Package 'metaDigitise' reference manual

Title:	Extract and Summarise Data from Published Figures
Description:	High-throughput, flexible and reproducible extraction of data from figures in primary research papers. metaDigitise() can extract data and / or automatically calculate summary statistics for users from box plots, bar plots (e.g., mean and errors), scatter plots and histograms.
Authors:	Joel Pick [aut], Shinichi Nakagawa [aut], Daniel Noble [aut, cre]
Maintainer:	Daniel Noble <[email protected]>
License:	GPL (>=2)
Version:	1.0.1
Built:	2024-11-12 03:55:26 UTC
Source:	https://github.com/daniel1noble/metadigitise

cal_coords

Description

Prompts user to enter axis coordinates, and their values. Modified from the digitize package

Usage

cal_coords(plot_type, cex, ...)
cal_coords(plot_type, cex, ...)

Arguments

`plot_type`	plot type
`cex`	size of points
`...`	further arguments passed to or from other methods.

calibrate

Description

Converts x and y coordinates from original plot coords to actual coords using previous identified coordinates. Modified from digitise package

Usage

calibrate(raw_data, calpoints, point_vals, log_axes, ...)
calibrate(raw_data, calpoints, point_vals, log_axes, ...)

Arguments

`raw_data`	The raw data
`calpoints`	The calibration points
`point_vals`	The point values
`log_axes`	whether x or y is logged
`...`	further arguments passed to or from other methods

cat_matrix

Description

prints a vector as a number list of items with a certain number of columns

Usage

cat_matrix(x, cols)
cat_matrix(x, cols)

Arguments

`x`	vector
`cols`	number of columns

CI95_to_sd

Description

Transforms symmetrical confidence interval to standard deviation

Usage

CI95_to_sd(CI, n)
CI95_to_sd(CI, n)

Arguments

`CI`	Interval difference from the mean
`n`	Sample Size

Value

Returns vector of standard deviations

Author(s)

Joel Pick

Examples

CI95_to_sd(CI = 2, n = 10)
CI95_to_sd(CI = 2, n = 10)

convert_group_data

Description

Converts, pre-calibrated points clicked into a meaningful dataframe

Usage

convert_group_data(cal_data, plot_type)
convert_group_data(cal_data, plot_type)

Arguments

`cal_data`	Calibrated data
`plot_type`	The type of plot

dir_details

Description

Function will gather important directory details about calibration files and figures needed for processing

Usage

dir_details(dir)
dir_details(dir)

Arguments

dir

the path name to the directory / folder where the files are located

Author(s)

Daniel Noble - [email protected]

Examples

 
# temporary directory
tmp_dir <- tempdir()

setup_calibration_dir(paste0(tmp_dir, "/"))

# Simulate data
set.seed(103)
x <- rnorm(20,0,1)
y <- rnorm(20,0,1)
means <- c(mean(x),mean(y))
ses <- c(sd(x)/sqrt(length(x))*1.96, sd(y)/sqrt(length(y))*1.96)

#Generate mock figures
png(filename = paste0(tmp_dir,"/mean_error.png"), width = 480, height = 480)
plot(means, ylim = c(min(means-ses)-0.1,max(means+ses)+0.1), xlim=c(0.5,2.5), 
xaxt="n", pch=19, cex=2, ylab="Variable +/- SE", xlab="Treatment", main="Mean Error")
arrows(1:length(means),means+ses, 1:length(means), means-ses, code=3, angle=90, length=0.1)
axis(1,1:length(means),names(means))
dev.off()
png(filename = paste0(tmp_dir, "/boxplot.png"), width = 480, height = 480)
boxplot(x,y, main="Boxplot")
dev.off()
png(filename = paste0(tmp_dir, "/histogram.png"),width = 480, height = 480)
hist(c(x,y), xlab= "variable", main="Histogram")
dev.off()
png(filename = paste0(tmp_dir, "/scatterplot.png"), width = 480, height = 480)
plot(x,y, main="Scatterplot")
dev.off()

#Obtain details on directory structure that are used for metaDigitise
data <- dir_details(tmp_dir)

# temporary directory
tmp_dir <- tempdir()

setup_calibration_dir(paste0(tmp_dir, "/"))

# Simulate data
set.seed(103)
x <- rnorm(20,0,1)
y <- rnorm(20,0,1)
means <- c(mean(x),mean(y))
ses <- c(sd(x)/sqrt(length(x))*1.96, sd(y)/sqrt(length(y))*1.96)

