Creating Your Data Science Portfolio

Meetup Description

Creating a data science portfolio can help you to develop your skills, and well as showcase them to the wider world. However, getting started on making your portfolio can feel like a huge task. In this talk, Nicola discussed how she used #Tidy Tuesday to gradually build an R portfolio.

We ran through an example of a #TidyTuesday contribution, including:

• downloading data

• initial data exploration

• creating and styling your plots

• sharing your data visualisations and code

…..and just like that, you have a portfolio!

Creating your Data Science Portfolio

Slides, code and link available here.

The session used the Big Tech Stock Prices from Tidy tuesday to kick start the portfolio.

1. Load data:

   #load the data set
   
   big_tech_stock_prices <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-02-07/big_tech_stock_prices.csv')

2. Data wrangling:

   library(tidyverse)
   
   library(lubridate)
   
   plot_data = big_tech_stock_prices %>%
   
   mutate(year = year(date)) %>%
   
   group_by(stock_symbol, year) %>%
   
   summarise(open = mean(open, na.rm = TRUE)) %>%
   
   ungroup() %>%
   
   filter(year <= 2022)

3. Make a basic line chart:

   ggplot(plot_data,
   
   aes(x = year, y = open, colour = stock_symbol)) +
   
   geom_line()

   

4. Try a sankey plot:

   library(ggsankey)
   
   ggplot(plot_data,
   
   aes(x = year, value = open, node = stock_symbol,
   
   fill = (stock_symbol == "ADBE"))) +
   
   geom_sankey_bump(colour = "transparent",
   
   alpha = 0.6) +
   
   scale_fill_manual(
   
   values = c("grey", "red")
   
   ) +
   
   theme_minimal()

generated sankey chart