Parcial 3
Juan David Betancourth López
8/11/2021
PONTIFICIA UNIVERSIDAD JAVERIANA CALI FACULTAD DE INGENIERIA Y CIENCIAS DEPARTAMENTO DE CIENCIAS NATURALES Y MATEMATICAS BIOESTADISTICA PARCIAL # 3 NOMBRE: Juan David Betancourth López_____________________________ CODIGO: 8952627______ 1. Dos tipos de moluscos A y B fueron sometidos a tres concentraciones distintas de agua de mar (100%, 75% y 50%) y se observó el consumo de oxígeno midiendo la proporción de O2 por unidad de peso seco del molusco.
load("C:/Users/Juan David Betancour/Downloads/YDRAY-moluscos.RData")
mean(BD_moluscos$c_agua, na.rm=TRUE)## [1] 75fmuestra=function(n)
muestra=sample(BD_moluscos$cons_o, size=n)
fmuestra(n=24)
resultados=sapply(rep(25,48), fmuestra)
1.14/sqrt(50)## [1] 0.1612203funcionP = function (P,E,Z,N){
n0= (P*(1-P))/((E/Z)^2)
nf= n0/(1+(n0/N))
return(nf)
}
funcionP(0.5, 0.03, 1.96, 5000)## [1] 879.4228###
require(ggplot2)## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.1.1ggplot(data=BD_moluscos, aes(y=cons_o, x=c_agua))+geom_point()+theme_bw()+xlab("Concentración de agua de mar")+ylab("Consumo de oxígeno")+geom_smooth()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 49.75## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 50.25## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 9.1868e-017## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 2525.1## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 49.75## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 50.25## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 9.1868e-017## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 2525.1
ggplot(data=BD_moluscos, aes(y=cons_o, x=molusco, colour=c_agua))+geom_point()+theme_bw()+xlab("Especie de molusco")+ylab("Consumo de oxígeno")+geom_smooth()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
require(ggplot2)
data(BD_moluscos)## Warning in data(BD_moluscos): data set 'BD_moluscos' not foundggplot(data = BD_moluscos,aes(y=cons_o,x=c_agua,))+geom_point()+theme_bw()+xlab("Concentración de agua de mar")+ylab("Consumo de oxígeno")+geom_smooth()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 49.75## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 50.25## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 9.1868e-017## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 2525.1## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 49.75## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 50.25## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 9.1868e-017## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 2525.1
load("C:/Users/Juan David Betancour/Downloads/YDRAY-moluscos.RData")
mean(BD_moluscos$c_agua)## [1] 75sd(BD_moluscos$c_agua)## [1] 20.62842min(BD_moluscos$c_agua)## [1] 50median(BD_moluscos$c_agua)## [1] 75max(BD_moluscos$c_agua)## [1] 100min(BD_moluscos$molusco)## [1] "A"max(BD_moluscos$molusco)## [1] "B"sum(BD_moluscos$cons_o)## [1] 446.63mean(BD_moluscos$cons_o)## [1] 9.304792sd(BD_moluscos$cons_o)## [1] 3.682652min(BD_moluscos$cons_o)## [1] 1.8median(BD_moluscos$cons_o)## [1] 9.7max(BD_moluscos$cons_o)## [1] 18.8###
attach(BD_moluscos)
#b)Coeficiente de correlación
#c)Regresión lineal
mod=lm(log(cons_o)~molusco)
summary(mod)##
## Call:
## lm(formula = log(cons_o) ~ molusco)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.44741 -0.28150 0.02414 0.29204 0.83836
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.25210 0.08915 25.26 <2e-16 ***
## moluscoB -0.21690 0.12608 -1.72 0.0921 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4367 on 46 degrees of freedom
## Multiple R-squared: 0.06045, Adjusted R-squared: 0.04003
## F-statistic: 2.96 on 1 and 46 DF, p-value: 0.09208require (faraway)## Loading required package: farawaydata(BD_moluscos)## Warning in data(BD_moluscos): data set 'BD_moluscos' not foundBD_moluscos## # A tibble: 48 x 3
## c_agua molusco cons_o
## <dbl> <chr> <dbl>
## 1 100 A 7.16
## 2 100 A 8.26
## 3 100 A 6.78
## 4 100 A 14
## 5 100 A 13.6
## 6 100 A 11.1
## 7 100 A 8.93
## 8 100 A 9.66
## 9 100 B 6.14
## 10 100 B 6.14
## # ... with 38 more rowsrequire(ggplot2)
ggplot(BD_moluscos, aes(x=c_agua,y=cons_o,fill=molusco))+geom_boxplot()
En el diagrama de caja y bigotes podemos ver que en general los moluscos de la especie A consumen más oxígeno. Además en los otros gráficos vemos que el mayor consumo de parte de ambas especies se da en la concentración de 50% agua de mar.
- Realice un análisis exploratorio que permita conocer como es el consumo de oxígeno en las distintas concentraciones de agua de mar. y si estas conclusiones son las mismas para cada tipo de molusco.
load("C:/Users/Juan David Betancour/Downloads/YDRAY-moluscos.RData")
sum(BD_moluscos$c_agua)## [1] 3600mean(BD_moluscos$c_agua)## [1] 75sd(BD_moluscos$c_agua)## [1] 20.62842min(BD_moluscos$c_agua)## [1] 50median(BD_moluscos$c_agua)## [1] 75max(BD_moluscos$c_agua)## [1] 100min(BD_moluscos$molusco)## [1] "A"max(BD_moluscos$molusco)## [1] "B"sum(BD_moluscos$cons_o)## [1] 446.63mean(BD_moluscos$cons_o)## [1] 9.304792sd(BD_moluscos$cons_o)## [1] 3.682652min(BD_moluscos$cons_o)## [1] 1.8median(BD_moluscos$cons_o)## [1] 9.7max(BD_moluscos$cons_o)## [1] 18.8En promedio el consumo de oxígeno de parte de ambas especies y en todas las concentraciones es de 9,304792.
require(ggplot2)
b<-ggplot(BD_moluscos,aes(x=c_agua,y=cons_o))+geom_col(aes(fill=molusco), position="dodge")
b+scale_fill_manual(values=c("red", "Green"))
qnorm(p = 0.025)## [1] -1.959964qnorm(p = 0.975)## [1] 1.959964funcionP = function (P,E,Z,N){
n0= (P*(1-P))/((E/Z)^2)
nf= n0/(1+(n0/N))
return(nf)
}
funcionP(0.5, 0.03, 1.96, 5000)## [1] 879.4228require(faraway)
data(BD_moluscos)## Warning in data(BD_moluscos): data set 'BD_moluscos' not foundBD_moluscos## # A tibble: 48 x 3
## c_agua molusco cons_o
## <dbl> <chr> <dbl>
## 1 100 A 7.16
## 2 100 A 8.26
## 3 100 A 6.78
## 4 100 A 14
## 5 100 A 13.6
## 6 100 A 11.1
## 7 100 A 8.93
## 8 100 A 9.66
## 9 100 B 6.14
## 10 100 B 6.14
## # ... with 38 more rowsmod_1=lm(cons_o~c_agua,data=BD_moluscos)
summary(mod_1)##
## Call:
## lm(formula = cons_o ~ c_agua, data = BD_moluscos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.5048 -2.1248 -0.4748 2.3504 7.7055
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.67385 1.87425 7.829 5.29e-10 ***
## c_agua -0.07159 0.02411 -2.969 0.00473 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.41 on 46 degrees of freedom
## Multiple R-squared: 0.1608, Adjusted R-squared: 0.1426
## F-statistic: 8.814 on 1 and 46 DF, p-value: 0.004734table(BD_moluscos$cons_o)##
## 1.8 3.68 4.47 4.95 5.2 5.44 5.49 5.75 5.8 6.14 6.37 6.38 6.49
## 1 1 1 1 2 1 1 1 1 2 1 1 1
## 6.78 7.16 7.18 8.26 8.39 8.93 9.63 9.66 9.74 9.9 9.96 10 10.2
## 1 1 2 1 1 1 1 1 2 1 1 1 1
## 10.4 10.5 11.1 11.11 11.6 11.8 12.3 13.2 13.4 13.6 14 14.5 14.6
## 2 1 1 2 1 1 1 1 1 1 1 2 1
## 17.7 18.8
## 1 1BD_moluscos$cons_o_=as.factor(BD_moluscos$cons_o)
BD_moluscos$cons_o_## [1] 7.16 8.26 6.78 14 13.6 11.1 8.93 9.66 6.14 6.14 3.68 10
## [13] 10.4 11.6 5.49 5.8 5.2 13.2 5.2 8.39 7.18 10.4 6.37 7.18
## [25] 4.47 4.95 9.96 6.49 5.75 5.44 1.8 9.9 11.11 10.5 9.74 14.6
## [37] 18.8 11.11 9.74 11.8 9.63 14.5 6.38 10.2 13.4 17.7 14.5 12.3
## 41 Levels: 1.8 3.68 4.47 4.95 5.2 5.44 5.49 5.75 5.8 6.14 6.37 6.38 ... 18.8###
mean(BD_moluscos$cons_o, na.rm=TRUE)## [1] 9.304792median(BD_moluscos$cons_o, na.rm=TRUE)## [1] 9.7sd(BD_moluscos$cons_o, na.rm=TRUE)## [1] 3.682652min(BD_moluscos$cons_o, na.rm=TRUE)## [1] 1.8max(BD_moluscos$cons_o, na.rm=TRUE)## [1] 18.8length(BD_moluscos$cons_o)## [1] 48boxplot(BD_moluscos$cons_o, main = "Boxplot consumo de oxígeno", ylab = "Consumo de Oxígeno",col="Beige")
En el diagrama de barras se puede ver claramente como en la concentración de 50% agua de mar ocurre el mayor consumo de oxígeno y que la especie A consume más oxígeno que la especie B en todas las concentraciones. Además en la concentración de 50%m agua de mar se presentó el menor consumo de oxígeno de parte de ambas especies,
- Estime el modelo de diseño de experimentos el cual permita evaluar el efecto de la concentración de agua de mar y los tipos de molusco sobre el consumo de oxigeno. Interprete los coeficientes del modelo, el valor p y realice un post anova de considerarlo necesario para los factores.
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## [154] "Juan David Betancourth López COVER LETTER -GUIDANCE-MOD-2021-2-WORKSHOP-ARMAND (1).docx"
## [155] "Juan David Betancourth López COVER LETTER -GUIDANCE-MOD-2021-2-WORKSHOP-ARMAND.docx"
## [156] "Juan David Betancourth Shop till you drop-ENGLISH FOR WORK -MOD-ARMAND-2021-1 (1).pdf"
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## [398] "zoom_0(58).mp4"require(faraway)
data(BD_moluscos)## Warning in data(BD_moluscos): data set 'BD_moluscos' not foundBD_moluscos## # A tibble: 48 x 4
## c_agua molusco cons_o cons_o_
## <dbl> <chr> <dbl> <fct>
## 1 100 A 7.16 7.16
## 2 100 A 8.26 8.26
## 3 100 A 6.78 6.78
## 4 100 A 14 14
## 5 100 A 13.6 13.6
## 6 100 A 11.1 11.1
## 7 100 A 8.93 8.93
## 8 100 A 9.66 9.66
## 9 100 B 6.14 6.14
## 10 100 B 6.14 6.14
## # ... with 38 more rowsrequire(ggplot2)
ggplot(BD_moluscos,aes(x=c_agua, y=cons_o))+geom_point()+theme_bw()
ggplot(BD_moluscos,aes(x=molusco, y=cons_o))+geom_point()+theme_bw()
attach(BD_moluscos)## The following objects are masked from BD_moluscos (pos = 4):
##
## c_agua, cons_o, moluscorequire(agricolae)## Loading required package: agricolae## Warning: package 'agricolae' was built under R version 4.1.1require(faraway)
require(ggplot2)
data(BD_moluscos)## Warning in data(BD_moluscos): data set 'BD_moluscos' not foundBD_moluscos## # A tibble: 48 x 4
## c_agua molusco cons_o cons_o_
## <dbl> <chr> <dbl> <fct>
## 1 100 A 7.16 7.16
## 2 100 A 8.26 8.26
## 3 100 A 6.78 6.78
## 4 100 A 14 14
## 5 100 A 13.6 13.6
## 6 100 A 11.1 11.1
## 7 100 A 8.93 8.93
## 8 100 A 9.66 9.66
## 9 100 B 6.14 6.14
## 10 100 B 6.14 6.14
## # ... with 38 more rowsBD_moluscos$c_agua_=as.factor(BD_moluscos$c_agua)
attach(BD_moluscos)## The following objects are masked from BD_moluscos (pos = 4):
##
## c_agua, cons_o, cons_o_, molusco## The following objects are masked from BD_moluscos (pos = 6):
##
## c_agua, cons_o, moluscotapply(cons_o,c_agua_,mean,na.rm=TRUE)## 50 75 100
## 12.25062 6.99250 8.67125#ANOVA
mod=lm(cons_o~c_agua_,data=BD_moluscos)
summary(mod)##
## Call:
## lm(formula = cons_o ~ c_agua_, data = BD_moluscos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.8706 -2.0445 -0.4766 2.2494 6.5494
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.2506 0.7515 16.302 < 2e-16 ***
## c_agua_75 -5.2581 1.0627 -4.948 1.09e-05 ***
## c_agua_100 -3.5794 1.0627 -3.368 0.00156 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.006 on 45 degrees of freedom
## Multiple R-squared: 0.3621, Adjusted R-squared: 0.3338
## F-statistic: 12.77 on 2 and 45 DF, p-value: 4.043e-05anova(mod)## Analysis of Variance Table
##
## Response: cons_o
## Df Sum Sq Mean Sq F value Pr(>F)
## c_agua_ 2 230.82 115.408 12.773 4.043e-05 ***
## Residuals 45 406.59 9.035
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1table(c_agua_)## c_agua_
## 50 75 100
## 16 16 16### Postanova (prueba de comparación múltiple)
compara=LSD.test(mod,"c_agua_")
compara## $statistics
## MSerror Df Mean CV t.value LSD
## 9.03543 45 9.304792 32.30485 2.014103 2.14048
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none c_agua_ 3 0.05
##
## $means
## cons_o std r LCL UCL Min Max Q25 Q50 Q75
## 100 8.67125 3.000940 16 7.157702 10.184798 3.68 14.0 6.140 8.595 10.5750
## 50 12.25062 3.199643 16 10.737077 13.764173 6.38 18.8 10.085 11.455 14.5000
## 75 6.99250 2.804093 16 5.478952 8.506048 1.80 13.2 5.200 6.430 8.7675
##
## $comparison
## NULL
##
## $groups
## cons_o groups
## 50 12.25062 a
## 100 8.67125 b
## 75 6.99250 b
##
## attr(,"class")
## [1] "group"data("BD_moluscos")## Warning in data("BD_moluscos"): data set 'BD_moluscos' not foundBD_moluscos## # A tibble: 48 x 5
## c_agua molusco cons_o cons_o_ c_agua_
## <dbl> <chr> <dbl> <fct> <fct>
## 1 100 A 7.16 7.16 100
## 2 100 A 8.26 8.26 100
## 3 100 A 6.78 6.78 100
## 4 100 A 14 14 100
## 5 100 A 13.6 13.6 100
## 6 100 A 11.1 11.1 100
## 7 100 A 8.93 8.93 100
## 8 100 A 9.66 9.66 100
## 9 100 B 6.14 6.14 100
## 10 100 B 6.14 6.14 100
## # ... with 38 more rowsmod3=lm(cons_o~c_agua,data=rabbit)
anova(mod3)## Analysis of Variance Table
##
## Response: cons_o
## Df Sum Sq Mean Sq F value Pr(>F)
## c_agua 1 102.50 102.495 8.8141 0.004734 **
## Residuals 46 534.91 11.629
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1###Presente un informe con los resultados que den cuenta de los dos puntos a y b anexando al final el código en R que genera los resultados. Tiene una hora máximo para contestar las preguntas las cuales tienen el mismo peso sobre la nota final del parcial.
