Lecture 01a – Welcome to ENVX1002
ENVX1002 Statistics in Life and Environmental Sciences
Apr 2026
About us…
Liana Pozza - Unit Coordinator
- Room 303, Level 3, Biomedical Building C81, 1 Central Ave, Eveleigh
- Ph: 02 8627 1012
- Email: liana.pozza@sydney.edu.au
Your Lecturers
Januar Harianto
Weeks 1 – 4
Floris van Ogtrop
Weeks 5 – 8
Liana Pozza
Weeks 9 – 12
Where are the Labs?
- Lab sessions include extra time (~30 minutes) for travel – already programmed in the timetable (so clashes are avoided)
- We are working on securing a free shuttle service between campus and the labs - stay tuned!
- Take advantage of the new community access gates at Redfern Station: saves 5 minutes
Campus bus
There is currently a bus that goes to Redfern Station. You can find the timetable here: Campus Bus Timetables
We are looking to secure a bus service from main campus to C81, but need an idea of numbers first.
If you are interested in catching a shuttle bus to C81, please fill out this short survey:
MS Excel
- Widely used for data entry and basic analysis
- A standard tool in many industries, including science, often to store data
- Can be a useful complement to R for data cleaning and simple calculations
- A stepping stone to more advanced tools?
R
- A free, open-source programming language
- Widely used for data analysis and statistics
- Standard tool in scientific research
- Extensive collection of packages for data science
- Strong support for creating publication-quality graphics
- Large, active community for help and resources
RStudio
- NOT the same as R – it’s an integrated development environment (IDE)
- Runs R (…and Python, and SQL, and more)
- Makes it easier to write and run R code by providing a significantly more user-friendly interface
Satisfying when it works
Click to see the code for this animation
# Load required packages
library(gapminder) # Dataset of country statistics over time
library(gganimate) # For creating animations in ggplot
library(tidyverse) # Collection of data science packages
# Create an animated plot showing how life expectancy relates to GDP
# across different continents over time
ggplot(
gapminder,
aes(gdpPercap, lifeExp, # GDP per capita vs life expectancy
size = pop, # Point size represents population
colour = country
)
) + # Each country gets its own color
geom_point(
alpha = 0.7, # Semi-transparent points
show.legend = FALSE
) + # Hide legend for cleaner look
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) + # Set min/max point sizes
scale_x_log10() + # Log scale for GDP (wide range)
facet_wrap(~continent) + # Separate plot for each continent
labs(
title = "Year: {frame_time}",
x = "GDP per capita",
y = "Life expectancy"
) +
transition_time(year) + # Animate through years
ease_aes("linear") # Smooth transitions
R, RStudio, Quarto!?
- Again, it’s normal to feel overwhelmed at first
- These technologies are complementary – everything is implemented in RStudio
- The lectures and practical classes will guide you through the process
