Para una aleatorización a nivel de aglomerados, se puede emplear el siguiente cálculo.
\[MDE=(t_{1-\kappa}+t_\alpha) \sqrt{\frac{1}{Jp(1-p)}} \sqrt{\rho + \frac{(1-\rho)\sigma^2}{n}}\]
Reordenando y despejando \(J\) tenemos:
\[J=(\frac{t_{1-\kappa}+t_\alpha}{MDE})^2 \frac{1}{p(1-p)} ( \rho + \frac{(1-\rho)\sigma^2}{n} )\]
Se estandariza la siguiente notación: Para facilitar el análisis de los parámetros en el modelo y obtener un cálculo de poder inmediato, redefinimos la variabilidad en términos del coeficiente de correlación intraclase \(\rho\); este se expresaría como un ratio de la variabilidad entre clusters respecto a la variabilidad total, tal como sigue:
\[\rho=\frac{\tau}{\tau+\sigma^2}\] Dado que \(\tau+\sigma^2\) es la variabilidad total, la podemos restringir a 1. De ese modo, \(\rho=\tau\), lo que implica además que \(1-\rho=\sigma^2\).
Reemplazando los datos como supuestos tenemos:
t_mk=0.84
t_alpha=1.96
MDE=0.2
p=0.5
rho=0.15
sigma2=1-rho
n=12
J=((t_mk+t_alpha)/MDE)^2*(1/(p*(1-p)))*(rho+(((1-p)*sigma2)/n))
J## [1] 145.3667#lima
setwd("C:/Users/sant_/Documents/Consultorias/2019/nico/cenagro")
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(foreign)
##Bases generales
lima_rec02 <- read.dbf("lima/351-Modulo231/03_IVCENAGRO_REC02.dbf")
lima_rec04a <- read.dbf("lima/351-Modulo236/08_IVCENAGRO_REC04A.dbf")
## Pollos
lima_pollos <- filter(lima_rec04a, P067_01==761)
## Ganado lechero (solo vacas)
lima_vacas <- filter(lima_rec04a, P067_01==674)
## Papas
lima_papas <- filter(lima_rec02, (
P024_03==2610 | P024_03==2611 | P024_03==2612 | P024_03==2613 | P024_03==2614 |
P024_03==2615 | P024_03==5049 | P024_03==5062 | P024_03==5074 | P024_03==5075 |
P024_03==5076 | P024_03==5077 | P024_03==5078 | P024_03==5079 | P024_03==5080 |
P024_03==5081 | P024_03==5090 | P024_03==5091 | P024_03==5113 | P024_03==5122 |
P024_03==5123 | P024_03==5124 | P024_03==5125 | P024_03==5126 | P024_03==5130 |
P024_03==5135 | P024_03==5136 | P024_03==5137 | P024_03==5142 | P024_03==5145 |
P024_03==5146 | P024_03==5155 | P024_03==5157 | P024_03==5159 | P024_03==5160 |
P024_03==5170 | P024_03==5183 | P024_03==5184 | P024_03==5185 | P024_03==5213 |
P024_03==5214 | P024_03==5231 | P024_03==5304 | P024_03==5344 | P024_03==5345 |
P024_03==5346 | P024_03==5347 | P024_03==5395 | P024_03==5399 | P024_03==5406 |
P024_03==5412 | P024_03==5436 | P024_03==5440 | P024_03==5441 | P024_03==5442 |
P024_03==5458 | P024_03==5496 | P024_03==5512 | P024_03==5554 | P024_03==5586 |
P024_03==5651 | P024_03==5658 | P024_03==5689 | P024_03==5733 | P024_03==5819 |
P024_03==5942 | P024_03==5952 | P024_03==6099 | P024_03==6147 | P024_03==6148 |
P024_03==6149 | P024_03==6482 | P024_03==6811 | P024_03==6898 | P024_03==6940 |
P024_03==7125 | P024_03==7126 | P024_03==7127 | P024_03==7219 | P024_03==8000 |
P024_03==8001 | P024_03==8002 | P024_03==8017 | P024_03==8074 | P024_03==8086 |
P024_03==8163 | P024_03==8194 | P024_03==8207 | P024_03==8217 | P024_03==8218 |
P024_03==8219 | P024_03==8233 | P024_03==8253 | P024_03==8275 | P024_03==8276 |
P024_03==8286 | P024_03==8287 | P024_03==8320 | P024_03==8404 | P024_03==8408 ))
## Naranjas y mandarinas
lima_narmand <- filter(lima_rec02, (
P024_03==1146 | P024_03==5533 | P024_03==5568 | P024_03==5593 | P024_03==5611 |
P024_03==5641 | P024_03==5751 | P024_03==5754 | P024_03==5762 | P024_03==5765 |
P024_03==5810 | P024_03==5929 | P024_03==5931 | P024_03==6004 | P024_03==6045 |
P024_03==6066 | P024_03==6087 | P024_03==6088 | P024_03==6089 | P024_03==6096 |
P024_03==6097 | P024_03==6098 | P024_03==6099 | P024_03==6100 | P024_03==6101 |
P024_03==6102 | P024_03==6103 | P024_03==6121 | P024_03==6190 | P024_03==6216 |
P024_03==6312 | P024_03==6313 | P024_03==6378 | P024_03==6405 | P024_03==6419 |
P024_03==6420 | P024_03==6421 | P024_03==6422 | P024_03==6423 | P024_03==6424 |