load("C:/Users/Juan David Betancour/Downloads/YDRAY-moluscos.RData")
sum(BD_moluscos$c_agua)## [1] 3600mean(BD_moluscos$c_agua)## [1] 75sd(BD_moluscos$c_agua)## [1] 20.62842min(BD_moluscos$c_agua)## [1] 50median(BD_moluscos$c_agua)## [1] 75max(BD_moluscos$c_agua)## [1] 100min(BD_moluscos$molusco)## [1] "A"max(BD_moluscos$molusco)## [1] "B"sum(BD_moluscos$cons_o)## [1] 446.63mean(BD_moluscos$cons_o)## [1] 9.304792sd(BD_moluscos$cons_o)## [1] 3.682652min(BD_moluscos$cons_o)## [1] 1.8median(BD_moluscos$cons_o)## [1] 9.7max(BD_moluscos$cons_o)## [1] 18.8hist(BD_moluscos$cons_o, xlab ="Consumo de oxígeno", ylab="Frecuencia" , xlim=c(1.80, 18.80), ylim=c(0, 15),breaks=15, col="orange", main="Histograma de consumo de oxígeno")
boxplot(BD_moluscos$cons_o, col="green", main="Boxplot de consumo de oxígeno")
hist(BD_moluscos$c_agua, col="yellow", main="Histograma del Consumo de agua", xlab ="Concentración de agua de mar", ylab="Frecuencia" , xlim=c(50, 100), ylim=c(0, 49),breaks=15)
knitr::opts_chunk$set(echo = TRUE)
fP=function(Z,S,E,N){
n0=((Z*S/E)^2)
return(n0/1+(n0/N))
}
fP(1.96,1.5,0.5,20000)## [1] 34.57613lm(cons_o~c_agua+molusco,data=gala)##
## Call:
## lm(formula = cons_o ~ c_agua + molusco, data = gala)
##
## Coefficients:
## (Intercept) c_agua moluscoB
## 15.36948 -0.07159 -1.39125###
require(agricolae)
require(faraway)
require(ggplot2)
attach(BD_moluscos)## The following objects are masked from BD_moluscos (pos = 3):
##
## c_agua, cons_o, molusco## The following objects are masked from BD_moluscos (pos = 5):
##
## c_agua, cons_o, molusco## The following objects are masked from BD_moluscos (pos = 7):
##
## c_agua, cons_o, moluscotapply(cons_o,molusco, mean)## A B
## 10.000417 8.609167ggplot(BD_moluscos, aes(x= cons_o, y= molusco, fill= cons_o))+ geom_boxplot()+
theme_bw()
mod6= lm(cons_o~molusco, data=BD_moluscos)
summary(mod6)##
## Call:
## lm(formula = cons_o ~ molusco, data = BD_moluscos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.8092 -2.8254 -0.2604 1.7930 9.0908
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.0004 0.7459 13.408 <2e-16 ***
## moluscoB -1.3913 1.0548 -1.319 0.194
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.654 on 46 degrees of freedom
## Multiple R-squared: 0.03644, Adjusted R-squared: 0.01549
## F-statistic: 1.74 on 1 and 46 DF, p-value: 0.1937anova(mod6)## Analysis of Variance Table
##
## Response: cons_o
## Df Sum Sq Mean Sq F value Pr(>F)
## molusco 1 23.23 23.227 1.7396 0.1937
## Residuals 46 614.18 13.352table(cons_o)## cons_o
## 1.8 3.68 4.47 4.95 5.2 5.44 5.49 5.75 5.8 6.14 6.37 6.38 6.49
## 1 1 1 1 2 1 1 1 1 2 1 1 1
## 6.78 7.16 7.18 8.26 8.39 8.93 9.63 9.66 9.74 9.9 9.96 10 10.2
## 1 1 2 1 1 1 1 1 2 1 1 1 1
## 10.4 10.5 11.1 11.11 11.6 11.8 12.3 13.2 13.4 13.6 14 14.5 14.6
## 2 1 1 2 1 1 1 1 1 1 1 2 1
## 17.7 18.8
## 1 1##POSTANOVA
compara1= LSD.test(mod6, "c_agua")
compara1## NULL## RELACIÓN DE CONSUMO DE OXÍGENO Y CONCENTRACIÓN DE AGUA DE MAR
mod9=lm(BD_moluscos$cons_o~BD_moluscos$c_agua, data=BD_moluscos)
summary(mod9)##
## Call:
## lm(formula = BD_moluscos$cons_o ~ BD_moluscos$c_agua, data = BD_moluscos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.5048 -2.1248 -0.4748 2.3504 7.7055
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.67385 1.87425 7.829 5.29e-10 ***
## BD_moluscos$c_agua -0.07159 0.02411 -2.969 0.00473 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.41 on 46 degrees of freedom
## Multiple R-squared: 0.1608, Adjusted R-squared: 0.1426
## F-statistic: 8.814 on 1 and 46 DF, p-value: 0.004734- A continuación, encontrará usted los datos de riqueza (número de especies) de moluscos asociados a cantos intermareales de diferente tamaño en diferentes épocas del año. Teniendo en cuenta esto, usted quiere evaluar si la riqueza varía entre los diferentes tamaños de cantos, las diferentes épocas del año y si existe interacción entre el tamaño del canto y la época del año. Recuerde plantear las hipótesis de trabajo antes de empezar con los análisis. Adicionalmente, recuerde que el investigador debe estar seguro de que sus datos cumplen ciertas asunciones para poder utilizar el ANOVA. Utilice pruebas de comparación múltiple (postanova), de ser necesarias, para determinar cuál es el tamaño del canto donde se encuentra mayor número de especies para cada época del año. Utilice un alfa = 5%.
library(readxl)## Warning: package 'readxl' was built under R version 4.1.1Cantos_mareas <- read_excel("C:/Users/Juan David Betancour/OneDrive/Documentos/Cantos_mareas.xlsx")## New names:
## * `` -> ...1require(ggplot2)
c<-ggplot(Cantos_mareas,aes(x=size,y=especie))+geom_col(aes(fill=tiempo), position="dodge")
c+scale_fill_manual(values=c("red", "Green", "Yellow", "Purple"))
summary(Cantos_mareas)## ...1 tiempo size especie
## Min. : 1.00 Length:64 Length:64 Min. : 0.000
## 1st Qu.:16.75 Class :character Class :character 1st Qu.: 2.000
## Median :32.50 Mode :character Mode :character Median : 4.000
## Mean :32.50 Mean : 4.719
## 3rd Qu.:48.25 3rd Qu.: 7.000
## Max. :64.00 Max. :11.000mod=lm(cons_o~c_agua+molusco,data=BD_moluscos)
summary(mod)##
## Call:
## lm(formula = cons_o ~ c_agua + molusco, data = BD_moluscos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.8092 -2.2945 -0.6798 2.8297 7.3011
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.36948 1.91620 8.021 3.22e-10 ***
## c_agua -0.07159 0.02384 -3.002 0.00436 **
## moluscoB -1.39125 0.97343 -1.429 0.15985
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.372 on 45 degrees of freedom
## Multiple R-squared: 0.1972, Adjusted R-squared: 0.1616
## F-statistic: 5.528 on 2 and 45 DF, p-value: 0.007132g22=ggplot(data = Cantos_mareas,aes(y=especie,x=size))+geom_point()+theme_bw()+xlab("Tamaño del canto")+ylab("Riqueza de especies")+geom_smooth()Podemos ver que el canto de tamaño S1 presenta la mayor riqueza de especies miesntras que el S4 presenta la menor riqueza. En cuanto a tiempo el T3 presenta la mayor riqueza de especies mientras que T1 presenta la menor riqueza de especies.
library(readxl)
Cantos_mareas <- read_excel("C:/Users/Juan David Betancour/OneDrive/Documentos/Cantos_mareas.xlsx")## New names:
## * `` -> ...1Cantos_mareas$size=as.factor(Cantos_mareas$size)
Cantos_mareas$especie=as.factor(Cantos_mareas$especie)
Cantos_mareas$tiempo=as.factor(Cantos_mareas$tiempo)
require(faraway)
require(ggplot2)
data(Cantos_mareas)## Warning in data(Cantos_mareas): data set 'Cantos_mareas' not foundggplot(data=Cantos_mareas,aes(y=especie, x=size))+geom_point()+theme_bw()+xlab("Tamaño del canto")+ylab("Riqueza de especies")+geom_smooth()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
g1=ggplot(data=BD_moluscos, aes(y=especie,x=size,colour=tiempo))+geom_point()+theme_bw()+xlab("Tamaño del canto")+ylab("Riqueza de especies")+geom_smooth()+facet_grid(~tiempo)
require(plotly)## Loading required package: plotly##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutknitr::opts_chunk$set(echo = TRUE)
summary(Cantos_mareas)## ...1 tiempo size especie
## Min. : 1.00 T1:16 S1:16 3 : 9
## 1st Qu.:16.75 T2:16 S2:16 2 : 8
## Median :32.50 T3:15 S3:16 4 : 8
## Mean :32.50 T4:17 S4:16 7 : 8
## 3rd Qu.:48.25 1 : 6
## Max. :64.00 5 : 5
## (Other):20plot(Cantos_mareas)
###
##Pruebas de Comparación Múltiple - Postanova
require (agricolae)
require (faraway)
require (ggplot2)
data(Cantos_mareas)## Warning in data(Cantos_mareas): data set 'Cantos_mareas' not foundCantos_mareas## # A tibble: 64 x 4
## ...1 tiempo size especie
## <dbl> <fct> <fct> <fct>
## 1 1 T1 S1 7
## 2 2 T1 S1 5
## 3 3 T1 S1 7
## 4 4 T1 S1 7
## 5 5 T1 S2 3
## 6 6 T1 S2 7
## 7 7 T1 S2 8
## 8 8 T1 S2 11
## 9 9 T1 S3 1
## 10 10 T1 S3 3
## # ... with 54 more rowsCantos_mareas$especie_=as.factor(Cantos_mareas$especie)
##Exploratoria de Datos
#y=germ
g1=ggplot(data = Cantos_mareas,aes(y=especie,x=size))+geom_point()+theme_bw()+xlab("Tamaño del canto")+ylab("Riqueza de")+geom_smooth()
require(plotly)
ggplotly(g1)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'Si consideramos el tamaño de los cantos ignorando el tiempo tenemos que los tamaños S1 y S2 alcanzan la mayor riqueza de especies pero en los diferentes tiempos S2 presenta mayor “diversidad” dado que se presentan situaciones en las que la riqueza es menor que la de S1. El tamaño con menor riqueza es S4.