#Generate mock figures
png(filename = paste0(tmp_dir,"/mean_error.png"), width = 480, height = 480)
plot(means, ylim = c(min(means-ses)-0.1,max(means+ses)+0.1), xlim=c(0.5,2.5), 
xaxt="n", pch=19, cex=2, ylab="Variable +/- SE", xlab="Treatment", main="Mean Error")
arrows(1:length(means),means+ses, 1:length(means), means-ses, code=3, angle=90, length=0.1)
axis(1,1:length(means),names(means))
dev.off()
png(filename = paste0(tmp_dir, "/boxplot.png"), width = 480, height = 480)
boxplot(x,y, main="Boxplot")
dev.off()
png(filename = paste0(tmp_dir, "/histogram.png"),width = 480, height = 480)
hist(c(x,y), xlab= "variable", main="Histogram")
dev.off()
png(filename = paste0(tmp_dir, "/scatterplot.png"), width = 480, height = 480)
plot(x,y, main="Scatterplot")
dev.off()

#Obtain details on directory structure that are used for metaDigitise
data <- dir_details(tmp_dir)

edit_group

Description

Edit group points in scatterplots

Usage

edit_group(raw_data, group_id, calpoints, cex, ...)
edit_group(raw_data, group_id, calpoints, cex, ...)

Arguments

`raw_data`	data
`group_id`	group_id
`calpoints`	The calibration points
`cex`	point size
`...`	other functions to pass to internal_redraw

edit_metaDigitise

Description

Function for editing previous data extraction through 'metaDigitise'

Usage

edit_metaDigitise(object)
edit_metaDigitise(object)

Arguments

object

an R object of class ‘metaDigitise’

Value

Data.frame

Author(s)

Joel Pick

error_to_sd

Description

Transforms error to standard deviation

Usage

error_to_sd(error, n, error_type = c("se", "CI95", "sd", NA))
error_to_sd(error, n, error_type = c("se", "CI95", "sd", NA))

Arguments

`error`	some form of error
`n`	Sample Size
`error_type`	type of error measured

Value

Returns vector of standard errors

Author(s)

Joel Pick

extract_digitised

Description

Function for extracting the data from a metaDigitise list and creating either summary data or a list of the raw data.

Usage

extract_digitised(list, summary = TRUE)
extract_digitised(list, summary = TRUE)

Arguments

`list`	A list of objects returned from metaDigitise
`summary`	A logical 'TRUE' or 'FALSE' indicating whether metaDigitise should print summary statistics from each figure and group.

Value

The function will return a data frame with the data across all the digitised files

Function will get file information from the directory and the calibration files. It will also exclude files that have already been processed, as is judged by the match between file names in the calibration folder and the imported details object

Usage

get_notDone_file_details(dir)
get_notDone_file_details(dir)

Arguments

dir

Path name to the directory / folder where the figure files are located.

Value

Returns a list containing details on the images names and their paths, the calibration file names (or files already completed) as well as the paths to these files.

Author(s)

Daniel Noble - [email protected]

Examples


# temporary directory
tmp_dir <- tempdir()

# Simulate data
set.seed(103)
x <- rnorm(20,0,1)
y <- rnorm(20,0,1)
means <- c(mean(x),mean(y))
ses <- c(sd(x)/sqrt(length(x))*1.96, sd(y)/sqrt(length(y))*1.96)

#Generate mock figures
png(filename = paste0(tmp_dir,"/mean_error.png"), width = 480, height = 480)
plot(means, ylim = c(min(means-ses)-0.1,max(means+ses)+0.1), xlim=c(0.5,2.5), 
xaxt="n", pch=19, cex=2, ylab="Variable +/- SE", xlab="Treatment", main="Mean Error")
arrows(1:length(means),means+ses, 1:length(means), means-ses, code=3, angle=90, length=0.1)
axis(1,1:length(means),names(means))
dev.off()
png(filename = paste0(tmp_dir, "/boxplot.png"), width = 480, height = 480)
boxplot(x,y, main="Boxplot")
dev.off()
png(filename = paste0(tmp_dir, "/histogram.png"),width = 480, height = 480)
hist(c(x,y), xlab= "variable", main="Histogram")
dev.off()
png(filename = paste0(tmp_dir, "/scatterplot.png"), width = 480, height = 480)
plot(x,y, main="Scatterplot")
dev.off()

#Obtain file names that are incomplete within the tmp directory
data <- get_notDone_file_details(tmp_dir)

# temporary directory
tmp_dir <- tempdir()

# Simulate data
set.seed(103)
x <- rnorm(20,0,1)
y <- rnorm(20,0,1)
means <- c(mean(x),mean(y))
ses <- c(sd(x)/sqrt(length(x))*1.96, sd(y)/sqrt(length(y))*1.96)