P024_03==6470 | P024_03==6517 | P024_03==6539 | P024_03==6660 | P024_03==6991 |
P024_03==6992 | P024_03==7192 | P024_03==6650 | P024_03==6969 | P024_03==8384 ))#Ancash
setwd("C:/Users/sant_/Documents/Consultorias/2019/nico/cenagro")
library(dplyr)
library(foreign)
##Bases generales
ancash_rec02 <- read.dbf("ancash/338-Modulo231/03_IVCENAGRO_REC02.dbf")
ancash_rec04a <- read.dbf("ancash/338-Modulo236/08_IVCENAGRO_REC04A.dbf")
## Pollos
ancash_pollos <- filter(ancash_rec04a, P067_01==761)
## Ganado lechero (solo vacas)
ancash_vacas <- filter(ancash_rec04a, P067_01==674)
## Papas
ancash_papas <- filter(ancash_rec02, (
P024_03==2610 | P024_03==2611 | P024_03==2612 | P024_03==2613 | P024_03==2614 |
P024_03==2615 | P024_03==5049 | P024_03==5062 | P024_03==5074 | P024_03==5075 |
P024_03==5076 | P024_03==5077 | P024_03==5078 | P024_03==5079 | P024_03==5080 |
P024_03==5081 | P024_03==5090 | P024_03==5091 | P024_03==5113 | P024_03==5122 |
P024_03==5123 | P024_03==5124 | P024_03==5125 | P024_03==5126 | P024_03==5130 |
P024_03==5135 | P024_03==5136 | P024_03==5137 | P024_03==5142 | P024_03==5145 |
P024_03==5146 | P024_03==5155 | P024_03==5157 | P024_03==5159 | P024_03==5160 |
P024_03==5170 | P024_03==5183 | P024_03==5184 | P024_03==5185 | P024_03==5213 |
P024_03==5214 | P024_03==5231 | P024_03==5304 | P024_03==5344 | P024_03==5345 |
P024_03==5346 | P024_03==5347 | P024_03==5395 | P024_03==5399 | P024_03==5406 |
P024_03==5412 | P024_03==5436 | P024_03==5440 | P024_03==5441 | P024_03==5442 |
P024_03==5458 | P024_03==5496 | P024_03==5512 | P024_03==5554 | P024_03==5586 |
P024_03==5651 | P024_03==5658 | P024_03==5689 | P024_03==5733 | P024_03==5819 |
P024_03==5942 | P024_03==5952 | P024_03==6099 | P024_03==6147 | P024_03==6148 |
P024_03==6149 | P024_03==6482 | P024_03==6811 | P024_03==6898 | P024_03==6940 |
P024_03==7125 | P024_03==7126 | P024_03==7127 | P024_03==7219 | P024_03==8000 |
P024_03==8001 | P024_03==8002 | P024_03==8017 | P024_03==8074 | P024_03==8086 |
P024_03==8163 | P024_03==8194 | P024_03==8207 | P024_03==8217 | P024_03==8218 |
P024_03==8219 | P024_03==8233 | P024_03==8253 | P024_03==8275 | P024_03==8276 |
P024_03==8286 | P024_03==8287 | P024_03==8320 | P024_03==8404 | P024_03==8408 ))
## Naranjas y mandarinas
ancash_narmand <- filter(ancash_rec02, (
P024_03==1146 | P024_03==5533 | P024_03==5568 | P024_03==5593 | P024_03==5611 |
P024_03==5641 | P024_03==5751 | P024_03==5754 | P024_03==5762 | P024_03==5765 |
P024_03==5810 | P024_03==5929 | P024_03==5931 | P024_03==6004 | P024_03==6045 |
P024_03==6066 | P024_03==6087 | P024_03==6088 | P024_03==6089 | P024_03==6096 |
P024_03==6097 | P024_03==6098 | P024_03==6099 | P024_03==6100 | P024_03==6101 |
P024_03==6102 | P024_03==6103 | P024_03==6121 | P024_03==6190 | P024_03==6216 |
P024_03==6312 | P024_03==6313 | P024_03==6378 | P024_03==6405 | P024_03==6419 |
P024_03==6420 | P024_03==6421 | P024_03==6422 | P024_03==6423 | P024_03==6424 |
P024_03==6470 | P024_03==6517 | P024_03==6539 | P024_03==6660 | P024_03==6991 |
P024_03==6992 | P024_03==7192 | P024_03==6650 | P024_03==6969 | P024_03==8384 ))#ica
setwd("C:/Users/sant_/Documents/Consultorias/2019/nico/cenagro")
library(dplyr)
library(foreign)
##Bases generales
ica_rec02 <- read.dbf("ica/347-Modulo231/03_IVCENAGRO_REC02.dbf")
ica_rec04a <- read.dbf("ica/347-Modulo236/08_IVCENAGRO_REC04A.dbf")
## Pollos
ica_pollos <- filter(ica_rec04a, P067_01==761)
## Ganado lechero (solo vacas)
ica_vacas <- filter(ica_rec04a, P067_01==674)
## Papas
ica_papas <- filter(ica_rec02, (
P024_03==2610 | P024_03==2611 | P024_03==2612 | P024_03==2613 | P024_03==2614 |
P024_03==2615 | P024_03==5049 | P024_03==5062 | P024_03==5074 | P024_03==5075 |
P024_03==5076 | P024_03==5077 | P024_03==5078 | P024_03==5079 | P024_03==5080 |
P024_03==5081 | P024_03==5090 | P024_03==5091 | P024_03==5113 | P024_03==5122 |