- Suponga que se desea realizar una investigación diseñada para conocer el efecto de cuatro tipos de dieta sobre el engorde de cerdos, donde se tienen 20 cerdos asignados aleatoriamente a cuatro grupos experimentales (cuatro dietas disponibles) y someter a cada grupo a un único tipo de dieta. Teniendo en cuenta lo anterior, usted quiere evaluar si existe diferencias entre los pesos corporales de los cerdos (en Kg) después de haber sido criados con esas dietas por un mes.
library(readxl)
Cerdos <- read_excel("C:/Users/Juan David Betancour/OneDrive/Documentos/Cerdos.xlsx")## New names:
## * `` -> ...1tapply(Cerdos$dieta, Cerdos$peso, mean, na.rm=TRUE)## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA## 60.3 60.8 61.7 61.9 63.1 64.2 65 66.7 67 67.7 68.6 68.7 69.6 71.5 71.8 73.3
## NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 74.3 75 75.2 77.1
## NA NA NA NAmean## function (x, ...)
## UseMethod("mean")
## <bytecode: 0x0000000014de3600>
## <environment: namespace:base>require(faraway)
require(ggplot2)
data(Cerdos)## Warning in data(Cerdos): data set 'Cerdos' not foundggplot(data=Cerdos,aes(x=peso))
plot(Cerdos)
x=c(60.8,67,65,68.6,61.7,68.7,67.7,75, 73.3, 71.8, 69.6, 77.1, 75.2, 71.5, 74.3, 61.9, 64.2, 63.1, 66.7, 60.3)
x## [1] 60.8 67.0 65.0 68.6 61.7 68.7 67.7 75.0 73.3 71.8 69.6 77.1 75.2 71.5 74.3
## [16] 61.9 64.2 63.1 66.7 60.3data("Cerdos")## Warning in data("Cerdos"): data set 'Cerdos' not foundCerdos## # A tibble: 20 x 3
## ...1 dieta peso
## <dbl> <chr> <dbl>
## 1 1 A 60.8
## 2 2 A 67
## 3 3 A 65
## 4 4 A 68.6
## 5 5 A 61.7
## 6 6 B 68.7
## 7 7 B 67.7
## 8 8 B 75
## 9 9 B 73.3
## 10 10 B 71.8
## 11 11 C 69.6
## 12 12 C 77.1
## 13 13 C 75.2
## 14 14 C 71.5
## 15 15 C 74.3
## 16 16 D 61.9
## 17 17 D 64.2
## 18 18 D 63.1
## 19 19 D 66.7
## 20 20 D 60.3attach(Cerdos)
ggplot(Cerdos,aes(x=dieta, y=peso))+geom_point()
###
data("Cerdos")## Warning in data("Cerdos"): data set 'Cerdos' not foundCerdos## # A tibble: 20 x 3
## ...1 dieta peso
## <dbl> <chr> <dbl>
## 1 1 A 60.8
## 2 2 A 67
## 3 3 A 65
## 4 4 A 68.6
## 5 5 A 61.7
## 6 6 B 68.7
## 7 7 B 67.7
## 8 8 B 75
## 9 9 B 73.3
## 10 10 B 71.8
## 11 11 C 69.6
## 12 12 C 77.1
## 13 13 C 75.2
## 14 14 C 71.5
## 15 15 C 74.3
## 16 16 D 61.9
## 17 17 D 64.2
## 18 18 D 63.1
## 19 19 D 66.7
## 20 20 D 60.3#y=gain
#x=treat
mod2=lm(peso~dieta, data=Cerdos)
anova(mod2)## Analysis of Variance Table
##
## Response: peso
## Df Sum Sq Mean Sq F value Pr(>F)
## dieta 3 377.71 125.902 14.239 8.833e-05 ***
## Residuals 16 141.47 8.842
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##No se realiza postanova
#y=gain
#x=block
mod3=lm(peso~dieta, data=Cerdos)
anova(mod3)## Analysis of Variance Table
##
## Response: peso
## Df Sum Sq Mean Sq F value Pr(>F)
## dieta 3 377.71 125.902 14.239 8.833e-05 ***
## Residuals 16 141.47 8.842
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1compara3=LSD.test(mod3,"block")
compara3## NULL- Determine si los pesos medios de los cerdos son estadísticamente iguales para las cuatro dietas. Si encuentra diferencias entre las dietas, realice la prueba de comparación múltiple correspondiente e indique cual dieta recomendaría.
library(readxl)
Cerdos <- read_excel("C:/Users/Juan David Betancour/Downloads/Cerdos.xlsx")## New names:
## * `` -> ...1#Data frame (Cerdos)
sum(Cerdos$peso)## [1] 1363.5mean(Cerdos$peso)## [1] 68.175sd(Cerdos$peso)## [1] 5.227345min(Cerdos$peso)## [1] 60.3median(Cerdos$peso)## [1] 68.15max(Cerdos$peso)## [1] 77.1library(readxl)
Cerdos <- read_excel("C:/Users/Juan David Betancour/OneDrive/Documentos/Cerdos.xlsx")## New names:
## * `` -> ...1require(ggplot2)
c<-ggplot(Cerdos,aes(x=dieta,y=peso))+geom_col(aes(fill=dieta), position="dodge")
c+scale_fill_manual(values=c("red", "green", "brown", "black"))
###
require (agricolae)
require (faraway)
require (ggplot2)
data(Cerdos)## Warning in data(Cerdos): data set 'Cerdos' not foundCerdos## # A tibble: 20 x 3
## ...1 dieta peso
## <dbl> <chr> <dbl>
## 1 1 A 60.8
## 2 2 A 67
## 3 3 A 65
## 4 4 A 68.6
## 5 5 A 61.7
## 6 6 B 68.7
## 7 7 B 67.7
## 8 8 B 75
## 9 9 B 73.3
## 10 10 B 71.8
## 11 11 C 69.6
## 12 12 C 77.1
## 13 13 C 75.2
## 14 14 C 71.5
## 15 15 C 74.3
## 16 16 D 61.9
## 17 17 D 64.2
## 18 18 D 63.1
## 19 19 D 66.7
## 20 20 D 60.3Cerdos$dieta_=as.factor(Cerdos$dieta)
##Exploratoria de Datos
#y=peso
require(plotly)
ggplotly(g1)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'- Se desea determinar el efecto que tienen dos factores (Temperatura y Sexo) sobre la tasa de consumo de oxígeno (mgO2) de una especie de cangrejo. La siguiente tabla muestra los datos colectados para 24 animales usados en el experimento.
library(readxl)
cangrejos <- read_excel("C:/Users/Juan David Betancour/OneDrive/Documentos/cangrejos.xlsx")## New names:
## * `` -> ...11:10## [1] 1 2 3 4 5 6 7 8 9 10require(faraway)
require(ggplot2)
data(cangrejos)## Warning in data(cangrejos): data set 'cangrejos' not foundggplot(data=cangrejos,aes(y=sexo,x=cons_ox,colour=temperatura))+geom_boxplot()+theme_bw()+xlab("Consumo de oxígeno")+ylab("Sexo")
###
library(readxl)
cangrejos <- read_excel("C:/Users/Juan David Betancour/OneDrive/Documentos/cangrejos.xlsx")## New names:
## * `` -> ...1require(ggplot2)
c<-ggplot(cangrejos,aes(x=temperatura,y=cons_ox))+geom_col(aes(fill=sexo), position="dodge")
c+scale_fill_manual(values=c("orange", "blue", "black", "gray"))
summary(cangrejos)## ...1 temperatura sexo cons_ox
## Min. : 1.00 Length:24 Length:24 Min. :1.400
## 1st Qu.: 6.75 Class :character Class :character 1st Qu.:1.800
## Median :12.50 Mode :character Mode :character Median :2.400
## Mean :12.50 Mean :2.388
## 3rd Qu.:18.25 3rd Qu.:2.925
## Max. :24.00 Max. :3.700require(faraway)
data(cangrejos)## Warning in data(cangrejos): data set 'cangrejos' not foundcangrejos## # A tibble: 24 x 4
## ...1 temperatura sexo cons_ox
## <dbl> <chr> <chr> <dbl>
## 1 1 Baja Macho 1.9
## 2 2 Baja Macho 1.8
## 3 3 Baja Macho 1.6
## 4 4 Baja Macho 1.4
## 5 5 Baja Hembra 1.8
## 6 6 Baja Hembra 1.7
## 7 7 Baja Hembra 1.4
## 8 8 Baja Hembra 1.5
## 9 9 Media Macho 2.3
## 10 10 Media Macho 2.1
## # ... with 14 more rows###
cangrejos$temperatura=as.factor(cangrejos$temperatura)
cangrejos$sexo=as.factor(cangrejos$sexo)
cangrejos$sexo=as.factor(cangrejos$sexo)
require(faraway)
require(ggplot2)
data(cangrejos)## Warning in data(cangrejos): data set 'cangrejos' not foundggplot(data=cangrejos,aes(y=cons_ox, x=temperatura))+geom_point()+theme_bw()+xlab("Temperatura")+ylab("Consumo de oxígeno")+geom_smooth()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
g1=ggplot(data=cangrejos, aes(y=cons_ox,x=temperatura,colour=sexo))+geom_point()+theme_bw()+xlab("Temperatura")+ylab("Consumo de oxígeno")+geom_smooth()+facet_grid(~sexo)
require(plotly)
###
##Pruebas de Comparación Múltiple - Postanova
require (agricolae)
require (faraway)
require (ggplot2)
data(cangrejos)## Warning in data(cangrejos): data set 'cangrejos' not foundcangrejos## # A tibble: 24 x 4
## ...1 temperatura sexo cons_ox
## <dbl> <fct> <fct> <dbl>
## 1 1 Baja Macho 1.9
## 2 2 Baja Macho 1.8
## 3 3 Baja Macho 1.6
## 4 4 Baja Macho 1.4
## 5 5 Baja Hembra 1.8
## 6 6 Baja Hembra 1.7
## 7 7 Baja Hembra 1.4
## 8 8 Baja Hembra 1.5
## 9 9 Media Macho 2.3
## 10 10 Media Macho 2.1
## # ... with 14 more rowscangrejos$temperatura_=as.factor(cangrejos$temperatura)
##Exploratoria de Datos
#y=germ- Realizar un análisis estadístico completo que permita conocer como es el consumo de oxígeno de los cangrejos en las distintas temperaturas (use ANOVA y postanova de ser necesario para las comparaciones múltiples).
library(readxl)
cangrejos <- read_excel("C:/Users/Juan David Betancour/OneDrive/Documentos/cangrejos.xlsx")## New names:
## * `` -> ...1require(ggplot2)
cangrejos$temperatura=as.factor(cangrejos$temperatura)
cangrejos$sexo=as.factor(cangrejos$sexo)
cangrejos$cons_ox=as.factor(cangrejos$cons_ox)
require(faraway)
require(ggplot2)
data(cangrejos)## Warning in data(cangrejos): data set 'cangrejos' not foundggplot(data=cangrejos,aes(y=cons_ox, x=temperatura))+geom_point()+theme_bw()+xlab("Temperatura")+ylab("Consumo de oxígeno")+geom_smooth()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
g1=ggplot(data=cangrejos, aes(y=cons_ox,x=temperatura,colour=sexo))+geom_point()+theme_bw()+xlab("Temperatura")+ylab("Consumo de oxígeno")+geom_smooth()+facet_grid(~sexo)
require(plotly)
###
require(ggplot2)
ggplot(cangrejos,aes(x=temperatura, y=cons_ox))+geom_point()+theme_bw()
ggplot(cangrejos,aes(x=sexo, y=cons_ox))+geom_point()+theme_bw()
attach(cangrejos)## The following object is masked from Cerdos:
##
## ...1############################
cangrejos$temperatura=as.factor(cangrejos$temperatura)
cangrejos$sexo=as.factor(cangrejos$sexo)
cangrejos$cons_ox=as.factor(cangrejos$cons_ox)
require(faraway)
require(ggplot2)
data(cangrejos)## Warning in data(cangrejos): data set 'cangrejos' not foundg1=ggplot(data=cangrejos, aes(y=cons_ox,x=temperatura,colour=sexo))+geom_point()+theme_bw()+xlab("Temperatura")+ylab("Consumo de oxígeno")+geom_smooth()+facet_grid(~sexo)
require(plotly)
###
##Pruebas de Comparación Múltiple - Postanova
require (agricolae)
require (faraway)
require (ggplot2)
data(cangrejos)## Warning in data(cangrejos): data set 'cangrejos' not foundcangrejos## # A tibble: 24 x 4
## ...1 temperatura sexo cons_ox
## <dbl> <fct> <fct> <fct>
## 1 1 Baja Macho 1.9
## 2 2 Baja Macho 1.8
## 3 3 Baja Macho 1.6
## 4 4 Baja Macho 1.4
## 5 5 Baja Hembra 1.8
## 6 6 Baja Hembra 1.7
## 7 7 Baja Hembra 1.4
## 8 8 Baja Hembra 1.5
## 9 9 Media Macho 2.3
## 10 10 Media Macho 2.1
## # ... with 14 more rowscangrejos$temperatura_=as.factor(cangrejos$temperatura)
##Exploratoria de Datos
#y=germ
require(plotly)
ggplotly(g1)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'- Cierto Agricultor interesado en el cultivo de la caña de azúcar desea saber en que zonas del mundo puede establecer su cultivo y obtener rendimientos (kg/ha) potenciales. El agricultor sabe que uno de los factores limitantes (mas no el único) es el clima y por ello investiga cuales son los rangos óptimos sobre los cuales se obtienen rendimientos potenciales, identificando lo siguiente: temperatura media entre (22.5 y 28 grados centígrados) y precitación anual entre 1500 y 3500 milímetros) o precipitación mensual entre 125 y 290 mm. Adicionalmente el agricultor tiene información que le indica que la región del Valle del Cauca en Colombia posee unos rendimientos altos y que otra aproximación para identificar las zonas a nivel global seria buscar otros sitios con estas condiciones (similares).
require(raster)## Loading required package: raster## Warning: package 'raster' was built under R version 4.1.1## Loading required package: sp## Warning: package 'sp' was built under R version 4.1.1##
## Attaching package: 'raster'## The following object is masked from 'package:plotly':
##
## selectrequire(rgdal)## Loading required package: rgdal## Warning: package 'rgdal' was built under R version 4.1.1## Please note that rgdal will be retired by the end of 2023,
## plan transition to sf/stars/terra functions using GDAL and PROJ
## at your earliest convenience.
##
## rgdal: version: 1.5-27, (SVN revision 1148)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.2.1, released 2020/12/29
## Path to GDAL shared files: C:/Users/Juan David Betancour/OneDrive/Documentos/R/win-library/4.1/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: C:/Users/Juan David Betancour/OneDrive/Documentos/R/win-library/4.1/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-5
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.
