Data Analysis
Consulting
Skills
Data visualization, for descriptive statistics
ggplot in R
Shiny dashboards
Stata
plotly in Python
Tableau
Codifying raw survey data
Writing meta-data documentation
Extracting, scraping, transforming, wrangling, tidying, exporting or analysing data, using R
Statistics
aggregation, with grouping
distributional analysis; density
multivariate regression
imputation
ARIMA or VAR time-series modelling
Using household survey data
- Specializing in the South African labour market
Report writing
Automation of scripts (e.g. in a virtual machine)
Using APIs for data collection, or running a bot.
Updating graphs daily
Using a High Performance Computer (HPC)
Fiverr
Survey data visualization
https://www.fiverr.com/aidanhorn/do-data-visualization-with-r-statistics-and-shiny-apps
Rate
Short-term projects: I can work remotely from Cape Town, for ZAR 500 per hour.
Longer-term academic projects, based on South African data, attract a lower rate. Especially if I am a co-author on a paper.
Technologies
Languages
Stata —since Aug 2017.
R —since May 2020.
Python (APIs, pandas, plotly, SciPy, scikit-learn ), since Sept 2021.
LaTeX —since 2015.
Git —since May 2022.
Bash (for using Linux CLI)
SQL —since April 2023.
Programs
Google Workspace (including Meet, Calendar, Sheets, Docs, Forms, and shared drives)
Dropbox
Office (including Word, Excel, PowerPoint, Teams, Outlook).
Excel: advanced functions, modelling, and basic VBA. I have an interest in BERT.
Texmaker + MiXTeX
Tableau
VS Code
R Studio
Stata
Notepad++
WinMerge
Portfolio
My work involved assistance with:
Codifying survey responses
Stata code for graphs
https://www.opensaldru.uct.ac.za/handle/11090/1005
I produced the statistical tables and graphs in this publication.
Since 2022-10-08. I run a Python script on a cron job daily, in my Linux virtual machine (VM) with Google Cloud Platform (GCP).
See Previous Research, a summary of my Résumé, and please contact me for my CV.
Testimonials
Further testimonials to be updated here.Prof Andrew Donaldson: andrew.donaldson@uct.ac.za
Dr Daan Steenkamp: daan@codera.co.za
Mr Grant Smith: grant@gmtplus.co.za
Contact
aidan@econometrics.co.za
Example ggplot code
Here is an example of how to write a simple graph in R, using the ggplot2 package. The data would usually be pulled in from a data file, but the code below shows how ggplot can be used to create a graph. This also shows how I lay out my code. The output is shown on the right.
base_graph = list( # can be used as default settings for all graphs in the project theme( plot.title.position = "plot", axis.title.x = element_text(margin=margin(t=5)), axis.title.y = element_text(margin=margin(r=10)), axis.text.x = element_text(size=rel(1.1), margin=margin(b=2)), panel.grid.major = element_blank(), panel.grid.minor = element_blank() ), scale_x_continuous( breaks = pretty_breaks(), expand = expansion(mult=c(0.015, 0.14)), labels = scales::percent_format(accuracy = 1) ))
employment_graph <- ggplot( data = data.frame( age = factor(seq(1, 5), seq(1, 5), c( "18-29", "30-39", "40-49", "50-59", "60-65" ) ), prop = c(0.63, 0.78, 0.90, 0.82, 0.68) ) %>% as_tibble() %>% mutate( percentage = paste0(round(prop*100), '%') ) , aes( x = prop, y = age )) + theme_minimal() + base_graph + geom_bar( stat = "identity", fill = "darkblue" ) + coord_flip() + geom_text( aes( x = prop*0.89 - 0.04, label = percentage, y = age ), stat = "identity", size = rel(3.5), check_overlap=T, color = "white" ) + labs( x = "Proportion", y = "Age", title = "Employment rate by age" )
employment_graph
png( filename="employment.png", width = 600, height = 500, res = 150)employment_graphdev.off()
