version 0.1.3

DESCRIPTION

@@ -1,23 +1,25 @@
 Package: BinMat
 Type: Package
-Title: Processes Binary Data Obtained from Fragment Analysis
-Version: 0.1.2
+Title: Processes Binary Data Obtained from Fragment Analysis (Such as
+        AFLPs, ISSRs, and RFLPs)
+Version: 0.1.3
 Authors@R: person("Clarke", "van Steenderen", email = "vsteenderen@gmail.com",
                   role = c("aut", "cre"), comment = c(ORCID = "0000-0002-4219-446X"))
-Description: A molecular genetics tool that processes binary data from fragment analysis, such as inter-simple sequence repeats (ISSRs) and amplified fragment length polymorphism (AFLP). It consolidates replicate sample pairs, outputs summary statistics, and produces hierarchical clustering trees and nMDS plots. This package was developed from the M.Sc. thesis entitled "A genetic analysis of the species and intraspecific lineages of Dactylopius Costa (Hemiptera:Dactylopiidae)" (van Steenderen, 2019, Rhodes University, Department of Zoology and Entomology, Center for Biological Control (CBC) <https://www.ru.ac.za/centreforbiologicalcontrol/>, Grahamstown, South Africa), <doi:10.13140/RG.2.2.28470.86083>. The GUI version of this package is available on the R Shiny online server at: <https://clarkevansteenderen.shinyapps.io/BINMAT/> , or it is accessible via GitHub by typing: shiny::runGitHub("BinMat", "CJMvS") into the console in R. Please see the vignette supplied with the package for a worked example, and detailed explanations of functions.   
+Description: A molecular genetics tool that processes binary data from fragment analysis. It consolidates replicate sample pairs, outputs summary statistics, and produces hierarchical clustering trees and nMDS plots. This package was developed from the M.Sc. thesis entitled "A genetic analysis of the species and intraspecific lineages of Dactylopius Costa (Hemiptera:Dactylopiidae)" (van Steenderen, 2019, Rhodes University, Department of Zoology and Entomology, Center for Biological Control (CBC) <https://www.ru.ac.za/centreforbiologicalcontrol/>, Grahamstown, South Africa), <doi:10.13140/RG.2.2.28470.86083>. The GUI version of this package is available on the R Shiny online server at: <https://clarkevansteenderen.shinyapps.io/BINMAT/> , or it is accessible via GitHub by typing: shiny::runGitHub("BinMat", "CJMvS") into the console in R.   
 License: GPL-3
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.0.2
+RoxygenNote: 7.1.2
 Depends: R (>= 3.0)
 Imports: pvclust (>= 2.0), magrittr, MASS (>= 7.3), stats (>= 3.4.0),
-        graphics (>= 3.4.0), base (>= 3.4.0)
+        graphics (>= 3.4.0), base (>= 3.4.0), ggpubr (>= 0.4.0), tibble
+        (>= 3.1.4)
 Suggests: knitr, rmarkdown
 VignetteBuilder: knitr
 NeedsCompilation: no
-Packaged: 2020-03-06 17:53:36 UTC; clarke
+Packaged: 2022-02-07 08:00:58 UTC; s1000334
 Author: Clarke van Steenderen [aut, cre]
     (<https://orcid.org/0000-0002-4219-446X>)
 Maintainer: Clarke van Steenderen <vsteenderen@gmail.com>
 Repository: CRAN
-Date/Publication: 2020-03-06 18:20:06 UTC
+Date/Publication: 2022-02-07 08:10:09 UTC

