plot_means {Kifidi} | R Documentation |
Plot Means
Description
This function plots the means of a summary data frame with optional error bars.
Usage
plot_means(summary_df,
main_title = "Mean Values by Group",
ylab = NULL,
xlab = NULL,
bar_color = "skyblue",
error_bar_color = "red",
bar_width = 0.7,
error_bar_length = 0.1,
axes = TRUE,
space = NULL,
density = NULL,
angle = 45,
col = NULL,
names_arg = NULL,
xlab_custom = NULL,
ylab_custom = NULL,
ann = TRUE,
xlim = NULL,
ylim = NULL,
xaxt = "s",
las = NULL)
Arguments
summary_df |
A summary data frame containing the means and standard errors for each group. |
main_title |
Main title for the plot. Default is "Mean Values by Group". |
ylab |
Label for the y-axis. |
xlab |
Label for the x-axis. |
bar_color |
Color for the bars. Default is "skyblue". |
error_bar_color |
Color for the error bars. Default is "red". |
bar_width |
Width of the bars. Default is 0.7. |
error_bar_length |
Length of the error bars. Default is 0.1. |
axes |
Logical value indicating whether to draw axes on the plot. Default is TRUE. |
space |
Spacing between bars. |
density |
Density of shading lines. |
angle |
Angle of shading lines. |
col |
Color of shading lines. |
names_arg |
Vector of names for the x-axis. |
xlab_custom |
Custom label for the x-axis. Default is "Groups". |
ylab_custom |
Custom label for the y-axis. Default is "Mean". |
ann |
Logical value indicating whether to draw annotations on the plot. Default is TRUE. |
xlim |
Limits for the x-axis. |
ylim |
Limits for the y-axis. |
xaxt |
Type of x-axis labeling. |
las |
Style of axis labels. |
Details
If the summary data frame contains two grouping variables (Group1 and Group2), they will be combined to form the x-axis labels.
Value
This function produces a bar plot with optional error bars.
Note
Additional notes can be added here.
Author(s)
Oswald Omuron
References
Please refer to the documentation of the barplot
and arrows
functions in the base R package.
See Also
The summary
function for creating summary data frames.
Examples
# Example data
example_data <- c(
445, 372, 284, 247, 328, 98.8, 108.7, 100.8, 123.6, 129.9, 133.3,
130.1, 123.1, 186.6, 215, 19.4, 19.3, 27.8, 26, 22, 30.9, 19.8,
16.5, 20.2, 31, 21.1, 16.5, 19.7, 18.9, 27, 161.8, 117, 94.6, 97.5,
142.7, 109.9, 118.3, 111.4, 96.5, 109, 114.1, 114.9, 101.2, 112.7,
111.1, 194.8, 169.9, 159.1, 100.8, 130.8, 93.6, 105.7, 178.4, 203,
172.2, 127.3, 128.3, 110.9, 124.1, 179.1, 293, 197.5, 139.1, 98.1,
84.6, 81.4, 87.2, 71.1, 70.3, 120.4, 194.5, 167.5, 121, 86.5, 81.7
)
example_group1 <- c(
rep("Palm", 15), rep("Papyrus", 10), rep("Typha", 15),
rep("Eucalyptus", 15), rep("Rice farm", 20)
)
example_group2 <- rep(c(50, 40, 30, 20, 10), 15)
# Create dataframe
example_df <- data.frame(
Vegetation_types = example_group1,
Depth_revised = example_group2,
EC_uS_cm = example_data
)
# Summarize by one grouping variable
summary_one_group <- summarize_data(
example_df$EC_uS_cm,
example_df$Vegetation_types
)
print(summary_one_group)
# Summarize by two grouping variables
summary_two_groups <- summarize_data(
example_df$EC_uS_cm,
example_df$Vegetation_types,
example_df$Depth_revised
)
print(summary_two_groups)
# Plotting the summarized data
plot_means(summary_two_groups, ylim=c(0,350), las=2,
space = c(0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0)
)