Révisions pour hltb-stats.r

1 file changed, 39 insertions

hltb-stats.r(fichier créé)

		@@ -0,0 +1,39 @@
1	+	library(ggplot2)
2	+	library(readr)
3	+	library(tidyr) # pivot_longer
4	+	library(ggbreak) # data break point
5	+	library(scales) # custom trans
6	+
7	+	data <- read_csv("1.csv", col_types = cols(
8	+	Lastmod = col_date(format = "%Y-%m-%d"),
9	+	.default = col_integer() # cool import
10	+	))
11	+
12	+	# use custom trans to multiply DP 50- to 5x
13	+	# as DP 2000+ occpies too much space in y axis
14	+	custom_trans <- trans_new(
15	+	name = "custom",
16	+	transform = function(x) ifelse(x < 50, x * 5, x), # if 50-, 5x
17	+	# "Retired" starts at 400, so we are safe here
18	+	inverse = function(x) ifelse(x < 250, x / 5, x) # if 250-, 1/5
19	+	)
20	+
21	+	# simplify data to avoid redundant lines
22	+	data_long <- data %>%
23	+	pivot_longer(cols = -Lastmod, names_to = "Tag", values_to = "Count")
24	+
25	+	p <- ggplot(data_long, aes(x = Lastmod, y = Count, color = Tag)) +
26	+	geom_line(alpha = 0.8) +
27	+	geom_point() +
28	+	scale_color_manual(values = c(
29	+	"Playing" = "#2f933a", "Backlogs" = "#2b7ab9", "Replays" = "#439de3",
30	+	"Stalled" = "#1b7168", "Completed" = "#934b93", "Retired" = "#cb3a3b"
31	+	)) + # HLTB style
32	+	scale_y_continuous(trans = custom_trans) + # apply custom trans
33	+	scale_y_break(c(40, 380)) + # skip gap in y axis
34	+	scale_y_break(c(700, 2500)) +
35	+	labs(title = "HLTB Trend 2024", x = "Date", y = "Count", color = "Tag") +
36	+	theme_minimal() +
37	+	theme(axis.text.x = element_text(angle = 45, hjust = 1))
38	+
39	+	ggsave("hltb-trend-2024.png", plot = p, width = 10, height = 6, dpi = 300)

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