## Overwritten PROJ_LIB was C:/Users/Juan David Betancour/OneDrive/Documentos/R/win-library/4.1/rgdal/projrequire(sp)
require(raster)
require(rgdal)
require(sp)
global=shapefile("C:/Users/Juan David Betancour/Downloads/YDRAY-shape_global/shape_global/g2008_0.shp")
global## class : SpatialPolygonsDataFrame
## features : 534
## extent : -180, 180, -55.72333, 83.62742 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## variables : 11
## names : AREA, PERIMETER, G2008_0_, G2008_0_ID, ADM0_CODE, ADM0_NAME, LAST_UPDAT, CONTINENT, REGION, STR_YEAR0, EXP_YEAR0
## min values : 0.10069705556089, 1.40729013556, 10019, 1, 1, Afghanistan, 20050415, Africa, Australia and New Zealand, 0, 0
## max values : 2825.6935742546, 969.26620675757, 9999, 9998, 98, Zimbabwe, 20081118, Oceania, Western Europe, 2006, 0- Utilizando los datos de clima de línea base a nivel global, genere un código en R que pemita construir mapas de aptitud en términos climáticos para la caña de azúcar (con base en los rangos óptimos). Grafique los mapas con una escala de colores adecuada.
nombres=list.files("~/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/",full.names = TRUE)
require(raster)
require(rgdal)
require(sp)
require(raster)
require(rgdal)
require(sp)
global=shapefile("C:/Users/Juan David Betancour/Downloads/YDRAY-shape_global/shape_global/g2008_0.shp")
global## class : SpatialPolygonsDataFrame
## features : 534
## extent : -180, 180, -55.72333, 83.62742 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## variables : 11
## names : AREA, PERIMETER, G2008_0_, G2008_0_ID, ADM0_CODE, ADM0_NAME, LAST_UPDAT, CONTINENT, REGION, STR_YEAR0, EXP_YEAR0
## min values : 0.10069705556089, 1.40729013556, 10019, 1, 1, Afghanistan, 20050415, Africa, Australia and New Zealand, 0, 0
## max values : 2825.6935742546, 969.26620675757, 9999, 9998, 98, Zimbabwe, 20081118, Oceania, Western Europe, 2006, 0require(raster)
require(rgdal)
require(sp)
require(raster)
require(rgdal)
require(sp)
nombres=list.files("C:/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/", full.names = TRUE)
temps=stack(nombres)
names(temps)=month.name
plot(temps)
plot(temps[[1:2]])
nombres=list.files("Desktop/temp/",full.names = TRUE)
names(temps)=month.name
plot(temps)
plot(temps[[1:2]])
temp_opt=temps>22.5 & temps<28
plot(temp_opt)
aptitud_temp=sum(temp_opt)
plot(aptitud_temp)
table(aptitud_temp[])##
## 0 1 2 3 4 5 6 7 8 9 10
## 543083 15395 56457 45955 25053 16547 11345 10570 13337 9917 9128
## 11 12
## 4864 46402temps[[1]]>5## class : RasterLayer
## dimensions : 1080, 2160, 2332800 (nrow, ncol, ncell)
## resolution : 0.1666667, 0.1666667 (x, y)
## extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## source : memory
## names : layer
## values : 0, 1 (min, max)plot(temps[[1]]>22.5&temps[[1]]<28)
opt_ene=temps[[1]]>22.5&temps[[1]]<28
table(opt_ene[])##
## FALSE TRUE
## 693540 11451357901/(750152+57901)## [1] 0.0716549557901*340## [1] 19686340global@data## AREA PERIMETER G2008_0_ G2008_0_ID ADM0_CODE
## 0 649.4221006 900.013634 2 1 98
## 1 0.1422211 4.510463 5 4 98
## 2 0.1156834 3.409226 15 14 98
## 3 0.1173564 2.728860 16 15 98
## 4 95.1420039 345.882703 18 17 46
## 5 0.2801303 6.484889 19 18 98
## 6 0.9306178 8.583795 40 39 98
## 7 0.3380294 4.706944 46 45 98
## 8 0.1562633 2.158037 50 49 98
## 9 0.1685253 3.316294 59 58 204
## 10 0.1550204 3.574494 66 65 204
## 11 0.1876524 3.786579 83 82 204
## 12 0.2724445 6.368011 93 92 204
## 13 19.1470731 85.289162 97 96 46
## 14 4.3171399 26.003137 102 101 204
## 15 0.2247931 3.371740 108 107 204
## 16 0.8455203 6.887400 111 110 204
## 17 0.2199999 3.524273 117 116 204
## 18 0.2101825 4.760534 118 117 204
## 19 0.4761179 7.457562 119 118 204
## 20 0.1452579 2.796307 132 131 204
## 21 1.3474735 24.331637 134 133 204
## 22 0.1195275 2.419301 136 135 204
## 23 0.1701549 4.114697 137 136 204
## 24 1.0104626 7.947547 140 139 204
## 25 0.5296560 10.076675 141 140 204
## 26 0.1756594 3.638152 147 146 204
## 27 6.5122763 42.829639 174 173 234
## 28 0.4696665 6.153708 182 181 204
## 29 0.2413749 4.045263 189 188 204
## 30 0.3015316 3.793434 196 195 204
## 31 0.3285165 4.321472 204 203 234
## 32 0.1019201 2.234947 215 214 98
## 33 6.2291968 33.140115 221 220 204
## 34 0.4291783 4.373820 222 221 46
## 35 0.1272016 3.347738 232 231 204
## 36 0.1220813 2.694179 236 235 204
## 37 0.7046931 10.385062 239 238 204
## 38 15.5801659 81.819341 240 239 234
## 39 0.1248217 3.311688 285 284 204
## 40 4.3401813 22.910516 311 310 204
## 41 4.6304807 26.912179 315 314 46
## 42 0.1346124 2.780515 357 356 46
## 43 0.2615033 4.937006 376 375 234
## 44 2.0917566 14.473261 385 384 46
## 45 1.1211007 10.368883 399 398 46
## 46 0.5299931 5.182223 416 415 234
## 47 1.9253399 11.641147 462 461 234
## 48 1.9278605 11.298947 490 489 46
## 49 0.2988444 3.665055 501 500 46
## 50 0.2463608 3.512637 542 541 46
## 51 0.8582896 7.891501 550 549 46
## 52 0.4805420 5.196366 559 558 46
## 53 2825.6935743 969.266207 566 565 204
## 54 0.5131293 4.277725 571 570 46
## 55 5.5974693 33.863998 584 583 46
## 56 0.1055759 2.696335 601 600 98
## 57 0.1169094 3.619780 639 638 204
## 58 17.9741445 74.024609 658 657 46
## 59 14.5913205 76.187273 668 667 204
## 60 0.1942223 3.083183 685 684 46
## 61 13.7333545 68.845942 697 696 46
## 62 0.2075044 2.557970 700 699 46
## 63 0.1140676 2.954141 721 720 46
## 64 0.1949519 3.458843 748 747 98
## 65 5.3827133 40.058743 760 759 46
## 66 0.3737773 4.664614 764 763 46
## 67 0.3843246 4.337254 875 874 46
## 68 0.1353576 4.820864 878 877 204
## 69 7.9233284 26.956297 899 898 204
## 70 0.5123389 4.982703 920 919 46
## 71 0.1151092 3.200140 922 921 46
## 72 0.1457398 3.465292 930 929 46
## 73 0.1657247 2.982177 976 975 46
## 74 0.1709153 2.392900 993 992 204
## 75 0.1348508 3.143367 1019 1018 46
## 76 2.2347433 10.276424 1023 1022 46
## 77 1.9541133 11.634462 1031 1030 204
## 78 0.3640943 3.182902 1054 1053 46
## 79 0.3900877 5.783986 1056 1055 98
## 80 0.1946659 2.816093 1078 1077 98
## 81 19.4872297 33.872001 1138 1137 46
## 82 0.5673586 4.587226 1139 1138 204
## 83 0.2634075 2.515339 1182 1181 204
## 84 6.9940505 23.127748 1192 1191 46
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## 86 9.0119916 39.459187 1244 1243 46
## 87 1.5089002 8.368559 1257 1256 204
## 88 113.5309800 447.506140 1271 1270 46
## 89 3.1045275 12.996773 1288 1287 46
## 90 1.2673760 7.437017 1289 1288 46
## 91 0.5576245 4.604941 1355 1354 204
## 92 0.6726686 10.094958 1372 1371 98
## 93 8.4964540 52.725856 1375 1374 204
## 94 54.0109992 120.279141 1389 1388 46
## 95 0.1123364 1.428357 1445 1444 46
## 96 0.1139210 2.236201 1483 1482 204
## 97 0.2258829 2.664396 1492 1491 204
## 98 0.4626420 7.089851 1514 1513 98
## 99 0.9384181 8.253386 1595 1594 98
## 100 0.2870843 3.373888 1699 1698 204
## 101 1231.5960441 889.370548 1818 1817 46
## 102 0.1071776 2.606278 1870 1869 204
## 103 1.3108141 9.349516 1871 1870 204
## 104 0.6231518 4.795167 1884 1883 204
## 105 0.1845418 4.386511 1930 1929 204
## 106 267.1289963 301.393893 1934 1933 259
## 107 0.1296699 1.884040 1937 1936 98
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## 109 0.1099812 4.438485 1975 1974 186
## 110 56.1796892 323.048213 1986 1985 186
## 111 0.9295592 6.918095 1999 1998 98
## 112 0.1238582 2.303346 2005 2004 46
## 113 0.2013179 8.785727 2054 2053 186
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## 116 0.1198854 1.914074 2142 2141 204
## 117 0.7672111 8.096833 2232 2231 204
## 118 1.9953158 13.309833 2283 2282 98
## 119 62.2085583 83.542538 2405 2404 84
## 120 0.1553865 2.989730 2406 2405 186
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## 122 0.1513645 3.246713 2457 2456 98
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## 129 1.1319177 6.852285 2752 2751 204
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## 132 0.1162574 2.440700 2845 2844 186
## 133 77.9791224 120.028940 2952 2951 236
## 134 0.1027399 2.730631 2953 2952 46
## 135 0.1988531 7.932596 2972 2971 186
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## 139 0.1150910 4.478381 3347 3346 186
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## ADM0_NAME LAST_UPDAT CONTINENT
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## 434 Indonesia 20080925 Asia
## 435 Indonesia 20080925 Asia
## 436 Indonesia 20080925 Asia
## 437 Papua New Guinea 20050720 Oceania
## 438 Burundi 20061023 Africa
## 439 Indonesia 20080925 Asia
## 440 Papua New Guinea 20050720 Oceania
## 441 Papua New Guinea 20050720 Oceania
## 442 Indonesia 20080925 Asia
## 443 Indonesia 20080925 Asia
## 444 Indonesia 20080925 Asia
## 445 Papua New Guinea 20050720 Oceania
## 446 Indonesia 20080925 Asia
## 447 Angola 20050415 Africa
## 448 Indonesia 20080925 Asia
## 449 Papua New Guinea 20050720 Oceania
## 450 Indonesia 20080925 Asia
## 451 United Republic of Tanzania 20061122 Africa
## 452 Angola 20050415 Africa
## 453 Indonesia 20080925 Asia
## 454 Indonesia 20080925 Asia
## 455 Indonesia 20080925 Asia
## 456 Solomon Islands 20050415 Oceania
## 457 Indonesia 20080925 Asia
## 458 Indonesia 20080925 Asia
## 459 Indonesia 20080925 Asia
## 460 Solomon Islands 20050415 Oceania
## 461 Indonesia 20080925 Asia
## 462 Solomon Islands 20050415 Oceania
## 463 Indonesia 20080925 Asia
## 464 Indonesia 20080925 Asia
## 465 Indonesia 20080925 Asia
## 466 Indonesia 20080925 Asia
## 467 Indonesia 20080925 Asia
## 468 Indonesia 20080925 Asia
## 469 Solomon Islands 20050415 Oceania
## 470 Timor-Leste 20061003 Asia
## 471 Indonesia 20080925 Asia
## 472 Zambia 20070731 Africa
## 473 Solomon Islands 20050415 Oceania
## 474 Indonesia 20080925 Asia
## 475 Papua New Guinea 20050720 Oceania
## 476 Malawi 20070803 Africa
## 477 Bolivia 20051228 Americas
## 478 Solomon Islands 20050415 Oceania
## 479 Indonesia 20080925 Asia
## 480 Australia 20050415 Oceania
## 481 Australia 20050415 Oceania
