combine facet_grid (ggplot2) with denscomp (fitdistrplus)
First off, I am an R newbie. I am trying to apply density plots to various groups within my data. Using fitdistrplus, I have created a single distribution density plot for all of my data.
plot(my_data, pch=20)
plotdist(my_data$Capture_Rate, histo = TRUE, demp = TRUE)
fit_w <- fitdist(my_data$Capture_Rate, "weibull")
fit_g <- fitdist(my_data$Capture_Rate, "gamma")
fit_ln <- fitdist(my_data$Capture_Rate, "lnorm")
par(mfrow=c(2,2))
plot.legend <- c("Weibull", "lognormal", "gamma")
denscomp(list(fit_w, fit_ln, fit_g), legendtext = plot.legend)
Using facet_grid in ggplot, I have created a grid of histograms for each grouping of my data.
df_data <- data.frame(my_data)
cdat <- ddply(df_data, c("sYear", "Season"), summarise, Capture_Rate.mean=mean(Capture_Rate))
ggplot(df_data, aes(x=Capture_Rate, fill=sYear))+
geom_histogram(binwidth = .025,
alpha = .5,
position = "identity")+
#geom_density(alpha=.2, fill="#FF6666")+
geom_vline(data=cdat, aes(xintercept=Capture_Rate.mean),
color="red", linetype="dashed", size=1)+
facet_grid(Season ~ sYear)
What I am looking for is to combine the two results where I get a density plot for each histogram in my grouping grid. Thank you for the assistance.
Sample Data:
a <- dput(my_data)
structure(list(Schedule_Name = structure(c(1L, 1L, 1L, 1L, 1L,
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1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Actuals ", class = "factor"),
Sub_Fleet = structure(c(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), .Label = "38K", class = "factor"), sDate = structure(c(17664,
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18081, 18082, 18083, 18084, 18085, 18086, 18087, 18088, 18089,
18090, 18091, 18092), class = "Date"), Active_Tails = c(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,
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71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L,
71L, 71L, 71L, 71L, 71L, 71L, 71L), MX_Credits = c(1L, 1L,
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4L, 3L, 3L, 4L, 2L, 5L, 5L, 4L, 4L, 6L, 7L, 2L, 4L, 6L, 4L,
7L, 9L, 6L, 4L, 7L, 3L, 9L, 6L, 9L, 7L, 7L, 8L, 7L, 5L, 8L,
10L, 11L, 9L, 6L, 8L, 5L, 7L, 6L, 9L, 10L, 8L, 10L, 7L, 9L,
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16L, 15L, 10L, 13L, 16L, 12L, 10L, 14L, 17L, 12L, 12L, 13L,
15L, 18L, 14L, 24L, 15L, 20L, 17L, 17L, 14L, 22L, 19L, 21L,
23L, 16L, 19L, 23L, 16L, 22L, 17L, 17L, 15L, 22L, 21L, 16L,
19L, 19L, 18L, 14L, 23L, 23L, 25L, 17L, 15L, 22L, 21L, 17L,
19L, 17L, 20L, 23L, 22L, 22L, 22L, 19L, 19L, 25L, 22L, 25L,
25L, 21L, 22L, 24L, 24L, 22L, 20L, 26L, 22L, 22L, 26L, 25L,
24L, 27L, 27L, 26L, 24L, 28L, 23L, 27L, 25L, 25L, 27L, 27L,
23L, 28L, 23L, 23L, 29L, 32L, 23L, 19L, 30L, 27L, 30L, 29L,
25L, 29L, 26L, 24L, 30L, 30L, 33L, 24L, 31L, 30L, 28L, 28L,
29L, 35L, 33L, 30L, 33L, 35L, 37L, 32L, 32L, 36L, 30L, 31L,
33L, 33L, 31L, 33L, 33L, 37L, 33L, 33L, 38L, 37L, 37L, 38L,