P024_03==5123 | P024_03==5124 | P024_03==5125 | P024_03==5126 | P024_03==5130 |
P024_03==5135 | P024_03==5136 | P024_03==5137 | P024_03==5142 | P024_03==5145 |
P024_03==5146 | P024_03==5155 | P024_03==5157 | P024_03==5159 | P024_03==5160 |
P024_03==5170 | P024_03==5183 | P024_03==5184 | P024_03==5185 | P024_03==5213 |
P024_03==5214 | P024_03==5231 | P024_03==5304 | P024_03==5344 | P024_03==5345 |
P024_03==5346 | P024_03==5347 | P024_03==5395 | P024_03==5399 | P024_03==5406 |
P024_03==5412 | P024_03==5436 | P024_03==5440 | P024_03==5441 | P024_03==5442 |
P024_03==5458 | P024_03==5496 | P024_03==5512 | P024_03==5554 | P024_03==5586 |
P024_03==5651 | P024_03==5658 | P024_03==5689 | P024_03==5733 | P024_03==5819 |
P024_03==5942 | P024_03==5952 | P024_03==6099 | P024_03==6147 | P024_03==6148 |
P024_03==6149 | P024_03==6482 | P024_03==6811 | P024_03==6898 | P024_03==6940 |
P024_03==7125 | P024_03==7126 | P024_03==7127 | P024_03==7219 | P024_03==8000 |
P024_03==8001 | P024_03==8002 | P024_03==8017 | P024_03==8074 | P024_03==8086 |
P024_03==8163 | P024_03==8194 | P024_03==8207 | P024_03==8217 | P024_03==8218 |
P024_03==8219 | P024_03==8233 | P024_03==8253 | P024_03==8275 | P024_03==8276 |
P024_03==8286 | P024_03==8287 | P024_03==8320 | P024_03==8404 | P024_03==8408 ))
## Naranjas y mandarinas
ica_narmand <- filter(ica_rec02, (
P024_03==1146 | P024_03==5533 | P024_03==5568 | P024_03==5593 | P024_03==5611 |
P024_03==5641 | P024_03==5751 | P024_03==5754 | P024_03==5762 | P024_03==5765 |
P024_03==5810 | P024_03==5929 | P024_03==5931 | P024_03==6004 | P024_03==6045 |
P024_03==6066 | P024_03==6087 | P024_03==6088 | P024_03==6089 | P024_03==6096 |
P024_03==6097 | P024_03==6098 | P024_03==6099 | P024_03==6100 | P024_03==6101 |
P024_03==6102 | P024_03==6103 | P024_03==6121 | P024_03==6190 | P024_03==6216 |
P024_03==6312 | P024_03==6313 | P024_03==6378 | P024_03==6405 | P024_03==6419 |
P024_03==6420 | P024_03==6421 | P024_03==6422 | P024_03==6423 | P024_03==6424 |
P024_03==6470 | P024_03==6517 | P024_03==6539 | P024_03==6660 | P024_03==6991 |
P024_03==6992 | P024_03==7192 | P024_03==6650 | P024_03==6969 | P024_03==8384 ))#junin
setwd("C:/Users/sant_/Documents/Consultorias/2019/nico/cenagro")
library(dplyr)
library(foreign)
##Bases generales
junin_rec02 <- read.dbf("junin/348-Modulo231/03_IVCENAGRO_REC02.dbf")
junin_rec04a <- read.dbf("junin/348-Modulo236/08_IVCENAGRO_REC04A.dbf")
## Pollos
junin_pollos <- filter(junin_rec04a, P067_01==761)
## Ganado lechero (solo vacas)
junin_vacas <- filter(junin_rec04a, P067_01==674)
## Papas
junin_papas <- filter(junin_rec02, (
P024_03==2610 | P024_03==2611 | P024_03==2612 | P024_03==2613 | P024_03==2614 |
P024_03==2615 | P024_03==5049 | P024_03==5062 | P024_03==5074 | P024_03==5075 |
P024_03==5076 | P024_03==5077 | P024_03==5078 | P024_03==5079 | P024_03==5080 |
P024_03==5081 | P024_03==5090 | P024_03==5091 | P024_03==5113 | P024_03==5122 |
P024_03==5123 | P024_03==5124 | P024_03==5125 | P024_03==5126 | P024_03==5130 |
P024_03==5135 | P024_03==5136 | P024_03==5137 | P024_03==5142 | P024_03==5145 |
P024_03==5146 | P024_03==5155 | P024_03==5157 | P024_03==5159 | P024_03==5160 |
P024_03==5170 | P024_03==5183 | P024_03==5184 | P024_03==5185 | P024_03==5213 |
P024_03==5214 | P024_03==5231 | P024_03==5304 | P024_03==5344 | P024_03==5345 |
P024_03==5346 | P024_03==5347 | P024_03==5395 | P024_03==5399 | P024_03==5406 |
P024_03==5412 | P024_03==5436 | P024_03==5440 | P024_03==5441 | P024_03==5442 |
P024_03==5458 | P024_03==5496 | P024_03==5512 | P024_03==5554 | P024_03==5586 |
P024_03==5651 | P024_03==5658 | P024_03==5689 | P024_03==5733 | P024_03==5819 |
P024_03==5942 | P024_03==5952 | P024_03==6099 | P024_03==6147 | P024_03==6148 |
P024_03==6149 | P024_03==6482 | P024_03==6811 | P024_03==6898 | P024_03==6940 |
