Parcial 3
Ana Marcela Gallego Muñoz & Juan José Arcila
9/11/2021
Punto 1:
Se someten dos tipos de molusco: A y B a tres concentraciones distintas de agua de mar (50%, 75%, 100%) y se observa el consumo de oxígeno
Punto 1.a: Realizar un análisis exploratorio entre el consumo de oxígeno y la concentración de agua, interpretar si el comportamiento es similar para cada tipo de molusco.
library(readxl)
Punto_1 <- read_excel("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/parcial 3/Punto 1.xlsx")
View(Punto_1)
require(ggplot2)## Loading required package: ggplot2require(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':
##
## layoutattach(Punto_1)
summary(cons_o)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.800 6.312 9.700 9.305 11.232 18.800Para el consumo de oxígeno de este estudio se reportó que el promedio de consumo es de 9.7, el valor mínimo de consumo es 1.8 mientras que el valor máximo es de 18.8. En cuanto a los cuartiles, se observa que el 25% de los datos recolectados son menores o iguales a 6.31 en el caso del cuartil 1, en el cuartil 3 se observa que el 75% de los datos son menores o iguales a 11.23.
graf_molusco= ggplot(Punto_1,aes(x=c_agua, y=cons_o,fill=molusco))+geom_boxplot(position = "dodge")+
facet_grid(molusco~c_agua)+xlab("Concentración de agua de mar")+
theme(axis.text.x = element_blank() , axis.ticks.x = element_blank())+
ylab("Consumo de oxígeno en moluscos")+ggtitle("relación entre el consumo de oxígeno y la concentración de agua")
graf_molusco
Como se observa en el diagrama de cajas y bigotes, en general los moluscos consumen grandes cantidades de oxígeno en concentraciones de agua de mar al 50% en comparación con los demás escenarios, en el agua de mar con concentración del 75% es donde menos oxígeno se consume, en comparación con el 50% y el 100%, y en concentraciones del 100% el consumo es mayor que en concentraciones al 75%, pero se mantiene en menor cantidad que en concentraciones del 50%. En cuanto al consumo de oxígeno entre los dos tipos de moluscos, el molusco A tiende a consumir más oxígeno en concentraciones del 75% y 100% a comparación del B, sin embargo en concentraciones del 50%, el molusco B es el que más oxígeno consume.
Punto 1.B: Se estima un modelo de diseño de experimentos para evaluar el efecto de la concentración de agua y los tipos de molusco sobre el consumo de oxígeno.
library(readxl)
Punto_1 <- read_excel("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/parcial 3/Punto 1.xlsx")
View(Punto_1)
require(agricolae)## Loading required package: agricolae## Warning: package 'agricolae' was built under R version 4.1.1attach(Punto_1)## The following objects are masked from Punto_1 (pos = 4):
##
## c_agua, cons_o, moluscoc_agua1=as.factor(c_agua)
mod_agua=lm(cons_o~c_agua1,data = Punto_1)
summary(mod_agua)##
## Call:
## lm(formula = cons_o ~ c_agua1, data = Punto_1)
##
## 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_agua175 -5.2581 1.0627 -4.948 1.09e-05 ***
## c_agua1100 -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_agua)## Analysis of Variance Table
##
## Response: cons_o
## Df Sum Sq Mean Sq F value Pr(>F)
## c_agua1 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 ' ' 1Al realizar el summary del modelo entre la concentración de agua y el consumo de oxígeno, se observa que esta variable es significativa, ya que su valor P observado al final del summary es igual a 4.043e-05 y se debe tener en cuenta que entre mas pequeño sea el valor P esto representa una mayor significancia estadítica entre estas dos variables. También, se puede ver que el valor tomado como intercepto fue la concentración de agua al 50% y a partir de ahí se comparan las otras dos concentraciones con base a dicho intercepto. En cuanto a la concentración de agua de mar al 75% hay una reducción en el consumo de oxígeno en un 5.25% respecto al intercepto, mientras que en concentraciones de agua al 100% hay una reducción en el cosumo de 3.58% respecto al intercepto; Puesto que este modelo tiene un alto grado de significancia estadística es factible realizar un postanova.
comp_agua_oxi= LSD.test(mod_agua,"c_agua1")
comp_agua_oxi## $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_agua1 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"Al realizar el postanova se puede corroborar que el consumo de oxígeno alcanza sus valores más altos en concentraciones de agua al 50% con un valor de 12.25, con una desviación estándar de 3.2 y un intervalo de confianza entre 10.73 y 13.76. Las concentraciones de agua al 100% representan un consumo de oxígeno moderado con un valor de 8.67 con una desviación estándar de 3 y un intervalo de confianza entre 7.15 y 10.18 y las concentraciones de agua al 75% son las de menor consumo de oxígeno con un valor de 7, una desviación estándar de 2.8 y un intervalo de confianza entre 5.4 y 8.5. Con los intervalos de confianza se realizan agrupaciones como se muestra en la sección de “groups”, para este modelo se denotan dos grupos: a y b, en donde se agrupan las 3 concentraciones de agua de mar respecto al consumo de oxígeno. En el grupo a está la concentración de agua al 50%, mientras que en el grupo b están las concentraciones al 75% y 100%, estos dos en un mismo grupo. ya que los intervalos de confianza de ambos se solapan en un punto entre sí.
molusco1=as.factor(molusco)
mod_molusco=lm(cons_o~molusco1,data = Punto_1)
summary(mod_molusco)##
## Call:
## lm(formula = cons_o ~ molusco1, data = Punto_1)
##
## 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 ***
## molusco1B -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(mod_molusco)## Analysis of Variance Table
##
## Response: cons_o
## Df Sum Sq Mean Sq F value Pr(>F)
## molusco1 1 23.23 23.227 1.7396 0.1937
## Residuals 46 614.18 13.352Al realizar un modelo entre el consumo de oxígeno y el tipo de molusco se observa que no hay mucha significancia estadística entre estas dos variables, ya que el valor P es 0.1937, lo que es muy alto comparado con el valor P obtenido en el anterior modelo. Esto quiere decir que el tipo de molusco no afecta el consumo de oxígeno como sí lo afecta las concentraciones de agua de mar. No se realiza un postanova debido a la poca significancia estadística.
