Commit: f137675fd64a56dc01bf7872940a590d4628b924 - myprojects-hg-reborn (hg)

Commit MetaInfo

• Revisión: f137675fd64a56dc01bf7872940a590d4628b924 (tree)
• Tiempo: 2023-01-25 18:34:08
• Autor: Lorenzo Isella <lorenzo.isella@gmai...>
• Commiter: Lorenzo Isella

Log Message

A script to make a panel plot which highlights a single line in each panel.

Cambiar Resumen

• add: R-codes/panel_plot_ilya.R

Diferencia incremental

diff -r b849bec577de -r f137675fd64a R-codes/panel_plot_ilya.R
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/R-codes/panel_plot_ilya.R	Wed Jan 25 10:34:08 2023 +0100
@@ -0,0 +1,157 @@
+rm(list=ls())
+
+
+## see https://www.r-bloggers.com/2023/01/show-all-data-in-the-background-of-your-faceted-ggplot/
+
+
+library(tidyverse)
+library(lubridate)
+
+source("/home/lorenzo/myprojects-hg/R-codes/stat_lib.R")
+
+df_reg <- structure(list(quart = c("2017 Q1", "2017 Q2", "2017 Q3", "2017 Q4", 
+"2018 Q1", "2018 Q2", "2018 Q3", "2018 Q4", "2019 Q1", "2019 Q2", 
+"2019 Q3"), reg_avg = c(74L, 69L, 69L, 73L, 78L, 67L, 61L, 63L, 
+71L, 68L, 61L)), row.names = c(NA, -11L), class = c("tbl_df", 
+"tbl", "data.frame"))
+
+nn <- nrow(df_reg)
+
+
+df <- structure(list(quart = c("2017 Q1", "2017 Q1", "2017 Q1", "2017 Q1", 
+"2017 Q1", "2017 Q1", "2017 Q1", "2017 Q1", "2017 Q1", "2017 Q1", 
+"2017 Q1", "2017 Q1", "2017 Q1", "2017 Q1", "2017 Q2", "2017 Q2", 
+"2017 Q2", "2017 Q2", "2017 Q2", "2017 Q2", "2017 Q2", "2017 Q2", 
+"2017 Q2", "2017 Q2", "2017 Q2", "2017 Q2", "2017 Q2", "2017 Q2", 
+"2017 Q3", "2017 Q3", "2017 Q3", "2017 Q3", "2017 Q3", "2017 Q3", 
+"2017 Q3", "2017 Q3", "2017 Q3", "2017 Q3", "2017 Q3", "2017 Q3", 
+"2017 Q3", "2017 Q3", "2017 Q4", "2017 Q4", "2017 Q4", "2017 Q4", 
+"2017 Q4", "2017 Q4", "2017 Q4", "2017 Q4", "2017 Q4", "2017 Q4", 
+"2017 Q4", "2017 Q4", "2017 Q4", "2017 Q4", "2018 Q1", "2018 Q1", 
+"2018 Q1", "2018 Q1", "2018 Q1", "2018 Q1", "2018 Q1", "2018 Q1", 
+"2018 Q1", "2018 Q1", "2018 Q1", "2018 Q1", "2018 Q1", "2018 Q1", 
+"2018 Q2", "2018 Q2", "2018 Q2", "2018 Q2", "2018 Q2", "2018 Q2", 
+"2018 Q2", "2018 Q2", "2018 Q2", "2018 Q2", "2018 Q2", "2018 Q2", 
+"2018 Q2", "2018 Q2", "2018 Q3", "2018 Q3", "2018 Q3", "2018 Q3", 
+"2018 Q3", "2018 Q3", "2018 Q3", "2018 Q3", "2018 Q3", "2018 Q3", 
+"2018 Q3", "2018 Q3", "2018 Q3", "2018 Q3", "2018 Q4", "2018 Q4", 
+"2018 Q4", "2018 Q4", "2018 Q4", "2018 Q4", "2018 Q4", "2018 Q4", 
+"2018 Q4", "2018 Q4", "2018 Q4", "2018 Q4", "2018 Q4", "2018 Q4", 
+"2019 Q1", "2019 Q1", "2019 Q1", "2019 Q1", "2019 Q1", "2019 Q1", 
+"2019 Q1", "2019 Q1", "2019 Q1", "2019 Q1", "2019 Q1", "2019 Q1", 
+"2019 Q1", "2019 Q1", "2019 Q2", "2019 Q2", "2019 Q2", "2019 Q2", 
+"2019 Q2", "2019 Q2", "2019 Q2", "2019 Q2", "2019 Q2", "2019 Q2", 
+"2019 Q2", "2019 Q2", "2019 Q2", "2019 Q2", "2019 Q3", "2019 Q3", 
+"2019 Q3", "2019 Q3", "2019 Q3", "2019 Q3", "2019 Q3", "2019 Q3", 
+"2019 Q3", "2019 Q3", "2019 Q3", "2019 Q3", "2019 Q3", "2019 Q3"