P024_03==7125 | P024_03==7126 | P024_03==7127 | P024_03==7219 | P024_03==8000 |
P024_03==8001 | P024_03==8002 | P024_03==8017 | P024_03==8074 | P024_03==8086 |
P024_03==8163 | P024_03==8194 | P024_03==8207 | P024_03==8217 | P024_03==8218 |
P024_03==8219 | P024_03==8233 | P024_03==8253 | P024_03==8275 | P024_03==8276 |
P024_03==8286 | P024_03==8287 | P024_03==8320 | P024_03==8404 | P024_03==8408 ))
## Naranjas y mandarinas
junin_narmand <- filter(junin_rec02, (
P024_03==1146 | P024_03==5533 | P024_03==5568 | P024_03==5593 | P024_03==5611 |
P024_03==5641 | P024_03==5751 | P024_03==5754 | P024_03==5762 | P024_03==5765 |
P024_03==5810 | P024_03==5929 | P024_03==5931 | P024_03==6004 | P024_03==6045 |
P024_03==6066 | P024_03==6087 | P024_03==6088 | P024_03==6089 | P024_03==6096 |
P024_03==6097 | P024_03==6098 | P024_03==6099 | P024_03==6100 | P024_03==6101 |
P024_03==6102 | P024_03==6103 | P024_03==6121 | P024_03==6190 | P024_03==6216 |
P024_03==6312 | P024_03==6313 | P024_03==6378 | P024_03==6405 | P024_03==6419 |
P024_03==6420 | P024_03==6421 | P024_03==6422 | P024_03==6423 | P024_03==6424 |
P024_03==6470 | P024_03==6517 | P024_03==6539 | P024_03==6660 | P024_03==6991 |
P024_03==6992 | P024_03==7192 | P024_03==6650 | P024_03==6969 | P024_03==8384 ))#cajamarca
setwd("C:/Users/sant_/Documents/Consultorias/2019/nico/cenagro")
library(dplyr)
library(foreign)
##Bases generales
cajamarca_rec02 <- read.dbf("cajamarca/342-Modulo231/03_IVCENAGRO_REC02.dbf")
cajamarca_rec04a <- read.dbf("cajamarca/342-Modulo236/08_IVCENAGRO_REC04A.dbf")
## Pollos
cajamarca_pollos <- filter(cajamarca_rec04a, P067_01==761)
## Ganado lechero (solo vacas)
cajamarca_vacas <- filter(cajamarca_rec04a, P067_01==674)
## Papas
cajamarca_papas <- filter(cajamarca_rec02, (
P024_03==2610 | P024_03==2611 | P024_03==2612 | P024_03==2613 | P024_03==2614 |
P024_03==2615 | P024_03==5049 | P024_03==5062 | P024_03==5074 | P024_03==5075 |
P024_03==5076 | P024_03==5077 | P024_03==5078 | P024_03==5079 | P024_03==5080 |
P024_03==5081 | P024_03==5090 | P024_03==5091 | P024_03==5113 | P024_03==5122 |
P024_03==5123 | P024_03==5124 | P024_03==5125 | P024_03==5126 | P024_03==5130 |
P024_03==5135 | P024_03==5136 | P024_03==5137 | P024_03==5142 | P024_03==5145 |
P024_03==5146 | P024_03==5155 | P024_03==5157 | P024_03==5159 | P024_03==5160 |
P024_03==5170 | P024_03==5183 | P024_03==5184 | P024_03==5185 | P024_03==5213 |
P024_03==5214 | P024_03==5231 | P024_03==5304 | P024_03==5344 | P024_03==5345 |
P024_03==5346 | P024_03==5347 | P024_03==5395 | P024_03==5399 | P024_03==5406 |
P024_03==5412 | P024_03==5436 | P024_03==5440 | P024_03==5441 | P024_03==5442 |
P024_03==5458 | P024_03==5496 | P024_03==5512 | P024_03==5554 | P024_03==5586 |
P024_03==5651 | P024_03==5658 | P024_03==5689 | P024_03==5733 | P024_03==5819 |
P024_03==5942 | P024_03==5952 | P024_03==6099 | P024_03==6147 | P024_03==6148 |
P024_03==6149 | P024_03==6482 | P024_03==6811 | P024_03==6898 | P024_03==6940 |
P024_03==7125 | P024_03==7126 | P024_03==7127 | P024_03==7219 | P024_03==8000 |
P024_03==8001 | P024_03==8002 | P024_03==8017 | P024_03==8074 | P024_03==8086 |
P024_03==8163 | P024_03==8194 | P024_03==8207 | P024_03==8217 | P024_03==8218 |
P024_03==8219 | P024_03==8233 | P024_03==8253 | P024_03==8275 | P024_03==8276 |
P024_03==8286 | P024_03==8287 | P024_03==8320 | P024_03==8404 | P024_03==8408 ))
## Naranjas y mandarinas
cajamarca_narmand <- filter(cajamarca_rec02, (
P024_03==1146 | P024_03==5533 | P024_03==5568 | P024_03==5593 | P024_03==5611 |
P024_03==5641 | P024_03==5751 | P024_03==5754 | P024_03==5762 | P024_03==5765 |
P024_03==5810 | P024_03==5929 | P024_03==5931 | P024_03==6004 | P024_03==6045 |
P024_03==6066 | P024_03==6087 | P024_03==6088 | P024_03==6089 | P024_03==6096 |
P024_03==6097 | P024_03==6098 | P024_03==6099 | P024_03==6100 | P024_03==6101 |