MD5

@@ -1,36 +1,36 @@
-c3c5e603eadbf21ad163bf62180cfd36 *DESCRIPTION
+ccf880aa567aab03603c1666b342dabd *DESCRIPTION
 ddbeeb32dd02f10bd1d99a4494c4e192 *NAMESPACE
-078180d085ca97c033e6289db5e04f4b *R/BinMatInput_ordination.R
-082e0ca4ed12ccedc0d553e6cd29d873 *R/BinMatInput_reps.R
-cfcf931474bc6c4bb028f7c9df2ce8f4 *R/check_data.R
+733037d1264f7b8abb95bed40ed7dd8c *R/BinMatInput_ordination.R
+47487718699dc10a65f10b434af64f98 *R/BinMatInput_reps.R
+4ec7d2185ec4a8990ac13daf369cea40 *R/check_data.R
 fedd397e38070517a149c174c2259e4a *R/consolidate.R
-c49a809c04a650b10c98c920d78b63d0 *R/errors.R
+4dd23b492eb7f0dad07e57c78935cb57 *R/errors.R
 de87dddf47b0b4d0b2bd96d4bcff4fd8 *R/group_names.R
-5b5b69e2a0f30a477817a45ca732166b *R/nmds.R
-36835ad82e7d87067698504df1e7ccb9 *R/peakRemove.R
-0759a1e7ef7b6ba7d3284cb8000d5bfb *R/peaks_consolidated.R
-83eb445f1d6494cf4d48ecd95ceed98e *R/peaks_replicates.R
-fa657ae34e4f0cce38d4f3ee587e0143 *R/scree.R
-098a7f43822051515812735c797de625 *R/shepard.R
-fb531c1ec001d1ed5470159ef4fb395c *R/upgma.R
-6a99094965ca95c1d941d06b4b0d1e9d *build/vignette.rds
+4632f13fc01e415503527cca4715c8f2 *R/nmds.R
+3b5e29d12b637efbcf6a7ba9fac88e3a *R/peakRemove.R
+a271dfa8b01fa6cd62092182594cbdad *R/peaks_consolidated.R
+3237d3bfcef8fab5707c926b5f85380f *R/peaks_replicates.R
+2a5f074965089cb6c27f2a53e9760ca9 *R/scree.R
+6d5fc44c6ffe366925f90b5a85d2096f *R/shepard.R
+02d4411f71120f7304a51fd8a8454060 *R/upgma.R
+f0f46601079db3b4afdc555abd66dd70 *build/vignette.rds
 a9bf62bcc5b7057d08b7050aa330f97f *data/BinMatInput_ordination.rda
 f4959bc1b00cd58e0953cc6653d5c191 *data/BinMatInput_reps.rda
-75a5f9380939bf25ced1af2439b30d47 *inst/doc/BinMat.R
-373cf0ec38f29706a275214e0f805351 *inst/doc/BinMat.Rmd
-1cfe6068ac9bb0e6c9896ffdadb1415c *inst/doc/BinMat.html
-94b1f63003bf13329d7178502fc9a488 *man/BinMatInput_ordination.Rd
-9aa70b4d098169b497ac0ac77c67d377 *man/BinMatInput_reps.Rd
-2a5ffde37f48e0f2ba893fc8e5fe3359 *man/check.data.Rd
+bbf9afef94f136d0f31d7562e9ce2275 *inst/doc/BinMat.R
+bc5bc9fddd5e6e1fa4519f342ccb9afa *inst/doc/BinMat.Rmd
+9fc4ad109bbd3e4d9ad50783f9556e2a *inst/doc/BinMat.html
+d56e9be99a6601ca67569789d41f8616 *man/BinMatInput_ordination.Rd
+75f18c1c416a63df7b204dba336a366a *man/BinMatInput_reps.Rd
+7b6d01567ba5562a6f15a68b0b093536 *man/check.data.Rd
 15e19aa2662e7be9219d77ec747a00b0 *man/consolidate.Rd
-787b15e3d26326a6ac8abeacd1cde0e9 *man/errors.Rd
+7f74098399e3e1fcde2e6ad3807272bc *man/errors.Rd
 e2ee5ac37b210342fba90c4f6b40c082 *man/group.names.Rd
-564fe135e1a27bde9d6d7d6dd5a8b0fe *man/nmds.Rd
-c55cdd4b10c8e1322c880f61f476eceb *man/peak.remove.Rd
-10e2b7f803ed4b1df735ebc9f7839128 *man/peaks.consolidated.Rd
-0672df0ac30e4af122f04b85bf2dbb36 *man/peaks.original.Rd
-97a6fd815ba7347a51657d68beb2c6d7 *man/scree.Rd
-3b38d759ae4e116ac34b6ec378a7eedd *man/shepard.Rd
-2b33a1da8d3796ada9fbe8b65a61a1d1 *man/upgma.Rd
-373cf0ec38f29706a275214e0f805351 *vignettes/BinMat.Rmd
+00266c04f007c996470739f2c473e9c8 *man/nmds.Rd
+aa78fd00595121a36d42897de779505b *man/peak.remove.Rd
+e7e49b1464fd4f9d317ec4e0b3fba90e *man/peaks.consolidated.Rd
+692eaa1923b9579b3c850462ba9cdcce *man/peaks.original.Rd
+57eb9c7031779018cd537ff61d8392c8 *man/scree.Rd
+e3fbb4e39814d1a410351ce384f67382 *man/shepard.Rd
+730e6b240744b5ccdd11c264635d6f47 *man/upgma.Rd
+bc5bc9fddd5e6e1fa4519f342ccb9afa *vignettes/BinMat.Rmd
 af8503436cc5b48444ef79deddfbb5a5 *vignettes/logo.png

