Merge pull request #46 from nanxstats/continuous-example

Compact continuous example

Author: nanxstats

DESCRIPTION

@@ -27,7 +27,6 @@ Suggests:
     gridExtra,
     knitr,
     ragg,
-    reshape2,
     rmarkdown
 VignetteBuilder: knitr
 Encoding: UTF-8

NEWS.md

@@ -11,6 +11,8 @@
   This change enhances package development transparency, reduces unnecessary
   indirection, and simplifies contributions by avoiding the construction
   of the palette data using the R script in `data-raw/` (#42).
+- For continuous palette examples, remove the reshape2 dependency and
+  use more compact grid layout to reduce output image size (#45).
 
 # ggsci 3.1.0
 

R/continuous-gsea.R

@@ -83,11 +83,14 @@ pal_gsea <- function(palette = c("default"), n = 12, alpha = 1, reverse = FALSE)
 #'
 #' @examples
 #' library("ggplot2")
-#' library("reshape2")
-#' data("mtcars")
 #'
+#' data("mtcars")
 #' cor <- cor(mtcars)
-#' cor_melt <- melt(cor)
+#' cor_melt <- data.frame(
+#'   Var1 = rep(seq_len(nrow(cor)), times = ncol(cor)),
+#'   Var2 = rep(seq_len(ncol(cor)), each = nrow(cor)),
+#'   value = as.vector(cor)
+#' )
 #'
 #' ggplot(
 #'   cor_melt,

R/continuous-material.R

@@ -113,11 +113,14 @@ pal_material <- function(
 #'
 #' @examples
 #' library("ggplot2")
-#' library("reshape2")
-#' data("mtcars")
 #'
+#' data("mtcars")
 #' cor <- abs(cor(mtcars))
-#' cor_melt <- melt(cor)
+#' cor_melt <- data.frame(
+#'   Var1 = rep(seq_len(nrow(cor)), times = ncol(cor)),
+#'   Var2 = rep(seq_len(ncol(cor)), each = nrow(cor)),
+#'   value = as.vector(cor)
+#' )
 #'
 #' ggplot(
 #'   cor_melt,

README.Rmd

@@ -279,11 +279,13 @@ grid.arrange(p1_frontiers, p2_frontiers, ncol = 2)
 ### GSEA
 
 ```{r}
-library("reshape2")
-
 data("mtcars")
-cor <- cor(unname(cbind(mtcars, mtcars, mtcars, mtcars)))
-cor_melt <- melt(cor)
+cor <- cor(unname(mtcars))
+cor_melt <- data.frame(
+  Var1 = rep(seq_len(nrow(cor)), times = ncol(cor)),
+  Var2 = rep(seq_len(ncol(cor)), each = nrow(cor)),
+  value = as.vector(cor)
+)
 
 p3 <- ggplot(cor_melt, aes(x = Var1, y = Var2, fill = value)) +
   geom_tile(colour = "black", linewidth = 0.3) +
@@ -299,12 +301,16 @@ grid.arrange(p3_gsea, p3_gsea_inv, ncol = 2)
 
 ### Material Design
 
-```{r, ggsci-material, fig.height=7.12}
+```{r, ggsci-material, fig.height=3.8}
 set.seed(42)
-k <- 9
+k <- 6
 x <- diag(k)
 x[upper.tri(x)] <- runif(sum(1:(k - 1)), 0, 1)
-x_melt <- melt(x)
+x_melt <- data.frame(
+  Var1 = rep(seq_len(nrow(x)), times = ncol(x)),
+  Var2 = rep(seq_len(ncol(x)), each = nrow(x)),
+  value = as.vector(x)
+)
 
 p4 <- ggplot(x_melt, aes(x = Var1, y = Var2, fill = value)) +
   geom_tile(colour = "black", linewidth = 0.3) +
@@ -331,7 +337,7 @@ grid.arrange(
   p4 + scale_fill_material("orange"), p4 + scale_fill_material("deep-orange"),
   p4 + scale_fill_material("brown"), p4 + scale_fill_material("grey"),
   p4 + scale_fill_material("blue-grey"),
-  ncol = 6
+  ncol = 8
 )
 ```
 

man/figures/README-ggsci-gsea-1.png

@@ -424,16 +424,15 @@ We will use a correlation matrix visualization (a special type of heatmap)
 to demonstrate the continuous color palettes in ggsci.
 
 ```{r}
-library("reshape2")
-
 data("mtcars")
-cor <- cor(unname(cbind(mtcars, mtcars, mtcars, mtcars)))
-cor_melt <- melt(cor)
+cor <- cor(unname(mtcars))
+cor_melt <- data.frame(
+  Var1 = rep(seq_len(nrow(cor)), times = ncol(cor)),
+  Var2 = rep(seq_len(ncol(cor)), each = nrow(cor)),
+  value = as.vector(cor)
+)
 
-p3 <- ggplot(
-  cor_melt,
-  aes(x = Var1, y = Var2, fill = value)
-) +
+p3 <- ggplot(cor_melt, aes(x = Var1, y = Var2, fill = value)) +
   geom_tile(colour = "black", linewidth = 0.3) +
   theme_void() +
   theme(
@@ -461,13 +460,15 @@ The Material Design color palettes are from the
 We generate a random matrix first:
 
 ```{r}
-library("reshape2")
-
 set.seed(42)
-k <- 9
+k <- 6
 x <- diag(k)
 x[upper.tri(x)] <- runif(sum(1:(k - 1)), 0, 1)
-x_melt <- melt(x)
+x_melt <- data.frame(
+  Var1 = rep(seq_len(nrow(x)), times = ncol(x)),
+  Var2 = rep(seq_len(ncol(x)), each = nrow(x)),
+  value = as.vector(x)
+)
 
 p4 <- ggplot(x_melt, aes(x = Var1, y = Var2, fill = value)) +
   geom_tile(colour = "black", linewidth = 0.3) +
@@ -486,7 +487,7 @@ p4 <- ggplot(x_melt, aes(x = Var1, y = Var2, fill = value)) +
 
 Plot the matrix with the 19 material design color palettes:
 
-```{r, fig.height=7.12}
+```{r, fig.height=3.8}
 grid.arrange(
   p4 + scale_fill_material("red"), p4 + scale_fill_material("pink"),
   p4 + scale_fill_material("purple"), p4 + scale_fill_material("deep-purple"),
@@ -498,7 +499,7 @@ grid.arrange(
   p4 + scale_fill_material("orange"), p4 + scale_fill_material("deep-orange"),
   p4 + scale_fill_material("brown"), p4 + scale_fill_material("grey"),
   p4 + scale_fill_material("blue-grey"),
-  ncol = 6
+  ncol = 8
 )
 ```
 
@@ -517,8 +518,7 @@ palette generator functions in the table above. For example:
 mypal <- pal_npg("nrc", alpha = 0.7)(9)
 mypal
 
-library("scales")
-show_col(mypal)
+scales::show_col(mypal)
 ```
 
 You will be able to use the generated hex color codes for such