#Generate mock figures
png(filename = paste0(tmp_dir,"/mean_error.png"), width = 480, height = 480)
plot(means, ylim = c(min(means-ses)-0.1,max(means+ses)+0.1), xlim=c(0.5,2.5), 
xaxt="n", pch=19, cex=2, ylab="Variable +/- SE", xlab="Treatment", main="Mean Error")
arrows(1:length(means),means+ses, 1:length(means), means-ses, code=3, angle=90, length=0.1)
axis(1,1:length(means),names(means))
dev.off()
png(filename = paste0(tmp_dir, "/boxplot.png"), width = 480, height = 480)
boxplot(x,y, main="Boxplot")
dev.off()
png(filename = paste0(tmp_dir, "/histogram.png"),width = 480, height = 480)
hist(c(x,y), xlab= "variable", main="Histogram")
dev.off()
png(filename = paste0(tmp_dir, "/scatterplot.png"), width = 480, height = 480)
plot(x,y, main="Scatterplot")
dev.off()

#Obtain file names that are incomplete within the tmp directory
data <- get_notDone_file_details(tmp_dir)

getExtracted

Description

Extracts data from a directory that has been previously digitised using metaDigitise()

Usage

getExtracted(dir, summary = TRUE)
getExtracted(dir, summary = TRUE)

Arguments

`dir`	The directory where figures have already been digitised. There
`summary`	Logical indicating whether summarised (default) or calibrated data should be returned.

Value

Returns a data frame (summary = TRUE) or a list with slots for each plot type (summary = FALSE)

Examples


# Make some mock metaDigitise object
	mock_metaDig <- list(
			image_file = "./image.png",
			flip=FALSE,
			rotate=0,
			plot_type="mean_error",
			variable="y",
			calpoints = data.frame(x=c(0,0),y=c(0,100)), 
			point_vals = c(1,2), 
			entered_N=TRUE,
			raw_data = data.frame(id=rep("control",2), 
						x=c(60,60), 
						y=c(75,50), 
						n=rep(20,2)),
						knownN = NULL,
						error_type="sd", 
					processed_data=data.frame(
						id=as.factor("control"),
						mean=1.5, 
						error=0.25, 
						n=20, 	
						variable="y", 
						stringsAsFactors = FALSE)	
					)
class(mock_metaDig) <- 'metaDigitise'

# write image file to tmpdir()
		dir <- tempdir()

# Setup directory as it would be if digitised images existed		
		setup_calibration_dir(dir)

# Save the digitised data
		saveRDS(mock_metaDig, file = paste0(dir, "/caldat/", "image"))

#metaDigitise figures
data <- getExtracted(dir)

# Make some mock metaDigitise object
	mock_metaDig <- list(
			image_file = "./image.png",
			flip=FALSE,
			rotate=0,
			plot_type="mean_error",
			variable="y",
			calpoints = data.frame(x=c(0,0),y=c(0,100)), 
			point_vals = c(1,2), 
			entered_N=TRUE,
			raw_data = data.frame(id=rep("control",2), 
						x=c(60,60), 
						y=c(75,50), 
						n=rep(20,2)),
						knownN = NULL,
						error_type="sd", 
					processed_data=data.frame(
						id=as.factor("control"),
						mean=1.5, 
						error=0.25, 
						n=20, 	
						variable="y", 
						stringsAsFactors = FALSE)	
					)
class(mock_metaDig) <- 'metaDigitise'

# write image file to tmpdir()
		dir <- tempdir()

# Setup directory as it would be if digitised images existed		
		setup_calibration_dir(dir)

# Save the digitised data
		saveRDS(mock_metaDig, file = paste0(dir, "/caldat/", "image"))

#metaDigitise figures
data <- getExtracted(dir)

getVals

Description

Gets values needed to calibrate axis coordinated. Modified from the digitize package

Usage

getVals(calpoints, ...)
getVals(calpoints, ...)

Arguments

`calpoints`	Calibration points
`...`	further arguments passed to or from other methods.

grandMean

Description

Pooled mean of a set of group means

Usage

grandMean(mean, n)
grandMean(mean, n)

Arguments

`mean`	Mean
`n`	Sample size

Value

Returns vector of pooled mean

Author(s)

Joel Pick

Examples

grandMean(mean = 10, n = 30)
grandMean(mean = 10, n = 30)

grandSD

Description

Pooled standard deviation of a set of groups

Usage

grandSD(mean, sd, n, equal = FALSE)
grandSD(mean, sd, n, equal = FALSE)

Arguments

`mean`	Mean
`sd`	standard deviation
`n`	Sample size
`equal`	Logical: Whether to calculate pooled SD assuming groups have the same means (TRUE) or different means (FALSE)

Value

Returns vector of pooled mean

Author(s)