## 482 Australia 20050415 Oceania
## 483 Madagascar 20081118 Africa
## 484 Mozambique 20070802 Africa
## 485 Samoa 20050415 Oceania
## 486 Australia 20050415 Oceania
## 487 Vanuatu 20050415 Oceania
## 488 Zimbabwe 20080724 Africa
## 489 Vanuatu 20050415 Oceania
## 490 Fiji 20050829 Oceania
## 491 Namibia 20050415 Africa
## 492 Fiji 20050829 Oceania
## 493 Chile 20050415 Americas
## 494 Botswana 20050415 Africa
## 495 Paraguay 20060112 Americas
## 496 Mauritius 20050415 Africa
## 497 New Caledonia 20050415 Oceania
## 498 New Caledonia 20050415 Oceania
## 499 Réunion 20050415 Africa
## 500 Argentina 20050415 Americas
## 501 South Africa 20050415 Africa
## 502 Australia 20050415 Oceania
## 503 Swaziland 20050415 Africa
## 504 Lesotho 20050415 Africa
## 505 Uruguay 20050415 Americas
## 506 New Zealand 20050415 Oceania
## 507 Australia 20050415 Oceania
## 508 Australia 20050415 Oceania
## 509 Australia 20050415 Oceania
## 510 New Zealand 20050415 Oceania
## 511 Australia 20050415 Oceania
## 512 Chile 20050415 Americas
## 513 Chile 20050415 Americas
## 514 Chile 20050415 Americas
## 515 New Zealand 20050415 Oceania
## 516 Chile 20050415 Americas
## 517 Chile 20050415 Americas
## 518 Chile 20050415 Americas
## 519 Chile 20050415 Americas
## 520 Chile 20050415 Americas
## 521 Falkland Islands (Malvinas) 20050415 Americas
## 522 Falkland Islands (Malvinas) 20050415 Americas
## 523 Chile 20050415 Americas
## 524 Chile 20050415 Americas
## 525 Argentina 20050415 Americas
## 526 Chile 20050415 Americas
## 527 Chile 20050415 Americas
## 528 Chile 20050415 Americas
## 529 Chile 20050415 Americas
## 530 Chile 20050415 Americas
## 531 Chile 20050415 Americas
## 532 Chile 20050415 Americas
## 533 Gambia 20050415 Africa
## REGION STR_YEAR0 EXP_YEAR0
## 0 Northern America 0 0
## 1 Northern America 0 0
## 2 Northern America 0 0
## 3 Northern America 0 0
## 4 Northern America 0 0
## 5 Northern America 0 0
## 6 Northern America 0 0
## 7 Northern America 0 0
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## 389 South-Eastern Asia 0 0
## 390 Central America 0 0
## 391 South America 0 0
## 392 Western Africa 0 0
## 393 South-Eastern Asia 0 0
## 394 South-Eastern Asia 0 0
## 395 South-Eastern Asia 0 0
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## 483 Eastern Africa 0 0
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## 492 Melanesia 0 0
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## 504 Southern Africa 0 0
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## 506 Australia and New Zealand 0 0
## 507 Australia and New Zealand 0 0
## 508 Australia and New Zealand 0 0
## 509 Australia and New Zealand 0 0
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## 515 Australia and New Zealand 0 0
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## 533 Western Africa 0 0plot(global)
pos=global$REGION=="South America"|global$REGION=="Central America"|global$REGION=="North America"|global$REGION=="Asia"|global$REGION=="Oceania"|global$REGION=="Europe"|global$REGION=="Africa"
mundo=global=global[pos,]
plot(mundo)
require(raster)
require(rgdal)
require(sp)
nombres=list.files("~/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/",full.names = TRUE)
names(temps)=month.name
plot(temps)
plot(temps[[1:2]])
nombres=list.files("~/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/", full.names = TRUE)
#######
temp=raster("C:/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/wc2.1_10m_tavg_01.tif")
lista=list.files("C:/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/",full.name=TRUE)- Identifique 2 o 3 países con áreas de alto potencial para la caña de azúcar y realice un corte para estas zonas con el shape global. Grafique los mapas con una escala de colores adecuada.
pos=global$REGION=="South America"|global$REGION=="Central America"|global$REGION=="North America"|global$REGION=="Asia"|global$REGION=="Oceania"|global$REGION=="Europe"|global$REGION=="Africa"
#####
library(readxl)
require(raster)
######
pos=global$REGION=="South America"|global$REGION=="Central America"
pos=global$REGION=="South America"|global$REGION=="Central America"
#######
#####
radia_01=raster("C:/Users/Juan David Betancour/Downloads/wc2.1_10m_srad/wc2.1_10m_srad_01.tif")
plot(radia_01)
global@data## AREA PERIMETER G2008_0_ G2008_0_ID ADM0_CODE
## 298 173.6764388 156.369231 13552 13551 162
## 306 0.1110900 1.652374 14106 14105 162
## 348 1.8562346 8.053743 17973 17972 28
## 350 9.1994782 18.679510 18068 18067 103
## 351 9.4448696 22.798962 18417 18416 111
## 354 10.7397096 20.650685 18588 18587 180
## 357 1.7380314 7.880556 18696 18695 75
## 369 92.7790064 84.335022 19299 19298 57
## 372 74.5446966 78.049646 19474 19473 263
## 379 4.2157136 20.048606 20285 20284 61
## 390 6.0459110 35.142682 21339 21338 191
## 391 17.1371875 28.647889 21877 21876 107
## 396 11.8919969 18.670423 22646 22645 233
## 397 6.7898240 13.510355 22756 22755 86
## 400 707.2241343 259.992466 22920 22919 37
## 418 20.0426382 28.596403 23589 23588 73
## 420 0.1775839 2.195180 23910 23909 37
## 421 0.3828820 4.719463 23983 23982 73
## 423 106.5412886 80.703190 24043 24042 195
## 477 92.0001191 58.024225 27797 27796 33
## 493 65.3951040 209.804782 29081 29080 51
## 495 35.2924670 34.706864 29275 29274 194
## 500 275.3795691 138.838751 29576 29575 12
## 505 17.1540232 20.987486 29874 29873 260
## 512 0.9364827 8.458475 30221 30220 51
## 513 0.2348970 4.364587 30413 30412 51
## 514 0.1028359 2.221707 30486 30485 51
## 516 0.1496873 6.770519 30753 30752 51
## 517 0.1267633 3.541468 30769 30768 51
## 518 0.7545058 22.918716 30801 30800 51
## 519 0.1308403 6.412690 30967 30966 51
## 520 0.1044784 5.532262 31039 31038 51
## 521 0.8430473 21.059890 31094 31093 81
## 522 0.5802136 15.758739 31123 31122 81
## 523 3.7085012 37.091502 31409 31408 51
## 524 0.6709721 12.361302 31451 31450 51
## 525 2.8060580 12.093473 31457 31456 12
## 526 0.1770267 10.557380 31471 31470 51
## 527 0.5048305 13.986042 31562 31561 51
## 528 0.1782853 3.453864 31587 31586 51
## 529 0.1535353 5.702080 31610 31609 51
## 530 0.1606732 5.856791 31626 31625 51
## 531 0.3601259 4.434421 31802 31801 51
## 532 0.5860953 18.451625 31809 31808 51
## ADM0_NAME LAST_UPDAT CONTINENT REGION STR_YEAR0
## 298 Mexico 20050415 Americas Central America 0
## 306 Mexico 20050415 Americas Central America 0
## 348 Belize 20050415 Americas Central America 0
## 350 Guatemala 20070911 Americas Central America 0
## 351 Honduras 20050415 Americas Central America 0
## 354 Nicaragua 20050415 Americas Central America 0
## 357 El Salvador 20050415 Americas Central America 0
## 369 Colombia 20050415 Americas South America 0
## 372 Venezuela 20050415 Americas South America 0
## 379 Costa Rica 20050415 Americas Central America 0
## 390 Panama 20050415 Americas Central America 0
## 391 Guyana 20050415 Americas South America 0
## 396 Suriname 20050415 Americas South America 0
## 397 French Guiana 20050415 Americas South America 0
## 400 Brazil 20050415 Americas South America 0
## 418 Ecuador 20050415 Americas South America 0
## 420 Brazil 20050415 Americas South America 0
## 421 Ecuador 20050415 Americas South America 0
## 423 Peru 20050415 Americas South America 0
## 477 Bolivia 20051228 Americas South America 0
## 493 Chile 20050415 Americas South America 0
## 495 Paraguay 20060112 Americas South America 0
## 500 Argentina 20050415 Americas South America 0
## 505 Uruguay 20050415 Americas South America 0
## 512 Chile 20050415 Americas South America 0
## 513 Chile 20050415 Americas South America 0
## 514 Chile 20050415 Americas South America 0
## 516 Chile 20050415 Americas South America 0
## 517 Chile 20050415 Americas South America 0
## 518 Chile 20050415 Americas South America 0
## 519 Chile 20050415 Americas South America 0
## 520 Chile 20050415 Americas South America 0
## 521 Falkland Islands (Malvinas) 20050415 Americas South America 0
## 522 Falkland Islands (Malvinas) 20050415 Americas South America 0
## 523 Chile 20050415 Americas South America 0
## 524 Chile 20050415 Americas South America 0
## 525 Argentina 20050415 Americas South America 0
## 526 Chile 20050415 Americas South America 0
## 527 Chile 20050415 Americas South America 0
## 528 Chile 20050415 Americas South America 0
## 529 Chile 20050415 Americas South America 0
## 530 Chile 20050415 Americas South America 0
## 531 Chile 20050415 Americas South America 0
## 532 Chile 20050415 Americas South America 0
## EXP_YEAR0
## 298 0
## 306 0
## 348 0
## 350 0
## 351 0
## 354 0
## 357 0
## 369 0
## 372 0
## 379 0
## 390 0
## 391 0
## 396 0
## 397 0
## 400 0
## 418 0
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## 421 0
## 423 0
## 477 0
## 493 0
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## 500 0
## 505 0
## 512 0
## 513 0
## 514 0
## 516 0
## 517 0
## 518 0
## 519 0
## 520 0
## 521 0
## 522 0
## 523 0
## 524 0
## 525 0
## 526 0
## 527 0
## 528 0
## 529 0
## 530 0
## 531 0
## 532 0plot(global)
pos=global$REGION=="South America"|global$REGION=="Central America"
sur_america=global=global[pos,]
plot(sur_america)
pos=sur_america$ADM0_NAME=="Colombia"|sur_america$ADM0_NAME=="Brazil" |global$ADM0_NAME=="Mexico"
pos=sur_america$ADM0_NAME=="Colombia"
colombia=sur_america[pos,]
plot(colombia,add=TRUE,col="red")
pos=sur_america$ADM0_NAME=="Brazil"
brazil=sur_america[pos,]
plot(brazil,add=TRUE,col="red")
pos=mundo$ADM0_NAME=="Mexico"
mexico=global[pos,]
plot(mexico,add=TRUE,col="red")
colombia=sur_america[pos,]
brazil=sur_america[pos,]
mexico=global[pos,]
plot(colombia)
plot(sur_america)
plot(colombia,add=TRUE,col="red")
plot(brazil,add=TRUE,col="red")
plot(mexico,add=TRUE,col="red")
- Identificar algunos puntos (2 o 3) al azar en la región del valle del cauca (use google maps) y extraer la información de clima. Grafique las series de tiempo de temperatura y precipitación.
