first commit
Marvin Johanning
commit
89d4e756c0
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install.packages("ggmap")
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install.packages("sf")
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install.packages("ggrepel")
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library("ggmap")
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library(tidyverse)
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library(sf)
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library(mapview)
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library(dplyr)
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library(ggrepel)
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register_google(key = "[AIzaSyDTykAdRVF2lO-7JKsLLZwoP534vRqyqI4]")
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#myLocation<-c(8.50, 52.04, 8.61, 52)
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#myLocation<-c(7, 47.75, 8.5, 48.25)
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myLocation<-c(8.50, 52, 8.61, 52.04)
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bw_map <- get_googlemap("bielefeld stauteich III", zoom = 14, maptype = "satellite")
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setwd("/Users/mjo-air/Programming/R")
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locations <- read.csv("ornitho.txt", sep = "\t")[,c('COORD_LAT', 'COORD_LON', 'TOTAL_COUNT', 'NAME_SPECIES', 'PLACE')]
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locations <- locations %>% slice(-1)
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print(locations)
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locations <- read.csv("birding.csv", stringsAsFactors = F, na.strings="`")[,c('Location.1', 'Longitude', 'Latitude', 'Number', 'Common')]
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locations <- read.csv("birding.csv", stringsAsFactors = F, na.strings="`")[,c('Location.1', 'Longitude', 'Latitude', 'Common')]
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locations$Number<-gsub(">","",as.character(locations$Number))
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locations$Number<-gsub("~","",as.character(locations$Number))
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locations <- locations %>% mutate_at(4, ~replace_na(.,0))
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locs <- as_tibble(locations)
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locs <- locs %>% mutate_at(4, ~replace_na(.,0))
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print(locs)
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locs$COORD_LON %<>% as.double
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locs$COORD_LAT %<>% as.double
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locs$TOTAL_COUNT %<>% as.integer
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locs$Number %<>% as.integer
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locations_sf <- st_as_sf(locs, coords = c("COORD_LON", "COORD_LAT"), crs = 4326)
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locations_sf <- st_as_sf(per_location, coords = c("Longitude", "Latitude"), crs = 4326)
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mapview(locations_sf, zcol="sum")
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per_location_no_dup <- locs[!duplicated(locs$Location.1),]
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per_location_no_dup <- unique(locs)
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per_location <- locs %>%
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group_by(Location.1, Longitude, Latitude) %>%
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summarise(sum = n()) %>%
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arrange(desc(sum))
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per_location <- per_location_no_dup %>%
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group_by(Location.1, Longitude, Latitude) %>%
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summarise(sum = n()) %>%
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arrange(desc(sum))
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per_location <- locs %>%
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group_by(Location.1, Longitude, Latitude) %>%
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summarise(sum = sum(Number)) %>%
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arrange(desc(sum))
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per_location[!duplicated(per_location$Common),]
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print(per_location)
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print(per_location_no_dup)
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ggmap(bw_map) +
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geom_point(data = per_location,
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aes(x = Longitude, y = Latitude, size=sum),
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color = "red", alpha = 0.5) +
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geom_text_repel(data = per_location, aes(x = Longitude, y = Latitude, label = Location.1)) +
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guides(color = guide_legend(override.aes = list(size = 6))) +
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scale_size_continuous(range = c(2, 9))
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barplot(height=per_location$sum, names=per_location$Location.1, las=2)
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barplot(data$average , border=F , names.arg=data$name ,
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las=2 ,
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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)) ,
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ylim=c(0,13) ,
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main="" )
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ggmap(bw_map) +
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geom_point(data = locs, aes(x = lon, y = lat))
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lon <- distinct(data, Longitude)
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lat <- distinct(data, Latitude)
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locs %>%
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group_by(Location.1) %>%
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summarise(across(everything(), sum))
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mapview(c(lon, lat))
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locations_sf <- st_as_sf(locations, coords = c("lon", "lat"), crs = 4326)
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per_source <- locs %>%
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group_by(TOTAL_COUNT) %>%
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summarise(count = n()) %>%
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arrange(desc(count))
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print(per_source)
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print(data)
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retval <- subset(data, Location.2 == "Heeper Fichten")
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print(retval$Common)
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library("ggmap")
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library(tidyverse)
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library(sf)
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library(mapview)
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library(dplyr)
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library(ggrepel)
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#Set working directory
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setwd("/Users/mjo-air/Programming/R")
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#Choose Scythebird export
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locations <- read.csv(file=file.choose(), stringsAsFactors = F)[,c('Location.1', 'Common', 'Number')]
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#Remove approximate, bigger than etc. signs
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locations$Number<-gsub(">","",as.character(locations$Number))
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locations$Number<-gsub("~","",as.character(locations$Number))
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#Do a tibblings
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locations <- as_tibble(locations)
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#Change data types from character to double and integer
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locations$Number %<>% as.integer
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#Total number of birds by species per location
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per_location_by_species <- locations %>%
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group_by(Common, Location.1) %>%
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summarise(sum = sum(Number)) %>%
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arrange(desc(sum))
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per_location_by_species <- subset(per_location_by_species, Common!="Stockente")
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#Bar plot
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ggplot(per_location_by_species, aes(sum, Common, fill = Location.1)) +
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geom_bar(stat="identity", position = "dodge") +
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labs(title="Number of birds per location")
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print(per_location_by_species)
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# Create mapview map
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locations_sf <- st_as_sf(per_location, coords = c("COORD_LON", "COORD_LAT"), crs = 4326)
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mapview(locations_sf, zcol="sum")
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print(locations)
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@ -0,0 +1,88 @@
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library("ggmap")
