r-Kifidi

Summary Table and Means Plots.

Kifidi

Note: Don’t forget to activate the Kifidi library before using any functions from this package:

library(Kifidi)

1. Overview

The Kifidi package provides tools for summarizing and analyzing environmental data based on grouping variables. It is designed to facilitate statistical summaries of data sets containing numeric measurements and associated categorical grouping factors. The package is particularly useful for environmental and ecological studies where data is often collected across different categories, such as vegetation types, depth, or sampling locations.

2. Key Functions

2.1 summarize_data

Description:

The summarize_data function provides statistical summaries (mean, standard deviation, sample size, etc.) of a numeric column grouped by one or two categorical variables.

Usage:

summarize_data(column_data, group_var1, group_var2 = NULL)

Arguments:

• column_data: A numeric vector containing the data to be summarized.
• group_var1: A factor or vector to group the data by (e.g., vegetation type).
• group_var2: (Optional) A second factor or vector to group the data by (e.g., depth).

Output: A data frame containing the following columns:

• Group1: The first grouping variable (values from group_var1).
• Group2: The second grouping variable (values from group_var2), if provided.
• Mean: The mean of the data for each group.
• SD: The standard deviation for each group.
• N: The sample size for each group.
• Min: The minimum value for each group.
• Max: The maximum value for each group.
• Median: The median value for each group.
• SE: The standard error of the mean for each group.

  # 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)

2.2 plot_means The plot_means function creates a bar plot of means with optional error bars but based on the output of the first function: the summarize_data function.

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.

Example:

# 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))

3. Why Kifidi?

Kifidi stands out from other data summarization and visualization packages for the following reasons:

Specialization for Environmental and Ecological Data: While other general-purpose summarization and plotting tools are available in R, Kifidi is specifically tailored to handle environmental and ecological datasets. Its functions are designed with researchers and practitioners in these fields in mind, streamlining their workflows and reducing the need for complex code.

• Simplified Workflow: Kifidi focuses on making data processing easier by providing just two intuitive functions that cover the most common tasks in data analysis — summarizing datasets and plotting means. This makes it ideal for users who want a straightforward tool without needing to learn extensive syntax or customize numerous options.

• Error Bars Support: The plot_means() function provides built-in support for visualizing uncertainty through error bars, allowing users to add standard errors or other uncertainty metrics directly to their plots without additional steps.

• Quick Summarization to the Global Environment: The summarize_data() function quickly processes data and assigns a summary object to the global environment, making it easily accessible for further analysis or visualization. This feature facilitates a fast, iterative workflow where users can immediately work with summarized results.

• Open Source and Extensible: Kifidi is hosted on GitHub, allowing users to contribute or extend the package as needed. It is licensed under GPL-3, ensuring that the community can continue to use, modify, and share the tool.

In summary, Kifidi is ideal for users working in environmental and ecological research who need a simple, efficient solution for summarizing and visualizing their data. The package’s targeted functionality and ease of use make it a valuable tool for researchers who prioritize efficiency and simplicity in their workflows.