This is my first interactive chart to present Covid Cases in UK regions. This uses an example given in class.

This is my second chart using data to show UK productivity. This was produced using an example provided in class.

This is my third chart presenting the most common brands that litter the UK's beaches. I produced this myself using data sourced from the Rapid Charts site.

This is my fourth chart which I created myself which uses data from a government API.

This is my fifth chart showing double vaccinations using data hosted on Github that was originally collected from a government API.

This is my sixth chart which presents shows the gender pay gap across OECD countries between 2016-2020. I changed this data by adding in a variable "NOR*2" which is the Norway values multiplied by 2. This data is from the OECD site.

This is my seventh chart which presents cumulative LFD tests using "in-line" JSON data that I wrote myself. This was also collected from the government API.

This is my eighth chart which draws on data from an API about carbon emissions across many countries. I specifically edited the URL to be UK specific spanning data from January to September (inclusive) of 2021.

This is my ninth chart which draws on data from the police API that takes record of offences committed at a given latitude/longitude set of coordinates. This example takes the coordinates from the Highbury and Islington tube station and shows the crimes committed during the latest month that data is available for.

The next 4 charts present data collected from the FRED API. They were batch downloaded using loops in Google Colaboratory and then these JSON files were uploaded to Github. All of the files used to create these charts can be found on my Github under portfolio/Skill 6.

This chart was created using data scraped from the World Athletics World Records page. I used Google Colaboratory to manipulate the data to show the distribution of total, female and male records and this file can be found under Portfolio/Skill 7 of my Github page.

At the Talking Economics conference on 19th November, Andrea Aldridge claimed that "the pandemic has widened the educational attainment gap between girls and boys". I investigated this claim by finding data about the proportion of GCSEs that were awarded a pass (C/4) or higher by gender from Statista (originally collected from the JCQ publication). I then created a chart which shows the year on year difference between the proportion of female and male students who achieved a pass or higher. Since 2020 (i.e. the beginning of the pandemic), we can observe negative change in the difference between male and female grades as indicated by the red negative bars. This means that the grade gap between female and male GCSE students has actually contracted since the pandemic thus refuting Andrea's claim according to this evidence.

For chart 16, I also took inspiration from the Talking Economics conference. During the climate crisis panel, Dimitri Zenghleis claimed that "low corruption leads to prosperity". I investigated this claim by merging data from two different sources about the Corruptions Perception Index (CPI) and the GDP per capita of various countries using Google Colaboratory and then layering a best fit line using the regression analysis output. From this chart I have produced, we can see that there is a relationship between a high CPI index which indicates low corruption and a high GDP per capita to indicate prosperity, thereby corrobating Dimitri's claim.

For charts 17 and 18, I have presented two charts included in my project. Chart 17 was made by feeding the top 20 most populous UK cities scraped from Statista and then fed into a URL base in Google Colaboratory to collect data from the Land Registry website. It is interactive because the 2020 population estimates are revealed when your mouse hovers over each chart. Chart 18 was made by cleaning and merging two datasets from the Metropolitan Police (MP) and Land Registry and is interactive because you can highlight the data points of individual boroughs via the dropdown menu and zoom in if you scroll over the chart.