## ----setup, include=FALSE------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, warnings = FALSE, message = FALSE,
#fig.width =
fig.height = 3)
## ------------------------------------------------------------------------
#load devtools
library(devtools)
#download wildlifeR from github
install_github("brouwern/wildlifeR")
#load wildlifeR into your current R sessions
library(wildlifeR)
## ------------------------------------------------------------------------
data(BBS_PA)
## ------------------------------------------------------------------------
dim(BBS_PA)
## ------------------------------------------------------------------------
data(AOU_species_codes)
## ------------------------------------------------------------------------
library(dplyr)
## ------------------------------------------------------------------------
AOU_species_codes %>% filter(alpha.code == "PIWO")
## ------------------------------------------------------------------------
BBS_PA_PIWO <- BBS_PA %>% filter(Aou == "4050")
## ----eval=TRUE-----------------------------------------------------------
BBS_PA_PIWO_2 <- BBS_PA_PIWO %>% filter(Year == 2006)
## ------------------------------------------------------------------------
data(BBS_PA_landcover_1km)
## ------------------------------------------------------------------------
BBS_PA_landcover_1km_2 <- BBS_PA_landcover_1km %>%
select(Route,
NLCD.41, # Deciduous Forest
NLCD.42, # Evergreen
NLCD.43, # Mixed
SUM)
## ------------------------------------------------------------------------
BBS_PA_landcover_1km_2$decid.percent <- BBS_PA_landcover_1km_2$NLCD.41/BBS_PA_landcover_1km_2$SUM
BBS_PA_landcover_1km_2$conifer.percent <- BBS_PA_landcover_1km_2$NLCD.42/BBS_PA_landcover_1km_2$SUM
BBS_PA_landcover_1km_2$mixed.forest.percent <- BBS_PA_landcover_1km_2$NLCD.42/BBS_PA_landcover_1km_2$SUM
## ------------------------------------------------------------------------
BBS_PA_PIWO_3 <- full_join(BBS_PA_PIWO_2 ,
BBS_PA_landcover_1km_2,
by = "Route")
## ------------------------------------------------------------------------
BBS_PA_PIWO_3$Year <- 2006
BBS_PA_PIWO_3$Aou <- 6080
BBS_PA_PIWO_3$name <- "PIWO"
## ------------------------------------------------------------------------
BBS_PA_PIWO_4 <- NA_to_zero(dat = BBS_PA_PIWO_3,
column = "SpeciesTotal")
## ------------------------------------------------------------------------
#with NAs
summary(BBS_PA_PIWO_3$SpeciesTotal)
#with NAs removed by set_NA_SpeciesTotal_to_zero()
summary(BBS_PA_PIWO_4$SpeciesTotal)
## ----eval = FALSE--------------------------------------------------------
# write.csv(BBS_PA_PIWO_4, file = "PIWO_vs_forest_cover.csv")
## ------------------------------------------------------------------------
PIWO_00_05_10 <- sample_BBS_routes(dat = BBS_PA_PIWO,
years = c(2000,2005,2010),
aou.code = 4050)
## ------------------------------------------------------------------------
library(ggplot2)
library(ggpubr)
ggerrorplot(data = PIWO_00_05_10,
y = "SpeciesTotal",
x = "Year",
desc_stat = "mean_ci")
## ------------------------------------------------------------------------
write.csv(PIWO_00_05_10, file = "PIWO_00_05_10.csv")
## ------------------------------------------------------------------------
## number of birds seen
plot.spp.total <- ggboxplot(data = BBS_PA_PIWO_4,
y = "SpeciesTotal",
xlab = "")
## Deciduous landcover
plot.decid.percent <- ggboxplot(data = BBS_PA_PIWO_4,
y = "decid.percent",
xlab = "")
## Coniferous landcover
plot.confir.percent <- ggboxplot(data = BBS_PA_PIWO_4,
y = "conifer.percent",
xlab = "")
## Mixed forest landcover
plot.mixed.percent <- ggboxplot(data = BBS_PA_PIWO_4,
y = "mixed.forest.percent",
xlab = "")