require(raster)
require(rgdal)
require(sp)
require(raster)
require(rgdal)
require(sp)
global=shapefile("C:/Users/Juan David Betancour/Downloads/YDRAY-shape_global/shape_global/g2008_0.shp")
global## class : SpatialPolygonsDataFrame
## features : 534
## extent : -180, 180, -55.72333, 83.62742 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## variables : 11
## names : AREA, PERIMETER, G2008_0_, G2008_0_ID, ADM0_CODE, ADM0_NAME, LAST_UPDAT, CONTINENT, REGION, STR_YEAR0, EXP_YEAR0
## min values : 0.10069705556089, 1.40729013556, 10019, 1, 1, Afghanistan, 20050415, Africa, Australia and New Zealand, 0, 0
## max values : 2825.6935742546, 969.26620675757, 9999, 9998, 98, Zimbabwe, 20081118, Oceania, Western Europe, 2006, 0radia_01=raster("C:/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/wc2.1_10m_tavg_01.tif")
lista=list.files("C:/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/",full.name=TRUE)
pos=sur_america$ADM0_NAME=="Colombia"|sur_america$ADM0_NAME=="Peru"
col_peru=sur_america[pos,]
pos=sur_america$ADM0_NAME=="Colombia"|sur_america$ADM0_NAME=="Brazil"|sur_america$ADM0_NAME=="Paraguay" |global$ADM0_NAME=="Panama"## Warning in sur_america$ADM0_NAME == "Colombia" | sur_america$ADM0_NAME == :
## longitud de objeto mayor no es múltiplo de la longitud de uno menorpos=sur_america$ADM0_NAME=="Colombia"
colombia=sur_america[pos,]
plot(colombia)
global@data## AREA PERIMETER G2008_0_ G2008_0_ID ADM0_CODE
## 0 649.4221006 900.013634 2 1 98
## 1 0.1422211 4.510463 5 4 98
## 2 0.1156834 3.409226 15 14 98
## 3 0.1173564 2.728860 16 15 98
## 4 95.1420039 345.882703 18 17 46
## 5 0.2801303 6.484889 19 18 98
## 6 0.9306178 8.583795 40 39 98
## 7 0.3380294 4.706944 46 45 98
## 8 0.1562633 2.158037 50 49 98
## 9 0.1685253 3.316294 59 58 204
## 10 0.1550204 3.574494 66 65 204
## 11 0.1876524 3.786579 83 82 204
## 12 0.2724445 6.368011 93 92 204
## 13 19.1470731 85.289162 97 96 46
## 14 4.3171399 26.003137 102 101 204
## 15 0.2247931 3.371740 108 107 204
## 16 0.8455203 6.887400 111 110 204
## 17 0.2199999 3.524273 117 116 204
## 18 0.2101825 4.760534 118 117 204
## 19 0.4761179 7.457562 119 118 204
## 20 0.1452579 2.796307 132 131 204
## 21 1.3474735 24.331637 134 133 204
## 22 0.1195275 2.419301 136 135 204
## 23 0.1701549 4.114697 137 136 204
## 24 1.0104626 7.947547 140 139 204
## 25 0.5296560 10.076675 141 140 204
## 26 0.1756594 3.638152 147 146 204
## 27 6.5122763 42.829639 174 173 234
## 28 0.4696665 6.153708 182 181 204
## 29 0.2413749 4.045263 189 188 204
## 30 0.3015316 3.793434 196 195 204
## 31 0.3285165 4.321472 204 203 234
## 32 0.1019201 2.234947 215 214 98
## 33 6.2291968 33.140115 221 220 204
## 34 0.4291783 4.373820 222 221 46
## 35 0.1272016 3.347738 232 231 204
## 36 0.1220813 2.694179 236 235 204
## 37 0.7046931 10.385062 239 238 204
## 38 15.5801659 81.819341 240 239 234
## 39 0.1248217 3.311688 285 284 204
## 40 4.3401813 22.910516 311 310 204
## 41 4.6304807 26.912179 315 314 46
## 42 0.1346124 2.780515 357 356 46
## 43 0.2615033 4.937006 376 375 234
## 44 2.0917566 14.473261 385 384 46
## 45 1.1211007 10.368883 399 398 46
## 46 0.5299931 5.182223 416 415 234
## 47 1.9253399 11.641147 462 461 234
## 48 1.9278605 11.298947 490 489 46
## 49 0.2988444 3.665055 501 500 46
## 50 0.2463608 3.512637 542 541 46
## 51 0.8582896 7.891501 550 549 46
## 52 0.4805420 5.196366 559 558 46
## 53 2825.6935743 969.266207 566 565 204
## 54 0.5131293 4.277725 571 570 46
## 55 5.5974693 33.863998 584 583 46
## 56 0.1055759 2.696335 601 600 98
## 57 0.1169094 3.619780 639 638 204
## 58 17.9741445 74.024609 658 657 46
## 59 14.5913205 76.187273 668 667 204
## 60 0.1942223 3.083183 685 684 46
## 61 13.7333545 68.845942 697 696 46
## 62 0.2075044 2.557970 700 699 46
## 63 0.1140676 2.954141 721 720 46
## 64 0.1949519 3.458843 748 747 98
## 65 5.3827133 40.058743 760 759 46
## 66 0.3737773 4.664614 764 763 46
## 67 0.3843246 4.337254 875 874 46
## 68 0.1353576 4.820864 878 877 204
## 69 7.9233284 26.956297 899 898 204
## 70 0.5123389 4.982703 920 919 46
## 71 0.1151092 3.200140 922 921 46
## 72 0.1457398 3.465292 930 929 46
## 73 0.1657247 2.982177 976 975 46
## 74 0.1709153 2.392900 993 992 204
## 75 0.1348508 3.143367 1019 1018 46
## 76 2.2347433 10.276424 1023 1022 46
## 77 1.9541133 11.634462 1031 1030 204
## 78 0.3640943 3.182902 1054 1053 46
## 79 0.3900877 5.783986 1056 1055 98
## 80 0.1946659 2.816093 1078 1077 98
## 81 19.4872297 33.872001 1138 1137 46
## 82 0.5673586 4.587226 1139 1138 204
## 83 0.2634075 2.515339 1182 1181 204
## 84 6.9940505 23.127748 1192 1191 46
## 85 0.2917631 3.784011 1202 1201 46
## 86 9.0119916 39.459187 1244 1243 46
## 87 1.5089002 8.368559 1257 1256 204
## 88 113.5309800 447.506140 1271 1270 46
## 89 3.1045275 12.996773 1288 1287 46
## 90 1.2673760 7.437017 1289 1288 46
## 91 0.5576245 4.604941 1355 1354 204
## 92 0.6726686 10.094958 1372 1371 98
## 93 8.4964540 52.725856 1375 1374 204
## 94 54.0109992 120.279141 1389 1388 46
## 95 0.1123364 1.428357 1445 1444 46
## 96 0.1139210 2.236201 1483 1482 204
## 97 0.2258829 2.664396 1492 1491 204
## 98 0.4626420 7.089851 1514 1513 98
## 99 0.9384181 8.253386 1595 1594 98
## 100 0.2870843 3.373888 1699 1698 204
## 101 1231.5960441 889.370548 1818 1817 46
## 102 0.1071776 2.606278 1870 1869 204
## 103 1.3108141 9.349516 1871 1870 204
## 104 0.6231518 4.795167 1884 1883 204
## 105 0.1845418 4.386511 1930 1929 204
## 106 267.1289963 301.393893 1934 1933 259
## 107 0.1296699 1.884040 1937 1936 98
## 108 0.1070814 1.426966 1950 1949 98
## 109 0.1099812 4.438485 1975 1974 186
## 110 56.1796892 323.048213 1986 1985 186
## 111 0.9295592 6.918095 1999 1998 98
## 112 0.1238582 2.303346 2005 2004 46
## 113 0.2013179 8.785727 2054 2053 186
## 114 0.1063340 1.730629 2085 2084 204
## 115 0.1419895 4.089793 2126 2125 186
## 116 0.1198854 1.914074 2142 2141 204
## 117 0.7672111 8.096833 2232 2231 204
## 118 1.9953158 13.309833 2283 2282 98
## 119 62.2085583 83.542538 2405 2404 84
## 120 0.1553865 2.989730 2406 2405 186
## 121 0.4797412 4.339750 2432 2431 204
## 122 0.1513645 3.246713 2457 2456 98
## 123 2.9740955 18.616853 2472 2471 46
## 124 0.1721906 4.797023 2488 2487 186
## 125 0.2091178 5.040179 2531 2530 46
## 126 0.1058375 2.250050 2562 2561 46
## 127 0.1086861 3.957967 2639 2638 46
## 128 0.3706972 10.207533 2671 2670 186
## 129 1.1319177 6.852285 2752 2751 204
## 130 0.1563569 2.511808 2788 2787 46
## 131 0.2450018 3.427972 2817 2816 46
## 132 0.1162574 2.440700 2845 2844 186
## 133 77.9791224 120.028940 2952 2951 236
## 134 0.1027399 2.730631 2953 2952 46
## 135 0.1988531 7.932596 2972 2971 186
## 136 22.5928595 56.000600 3008 3007 204
## 137 0.4948752 11.092739 3013 3012 186
## 138 0.1413798 2.431784 3143 3142 46
## 139 0.1150910 4.478381 3347 3346 186
## 140 0.1039655 4.443468 3442 3441 186
## 141 2.0342285 6.139754 3450 3449 46
## 142 0.2433484 2.239214 3488 3487 46
## 143 0.3653738 3.469559 3618 3617 46
## 144 0.1688181 4.801699 4161 4160 204
## 145 19.4616051 88.516856 4250 4249 114
## 146 0.1956017 5.179043 4406 4405 46
## 147 0.1524083 2.502513 4436 4435 46
## 148 8.0772729 26.881168 4483 4482 46
## 149 0.1513316 3.386262 4501 4500 98
## 150 0.8844887 9.157342 5704 5703 259
## 151 0.1444218 3.980247 5772 5771 46
## 152 0.1014963 3.060161 5800 5799 186
## 153 0.2424292 3.648900 5915 5914 46
## 154 0.9996153 5.466186 6143 6142 46
## 155 0.1407341 4.413885 6189 6188 46
## 156 0.5409064 3.589032 6383 6382 46
## 157 0.1694963 4.732265 6600 6599 46
## 158 0.1481716 1.634856 7024 7023 46
## 159 0.1228593 7.022435 7103 7102 256
## 160 0.6732233 6.479992 7176 7175 259
## 161 0.1215233 6.031179 7180 7179 84
## 162 0.1321749 4.079233 7238 7237 259
## 163 0.1062296 3.859258 7527 7526 98
## 164 6.3916590 18.790622 7885 7884 78
## 165 0.3028397 3.923439 8064 8063 204
## 166 0.1610797 3.388845 8144 8143 78
## 167 29.9400722 113.678110 8363 8362 256
## 168 0.4118817 6.682140 8374 8373 78
## 169 0.3296084 9.788115 8420 8419 256
## 170 0.2799383 7.752261 8438 8437 259
## 171 0.6533027 12.682656 8462 8461 259
## 172 0.8079918 16.923113 8501 8500 259
## 173 9.5034493 20.870937 8576 8575 140
## 174 1.4312525 20.601659 8611 8610 259
## 175 0.4575684 5.630852 8636 8635 236
## 176 4.3948086 19.060827 8694 8693 69
## 177 0.2436780 6.883683 8704 8703 256
## 178 0.6018615 15.081586 8751 8750 259
## 179 0.1990089 3.641259 8813 8812 236
## 180 0.4092274 6.555268 8883 8882 259
## 181 0.2849475 10.611023 8920 8919 259
## 182 1.9439402 20.062275 9003 9002 259
## 183 0.1313876 4.619109 9031 9030 256
## 184 0.2686609 9.400401 9041 9040 46
## 185 9.1769581 17.867213 9121 9120 147
## 186 0.9302204 23.891507 9155 9154 259
## 187 0.1279312 3.738411 9158 9157 259
## 188 28.1405111 36.627182 9238 9237 26
## 189 1.0004816 12.120008 9274 9273 69
## 190 0.4152594 7.491809 9363 9362 259
## 191 0.4277331 6.041172 9478 9477 69
## 192 343.0547933 148.071137 9542 9541 132
## 193 9.3229378 64.109557 9560 9559 119
## 194 0.1773739 3.114959 9566 9565 204
## 195 1.8882188 10.406931 9598 9597 204
## 196 1.9589834 12.193554 9618 9617 256
## 197 0.2634399 2.747610 9636 9635 204
## 198 45.6046486 63.559561 9691 9690 93
## 199 0.5688918 5.690894 9694 9693 259
## 200 0.1722472 3.439853 9740 9739 69
## 201 40.9022540 40.389765 9782 9781 198
## 202 0.1337871 5.243497 9827 9826 93
## 203 9.4403408 36.664005 9898 9897 204
## 204 0.8843181 12.034130 9944 9943 46
## 205 0.3712934 10.787999 9999 9998 259
## 206 0.1857146 4.840635 10019 10018 46
## 207 0.1333251 3.119120 10093 10092 46
## 208 0.2395473 4.201318 10108 10107 259
## 209 944.6688489 363.832890 10109 10108 53
## 210 4.4506284 23.532526 10137 10136 177
## 211 0.3021844 6.510717 10178 10177 46
## 212 0.3472373 12.058150 10187 10186 46
## 213 0.4005374 3.299425 10194 10193 46
## 214 0.1203187 3.002985 10233 10232 259
## 215 0.1094873 2.511445 10368 10367 46
## 216 0.1418312 6.138344 10371 10370 259
## 217 73.8294126 87.949083 10377 10376 254
## 218 184.7323981 89.054482 10418 10417 167
## 219 13.2778135 99.310748 10539 10538 46
## 220 3.8973666 14.678572 10559 10558 27
## 221 63.4153370 77.274928 10642 10641 85
## 222 9.8348333 22.603090 10648 10647 65
## 223 3.9589275 34.434231 10670 10669 46
## 224 0.2610961 3.088538 10694 10693 204
## 225 0.3271367 3.385718 10800 10799 148
## 226 0.9847631 6.725663 10831 10830 46
## 227 5.9957871 16.124718 10870 10869 223
## 228 816.7711235 407.705812 10940 10939 259
## 229 23.7863602 84.832446 10951 10950 46
## 230 10.0407875 26.029136 10993 10992 18
## 231 11.0478708 21.424557 11120 11119 113
## 232 4.0127464 17.453739 11141 11140 165
## 233 27.5837808 31.439618 11167 11166 203
## 234 4.8622629 18.109517 11205 11204 237
## 235 0.1759389 2.877367 11263 11262 259
## 236 27.9783885 58.281124 11319 11318 122
## 237 0.6919158 11.277899 11325 11324 46
## 238 1.2247470 18.971125 11328 11327 46
## 239 2.3653477 11.719966 11354 11353 224
## 240 5.9944896 33.077494 11377 11376 62
## 241 0.1674460 3.099225 11412 11411 204
## 242 9.9347312 24.909030 11421 11420 2648
## 243 0.3378223 5.947208 11482 11481 46
## 244 48.6613955 65.904674 11560 11559 261
## 245 0.3683696 6.422340 11571 11570 136
## 246 8.6900854 26.097209 11582 11581 126
## 247 5.7557681 15.849995 11644 11643 34
## 248 0.1718310 3.681980 11774 11773 136
## 249 12.2416826 23.354462 11851 11850 41
## 250 52.3449389 59.152590 11943 11942 229
## 251 7.6048293 19.911341 11990 11989 92
## 252 1.5155429 9.414241 11992 11991 2647
## 253 21.4710317 43.276247 12018 12017 138
## 254 0.1071601 4.682256 12035 12034 62
## 255 0.9534751 7.923174 12040 12039 85
## 256 12.9231393 41.610641 12041 12040 67
## 257 57.9158357 47.137676 12055 12054 250
## 258 16.9070746 26.175137 12064 12063 19
## 259 3.0549173 12.635512 12069 12068 3
## 260 2.7458410 9.011820 12094 12093 241
## 261 9.3160805 25.973716 12113 12112 199
## 262 2.5400805 12.066324 12117 12116 249
## 263 78.7147204 79.096402 12118 12117 249
## 264 11.2081188 54.395568 12126 12125 97
## 265 23.0082927 74.073764 12138 12137 126
## 266 3.1372199 14.008942 12157 12156 13
## 267 2.5326486 11.968141 12162 12161 122
## 268 0.3313231 7.773665 12168 12167 259
## 269 14.6621431 38.243714 12174 12173 239