P024_03==6102 | P024_03==6103 | P024_03==6121 | P024_03==6190 | P024_03==6216 |
P024_03==6312 | P024_03==6313 | P024_03==6378 | P024_03==6405 | P024_03==6419 |
P024_03==6420 | P024_03==6421 | P024_03==6422 | P024_03==6423 | P024_03==6424 |
P024_03==6470 | P024_03==6517 | P024_03==6539 | P024_03==6660 | P024_03==6991 |
P024_03==6992 | P024_03==7192 | P024_03==6650 | P024_03==6969 | P024_03==8384 ))#lalibertad
setwd("C:/Users/sant_/Documents/Consultorias/2019/nico/cenagro")
library(dplyr)
library(foreign)
##Bases generales
lalibertad_rec02 <- read.dbf("lalibertad/349-Modulo231/03_IVCENAGRO_REC02.dbf")
lalibertad_rec04a <- read.dbf("lalibertad/349-Modulo236/08_IVCENAGRO_REC04A.dbf")
## Pollos
lalibertad_pollos <- filter(lalibertad_rec04a, P067_01==761)
## Ganado lechero (solo vacas)
lalibertad_vacas <- filter(lalibertad_rec04a, P067_01==674)
## Papas
lalibertad_papas <- filter(lalibertad_rec02, (
P024_03==2610 | P024_03==2611 | P024_03==2612 | P024_03==2613 | P024_03==2614 |
P024_03==2615 | P024_03==5049 | P024_03==5062 | P024_03==5074 | P024_03==5075 |
P024_03==5076 | P024_03==5077 | P024_03==5078 | P024_03==5079 | P024_03==5080 |
P024_03==5081 | P024_03==5090 | P024_03==5091 | P024_03==5113 | P024_03==5122 |
P024_03==5123 | P024_03==5124 | P024_03==5125 | P024_03==5126 | P024_03==5130 |
P024_03==5135 | P024_03==5136 | P024_03==5137 | P024_03==5142 | P024_03==5145 |
P024_03==5146 | P024_03==5155 | P024_03==5157 | P024_03==5159 | P024_03==5160 |
P024_03==5170 | P024_03==5183 | P024_03==5184 | P024_03==5185 | P024_03==5213 |
P024_03==5214 | P024_03==5231 | P024_03==5304 | P024_03==5344 | P024_03==5345 |
P024_03==5346 | P024_03==5347 | P024_03==5395 | P024_03==5399 | P024_03==5406 |
P024_03==5412 | P024_03==5436 | P024_03==5440 | P024_03==5441 | P024_03==5442 |
P024_03==5458 | P024_03==5496 | P024_03==5512 | P024_03==5554 | P024_03==5586 |
P024_03==5651 | P024_03==5658 | P024_03==5689 | P024_03==5733 | P024_03==5819 |
P024_03==5942 | P024_03==5952 | P024_03==6099 | P024_03==6147 | P024_03==6148 |
P024_03==6149 | P024_03==6482 | P024_03==6811 | P024_03==6898 | P024_03==6940 |
P024_03==7125 | P024_03==7126 | P024_03==7127 | P024_03==7219 | P024_03==8000 |
P024_03==8001 | P024_03==8002 | P024_03==8017 | P024_03==8074 | P024_03==8086 |
P024_03==8163 | P024_03==8194 | P024_03==8207 | P024_03==8217 | P024_03==8218 |
P024_03==8219 | P024_03==8233 | P024_03==8253 | P024_03==8275 | P024_03==8276 |
P024_03==8286 | P024_03==8287 | P024_03==8320 | P024_03==8404 | P024_03==8408 ))
## Naranjas y mandarinas
lalibertad_narmand <- filter(lalibertad_rec02, (
P024_03==1146 | P024_03==5533 | P024_03==5568 | P024_03==5593 | P024_03==5611 |
P024_03==5641 | P024_03==5751 | P024_03==5754 | P024_03==5762 | P024_03==5765 |
P024_03==5810 | P024_03==5929 | P024_03==5931 | P024_03==6004 | P024_03==6045 |
P024_03==6066 | P024_03==6087 | P024_03==6088 | P024_03==6089 | P024_03==6096 |
P024_03==6097 | P024_03==6098 | P024_03==6099 | P024_03==6100 | P024_03==6101 |
P024_03==6102 | P024_03==6103 | P024_03==6121 | P024_03==6190 | P024_03==6216 |
P024_03==6312 | P024_03==6313 | P024_03==6378 | P024_03==6405 | P024_03==6419 |
P024_03==6420 | P024_03==6421 | P024_03==6422 | P024_03==6423 | P024_03==6424 |
P024_03==6470 | P024_03==6517 | P024_03==6539 | P024_03==6660 | P024_03==6991 |
P024_03==6992 | P024_03==7192 | P024_03==6650 | P024_03==6969 | P024_03==8384 ))#Reportes generales
Nro_productores=cbind(c(nrow(cajamarca_vacas),nrow(lalibertad_vacas),nrow(lima_vacas),nrow(ancash_vacas),nrow(ica_vacas),nrow(junin_vacas)),
c(nrow(cajamarca_papas),nrow(lalibertad_papas),nrow(lima_papas),nrow(ancash_papas),nrow(ica_papas),nrow(junin_papas)),
c(nrow(cajamarca_narmand),nrow(lalibertad_narmand),nrow(lima_narmand),nrow(ancash_narmand),nrow(ica_narmand),nrow(junin_narmand)),
