Basic examples of screening studies, extracting data, and meta-analysis with the metagear package for R
Marc J. Lajeunesse
University of South Florida, August 5th 2015 (for metagear v. 0.2)
Introduction
The metagear package for R contains tools for facilitating systematic reviews, data extraction, and meta-analyses. It aims to facilitate research synthesis as a whole, by providing a single source for several of the common tasks involved in screening studies, extracting outcomes from studies, and performing statistical analyses on these outcomes using meta-analysis. Below are a few illustrative examples of applications of these functionalities.
Updates to these examples will be posted on our research webpage at USF.
For the source code of metagear see: http://cran.r-project.org/web/packages/metagear/index.html.
Delegating reference screening effort to a team
One of the first tasks of a systematic review is to screen the titles and abstracts of study references to assess their relevance for the synthesis project. For example, after a bibliographic search using Web of Science, there may be thousands of references generated; references from experimental studies, modeling studies, review papers, commentaries, etc. These need to be reviewed individually as a first pass to exclude those that do not fit the synthesis project; such as excluding simulation studies that do not report experimental outcomes useful for estimating an effect size.
However, individually screening thousands of references is time consuming, and large synthesis projects may benefit from delegating this screening effort to a research team. Having multiple people screen references also provides an opportunity to assess the repeatability of these screening decisions.
In this example, we have the following goals:
- Initialize a dataframe containing bibliographic data (tile, abstract, journal) from multiple study references.
- Distribute these references randomly to two team members.
- Merge and summarize the screening efforts of this team.
First, let's start by loading and exploring the contents of a pre-packaged dataset from metagear that contains the bibliographic information of 11 journal articles (example_references_metagear). These data are a subset of references generated from a search in Web of Science for "Genome size", and contain the abstracts, titles, volume, page numbers, and authors of these references.
# load package
library(metagear)
# load a bibliographic dataset with the authors, titles, and abstracts of multiple study references
data(example_references_metagear)
# display the bibliographic variables in this dataset
names(example_references_metagear)## [1] "AUTHORS" "YEAR" "TITLE" "JOURNAL" "VOLUME" "LPAGES" "UPAGES" "DOI" "ABSTRACT"# display the various Journals that these references were published in
example_references_metagear["JOURNAL"]## JOURNAL
## 1 BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS
## 2 EVOLUTIONARY ECOLOGY RESEARCH
## 3 AMERICAN NATURALIST
## 4 GENE
## 5 VIRUS GENES
## 6 JOURNAL OF SHELLFISH RESEARCH
## 7 JOURNAL OF GENERAL MICROBIOLOGY
## 8 APPLIED GEOCHEMISTRY
## 9 JOURNAL OF DRUG DELIVERY SCIENCE AND TECHNOLOGY
## 10 BIOLOGIA PLANTARUM
## 11 GENOMICSOur next step is to initialize/prime this dataset for screening tasks. Our goal is to distribute screening efforts to two screeners/reviewers: "Christina" and "Luc". Here each reviewer will screen a separate subset of these references (a forthcoming example will review how to set up a dual screening design where each member screens the same references). The dataset first needs to be initialized as follows:
# prime the study-reference dataset
theRefs <- effort_initialize(example_references_metagear)
# display the new columns added by effort_initialize
names(theRefs)## [1] "STUDY_ID" "REVIEWERS" "INCLUDE" "AUTHORS" "YEAR" "TITLE" "JOURNAL" "VOLUME" "LPAGES" "UPAGES" "DOI" "ABSTRACT"Note that the effort_initialize() function added three new columns: "STUDY_ID" which is a unique number for each reference (e.g., from 1 to 11), "REVIEWERS" an empty column with NAs that will be later populated with our reviewers (e.g., Christina and Luc), and finally the "INCLUDE" column, which will later contain the screening efforts by the two reviewers.
Screening efforts are essentially how individual study references get coded for inclusion in the synthesis project; currently the "INCLUDE" column has each reference coded as "not vetted", indicating that each reference has yet to be screened.
Our next task is to delegate screening efforts to our two reviewers Christina and Luc. Our goal is to randomly distribute these references to each reviewer.