R/BinMatInput_ordination.R

@@ -1,16 +1,16 @@
-#' Example input data containing a consolidated binary matrix with grouping information
-#' @docType data
-#'
-#' @usage data(BinMatInput_ordination)
-#'
-#' @format A dataframe with columns for loci, and rows for samples. Grouping information is in the second column.
-#' @examples data(BinMatInput_ordination)
-#' mat = BinMatInput_ordination
-#' group.names(mat)
-#' scree(mat)
-#' shepard(mat)
-#' clrs = c("red", "green", "black")
-#' shp = c(16,16,16)
-#' nmds(mat, colours = clrs, shapes = shp, labs = TRUE)
-
-"BinMatInput_ordination"
+#' Example input data containing a consolidated binary matrix with groups
+#' @docType data
+#'
+#' @usage data(BinMatInput_ordination)
+#'
+#' @format A dataframe with columns for loci, and rows of replicate pairs. Grouping information is in the second column.
+#' @examples data(BinMatInput_ordination)
+#' mat = BinMatInput_ordination
+#' group.names(mat)
+#' scree(mat)
+#' shepard(mat)
+#' clrs = c("red", "green", "black")
+#' shp = c(16,16,16)
+#' nmds(mat, colours = clrs, shapes = shp, labs = TRUE)
+
+"BinMatInput_ordination"

R/BinMatInput_reps.R

@@ -1,19 +1,19 @@
-#' Example input data containing a binary matrix comprising replicate sample pairs
-#'
-#' @docType data
-#'
-#' @usage data(BinMatInput_reps)
-#'
-#' @format A dataframe with columns for loci, and rows for replicate sample pairs.
-#'
-#' @examples data(BinMatInput_reps)
-#' mat = BinMatInput_reps
-#' check.data(mat)
-#' cons = consolidate(mat)
-#' pks = peaks.consolidated(cons)
-#' err = errors(cons)
-#' rem = peak.remove(cons, 4)
-#' clust = upgma(cons)
-#'
-#'
-"BinMatInput_reps"
+#' Example input data containing a binary matrix comprising replicate pairs
+#'
+#' @docType data
+#'
+#' @usage data(BinMatInput_reps)
+#'
+#' @format A dataframe with columns for loci, and rows of replicate pairs.
+#'
+#' @examples data(BinMatInput_reps)
+#' mat = BinMatInput_reps
+#' check.data(mat)
+#' cons = consolidate(mat)
+#' pks = peaks.consolidated(cons)
+#' err = errors(cons)
+#' rem = peak.remove(cons, 4)
+#' clust = upgma(cons)
+#'
+#'
+"BinMatInput_reps"

R/check_data.R

@@ -1,25 +1,25 @@
-#' @title Checks binary matrix for unwanted characters.
-#'
-#' @description Checks for unwanted values (other than 1, 0, and ?) in the data set.
-#'
-#' @param x A CSV file containing replicate sample pairs of binary data.
-#'
-#' @return Index positions where unwanted values occur (row, column).
-#'
-#' @examples data(BinMatInput_reps)
-#' mat = BinMatInput_reps
-#' check.data(mat)
-#'
-#' @export
-
-check.data = function(x){
-  row.names(x) <- x[[1]]
-  x[,1] <- NULL
-  x[,] <- sapply(x[,], as.numeric)
-  answer = which(x != 0 & x != 1 & x != "?", arr.ind = TRUE)
-  if(length(answer) > 0) message(answer)
-  else {message("None found.")}
-
-}
-
-
+#' @title Checks binary matrix for unwanted characters.
+#'
+#' @description Checks for unwanted values (other than 1, 0, and ?).
+#'
+#' @param x A CSV file containing replicate pairs of binary data.
+#'
+#' @return Index positions where unwanted values occur (row, column).
+#'
+#' @examples data(BinMatInput_reps)
+#' mat = BinMatInput_reps
+#' check.data(mat)
+#'
+#' @export
+
+check.data = function(x){
+  row.names(x) <- x[[1]]
+  x[,1] <- NULL
+  x[,] <- sapply(x[,], as.numeric)
+  answer = which(x != 0 & x != 1 & x != "?", arr.ind = T)
+  if(length(answer) > 0) print(answer)
+  else {writeLines("None found.")}
+
+}
+
+