Punto 2: Se obtienen datos de riqueza (número de especies) de moluscos asociados a cantos intermareales de diferente tamaño (S1 a S4, siendo S1 el mas pequeño y S el más grande) en diferentes épocas del año (T1 a T4). Se desea saber si la riqueza varía entre los tamaños de canto, las diferentes epocas del año y si existe interacción entre el tamaño de canto y la época del año.
Se tiene la hipótesis que la riqueza de especies varía dependiendo del tamaño de canto intermareal y no en el tiempo. Para responder a esto se realiza un modelo de diseño de experimentos entre la riqueza de especies, el tamaño de canto y el tiempo. Además de hacer una prueba de comparación múltiple en caso que sea necesario.
library(readxl)
Punto_2 <- read_excel("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/parcial 3/Punto 2.xlsx")
View(Punto_2)
require(agricolae)
attach(Punto_2)
require(ggplot2)graf_canto=ggplot(Punto_2,aes(x=canto,y=especies,fill=canto))+geom_boxplot(position = "dodge")+theme_bw()+
ylab("Riqueza de especies de moluscos")+xlab("Tamaño de canto")+ggtitle("Relación entre la riqueza de especies de moluscos, el tamaño de canto y el tiempo")+facet_grid(tiempo~canto)+theme(axis.text.x = element_blank(),axis.ticks.x = element_blank())
graf_canto
Como se observa en la gráfica los tamaños de canto S1 son lo que tienen mayor riqueza de especies en las cuatro épocas del año estudiadas, el tamaño de canto S2 tiene la misma riqueza de especies que S1 en el segundo trimestre, pero en general posee menor riqueza, S3 tiene menor riqueza de especies que S1 y S2 en cualquier época del año y el tamaño de canto con la menor riqueza de especies es el tamaño S4 que en ninguna época del año iguala a los otros tres tamaños anteriores. Para corroborar si los tamaños de canto y las epocas del año tienen significancia estadística respecto a la riqueza de especies, se realiza un modelo anova y un postanova si las variables tienen un alto grado de significancia.
canto1=as.factor(canto)
mod_canto=lm(especies~canto1,data = Punto_2)
summary(mod_canto)##
## Call:
## lm(formula = especies ~ canto1, data = Punto_2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.8750 -1.3281 -0.3125 1.2500 5.6250
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.3125 0.5538 13.205 < 2e-16 ***
## canto1S2 -1.4375 0.7831 -1.836 0.0714 .
## canto1S3 -3.9375 0.7831 -5.028 4.77e-06 ***
## canto1S4 -5.0000 0.7831 -6.385 2.77e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.215 on 60 degrees of freedom
## Multiple R-squared: 0.4598, Adjusted R-squared: 0.4328
## F-statistic: 17.02 on 3 and 60 DF, p-value: 4.096e-08anova(mod_canto)## Analysis of Variance Table
##
## Response: especies
## Df Sum Sq Mean Sq F value Pr(>F)
## canto1 3 250.56 83.521 17.023 4.096e-08 ***
## Residuals 60 294.38 4.906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Al realizar el modelo ente el canto y la riqueza de especies se observa que el valor P que representa el grado de significancia estadística es muy bajo, indicando que la variable del canto es muy significacitiva. En cuanto a los coeficientes, se toma como intercepto el valor estimado del tamaño de canto S1 el cual es 7.32, a partir de ahí se puede observar que la riqueza de especies para el tipo de canto S2 se reduce en 1.4% respecto al intercepto, en el tipo de canto S3 se reducen en 4% respecto al intercepto y para el tipo de canto S4 se reducen en 5% respecto al intercepto. Ya que la variable del canto tiene un alto grado de significancia estadística se realiza la prueba de comparación multiple postanova.
comp_esp_canto=LSD.test(mod_canto,"canto1")
comp_esp_canto## $statistics
## MSerror Df Mean CV t.value LSD
## 4.90625 60 4.71875 46.94052 2.000298 1.566479
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none canto1 4 0.05
##
## $means
## especies std r LCL UCL Min Max Q25 Q50 Q75
## S1 7.3125 1.922455 16 6.204832 8.420168 4 11 6.75 7 8.00
## S2 5.8750 2.963669 16 4.767332 6.982668 0 11 3.75 6 8.00
## S3 3.3750 2.276694 16 2.267332 4.482668 0 9 2.00 3 4.25
## S4 2.3125 1.400893 16 1.204832 3.420168 0 5 1.00 2 3.25
##
## $comparison
## NULL
##
## $groups
## especies groups
## S1 7.3125 a
## S2 5.8750 a
## S3 3.3750 b
## S4 2.3125 b
##
## attr(,"class")
## [1] "group"Al realizar la prueba postanova se forman dos grupos: a y b, en el grupo a están los tipos de canto S1 y S2, puesto que los intervalos de confianza se superponen en un punto, en el caso de S1 el intervalo de confianza va desde 6.2 hasta 8.42 con un valor promedio de 7.31 y en S2 el intervalo va desde 4.7 hasta 6.9 con un promedio de 5.8. En el grupo B se ubican los tamaños de canto S3 y S4, en donde sucede la misma situación del grupo a; en el tamaño de canto S3 hay un intervalo de confianza que va desde 2.26 hasta 4.48 con un promedio de 3.37 y en S4 el intervalo de confianza va desde 1.20 hasta 3.42 con un promedio de 2.31. Esto nos indica que los tipos de cantos con mayor número de especies son lo tamaños S1 y S2, siendo S1 mayor que S2, mientras que S3 y S4 son los tamaños de canto que tienen la menor riqueza de especies.
