For these exercise download and import the data files containing my Ebird observations from here: https://github.com/mbtoomey/Biol_7263/blob/main/Data/MBT_ebird.csv?raw=true
#load packages library(dplyr) library(tidyverse) MBT_ebird<-read_csv(“https://github.com/mbtoomey/Biol_7263/blob/main/Data/MBT_ebird.csv?raw=true”) head(MBT_ebird)
#In which year did I observe the most individual birds? How many?
MBT_ebird <- group_by(MBT_ebird, year) ungroup(MBT_ebird)
#this pulled just a column of the years MBT_years <- select(MBT_ebird, matches(“year”))
#this pulled the coulmns i wanted but did not consolidate the years like i wanted it too MBT_years2 <- select(MBT_ebird, common_name, count, year)
#this returned a tibble with the year and total amount of birds seen
for that year each in their own column summarize(MBT_ebird,
birdtotal=max(count_tot, na.rm=TRUE)) # in 2020 the most individual
birds were observed at 3154. # A tibble: 13 × 2 year birdtotal
#In that year how many different species of birds did I observe?
#filter for just the 2020 data MBT_ebird2020 <- filter(MBT_ebird, year == 2020)
MBT_ebird2020 <- group_by(MBT_ebird2020, scientific_name) #this give us the amount of species record for 2020 (146) and the following value is a 1 indicating the year 2020
[1] 146 1
#In which state did I most frequently observe Red-winged Blackbirds?
dim(summarize(MBT_ebird2020))
MBT_ebird <- group_by(MBT_ebird, location, common_name, count) dim(summarize(MBT_ebird)) ungroup(MBT_ebird)
#this put out a tibble with coulmns grouped by location, common name, count, and rwbb summarize(MBT_ebird, rwbb=max(count_tot, na.rm=TRUE))
#this pulled out only the entries for RWBB MBT_ebirdRWBB <- filter(MBT_ebird, common_name == “Red-winged Blackbird”)
MBT_ebirdRWBB <- group_by(MBT_ebirdRWBB, location) # this takes
the data that has been summarized by location and condeses the count_tot
of each bird observed in a state and condenses it
summarize(MBT_ebirdRWBB, RWBB_sum=sum(count_tot)) #The state with the
most observations of RWBB is Missouri # A tibble: 5 × 2 location
RWBB_sum
#Filter observations for a duration between 5 and 200 minutes. Calculate the mean rate per checklist that I encounter species each year. Specifically, calculate the number of species in each checklist divided by duration and then take the mean for the year.
MBT_ebird_duration <- filter(MBT_ebird, duration <= “200” | duration >= “5”) MBT_ebird_duration <- filter(MBT_ebird_duration, duration != “0”) MBT_ebird_duration <- filter(MBT_ebird_duration, duration != “2”)
MBT_ebirdmutate <- mutate(MBT_ebird_duration, rate = n_distinct(common_name)/(duration))
MBT_ebirdmean %>% group_by(year) %>% mutate(yrmean = mean(rate)) view(MBT_ebirdmean)
#Create a tibble that includes the complete observations for the top 10 most frequently observed species. First generate a top 10 list and then use this list to filter all observations. Export this tibble as a .csv file saved to a folder called “Results” folder within your R project and add link to the markdown document.
#create a list of 10 MBT_ebird10 <- MBT_ebird %>% count(common_name) %>% arrange(by=(desc(n))) MBT_ebird10 <- as.list(MBT_ebird10$common_name[1:10])
#convert this list to a tibble