# randomly distribute screening effort to a team
theTeam <- c("Christina", "Luc")
theRefs_unscreened <- effort_distribute(theRefs, reviewers = theTeam)
# display screening tasks
theRefs_unscreened[c("STUDY_ID", "REVIEWERS")]## STUDY_ID REVIEWERS
## 1 1 Luc
## 2 2 Christina
## 3 3 Luc
## 4 4 Christina
## 5 5 Christina
## 6 6 Luc
## 7 7 Luc
## 8 8 Luc
## 9 9 Christina
## 10 10 Christina
## 11 11 ChristinaThe screening efforts can also be delegated unevenly, such as below where Luc will take on 80% of the screening effort:
# randomly distribute screening effort to a team, but with Luc handeling 80% of the work
theRefs_unscreened <- effort_distribute(theRefs, reviewers = theTeam, effort = c(20, 80))
theRefs_unscreened[c("STUDY_ID", "REVIEWERS")]## STUDY_ID REVIEWERS
## 1 1 Christina
## 2 2 Luc
## 3 3 Luc
## 4 4 Luc
## 5 5 Christina
## 6 6 Luc
## 7 7 Luc
## 8 8 Luc
## 9 9 Luc
## 10 10 Luc
## 11 11 LucThe effort can also be redistributed with the effort_redistribute() function. In the above example we assigned Luc 80% of the work. Now let's redistribute half of Luc's work to a new team member "Patsy".
theRefs_Patsy <- effort_redistribute(theRefs_unscreened,
reviewer = "Luc",
remove_effort = "50", # move 50% of Luc's work to Patsy
reviewers = c("Luc", "Patsy")) # team members loosing and picking up work
theRefs_Patsy[c("STUDY_ID", "REVIEWERS")]## STUDY_ID REVIEWERS
## 1 1 Christina
## 5 5 Christina
## 2 2 Patsy
## 3 3 Luc
## 4 4 Luc
## 6 6 Patsy
## 7 7 Luc
## 8 8 Luc
## 9 9 Patsy
## 10 10 Luc
## 11 11 PatsyThe references have now been randomly assigned to either Christina or Luc. The whole initialization of the reference dataset with effort_initialize() can be abbreviated with effort_distribute(example_references_metagear, reviewers = c("Christina", "Luc"), initialize = TRUE).
Now that screening tasks have been distributed, the next stage is for reviewers to start the manual screening of each assigned reference. This is perhaps best done by providing a separate file of these references to Christina and Luc. They can then work on screening these references separately and remotely. Once the screening is complete, we can then merge these files into a complete dataset (we'll get to this later).
The effort_distribute() function can also save to file each reference subset; these can be given to Christina and Luc to start their work. This is done by setting the 'save_split' parameter to TRUE.
# randomly distribute screening effort to a team, but with Luc handling 80% of the work,
# but also saving these screening tasks to separate files for each team member
theRefs_unscreened <- effort_distribute(theRefs, reviewers = theTeam, effort = c(20, 80), save_split = TRUE)## 2 files saved in: C:/Users/lajeunesse@usf.edu/DesktoptheRefs_unscreened[c("STUDY_ID", "REVIEWERS")]## STUDY_ID REVIEWERS
## 1 1 Luc
## 2 2 Christina
## 3 3 Luc
## 4 4 Luc
## 5 5 Luc
## 6 6 Christina
## 7 7 Luc
## 8 8 Luc
## 9 9 Luc
## 10 10 Luc
## 11 11 Luclist.files(pattern = "effort")## [1] "effort_Christina.csv" "effort_Luc.csv"These two effort_*.csv files contain the assigned references for Christina and Luc. These can be passed on to each team member so that they can begin screening/coding each reference for inclusion in the synthesis project.
References should be coded as "YES" or "NO" for inclusion, but can also be coded as "MAYBE" if bibliographic information is missing or there is inadequate information to make a proper assessment of the study.
The abstract_screener() function can be used to facilitate this screening process (an example is forthcoming), but for the sake of introducing how screening efforts can be merged and summarized, I manually coded all the references in both of Christina's and Luc's effort_*.csv files. Essentially, I randomly coded each references as either "YES", "NO", or "MAYBE". These files now contain the completed screening efforts.
We can merge these two files with the completed screening efforts using the effort_merge() function, as well as summarize the outcome of screening tasks using the effort_summary() function.