+), dealer = structure(c(9L, 2L, 3L, 1L, 14L, 5L, 10L, 11L, 13L, 
+12L, 4L, 8L, 7L, 6L, 9L, 2L, 3L, 1L, 14L, 5L, 10L, 11L, 13L, 
+12L, 4L, 8L, 7L, 6L, 9L, 2L, 3L, 1L, 14L, 5L, 10L, 11L, 13L, 
+12L, 4L, 8L, 7L, 6L, 9L, 2L, 3L, 1L, 14L, 5L, 10L, 11L, 13L, 
+12L, 4L, 8L, 7L, 6L, 9L, 2L, 3L, 1L, 14L, 5L, 10L, 11L, 13L, 
+12L, 4L, 8L, 7L, 6L, 9L, 2L, 3L, 1L, 14L, 5L, 10L, 11L, 13L, 
+12L, 4L, 8L, 7L, 6L, 9L, 2L, 3L, 1L, 14L, 5L, 10L, 11L, 13L, 
+12L, 4L, 8L, 7L, 6L, 9L, 2L, 3L, 1L, 14L, 5L, 10L, 11L, 13L, 
+12L, 4L, 8L, 7L, 6L, 9L, 2L, 3L, 1L, 14L, 5L, 10L, 11L, 13L, 
+12L, 4L, 8L, 7L, 6L, 9L, 2L, 3L, 1L, 14L, 5L, 10L, 11L, 13L, 
+12L, 4L, 8L, 7L, 6L, 9L, 2L, 3L, 1L, 14L, 5L, 10L, 11L, 13L, 
+12L, 4L, 8L, 7L, 6L), levels = c("Lakeside", "Draper", "Filmore", 
+"Sealy", "North", "Wildland", "Westlake", "Southlake", "Beacon", 
+"Oakley", "Orly", "Rosedale", "Pierce", "Mare valley"), class = "factor"), 
+    value = c(69L, 130L, 85L, 117L, 16L, 74L, 61L, 51L, 35L, 
+    65L, 93L, 72L, 83L, 83L, 77L, 86L, 97L, 107L, 17L, 71L, 55L, 
+    61L, 32L, 55L, 89L, 64L, 73L, 79L, 63L, 90L, 92L, 127L, 18L, 
+    68L, 55L, 67L, 32L, 59L, 79L, 58L, 73L, 81L, 61L, 100L, 95L, 
+    132L, 21L, 72L, 72L, 51L, 49L, 53L, 82L, 77L, 85L, 75L, 67L, 
+    106L, 92L, 145L, 21L, 69L, 79L, 51L, 49L, 59L, 94L, 87L, 
+    87L, 79L, 67L, 91L, 83L, 112L, 17L, 71L, 60L, 66L, 42L, 52L, 
+    83L, 70L, 54L, 72L, 57L, 89L, 103L, 107L, 17L, 56L, 42L, 
+    52L, 23L, 41L, 84L, 66L, 59L, 51L, 62L, 87L, 109L, 95L, 17L, 
+    66L, 44L, 60L, 48L, 37L, 81L, 59L, 64L, 46L, 68L, 93L, 94L, 
+    126L, 21L, 83L, 65L, 66L, 38L, 63L, 89L, 66L, 59L, 66L, 61L, 
+    96L, 104L, 133L, 13L, 79L, 80L, 47L, 24L, 49L, 70L, 65L, 
+    68L, 57L, 54L, 103L, 88L, 105L, 22L, 66L, 37L, 63L, 26L, 
+    52L, 66L, 55L, 51L, 69L), avg_sales = c(64.1818181818182, 
+    97.3636363636364, 94.7272727272727, 118.727272727273, 18.1818181818182, 
+    70.4545454545455, 59.0909090909091, 57.7272727272727, 36.1818181818182, 
+    53.1818181818182, 82.7272727272727, 67.1818181818182, 68.7272727272727, 
+    68.9090909090909, 64.1818181818182, 97.3636363636364, 94.7272727272727, 
+    118.727272727273, 18.1818181818182, 70.4545454545455, 59.0909090909091, 
+    57.7272727272727, 36.1818181818182, 53.1818181818182, 82.7272727272727, 
+    67.1818181818182, 68.7272727272727, 68.9090909090909, 64.1818181818182, 
+    97.3636363636364, 94.7272727272727, 118.727272727273, 18.1818181818182, 
+    70.4545454545455, 59.0909090909091, 57.7272727272727, 36.1818181818182, 
+    53.1818181818182, 82.7272727272727, 67.1818181818182, 68.7272727272727, 
+    68.9090909090909, 64.1818181818182, 97.3636363636364, 94.7272727272727, 
+    118.727272727273, 18.1818181818182, 70.4545454545455, 59.0909090909091, 
+    57.7272727272727, 36.1818181818182, 53.1818181818182, 82.7272727272727, 
+    67.1818181818182, 68.7272727272727, 68.9090909090909, 64.1818181818182, 
+    97.3636363636364, 94.7272727272727, 118.727272727273, 18.1818181818182, 