R/errors.R

@@ -1,46 +1,48 @@
-#' @title Calculates Jaccard and Euclidean error rates.
-#'
-#' @description Calculates the Jaccard and Euclidean error rates for the dataset. Jaccard's error does not take shared absences of bands as being biologically meaningful. Jaccard Error = (f10 + f01)/(f10 + f01 + f11) and Euclidean Error = (f10 + f01)/(f10 + f01 + f11 + f00). At each locus, f01 and f10 indicates a case where a 0 was present in one replicate, and a 1 in the other. f11 indicates the shared presence of a band in both replicates, and f00 indicates a shared absence. For example, if a replicate pair comprises Rep1 = 00101 and Rep2 = 01100, Jaccard Error = (1+1)/(1+1+1) = 2/3 = 0.67, Euclidean Error = (1+1)/(1+1+1+2) = 2/5 = 0.4.
-#'
-#' @param x Consolidated binary matrix.
-#'
-#' @return JE (Jaccard Error), EE (Euclidean Error), and standard deviations.
-#'
-#' @examples data(BinMatInput_reps)
-#' mat = BinMatInput_reps
-#' cons = consolidate(mat)
-#' errors(cons)
-#'
-#' @export
-
-errors = function(x){
-
-  mismatch_err = matrix(nrow=nrow(x), ncol = 1)
-  jacc_err = matrix(nrow=nrow(x), ncol = 1)
-
-
-  for(i in 1:nrow(x)) {
-    # find the number of 1s, Os and question marks
-    ones = length(which(x[i,] == 1))
-    zeroes = length(which(x[i,] == 0))
-    questions = length(which(x[i,] == "?"))
-    sum_bands = ones + questions
-
-    mismatch_err[i,] = (questions/(questions + ones + zeroes))
-    jacc_err[i,] = (questions/(questions + ones))
-
-  }
-
-  error_table = data.frame("Errors" = matrix(ncol = 1, nrow = 8))
-  error_table[1,] = "Average Euclidean Error:"
-  error_table[2,] = round(base::mean(mismatch_err[,1]),4)
-  error_table[3,] = "Euclidean error St. dev:"
-  error_table[4,] = round(stats::sd(mismatch_err[,1]),4)
-  error_table[5,] = "Average Jaccard:"
-  error_table[6,] = round(base::mean(jacc_err[,1]),4)
-  error_table[7,] = "Jaccard error St.dev:"
-  error_table[8,] = round(stats::sd(jacc_err[,1]),4)
-
-return(error_table)
-
-}
+#' @title Calculates Jaccard and Euclidean error rates.
+#'
+#' @description Calculates the Jaccard and Euclidean error rates for the dataset. Jaccard's error does not take shared absences of bands as being biologically meaningful. JE = (f10 + f01)/(f10 + f01 + f11) and EE = (f10 + f01)/(f10 + f01 + f11 + f00). At each locus, f01 and f10 indicates a case where a 0 was present in one replicate, and a 1 in the other. f11 indicates the shared presence of a band in both replicates, and f00 indicates a shared absence. For example, if a replicate pair comprises Rep1 = 00101 and Rep2 = 01100, JE = (1+1)/(1+1+1) = 2/3 = 0.67, EE = (1+1)/(1+1+1+2) = 2/5 = 0.4.
+#'
+#' @param x Consolidated binary matrix.
+#'
+#' @return JE (Jaccard Error), EE (Euclidean Error), and standard deviations.
+#'
+#' @examples data(BinMatInput_reps)
+#' mat = BinMatInput_reps
+#' cons = consolidate(mat)
+#' errors(cons)
+#'
+#' @export
+
+errors = function(x){
+
+  mismatch_err = matrix(nrow=nrow(x), ncol = 1)
+  jacc_err = matrix(nrow=nrow(x), ncol = 1)
+
+
+  for(i in 1:nrow(x)) {
+    # find the number of 1s, Os and question marks
+    ones = length(which(x[i,] == 1))
+    zeroes = length(which(x[i,] == 0))
+    questions = length(which(x[i,] == "?"))
+    sum_bands = ones + questions
+
+    mismatch_err[i,] = (questions/(questions + ones + zeroes))
+    jacc_err[i,] = (questions/(questions + ones))
+
+  }
+
+  error_table = data.frame("Errors" = matrix(ncol = 2, nrow = 4))
+  error_table[1,1] = "Average Euclidean Error:"
+  error_table[1,2] = round(base::mean(mismatch_err[,1]),4)
+  error_table[2,1] = "Euclidean error St. dev:"
+  error_table[2,2] = round(stats::sd(mismatch_err[,1]),4)
+  error_table[3,1] = "Average Jaccard:"
+  error_table[3,2] = round(base::mean(jacc_err[,1]),4)
+  error_table[4,1] = "Jaccard error St.dev:"
+  error_table[4,2] = round(stats::sd(jacc_err[,1]),4)
+
+  colnames(error_table) = c("Metric", "Value")
+
+return(error_table)
+
+}