset:
Identify the source of the data.
The data for this project was sourced from https://data.world/brocolidata/world-cup-matches-1930-2018.
State when and how it was originally collected (by the original data curator, not necessarily how you found the data).
1. FIFA Official Website: FIFA keeps the records of the data of all previous World Cup matches in the official archive. The data was originally collected and curated by the official data curator responsible for maintaining historical FIFA World Cup match records.
Write a brief description of the observations.
The dataset comprises records of FIFA World Cup matches spanning from 1930 to 2018. It includes detailed information about teams, players, match locations, and match statistics. This dataset provides valuable historical insights into the World Cup, including team performance, historical records, and the evolution of the tournament.
Address ethical concerns about the data, if any.
We do not have any ethical concerns. Our data was extracted from an transparent, legal environment and did not required consent.
Glimpse of data
# add code here
world_cup_raw <- read_csv("data/world_cup_matches.csv")Rows: 901 Columns: 18
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (12): match_link, phase_label, phase_type, home_team, score, away_team,...
dbl (4): edition, attendance_number, home_score, away_score
lgl (1): is_wo
dttm (1): match_datetime
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#glimpse of the dataset
skim(world_cup_raw) | Name | world_cup_raw |
| Number of rows | 901 |
| Number of columns | 18 |
| _______________________ | |
| Column type frequency: | |
| character | 12 |
| logical | 1 |
| numeric | 4 |
| POSIXct | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| match_link | 2 | 1.00 | 42 | 121 | 0 | 899 | 0 |
| phase_label | 0 | 1.00 | 7 | 14 | 0 | 16 | 0 |
| phase_type | 0 | 1.00 | 6 | 6 | 0 | 2 | 0 |
| home_team | 0 | 1.00 | 4 | 22 | 0 | 81 | 0 |
| score | 0 | 1.00 | 3 | 12 | 0 | 64 | 0 |
| away_team | 0 | 1.00 | 4 | 22 | 0 | 85 | 0 |
| winner_team | 199 | 0.78 | 4 | 22 | 0 | 65 | 0 |
| winner_home_away | 199 | 0.78 | 4 | 4 | 0 | 2 | 0 |
| aet | 0 | 1.00 | 4 | 19 | 0 | 3 | 0 |
| location | 1 | 1.00 | 14 | 44 | 0 | 224 | 0 |
| referee_name | 1 | 1.00 | 6 | 31 | 0 | 402 | 0 |
| referee_country | 1 | 1.00 | 4 | 20 | 0 | 88 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| is_wo | 0 | 1 | 0 | FAL: 900, TRU: 1 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| edition | 0 | 1 | 1986.86 | 23.19 | 1930 | 1970 | 1990 | 2006 | 2018 | ▂▃▅▆▇ |
| attendance_number | 0 | 1 | 44852.89 | 23088.35 | 0 | 30454 | 41835 | 60578 | 173850 | ▅▇▂▁▁ |
| home_score | 0 | 1 | 1.78 | 1.61 | 0 | 1 | 1 | 3 | 10 | ▇▂▁▁▁ |
| away_score | 0 | 1 | 1.05 | 1.07 | 0 | 0 | 1 | 2 | 7 | ▇▂▁▁▁ |
Variable type: POSIXct
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| match_datetime | 0 | 1 | 1930-07-13 15:00:00 | 2018-07-15 18:00:00 | 1990-06-23 21:00:00 | 662 |
Data 3
Problem or question
Identify the problem you will solve or the question you will answer
The objective of this project is to demonstrate the correlation, if any, between the major cryptocurrencies and the real-world key macroeconomic indicators.
Explain why you think this topic is important.
Cryptocurrency has been increasingly important to Internet Users and the modern-day economy. Our research will demonstrate how cryptocurrency is connected to the real world.
Identify the types of data/variables you will use.
-date: date
-BTCUSDT: num
-ETHUSDT: num
-BNBUSDT: num
-XRPUSDT: num
-DOGEUSDT: num
-ADAUSDT: num
-MATICUSDT: num
-LTCUSDT: num
-FEDFUNDS: num
-GDP: num
State the major deliverable(s) you will create to solve this problem/answer this question.
The major deliverables for this project proposal will be a formatted research paper to present our findings and an interactive visualization web app that can be embedded to other websites.
Introduction and data
Dataset: AggregatedMarketCapTop25_Sample
If you are using a dataset:
Identify the source of the data.
- www.cryptodatadownload.com
State when and how it was originally collected (by the original data curator, not necessarily how you found the data).
- The data is collected using Binance API by cryptodatadownload.com. It contains the top 25 cryptocurrency trading pairs since 2017/7.
Write a brief description of the observations.
- The data contains 2054 rows, but only Bitcoin and Ethereum populate most of them, some of the coins with smaller market caps seem to fluctuate more often, and many of them didn’t exist until 2020.
Address ethical concerns about the data, if any.
- The data was collected by a third party under their free-of-use license and is for analysis purposes only.
Glimpse of data
# add code here
crypto_raw <- read_csv("data/AggregatedMarketCapTop25_Sample.csv")New names:
Rows: 2053 Columns: 30
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(3): date, DATE...27, DATE...29 dbl (27): BTCUSDT, ETHUSDT, BNBUSDT, XRPUSDT,
DOGEUSDT, ADAUSDT, MATICUSDT, ...
ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
Specify the column types or set `show_col_types = FALSE` to quiet this message.
