Proportion of locations per day-CodePudding

I need to calculate the proportion of estimated locations considered to be area-restricted search (ARS) per day, in order to calculate the effect of bathymetry on these locations. How can I do it?

I was trying to do it this way:

AA_jub_day$YMD <- as.Date(AA_jub_day$YMD, "%Y,%m,%d")

AA_jub_day %>%
  group_by(YMD, States) %>%
  summarise(n = n()) %>%
  mutate(proportion = n / sum(n))

but I can't think of a way out.

Part of my data:

> dput(AA_jub_day)
structure(list(ID = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
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1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L), levels = c("24641.05", "84485.18"), class = "factor"), 
    sex = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
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    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
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    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
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    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L), levels = "F", class = "factor"), bat = structure(list(
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    17910, 17910, 17910, 17910, 17911, 17911, 17911, 17911, 17912, 
    17912, 17912, 17912, 17913, 17913, 17913, 17913, 17914, 17914, 
    17914, 17914, 17915, 17915, 17915, 17915, 17916, 17916, 17916, 
    17916, 17917, 17917, 17917, 17917, 17918, 17918, 17918, 17918, 
    17919, 17919, 17919, 17919, 17920), class = "Date")), class = "data.frame", row.names = c("13981", 
"13982", "13983", "13984", "13985", "13986", "13987", "13988", 
"13989", "13990", "13991", "13992", "13993", "13994", "13995", 
"13996", "13997", "13998", "13999", "14000", "14001", "14002", 
"14003", "14004", "14005", "14006", "14007", "14008", "14009", 
"14010", "14011", "14012", "14013", "14014", "14015", "14016", 
"14017", "14018", "14019", "14020", "14021", "14022", "14023", 
"14024", "14025", "14026", "14027", "14028", "14029", "14030", 
"14031", "14032", "14033", "14034", "14035", "14036", "14037", 
"14038", "14039", "14040", "14041", "14042", "14043", "14044", 
"14045", "14046", "14047", "14048", "14049", "14050", "14051", 
"14052", "14053", "14054", "14055", "14056", "14057", "14058", 
"14059", "14060", "14061", "14062", "14063", "14064", "14065", 
"14066", "14067", "14068", "14069", "14070", "14071", "14072", 
"14073", "14074", "14075", "14076", "14077", "14078", "14079", 
"14080", "14081", "14082", "14083", "14084", "14085", "14086", 
"14087", "14088", "14089", "14090", "14091", "14092", "14093", 
"14094", "14095", "14096", "14097", "14098", "14099", "14100", 
"14101", "14102", "14103", "14104", "14105", "14106", "14107", 
"14108", "14109", "14110", "14111", "14112", "14113", "14114", 
"14115", "14116", "14117", "14118", "14119", "14120", "14121", 
"14122", "14123", "14124", "14125", "14126", "14127", "14128", 
"14129", "14130", "14131", "14132", "14133", "14134", "14135", 
"14136", "14137", "14138", "14139", "14140", "14141", "14142", 
"14143", "14144", "14145", "14146", "14147", "14148", "14149", 
"14150", "14151", "14152", "19261", "19262", "19263", "19264", 
"19265", "19266", "19267", "19268", "19269", "19270", "19271", 
"19272", "19273", "19274", "19275", "19276", "19277", "19278", 
"19279", "19280", "19281", "19282", "19283", "19284", "19285", 
"19286", "19287", "19288", "19289", "19290", "19291", "19292", 
"19293", "19294", "19295", "19296", "19297", "19298", "19299", 
"19300", "19301", "19302", "19303", "19304", "19305", "19306", 
"19307", "19308", "19309", "19310"))

In the end I want to generate a plot like this

CodePudding user response：

When you said you wanted to "calculate the effect of bathymetry", I assumed that you were referring to the variable bat$depth. I don't see anything immediately wrong with your original code, you just needed to add bat$depth to your group_by argument. You can also add a month variable (done using lubridate) and use other visualization packages to recreate the plot you included as an example; I used a combination of cowplot and patchwork to get a plot that combine the geom_smooth() line and the colorful dots that indicate month.

library(tidyverse)
library(lubridate)
library(cowplot)
library(patchwork)

df <- df %>%
  mutate(YMD = as.Date(df$YMD, "%Y,%m,%d")) %>%
  group_by(YMD, States, bat$depth) %>%
  summarise(n = n()) %>%
  mutate(proportion = n / sum(n)) %>%
  mutate(month = month(YMD, label = TRUE, abbr = FALSE))

p1 <- ggplot(df, aes(x = `bat$depth`, y = proportion))  
  geom_smooth(color = "black")  
  theme_bw()   
  theme(plot.margin = unit(c(0,0,0,0), "cm"))
 
p2 <- ggplot(df, aes(x = `bat$depth`, y = month))  
  geom_point(aes(color = month), shape = 15)  
  coord_fixed(ratio = 150)  
  xlab("")   ylab("")  
  theme_bw()   
  theme(legend.title=element_blank())  
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank())  
  theme(axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())  
  theme(plot.margin = unit(c(0,0,0,0), "cm"))   
  theme(legend.position = "right")   
  theme(legend.key.size = unit(0.01, "cm"),
        legend.margin = margin(0,0,0,0))

getleg <- get_legend(p2)
p2 <- p2   theme(legend.position = "none")
p3 <- p2/p1   theme(aspect.ratio = 1/2)
final <- p3   inset_element(getleg, .01, .01, .2, .2)
final