## 270 0.3800419 4.078152 12256 12255 229
## 271 0.5624821 4.316580 12275 12274 19
## 272 161.4279644 85.074494 12276 12275 117
## 273 0.1719847 3.227027 12330 12329 97
## 274 0.3814282 6.055891 12406 12405 97
## 275 9.4036343 40.348369 12450 12449 202
## 276 62.5136985 54.507300 12474 12473 1
## 277 2.6184062 10.243291 12512 12511 122
## 278 42.1220557 35.744387 12669 12668 118
## 279 15.1063466 26.641928 12675 12674 248
## 280 18.5884992 24.525755 12679 12678 238
## 281 213.4347272 73.435570 12733 12732 4
## 282 18.2739798 28.192597 12735 12734 40781
## 283 73.9024637 75.763547 12775 12774 188
## 284 0.1418881 2.062376 12842 12841 97
## 285 3.0289496 10.773974 12898 12897 2
## 286 38.7522060 38.419673 12903 12902 169
## 287 0.8905721 7.103332 12926 12925 64
## 288 0.8259251 8.699382 12927 12926 97
## 289 0.9878983 6.652312 13111 13110 141
## 290 1.7914091 14.698661 13217 13216 126
## 291 3.5509762 26.709177 13394 13393 126
## 292 0.1801344 2.125056 13444 13443 202
## 293 8.4385980 16.582734 13461 13460 130
## 294 1.9681663 11.708843 13467 13466 121
## 295 0.1472065 2.141348 13479 13478 52
## 296 260.9166909 215.359768 13483 13482 115
## 297 147.2770070 58.838305 13486 13485 145
## 298 173.6764388 156.369231 13552 13551 162
## 299 0.5543983 4.135027 13568 13567 267
## 300 171.2594775 79.202247 13602 13601 215
## 301 88.9348898 58.166517 13628 13627 40765
## 302 0.1341296 1.817652 13648 13647 52
## 303 13.5513362 27.944825 13764 13763 175
## 304 1.5201845 6.996067 13843 13842 137
## 305 6.2426521 18.810388 14032 14031 15
## 306 0.1110900 1.652374 14106 14105 162
## 307 0.1534484 2.388934 14225 14224 229
## 308 0.1870707 2.292254 14236 14235 229
## 309 57.4672732 114.639643 14237 14236 171
## 310 3.4373990 10.551868 14274 14273 31
## 311 0.1410611 1.695063 14284 14283 229
## 312 23.9716772 29.383481 14387 14386 268
## 313 89.9271074 55.172664 14420 14419 159
## 314 0.1318939 2.813031 14471 14470 117
## 315 0.1052842 5.020651 14493 14492 20
## 316 0.1090925 3.571577 14506 14505 126
## 317 11.1826294 45.969168 14603 14602 23
## 318 0.1416269 4.392305 14686 14685 187
## 319 1.0178605 7.396113 14736 14735 201
## 320 6.2457840 18.833947 14743 14742 255
## 321 3.1800016 10.346173 14962 14961 53
## 322 0.3272789 4.339927 14984 14983 20
## 323 106.5892335 68.786522 15029 15028 155
## 324 26.4578063 29.828360 15034 15033 187
## 325 100.7822523 52.866239 15460 15459 181
## 326 107.1353108 55.699281 15490 15489 50
## 327 27.5480827 87.772921 15520 15519 264
## 328 9.3022940 42.740425 15596 15595 63
## 329 1.5694946 7.443698 15612 15611 40760
## 330 0.1370007 3.712889 15726 15725 23
## 331 19.6914941 44.586474 15953 15952 139
## 332 0.1253764 1.537596 16150 16149 259
## 333 208.2466054 81.939101 16158 16157 40764
## 334 0.1720383 2.054439 16371 16370 40762
## 335 0.1957079 2.655009 16415 16414 63
## 336 0.1359600 2.403433 16615 16614 259
## 337 0.1374964 2.135961 16774 16773 20
## 338 0.1662608 2.052693 16969 16968 259
## 339 43.0908396 76.659001 17660 17659 240
## 340 0.8992578 4.407968 17695 17694 259
## 341 2.9024031 12.621960 17712 17711 53
## 342 2.2335545 18.624939 17762 17761 108
## 343 4.1175399 15.230512 17766 17765 72
## 344 37.9277415 33.529572 17896 17895 269
## 345 8.8510497 43.618293 17948 17947 196
## 346 0.9410966 6.877487 17964 17963 123
## 347 0.7495070 5.543672 17968 17967 200
## 348 1.8562346 8.053743 17973 17972 28
## 349 10.0263198 27.354477 18056 18055 77
## 350 9.1994782 18.679510 18068 18067 103
## 351 9.4448696 22.798962 18417 18416 111
## 352 16.4904167 28.770730 18530 18529 217
## 353 22.7197639 31.489075 18578 18577 42
## 354 10.7397096 20.650685 18588 18587 180
## 355 92.8654058 49.839295 18614 18613 79
## 356 15.0817114 25.003084 18675 18674 44
## 357 1.7380314 7.880556 18696 18695 75
## 358 0.1225293 2.614808 18750 18749 196
## 359 74.9607362 45.893725 18841 18840 182
## 360 0.1108065 4.004652 18934 18933 115
## 361 0.8194097 5.904273 18948 18947 196
## 362 38.0988700 47.451105 19063 19062 45
## 363 0.1248321 3.465692 19110 19109 115
## 364 0.3012651 2.960132 19173 19172 269
## 365 1.7980916 7.967519 19178 19177 70
## 366 0.2664208 5.235774 19238 19237 196
## 367 1.0366246 10.143113 19247 19246 196
## 368 20.1903483 37.643315 19288 19287 106
## 369 92.7790064 84.335022 19299 19298 57
## 370 9.5260462 19.238445 19323 19322 29
## 371 0.1049095 3.990795 19442 19441 115
## 372 74.5446966 78.049646 19474 19473 263
## 373 51.8171192 52.123510 19632 19631 226
## 374 0.9491812 9.713523 19697 19696 196
## 375 0.5844888 7.106740 19966 19965 196
## 376 2.6632903 17.463541 20065 20064 105
## 377 0.9413079 16.936720 20135 20134 196
## 378 0.3638733 5.542802 20234 20233 196
## 379 4.2157136 20.048606 20285 20284 61
## 380 19.5890809 24.407268 20317 20316 94
## 381 4.6855192 15.551464 20360 20359 243
## 382 50.7300296 49.244668 20467 20466 49
## 383 1.0537358 7.400626 20469 20468 196
## 384 0.4008059 3.913016 20586 20585 246
## 385 26.3871993 33.300757 20628 20627 66
## 386 0.3110512 3.186960 20944 20943 196
## 387 5.8845597 15.256795 21055 21054 221
## 388 5.3943894 15.620056 21184 21183 231
## 389 7.7132241 35.344351 21196 21195 196
## 390 6.0459110 35.142682 21339 21338 191
## 391 17.1371875 28.647889 21877 21876 107
## 392 7.8384883 19.959624 21881 21880 144
## 393 15.9663118 46.977015 22310 22309 153
## 394 0.1013488 1.801409 22402 22401 196
## 395 10.6387695 21.817370 22421 22420 153
## 396 11.8919969 18.670423 22646 22645 233
## 397 6.7898240 13.510355 22756 22755 86
## 398 34.9353840 50.601593 22798 22797 116
## 399 189.9223534 92.270736 22876 22875 68
## 400 707.2241343 259.992466 22920 22919 37
## 401 0.3725507 3.943208 23037 23036 40
## 402 0.2591785 2.838037 23051 23050 61013
## 403 0.1059198 1.828787 23074 23073 40
## 404 47.2715315 42.779034 23121 23120 133
## 405 43.1350453 60.567848 23147 23146 116
## 406 0.1341689 2.419608 23162 23161 116
## 407 19.6120455 22.508218 23164 23163 253
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## ADM0_NAME LAST_UPDAT CONTINENT
## 0 Greenland 20050415 Americas
## 1 Greenland 20050415 Americas
## 2 Greenland 20050415 Americas
## 3 Greenland 20050415 Americas
## 4 Canada 20050415 Americas
## 5 Greenland 20050415 Americas
## 6 Greenland 20050415 Americas
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## 8 Greenland 20050415 Americas
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## 10 Russian Federation 20050415 Europe
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## 15 Russian Federation 20050415 Europe
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## 18 Russian Federation 20050415 Europe
## 19 Russian Federation 20050415 Europe
## 20 Russian Federation 20050415 Europe
## 21 Russian Federation 20050415 Europe
## 22 Russian Federation 20050415 Europe
## 23 Russian Federation 20050415 Europe
## 24 Russian Federation 20050415 Europe
## 25 Russian Federation 20050415 Europe
## 26 Russian Federation 20050415 Europe
## 27 Svalbard and Jan Mayen Islands 20050415 Europe
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## 81 Canada 20050415 Americas
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## 83 Russian Federation 20050415 Europe
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## 85 Canada 20050415 Americas
## 86 Canada 20050415 Americas
## 87 Russian Federation 20050415 Europe
## 88 Canada 20050415 Americas
## 89 Canada 20050415 Americas
## 90 Canada 20050415 Americas
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## 93 Russian Federation 20050415 Europe
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## 95 Canada 20050415 Americas
## 96 Russian Federation 20050415 Europe
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## 99 Greenland 20050415 Americas
## 100 Russian Federation 20050415 Europe
## 101 Canada 20050415 Americas
## 102 Russian Federation 20050415 Europe
## 103 Russian Federation 20050415 Europe
## 104 Russian Federation 20050415 Europe
## 105 Russian Federation 20050415 Europe
## 106 United States of America 20060103 Americas
## 107 Greenland 20050415 Americas
## 108 Greenland 20050415 Americas
## 109 Norway 20060103 Europe
## 110 Norway 20060103 Europe
## 111 Greenland 20050415 Americas
## 112 Canada 20050415 Americas
## 113 Norway 20060103 Europe
## 114 Russian Federation 20050415 Europe
## 115 Norway 20060103 Europe
## 116 Russian Federation 20050415 Europe
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## 119 Finland 20050415 Europe
## 120 Norway 20060103 Europe
## 121 Russian Federation 20050415 Europe
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## 124 Norway 20060103 Europe
## 125 Canada 20050415 Americas
## 126 Canada 20050415 Americas
## 127 Canada 20050415 Americas
## 128 Norway 20060103 Europe
## 129 Russian Federation 20050415 Europe
## 130 Canada 20050415 Americas
## 131 Canada 20050415 Americas
## 132 Norway 20060103 Europe
## 133 Sweden 20050415 Europe
## 134 Canada 20050415 Americas
## 135 Norway 20060103 Europe
## 136 Russian Federation 20050415 Europe
## 137 Norway 20060103 Europe
## 138 Canada 20050415 Americas
## 139 Norway 20060103 Europe
## 140 Norway 20060103 Europe
## 141 Canada 20050415 Americas
## 142 Canada 20050415 Americas
## 143 Canada 20050415 Americas
## 144 Russian Federation 20050415 Europe
## 145 Iceland 20050415 Europe
## 146 Canada 20050415 Americas
## 147 Canada 20050415 Americas
## 148 Canada 20050415 Americas
## 149 Greenland 20050415 Americas
## 150 United States of America 20060103 Americas
## 151 Canada 20050415 Americas
## 152 Norway 20060103 Europe
## 153 Canada 20050415 Americas
## 154 Canada 20050415 Americas
## 155 Canada 20050415 Americas
## 156 Canada 20050415 Americas
## 157 Canada 20050415 Americas
## 158 Canada 20050415 Americas
## 159 U.K. of Great Britain and Northern Ireland 20050825 Europe
## 160 United States of America 20060103 Americas
## 161 Finland 20050415 Europe
## 162 United States of America 20060103 Americas
## 163 Greenland 20050415 Americas
## 164 Estonia 20050415 Europe
## 165 Russian Federation 20050415 Europe
## 166 Estonia 20050415 Europe
## 167 U.K. of Great Britain and Northern Ireland 20050825 Europe
## 168 Estonia 20050415 Europe
## 169 U.K. of Great Britain and Northern Ireland 20050825 Europe
## 170 United States of America 20060103 Americas
## 171 United States of America 20060103 Americas
## 172 United States of America 20060103 Americas
## 173 Latvia 20050415 Europe
## 174 United States of America 20060103 Americas
## 175 Sweden 20050415 Europe
## 176 Denmark 20060307 Europe
## 177 U.K. of Great Britain and Northern Ireland 20050825 Europe
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## 179 Sweden 20050415 Europe
## 180 United States of America 20060103 Americas
## 181 United States of America 20060103 Americas
## 182 United States of America 20060103 Americas
## 183 U.K. of Great Britain and Northern Ireland 20050825 Europe
## 184 Canada 20050415 Americas
## 185 Lithuania 20050415 Europe
## 186 United States of America 20060103 Americas
## 187 United States of America 20060103 Americas
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## 189 Denmark 20060307 Europe
## 190 United States of America 20060103 Americas
## 191 Denmark 20060307 Europe
## 192 Kazakhstan 20071022 Asia
## 193 Ireland 20050415 Europe
## 194 Russian Federation 20050415 Europe
## 195 Russian Federation 20050415 Europe
## 196 U.K. of Great Britain and Northern Ireland 20050825 Europe
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## 198 Germany 20051229 Europe
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## 200 Denmark 20060307 Europe
## 201 Poland 20050415 Europe
## 202 Germany 20051229 Europe
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## 205 United States of America 20060103 Americas
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## 208 United States of America 20060103 Americas
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## 212 Canada 20050415 Americas
## 213 Canada 20050415 Americas
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## 215 Canada 20050415 Americas
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## 220 Belgium 20051228 Europe