Joel Pick

Examples

grandSD(mean = 10, sd = 3, n = 40)
grandSD(mean = 10, sd = 3, n = 40)

group_scatter_extract

Description

Extraction of data from scatterplots

Usage

group_scatter_extract(
  edit = FALSE,
  raw_data = data.frame(stringsAsFactors = TRUE),
  cex,
  ...
)
group_scatter_extract(
  edit = FALSE,
  raw_data = data.frame(stringsAsFactors = TRUE),
  cex,
  ...
)

Arguments

`edit`	logical; whether in edit mode
`raw_data`	raw data
`cex`	point size
`...`	arguments passed to internal_redraw

histogram_extract

Description

Extraction of data from histograms

Usage

histogram_extract(edit = FALSE, raw_data = data.frame(), calpoints, cex, ...)
histogram_extract(edit = FALSE, raw_data = data.frame(), calpoints, cex, ...)

Arguments

`edit`	logical; whether in edit mode
`raw_data`	raw data
`calpoints`	The calibration points
`cex`	point size
`...`	arguments to pass to internal_redraw

import_menu

Description

Imports metaDigitise() calibration files from a directory that is partially or fully digitised already

Usage

import_menu(dir, summary)
import_menu(dir, summary)

Arguments

`dir`	The directory where figures have already been digitised
`summary`	Logical indicating whether the imported data should be returned in summarised or processed form.

Value

Returns a list (summary = FALSE) or data frame (summary = TRUE)

import_metaDigitise

Description

Imports metaDigitise() calibration files from a directory that is partially or fully digitised already

Usage

import_metaDigitise(dir, summary)
import_metaDigitise(dir, summary)

Arguments

`dir`	The directory where figures have already been digitised
`summary`	Logical indicating whether the imported data should be returned in summarised form ('TRUE') or not ('FALSE')

Value

Returns a list (summary = FALSE) or data frame (summary = TRUE)

Author(s)

Daniel Noble - [email protected]

internal_digitise

Description

Extracts points from a single figure and processes data

Usage

internal_digitise(image_file, plot_type = NULL, cex)
internal_digitise(image_file, plot_type = NULL, cex)

Arguments

`image_file`	Image file
`plot_type`	Type of plot from "mean_error","boxplot","scatterplot" or"histogram". Function will prompt if not entered by user.
`cex`	point size for replotting

Value

List of user inputs and transformed data from digitisation

Author(s)

Joel Pick

internal_redraw

Description

Redraws figure and extraction data

Usage

internal_redraw(
  image_file,
  flip = FALSE,
  rotate = 0,
  plot_type = NULL,
  variable = NULL,
  cex = NULL,
  calpoints = NULL,
  point_vals = NULL,
  raw_data = NULL,
  rotation = TRUE,
  calibration = TRUE,
  points = TRUE,
  ...
)
internal_redraw(
  image_file,
  flip = FALSE,
  rotate = 0,
  plot_type = NULL,
  variable = NULL,
  cex = NULL,
  calpoints = NULL,
  point_vals = NULL,
  raw_data = NULL,
  rotation = TRUE,
  calibration = TRUE,
  points = TRUE,
  ...
)

Arguments

`image_file`	Image filename
`flip`	whether to flip figure
`rotate`	how much to rotate figure
`plot_type`	plot_type
`variable`	variable
`cex`	relative size of points and text
`calpoints`	The calibration points
`point_vals`	The point values
`raw_data`	The raw data
`rotation`	logical, should figure be rotated
`calibration`	logical, should calibration be redrawn
`points`	logical, should points be redrawn
`...`	further arguments passed to or from other methods.

is.even

Description

Checks whether a integer is even

Usage

is.even(x)
is.even(x)

Arguments

`x`	integer value

Value

Logical (TRUE or FALSE) indicating whether value is an even number or not

is.wholenumber

Description

Checks whether value is a whole number

Usage

is.wholenumber(x, tol = .Machine$double.eps^0.5)
is.wholenumber(x, tol = .Machine$double.eps^0.5)

Arguments

`x`	object to be tested
`tol`	tolerance

Value

Logical value (TRUE or FALSE)

isNumeric

Description

Checks whether a character is a number

Usage

isNumeric(x)
isNumeric(x)

Arguments

`x`	character to be tested

Value

Logical (TRUE or FALSE) indicating whether value is numeric or not

knownN

Description

prints a vector as a number list of items with a certain number of columns

Usage

knownN(plot_type, processed_data, knownN = NULL, ...)
knownN(plot_type, processed_data, knownN = NULL, ...)

Arguments

`plot_type`	plot type
`processed_data`	raw_data
`knownN`	previously entered N
`...`	arguments from other calls

load_metaDigitise

Description

Loads metaDigitise calibration / data files from a directory containing a set of figures that are partially or fully digitised already.