• `DATE` -> `DATE...27`
• `DATE` -> `DATE...29`#glimpse of the dataset
skim(crypto_raw) | Name | crypto_raw |
| Number of rows | 2053 |
| Number of columns | 30 |
| _______________________ | |
| Column type frequency: | |
| character | 3 |
| numeric | 27 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| date | 0 | 1.00 | 8 | 14 | 0 | 2053 | 0 |
| DATE…27 | 1978 | 0.04 | 8 | 9 | 0 | 75 | 0 |
| DATE…29 | 2029 | 0.01 | 8 | 9 | 0 | 24 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| BTCUSDT | 0 | 1.00 | 19026.29 | 16617.35 | 0.00 | 7197.32 | 10569.04 | 29468.10 | 67525.83 | ▇▂▂▂▁ |
| ETHUSDT | 0 | 1.00 | 1076.85 | 1161.87 | 0.00 | 210.77 | 460.55 | 1635.74 | 4807.98 | ▇▂▁▁▁ |
| BNBUSDT | 0 | 1.00 | 140.44 | 180.08 | 0.00 | 12.81 | 22.97 | 289.90 | 676.15 | ▇▁▂▁▁ |
| XRPUSDT | 0 | 1.00 | 0.41 | 0.32 | 0.00 | 0.23 | 0.33 | 0.52 | 1.83 | ▇▅▂▁▁ |
| DOGEUSDT | 0 | 1.00 | 0.06 | 0.10 | 0.00 | 0.00 | 0.00 | 0.08 | 0.69 | ▇▁▁▁▁ |
| ADAUSDT | 0 | 1.00 | 0.43 | 0.62 | 0.00 | 0.04 | 0.10 | 0.50 | 2.97 | ▇▁▁▁▁ |
| MATICUSDT | 0 | 1.00 | 0.41 | 0.63 | 0.00 | 0.00 | 0.02 | 0.82 | 2.88 | ▇▂▁▁▁ |
| LTCUSDT | 0 | 1.00 | 92.69 | 66.04 | 0.00 | 49.34 | 70.20 | 132.09 | 387.80 | ▇▃▂▁▁ |
| TRXUSDT | 0 | 1.00 | 0.04 | 0.03 | 0.00 | 0.02 | 0.03 | 0.06 | 0.16 | ▇▃▂▁▁ |
| DOTUSDT | 0 | 1.00 | 7.66 | 12.02 | 0.00 | 0.00 | 0.00 | 9.35 | 53.82 | ▇▁▁▁▁ |
| SOLUSDT | 0 | 1.00 | 25.15 | 49.41 | 0.00 | 0.00 | 0.00 | 31.16 | 258.44 | ▇▁▁▁▁ |
| AVAXUSDT | 0 | 1.00 | 15.19 | 26.90 | 0.00 | 0.00 | 0.00 | 18.12 | 134.84 | ▇▁▁▁▁ |
| XMRUSDT | 0 | 1.00 | 102.02 | 95.18 | 0.00 | 0.00 | 80.92 | 160.42 | 484.00 | ▇▃▂▁▁ |
| ATOMUSDT | 0 | 1.00 | 8.26 | 10.14 | 0.00 | 0.00 | 4.31 | 12.09 | 44.27 | ▇▂▁▁▁ |
| TONUSDT | 0 | 1.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▁▁▇▁▁ |
| ETCUSDT | 0 | 1.00 | 16.01 | 18.50 | 0.00 | 4.66 | 7.39 | 22.64 | 133.86 | ▇▂▁▁▁ |
| BCHUSDT | 0 | 1.00 | 196.94 | 236.89 | 0.00 | 0.00 | 117.80 | 310.00 | 1547.51 | ▇▂▁▁▁ |
| XLMUSDT | 0 | 1.00 | 0.15 | 0.13 | 0.00 | 0.06 | 0.11 | 0.22 | 0.73 | ▇▂▁▁▁ |
| ALGOUSDT | 0 | 1.00 | 0.42 | 0.51 | 0.00 | 0.00 | 0.26 | 0.63 | 2.38 | ▇▂▁▁▁ |
| VETUSDT | 0 | 1.00 | 0.03 | 0.04 | 0.00 | 0.00 | 0.01 | 0.03 | 0.25 | ▇▁▁▁▁ |
| FILUSDT | 0 | 1.00 | 15.62 | 31.01 | 0.00 | 0.00 | 0.00 | 19.61 | 190.86 | ▇▁▁▁▁ |
| NEARUSDT | 0 | 1.00 | 2.31 | 3.88 | 0.00 | 0.00 | 0.00 | 3.37 | 20.18 | ▇▁▁▁▁ |
| EOSUSDT | 0 | 1.00 | 3.02 | 2.33 | 0.00 | 1.18 | 2.74 | 4.20 | 14.72 | ▇▅▁▁▁ |
| HBARUSDT | 0 | 1.00 | 0.08 | 0.11 | 0.00 | 0.00 | 0.03 | 0.08 | 0.50 | ▇▁▁▁▁ |
| THETAUSDT | 0 | 1.00 | 1.60 | 2.72 | 0.00 | 0.00 | 0.14 | 1.45 | 14.20 | ▇▁▁▁▁ |
| FEDFUNDS | 1978 | 0.04 | 1.67 | 1.56 | 0.05 | 0.09 | 1.55 | 2.38 | 5.33 | ▇▇▃▁▂ |
| GDP | 2029 | 0.01 | 22706.76 | 2366.58 | 19692.60 | 20888.08 | 21804.45 | 24748.23 | 27063.01 | ▇▇▂▃▃ |