Creating table for multiple conditional means and Mann-Whitney-U p-values

I used following code to find the individual values and create the table below

Is there an elegant way to create a table with the respective means, the difference between them, and the corresponding p-value within R itself? I'm thinking something along those lines

Thank you, much appreciated!

core_mv_outliers <- structure(list(damage_ratio = structure(c(1.76470588235294, 0.0123076923076923, 
0.25, 0.0761421319796954, 0.1875), format.spss = "F8.2", display_width = 20L), 
    Elevation_dummy = structure(c(1L, 1L, 1L, 1L, 2L), .Label = c("0", 
    "1"), class = "factor"), Shields_bags_dummy = structure(c(1L, 
    1L, 1L, 1L, 1L), .Label = c("0", "1"), class = "factor"), 
    Wall_coat_dummy = structure(c(1L, 2L, 1L, 2L, 1L), .Label = c("0", 
    "1"), class = "factor"), Sump_pump_dummy = structure(c(1L, 
    2L, 1L, 2L, 1L), .Label = c("0", "1"), class = "factor"), 
    Materials_dummy = structure(c(1L, 1L, 1L, 1L, 1L), .Label = c("0", 
    "1"), class = "factor"), Floor_dummy = structure(c(1L, 1L, 
    1L, 1L, 1L), .Label = c("0", "1"), class = "factor"), Electricals_above_dummy = structure(c(1L, 
    2L, 1L, 1L, 1L), .Label = c("0", "1"), class = "factor"), 
    Expensive_contents_dummy = structure(c(1L, 2L, 1L, 1L, 1L
    ), .Label = c("0", "1"), class = "factor")), row.names = c(NA, 
-5L), class = c("tbl_df", "tbl", "data.frame"))

 core_mv_outliers %>%
  group_by(Elevation_dummy) %>%
  summarise(mean_damage_ratio = mean(damage_ratio))

wilcox.test(damage_ratio ~ Elevation_dummy, data = core_mv_outliers)

core_mv_outliers %>%
  group_by(Shields_bags_dummy) %>%
  summarise(mean_damage_ratio = mean(damage_ratio))

wilcox.test(damage_ratio ~ Shields_bags_dummy, data = core_mv_outliers)

core_mv_outliers %>%
  group_by(Wall_coat_dummy) %>%
  summarise(mean_damage_ratio = mean(damage_ratio))

wilcox.test(damage_ratio ~ Wall_coat_dummy, data = core_mv_outliers)

core_mv_outliers %>%
  group_by(Sump_pump_dummy) %>%
  summarise(mean_damage_ratio = mean(damage_ratio))

wilcox.test(damage_ratio ~ Sump_pump_dummy, data = core_mv_outliers)

core_mv_outliers %>%
  group_by(Materials_dummy) %>%
  summarise(mean_damage_ratio = mean(damage_ratio))

wilcox.test(damage_ratio ~ Materials_dummy, data = core_mv_outliers)

core_mv_outliers %>%
  group_by(Floor_dummy) %>%
  summarise(mean_damage_ratio = mean(damage_ratio))

wilcox.test(damage_ratio ~ Floor_dummy, data = core_mv_outliers)

core_mv_outliers %>%
  group_by(Electricals_above_dummy) %>%
  summarise(mean_damage_ratio = mean(damage_ratio))

wilcox.test(damage_ratio ~ Electricals_above_dummy, data = core_mv_outliers)

core_mv_outliers %>%
  group_by(Expensive_contents_dummy) %>%
  summarise(mean_damage_ratio = mean(damage_ratio))

wilcox.test(damage_ratio ~ Expensive_contents_dummy, data = core_mv_outliers)