## ------------------------------------------------------------------------
library(cowplot)
plot_grid(plot.spp.total,
plot.decid.percent,
plot.confir.percent,
plot.mixed.percent,
labels = c("A", "B",
"C","D"))
## ------------------------------------------------------------------------
## number of birds seen
plot.spp.total <- gghistogram(data = BBS_PA_PIWO_4,
x = "SpeciesTotal",
xlab = "")
## Deciduous landcover
plot.decid.percent <- gghistogram(data = BBS_PA_PIWO_4,
x = "decid.percent",
xlab = "")
## Coniferous landcover
plot.confir.percent <- gghistogram(data = BBS_PA_PIWO_4,
x = "conifer.percent",
xlab = "")
## Mixed forest landcover
plot.mixed.percent <- gghistogram(data = BBS_PA_PIWO_4,
x = "mixed.forest.percent",
xlab = "")
## ------------------------------------------------------------------------
library(cowplot)
plot_grid(plot.spp.total,
plot.decid.percent,
plot.confir.percent,
plot.mixed.percent,
labels = c("A", "B",
"C","D"))
## ------------------------------------------------------------------------
library(ggplot2)
library(ggpubr)
ggscatter(data = BBS_PA_PIWO_4,
y = "SpeciesTotal",
x = "conifer.percent") +
geom_smooth(se = FALSE)
## ------------------------------------------------------------------------
ggboxplot(data = PIWO_00_05_10,
y = "SpeciesTotal",
x = "Year")
## ------------------------------------------------------------------------
gghistogram(data = PIWO_00_05_10,
x = "SpeciesTotal",
facet.by = "Year")
## ------------------------------------------------------------------------
ggerrorplot(data = PIWO_00_05_10,
y = "SpeciesTotal",
x = "Year",
desc_stat = "mean_ci")
## ------------------------------------------------------------------------
m.null <- lm(SpeciesTotal ~ 1, BBS_PA_PIWO_4)
m.decid <- lm(SpeciesTotal ~ decid.percent, BBS_PA_PIWO_4)
m.mixed <- lm(SpeciesTotal ~ mixed.forest.percent, BBS_PA_PIWO_4)
## ------------------------------------------------------------------------
library(broom)
tidy(m.null)
tidy(m.decid)
tidy(m.mixed)
## ------------------------------------------------------------------------
summary(m.null)
## ------------------------------------------------------------------------
summary(m.decid)
## ------------------------------------------------------------------------
summary(m.mixed)
## ------------------------------------------------------------------------
anova(m.null, m.decid)
## ------------------------------------------------------------------------
anova(m.null, m.mixed)
## ------------------------------------------------------------------------
library(bbmle)
AICtab(m.null,
m.decid,
m.mixed)
## ------------------------------------------------------------------------
PIWO_00_05_10$Year <- factor(PIWO_00_05_10$Year)
## ------------------------------------------------------------------------
m.time.null <- lm(SpeciesTotal ~ 1 , data = PIWO_00_05_10)
m.time.year <- lm(SpeciesTotal ~ Year , data = PIWO_00_05_10)
## ------------------------------------------------------------------------
library(broom)
tidy(m.time.null)
tidy(m.time.year)
## ------------------------------------------------------------------------
library(bbmle)
AICtab(m.time.null,
m.time.year)
## ------------------------------------------------------------------------
m.time.year.aov <- aov(SpeciesTotal ~ Year , data = PIWO_00_05_10)
## ------------------------------------------------------------------------
TukeyHSD(m.time.year.aov)
## ------------------------------------------------------------------------
tukey.out <- TukeyHSD(m.time.year.aov)
## ------------------------------------------------------------------------
plotTukeysHSD(tukey.out)
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