34L, 36L, 38L, 28L, 35L, 30L, 33L, 38L, 39L, 30L, 34L, 32L,
28L, 37L, 33L, 36L, 39L, 33L, 36L, 34L, 39L, 28L, 39L, 39L,
32L, 30L, 35L, 33L, 37L, 25L, 32L, 30L, 28L, 39L, 36L, 33L,
38L, 40L, 37L, 33L, 35L, 43L, 30L, 32L, 40L, 36L, 30L, 31L,
41L, 29L, 31L, 38L, 41L, 34L, 35L, 42L, 34L, 33L, 40L, 33L,
31L, 38L, 37L, 29L, 33L, 35L, 38L, 34L, 33L, 36L, 39L, 33L,
33L, 31L, 33L, 36L, 33L, 38L, 33L, 30L, 28L, 30L, 28L, 37L,
34L, 33L, 33L, 34L, 35L, 31L, 38L, 30L, 35L, 30L, 45L, 35L,
31L, 30L, 26L, 26L, 35L, 34L, 26L, 34L, 36L, 31L, 31L), Capture_Rate = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5,
1, 1, 0.5, 1, 0.5, 1, 1, 1, 1, 1, 0.5, 0.5, 1, 1, 0.5, 1,
0.33, 1, 1, 0.75, 0.5, 1, 1, 0.25, 0.6, 0.4, 0.8, 0.8, 0.6,
0.6, 0.8, 0.4, 1, 1, 0.67, 0.67, 1, 1, 0.25, 0.4, 0.6, 0.4,
0.64, 0.82, 0.55, 0.36, 0.64, 0.25, 0.75, 0.5, 0.75, 0.58,
0.58, 0.62, 0.54, 0.36, 0.57, 0.71, 0.79, 0.6, 0.38, 0.5,
0.31, 0.44, 0.38, 0.56, 0.63, 0.47, 0.56, 0.39, 0.47, 0.58,
0.47, 0.5, 0.52, 0.38, 0.48, 0.5, 0.5, 0.39, 0.33, 0.36,
0.5, 0.5, 0.62, 0.58, 0.4, 0.5, 0.62, 0.44, 0.37, 0.5, 0.61,
0.43, 0.43, 0.46, 0.52, 0.6, 0.47, 0.77, 0.47, 0.61, 0.52,
0.5, 0.41, 0.65, 0.54, 0.6, 0.64, 0.44, 0.53, 0.62, 0.43,
0.59, 0.46, 0.45, 0.38, 0.54, 0.51, 0.39, 0.46, 0.46, 0.44,
0.34, 0.56, 0.53, 0.58, 0.4, 0.35, 0.51, 0.49, 0.4, 0.44,
0.4, 0.44, 0.5, 0.48, 0.48, 0.48, 0.41, 0.41, 0.53, 0.46,
0.52, 0.51, 0.43, 0.45, 0.49, 0.48, 0.43, 0.39, 0.5, 0.42,
0.42, 0.5, 0.47, 0.45, 0.5, 0.49, 0.47, 0.44, 0.51, 0.41,
0.48, 0.45, 0.43, 0.47, 0.47, 0.4, 0.47, 0.39, 0.39, 0.48,
0.53, 0.38, 0.32, 0.49, 0.44, 0.48, 0.46, 0.4, 0.46, 0.4,
0.37, 0.46, 0.45, 0.5, 0.36, 0.47, 0.45, 0.42, 0.42, 0.43,
0.52, 0.49, 0.45, 0.49, 0.51, 0.54, 0.47, 0.47, 0.52, 0.43,
0.45, 0.48, 0.48, 0.45, 0.48, 0.48, 0.54, 0.48, 0.48, 0.55,
0.54, 0.54, 0.55, 0.49, 0.52, 0.55, 0.41, 0.51, 0.43, 0.48,
0.55, 0.57, 0.43, 0.49, 0.46, 0.4, 0.53, 0.48, 0.51, 0.56,
0.46, 0.51, 0.49, 0.55, 0.39, 0.55, 0.55, 0.45, 0.42, 0.49,
0.46, 0.52, 0.35, 0.46, 0.43, 0.39, 0.55, 0.51, 0.46, 0.54,
0.56, 0.52, 0.46, 0.49, 0.61, 0.42, 0.45, 0.56, 0.51, 0.42,
0.44, 0.58, 0.41, 0.44, 0.54, 0.58, 0.48, 0.49, 0.59, 0.48,
0.46, 0.56, 0.46, 0.44, 0.54, 0.52, 0.41, 0.46, 0.49, 0.54,
0.48, 0.46, 0.51, 0.55, 0.46, 0.46, 0.44, 0.46, 0.51, 0.46,
0.54, 0.46, 0.42, 0.39, 0.42, 0.39, 0.52, 0.48, 0.46, 0.46,
0.48, 0.49, 0.44, 0.54, 0.42, 0.49, 0.42, 0.63, 0.49, 0.44,
0.42, 0.37, 0.37, 0.49, 0.48, 0.37, 0.48, 0.51, 0.44, 0.44
), Total_SPR_IML = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L), Capture_Rate_w_SPR_IML = c(1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5, 0.5, 1, 1, 0.5,
1, 0.5, 1, 1, 1, 1, 1, 0.5, 0.5, 1, 1, 0.5, 1, 0.33, 1, 1,
0.75, 0.5, 1, 1, 0.25, 0.6, 0.4, 0.8, 0.8, 0.6, 0.6, 0.8,
0.4, 1, 1, 0.67, 0.67, 1, 1, 0.25, 0.4, 0.6, 0.4, 0.64, 0.82,
0.55, 0.36, 0.64, 0.25, 0.75, 0.5, 0.75, 0.58, 0.58, 0.62,
0.54, 0.36, 0.57, 0.71, 0.79, 0.6, 0.38, 0.5, 0.31, 0.44,
0.38, 0.56, 0.63, 0.47, 0.56, 0.39, 0.47, 0.58, 0.47, 0.5,
