Type: Package
Title: Summary Table and Means Plots
Version: 0.1.0
Maintainer: Oswald Omuron <oswaldomuron@gmail.com>
Description: Optimized for handling complex datasets in environmental and ecological research, this package offers functionality that is not fully met by general-purpose packages. It provides two key functions, 'summarize_data()', which summarizes datasets, and 'plot_means()', which creates plots with error bars. The 'plot_means()' function incorporates error bars by default, allowing quick visualization of uncertainties, crucial in ecological studies. It also streamlines workflows for grouped datasets (e.g., by species or treatment), making it particularly user-friendly and reducing the complexity and time required for data summarization and visualization.
License: GPL-3
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2024-10-09 06:37:02 UTC; Oswald
Author: Oswald Omuron [aut, cre]
Repository: CRAN
Date/Publication: 2024-10-11 08:40:06 UTC

Kifidi package by Oswald Omuron

Description

Kifidi v.0.1.0

Details

Package: Kifidi
Type: Package
Title: Kifidi v.0.1.0
Version: 0.1.0
Author: Oswald Omuron
Maintainer: Oswald Omuron <oswaldomuron@gmail.com>
Description: An overview of how to use the package, including the most important functions under See Also below.
License: GPL-3

Author(s)

Oswald Omuron

Maintainer: Oswald Omuron <oswaldomuron@gmail.com>

References

https://github.com/OswaldOmuron/Kifidi

See Also

Optional links to other man pages, e.g. summarize_data, plot_means

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)
  )

Summarize Data by Groups

Description

This function summarizes the provided data column by one or two grouping variables. It calculates the mean, standard deviation, sample size, minimum, maximum, median, and standard error.

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.

group_var2

An optional second factor or vector to group the data by.

Details

If only one grouping variable is provided, the function will summarize the data by that variable. If two grouping variables are provided, it will summarize the data by both variables.

Value

A data frame with the following columns:

Group1

The first grouping variable (from group_var1).

Group2

The second grouping variable (from group_var2), if provided.

Mean

The mean of the column_data for each group.

SD

The standard deviation of the column_data for each group.

N

The sample size for each group.

Min

The minimum value of the column_data for each group.

Max

The maximum value of the column_data for each group.

Median

The median value of the column_data for each group.

SE

The standard error of the mean for each group.

Output

A data frame with the above columns.

Note

The grouping variables and the data column can be of different lengths.

Author(s)

Oswald Omuron

References

No references available.

See Also

aggregate, summarize_data

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)