Se realiza un modelo anova entre la riqueza de especies y las epocas del año:
tiempo1=as.factor(tiempo)
mod_tiempo=lm(especies~tiempo1,data = Punto_2)
summary(mod_tiempo)##
## Call:
## lm(formula = especies ~ tiempo1, data = Punto_2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.938 -2.375 -0.375 2.438 6.625
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.5625 0.7504 6.080 9.04e-08 ***
## tiempo1T2 0.4375 1.0613 0.412 0.682
## tiempo1T3 0.3750 1.0613 0.353 0.725
## tiempo1T4 -0.1875 1.0613 -0.177 0.860
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.002 on 60 degrees of freedom
## Multiple R-squared: 0.007914, Adjusted R-squared: -0.04169
## F-statistic: 0.1595 on 3 and 60 DF, p-value: 0.9231anova(mod_tiempo)## Analysis of Variance Table
##
## Response: especies
## Df Sum Sq Mean Sq F value Pr(>F)
## tiempo1 3 4.31 1.4375 0.1595 0.9231
## Residuals 60 540.63 9.0104Al realizar el modelo lineal se observa que la vairable del tiempo no demuestra ningún tipo de relación con la riqueza de especies debido al valor p, el cual es 0.92 que es mucho mas alto que el valor p obtenido en el modelo de los tipos de canto y la riqueza de especies y no tiene relevancia significativa, debido a esto no se realiza una prueba postanova.
A partir de lo anterior, se puede concluir que la riqueza de especies está relacionada con el tipo del canto, en donde la mayor cantidad de especies tienen tamaños de canto muy pequeñs y los tamaños de canto más grandes son los que menor riqueza tienen, esta tendencia se mantiene a lo largo del año sin ningún tipo de preferencia a una epoca en específico, lo que quiere decir que el tiempo no tiene relación con la riqueza de especies.
Punto 3
La investigación diseñada se basa en conocer el efecto de cuatro tipos de dieta sobre el engorde de cerdos, donde se tienen 20 cerdos asginados aleatoriamente a 4 grupos experimentales cada uno con su respectiva dieta. Se quiere evaluar si existen diferencias entre los pesos corporales de los cerdos (Kg) después de haber sido criados con esas dietas por un mes.
library(readxl)
cerdos <- read_excel("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/parcial 3/cerdos.xlsx",
col_types = c("text", "numeric"))
View(cerdos)require(agricolae)
require(ggplot2)
#X= Dieta
#y= Peso cerdos
cerdos$Dieta=as.factor(cerdos$Dieta)
mod1=lm(Peso~Dieta,data=cerdos)
summary(mod1)##
## Call:
## lm(formula = Peso ~ Dieta, data = cerdos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.940 -2.680 0.440 2.095 3.980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 64.620 1.330 48.593 < 2e-16 ***
## DietaDieta B 6.680 1.881 3.552 0.002655 **
## DietaDieta C 8.920 1.881 4.743 0.000221 ***
## DietaDieta D -1.380 1.881 -0.734 0.473691
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.974 on 16 degrees of freedom
## Multiple R-squared: 0.7275, Adjusted R-squared: 0.6764
## F-statistic: 14.24 on 3 and 16 DF, p-value: 8.833e-05anova(mod1)## 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 ' ' 1compara=LSD.test(mod1,"Dieta")
compara## $statistics
## MSerror Df Mean CV t.value LSD
## 8.842 16 68.175 4.361643 2.119905 3.986775
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none Dieta 4 0.05
##
## $means
## Peso std r LCL UCL Min Max Q25 Q50 Q75
## Dieta A 64.62 3.345445 5 61.80092 67.43908 60.8 68.6 61.7 65.0 67.0
## Dieta B 71.30 3.068387 5 68.48092 74.11908 67.7 75.0 68.7 71.8 73.3
## Dieta C 73.54 2.987139 5 70.72092 76.35908 69.6 77.1 71.5 74.3 75.2
## Dieta D 63.24 2.416195 5 60.42092 66.05908 60.3 66.7 61.9 63.1 64.2
##
## $comparison
## NULL
##
## $groups
## Peso groups
## Dieta C 73.54 a
## Dieta B 71.30 a
## Dieta A 64.62 b
## Dieta D 63.24 b
##
## attr(,"class")
## [1] "group"Según el modelo de diseño de experimentos, la dieta A es seleccionada como el intercepto donde el promedio del peso está en 64.62 kg y esta es empleada para comparar los resultados con las demás dietas. Por lo cual, cuando se compara la dieta A con la dieta B se presenta un aumento del 6.68% en el peso promedio de los cerdos; cuado se compara la dieta A con la dieta C se tiene un incremento del 8.92% en el peso promedio y cuando la dieta A se compara con la dieta D el peso promedio de los cerdos presenta una disminución del 1.38%. De este modelo, podemos concluir que el peso de los cerdos aumento más al darles la dieta C, después el mejor resultado se obtuvo con la dieta B, mientras que las dietas A y D no tuvieron resultados favorecedores.
Seguidamente, el análisis ANOVA permitió observar que hay un efecto significativo de la dieta sobre el peso, queriendo decir que sí es un factor relevante para evaluar las variaciones de peso que se presentaron en los 20 cerdos del estudio. Por esto, se realizó una prueba de comparación multiple Postanova, en la cual se compararon los límites de los intervalos de confianza para cada dieta, ya que se quería evaluar que tan significativa era la diferencia entre ellas. Por lo anterior, se obtuvo que las dietas C y B se solapan en sus intervalos de confianza, por lo cual están en un mismo grupo y, además son las que presentan los mayores promedios de peso, estando primero C y luego B. Para las dietas A y D, sus intervalos también se solapan, están ambas en un mismo grupo, pero los valores promedios del peso son menores que los de las dietas C y B.
