Cluster visualisation images

R/clusterviz.R

@@ -14,14 +14,80 @@ str(aggdf)
 
 ggplot(aggdf, aes(y = kwh_tot_mean, x = cluster)) + geom_boxplot()
 
-ggplot(aggdf, aes(x = read_time, y = kwh_tot_mean, color = cluster)) + 
-    geom_line() + facet_grid(cluster ~ .) +
-    labs(title = "Cluster behaviour over full year", x = "Date and time", y = "kwh") +
-    scale_color_manual(values = cbp)
+facall <- ggplot(aggdf, aes(x = read_time, y = kwh_tot_mean, color = cluster, fill = cluster)) + 
+    geom_line(size = 1.5) + geom_ribbon(aes(ymin = kwh_tot_CI_low, ymax = kwh_tot_CI_high), alpha = 0.2, color = NA) +
+    labs(title = "Cluster behaviour over full year, 2017", x = "Date", y = "kwh") +
+    scale_color_manual(values = cbp) +
+    scale_fill_manual(values = cbp) +
+    theme(legend.position = "none") +
+    scale_x_datetime(date_breaks = "1 month", date_labels = "%-d %B")
 
-midjan <- filter(aggdf, read_time > as.POSIXct("2017-01-15"), read_time <= as.POSIXct("2017-01-21"))
+allcon <- facall + facet_grid(cluster ~ .)
+allfre <- facall + facet_grid(cluster ~ ., scales = "free")
+
+midjan <- filter(aggdf, read_time >= as.POSIXct("2017-01-15", tz = "UTC"), read_time <= as.POSIXct("2017-01-22", tz = "UTC"))
+
+facjan <- ggplot(midjan, aes(x = read_time, y = kwh_tot_mean, color = cluster, fill = cluster)) + 
+    geom_line(size = 1.5) + geom_ribbon(aes(ymin = kwh_tot_CI_low, ymax = kwh_tot_CI_high), alpha = 0.2, color = NA) +
+    labs(title = "Cluster behaviour over third week of January", x = "Date", y = "kwh") +
+    scale_color_manual(values = cbp) +
+    scale_fill_manual(values = cbp) +
+    theme(legend.position = "none") +
+    scale_x_datetime(date_breaks = "1 day", date_labels = "%a, %-d %B %Y")
+
+jancon <- facjan + facet_grid(cluster ~ .)
+janfre <- facjan + facet_grid(cluster ~ ., scales = "free")
+
+
+midap <- filter(aggdf, read_time >= as.POSIXct("2017-04-16", tz = "UTC"), read_time <= as.POSIXct("2017-04-23", tz = "UTC"))
+
+facap <- ggplot(midap, aes(x = read_time, y = kwh_tot_mean, color = cluster, fill = cluster)) + 
+    geom_line(size = 1.5) + geom_ribbon(aes(ymin = kwh_tot_CI_low, ymax = kwh_tot_CI_high), alpha = 0.2, color = NA) +
+    labs(title = "Cluster behaviour over third week of April 2017", x = "Date", y = "kwh") +
+    scale_color_manual(values = cbp) +
+    scale_fill_manual(values = cbp) +
+    theme(legend.position = "none") +
+    scale_x_datetime(date_breaks = "1 day", date_labels = "%a, %-d %B %Y")
+
+apcon <- facap + facet_grid(cluster ~ .)
+apfre <- facap + facet_grid(cluster ~ ., scales = "free")
+
+
+midjul <- filter(aggdf, read_time >= as.POSIXct("2017-07-16", tz = "UTC"), read_time <= as.POSIXct("2017-07-23", tz = "UTC"))
+
+facjul <- ggplot(midjul, aes(x = read_time, y = kwh_tot_mean, color = cluster, fill = cluster)) + 
+    geom_line(size = 1.5) + geom_ribbon(aes(ymin = kwh_tot_CI_low, ymax = kwh_tot_CI_high), alpha = 0.2, color = NA) +
+    labs(title = "Cluster behaviour over third week of July 2017", x = "Date", y = "kwh") +
+    scale_color_manual(values = cbp) +
+    scale_fill_manual(values = cbp) +
+    theme(legend.position = "none") +
+    scale_x_datetime(date_breaks = "1 day", date_labels = "%a, %-d %B %Y")
+
+julcon <- facjul + facet_grid(cluster ~ .)
+julfre <- facjul + facet_grid(cluster ~ ., scales = "free")
+
+
+midoct <- filter(aggdf, read_time >= as.POSIXct("2017-10-15", tz = "UTC"), read_time <= as.POSIXct("2017-10-22", tz = "UTC"))
+
+facoct <- ggplot(midoct, aes(x = read_time, y = kwh_tot_mean, color = cluster, fill = cluster)) + 
+    geom_line(size = 1.5) + geom_ribbon(aes(ymin = kwh_tot_CI_low, ymax = kwh_tot_CI_high), alpha = 0.2, color = NA) +
+    labs(title = "Cluster behaviour over third week of October 2017", x = "Date", y = "kwh") +
+    scale_color_manual(values = cbp) +
+    scale_fill_manual(values = cbp) +
+    theme(legend.position = "none") +
+    scale_x_datetime(date_breaks = "1 day", date_labels = "%a, %-d %B %Y")
+
+octcon <- facoct + facet_grid(cluster ~ .)
+octfre <- facoct + facet_grid(cluster ~ ., scales = "free")
+
+ggsave("all-9-fix.png", allcon, path = "../img/", dpi = "retina", width = 40, height = 25, units = "cm")
+ggsave("all-9-fre.png", allfre, path = "../img/", dpi = "retina", width = 40, height = 25, units = "cm")
+ggsave("jan-9-fix.png", jancon, path = "../img/", dpi = "retina", width = 40, height = 25, units = "cm")
+ggsave("jan-9-fre.png", janfre, path = "../img/", dpi = "retina", width = 40, height = 25, units = "cm")
+ggsave("apr-9-fix.png",  apcon, path = "../img/", dpi = "retina", width = 40, height = 25, units = "cm")
+ggsave("apr-9-fre.png",  apfre, path = "../img/", dpi = "retina", width = 40, height = 25, units = "cm")
+ggsave("jul-9-fix.png", julcon, path = "../img/", dpi = "retina", width = 40, height = 25, units = "cm")
+ggsave("jul-9-fre.png", julfre, path = "../img/", dpi = "retina", width = 40, height = 25, units = "cm")
+ggsave("oct-9-fix.png", octcon, path = "../img/", dpi = "retina", width = 40, height = 25, units = "cm")
+ggsave("oct-9-fre.png", octfre, path = "../img/", dpi = "retina", width = 40, height = 25, units = "cm")
 
-ggplot(midjan, aes(x = read_time, y = kwh_tot_mean, color = cluster)) + 
-    geom_line() + facet_grid(cluster ~ .) +
-    labs(title = "Cluster behaviour over third week of January", x = "Date and time", y = "kwh") +
-    scale_color_manual(values = cbp)