# 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()