+    70.4545454545455, 59.0909090909091, 57.7272727272727, 36.1818181818182, 
+    53.1818181818182, 82.7272727272727, 67.1818181818182, 68.7272727272727, 
+    68.9090909090909, 64.1818181818182, 97.3636363636364, 94.7272727272727, 
+    118.727272727273, 18.1818181818182, 70.4545454545455, 59.0909090909091, 
+    57.7272727272727, 36.1818181818182, 53.1818181818182, 82.7272727272727, 
+    67.1818181818182, 68.7272727272727, 68.9090909090909, 64.1818181818182, 
+    97.3636363636364, 94.7272727272727, 118.727272727273, 18.1818181818182, 
+    70.4545454545455, 59.0909090909091, 57.7272727272727, 36.1818181818182, 
+    53.1818181818182, 82.7272727272727, 67.1818181818182, 68.7272727272727, 
+    68.9090909090909, 64.1818181818182, 97.3636363636364, 94.7272727272727, 
+    118.727272727273, 18.1818181818182, 70.4545454545455, 59.0909090909091, 
+    57.7272727272727, 36.1818181818182, 53.1818181818182, 82.7272727272727, 
+    67.1818181818182, 68.7272727272727, 68.9090909090909, 64.1818181818182, 
+    97.3636363636364, 94.7272727272727, 118.727272727273, 18.1818181818182, 
+    70.4545454545455, 59.0909090909091, 57.7272727272727, 36.1818181818182, 
+    53.1818181818182, 82.7272727272727, 67.1818181818182, 68.7272727272727, 
+    68.9090909090909, 64.1818181818182, 97.3636363636364, 94.7272727272727, 
+    118.727272727273, 18.1818181818182, 70.4545454545455, 59.0909090909091, 
+    57.7272727272727, 36.1818181818182, 53.1818181818182, 82.7272727272727, 
+    67.1818181818182, 68.7272727272727, 68.9090909090909, 64.1818181818182, 
+    97.3636363636364, 94.7272727272727, 118.727272727273, 18.1818181818182, 
+    70.4545454545455, 59.0909090909091, 57.7272727272727, 36.1818181818182, 
+    53.1818181818182, 82.7272727272727, 67.1818181818182, 68.7272727272727, 
+    68.9090909090909)), row.names = c(NA, -154L), class = c("tbl_df", 
+"tbl", "data.frame"))
+
+
+
+# plot
+
+df <- df |>
+    mutate(new=dealer)
+
+gpl <- df  |>  
+    ## mutate(new = dealer)  |>  
+    ggplot(aes(quart, value))+
+    geom_hline(yintercept = 0)+
+    geom_line(
+        data = df |>  select(-dealer),
+        aes(group = new),
+        color = "grey",
+        linewidth = .5,
+        alpha = 0.5
+    ) +
+      my_ggplot_theme2("right")+
+
+    facet_wrap(~dealer, ncol = 5) +
+    geom_line(
+        data = df_reg, aes(y = reg_avg, group = 1
+                           ),
+        size = .5, color = "black"
+    )  +
+    
+    geom_line(aes(group = dealer), size = .5, color="red") +
+    
+    scale_x_discrete(labels = seq(1:nn), expand = c(0.1,0.1))+
+    scale_y_continuous(sec.axis = dup_axis()) +
+    geom_text(
+        data = df |>  filter(dealer == "Mare valley", quart == "2019 Q3")  |> 
+            transmute(dealer, quart, value = 90),
+        label = "Regional average", hjust = 1, size = 3,
+        color = "black"
+    )
+
+ggsave("panel_ilya.pdf", gpl, width=15,height=10)
+
+
+print("So far so good")