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## 222 Czech Republic 20050415 Europe
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## 224 Russian Federation 20050415 Europe
## 225 Luxembourg 20050415 Europe
## 226 Canada 20050415 Americas
## 227 Slovakia 20050415 Europe
## 228 United States of America 20060103 Americas
## 229 Canada 20050415 Americas
## 230 Austria 20050415 Europe
## 231 Hungary 20050415 Europe
## 232 Republic of Moldova 20060315 Europe
## 233 Romania 20050415 Europe
## 234 Switzerland 20050415 Europe
## 235 United States of America 20060103 Americas
## 236 Italy 20050415 Europe
## 237 Canada 20050415 Americas
## 238 Canada 20050415 Americas
## 239 Slovenia 20050415 Europe
## 240 Croatia 20050415 Europe
## 241 Russian Federation 20050415 Europe
## 242 Serbia 20081118 Europe
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## 244 Uzbekistan 20050822 Asia
## 245 Kuril islands 20050415 Asia
## 246 Japan 20050711 Asia
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## 248 Kuril islands 20050415 Asia
## 249 Bulgaria 20050415 Europe
## 250 Spain 20050415 Europe
## 251 Georgia 20050415 Asia
## 252 Montenegro 20061004 Europe
## 253 Kyrgyzstan 20050415 Asia
## 254 Croatia 20050415 Europe
## 255 France 20050415 Europe
## 256 Dem People's Rep of Korea 20050822 Asia
## 257 Turkmenistan 20050415 Asia
## 258 Azerbaijan 20050415 Asia
## 259 Albania 20050415 Europe
## 260 The former Yugoslav Republic of Macedonia 20050415 Europe
## 261 Portugal 20050415 Europe
## 262 Turkey 20050415 Asia
## 263 Turkey 20050415 Asia
## 264 Greece 20060112 Europe
## 265 Japan 20050711 Asia
## 266 Armenia 20050415 Asia
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## 268 United States of America 20060103 Americas
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## 270 Spain 20050415 Europe
## 271 Azerbaijan 20050415 Asia
## 272 Iran (Islamic Republic of) 20060309 Asia
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## 274 Greece 20060112 Europe
## 275 Republic of Korea 20050415 Asia
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## 277 Italy 20050415 Europe
## 278 Iraq 20050713 Asia
## 279 Tunisia 20050728 Africa
## 280 Syrian Arab Republic 20050415 Asia
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## 283 Pakistan 20051108 Asia
## 284 Greece 20060112 Europe
## 285 Aksai Chin 20050415 Asia
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## 288 Greece 20060112 Europe
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## 290 Japan 20050711 Asia
## 291 Japan 20050711 Asia
## 292 Republic of Korea 20050415 Asia
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## 294 Israel 20050415 Asia
## 295 China/India 20050415 Asia
## 296 India 20080908 Asia
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## 299 West Bank 20050415 Asia
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## 301 Egypt 20060112 Africa
## 302 China/India 20050415 Asia
## 303 Nepal 20071022 Asia
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## 305 Arunachal Pradesh 20080908 Asia
## 306 Mexico 20050415 Americas
## 307 Spain 20050415 Europe
## 308 Spain 20050415 Europe
## 309 Myanmar 20060927 Asia
## 310 Bhutan 20050415 Asia
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## 313 Mauritania 20050415 Africa
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## 316 Japan 20050711 Asia
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## 318 Oman 20050415 Asia
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## 320 United Arab Emirates 20050415 Asia
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## 324 Oman 20050415 Asia
## 325 Niger 20050415 Africa
## 326 Chad 20070914 Africa
## 327 Viet Nam 20081016 Asia
## 328 Cuba 20050415 Americas
## 329 Hala'ib triangle 20060112 Africa
## 330 Bangladesh 20080828 Asia
## 331 Lao People's Democratic Republic 20081028 Asia
## 332 United States of America 20060103 Americas
## 333 Sudan 20080717 Africa
## 334 Ma'tan al-Sarra 20060112 Africa
## 335 Cuba 20050415 Americas
## 336 United States of America 20060103 Americas
## 337 Bahamas 20050415 Americas
## 338 United States of America 20060103 Americas
## 339 Thailand 20050415 Asia
## 340 United States of America 20060103 Americas
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## 348 Belize 20050415 Americas
## 349 Eritrea 20050415 Africa
## 350 Guatemala 20070911 Americas
## 351 Honduras 20050415 Americas
## 352 Senegal 20071022 Africa
## 353 Burkina Faso 20051028 Africa
## 354 Nicaragua 20050415 Americas
## 355 Ethiopia 20061016 Africa
## 356 Cambodia 20070910 Asia
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## 358 Philippines 20071015 Asia
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## 360 India 20080908 Asia
## 361 Philippines 20071015 Asia
## 362 Cameroon 20051229 Africa
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## 364 Yemen 20050415 Asia
## 365 Djibouti 20050415 Africa
## 366 Philippines 20071015 Asia
## 367 Philippines 20071015 Asia
## 368 Guinea 20060103 Africa
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## 370 Benin 20050415 Africa
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## 373 Somalia 20061128 Africa
## 374 Philippines 20071015 Asia
## 375 Philippines 20071015 Asia
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## 380 Ghana 20050415 Africa
## 381 Togo 20070807 Africa
## 382 Central African Republic 20071010 Africa
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## 384 Trinidad and Tobago 20050415 Americas
## 385 Côte d'Ivoire 20081113 Africa
## 386 Philippines 20071015 Asia
## 387 Sierra Leone 20050415 Africa
## 388 Sri Lanka 20060921 Asia
## 389 Philippines 20071015 Asia
## 390 Panama 20050415 Americas
## 391 Guyana 20050415 Americas
## 392 Liberia 20050727 Africa
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## 394 Philippines 20071015 Asia
## 395 Malaysia 20050719 Asia
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## 397 French Guiana 20050415 Americas
## 398 Indonesia 20080925 Asia
## 399 Democratic Republic of the Congo 20081104 Africa
## 400 Brazil 20050415 Americas
## 401 Brunei Darussalam 20061128 Asia
## 402 Ilemi triangle 20071021 Africa
## 403 Brunei Darussalam 20061128 Asia
## 404 Kenya 20081111 Africa
## 405 Indonesia 20080925 Asia
## 406 Indonesia 20080925 Asia
## 407 Uganda 20060929 Africa
## 408 Equatorial Guinea 20050415 Africa
## 409 Congo 20050415 Africa
## 410 Indonesia 20080925 Asia
## 411 Indonesia 20080925 Asia
## 412 Equatorial Guinea 20050415 Africa
## 413 Gabon 20051229 Africa
## 414 Indonesia 20080925 Asia
## 415 Indonesia 20080925 Asia
## 416 Indonesia 20080925 Asia
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## 419 Indonesia 20080925 Asia
## 420 Brazil 20050415 Americas
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## 422 Indonesia 20080925 Asia
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## 424 Indonesia 20080925 Asia
## 425 Indonesia 20080925 Asia
## 426 Indonesia 20080925 Asia
## 427 Indonesia 20080925 Asia
## 428 Indonesia 20080925 Asia
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## 432 Indonesia 20080925 Asia
## 433 Indonesia 20080925 Asia
## 434 Indonesia 20080925 Asia
## 435 Indonesia 20080925 Asia
## 436 Indonesia 20080925 Asia
## 437 Papua New Guinea 20050720 Oceania
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## 441 Papua New Guinea 20050720 Oceania
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## 443 Indonesia 20080925 Asia
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## 470 Timor-Leste 20061003 Asia
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## 492 Fiji 20050829 Oceania
## 493 Chile 20050415 Americas
## 494 Botswana 20050415 Africa
## 495 Paraguay 20060112 Americas
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## 497 New Caledonia 20050415 Oceania
## 498 New Caledonia 20050415 Oceania
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## 514 Chile 20050415 Americas
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## 518 Chile 20050415 Americas
## 519 Chile 20050415 Americas
## 520 Chile 20050415 Americas
## 521 Falkland Islands (Malvinas) 20050415 Americas
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## REGION STR_YEAR0 EXP_YEAR0
## 0 Northern America 0 0
## 1 Northern America 0 0
## 2 Northern America 0 0
## 3 Northern America 0 0
## 4 Northern America 0 0
## 5 Northern America 0 0
## 6 Northern America 0 0
## 7 Northern America 0 0
## 8 Northern America 0 0
## 9 Eastern Europe 0 0
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## 17 Eastern Europe 0 0
## 18 Eastern Europe 0 0
## 19 Eastern Europe 0 0
## 20 Eastern Europe 0 0
## 21 Eastern Europe 0 0
## 22 Eastern Europe 0 0
## 23 Eastern Europe 0 0
## 24 Eastern Europe 0 0
## 25 Eastern Europe 0 0
## 26 Eastern Europe 0 0
## 27 Northern Europe 0 0
## 28 Eastern Europe 0 0
## 29 Eastern Europe 0 0
## 30 Eastern Europe 0 0
## 31 Northern Europe 0 0
## 32 Northern America 0 0
## 33 Eastern Europe 0 0
## 34 Northern America 0 0
## 35 Eastern Europe 0 0
## 36 Eastern Europe 0 0
## 37 Eastern Europe 0 0
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## 39 Eastern Europe 0 0
## 40 Eastern Europe 0 0
## 41 Northern America 0 0
## 42 Northern America 0 0
## 43 Northern Europe 0 0
## 44 Northern America 0 0
## 45 Northern America 0 0
## 46 Northern Europe 0 0
## 47 Northern Europe 0 0
## 48 Northern America 0 0
## 49 Northern America 0 0
## 50 Northern America 0 0
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## 481 Australia and New Zealand 0 0
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## 486 Australia and New Zealand 0 0
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## 505 South America 0 0
## 506 Australia and New Zealand 0 0
## 507 Australia and New Zealand 0 0
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## 516 South America 0 0
## 517 South America 0 0
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## 523 South America 0 0
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## 531 South America 0 0
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## 533 Western Africa 0 0plot(global)
pos=global$REGION=="South America"|global$REGION=="Central America"
sur_america=global=global[pos,]
plot(sur_america)
plot(sur_america)
plot(colombia,add=TRUE,col="red")
plot(col_peru)
plot(colombia,add=TRUE)
mapas=list.files("C:/Users/Juan David Betancour/Downloads/wc2.1_10m_srad/", full.names = TRUE)
mapas=list.files("C:/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/", full.names = TRUE)
mapas## [1] "C:/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/wc2.1_10m_tavg_01.tif"
## [2] "C:/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/wc2.1_10m_tavg_02.tif"
## [3] "C:/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/wc2.1_10m_tavg_03.tif"
## [4] "C:/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/wc2.1_10m_tavg_04.tif"
## [5] "C:/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/wc2.1_10m_tavg_05.tif"
## [6] "C:/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/wc2.1_10m_tavg_06.tif"
## [7] "C:/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/wc2.1_10m_tavg_07.tif"
## [8] "C:/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/wc2.1_10m_tavg_08.tif"
## [9] "C:/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/wc2.1_10m_tavg_09.tif"
## [10] "C:/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/wc2.1_10m_tavg_10.tif"
## [11] "C:/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/wc2.1_10m_tavg_11.tif"
## [12] "C:/Users/Juan David Betancour/Downloads/wc2.1_10m_tavg/wc2.1_10m_tavg_12.tif"radiaciones=stack(mapas)
plot(radiaciones)
radiaciones_col=crop(radiaciones,colombia)
radiaciones_col=mask(radiaciones_col,colombia)
names(radiaciones_col)=month.name
loc=cbind(-76.538575, 3.626382)
points(loc,col="blue",pch=16)
#####
loc=cbind(-76.592366, 3.995379)
points(loc,col="purple",pch=16)
##########################################Enviar el informe en Rpubs al correo david.arango@javerianacali.edu.co el informe debe contener, los códigos, resultados e interpretaciones y se puede realizar en grupos de 2 integrantes. La sustentación es individual