## ----setup, include=FALSE------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, warnings = FALSE, message = FALSE)
## ---- eval = TRUE--------------------------------------------------------
library(dplyr)
library(wildlifeR)
## ---- eval = TRUE--------------------------------------------------------
## BBS data for PA
data(BBS_PA)
##Landcover data for BBS routes in PA
data(BBS_PA_landcover_1km)
## ------------------------------------------------------------------------
head(BBS_PA)
## ---- eval= TRUE---------------------------------------------------------
dim(BBS_PA)
summary(BBS_PA)
## ------------------------------------------------------------------------
50*202*137
## ------------------------------------------------------------------------
summary(BBS_PA$StopTotal)
## ---- eval = TRUE--------------------------------------------------------
#load dplyr if you haven't already
#library(dplyr)
#look at the names of the full dataframe
names(BBS_PA)
#use select() to isolate focal columns
## and put into a new dataframe
##(Note that ths A in Aou is uppercase
## while the rest of the letters are lowercase)
#BBS_PA2 <- select(.data = BBS_PA,
# Year, Aou, Route, SpeciesTotal)
#dplyr was not working for me so I did this another way of doing
#this
BBS_PA2 <- BBS_PA[,c("Year", "Aou", "Route", "SpeciesTotal")]
#look at columns in new dataframe
names(BBS_PA2)
## ---- echo=FALSE---------------------------------------------------------
i.6080 <- which(BBS_PA2$Aou == 6080)[35:37]
temp <- BBS_PA2[c(i.6080-1,i.6080[1],i.6080-2,i.6080[2],i.6080+1,i.6080[3],i.6080+2),]
temp$" " <- ifelse(temp$Aou == 6080, "we want this row","")
print(temp)
## ---- echo = FALSE-------------------------------------------------------
temp %>% filter(Aou == 6080)
## ---- eval = TRUE--------------------------------------------------------
library(dplyr)
BBS_PA_SCTA <- BBS_PA2 %>% filter(Aou == 6080)
## ------------------------------------------------------------------------
library(ggplot2)
library(ggpubr)
ggscatter(data = BBS_PA_SCTA,
y = "SpeciesTotal",
x = "Year")
## ------------------------------------------------------------------------
summary(BBS_PA_SCTA)
## ---- warning=FALSE------------------------------------------------------
gghistogram(BBS_PA_SCTA,
x = "SpeciesTotal")
## ----eval=FALSE----------------------------------------------------------
# BBS_PA_SCTA_2 <- BBS_PA_SCTA %>% filter(Year == 2006)
## ----nclude=FALSE--------------------------------------------------------
BBS_PA_SCTA_2 <- BBS_PA_SCTA %>% dplyr::filter(Year == 2006)
## ------------------------------------------------------------------------
dim(BBS_PA)
dim(BBS_PA_SCTA)
dim(BBS_PA_SCTA_2)
## ---- warnings =FALSE----------------------------------------------------
gghistogram(data = BBS_PA_SCTA_2,
x = "SpeciesTotal")
## ------------------------------------------------------------------------
names(BBS_PA_landcover_1km)
## ----eval=FALSE----------------------------------------------------------
# BBS_PA_landcover_1km_2 <- BBS_PA_landcover_1km %>%
# select(Route, NLCD.41, NLCD.42, NLCD.43, SUM)
## ----include=FALSE-------------------------------------------------------
BBS_PA_landcover_1km_2 <- BBS_PA_landcover_1km %>%
dplyr::select(Route, NLCD.41, NLCD.42, NLCD.43, SUM)
## ------------------------------------------------------------------------
forest.total <- rowSums(BBS_PA_landcover_1km_2[c("NLCD.41",
"NLCD.42",
"NLCD.43")])
## ------------------------------------------------------------------------
BBS_PA_landcover_1km_2$forest.total <- forest.total
## ------------------------------------------------------------------------
head(BBS_PA_landcover_1km_2)
## ------------------------------------------------------------------------
forest.percent <- BBS_PA_landcover_1km_2$forest.total / BBS_PA_landcover_1km_2$SUM
## ------------------------------------------------------------------------
BBS_PA_landcover_1km_2$forest.percent <- forest.percent
## ------------------------------------------------------------------------
BBS_PA_landcover_1km_2$forest.percent <- BBS_PA_landcover_1km_2$forest.total / BBS_PA_landcover_1km_2$SUM
## ----eval=FALSE----------------------------------------------------------
# BBS_PA_landcover_1km_3 <- BBS_PA_landcover_1km_2 %>%
# select(Route, forest.total, SUM, forest.percent,
# NLCD.41,NLCD.42,NLCD.43)
## ----include=FALSE-------------------------------------------------------
BBS_PA_landcover_1km_3 <- BBS_PA_landcover_1km_2 %>%
dplyr::select(Route, forest.total, SUM, forest.percent,
NLCD.41,NLCD.42,NLCD.43)
## ---- eval = TRUE--------------------------------------------------------
BBS_PA_SCTA_3 <- full_join(BBS_PA_SCTA_2 ,
BBS_PA_landcover_1km_3,
by = "Route")
## ------------------------------------------------------------------------
head(BBS_PA_SCTA_3)
## ------------------------------------------------------------------------
#the BBS data that were merged
dim(BBS_PA_SCTA_2)
#the landcover data that were merged
dim(BBS_PA_landcover_1km_3)
#the final merged dataframe
dim(BBS_PA_SCTA_3)
## ------------------------------------------------------------------------
summary(BBS_PA_SCTA_3)
## ------------------------------------------------------------------------
BBS_PA_SCTA_3$Year <- 2006
BBS_PA_SCTA_3$Aou <- 6080
## ------------------------------------------------------------------------
BBS_PA_SCTA_3$name <- "SCTA"
## ------------------------------------------------------------------------
BBS_PA_SCTA_3$name <- factor(BBS_PA_SCTA_3$name)
## ------------------------------------------------------------------------
summary(BBS_PA_SCTA_3)
## ------------------------------------------------------------------------
is.na(BBS_PA_SCTA_3$SpeciesTotal)
## ------------------------------------------------------------------------
BBS_PA_SCTA_4 <- NA_to_zero(dat = BBS_PA_SCTA_3,column = "SpeciesTotal")
## ------------------------------------------------------------------------
#with NAs
summary(BBS_PA_SCTA_3$SpeciesTotal)
#with NAs removed by NA_to_zero()
summary(BBS_PA_SCTA_4$SpeciesTotal)
## ------------------------------------------------------------------------
BBS_PA_SCTA_3 <- NA_to_zero(dat = BBS_PA_SCTA_3,
column = "SpeciesTotal")
## ------------------------------------------------------------------------
ggscatter(data = BBS_PA_SCTA_3,
y = "SpeciesTotal",
x = "forest.percent")
## ----eval = F------------------------------------------------------------
# BBS_PA_SCTA_4 <- BBS_PA_SCTA_3 %>% select(year = Year,
# aou = Aou,
# route = Route,
# name = name,
# spp.tot = SpeciesTotal,
# for.tot = forest.total,
# NLCD.sum = SUM,
# for.percent = forest.percent)
## ----eval = T------------------------------------------------------------
BBS_PA_SCTA_4 <- BBS_PA_SCTA_3 %>% dplyr::select(year = Year,
aou = Aou,
route = Route,
name = name,
spp.tot = SpeciesTotal,
for.tot = forest.total,
NLCD.sum = SUM,
for.percent = forest.percent)
## ---- eval = FALSE-------------------------------------------------------
# write.csv(BBS_PA_SCTA_4, file = "SCTA_vs_forest_cover.csv")
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