Minor edits to the vignette, pander not required any longer.

DESCRIPTION

@@ -20,7 +20,6 @@ Imports:
 Suggests:
     dplyr,
     knitr,
-    pander,
     shiny,
     tibble
 VignetteBuilder: knitr

inst/examples/example.R

@@ -14,7 +14,7 @@ m
 
 # default call; group arc length is determined by the row sums; here, this is
 # the number of people with different hair colors asked what hair color they
-# prefer; this probably reflects how many people there were with the different
+# prefer; this reflects how many people there were with the different
 # hair colors
 chorddiag(m, showGroupnames = T)
 
@@ -23,8 +23,8 @@ chorddiag(m, showGroupnames = T)
 groupColors <- c("#000000", "#FFDD89", "#957244", "#F26223")
 chorddiag(m, groupColors = groupColors, groupnamePadding = 50, margin = 100,
           tooltipGroupConnector = " prefer ")
-# by transposing the matrix, group arc length is determined by the hair color
-# numbers prefered:
+# by transposing the matrix, group arc length is determined by how ofthen the
+# different hair colors were prefered:
 chorddiag(t(m), groupColors = groupColors, groupnamePadding = 50, margin = 100,
           tooltipGroupConnector = " prefered by ")
 # we can also normalize preference by row sums thus expressing for each hair
@@ -35,72 +35,22 @@ chorddiag(100*m/rowSums(m), groupColors = groupColors, groupnamePadding = 30,
           tooltipGroupConnector = " prefer ", tooltipUnit = " %",
           precision = 2)
 
-
-# world migrant stocks
-library(migration.indices)
-library(RColorBrewer)
-data("migration.world")   # this 226x226 matrix has migrant stock numbers from 1990
-                          # where row name gives the country of origin and column
-                          # name gives the destination country
-sort.by.orig <- sort(rowSums(migration.world), decreasing = TRUE, index.return = TRUE)
-mig.sorted.by.orig <- migration.world[sort.by.orig$ix, sort.by.orig$ix]
-row.names(mig.sorted.by.orig) <- names(sort.by.orig$x)
-
-n <- dim(mig.sorted.by.orig)[1]
-groupColors <- rep(brewer.pal(12, "Set3")[c(1:8, 10:12)], length.out = n)
-groupNames <- rep("", n)
-ix <- c(1:50, seq(52, 100, by = 2),
-        seq(105, 150, by = 5),
-        160, 180, 226)
-groupNames[ix] <- colnames(mig.sorted.by.orig)[ix]
-tooltipNames <- colnames(mig.sorted.by.orig)
-chorddiag(mig.sorted.by.orig, groupPadding = 0,
-          showTicks = FALSE, margin = 120,
-          chordedgeColor = NULL, groupColors = groupColors,
-          groupnameFontsize = 10,
-          groupNames = groupNames,
-          groupnamePadding = 10,
-          tooltipNames = tooltipNames,
-          fadeLevel = 0)
-
-mig.transposed <- t(migration.world)
-sort.by.dest <- sort(rowSums(mig.transposed), decreasing = TRUE, index.return = TRUE)
-mig.sorted.by.dest <- mig.transposed[sort.by.dest$ix, sort.by.dest$ix]
-row.names(mig.sorted.by.dest) <- names(sort.by.dest$x)
-
-n <- dim(mig.sorted.by.dest)[1]
-groupColors <- rep(brewer.pal(12, "Set3")[c(1:8, 10:12)], length.out = n)
-groupNames <- rep("", n)
-ix <- c(1:40, seq(42, 60, by = 2),
-        seq(65, 100, by = 5),
-        110, 130, 150, 226)
-groupNames[ix] <- colnames(mig.sorted.by.dest)[ix]
-tooltipNames <- colnames(mig.sorted.by.dest)
-chorddiag(mig.sorted.by.dest, groupPadding = 0,
-          showTicks = FALSE, margin = 120,
-          chordedgeColor = NULL, groupColors = groupColors,
-          groupnameFontsize = 10,
-          groupNames = groupNames,
-          groupnamePadding = 10,
-          tooltipNames = tooltipNames,
-          tooltipGroupConnector = " &#x25c0; ",
-          fadeLevel = 0)
-
-
 # Bipartite chord diagram with Titanic data
 if (requireNamespace("dplyr", quietly = TRUE)) {
-    titanic_tbl <- dplyr::tbl_dt(Titanic)
+    library(dplyr)
+    titanic_tbl <- tibble::as_tibble(Titanic)
     titanic_tbl <- titanic_tbl %>%
-        mutate_each(funs(factor), Class:Survived)
+        mutate(across(where(is.character), as.factor))
     by_class_survival <- titanic_tbl %>%
         group_by(Class, Survived) %>%
-        summarize(Count = sum(N))
-    m <- matrix(by_class_survival$Count, nrow = 4, ncol = 2)
-    dimnames(m) <- list(Class = levels(titanic_tbl$Class),
-                        Survival = levels(titanic_tbl$Survived))
-    m
+        summarise(Count = sum(n)) %>%
+        ungroup()
+    titanic.mat <- matrix(by_class_survival$Count, nrow = 4, ncol = 2, byrow = TRUE)
+    dimnames(titanic.mat ) <- list(Class = levels(titanic_tbl$Class),
+                                   Survival = levels(titanic_tbl$Survived))
+    print(titanic.mat)
     groupColors <- c("#2171b5", "#6baed6", "#bdd7e7", "#bababa", "#d7191c", "#1a9641")
-    chorddiag(m, type = "bipartite", groupColors = groupColors,
-              tooltipGroupConnector = " in ",
+    chorddiag(titanic.mat, type = "bipartite",
+              groupColors = groupColors,
               tickInterval = 50)
 }

inst/examples/shiny/server.R

@@ -1,3 +1,7 @@
+if (!requireNamespace("shiny", quietly = TRUE)) {
+    stop("Package \"shiny\" needed to run the example app. Please install it.",
+         call. = FALSE)
+}
 library(shiny)
 library(chorddiag)
 

vignettes/chorddiagram-vignette.Rmd

@@ -20,10 +20,6 @@ if (!requireNamespace("dplyr", quietly = TRUE)) {
     stop("Package \"dplyr\" needed to build the vignette. Please install it.",
       call. = FALSE)
 }
-if (!requireNamespace("pander", quietly = TRUE)) {
-    stop("Package \"pander\" needed to build the vignette. Please install it.",
-      call. = FALSE)
-}
 ```
 
 
@@ -78,11 +74,6 @@ dimnames(m) <- list(have = haircolors,
 print(m)
 ```
 
-```{r, echo = FALSE, results = 'asis'}
-pander::pandoc.table(m, style = "rmarkdown",
-                     caption = "Hair color preference data. Row names show what hair color people have, and column names show what hair color they prefer for a dating partner.")
-```
-
 Then, we can pass this matrix to the `chorddiag` function to create the chord diagram:
 
 ```{r, eval = FALSE, fig.width = 8}
@@ -136,12 +127,6 @@ print(titanic.mat)
 Note that we labeled the dimensions of the matrix by assigning a named list to `dimnames`.
 The dimension labels (here: "Class" and "Survival") will automatically be used in the chord diagram.
 
-```{r, echo = FALSE, results = 'asis'}
-pander::pandoc.table(titanic.mat, 
-                     type = "rmarkdown",
-                     caption = "Titanic data. Counts of people who died ('No') or survived ('Yes') grouped by membership to passenger class and crew.")
-```
-
 We can create a "bipartite" chord diagram for this matrix by setting `type = "bipartite"`.
 
 ```{r, eval = FALSE}