birding-stats/Untitled.R

121 lines
3.3 KiB
R

install.packages("ggmap")
install.packages("sf")
install.packages("ggrepel")
library("ggmap")
library(tidyverse)
library(sf)
library(mapview)
library(dplyr)
library(ggrepel)
register_google(key = "[AIzaSyDTykAdRVF2lO-7JKsLLZwoP534vRqyqI4]")
#myLocation<-c(8.50, 52.04, 8.61, 52)
#myLocation<-c(7, 47.75, 8.5, 48.25)
myLocation<-c(8.50, 52, 8.61, 52.04)
bw_map <- get_googlemap("bielefeld stauteich III", zoom = 14, maptype = "satellite")
setwd("/Users/mjo-air/Programming/R")
locations <- read.csv("ornitho.txt", sep = "\t")[,c('COORD_LAT', 'COORD_LON', 'TOTAL_COUNT', 'NAME_SPECIES', 'PLACE')]
locations <- locations %>% slice(-1)
print(locations)
locations <- read.csv("birding.csv", stringsAsFactors = F, na.strings="`")[,c('Location.1', 'Longitude', 'Latitude', 'Number', 'Common')]
locations <- read.csv("birding.csv", stringsAsFactors = F, na.strings="`")[,c('Location.1', 'Longitude', 'Latitude', 'Common')]
locations$Number<-gsub(">","",as.character(locations$Number))
locations$Number<-gsub("~","",as.character(locations$Number))
locations <- locations %>% mutate_at(4, ~replace_na(.,0))
locs <- as_tibble(locations)
locs <- locs %>% mutate_at(4, ~replace_na(.,0))
print(locs)
locs$COORD_LON %<>% as.double
locs$COORD_LAT %<>% as.double
locs$TOTAL_COUNT %<>% as.integer
locs$Number %<>% as.integer
locations_sf <- st_as_sf(locs, coords = c("COORD_LON", "COORD_LAT"), crs = 4326)
locations_sf <- st_as_sf(per_location, coords = c("Longitude", "Latitude"), crs = 4326)
mapview(locations_sf, zcol="sum")
per_location_no_dup <- locs[!duplicated(locs$Location.1),]
per_location_no_dup <- unique(locs)
per_location <- locs %>%
group_by(Location.1, Longitude, Latitude) %>%
summarise(sum = n()) %>%
arrange(desc(sum))
per_location <- per_location_no_dup %>%
group_by(Location.1, Longitude, Latitude) %>%
summarise(sum = n()) %>%
arrange(desc(sum))
per_location <- locs %>%
group_by(Location.1, Longitude, Latitude) %>%
summarise(sum = sum(Number)) %>%
arrange(desc(sum))
per_location[!duplicated(per_location$Common),]
print(per_location)
print(per_location_no_dup)
ggmap(bw_map) +
geom_point(data = per_location,
aes(x = Longitude, y = Latitude, size=sum),
color = "red", alpha = 0.5) +
geom_text_repel(data = per_location, aes(x = Longitude, y = Latitude, label = Location.1)) +
guides(color = guide_legend(override.aes = list(size = 6))) +
scale_size_continuous(range = c(2, 9))
barplot(height=per_location$sum, names=per_location$Location.1, las=2)
barplot(data$average , border=F , names.arg=data$name ,
las=2 ,
col=c(rgb(0.3,0.1,0.4,0.6) , rgb(0.3,0.5,0.4,0.6) , rgb(0.3,0.9,0.4,0.6) , rgb(0.3,0.9,0.4,0.6)) ,
ylim=c(0,13) ,
main="" )
ggmap(bw_map) +
geom_point(data = locs, aes(x = lon, y = lat))
lon <- distinct(data, Longitude)
lat <- distinct(data, Latitude)
locs %>%
group_by(Location.1) %>%
summarise(across(everything(), sum))
mapview(c(lon, lat))
locations_sf <- st_as_sf(locations, coords = c("lon", "lat"), crs = 4326)
per_source <- locs %>%
group_by(TOTAL_COUNT) %>%
summarise(count = n()) %>%
arrange(desc(count))
print(per_source)
print(data)
retval <- subset(data, Location.2 == "Heeper Fichten")
print(retval$Common)