c(nrow(cajamarca_pollos),nrow(lalibertad_pollos),nrow(lima_pollos),nrow(ancash_pollos),nrow(ica_pollos),nrow(junin_pollos)))
rownames(Nro_productores) <- c("Cajamarca","La Libertad","Lima","Ancash","Ica","Junín")
colnames(Nro_productores) <- c("Ganado lechero","Papas","Cítricos","Pollos")
Nro_productores## Ganado lechero Papas Cítricos Pollos
## Cajamarca 122397 103050 8 71518
## La Libertad 30919 47250 8 24396
## Lima 15481 10695 2500 7075
## Ancash 34695 78324 23 9895
## Ica 2859 666 131 3240
## Junín 25380 62610 381 13872lalibertad_vacas <- mutate(lalibertad_vacas, ubigeo=paste(lalibertad_vacas$P001,lalibertad_vacas$P002,lalibertad_vacas$P003))
Tlalibertad_vacas <- group_by(lalibertad_vacas, ubigeo)
vacas <- data.frame(summarize(Tlalibertad_vacas, SuperficieTotal= sum(P067_03),
Media=mean(P067_03),
Minimo=min(P067_03),
Maximo=max(P067_03)))
vacas## ubigeo SuperficieTotal Media Minimo Maximo
## 1 13 01 01 531 19.666667 1 178
## 2 13 01 02 37 7.400000 1 17
## 3 13 01 04 1405 29.893617 1 335
## 4 13 01 06 812 3.741935 1 42
## 5 13 01 07 697 8.011494 1 150
## 6 13 01 08 46 1.314286 1 3
## 7 13 01 09 2305 329.285714 1 1232
## 8 13 01 10 407 3.230159 1 138
## 9 13 01 11 224 8.615385 1 70
## 10 13 02 01 162 2.655738 1 15
## 11 13 02 02 150 2.142857 1 6
## 12 13 02 03 176 3.087719 1 10
## 13 13 02 04 11 2.200000 1 5
## 14 13 02 05 1147 8.891473 1 450
## 15 13 02 06 673 3.759777 1 80
## 16 13 02 07 625 20.161290 1 485
## 17 13 02 08 995 14.420290 1 838
## 18 13 03 01 1611 3.948529 1 35
## 19 13 03 02 486 3.000000 1 20
## 20 13 03 03 609 3.421348 1 18
## 21 13 03 04 200 2.631579 1 15
## 22 13 03 05 1136 4.437500 1 75
## 23 13 03 06 147 3.585366 1 15
## 24 13 04 01 373 2.453947 1 16
## 25 13 04 02 1319 2.937639 1 25
## 26 13 04 03 293 2.547826 1 40
## 27 13 05 01 1621 1.529245 1 20
## 28 13 05 02 917 1.493485 1 9
## 29 13 05 03 1267 1.506540 1 9
## 30 13 05 04 1291 1.754076 1 15
## 31 13 06 01 1931 1.465099 1 7
## 32 13 06 02 1835 1.755981 1 12
## 33 13 06 04 630 1.438356 1 11
## 34 13 06 05 939 2.871560 1 25
## 35 13 06 06 270 2.125984 1 15
## 36 13 06 08 700 1.670644 1 10
## 37 13 06 10 173 1.616822 1 4
## 38 13 06 11 1500 1.557632 1 13
## 39 13 06 13 1186 1.642659 1 10
## 40 13 06 14 4694 2.076991 1 30
## 41 13 07 01 348 2.829268 0 15
## 42 13 07 02 141 2.820000 1 20
## 43 13 07 03 1197 46.038462 1 1092
## 44 13 07 04 4 1.333333 1 2
## 45 13 07 05 349 2.860656 1 24
## 46 13 08 01 1540 2.475884 1 30
## 47 13 08 02 740 2.624113 1 27
## 48 13 08 03 1648 2.209115 1 20
## 49 13 08 04 794 1.800454 1 10
## 50 13 08 05 248 3.100000 1 10
## 51 13 08 06 476 1.830769 1 7
## 52 13 08 07 63 3.150000 1 10
## 53 13 08 08 1649 3.239686 1 30
## 54 13 08 09 295 3.172043 1 19
## 55 13 08 10 289 2.627273 1 15
## 56 13 08 11 428 1.981481 0 18
## 57 13 08 12 343 1.834225 1 15
## 58 13 08 13 349 1.866310 1 17
## 59 13 09 01 2183 1.655042 1 10
## 60 13 09 02 1147 1.745814 1 23
## 61 13 09 03 637 2.081699 1 30
## 62 13 09 04 362 1.306859 1 6
## 63 13 09 05 782 1.551587 1 9
## 64 13 09 06 1410 1.707022 1 12
## 65 13 09 07 1302 1.745308 1 11
## 66 13 09 08 506 1.888060 1 12
## 67 13 10 01 3975 1.810109 1 30
## 68 13 10 02 889 1.746562 1 12
## 69 13 10 03 1229 2.118966 1 10
## 70 13 10 04 397 2.089474 1 12
## 71 13 10 05 441 1.621324 1 15
## 72 13 10 06 2059 2.118313 1 30
## 73 13 10 07 579 1.539894 1 6
## 74 13 10 08 1100 3.273810 1 18
## 75 13 11 01 1753 2.566618 1 24
## 76 13 11 02 751 1.925641 1 8
## 77 13 11 03 383 2.201149 1 15
## 78 13 11 04 1191 2.465839 1 16
## 79 13 12 01 3969 10.125000 1 2327
## 80 13 12 02 1630 4.630682 1 72
## 81 13 12 03 117 2.785714 1 18lalibertad_papas <- mutate(lalibertad_papas, ubigeo=paste(lalibertad_papas$P001,lalibertad_papas$P002,lalibertad_papas$P003))
Tlalibertad_papas <- group_by(lalibertad_papas, ubigeo)
papas <- data.frame(summarize(Tlalibertad_papas, SuperficieTotal= sum(P025),
