R Projects
R Skills
- Cleaned and transformed datasets using dplyr and tidyr for efficient wrangling
- Created visualisations with ggplot2 and plotly to explore trends and distributions
- Built statistical models using functions like
lm(),glm(), and the caret package - Performed time series analysis with forecast, zoo, and xts
- Generated automated reports and notebooks using R Markdown
- Built interactive dashboards with Shiny for stakeholder-facing reporting
- Imported and exported data using readr, readxl, and openxlsx
- Applied inferential statistics: t-tests, ANOVA, and confidence interval estimation
- Handled missing data using
na.omit(),mutate(), and tidyverse logic - Created reusable scripts and functions to streamline repetitive analysis
- Explored data using
summary(),str(), and the skimr package - Conducted geospatial analysis with sf and leaflet
- Integrated R with SQL queries, Excel files, and REST APIs
- Followed best practices for clean, modular, and reproducible R coding
This section highlights my use of R for data cleaning, transformation, and visualisation, especially using libraries like tidyverse and ggplot2.
- Tools: R, tidyverse, ggplot2, caret
- Key Topics: Data wrangling, linear regression, visualisation
- Dataset: UK housing market data (cleaned and transformed)
- Goal: Identify key price drivers and predict sale prices using regression modelling