Correct the wrong script and plot the figure

# Importing Edinburgh Biodiversity Data, publicly available from the NBN Gateway https://data.nbn.org.uk/ # Add informative header # Add libraries for packages # Insert code to import data here # It's good practice to always check your imported data before starting analysis Head(edidiv) tail(edidiv) str(edidiv) dim(edidiv) summary(edidiv) # We can create a vector (a series of values) listing how many species from each taxa have been recorded in Edinburgh # We will filter out the data for each taxon group and then count the unique species within it # Here we are only hinting to all the cool stuff we can do with dplyr - we will learn more in later tutorials. ##### the following code is to identify each of classification of specises ###### Beetle <- filter(edidiv, taxonGroup == "Beetle") Bird <- filter(edidiv, taxonGroup == "Bird") Butterfly <- filter(edidiv, TaxonGroup == "Butterfly") Dragonfly <- filter(edidiv, taxonGroup == "Dragonfly") FloweringPlants <- filter(edidiv, taxonGroup == "Flowering.Plants") Fungus <- filter(edidiv, taxonGroup == "Fungus") Hymenopteran <- filter(edidiv, taxonGroup == "Hymenopteran") Lichen <- filter(edidiv, taxonGroup == "Lichen") Liverwort <- filter(edidiv, taxonGroup == "Liverwort") Mammal <- filter(edidiv, taxonGroup == "Mammal") Mollusc <- filter(edidiv, taxonGroup == "Mollusc") # To find out the number of different species in each taxa, we will use the function unique() together (length) a <- length(unique(Beetle$taxonName)) b <- length(unique(Bird$taxonName)) c <- length(unique(Butterfly$taxonName)) d <- length(unique(Dragonfly$taxonName)) e <- length(unique(FloweringPlants$taxonName)) f <- length(unique(Fungus$taxonname)) g <- length(unique(Hymenopteran$taxonName)) h <- length(unique(Lichen$taxonName)) i <- length(unique(Liverwort$taxonName)) j <- length(unique(Mammal$taxonName)) k <- length(unique(Mollusc$taxonName)) # We can now combine all those object in one vector using the c() function and add labels using names() biodiv <- c(a,b,c,d,e,f,g,h,i,j,k) names(biodiv) <- c("Beetle", "Bird", "Butterfly", "Dragonfly", "Fl.Plants", "Fungus", "Hymenopteran", "Lichen", "Liverwort", "Mammal", "Mollusc") # We have all the values now, so we can visualise them quickly with the barplot() function barplot(biodiv) # There's a few things not quite right that we should fix # Add in axis titles # Make all column labels visible # Set the limits for the y axis # We also want to save our plot png("barplot.png", width=950, height=500) barplot(biodiv, xlab="Taxa", ylab="Number of species", ylim=c(0,900), cex.axis=1.5, cex.lab=1.5) dev.off() # The plot has been saved in your working directory # To confirm where that was, you can use getwd(), and to change it, you can use setwd() getwd() # This was a vector of values, each with a label, which is suitable when dealing with just one set of values # In most cases you will have more variables and categories # For that we will use data frames - we can save a data frame as a csv file to use again later taxa <- c("Beetle", "Bird", "Butterfly", "Dragonfly", "Fl.Plants", "Fungus", "Hymenopteran", "Lichen", "Liverwort", "Mammal", "Mollusc") taxa_f <- factor(taxa) richness <- c(a,b,c,d,e,f,g,h,i,j,k) biodata <- data.frame(taxa_f, richness) barplot(biodata) write.csv(biodata, file="biodata.csv") # If we want to make the same barplot using the data frame, not the vector, we need to slightly change the code # We need to tell the barplot() function exactly what we want it to plot, in our case the richness png("barplot2.png", width=950, height=500) barplot(biodata$richness, names.arg=c("Beetle", "Bird", "Butterfly", "Dragonfly", "Fl.Plants", "Fungus", "Hymenopteran", "Lichen", "Liverwort", "Mammal", "Mollusc"), xlab="Taxa", ylab="Number of species", ylim=c(0,600), cex.axis=1.5, cex.lab=1.5) dev.off() # In this tutorial we found out how many species from a range of taxa have been recorded.

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