How can I make one legend for crop type and nitrogen treatment, as shown here [![enter image description here][1]][1]? I wanted to make solid line with geom_point for high Nitrogen and dahsed line with geom_point for low nitrogen.
Here is the data
library(dplyr)
library(ggplot2)
# Create a dummy dataset
set.seed(123)
dummy_data <- data.frame(
Rotation_Group = rep("Dummy Rotation Group", 32),
Sp = rep(c("B/B", "B/Bd", "C/B", "P/B"), each = 8), # Crop types
Nitrogen = rep(c("High N", "Low N"), times = 16), # Nitrogen treatment
Date = rep(seq(as.Date("2024-03-12"), by = "months", length.out = 4), each = 2, times = 4), # Sampling dates
min_n_kgha = c(
# Mineral N data (random numbers for the example)
sample(50:120, 8), sample(30:80, 8),
sample(50:100, 8), sample(30:70, 8)
)
)
plot_rotation_group <- function(rotation_group_label) {
# Filter data for the specific rotation group
mn_aggregated <- dummy_data %>%
filter(Rotation_Group == rotation_group_label) %>%
group_by(Sp, Nitrogen, Date) %>%
summarise(mean_min_n_kgha = mean(min_n_kgha, na.rm = TRUE), .groups = "drop")
# Convert the Date to a factor for equally spaced categorical labels
mn_aggregated$Date_Factor <- factor(mn_aggregated$Date, levels = unique(mn_aggregated$Date))
# Generate the plot for this specific group
ggplot(mn_aggregated, aes(x = Date_Factor, y = mean_min_n_kgha, colour = Sp, group = interaction(Sp, Nitrogen))) +
geom_point(size = 4) + # Add points for each data observation
geom_smooth(aes(linetype = Nitrogen), linewidth = 1.5, method = 'loess', formula = 'y ~ x', se = FALSE) + # Add smooth trend lines without confidence intervals
# Line type: solid for High N, dashed for Low N
scale_linetype_manual(values = c("High N" = "solid", "Low N" = "dashed")) +
# Use scale_x_discrete to space the dates equally and add some padding on either side
scale_x_discrete(labels = format(unique(mn_aggregated$Date), "%b %d"),
expand = expansion(mult = c(0.1, 0.1))) + # Add padding to the x-axis
# Ensure y-axis starts at 0
scale_y_continuous(limits = c(0, NA), breaks = seq(0, 500, 20)) + # Force the y-axis to start at 0, let max scale automatically
labs(x = "Date of sampling", y = "Mineral N (kg/ha)", title = rotation_group_label) + # Custom title for the plot
# Adjust theme for more space and better readability
theme_minimal() +
theme(plot.margin = unit(c(1, 1, 1, 1), "cm")) + # Adjust margins around the plot
# Separate color and linetype legends with extended line length for linetype
guides(
colour = guide_legend(
title = "Crop Type", # Legend for different species/crops
override.aes = list(size = 4)
),
linetype = guide_legend(
title = "Nitrogen Treatment", # Separate legend for High N and Low N linetype
override.aes = list(size = 1.5, keywidth = 2) # Extend the line in the linetype legend
)
)
}
# Test the plot with the dummy data
plot_rotation_group("Dummy Rotation Group")```
[![enter image description here][2]][2]
[1]: https://i.sstatic.net/2C3syBM6.png
[2]: https://i.sstatic.net/Z4KdEuHm.png
One option would be to map the “interaction” of both variables on color and linetype, then use corresponding color and linetype palettes:
library(tidyverse)
plot_rotation_group <- function(rotation_group_label) {
# Filter data for the specific rotation group
mn_aggregated <- dummy_data %>%
filter(Rotation_Group == rotation_group_label) %>%
group_by(Sp, Nitrogen, Date) %>%
summarise(mean_min_n_kgha = mean(min_n_kgha, na.rm = TRUE), .groups = "drop")
# Convert the Date to a factor for equally spaced categorical labels
mn_aggregated$Date_Factor <- factor(mn_aggregated$Date, levels = unique(mn_aggregated$Date))
pal_color <- mn_aggregated |>
distinct(Sp) |>
mutate(color = scales::hue_pal()(n()))
pal_lty <- c("High N" = "solid", "Low N" = "dashed") |>
tibble::enframe("Nitrogen", "linetype")
pals <- mn_aggregated |>
distinct(Sp, Nitrogen) |>
left_join(pal_color) |>
left_join(pal_lty) |>
unite(category, Sp, Nitrogen, sep = " ")
pal_color <- pals |>
select(category, color) |>
tibble::deframe()
pal_lty <- pals |>
select(category, linetype) |>
tibble::deframe()
# Generate the plot for this specific group
ggplot(mn_aggregated, aes(
x = Date_Factor, y = mean_min_n_kgha,
colour = paste(Sp, Nitrogen),
linetype = paste(Sp, Nitrogen)
)) +
geom_point(size = 4) + # Add points for each data observation
geom_smooth(
aes(group = interaction(Sp, Nitrogen)),
linewidth = 1.5,
method = "loess", formula = "y ~ x", se = FALSE
) + # Add smooth trend lines without confidence intervals
scale_linetype_manual(
values = pal_lty
) +
scale_color_manual(
values = pal_color
) +
# Use scale_x_discrete to space the dates equally and add some padding on either side
scale_x_discrete(
labels = format(unique(mn_aggregated$Date), "%b %d"),
expand = expansion(mult = c(0.1, 0.1))
) + # Add padding to the x-axis
# Ensure y-axis starts at 0
scale_y_continuous(limits = c(0, NA), breaks = seq(0, 500, 20)) + # Force the y-axis to start at 0, let max scale automatically
labs(x = "Date of sampling", y = "Mineral N (kg/ha)", title = rotation_group_label) + # Custom title for the plot
# Adjust theme for more space and better readability
theme_minimal() +
theme(
plot.margin = unit(c(1, 1, 1, 1), "cm"),
legend.key.width = unit(2, "cm")
) + # Adjust margins around the plot
# Separate color and linetype legends with extended line length for linetype
guides(
colour = guide_legend(
title = "Crop Type and Nitrogen Treatment",
override.aes = list(size = 4, linewidth = 1.5, keywidth = 2)
),
linetype = guide_legend(
title = "Crop Type and Nitrogen Treatment"
#override.aes = list(size = 4, linewidth = 1.5, keywidth = 2)
)
)
}
# Test the plot with the dummy data
plot_rotation_group("Dummy Rotation Group")
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