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# Set your local data here. note that when used in teal the `data` must be available to the module CO2 <- datasets::CO2 # nolint: object_name_linter.
# Convert Treatment and Type to factors (if not already) CO2$Treatment <- as.factor(CO2$Treatment) # nolint: object_name_linter. CO2$Type <- as.factor(CO2$Type) # nolint: object_name_linter.
library(dplyr) # Summarize mean uptake by Type and Treatment CO2 |> group_by(Type, Treatment) |> summarise(mean_uptake = mean(uptake), .groups = "drop")
library(ggplot2) ggplot(CO2, aes(x = conc, y = uptake, color = Treatment, shape = Type)) + geom_point() + geom_smooth(formula = "y ~ x", method = "lm", se = FALSE) + labs( title = "CO2 Uptake vs. Concentration", x = "CO2 Concentration (mL/L)", y = "CO2 Uptake (umol/m^2 sec)", color = "Treatment", shape = "Plant Type" ) + theme_minimal()
ggplot(CO2, aes(x = Plant, y = uptake, fill = Treatment)) + geom_boxplot() + labs( title = "CO2 Uptake Distribution by Plant", x = "Plant", y = "CO2 Uptake (umol/m^2 sec)", fill = "Treatment" ) + theme_minimal()
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