Usage

load_metaDigitise(doneCalFiles, names)
load_metaDigitise(doneCalFiles, names)

Arguments

`doneCalFiles`	The metaDigitise objects that have already been completed in the directory
`names`	The names of the finished metaDigitise objects

Value

Returns a list of metaDigitised objects that have already been completed

Author(s)

Daniel Noble - [email protected]

locator_mD

Description

Wrapper function for locator, with more control over point size etc

Usage

locator_mD(nPoints = 1, line = TRUE, cex = 1, col = "red", ...)
locator_mD(nPoints = 1, line = TRUE, cex = 1, col = "red", ...)

Arguments

`nPoints`	number of points in a sequence
`line`	logical; plot lines between points
`cex`	size of points
`col`	colour of points
`...`	further arguments passed to or from other methods.

Value

Plots clicked points, and returns their x.y coordinates as a data.frame

getVals

Description

Ask user for information about whether axes are on log scale

Usage

logAxes(...)
logAxes(...)

Arguments

...

further arguments passed to or from other methods.

MB_extract

Description

Extraction of data from boxplots of mean_error plots, from multiple groups

Usage

MB_extract(
  edit = FALSE,
  plot_type,
  entered_N,
  raw_data = data.frame(stringsAsFactors = TRUE),
  cex,
  ...
)
MB_extract(
  edit = FALSE,
  plot_type,
  entered_N,
  raw_data = data.frame(stringsAsFactors = TRUE),
  cex,
  ...
)

Arguments

`edit`	logical; whether in edit mode
`plot_type`	The type of plot
`entered_N`	ask for sample sizes?
`raw_data`	raw data
`cex`	point size
`...`	further arguments to MB_extract

metaDigitise

Description

Single or batch processing of figures with .png, .jpg, .tiff, .pdf extensions within a set directory. metaDigitise() consolidates the data and exports the data for each image and image type. It can also summarise the data, provide the raw data (if scatterplots) and automatically imports previously finished data and merges it with newly digitised data. metaDigitise() also allows users to check their calibration along with editing previous digitisations.

Usage

metaDigitise(dir, summary = TRUE, cex = 1)
metaDigitise(dir, summary = TRUE, cex = 1)

Arguments

`dir`	the path name to the directory / folder where the files are located
`summary`	whether the digitised data should be returned as a summary (TRUE) or as a concatenated list of similar types.
`cex`	relative size of points and text in replotting of digitisation. Default is 1.

Details

metaDigitise() can be used on a directory with a whole host of different figure types (mean and error, scatter plots, box plots and histograms) and file types (.jpeg, .png, .tiff, .pdf). There are three major options provided to users:

If the "1: Process new images" option is chosen, it will automatically cycle through all figures not already completed within a directory in order, prompting the user for specific information as they go. At the end of each figure users will be asked if they would like to continue or not, providing flexibility to leave a job should should they need to. As figures are digitised it will automatically write metaDigitise() object files (in .RDS format containing processed and calibration data along with directory and file details), into a special caldat/ folder within the directory. Importantly, as new files are added to a directory that has already been "completed", metaDigitise() will recognize these unfinished files and only cycle through the digitisation of these new files. This easily allows users to pick up from where they left off. It will also automatically re-merge completed figure with any newly digitised figures at the end of this process keeping everything together throughout the process.

If the "2: Import existing data" is chosen, all existing files that have already been digitised will be automatically imported from the given directory.

Finally, metaDigitise is built for ease of editing and reproducibility in mind. Hence, if "3: Edit existing data" is chosen by the user then users will have the options to "1: Cycle through images" (that are complete), overlaying digitisations with each figure and asking whether they would like to edit each figure or "2: Choose specific file to edit" allowing editing for a specific file. Here a list of all files are provided and the user simply needs to pick the one in the console they would like to view. Alternatively, the "3: Enter previously omitted sample sizes" option allows the user to go back and enter sample sizes that they may not have had on hand at the time of digitisation. This means that, so long as the caldat/ folder along with respective images are maintained, anyone using metaDigitise() can simply import existing digitisations, modify them and fix them. This folder can then be shared with colleagues to allow them to reproduce any data extraction.