Media=mean(P025),
Minimo=min(P025),
Maximo=max(P025)))
papas## ubigeo SuperficieTotal Media Minimo Maximo
## 1 13 01 01 0.3000 0.3000000 0.3000 0.30
## 2 13 01 06 14.8600 0.6460870 0.0100 4.00
## 3 13 01 07 0.2970 0.0594000 0.0035 0.25
## 4 13 01 08 0.2500 0.2500000 0.2500 0.25
## 5 13 01 10 0.8100 0.2700000 0.0600 0.50
## 6 13 02 01 0.3000 0.1500000 0.0500 0.25
## 7 13 02 02 0.3000 0.3000000 0.3000 0.30
## 8 13 02 08 0.5800 0.1450000 0.0060 0.50
## 9 13 03 01 181.8300 0.3819958 0.0050 3.00
## 10 13 03 02 248.8200 0.5733180 0.1500 4.00
## 11 13 03 03 85.4000 0.3846847 0.0500 3.50
## 12 13 03 04 44.4000 0.6253521 0.1000 2.00
## 13 13 03 05 236.4000 0.5710145 0.0500 3.00
## 14 13 03 06 30.9500 0.4761538 0.1000 2.00
## 15 13 04 02 1.0000 1.0000000 1.0000 1.00
## 16 13 05 01 1546.0961 0.5511929 0.0001 7.00
## 17 13 05 02 492.9600 0.7082759 0.0100 8.00
## 18 13 05 03 368.2300 0.5463353 0.0200 5.00
## 19 13 05 04 403.4300 0.6403651 0.0100 5.00
## 20 13 06 01 706.9397 0.5380059 0.0015 200.00
## 21 13 06 02 909.8890 0.6457693 0.0040 13.00
## 22 13 06 04 32.6792 0.1835910 0.0010 2.00
## 23 13 06 05 591.0750 1.3464123 0.0050 200.00
## 24 13 06 06 50.5400 0.2871591 0.0500 1.00
## 25 13 06 08 644.2063 0.6188341 0.0100 10.00
## 26 13 06 10 125.9400 0.3537640 0.0050 6.00
## 27 13 06 11 532.2567 0.6069062 0.0030 5.00
## 28 13 06 13 700.8335 0.2597604 0.0030 5.00
## 29 13 06 14 2896.9335 0.7264126 0.0010 300.00
## 30 13 08 01 695.7375 0.3297334 0.0025 100.00
## 31 13 08 02 1444.4070 4.0234178 0.0150 300.00
## 32 13 08 03 612.8200 0.4132299 0.0200 5.00
## 33 13 08 04 803.1408 0.9338847 0.0012 200.00
## 34 13 08 05 34.1300 0.5250769 0.0200 3.00
## 35 13 08 06 82.3900 0.2402041 0.0100 1.00
## 36 13 08 07 0.7500 0.3750000 0.2500 0.50
## 37 13 08 08 436.7045 0.6006939 0.0015 6.00
## 38 13 08 09 52.6600 0.6421951 0.0050 3.00
## 39 13 08 10 51.3500 0.7442029 0.1000 10.25
## 40 13 08 11 64.8700 0.1340289 0.0100 2.00
## 41 13 08 12 85.9600 0.2737580 0.0100 1.50
## 42 13 08 13 35.3100 0.3331132 0.0500 2.00
## 43 13 09 01 1953.3606 0.4332137 0.0025 7.00
## 44 13 09 02 1041.1140 0.4372591 0.0020 8.00
## 45 13 09 03 573.9600 0.6178256 0.0400 35.00
## 46 13 09 04 694.1954 0.4181900 0.0020 3.50
## 47 13 09 05 702.1600 0.3660897 0.0100 4.00
## 48 13 09 06 769.4975 0.4086551 0.0001 5.00
## 49 13 09 07 781.5295 0.4070466 0.0015 6.50
## 50 13 09 08 378.8460 0.3913698 0.0015 3.00
## 51 13 10 01 213.2800 0.6749367 0.0100 7.50
## 52 13 10 02 136.3700 0.4471148 0.0100 7.00
## 53 13 10 03 401.4000 0.5629734 0.0060 10.00
## 54 13 10 04 119.8612 0.5423584 0.0025 2.00
## 55 13 10 05 136.0048 0.2833433 0.0020 2.50
## 56 13 10 06 515.3020 0.5595027 0.0020 50.00
## 57 13 10 07 149.0600 0.6316102 0.0100 7.00
## 58 13 10 08 341.8780 0.5259662 0.0020 50.00
## 59 13 11 01 89.2700 0.3704149 0.0600 2.00
## 60 13 11 02 81.1649 0.2696508 0.0014 6.00
## 61 13 11 03 18.9501 0.2037645 0.0050 1.00
## 62 13 11 04 181.0400 0.3165035 0.0200 3.25
## 63 13 12 01 30.5000 1.7941176 0.2500 10.00
## 64 13 12 02 23.6300 2.9537500 0.7000 6.00lalibertad_narmand <- mutate(lalibertad_narmand, ubigeo=paste(lalibertad_narmand$P001,lalibertad_narmand$P002,lalibertad_narmand$P003))
Tlalibertad_narmand <- group_by(lalibertad_narmand, ubigeo)
citricos <- data.frame(summarize(Tlalibertad_narmand, SuperficieTotal= sum(P025),
Media=mean(P025),
Minimo=min(P025),
Maximo=max(P025)))
citricos## ubigeo SuperficieTotal Media Minimo Maximo
## 1 13 01 06 3.74 3.74 3.74 3.74
## 2 13 01 08 0.10 0.10 0.10 0.10
## 3 13 04 02 0.50 0.50 0.50 0.50
## 4 13 07 05 0.12 0.12 0.12 0.12
## 5 13 08 12 0.50 0.50 0.50 0.50
## 6 13 09 03 0.10 0.10 0.10 0.10
## 7 13 12 01 2.00 2.00 2.00 2.00
## 8 13 12 02 101.75 101.75 101.75 101.75lalibertad_pollos <- mutate(lalibertad_pollos, ubigeo=paste(lalibertad_pollos$P001,lalibertad_pollos$P002,lalibertad_pollos$P003))
Tlalibertad_pollos <- group_by(lalibertad_pollos, ubigeo)