Teniendo en cuenta los análisis anteriores, la dieta que recomendaria para tener mejores resultados a la hora de engordar los cerdos sería la dieta C, pues el promedio fue mayor y muy relevante según los resultados. Seguidamente, se recomendaría la dieta B pues tuvo el segundo mejor promedio, pero esta no tiene un efecto significativo tan relevante. Por último, no recomendaria la dieta A por no tener un promedio alto y tampoco la D pues esta en cambio presentó una disminución en el promedio y no tiene un efecto significativo.
Punto 4
Se desea determinar el efecto que tienen los factores temperatura y sexo sobre la tasa de consumo de oxígeno mgO2 de una especie de cangrejo, de la cual se usaron 24 aniamles en el experimento.
library(readxl)
punto_4 <- read_excel("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/parcial 3/punto 4.xlsx",
col_types = c("text", "text", "numeric"))
View(punto_4)require(agricolae)
require(ggplot2)
#X= Temperatura
#y= Tasa de consumo de oxígeno
punto_4$Temperatura=as.factor(punto_4$Temperatura)
mod2=lm(Oxigeno~Temperatura,data=punto_4)
summary(mod2)##
## Call:
## lm(formula = Oxigeno ~ Temperatura, data = punto_4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3875 -0.1562 0.0000 0.1688 0.3875
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.01250 0.07854 38.357 < 2e-16 ***
## TemperaturaBaja -1.37500 0.11107 -12.380 4.09e-11 ***
## TemperaturaMedia -0.62500 0.11107 -5.627 1.39e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2221 on 21 degrees of freedom
## Multiple R-squared: 0.8798, Adjusted R-squared: 0.8683
## F-statistic: 76.84 on 2 and 21 DF, p-value: 2.187e-10anova(mod2)## Analysis of Variance Table
##
## Response: Oxigeno
## Df Sum Sq Mean Sq F value Pr(>F)
## Temperatura 2 7.5833 3.7917 76.84 2.187e-10 ***
## Residuals 21 1.0363 0.0493
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1compara=LSD.test(mod2,"Temperatura")
compara## $statistics
## MSerror Df Mean CV t.value LSD
## 0.04934524 21 2.345833 9.469466 2.079614 0.2309805
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none Temperatura 3 0.05
##
## $means
## Oxigeno std r LCL UCL Min Max Q25 Q50 Q75
## Alta 3.0125 0.2232071 8 2.849172 3.175828 2.7 3.4 2.875 3.00 3.125
## Baja 1.6375 0.1922610 8 1.474172 1.800828 1.4 1.9 1.475 1.65 1.800
## Media 2.3875 0.2474874 8 2.224172 2.550828 2.0 2.7 2.250 2.40 2.600
##
## $comparison
## NULL
##
## $groups
## Oxigeno groups
## Alta 3.0125 a
## Media 2.3875 b
## Baja 1.6375 c
##
## attr(,"class")
## [1] "group"Según el modelo de diseño de experimentos, la temperatura alta fue seleccionada como el intercepto, donde la tasa de consumo de oxígeno estaba en un 3.01% y a partir de esta se compararonn las tasas de los demás niveles de temperatura. En el primer caso, se compara el nivel de temperatura alto con el nivel de temperatura bajo y se obtiene una dismunición en la tasa de consumo de oxígeno de un 1.37% y en el segundo caso, se compara el nivel de temperatura alto con el nivel de temperatura medio y se obtiene una dismunición en la tasa de consumo de oxígeno de un 0.62%. De este modelo, podemos concluir que los resultados son releantes, significativos y que la mejor tasa de consumo de oxígeno se obtiene a temperaturas altas disminuyendo progresivamente en los niveles medio y bajo.
Posterior a esto, se realizó un análisis ANOVA, en el cual se encontró que la temperatura tiene un efecto significativo sobre la tasa de consumo de oxígeno, por lo que sí es un factor relevante para evaluar las variaciones del consumo de oxígeno en los 24 individuos de la especie de cangrejos de interes. Debido a lo anterior, se llevó a cabo la prueba de comparación multiple Postanova, en la cual se compararon los límites de los intervalos de confianza para cada nivel de temperatura, ya que se quería evaluar que tan significativa era la diferencia entre estos. Con respecto a lo anterior, se obtuvo que cada nivel esta bien diferenciado entre ellos y no se solapan los intervalos. Adicional, se evidenció que efectivamente en el nivel de temperatura alto se obtiene el promedio más alto de la tasa de consumo de oxígeno, seguido por el nivel medio y, por último, esta el nivel de temperatura bajo.
#X= Sexo
#y= Tasa de consumo de oxígeno
punto_4$Sexo=as.factor(punto_4$Sexo)
mod3=lm(Oxigeno~Sexo,data=punto_4)
summary(mod3)##
## Call:
## lm(formula = Oxigeno ~ Sexo, data = punto_4)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.95833 -0.53958 0.04167 0.49167 1.06667
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.3583 0.1807 13.054 7.76e-12 ***
## SexoMacho -0.0250 0.2555 -0.098 0.923
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6258 on 22 degrees of freedom
## Multiple R-squared: 0.0004351, Adjusted R-squared: -0.045
## F-statistic: 0.009575 on 1 and 22 DF, p-value: 0.9229anova(mod3)## Analysis of Variance Table
##
## Response: Oxigeno
## Df Sum Sq Mean Sq F value Pr(>F)
## Sexo 1 0.0037 0.00375 0.0096 0.9229
## Residuals 22 8.6158 0.39163Después de evaluar la variable temperatura, se quiso evaluar la variable sexo para conocer si era un factor relevante en el análisis de la tasa de consumo de oxígeno. Según el modelo de diseño de experimentos, el sexo hembra se definió como el intercepto, donde la tasa de consumo de oxígeno se ubicaba en un 2.35% y esta se empleó para analizar el cambio con respecto al sexo macho. Para lo anterior, se encontró que del sexo hembra al sexo macho había una disminución del 0.02% en la tasa de consumo de oxígeno. Con respecto al modelo, se concluye que no hay una diferencia significativa en la tasa de consumo de oxígeno con relación al sexo de los cangrejos. Más allá, se realizó el ANOVA y como resultado no se encontro que el factor sexo fue significativo para la tasa de consumo de oxígeno. En conclusión, ambos análsis demuestran que la tasa de consumo de oxígeno en esta especie de cangrejo no tiene relación con el sexo del individuo.