# merge the effort_Luc.csv and effort_Christina.csv [WARNING: will merge all files named "effort_*" in directory]
theRefs_screened <- effort_merge()
theRefs_screened[c("STUDY_ID", "REVIEWERS", "INCLUDE")]## STUDY_ID REVIEWERS INCLUDE
## 1 2 Christina MAYBE
## 2 6 Christina YES
## 3 1 Luc YES
## 4 3 Luc YES
## 5 4 Luc NO
## 6 5 Luc NO
## 7 7 Luc MAYBE
## 8 8 Luc YES
## 9 9 Luc MAYBE
## 10 10 Luc YES
## 11 11 Luc MAYBEtheSummary <- effort_summary(theRefs_screened)## === SCREENING EFFORT SUMMARY ===
##
## 2 candidate studies identified
## 5 studies excluded
## 4 challenging studies needing additional screening
## ----
## 11 TOTAL SCREENED
##
## === SCREENING DESIGN SUMMARY ===
##
## MAYBE YES NO TOTAL %
## Christina 1 1 0 2 18.18182
## Luc 3 4 2 9 81.81818
## TOTAL 4 5 2 11 100.00000The summary of screening tasks describes the outcomes of which references had studies appropriate for the synthesis project, while also outlining which need to be re-assessed. The team should discuss these challenging references and decide if they are appropriate for inclusion or track down any additional/missing information needed to make proper assessment of their inclusion.
Downloading PDFs
Once references have been screened, metagear can be used to download and organize the full-texts of these references. However, note that the download success of these PDFs is entirely conditional on the journal subscription coverage of the host institution running metagear. Also note that metagear only supports the download of a PDF article if the DOI (digital object identifier) is available for that article.
In this example, we have the following goals:
- Download a single PDF with the
PDF_download()function. - Download multiple PDFs with the
PDFs_collect()function.
Let's start by loading the pre-packaged reference dataset in metagear that contains the bibliographic information of 11 journal articles (example_references_metagear). This dataset includes a column "DOI" which contains the DOI of each article (if available).
# load package
library(metagear)
# load a bibliographic dataset with the DOIs
data(example_references_metagear)
# display the year published of each study reference and their DOIs
example_references_metagear[c("JOURNAL", "DOI")]## JOURNAL DOI
## 1 BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS 10.1016/j.bbrc.2011.10.017
## 2 EVOLUTIONARY ECOLOGY RESEARCH <NA>
## 3 AMERICAN NATURALIST 10.1086/319928
## 4 GENE 10.1016/j.gene.2008.01.009
## 5 VIRUS GENES 10.1007/s11262-012-0864-0
## 6 JOURNAL OF SHELLFISH RESEARCH 10.2983/035.029.0428
## 7 JOURNAL OF GENERAL MICROBIOLOGY <NA>
## 8 APPLIED GEOCHEMISTRY 10.1016/S0883-2927(02)00054-9
## 9 JOURNAL OF DRUG DELIVERY SCIENCE AND TECHNOLOGY <NA>
## 10 BIOLOGIA PLANTARUM 10.1023/A:1012426306493
## 11 GENOMICS 10.1016/j.ygeno.2013.09.002Note that references collected from bibliographic databases like Web of Science will often be incomplete. For example, the study published in EVOLUTIONARY ECOLOGY RESEARCH does not have a DOI (described above as NA). This is because EVOLUTIONARY ECOLOGY RESEARCH is an independently published journal and does not provide DOIs for their research articles.
However, a DOI for the AMERICAN NATURALIST study is available, and let's use it to fetch the PDF.
# load package
PDF_download("10.1086/319928", theFileName = "AMNAT_metagear")## Collecting PDF from DOI: 10.1086/319928
## Extraction 1 of 2: HTML script.... successful
## Extraction 2 of 2: PDF download... successfulThe downloader provides information on the download success, and in this case a PDF was successfully retrieved. It was saved in the working directory of the R process (to see this directory use getwd()).
Now let's try downloading all the PDFs from our reference dataset. This can be done using the PDFs_collect() function.
