peng-ggpcp.R

R/crime-scatmat.R

@@ -2,12 +2,19 @@ library(dplyr)
 library(ggplot2)
 library(car)
 
-load(here("data", "crime.RData"))
+load(here::here("data", "crime.RData"))
 
 
 scatterplotMatrix(crime[, 2:8],
-    ellipse = list(levels = 0.68),
+    ellipse = list(levels = 0.68, fill=FALSE),
 #    regLine = list(lwd=3),
 #    diagonal = list(method = "boxplot"),
     smooth = FALSE,
     plot.points = FALSE)
+
+scatterplotMatrix(crime[, 2:8],
+  plot.points = FALSE,
+  ellipse = list(levels = 0.68, fill=FALSE),
+  smooth = list(spread = FALSE, 
+                lwd.smooth=2, lty.smooth = 1, col.smooth = "red"),
+  cex.labels = 2)

R/penguin/peng-ggpcp.R

@@ -0,0 +1,74 @@
+#' ---
+#' title: Penguin data, parallel coordinate plots
+#' ---
+#' 
+
+library(ggplot2)
+library(ggpcp)
+
+load(here::here("data", "peng.RData"))
+source("R/penguin/penguin-colors.R")
+cols <- peng.colors()
+
+# change levels of sex
+peng <- peng |>
+  mutate(sex = factor(sex, labels = c("Female", "Male")))
+
+# Fig 2
+# peng |> 
+#   pcp_select(4,3,5:6, sex, species) |>   #   var selection (sec 3.1)
+#   pcp_scale(method="uniminmax") |>       #   scale values (sec 3.2)
+#   pcp_arrange() |>                       #   arrange categ. data
+#   ggplot(aes_pcp()) +                     # create chart layers:
+#   geom_pcp_axes() +                     #   vertical lines for axes
+#   geom_pcp(aes(colour = species),       #   line  segments
+#            alpha = 0.8, overplot="none") +
+#   geom_pcp_labels() +                    #   label categories
+#   labs(x = "", y = "") +
+#   theme_penguins()
+
+peng |>
+  pcp_select(4, 3, 5:6, sex, species) |>
+  pcp_scale(method = "uniminmax") |>
+  pcp_arrange() |>
+  ggplot(aes_pcp()) +
+  geom_pcp_axes() +
+  geom_pcp(aes(colour = species), alpha = 0.8, overplot = "none") +
+  geom_pcp_labels() +
+  scale_color_manual("Species", values = cols) +
+  theme_bw(base_size = 14) +
+  scale_x_discrete(
+    expand = expansion(add = 0.3),
+    labels = c(
+      "Bill Depth", "Bill Length",
+      "Flipper Length", "Body Mass",
+      "Sex", "Species"
+    )
+  ) +
+  labs(x = "", y = "") +
+  theme(axis.title.y = element_blank(), axis.text.y = element_blank(), 
+        axis.ticks.y = element_blank(), legend.position = "none")
+
+# reorder levels to put species and islands together
+peng1 <- peng %>%
+  mutate(species = factor(species, levels = c("Chinstrap", "Adelie", "Gentoo"))) %>%
+  mutate(island = factor(island, levels = c("Dream", "Torgersen", "Biscoe")))
+
+peng1 %>%
+  pcp_select(species, island, bill_length:body_mass) %>%
+  pcp_scale() %>%
+  pcp_arrange(method = "from-left") %>%
+  ggplot(aes_pcp()) +
+  geom_pcp_axes() +
+  geom_pcp(aes(colour = species), alpha = 0.6, overplot = "none") +
+  geom_pcp_boxes(fill = "white", alpha = 0.5) +
+  geom_pcp_labels() +
+  theme_bw() +
+  labs(x = "", y = "") +
+  theme(
+    axis.text.y = element_blank(), axis.ticks.y = element_blank(),
+    legend.position = "none"
+  ) +
+  scale_colour_manual(values = c(cols[2], cols[1], cols[3])) 
+
+