Punto 5
Un agricultor interesado en el cultivo de caña de azucar 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 (más 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: la temperatura media entre 22.5 y 28 °C y precipitación 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 sería buscar otros sitios con estas condiciones (similares).
punto 5.a. Usando los datos del clima a nivel global se construyén los mapas de aptitud en términos climáticos para la caña de azuzar.
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/Usuario/Documents/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/Usuario/Documents/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/Usuario/Documents/R/win-library/4.1/rgdal/projrequire(sp)
nombres=list.files("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/", full.names = TRUE)
temps=stack(nombres)
names(temps)=month.name
plot(temps)
temp_opt=temps>22.5&temps<28
plot(temp_opt)
aptitud_temp=sum(temp_opt)
plot(aptitud_temp)
Para el agricultor es importante identificar las regiones del mundo donde se pueden dar las condiciones optimas para el cultivo de caña de azucar. El mapa anterior es un mapa de aptitud en términos de tempertatura para la caña de azúcar y se registran las zonas aptas según las temperaturas requeridas para este cultivo, que deben estar entre 22.5 a 28°C. Según este, las regiones más optimas según la temperatura están ubicadas sobre la línea ecutorial, lo que incluye países del norte de suramérica, africa central, sur de asia y norte de oceania.
nombres=list.files("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/", full.names = TRUE, pattern=".tif")
prec=stack(nombres)
names(prec)=month.name
plot(prec)
prec_opt=prec>125&prec<290
plot(prec_opt)
aptitud_prec=sum(prec_opt)
plot(aptitud_prec)
Ahora bien, para el agricultor también es importante identificar las regiones del mundo donde se pueden dar las condiciones optimas para el cultivo de caña de azucar según la precipitación. El mapa anterior es un mapa de aptitud en términos de precipitación mensual para la caña de azúcar y se registran las zonas aptas según la cantidad de precipitación requerida para este cultivo, que deben estar entre 125 mm a 290 mm mensualmente. Según este mapa, las regiones más optimas según la precipitación también están sobre la línea ecuatorial, pero son más restringidas ubicandose principalmente en países de sur américa como Perú, Bolivia, Colombia, Brasil; también en Africa en países como la Republica Democratica del Congo o en Asia en países como indonesia.
aptitudes=stack(aptitud_temp, aptitud_prec)
names(aptitudes)=c("temperatura","precipitaciones")
plot(aptitudes)
a=aptitud_temp>6
b=aptitud_prec>6
apt_global1=a*b
plot(apt_global1)
Ahora bien, los dos mapas anteriores dan referencias según los factores temperatura y precipitación de manera independiente, pero para la necesidad del agricultor es necesario encontrar zonas que cumplan con las condiciones simultaneamente. Es por esto que el mapa anterior es importante. En este las zonas verdes indican las regiones en las cuales la temperatura anual varía entre 22.5°C y 28°C y la cantidad de precipitación mensual cumple con estar entre 125 mm a 290 mm. Según lo anterior, las regiones que cumplen con estas características están al norte de sur américa en países como Colombia, Perú, Bolivia, Brazil, en el centro de Africa en países como La Republica Democratica del Congo, La Republica del Congo y en Asía en Indoneseia, malasia, entre otros.
punto 5.b. Identificar 2 o 3 países con áreas de alto potencial para la caña de azúcar.
global=shapefile("C:/Users/Usuario/Downloads/shape_global/g2008_0.shp")
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## ADM0_NAME LAST_UPDAT CONTINENT
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## 329 Hala'ib triangle 20060112 Africa
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## 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
## 341 China 20050415 Asia
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## 343 Dominican Republic 20050714 Americas
## 344 Yemen 20050415 Asia
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## 346 Jamaica 20050415 Americas
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## 348 Belize 20050415 Americas
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## 350 Guatemala 20070911 Americas
## 351 Honduras 20050415 Americas
## 352 Senegal 20071022 Africa
## 353 Burkina Faso 20051028 Africa
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## 380 Ghana 20050415 Africa
## 381 Togo 20070807 Africa
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## 385 Côte d'Ivoire 20081113 Africa
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## 390 Panama 20050415 Americas
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## 392 Liberia 20050727 Africa
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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
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## 407 Uganda 20060929 Africa
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## 412 Equatorial Guinea 20050415 Africa
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## 424 Indonesia 20080925 Asia
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## 427 Indonesia 20080925 Asia
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## 429 United Republic of Tanzania 20061122 Africa
## 430 Rwanda 20060103 Africa
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## 432 Indonesia 20080925 Asia
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## 442 Indonesia 20080925 Asia
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## 445 Papua New Guinea 20050720 Oceania
## 446 Indonesia 20080925 Asia
## 447 Angola 20050415 Africa
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## 451 United Republic of Tanzania 20061122 Africa
## 452 Angola 20050415 Africa
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## 454 Indonesia 20080925 Asia
## 455 Indonesia 20080925 Asia
## 456 Solomon Islands 20050415 Oceania
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## 470 Timor-Leste 20061003 Asia
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## 495 Paraguay 20060112 Americas
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## 500 Argentina 20050415 Americas
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## 515 New Zealand 20050415 Oceania
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## 520 Chile 20050415 Americas
