Lecture 07 — Real Climate Data in R
Downloading, summarizing, and modeling Duluth weather station data
2026-07-05
First Look — Plot the Raw Daily Data
Note
🔮 Predict first: This plots ~28,000 daily points. Before you see it — will a long-term warming trend be visible, or will something else dominate the picture?
# Plot every single daily temperature ---------------------
raw_temp_plot <- dlh_temp_df %>%
ggplot(aes(x = YEARMODA, y = TEMP)) +
geom_point(alpha = 0.2, size = 0.5) +
geom_line(alpha = 0.3) +
labs(
title = "Duluth Daily Mean Temperature, 1948–2025",
x = "Date",
y = "Temperature (°C)"
) +
theme_minimal()
raw_temp_plot
What do you see?
- A dense, fuzzy band — the seasonal cycle (winter ↔︎ summer) dominates the plot
- ~28,000 points overlapping each other
- Any long-term trend is completely hidden inside the seasonal noise
The problem: raw daily data shows you everything — which means it can hide the one thing you’re looking for.
Summarize by Month
# Plot the monthly means ----------------------------------
month_temp_plot <- dlh_month_df %>%
ggplot(aes(x = YEARMODA, y = TEMP)) +
geom_point(alpha = 0.4, size = 0.8) +
geom_line(alpha = 0.4) +
geom_smooth(method = "lm", color = "steelblue") +
labs(
title = "Duluth Monthly Mean Temperature",
x = "Date",
y = "Temperature (°C)"
) +
theme_minimal()
month_temp_plot
Better — but still busy.
- The seasonal cycle (summer peaks, winter dips) is now a clean, regular wave
- A faint regression line is visible, but the seasonal swing still dominates the y-axis range
first(YEARMODA) keeps one representative date per month so we can still plot on a time axis.
Summarize by Year — The Story Gets Clear
# A tibble: 6 × 3
YEAR YEARMODA TEMP
<int> <date> <dbl>
1 1948 1948-01-01 3.76
2 1949 1949-01-01 4.10
3 1950 1950-01-01 1.60
4 1951 1951-01-01 2.41
5 1952 1952-01-01 4.55
6 1953 1953-01-01 4.55
Now the trend is obvious.
- The seasonal noise is completely averaged out
- 78 points instead of 28,000 — and the story is clearer, not weaker
- The blue/red trend line is the same kind of
geom_smooth(method = "lm")line from Lecture 03
This is the power of group_by() %>% summarize(): the right level of summary reveals the signal.
Plot Both Seasons Together
# Plot both seasons with separate trend lines --------------
season_temp_plot <- season_year_df %>%
ggplot(aes(x = YEAR, y = TEMP, color = season)) +
geom_point(size = 1.8, alpha = 0.7) +
geom_smooth(method = "lm", se = TRUE) +
scale_color_manual(
values = c(
"summer" = "firebrick",
"winter" = "steelblue"
)
) +
labs(
title = "Duluth Summer vs. Winter Temperature Trends",
x = "Year",
y = "Mean Temp (°C)",
color = "Season"
) +
theme_minimal()
season_temp_plot
What to look for:
- Two clearly separated bands — winter is much colder than summer, as expected
- Compare the steepness of each trend line, not just the starting height
- A steeper slope means a faster rate of warming for that season