Test R Markdown

Data preparation

# 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.

Uptake by Plant Type and Treatment

library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

# Summarize mean uptake by Type and Treatment
CO2 |>
  group_by(Type, Treatment) |>
  summarise(mean_uptake = mean(uptake), .groups = "drop")

# A tibble: 4 × 3
  Type        Treatment  mean_uptake
  <fct>       <fct>            <dbl>
1 Quebec      nonchilled        35.3
2 Quebec      chilled           31.8
3 Mississippi nonchilled        26.0
4 Mississippi chilled           15.8

Visualization: Uptake by Concentration

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()

Visualization: Uptake Distribution by Plant

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()