Keep only unique tows
IDnoyear<-trawl.data %>%
distinct(ID, .keep_all = TRUE) %>%
separate(ID, into = c("year","ID2"), sep = 4, remove = FALSE)
IDnoyear %>%
group_by(EST_YEAR, SEASON) %>%
summarize(n = n()) %>%
ggplot() + geom_bar(aes(EST_YEAR, n, fill = SEASON),stat = "identity") +
theme(panel.grid = element_blank()) +
labs(x = "Year", y = "", title = "Tows per Year")
IDnoyear %>%
group_by(STRATUM, SEASON) %>%
summarize(n = n()) %>%
ggplot() + geom_bar(aes(STRATUM, n, fill = SEASON),stat = "identity") +
theme(panel.grid = element_blank()) +
labs(x = "Year", y = "", title = "Tows per Stratum")
ID2wlatlong<-IDnoyear %>%
group_by(ID2) %>%
summarize(n = n()) %>%
right_join(IDnoyear, by = "ID2") %>%
select(ID2, n, EST_YEAR, DECDEG_BEGLON, DECDEG_BEGLAT, SEASON, STRATUM) %>%
filter(n > 1) %>%
arrange(desc(n))
IDnoyear %>%
group_by(ID2) %>%
summarize(n = n()) %>%
right_join(IDnoyear, by = "ID2") %>%
select(ID2, n, EST_YEAR, DECDEG_BEGLON, DECDEG_BEGLAT, SEASON, STRATUM) %>%
group_by(n) %>%
ggplot(aes(n)) + geom_histogram() +
labs(x = "ID repeated", title = "Number of times each tow # occurs")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
uniqueID2<-as.vector(unique(ID2wlatlong$ID2))
ID2list<-list()
meanlist<-list()
for(i in uniqueID2){
firstrec<-ID2wlatlong %>%
filter(ID2 == i) %>%
mutate(sortID = seq(1:length(ID2wlatlong$ID2[ID2wlatlong$ID2 == i]))) %>%
filter(sortID == 1)
firstlon<-as.numeric(firstrec$DECDEG_BEGLON)
firstlat<-as.numeric(firstrec$DECDEG_BEGLAT)
xy<-cbind(firstlon,firstlat)
temp<-ID2wlatlong %>%
filter(ID2 == i)
uniqueyear = as.vector(unique(as.character(temp$EST_YEAR)))
for(j in uniqueyear){
tempj<-temp %>%
filter(EST_YEAR == j)
lon2<-as.numeric(tempj$DECDEG_BEGLON)
lat2<-as.numeric(tempj$DECDEG_BEGLAT)
xy2<-cbind(lon2,lat2)
dist<-distm (xy,xy2, fun = distHaversine)
ID2list[[i]][j] = as.numeric(diag(dist)/1000)
}
}
flatIDs<-do.call(rbind,ID2list)
## Warning in (function (..., deparse.level = 1) : number of columns of result
## is not a multiple of vector length (arg 2)
flatIDs %>%
as_tibble(flatIDs) %>%
mutate(ID2 = uniqueID2) %>%
gather(key = "EST_YEAR", value = "meandist", 1:21) %>%
distinct(ID2,meandist, .keep_all = TRUE) %>%
group_by(ID2) %>%
summarise(n = n(), meandistKM = mean(meandist)) %>%
arrange(desc(n)) %>%
ggplot(aes(meandistKM)) + geom_histogram() + labs(x = "Distance (km)", y = "",
title = "Distance between tows of same ID in different years")
## Warning: The `.name_repair` argument to `as_tibble()` takes precedence over
## the deprecated `validate` argument.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.