# (optional) initialize the reference dataset to help generate standardized fileNames (e.g., STUDY_ID numbers)
theRefs <- effort_initialize(example_references_metagear)
# fetch the PDFs
PDFs_collect(theRefs, DOIcolumn = "DOI", FileNamecolumn = "STUDY_ID", directory = getwd())## Collecting PDF from DOI: 10.1016/j.bbrc.2011.10.017
## Extraction 1 of 2: HTML script.... successful
## Extraction 2 of 2: PDF download... successful
## Collecting PDF from DOI: NA
## Extraction 1 of 2: HTML script.... cannot open: HTTP status was '404 Not Found'
## Extraction 2 of 2: PDF download... skipped
## Collecting PDF from DOI: 10.1086/319928
## Extraction 1 of 2: HTML script.... successful
## Extraction 2 of 2: PDF download... successful
## Collecting PDF from DOI: 10.1016/j.gene.2008.01.009
## Extraction 1 of 2: HTML script.... successful
## Extraction 2 of 2: PDF download... successful
## Collecting PDF from DOI: 10.1007/s11262-012-0864-0
## Extraction 1 of 2: HTML script.... successful
## Extraction 2 of 2: PDF download... successful
## Collecting PDF from DOI: 10.2983/035.029.0428
## Extraction 1 of 2: HTML script.... successful
## Extraction 2 of 2: PDF download... successful
## Collecting PDF from DOI: NA
## Extraction 1 of 2: HTML script.... cannot open: HTTP status was '404 Not Found'
## Extraction 2 of 2: PDF download... skipped
## Collecting PDF from DOI: 10.1016/S0883-2927(02)00054-9
## Extraction 1 of 2: HTML script.... successful
## Extraction 2 of 2: PDF download... successful
## Collecting PDF from DOI: NA
## Extraction 1 of 2: HTML script.... cannot open: HTTP status was '404 Not Found'
## Extraction 2 of 2: PDF download... skipped
## Collecting PDF from DOI: 10.1023/A:1012426306493
## Extraction 1 of 2: HTML script.... successful
## Extraction 2 of 2: PDF download... successful
## Collecting PDF from DOI: 10.1016/j.ygeno.2013.09.002
## Extraction 1 of 2: HTML script.... successful
## Extraction 2 of 2: PDF download... successful
##
## PDF download summary
## 8 = downloaded
## 3 = URL error
## Downloads located in: C:/Users/lajeunesse@usf.edu/DocumentsEight of the 11 references had successful PDF downloads; the remaining 3 did not have DOIs available. These PDFs will need to be checked to determine if their contents are the desired research articles. Also note that the downloading process will take time, and in general, it will take ~ 45 seconds to detect and download a single PDF.
Automated extraction of data from scatterplots
Extracting data from a figure image is a common challenge when trying to extract outcomes (effect sizes) from a study. The scrapping (reverse engineering) of data points from a scatterplot image can be automated with metagear.
In these examples, we have the following goals:
- Extract data points from an image containing a scatterplot using the
figure_scatterPlot()default parameters. - Tweak the parameters to extract data from scatterplots with various formats (e.g., different point shapes, or image sizes).
Example 1 | figure_scatterPlot() default settings
Metagear offers a pre-packaged scatterplot image, and so let's begin with extracting data from this image, before moving to more advanced applications of figure_scatterPlot(). First, let's load and display the image.
# load metagear package and .jpg image manipulation package EBImage
library(metagear)
library(EBImage)
# load the scatterplot image, source: Kam et al. (2003) Functional Ecology 17:496-503.
data(Kam_et_al_2003_Fig2)
# display the image
figure_display(Kam_et_al_2003_Fig2) 
Now let's use figure_scatterPlot() to scrape data from this image; however, because Kam_et_al_2003_Fig2 is pre-packaged with metagear it needs to be converted back to a .jpg before the image can be processed.
The figure_scatterPlot() will by default output three objects:
- The estimated regression fit of these detected points, as well as the estimated effect size and variance of the correlation presented in the figure.
- A raster image of the detected objects painted over the original image. Blue spheres are detected points, orange spheres are detected clusters of points that could not be separated, the X-axis in pink, and the Y-axis in green. The points and axes can also be extracted individually using the
figure_detectAllPoints()andfigure_detectAxis()functions. - The X and Y data from each detected point on the image, and information on whether that point was identified as a cluster.
Here are the results of using figure_scatterPlot() on Kam et al.'s (2002) figure.
# convert back to .jpg
figure_write(Kam_et_al_2003_Fig2, file = "Kam_et_al_2003_Fig2.jpg")
# load the scatterplot image, source: Kam et al. (2003) Functional Ecology 17:496-503.
rawData <- figure_scatterPlot("Kam_et_al_2003_Fig2.jpg") ## regression fit: Y = 11.92716 + 0.90769 * X, R-squared = 0.59496
## Pearson's r = 0.7713394, var(r) = 0.0034905, N = 49
The estimated regression coefficients are very similar to those originally reported by Kam et al.'s (2002) study; which were Y = 12.03 + 0.907 * X with an R2 = 0.59 and a sample size of N = 51.
Example 2 | tweaking defaults for image size
Now let's try to extract data from another image. This time the figure is relatively small and figure_scatterPlot() will need some adjustments based on this size difference. Also, this time we will scale the data extractions to the X- and Y-axis scale; this is useful to calculate the original regression coefficients. Here, the minimum and maximum presented in the figure for the X-axis is 0 to 50, and 0 to 70 for the Y-axis. However, note that re-scaling the data does not affect the effect size calculated from the figure, only the estimated regression coefficients. Let's download the image first from my website and then process it.