## 521 Falkland Islands (Malvinas) 20050415 Americas
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## 523 Chile 20050415 Americas
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## 528 Chile 20050415 Americas
## 529 Chile 20050415 Americas
## 530 Chile 20050415 Americas
## 531 Chile 20050415 Americas
## 532 Chile 20050415 Americas
## 533 Gambia 20050415 Africa
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## 533 Western Africa 0 0plot(global)
pos=global$REGION=="South America"
sur_america=global[pos,]
plot(sur_america)
pos=sur_america$ADM0_NAME=="Colombia"
colombia=sur_america[pos,]
apt_global1=a*b
plot(apt_global1)
plot(colombia)
temp_prec_1=crop(apt_global1,colombia)
plot(temp_prec_1)
plot(colombia,add=TRUE)
temp_prec_1=mask(temp_prec_1,colombia)
plot(temp_prec_1)
plot(colombia,add=TRUE)
global=shapefile("C:/Users/Usuario/Downloads/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, 0global@data## AREA PERIMETER G2008_0_ G2008_0_ID ADM0_CODE
## 0 649.4221006 900.013634 2 1 98
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## ADM0_NAME LAST_UPDAT CONTINENT
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## 198 Germany 20051229 Europe
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## 202 Germany 20051229 Europe
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## 240 Croatia 20050415 Europe
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## 242 Serbia 20081118 Europe
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## 251 Georgia 20050415 Asia
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## 260 The former Yugoslav Republic of Macedonia 20050415 Europe
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## 274 Greece 20060112 Europe
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## 280 Syrian Arab Republic 20050415 Asia
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## 288 Greece 20060112 Europe
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## 296 India 20080908 Asia
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## 299 West Bank 20050415 Asia
## 300 Saudi Arabia 20050415 Asia
## 301 Egypt 20060112 Africa
## 302 China/India 20050415 Asia
## 303 Nepal 20071022 Asia
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## 309 Myanmar 20060927 Asia
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## 326 Chad 20070914 Africa
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## 329 Hala'ib triangle 20060112 Africa
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## 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
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## 338 United States of America 20060103 Americas
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## 340 United States of America 20060103 Americas
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## 348 Belize 20050415 Americas
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## 350 Guatemala 20070911 Americas
## 351 Honduras 20050415 Americas
## 352 Senegal 20071022 Africa
## 353 Burkina Faso 20051028 Africa
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## 356 Cambodia 20070910 Asia
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## 362 Cameroon 20051229 Africa
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## 367 Philippines 20071015 Asia
## 368 Guinea 20060103 Africa
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## 381 Togo 20070807 Africa
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## 402 Ilemi triangle 20071021 Africa
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## 408 Equatorial Guinea 20050415 Africa
## 409 Congo 20050415 Africa
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## 411 Indonesia 20080925 Asia
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## REGION STR_YEAR0 EXP_YEAR0
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## 533 Western Africa 0 0plot(global)
pos=global$REGION=="South America"
sur_america=global[pos,]
plot(sur_america)
pos=sur_america$ADM0_NAME=="Peru"
Peru=sur_america[pos,]
apt_global1=a*b
plot(apt_global1)
plot(Peru)
temp_prec_2=crop(apt_global1,Peru)
plot(temp_prec_2)
plot(Peru,add=TRUE)
temp_prec_2=mask(temp_prec_2,Peru)
plot(temp_prec_2)
plot(Peru,add=TRUE)
global=shapefile("C:/Users/Usuario/Downloads/shape_global/g2008_0.shp")
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## extent : -180, 180, -55.72333, 83.62742 (xmin, xmax, ymin, ymax)
## crs : +proj=longlat +datum=WGS84 +no_defs
## variables : 11
## names : AREA, PERIMETER, G2008_0_, G2008_0_ID, ADM0_CODE, ADM0_NAME, LAST_UPDAT, CONTINENT, REGION, STR_YEAR0, EXP_YEAR0
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## ADM0_NAME LAST_UPDAT CONTINENT
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## 20 Russian Federation 20050415 Europe
## 21 Russian Federation 20050415 Europe
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## 135 Norway 20060103 Europe
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## 198 Germany 20051229 Europe
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## 202 Germany 20051229 Europe
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## 309 Myanmar 20060927 Asia
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## 533 Western Africa 0 0plot(global)
pos=global$REGION=="South America"
sur_america=global[pos,]
plot(sur_america)
pos=sur_america$ADM0_NAME=="Brazil"
Brazil=sur_america[pos,]
apt_global1=a*b
plot(apt_global1)
plot(Brazil)
temp_prec_3=crop(apt_global1,Brazil)
plot(temp_prec_3)
plot(Brazil,add=TRUE)
temp_prec_3=mask(temp_prec_3,Brazil)
plot(temp_prec_3)
plot(Brazil,add=TRUE)
En el punto 5.a se pudo identificar diferentes regiones con lugares que tuvieran las condiciones optimas para el cultivo de caña de azucar. En este punto, se señalan países con áreas de alto potencial para la caña de azúcar, escogiendose como ejemplo Colombia, Perú y Brasil. Cada países cuenta con su mapa individual y en cada uno se marcan las zonas verdes que simbolizan las regiones al interior de estos que cuentan con las condiciones climaticas de temperatura y precipitación ideales para poder cultivar la caña de azucar. Para Colombia, se ve que en zonas hacia el sur y centro del países pueden darse las condiciones. Luego en Perú, hacia el nororiente se ven zonas optimas y Por último, en Brasil las zonas optimas están hacia el noroccidente.
Punto 5.c Identificar algunos puntos (2 o 3) al azar en la región del valle del cauca usando google maps y extraer la información de clima.