Value

A data frame or list containing the raw digitised data or the processed, summary statistics from the digitised data

Author(s)

Joel Pick - [email protected]

Daniel Noble - [email protected]

Examples


# temporary directory
tmp_dir <- tempdir()

# Simulate data
set.seed(103)
x <- rnorm(20,0,1)
y <- rnorm(20,0,1)
means <- c(mean(x),mean(y))
ses <- c(sd(x)/sqrt(length(x))*1.96, sd(y)/sqrt(length(y))*1.96)

#Generate mock figures
png(filename = paste0(tmp_dir,"/mean_error.png"), width = 480, height = 480)
plot(means, ylim = c(min(means-ses)-0.1,max(means+ses)+0.1), xlim=c(0.5,2.5), 
xaxt="n", pch=19, cex=2, ylab="Variable +/- SE", xlab="Treatment", main="Mean Error")
arrows(1:length(means),means+ses, 1:length(means), means-ses, code=3, angle=90, length=0.1)
axis(1,1:length(means),names(means))
dev.off()
png(filename = paste0(tmp_dir, "/boxplot.png"), width = 480, height = 480)
boxplot(x,y, main="Boxplot")
dev.off()
png(filename = paste0(tmp_dir, "/histogram.png"),width = 480, height = 480)
hist(c(x,y), xlab= "variable", main="Histogram")
dev.off()
png(filename = paste0(tmp_dir, "/scatterplot.png"), width = 480, height = 480)
plot(x,y, main="Scatterplot")
dev.off()

#metaDigitise figures
## Not run: 
data <- metaDigitise(tmp_dir)

## End(Not run)

# temporary directory
tmp_dir <- tempdir()

# Simulate data
set.seed(103)
x <- rnorm(20,0,1)
y <- rnorm(20,0,1)
means <- c(mean(x),mean(y))
ses <- c(sd(x)/sqrt(length(x))*1.96, sd(y)/sqrt(length(y))*1.96)

#Generate mock figures
png(filename = paste0(tmp_dir,"/mean_error.png"), width = 480, height = 480)
plot(means, ylim = c(min(means-ses)-0.1,max(means+ses)+0.1), xlim=c(0.5,2.5), 
xaxt="n", pch=19, cex=2, ylab="Variable +/- SE", xlab="Treatment", main="Mean Error")
arrows(1:length(means),means+ses, 1:length(means), means-ses, code=3, angle=90, length=0.1)
axis(1,1:length(means),names(means))
dev.off()
png(filename = paste0(tmp_dir, "/boxplot.png"), width = 480, height = 480)
boxplot(x,y, main="Boxplot")
dev.off()
png(filename = paste0(tmp_dir, "/histogram.png"),width = 480, height = 480)
hist(c(x,y), xlab= "variable", main="Histogram")
dev.off()
png(filename = paste0(tmp_dir, "/scatterplot.png"), width = 480, height = 480)
plot(x,y, main="Scatterplot")
dev.off()

#metaDigitise figures
## Not run: 
data <- metaDigitise(tmp_dir)

## End(Not run)

order_lists

Description

Will re-order the processed data such that similar types of data are organised into a single list defined by their plot type.

Usage

order_lists(list, plot_types)
order_lists(list, plot_types)

Arguments

`list`	The list of metaDigitise objects that have already been finished within the caldat/ folder
`plot_types`	The list of plot types extracted from metaDigitised objects

Value

Returns a list ordered by the plot type

Author(s)

Daniel Noble - [email protected]

plot.metaDigitise

Description

Re-plots figure and extraction data

Usage

## S3 method for class 'metaDigitise'
plot(x, cex = NULL, ...)
## S3 method for class 'metaDigitise'
plot(x, cex = NULL, ...)

Arguments

`x`	an R object of class ‘metaDigitise’
`cex`	size of points
`...`	further arguments passed to or from other methods.

Author(s)

Joel Pick

point_extraction

Description

Extracts or edits point of a digitisation

Usage

point_extraction(object, edit = FALSE)
point_extraction(object, edit = FALSE)

Arguments

`object`	Object
`edit`	Logical (TRUE or FALSE) indicating whether a point would like to be edited

print_cal_instructions

Description

Prints instructions for calibration. Modified from the digitize package

Usage

print_cal_instructions(plot_type, ...)
print_cal_instructions(plot_type, ...)

Arguments

`plot_type`	plot type
`...`	further arguments passed to or from other methods.

print.metaDigitise

Description

Print method for class ‘metaDigitise’

Usage

## S3 method for class 'metaDigitise'
print(x, ...)
## S3 method for class 'metaDigitise'
print(x, ...)

Arguments

`x`	an R object of class ‘metaDigitise’
`...`	further arguments passed to or from other methods.

Author(s)

Joel Pick

process_data

Description

Processes points clicked into a meaningful dataframe

Usage

process_data(object)
process_data(object)

Arguments

object

object from metaDigitise

process_new_files

Description

Batch processes image files within a set directory, consolidates the data and exports the data for each image and type

Usage

process_new_files(dir, summary = TRUE, cex)
process_new_files(dir, summary = TRUE, cex)

Arguments

`dir`	the path name to the directory / folder where the files are located
`summary`	summary = TRUE or FALSE is most relevant as it will print a simple summary statistics that are the same across all files
`cex`	relative size of points and text in replotting of digitisation.

