# -----------------------------------
# STEP 1: Create Dataset
# -----------------------------------

# Example: Plant growth (cm) under 3 fertilizers
fertilizer <- rep(c("A", "B", "C"), each = 10)

height <- c(
  15,16,14,15,17,16,15,14,16,15,   # A
  18,19,17,18,20,19,18,17,19,18,   # B
  12,13,11,12,14,13,12,11,13,12    # C
)

data <- data.frame(fertilizer, height)

# View dataset
print(data)

# -----------------------------------
# STEP 2: Install & Load Libraries
# -----------------------------------

install.packages("dplyr")      # Run only once
install.packages("ggplot2")    # Run only once

library(dplyr)
library(ggplot2)

# -----------------------------------
# STEP 3: Calculate Mean & SD
# -----------------------------------

summary_data <- data %>%
  group_by(fertilizer) %>%
  summarise(
    mean_height = mean(height),
    sd_height = sd(height)
  )

print(summary_data)

# -----------------------------------
# STEP 4: Create Bar Chart with SD
# -----------------------------------

plot <- ggplot(summary_data, aes(x = fertilizer, y = mean_height, fill = fertilizer)) +

  # Bars with BLACK BORDER
  geom_bar(
    stat = "identity",
    width = 0.6,
    color = "black"
  ) +

  # Add Standard Deviation (Error Bars)
  geom_errorbar(
    aes(
      ymin = mean_height - sd_height,
      ymax = mean_height + sd_height
    ),
    width = 0.2,
    linewidth = 0.8
  ) +

  # Labels
  labs(
    title = "Bar Chart with Standard Deviation",
    x = "Fertilizer Type",
    y = "Mean Plant Height (cm)"
  ) +

  # Theme and centered title
  theme_minimal() +
  theme(
    legend.position = "none",
    plot.title = element_text(hjust = 0.5)
  ) +

  # Color palette
  scale_fill_brewer(palette = "Set2")

# Display plot
print(plot)