Ubicación 1: Temperatura
Temp_01=raster("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_01.tif")
plot(Temp_01)
plot(colombia,add=TRUE)
temp_01_col=crop(Temp_01,colombia)
plot(temp_01_col)
plot(colombia,add=TRUE)
temp_01_col=mask(temp_01_col,colombia)
plot(temp_01_col)
plot(colombia,add=TRUE)
lista=list.files("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/",full.name=TRUE)
lista## [1] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_01.tif"
## [2] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_02.tif"
## [3] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_03.tif"
## [4] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_04.tif"
## [5] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_05.tif"
## [6] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_06.tif"
## [7] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_07.tif"
## [8] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_08.tif"
## [9] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_09.tif"
## [10] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_10.tif"
## [11] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_11.tif"
## [12] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_12.tif"Temperature=stack(lista)
plot(Temperature)
Temperature_col=crop(Temperature,colombia)
Temperature_col=mask(Temperature_col,colombia)
names(Temperature_col)=month.name
plot(Temperature_col)
loc=cbind(-76.511252, 3.282328)
plot(temp_01_col)
points(loc,col="red",pch=16)
extract(temp_01_col,loc)## [1] 21.854serie_temperatura=extract(Temperature_col,loc)
serie_temperatura## January February March April May June July August
## [1,] 21.854 21.9875 22.12025 21.906 21.82075 21.702 21.9865 22.14975
## September October November December
## [1,] 21.98 21.53625 21.4225 21.576plot(serie_temperatura[1,],type="b")
Ubicación 1: Precipitación
Preci_01=raster("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_01.tif")
plot(Preci_01)
plot(colombia,add=TRUE)
Preci_01_col=crop(Preci_01,colombia)
plot(Preci_01_col)
plot(colombia,add=TRUE)
Preci_01_col=mask(Preci_01_col,colombia)
plot(Preci_01_col)
plot(colombia,add=TRUE)
mapa=list.files("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/",full.name=TRUE, pattern=".tif")
mapa## [1] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_01.tif"
## [2] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_02.tif"
## [3] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_03.tif"
## [4] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_04.tif"
## [5] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_05.tif"
## [6] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_06.tif"
## [7] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_07.tif"
## [8] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_08.tif"
## [9] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_09.tif"
## [10] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_10.tif"
## [11] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_11.tif"
## [12] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_12.tif"Precipitation=stack(mapa)
plot(Precipitation)
Precipitacion_col=crop(Precipitation,colombia)
Precipitacion_col=mask(Precipitacion_col,colombia)
names(Precipitacion_col)=month.name
plot(Precipitacion_col)
loc=cbind(-76.511252, 3.282328)
plot(Preci_01_col)
points(loc,col="red",pch=16)
extract(Preci_01_col,loc)## [1] 124serie_precipitacion=extract(Precipitacion_col,loc)
serie_precipitacion## January February March April May June July August September October
## [1,] 124 134 164 229 210 116 71 96 133 242
## November December
## [1,] 205 164plot(serie_precipitacion[1,],type="b")
La primer ubicación corresponde a las coordenadas 3.282328, -76.511252 encontradas dentro del departamente del Valle del Cauca. En el mapa de Colombia se señala este lugar con un punto de color rojo. Las gráficas de serie, tanto de temperatura como de precipitación muestran las variaciones resgistradas para estas variables durante el transcurso de los 12 meses del año para esta ubicación especifica. En la serie de temperatura para esta coordenada, al principio del año se registran temperaturas superiores 21.8°C, la temperatura más alta se registra cercana al octavo mes del año y el momento más frío alcanzando temperaturas cercanas 21.4°C se da en el onceavo mes del año. Por otro lado, para la serie de precipitaciones se registra que durante los primeros 4 meses del año se aumenta rápidamente la cantidad de precipitaciones, los valores más altos son superiores a 200 mm y se dan durante el 4 y 10 mes del año y, finalmente, la menor cantidad de precipitaciones se dan entre el sexto y octavo mes del año.
Ubicación 2: Temperatura
Temp_01=raster("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_01.tif")
plot(Temp_01)
plot(colombia,add=TRUE)
temp_01_col=crop(Temp_01,colombia)
plot(temp_01_col)
plot(colombia,add=TRUE)
temp_01_col=mask(temp_01_col,colombia)
plot(temp_01_col)
plot(colombia,add=TRUE)
lista=list.files("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/",full.name=TRUE)
lista## [1] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_01.tif"
## [2] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_02.tif"
## [3] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_03.tif"
## [4] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_04.tif"
## [5] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_05.tif"
## [6] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_06.tif"
## [7] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_07.tif"
## [8] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_08.tif"
## [9] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_09.tif"
## [10] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_10.tif"
## [11] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_11.tif"
## [12] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_12.tif"Temperature=stack(lista)
plot(Temperature)
Temperature_col=crop(Temperature,colombia)
Temperature_col=mask(Temperature_col,colombia)
names(Temperature_col)=month.name
plot(Temperature_col)
loc=cbind(-76.672100, 3.862438)
plot(temp_01_col)
points(loc,col="red",pch=16)
extract(temp_01_col,loc)## [1] 23.2525serie_temperatura=extract(Temperature_col,loc)
serie_temperatura## January February March April May June July August September
## [1,] 23.2525 23.45425 23.6465 23.57075 23.425 23.343 23.3775 23.30425 23.23075
## October November December
## [1,] 22.87025 22.8265 22.90775plot(serie_temperatura[1,],type="b")
Ubicación 2: Precipitación
Preci_01=raster("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_01.tif")
plot(Preci_01)
plot(colombia,add=TRUE)
Preci_01_col=crop(Preci_01,colombia)
plot(Preci_01_col)
plot(colombia,add=TRUE)
Preci_01_col=mask(Preci_01_col,colombia)
plot(Preci_01_col)
plot(colombia,add=TRUE)
mapa=list.files("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/",full.name=TRUE, pattern=".tif")
mapa## [1] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_01.tif"
## [2] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_02.tif"
## [3] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_03.tif"
## [4] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_04.tif"
## [5] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_05.tif"
## [6] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_06.tif"
## [7] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_07.tif"
## [8] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_08.tif"
## [9] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_09.tif"
## [10] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_10.tif"
## [11] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_11.tif"
## [12] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_12.tif"Precipitation=stack(mapa)
plot(Precipitation)
Precipitacion_col=crop(Precipitation,colombia)
Precipitacion_col=mask(Precipitacion_col,colombia)
names(Precipitacion_col)=month.name
plot(Precipitacion_col)
loc=cbind(-76.672100, 3.862438)
plot(Preci_01_col)
points(loc,col="red",pch=16)
extract(Preci_01_col,loc)## [1] 257serie_precipitacion=extract(Precipitacion_col,loc)
serie_precipitacion## January February March April May June July August September October
## [1,] 257 213 263 309 330 276 252 281 341 454
## November December
## [1,] 368 325plot(serie_precipitacion[1,],type="b")
La segunda ubicación corresponde a las coordenadas 3.862438, -76.672100 encontradas dentro del departamente del Valle del Cauca. En el mapa de Colombia se señala este lugar con un punto de color rojo. Las gráficas de serie, tanto de temperatura como de precipitación muestran las variaciones resgistradas para estas variables durante el transcurso de los 12 meses del año para esta ubicación especifica. En la serie de temperatura para esta coordenada, al principio del año se registran temperaturas superiores 23°C, la temperatura más alta se registra cercana al tercer mes del año siendo más de 23.6°C y el momento más frío alcanzando temperaturas cercanas a 22.8°C se da cerca del onceavo mes del año. Adicional a esto, la gráfica tiene un tendencia a disminuir los valores a lo que trascurre el año. Por otro lado, para la serie de precipitaciones se registra que durante los primeros 3 meses del año la cantidad de precipitaciones es baja, alrededor de 250 mm. A lo que transcurre el año, la cantidad de precipitaciones aumenta progresivamente dandose el valor más alto durante el decimo mes del año con alrededor de 450 mm. A pesar de un aumento en general, durante el sexto y octavo mes se vuelven a disminuir las precipitaciones entre 250 a 300 mm.