0.52, 0.38, 0.48, 0.5, 0.5, 0.39, 0.33, 0.36, 0.5, 0.5, 0.62,
0.58, 0.4, 0.5, 0.62, 0.44, 0.37, 0.5, 0.61, 0.43, 0.43,
0.46, 0.52, 0.6, 0.47, 0.77, 0.47, 0.61, 0.52, 0.5, 0.41,
0.65, 0.54, 0.6, 0.64, 0.44, 0.53, 0.62, 0.43, 0.59, 0.46,
0.45, 0.38, 0.54, 0.51, 0.39, 0.46, 0.46, 0.44, 0.34, 0.56,
0.53, 0.58, 0.4, 0.35, 0.51, 0.49, 0.4, 0.44, 0.4, 0.44,
0.5, 0.48, 0.48, 0.48, 0.41, 0.41, 0.53, 0.46, 0.52, 0.51,
0.43, 0.45, 0.49, 0.48, 0.43, 0.39, 0.5, 0.42, 0.42, 0.5,
0.47, 0.45, 0.5, 0.49, 0.47, 0.44, 0.51, 0.41, 0.48, 0.45,
0.43, 0.47, 0.47, 0.4, 0.47, 0.39, 0.39, 0.48, 0.53, 0.38,
0.32, 0.49, 0.44, 0.48, 0.46, 0.4, 0.46, 0.4, 0.37, 0.46,
0.45, 0.5, 0.36, 0.47, 0.45, 0.42, 0.42, 0.43, 0.52, 0.49,
0.45, 0.49, 0.51, 0.54, 0.47, 0.47, 0.52, 0.43, 0.45, 0.48,
0.48, 0.45, 0.48, 0.48, 0.54, 0.48, 0.48, 0.55, 0.54, 0.54,
0.55, 0.49, 0.52, 0.55, 0.41, 0.51, 0.43, 0.48, 0.55, 0.57,
0.43, 0.49, 0.46, 0.4, 0.53, 0.48, 0.51, 0.56, 0.46, 0.51,
0.49, 0.55, 0.39, 0.55, 0.55, 0.45, 0.42, 0.49, 0.46, 0.52,
0.35, 0.46, 0.43, 0.39, 0.55, 0.51, 0.46, 0.54, 0.56, 0.52,
0.46, 0.49, 0.61, 0.42, 0.45, 0.56, 0.51, 0.42, 0.44, 0.58,
0.41, 0.44, 0.54, 0.58, 0.48, 0.49, 0.59, 0.48, 0.46, 0.56,
0.46, 0.44, 0.54, 0.52, 0.41, 0.46, 0.49, 0.54, 0.48, 0.46,
0.51, 0.55, 0.46, 0.46, 0.44, 0.46, 0.51, 0.46, 0.54, 0.46,
0.42, 0.39, 0.42, 0.39, 0.52, 0.48, 0.46, 0.46, 0.48, 0.49,
0.44, 0.54, 0.42, 0.49, 0.42, 0.63, 0.49, 0.44, 0.42, 0.37,
0.37, 0.49, 0.48, 0.37, 0.48, 0.51, 0.44, 0.44), sYear = 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, 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, 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, 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, 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, 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), .Label = c("2018 -",
"2019 -"), class = "factor"), sYear_Month = structure(c(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, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L), .Label = c("2018-05",
"2018-06", "2018-07", "2018-08", "2018-09", "2018-10", "2018-11",
"2018-12", "2019-01", "2019-02", "2019-03", "2019-04", "2019-05",
"2019-06", "2019-07"), class = "factor"), Season = structure(c(3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("0.Winter 1H",
"1.Winter 2H", "2.Spring", "3.Summer", "4.Fall"), class = "factor"),
Year_Season = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 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, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L), .Label = c("2018-0.Winter 1H", "2018-2.Spring",
"2018-3.Summer", "2018-4.Fall", "2019-1.Winter 2H", "2019-2.Spring",
"2019-3.Summer"), class = "factor")), row.names = c(NA, 418L
), class = "data.frame")
Solution
So, the solution for the empirical density is going to slightly easier than do the theoretical distributions. First, let's setup some dummy data, since we don't have any of yours to play around with.