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library(tidyverse)
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library(sf)
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library(mapview)
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library(dplyr)
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library(ggrepel)
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library(magrittr)
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#Google API key
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register_google(key = "")
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#Google Maps view
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stauteich_3 <- get_googlemap("bielefeld stauteich III", zoom = 14, maptype = "satellite")
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#Set working directory
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setwd("/Users/mjo-air/Programming/R")
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#Choose Ornihto export
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locations <- read.csv(file=file.choose(), sep = "\t")[,c('COORD_LAT', 'COORD_LON', 'TOTAL_COUNT', 'NAME_SPECIES', 'PLACE')]
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#Remove first row
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locations <- locations %>% slice(-1)
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#Do a tibblings
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locations <- as_tibble(locations)
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#Change data types from character to double and integer
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locations$COORD_LON %<>% as.double
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locations$COORD_LAT %<>% as.double
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locations$TOTAL_COUNT %<>% as.integer
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#Total number of birds per location
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#per_location <- locations %>%
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# group_by(PLACE, COORD_LAT, COORD_LON) %>%
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#summarise(sum = sum(TOTAL_COUNT)) %>%
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#arrange(desc(sum))
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#Total number of birds if Psalm is stupid
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per_location <- locations %>%
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group_by(PLACE) %>%
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mutate(across(starts_with("COORD_"), last)) %>%
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ungroup()
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per_location <- per_location %>%
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group_by(PLACE, COORD_LAT, COORD_LON) %>%
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summarise(sum = sum(TOTAL_COUNT)) %>%
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arrange(desc(sum))
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#Remove text in brackets
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per_location$PLACE <- gsub("\\s*\\[[^\\)]+\\]","",as.character(per_location$PLACE))
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#Total number of birds by species per location
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per_location_by_species <- locations %>%
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group_by(NAME_SPECIES, PLACE) %>%
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summarise(sum = sum(TOTAL_COUNT)) %>%
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arrange(desc(sum))
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#HYPERTEST
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per_location_by_species <- locations %>%
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group_by(NAME_SPECIES, PLACE) %>%
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summarise(sum = sum(TOTAL_COUNT)) %>%
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arrange(desc(sum))
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#Bar plot
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ggplot(per_location_by_species, aes(sum, NAME_SPECIES, fill = PLACE)) +
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geom_bar(stat="identity", position = "dodge") +
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labs(title="Number of birds per location")
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print(per_location_by_species)
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# Create mapview map
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locations_sf <- st_as_sf(per_location, coords = c("COORD_LON", "COORD_LAT"), crs = 4326)
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mapview(locations_sf, zcol="sum")
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# Create Google Maps map
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ggmap(stauteich_3) +
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geom_point(data = per_location,
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aes(x = COORD_LON, y = COORD_LAT, size=sum),
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color = "red", alpha = 0.5) +
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geom_text_repel(data = per_location, aes(x = COORD_LON, y = COORD_LAT, label = PLACE)) +
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guides(color = guide_legend(override.aes = list(size = 1))) +
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scale_size_continuous(range = c(1, 15))
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print(per_location_by_species) %>% print(n=40)
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@ -0,0 +1,65 @@
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library("ggmap")
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library(tidyverse)
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library(sf)
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library(mapview)
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library(dplyr)
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library(ggrepel)
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library(grid)
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library(tidyverse)
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library(shadowtext)
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#Working directory
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setwd("/Users/mjo-air/Programming/R")
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#Colours
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BLUE <- "#076fa2"
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#API key for Google Maps
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register_google(key = "")
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#Map of Bielefeld, centered on Stauteich 3
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stauteich_3 <- get_googlemap("bielefeld stauteich III", zoom = 14, maptype = "satellite")
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#The variable used to get the sum of all birds that were seen at a particular location
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#all_birds_sum_var <- read.csv("birding.csv", stringsAsFactors = F, na.strings="`")[,c('Location.1', 'Longitude', 'Latitude', 'Number', 'Common')]
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#The variable used to get the sum of unique birds that were seen at a particular location
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unique_birds_sum_var <- read.csv("birding.csv", stringsAsFactors = F, na.strings="`")[,c('Location.1', 'Longitude', 'Latitude', 'Common')]
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#Convert to tibble
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locations <- as_tibble(unique_birds_sum_var)
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#Find how many unique sightings there are in this location
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per_location_unique <- unique(locations)
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#Do some grouping
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per_location <- per_location_unique %>%
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group_by(Location.1, Longitude, Latitude) %>%
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summarise(sum = n()) %>%
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arrange(desc(sum))
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#Show table
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print(per_location)
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#Do some converterinos
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locations_sf <- st_as_sf(per_location, coords = c("Longitude", "Latitude"), crs = 4326)
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#Show as HTML map
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mapview(locations_sf, zcol="sum")
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#Create barplot
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barplot(height=per_location$sum, names=per_location$Location.1, las=2)
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#Create hyperplot
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ggplot(per_location) +
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geom_col(aes(sum, Location.1), fill = BLUE, width = 0.6)
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#Google Maps map
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ggmap(bw_map) +
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geom_point(data = per_location,
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aes(x = Longitude, y = Latitude, size=sum),
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color = "red", alpha = 0.5) +
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geom_text_repel(data = per_location, aes(x = Longitude, y = Latitude, label = Location.1)) +
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guides(color = guide_legend(override.aes = list(size = 1))) +
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scale_size_continuous(range = c(1, 15))
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