Ubicación 3: Temperatura
Temp_01=raster("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_01.tif")
plot(Temp_01)
plot(colombia,add=TRUE)
temp_01_col=crop(Temp_01,colombia)
plot(temp_01_col)
plot(colombia,add=TRUE)
temp_01_col=mask(temp_01_col,colombia)
plot(temp_01_col)
plot(colombia,add=TRUE)
lista=list.files("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/",full.name=TRUE)
lista## [1] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_01.tif"
## [2] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_02.tif"
## [3] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_03.tif"
## [4] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_04.tif"
## [5] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_05.tif"
## [6] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_06.tif"
## [7] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_07.tif"
## [8] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_08.tif"
## [9] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_09.tif"
## [10] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_10.tif"
## [11] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_11.tif"
## [12] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/temp/wc2.1_10m_tavg_12.tif"Temperature=stack(lista)
plot(Temperature)
Temperature_col=crop(Temperature,colombia)
Temperature_col=mask(Temperature_col,colombia)
names(Temperature_col)=month.name
plot(Temperature_col)
loc=cbind(-76.085213, 4.442540)
plot(temp_01_col)
points(loc,col="red",pch=16)
extract(temp_01_col,loc)## [1] 23.4945serie_temperatura=extract(Temperature_col,loc)
serie_temperatura## January February March April May June July August
## [1,] 23.4945 23.71775 23.79775 23.4965 23.372 23.3205 23.83375 23.77975
## September October November December
## [1,] 23.497 22.80275 22.6935 23.02375plot(serie_temperatura[1,],type="b")
Ubicación 3: Precipitación
Preci_01=raster("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_01.tif")
plot(Preci_01)
plot(colombia,add=TRUE)
Preci_01_col=crop(Preci_01,colombia)
plot(Preci_01_col)
plot(colombia,add=TRUE)
Preci_01_col=mask(Preci_01_col,colombia)
plot(Preci_01_col)
plot(colombia,add=TRUE)
mapa=list.files("C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/",full.name=TRUE, pattern=".tif")
mapa## [1] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_01.tif"
## [2] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_02.tif"
## [3] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_03.tif"
## [4] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_04.tif"
## [5] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_05.tif"
## [6] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_06.tif"
## [7] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_07.tif"
## [8] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_08.tif"
## [9] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_09.tif"
## [10] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_10.tif"
## [11] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_11.tif"
## [12] "C:/Users/Usuario/Desktop/Universidad/semestre 5/Bioestadistica/prec/wc2.1_10m_prec_12.tif"Precipitation=stack(mapa)
plot(Precipitation)
Precipitacion_col=crop(Precipitation,colombia)
Precipitacion_col=mask(Precipitacion_col,colombia)
names(Precipitacion_col)=month.name
plot(Precipitacion_col)
loc=cbind(-76.085213, 4.442540)
plot(Preci_01_col)
points(loc,col="red",pch=16)
extract(Preci_01_col,loc)## [1] 81serie_precipitacion=extract(Precipitacion_col,loc)
serie_precipitacion## January February March April May June July August September October
## [1,] 81 102 141 193 182 127 74 105 135 214
## November December
## [1,] 168 110plot(serie_precipitacion[1,],type="b")
La tercera ubicación corresponde a las coordenadas 4.442540, -76.085213 encontradas dentro del departamente del Valle del Cauca. En el mapa de Colombia se señala este lugar con un punto de color rojo. Las gráficas de serie, tanto de temperatura como de precipitación muestran las variaciones resgistradas para estas variables durante el transcurso de los 12 meses del año para esta ubicación especifica. En la serie de temperatura para esta coordenada, al principio del año se registran temperaturas superiores 23.4°C, hay dos picos que regristran las temperaturas más altas, primero alrededor del cuarto mes del año y luego entre el sexto y octavo mes del año, y en ambas ocasiones las temperaturas son cercanas a los 23.8°C y el momento con menor temperatura, alcanzando una temperatura inferior a 22.8°C, se da en el onceavo mes del año. Por otro lado, para la serie de precipitaciones se registra que durante los primeros 4 meses del año se aumenta marcadamente la cantidad de precipitaciones, iniciando en 80 mm y llegando hasta más de 180 mm. Además, el valor más alto de precipitaciones es superior a 180 mm y se dan durante el decimo mes del año y, finalmente, la menor cantidad de precipitaciones se dan entre el sexto y octavo mes del año llegando a un valor menor que 80 mm.