ibartomeus

#question: Can we create an heuristic to parse this type of data:

#Input example:
Halictus crenicornis
GALICIA: 1♀, Monte do Gozo, Santiago de Compostela (La Coruña), 350 m, 5.VIII.2016, 29TNH404481. – 1♀, Río Castro, Cerdedo (Pontevedra), 20.VII.1996. – 1♀, Oca (Pontevedra), 20.VII.1996. 
ASTURIAS: 1♂, 1♀, Raitán, Carreño (Asturias), 130 m, 30TTP72, 17.VIII.2005. – 1♀, Poreño, Villaviciosa (Asturias), 43,426443º, -5,445950º, 13.V.2015, sobre flor de Centaurea nigra, C. Guardado leg. – 1♀, Poreño, Villaviciosa (Asturias), 43,426443º, -5,445950º, 27.V.2014, M. Miñarro leg. – 1♀, Muñiz (Asturias), 14.VII.2016, sobre flor de Taraxacum, D. Luna leg.

#Desired output (csv):
Species, CCAA, female, male, locality, province, elevation, date, UTM, latitude, longitude, notes
Halictus crenicornis, Galicia, 1, 0, "Monte do Gozo, Santiago de Compostela", La Coruña, 350, 5.VIII.2016, 29TNH404481, NA, NA, NA

ibartomeus

#This shows how to get the random slopes and CI's for each level in a hierarchical model

#dataset used
head(iris)

#what we want to investigate
#Is there a general relationship? and how it differs by species
plot(iris$Sepal.Width ~ iris$Petal.Width, col = iris$Species, las =1)

#Our model with random slope and intercept

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#Calculate how many sets there are in a set game.
 

#each card has 4 characteristics with 3 factors each
color <- c("red", "green", "purple")
shape <- c("round", "diamond", "curly")
texture <- c("fill", "empty", "striped")
number <- c("one", "two", "three")

create_board <- function(n = 12){

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#SevillaR talk

#The problem:

time <- c(2000:2015)
abundance <- rnorm(16, 150, 50) #poison??
plot(abundance ~ time, t = "l")


#can I detect a trend?

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#I have >1000 bees to check its name, so I want to automatize taxize for
# fixing misspellings when possible
# updating synonims to accepted names
# keeping ONLY accepted species (fully resolved at species level)

# this uses taxize > 0.7.6.9157 If you are using older version (e.g. what its now on CRAN) see the history of this file.
library(taxize)
library(dplyr)

#example: good, synomin, typo, unexisting, genus only. 

ibartomeus

#This is a quick demo performed for Sevilla R users group

#Elena asked about IUCN data. You can retrive this data using taxize: https://github.com/ropensci/taxize
#Elena also suggested vectorbase can be scrapped. Maybe for the next hackaton?

#package OriginR
library(originr)

#define species (don't worry about typos)
sp <- c("Apis mellifera", "carpobrotus edulis", "Lavandula stoechas",

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# This approach to assess multifunctionality is based in the idea that sites providing
    # best multiple functions will have not only a high mean value across function 
    # (approach 3 in Byrnes et al.) but also low variability in the function delivered  
    # across functions (i.e. Coef of var).

#I use Byrnes multifunc package to ilustrate it.
library(devtools)
install_github("multifunc", "jebyrnes")
library(multifunc)
library(ggplot2)

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library(plotrix)
#manually enter data
d <- data.frame(
  Bee_group = c("Bombus", "Dark", "Green", "Lasioglossum"),
  Efficiency = c(106.86207, 81.50000, 80.66667, 59.27586),
  Eff_SE = c(15.56551, 20.12130, 17.97529, 12.25689),
  Visitation = c(2.8333333, 0.2222222, 2.2222222, 5.6666667),
  Visit_SE = c(0.8333333, 0.1008317, 0.7119684, 1.0966378)
  )
#create a grid with value x (visitation), y (efficiency) and z = x*y.

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#who are the pollinators? (a plot)
#http://ibartomeus.wordpress.com/2012/12/17/who-are-the-pollinators

#enter data manually
d <- matrix(nrow = 6, ncol = 7)
d[,1] <- c(20.000,  10,	10,	10,	200.000,	100)
d[,2] <- c(6.000,	4,	8,	9,	100.000,	20)
d[,3] <- c(14.500,	6,	4,	9,	80.000,	4)
d[,4] <- c(30.000,	7,	2,	7,	50.000,	3)
d[,5] <- c(0.328, 10,	2,	3,	1.000,	5)

ibartomeus

---
title: "Sevillarusers - ggplot2 intro"
author: "Ra?l Ortiz"
date: "Tuesday, October 27, 2015"
output: pdf_document
---

# Introducci?n al paquete gr?fico "ggplot2".

## Establezco el directorio de trabajo.