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Equation \(A =\pi \times r^{2}\)

This is how you waould add H. Wickham as an in-text citation

After a statement:

(Wickham, 2011)

Date only after author name:

(2011)

Multiple references:

(Wickham, 2011; Wickham, Cook, Hofmann, Buja, & others, 2011)

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#Create dummy data
A <- c("a", "a", "b", "b")
B <- c(5, 10, 15,20)
dataframe <- data.frame(A, B)

#Plot figure
boxplot(B~A, data =  dataframe)

library(knitr)
kable(dataframe, digits = 2)

A	B
a	5
a	10
b	15
b	20

#install.packages(pander)
library(`pander`)

## Warning: package 'pander' was built under R version 4.0.4

plant <- c("a", "b", "c")
temperature <- c(20, 20, 20)
growth <- c(0.65, 0.95, 0.15)
dataframe <- data.frame(plant, temperature, growth)
emphasize.italics.cols(3)   # Make the 3rd column italics
pander(dataframe)           # Create the table

plant	temperature	growth
a	20	0.65
b	20	0.95
c	20	0.15

Data Exploration

A preliminary investigation into the biodiversity of Edinburgh, using data from the NBN Gateway.

What is the species richness across taxonomic groups?

A table of species richness:

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

richness <- 
  edidiv %>%
  group_by(taxonGroup) %>%
  summarise(Species_richness = n_distinct(taxonName)) 

pander(richness)

taxonGroup	Species_richness
Beetle	37
Bird	86
Butterfly	25
Dragonfly	11
Flowering.Plants	521
Fungus	219
Hymenopteran	112
Lichen	94
Liverwort	40
Mammal	33
Mollusc	97

A barplot of the table above:

barplot(richness$Species_richness, 
        names.arg = richness$taxonGroup, 
        xlab="Taxa", ylab="Number of species", 
        ylim=c(0,600)
        )

What is the most common species in each taxonomic group?

A table of the most common species:

#Create a vector of most abundant species per taxa
max_abund <-
  edidiv %>%
    group_by(taxonGroup) %>%
    summarise(taxonName = names(which.max(table(taxonName))))

#Add the vector to the data frame
richness_abund <-
inner_join(richness, max_abund, by = "taxonGroup")
richness_abund <- rename(richness_abund, Most_abundant =  taxonName, Taxon = taxonGroup)

richness_abund <- rename(richness_abund, 
                        "Most Abundant" = Most_abundant,
                        "Species Richness" = Species_richness) #Change the column names
emphasize.italics.cols(3) #Make the 3rd column italics
pander(richness_abund) #Create a table

Taxon	Species Richness	Most Abundant
Beetle	37	Coccinella septempunctata
Bird	86	Turdus merula
Butterfly	25	Maniola jurtina
Dragonfly	11	Ischnura elegans
Flowering.Plants	521	Urtica dioica
Fungus	219	Auricularia auricula-judae
Hymenopteran	112	Bombus (Bombus) terrestris
Lichen	94	Xanthoria parietina
Liverwort	40	Lophocolea bidentata
Mammal	33	Sciurus carolinensis
Mollusc	97	Cornu aspersum

Wickham, H. (2011). ggplot2. Wiley Interdisciplinary Reviews: Computational Statistics, 3(2), 180–185.

Wickham, H., Cook, D., Hofmann, H., Buja, A., & others. (2011). Tourr: An r package for exploring multivariate data with projections. Journal of Statistical Software, 40(2), 1–18.

Lab11