Taller 1 - R Basics
Iniciamos con la instalación de la librería “dplyr” y retificación de la direccion de nuestra carpeta de trabajo.
# Coding Club Workshop 1 - R Basics
# Learning how to import and explore data, and make graphs about Edinburgh's biodiversity
# Written by Andrea Galindo 08/03/2020 National University of Colombia
library(dplyr)## Warning: package 'dplyr' was built under R version 3.6.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniongetwd()## [1] "D:/Mis documentos/RStudio Works"Seguimos con la importación de la base de datos a trabajar y uso de comandos para facilitar su identificación.
edidiv <- read.csv("D:/Mis documentos/RStudio Works/edidiv_.csv")
head(edidiv) # Displays the first few rows
tail(edidiv) # Displays the last rows
str(edidiv) ## 'data.frame': 25684 obs. of 5 variables:
## $ organisationName: Factor w/ 28 levels "BATS & The Millennium Link",..: 14 14 14 8 8 28 28 28 28 28 ...
## $ gridReference : Factor w/ 1938 levels "NT200701","NT200712",..: 1314 569 569 1412 1412 1671 1671 1671 1671 1671 ...
## $ year : int 2000 2000 2000 2000 2000 2001 2001 2001 2001 2001 ...
## $ taxonName : Factor w/ 1275 levels "Acarospora fuscata",..: 1126 1126 1127 192 193 1202 365 977 472 947 ...
## $ taxonGroup : Factor w/ 11 levels "Beetle","Bird",..: 2 2 2 2 2 2 2 2 2 2 ...# Tells you whether the variables are continuous, integers, categorical or charactersLos siguientes son comandos de exploración de los datos.
head(edidiv$taxonGroup) # Displays the first few rows of this column only## [1] Bird Bird Bird Bird Bird Bird
## 11 Levels: Beetle Bird Butterfly Dragonfly Flowering.Plants ... Molluscclass(edidiv$taxonGroup) # Tells you what type of variable we're dealing with: it's character now but we want it to be a factor## [1] "factor"edidiv$taxonGroup <- as.factor(edidiv$taxonGroup) # What are we doing here?!
# More exploration
dim(edidiv) # Displays number of rows and columns## [1] 25684 5summary(edidiv) # Gives you a summary of the data## organisationName gridReference
## Biological Records Centre :6744 NT2673 : 2741
## RSPB :5809 NT2773 : 2031
## Butterfly Conservation :3000 NT2873 : 1247
## Scottish Wildlife Trust :2070 NT2570 : 1001
## Conchological Society of Great Britain & Ireland:1998 NT27 : 888
## The Wildlife Information Centre :1860 NT2871 : 767
## (Other) :4203 (Other):17009
## year taxonName taxonGroup
## Min. :2000 Maniola jurtina : 1710 Butterfly :9670
## 1st Qu.:2006 Aphantopus hyperantus: 1468 Bird :7366
## Median :2009 Turdus merula : 1112 Flowering.Plants:2625
## Mean :2009 Lycaena phlaeas : 972 Mollusc :2226
## 3rd Qu.:2011 Aglais urticae : 959 Hymenopteran :1391
## Max. :2016 Aglais io : 720 Mammal : 960
## (Other) :18743 (Other) :1446summary(edidiv$taxonGroup) # Gives you a summary of that particular variable (column) in your dataset## Beetle Bird Butterfly Dragonfly
## 426 7366 9670 421
## Flowering.Plants Fungus Hymenopteran Lichen
## 2625 334 1391 140
## Liverwort Mammal Mollusc
## 125 960 2226- Calcular la riqueza de especies
Hacemos uso de la siguiente función filter() para crear divisiones entre los objetos:
Beetle <- filter(edidiv, taxonGroup == "Beetle")
Bird <- filter(edidiv, taxonGroup == "Bird")
Butterfly<- filter(edidiv, taxonGroup == "Butterfly")
Dragonfly<- filter(edidiv, taxonGroup == "Dragonfly")
Flowering.Plants<- filter(edidiv, taxonGroup == "Flowering.Plants")
Hymenopteran<- filter(edidiv, taxonGroup == "Hymenopteran")
Lichen<- filter(edidiv, taxonGroup == "Lichen")
Liverwort<- filter(edidiv, taxonGroup == "Liverwort")
Fungus<- filter(edidiv, taxonGroup == "Fungus")
Mammal<- filter(edidiv, taxonGroup == "Mammal")
Mollusc<- filter(edidiv, taxonGroup == "Mollusc")O también podemos utilizar la siguientes funciones juntas: unique() que identifica diferentes especies y length() que realiza su conteo:
a <- length(unique(Beetle$taxonName))
b <- length(unique(Bird$taxonName))
c<- length(unique(Butterfly$taxonName))
d<- length(unique(Dragonfly$taxonName))
e<- length(unique(Flowering.Plants$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))- Creación de un vector
Hacemos esto usando la función c() que significa concatenar. También podemos agregar etiquetas con la función names(), para que los valores salgan de la base de datos:
biodiv <- c(a,b,c,d,e,f,g,h,i,j,k) # We are chaining together all the values; pay attention to the object names you have calculated and their order
names(biodiv) <- c("Beetle",
"Bird",
"Butterfly",
"Dragonfly",
"Flowering.Plants",
"Fungus",
"Hymenopteran",
"Lichen",
"Liverwort",
"Mammal",
"Mollusc")Para visualizar la riqueza de especies en un diagrama usamos la función barplot():
barplot(biodiv)
Si queremos visualizar la información de forma gráfica
png("barplot.png", width=1600, height=600) # look up the help for this function: you can customise the size and resolution of the image
barplot(biodiv, xlab="Taxa", ylab="Number of species", ylim=c(0,600), cex.names= 1.5, cex.axis=1.5, cex.lab=1.5)
dev.off()## png
## 2- Creación de un marco de datos
Utilizaremos la función data.frame(), pero primero crearemos un objeto que contenga los nombres de todos los taxones (una columna) y otro objeto con todos los valores para la riqueza de especies de cada taxón (otra columna).
# Creating an object called "taxa" that contains all the taxa names
taxa <- c("Beetle",
"Bird",
"Butterfly",
"Dragonfly",
"Flowering.Plants",
"Fungus",
"Hymenopteran",
"Lichen",
"Liverwort",
"Mammal",
"Mollusc")
# Turning this object into a factor, i.e. a categorical variable
taxa_f <- factor(taxa)
# Combining all the values for the number of species in an object called richness
richness <- c(a,b,c,d,e,f,g,h,i,j,k)
# Creating the data frame from the two vectors
biodata <- data.frame(taxa_f, richness)
# Saving the file
write.csv(biodata, file="biodata.csv") # it will be saved in your working directoryPara visualizar la información anterior usamos
png("barplot2.png", width=1600, height=600)
barplot(biodata$richness, names.arg=c("Beetle",
"Bird",
"Butterfly",
"Dragonfly",
"Flowering.Plants",
"Fungus",
"Hymenopteran",
"Lichen",
"Liverwort",
"Mammal",
"Mollusc"),
xlab="Taxa", ylab="Number of species", ylim=c(0,600))
dev.off()## png
## 2