Author(s)

Joel Pick - [email protected]

Daniel Noble - [email protected]

Examples


# temporary directory
tmp_dir <- tempdir()

# Simulate data
set.seed(103)
x <- rnorm(20,0,1)
y <- rnorm(20,0,1)
means <- c(mean(x),mean(y))
ses <- c(sd(x)/sqrt(length(x))*1.96, sd(y)/sqrt(length(y))*1.96)

#Generate mock mean error plot
png(filename = paste0(tmp_dir,"/mean_error.png"), width = 480, height = 480)
plot(means, ylim = c(min(means-ses)-0.1,max(means+ses)+0.1), xlim=c(0.5,2.5), 
xaxt="n", pch=19, cex=2, ylab="Variable +/- SE", xlab="Treatment", main="Mean Error")
arrows(1:length(means),means+ses, 1:length(means), means-ses, code=3, angle=90, length=0.1)
axis(1,1:length(means),names(means))
dev.off()

## Not run: 
#metaDigitise figures
	data <- process_new_files(paste0(tmp_dir, "/"), summary = TRUE, cex = 2)

## End(Not run)

# temporary directory
tmp_dir <- tempdir()

# Simulate data
set.seed(103)
x <- rnorm(20,0,1)
y <- rnorm(20,0,1)
means <- c(mean(x),mean(y))
ses <- c(sd(x)/sqrt(length(x))*1.96, sd(y)/sqrt(length(y))*1.96)

#Generate mock mean error plot
png(filename = paste0(tmp_dir,"/mean_error.png"), width = 480, height = 480)
plot(means, ylim = c(min(means-ses)-0.1,max(means+ses)+0.1), xlim=c(0.5,2.5), 
xaxt="n", pch=19, cex=2, ylab="Variable +/- SE", xlab="Treatment", main="Mean Error")
arrows(1:length(means),means+ses, 1:length(means), means-ses, code=3, angle=90, length=0.1)
axis(1,1:length(means),names(means))
dev.off()

## Not run: 
#metaDigitise figures
	data <- process_new_files(paste0(tmp_dir, "/"), summary = TRUE, cex = 2)

## End(Not run)

range_to_sd

Description

Converts a range to a standard deviation

Usage

range_to_sd(min, max, n)
range_to_sd(min, max, n)

Arguments

`min`	Minimum value
`max`	Maximum value
`n`	Sample size

Value

Returns vector of standard deviation

Author(s)

Joel Pick

Examples

range_to_sd(min = 3, max = 8, n = 40)
range_to_sd(min = 3, max = 8, n = 40)

redraw_calibration

Description

plots calibration data on graph

Usage

redraw_calibration(
  plot_type,
  variable,
  calpoints,
  point_vals,
  image_details,
  cex
)
redraw_calibration(
  plot_type,
  variable,
  calpoints,
  point_vals,
  image_details,
  cex
)

Arguments

`plot_type`	plot_type
`variable`	variable
`calpoints`	The calibration points
`point_vals`	The point values
`image_details`	image_details
`cex`	relative size of points and text

redraw_points

Description

plots clicked data on graph

Usage

redraw_points(plot_type, raw_data, image_details, cex)
redraw_points(plot_type, raw_data, image_details, cex)

Arguments

`plot_type`	plot_type
`raw_data`	The raw data
`image_details`	image_details
`cex`	relative size of points and text

rotate_graph

Description

Rotates/flips imported figures

Usage

redraw_rotation(image, flip, rotate)
redraw_rotation(image, flip, rotate)

Arguments

`image`	Image object from magick::image_read
`flip`	whether to flip figure
`rotate`	how much to rotate figure

rqm_to_mean

Description

Calculate the mean from the box plots

Usage

rqm_to_mean(min, LQ, median, UQ, max, n)
rqm_to_mean(min, LQ, median, UQ, max, n)

Arguments

`min`	Minimum value
`LQ`	Lower 75th quartile
`median`	Median
`UQ`	Upper 75th quartile
`max`	Maximum value
`n`	Sample size

Value

Returns vector of mean

Author(s)

Joel Pick

Examples

rqm_to_mean(min = 2, LQ = 3, median = 5, UQ = 6, max = 9, n = 30)
rqm_to_mean(min = 2, LQ = 3, median = 5, UQ = 6, max = 9, n = 30)

rqm_to_sd

Description

Calculate the standard deviation from box plots

Usage

rqm_to_sd(min, LQ, UQ, max, n)
rqm_to_sd(min, LQ, UQ, max, n)