# download the figure image from my website
figureSource <- "http://lajeunesse.myweb.usf.edu/metagear/example_2_scatterPlot.jpg"
download.file(figureSource, "example_2_scatterPlot.jpg", quiet = TRUE, mode = "wb")
aFig <- figure_read("example_2_scatterPlot.jpg", display = TRUE)
# because of the small size of the image the axis parameter needed adjustment from 5 to 3
rawData2 <- figure_scatterPlot("example_2_scatterPlot.jpg",
axis_thickness = 3, # adjusted from 5 to 3 to help detect the thin axis
X_min = 0, # minimum X-value reported in the plot
X_max = 50, # maximum X-value reported in the plot
Y_min = 0,
Y_max = 70)## regression fit: Y = -0.40746 + 1.26962 * X, R-squared = 0.51678
## Pearson's r = 0.7188738, var(r) = 0.0179617, N = 15
In this example, because of the small size of the figure, the axis_thinkness parameter needed to be reduced from 5 to 3. This was sufficient to detect the axis lines and extract the plotted data.
Example 3 | more tweaking based on color, size, and empty points
In this figure example, we have the case where the image is large (1122px by 780px), the plotted points are large but empty, and the axis lines are thin and grey. All of these issues complicate object detection on the figure.
# download the figure image from my website
figureSource <- "http://lajeunesse.myweb.usf.edu/metagear/example_3_scatterPlot.jpg"
download.file(figureSource, "example_3_scatterPlot.jpg", quiet = TRUE, mode = "wb")
aFig <- figure_read("example_3_scatterPlot.jpg", display = TRUE)
# tweaking the figure_scatterPlot() function to improve object detection
rawData3 <- figure_scatterPlot("example_3_scatterPlot.jpg",
binary_point_fill = TRUE, # set to TRUE to fill empty points
point_size = 9, # increase from 5 to 9 since points are large
binary_threshold = 0.8, # increase from 0.6 to 0.8 to include the grey objects
axis_thickness = 3, # decrease from 5 to 3 since axes are thin
X_min = 0,
X_max = 800,
Y_min = 0,
Y_max = 35)## regression fit: Y = 8.50394 + 0.02549 * X, R-squared = 0.45299
## Pearson's r = 0.6730416, var(r) = 0.0019557, N = 155
It looks like figure_scatterPlot() confused some of the regression summary text on the plot for points. This can be avoided by erasing all superfluous information on the figure prior to processing with figure_scatterPlot(). However, in our case we are interested in estimating these reported regression coefficients. We can quickly exclude these false detections since they reside within a specific range on the plot that does not include data (e.g., values above 25 for Y, and below 305 for X).
# remove false detected points from the regression summary presented within the plot
cleaned_rawData3 <- rawData3[ which(!(rawData3$X < 350 & rawData3$Y > 25)), ]
# estimate the regression coefficients
lm(Y ~ X, data = cleaned_rawData3)##
## Call:
## lm(formula = Y ~ X, data = cleaned_rawData3)
##
## Coefficients:
## (Intercept) X
## 6.45334 0.02893# and get R-squared
round(summary(lm(Y ~ X, data = cleaned_rawData3))$r.squared, 4)## [1] 0.6369The estimated regression coefficients are very similar to those presented within the plot.
Automated extraction of data from bar plots
Bar plots (or bar charts) are a common way to present information in groups or categories.
In these examples, we have the following goals:
- Extract data points from an image containing a bar plot using the
figure_barPlot()default parameters. - Tweak the parameters to extract data from bar plots with various formats (e.g., with bars with different shading indicating different groups, or bars presented horizontally rather than vertically).
Example 1 | figure_barPlot() default settings
Let's have a look at the bar plot image provided by metagear called Kortum_and_Acymyan_2013_Fig4; originally extracted from Kortum & Acymyan (2013; Journal of Usability Studies 9:14-24).
# load metagear package
library(metagear)
# load the scatterplot image, source: Kortum & Acymyan (2013) J. of Usability Studies 9:14-24).
data(Kortum_and_Acymyan_2013_Fig4)
# display the image
figure_display(Kortum_and_Acymyan_2013_Fig4)
Manual extraction of the bars and their errors will be time consuming here given that there are 42 separate data points to be gathered (i.e. 14 bars each with upper and lower error bars). Let's use figure_barPlot() with its default options to extract these 42 points.