set.seed(123)
# Setup some facets
idx <- expand.grid(c("A", "B"), c("C", "D"))
# For each facet, generate some numbers
df <- apply(idx, 1, function(x){
data.frame(row = x[[1]],
col = x[[2]],
# chose 10 as mean, since Weibull can't be negative
x = rnorm(100, 10))
})
df <- do.call(rbind, df)
Now for the empirical case, we can simply take the density in each facet. We can do this, because ggplot has included kernel density estimates as a stat function.
ggplot(df, aes(x)) +
geom_histogram(binwidth = 0.1) +
# To line up the histogram with KDE, we multiply y-values by binwidth
geom_line(aes(y = ..count..*0.1, colour = "empirical"), stat = "density") +
facet_grid(row ~ col)
Which looks like this:
Because we don't have any ggplot stat functions for the theoretical densities -at least not ones that are panel specific- we would have to pre-compute the xy-coordinates for the theoretical distributions in a separate data.frame:
# Loop over facets
dists <- apply(idx, 1, function(i){
# Grab data belonging to facet
dat <- df$x[df$row == i[[1]] & df$col == i[[2]]]
# Setup x-values
xseq <- seq(min(dat), max(dat), length.out = 100)
# Specify distributions of interest
dists <- c("weibull", "lnorm", "gamma")
# Loop over distributions
fits <- lapply(setNames(dists, dists), function(dist) {
# Estimate parameters
ests <- fitdist(dat, dist)$estimate
# Get y-values
y <- do.call(paste0("d", dist), c(list(x = xseq), as.list(ests)))
# Multiplied by length(dat) to match absolute counts
y * length(dat)
})
# Format everything neatly in a data.frame
out <- lapply(dists, function(j) {
data.frame(row = i[[1]],
col = i[[2]],
x = xseq,
y = fits[[j]],
distr = j)
})
# Combine all distributions
do.call(rbind, out)
})
# Combine all facets
dists <- do.call(rbind, dists)
Now that we've done that tedious work, we can finally plot it:
ggplot(df, aes(x)) +
geom_histogram(binwidth = 0.1) +
geom_line(data = dists, aes(y = y * 0.1, colour = distr)) +
facet_grid(row ~ col)
Adapt as necessary for your own data. Good luck!
EDIT: Now with example data
Assume df is the data.frame from which you've posted the dput() output. I've included a condition that checks if the length of the facet data is longer than 2 and wether the variance is non-zero, so as to skip data from which we wouldn't be able to make any estimates anyway. Furthermore, I've converted variable names to be compatible with how you named them in your data.frame.
idx <- expand.grid(levels(df$Season), levels(df$sYear))
# Loop over facets
dists <- apply(idx, 1, function(i){
dat <- df$Capture_Rate[df$Season == i[[1]] & df$sYear == i[[2]]]
print(length(dat))
if (length(dat) < 2 | var(dat) == 0) {
return(NULL)
}
xseq <- seq(min(dat), max(dat), length.out = 100)
dists <- c("weibull", "lnorm", "gamma")
fits <- lapply(setNames(dists, dists), function(dist) {
ests <- fitdist(dat, dist)$estimate
y <- do.call(paste0("d", dist), c(list(x = xseq), as.list(ests)))
y * length(dat)
})
out <- lapply(dists, function(j) {
data.frame(Season = i[[1]],
sYear = i[[2]],
x = xseq,
y = fits[[j]],
distr = j)
})
do.call(rbind, out)
})
dists <- do.call(rbind, dists)
ggplot(df, aes(x=Capture_Rate, fill=sYear))+
geom_histogram(binwidth = .025,
alpha = .5,
position = "identity") +
geom_line(data = dists, aes(x, y * .025, colour = distr), inherit.aes = FALSE) +
facet_grid(Season ~ sYear)
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