Arguments

`min`	Minimum value
`LQ`	Lower 75th quartile
`UQ`	Upper 75th quartile
`max`	Maximum value
`n`	Sample size

Value

Returns vector of standard deviation

Author(s)

Joel Pick

Examples

rqm_to_sd(min = 2, LQ = 3, UQ = 6, max = 9, n = 30)
rqm_to_sd(min = 2, LQ = 3, UQ = 6, max = 9, n = 30)

se_to_sd

Description

Transforms standard error to standard deviation

Usage

se_to_sd(se, n)
se_to_sd(se, n)

Arguments

`se`	Standard Error of the mean
`n`	Sample Size

Value

Returns vector of standard errors

Author(s)

Joel Pick

Examples

se_to_sd(se = 5, n = 10)
se_to_sd(se = 5, n = 10)

setup_calibration_dir

Description

Function will check whether the calibration directory has been setup and if not, create one.

Usage

setup_calibration_dir(dir)
setup_calibration_dir(dir)

Arguments

dir

Path name to the directory / folder where the files are located.

Value

Returns a caldat/ folder within the directory where all metaDigitise objects are stored.

Author(s)

Daniel Noble - [email protected]

Examples


# temporary directory
tmp_dir <- tempdir()

#Create the calibration folder in the directory specified that is used to store files.
setup_calibration_dir(paste0(tmp_dir, "/"))


# temporary directory
tmp_dir <- tempdir()

#Create the calibration folder in the directory specified that is used to store files.
setup_calibration_dir(paste0(tmp_dir, "/"))

single_MB_extract

Description

Takes points user defined points from a single group mean_error plot or boxplot, in a set order, and returns them.

Usage

single_MB_extract(plot_type, cex)
single_MB_extract(plot_type, cex)

Arguments

`plot_type`	Type of plot
`cex`	point size

specify_type

Description

Function that allows user to interface with function to specific each type of plot prior to digitising

Usage

specify_type()
specify_type()

Value

The function will return the type of plot specified by the user and feed this argument back into metDigitise

Author(s)

Daniel Noble - [email protected]

Joel Pick - [email protected]

summary.metaDigitise

Description

Summary method for class ‘metaDigitise’

Usage

## S3 method for class 'metaDigitise'
summary(object, ...)
## S3 method for class 'metaDigitise'
summary(object, ...)

Arguments

`object`	an R object of class ‘metaDigitise’
`...`	further arguments passed to or from other methods.

Value

Data.frame

Author(s)

Joel Pick

user_base

Description

asks user for base of logarithm, accept numeric or "e"

Usage

user_base(...)
user_base(...)

Arguments

...

arguments passed to other functions

user_calibrate

Description

Gets values needed to calibrate axis coordinated. Modified from the digitize package

Usage

user_calibrate(object)
user_calibrate(object)

Arguments

object

metaDigitise object

user_count

Description

asks user for count

Usage

user_count(question)
user_count(question)

Arguments

question

question

user_numeric

Description

asks user for numeric

Usage

user_numeric(question)
user_numeric(question)

Arguments

question

question

user_options

Description

asks user for option from specified list

Usage

user_options(question, allowed_answers)
user_options(question, allowed_answers)

Arguments

`question`	question
`allowed_answers`	allowed answers

user_rotate_graph

Description

Rotates/flips imported figures according to user input, in order to align them properly. Asks the user after each change if further alteration is required

Usage

user_rotate_graph(image_file)
user_rotate_graph(image_file)

Arguments

image_file

Image filename

user_unique

Description

asks user for option from specified list

Usage

user_unique(question, previous_answers)
user_unique(question, previous_answers)

Arguments

`question`	question
`previous_answers`	allowed answers

`dir`	parent directory
`summary`	logical; whether summary is returned
`cex`	relative size of text and points in replotting

Package 'metaDigitise'

Help Index

ask_variable

Description

Usage

Arguments

bulk_edit

Description

Usage

Arguments

Author(s)

cal_coords

Description

Usage

Arguments

calibrate

Description

Usage

Arguments

cat_matrix

Description

Usage

Arguments

CI95_to_sd

Description

Usage

Arguments

Value

Author(s)

Examples

convert_group_data

Description

Usage

Arguments

convert_histogram_data

Description

Usage

Arguments

delete_points

Description

Usage

Arguments

dir_details

Description

Usage

Arguments

Author(s)

Examples

edit_group

Description

Usage

Arguments

edit_metaDigitise

Description

Usage

Arguments

Value

Author(s)

enter_N

Description

Usage

Arguments

Author(s)

error_to_sd

Description

Usage

Arguments

Value

Author(s)

extract_digitised

Description

Usage

Arguments

Value

filename

Description

Usage

Arguments

get_notDone_file_details

Description