# convert metagear image object back to .jpg and then extract objects from this .jpg
figure_write(Kortum_and_Acymyan_2013_Fig4, file = "Kortum_and_Acymyan_2013_Fig4.jpg")
rawData <- figure_barPlot("Kortum_and_Acymyan_2013_Fig4.jpg")
In the above image, the detected points for each ballot were painted in blue. Let's have a closer look at these extracted data.
# display extracted points
as.vector(round(rawData, 2))## [1] 15.13 20.57 9.69 21.99 28.37 15.84 31.44 26.00 20.33 29.55 35.93 23.17 34.75 28.37 41.13 35.93 43.03 28.84 37.83 45.63 30.50 32.86 39.24 45.63 41.84 34.28 49.41 53.90 62.17 45.86 62.65 54.61 70.92 71.87 54.61 63.12 73.52 67.38 79.20 92.67 89.13 95.98Metagear is not clever enough to know what groupings these extractions belong too; however, the extractions will be sorted relative to their axis positioning. For example, there are three extractions that occupy the same X-axis range under the A ballot column. These three extractions will be grouped together in the figure_barPlot() output. With this in mind, a little data manipulation is needed to make better sense of these ballot data.
# extractions are in triplicates with an upper, mean, and lower values, so let's
# stack by three and sort within triplicates from lowest to highest
organizedData <- t(apply(matrix(rawData, ncol = 3, byrow = TRUE), 1, sort))
# rename rows and columns of these triplicates as presented in Kortum_and_Acymyan_2013_Fig4.jpg
theExtraction_names <- c("lower 95%CI", "mean SUS score", "upper 95%CI")
theBar_names <- toupper(letters[1:14])
dimnames(organizedData) <- list(theBar_names, theExtraction_names)
organizedData## lower 95%CI mean SUS score upper 95%CI
## A 9.692671 15.13002 20.56738
## B 15.839243 21.98582 28.36879
## C 20.330969 26.00473 31.44208
## D 23.167849 29.55083 35.93381
## E 28.368794 34.75177 41.13475
## F 28.841608 35.93381 43.02600
## G 30.496454 37.82506 45.62648
## H 32.860520 39.24350 45.62648
## I 34.278960 41.84397 49.40898
## J 45.862884 53.90071 62.17494
## K 54.609929 62.64775 70.92199
## L 54.609929 63.12057 71.86761
## M 67.375887 73.52246 79.19622
## N 89.125296 92.67139 95.98109Example 2 | tweaking defaults for horizontal columns
Now let's try to extract data from another image where bar-plot is presented horizontally (i.e. bars stem from the Y-axis).
# download the figure image from my website
figureSource <- "http://lajeunesse.myweb.usf.edu/metagear/example_2_barPlot.jpg"
download.file(figureSource, "example_2_barPlot.jpg", quiet = TRUE, mode = "wb")
aFig <- figure_read("example_2_barPlot.jpg", display = TRUE)
rawData2 <- figure_barPlot("example_2_barPlot.jpg",
horizontal = TRUE, # changed from FALSE since bars are horizontal
bar_width = 11, # raised from 9 since bars are wide relative to the figure
Y_min = 0,
Y_max = 10)## Warning in makeBrush(watershed_thickness, shape = "line", angle = theAngle): 'size' was rounded to the next odd number: 301
The function also detected the right-most vertical line (part of the the figure box) as a datapoint. The options of figure_barPlot() can be tweaked to avoid this issue; however, it might be easier to just exclude this extraction given that it has the largest plant biomass value (i.e. close to 10). Let's exclude this false datapoint and organize the dataset as presented in the figure.
# exclude the false detection
rawData2 <- rawData2[rawData2 < max(rawData2)]
# data are in triplicates with an upper, mean, and lower values, so let's
# stack by three and sort within triplicates from lowest to highest
organizedData <- t(apply(matrix(rawData2, ncol = 3, byrow = TRUE), 1, sort))
# rename rows and columns of these triplicates as presented in the figure
theExtraction_names <- c("lower error", "bar", "upper error")
theBar_names <- c("exclosure", "water", "fertilizer", "control")
dimnames(organizedData) <- list(theBar_names, theExtraction_names)
organizedData## lower error bar upper error
## exclosure 4.775438 5.466238 6.173633
## water 7.572347 7.974277 8.376206
## fertilizer 8.986066 9.276230 9.581994
## control 4.678975 4.983923 5.273312