pollos <- data.frame(summarize(Tlalibertad_pollos, SuperficieTotal= sum(P067_03),
Media=mean(P067_03),
Minimo=min(P067_03),
Maximo=max(P067_03)))
pollos## ubigeo SuperficieTotal Media Minimo Maximo
## 1 13 01 01 56608 1.769000e+03 3 28300
## 2 13 01 02 155 1.937500e+01 2 100
## 3 13 01 04 4365505 4.960801e+04 3 780000
## 4 13 01 06 1764293 3.869064e+03 1 450000
## 5 13 01 07 50773 6.680658e+02 1 49800
## 6 13 01 08 331408 2.529832e+03 1 330000
## 7 13 01 09 898032 1.122540e+05 8 256000
## 8 13 01 10 288011 1.523868e+03 1 285000
## 9 13 01 11 244 2.218182e+01 4 100
## 10 13 02 01 768 9.974026e+00 1 30
## 11 13 02 02 618954 6.128257e+03 1 124700
## 12 13 02 03 856 1.083544e+01 2 30
## 13 13 02 04 438 9.733333e+00 1 30
## 14 13 02 05 1192 9.240310e+00 1 100
## 15 13 02 06 361436 2.331845e+03 1 280000
## 16 13 02 07 83426 2.139128e+03 2 69553
## 17 13 02 08 2868 1.297738e+01 1 100
## 18 13 03 01 9 4.500000e+00 4 5
## 19 13 03 02 676 8.047619e+00 1 71
## 20 13 03 03 406 5.413333e+00 1 20
## 21 13 03 04 1 1.000000e+00 1 1
## 22 13 03 05 241 4.725490e+00 1 16
## 23 13 04 01 4282 1.259412e+01 1 100
## 24 13 04 02 616170 9.494145e+02 1 207360
## 25 13 04 03 3128 1.117143e+01 1 106
## 26 13 05 01 4525 5.677541e+00 1 50
## 27 13 05 02 2688 5.946903e+00 1 37
## 28 13 05 03 4724 6.462380e+00 1 40
## 29 13 05 04 3549 7.834437e+00 1 60
## 30 13 06 01 8770 6.124302e+00 1 100
## 31 13 06 02 3984 5.724138e+00 0 60
## 32 13 06 04 2832 5.732794e+00 0 30
## 33 13 06 05 2080 7.349823e+00 1 50
## 34 13 06 06 235 6.351351e+00 0 18
## 35 13 06 08 1816 6.283737e+00 1 39
## 36 13 06 10 635 5.934579e+00 2 16
## 37 13 06 11 4993 6.587071e+00 1 60
## 38 13 06 13 960 6.400000e+00 1 20
## 39 13 06 14 7282 5.605851e+00 0 90
## 40 13 07 01 1160777 8.793765e+03 1 571000
## 41 13 07 02 4411 1.091832e+01 1 150
## 42 13 07 03 182135 1.011861e+04 1 100000
## 43 13 07 04 1000052 1.666753e+05 2 800000
## 44 13 07 05 3895 1.228707e+01 2 200
## 45 13 08 01 1982 4.857843e+00 1 32
## 46 13 08 02 644 5.111111e+00 1 16
## 47 13 08 03 2664 4.649215e+00 0 28
## 48 13 08 04 437 4.965909e+00 1 40
## 49 13 08 05 16 4.000000e+00 1 7
## 50 13 08 06 44 5.500000e+00 2 12
## 51 13 08 07 12 6.000000e+00 2 10
## 52 13 08 08 2039 7.903101e+00 1 200
## 53 13 08 09 682 8.023529e+00 1 50
## 54 13 08 10 558 7.643836e+00 1 32
## 55 13 08 11 68 4.250000e+00 1 14
## 56 13 08 12 192 4.571429e+00 1 11
## 57 13 08 13 188 4.820513e+00 1 10
## 58 13 09 01 12556 6.512448e+00 1 2200
## 59 13 09 02 2557 4.623870e+00 0 40
## 60 13 09 03 2163 6.324561e+00 1 200
## 61 13 09 04 4001 4.746145e+00 1 25
## 62 13 09 05 3621 5.150782e+00 1 30
## 63 13 09 06 3314 5.450658e+00 0 200
## 64 13 09 07 3311 4.223214e+00 1 50
## 65 13 09 08 3144 5.328814e+00 1 50
## 66 13 10 01 5896 6.457831e+00 0 50
## 67 13 10 02 2032 6.233129e+00 1 80
## 68 13 10 03 2053 6.601286e+00 1 80
## 69 13 10 04 721 8.011111e+00 2 50
## 70 13 10 05 227 1.194737e+01 1 112
## 71 13 10 06 2418 5.623256e+00 1 30
## 72 13 10 07 1305 5.240964e+00 1 39
## 73 13 10 08 1073 4.452282e+00 1 20
## 74 13 11 01 4296 2.169697e+01 0 1800
## 75 13 11 02 1099 7.579310e+00 1 50
## 76 13 11 03 2696 1.095935e+01 1 200
## 77 13 11 04 2011 7.312727e+00 1 36
## 78 13 12 01 2771279 6.614031e+03 1 701000
## 79 13 12 02 1035049 3.891162e+03 1 220225
## 80 13 12 03 580084 4.462185e+04 2 190533cajamarca_vacas <- mutate(cajamarca_vacas, ubigeo=paste(cajamarca_vacas$P001,cajamarca_vacas$P002,cajamarca_vacas$P003))
Tcajamarca_vacas <- group_by(cajamarca_vacas, ubigeo)
vacas <- data.frame(summarize(Tcajamarca_vacas, SuperficieTotal= sum(P067_03),
Media=mean(P067_03),
Minimo=min(P067_03),
Maximo=max(P067_03)))
vacas## ubigeo SuperficieTotal Media Minimo Maximo
## 1 06 01 01 5863 2.737162 1 371
## 2 06 01 02 1871 1.804243 1 10
## 3 06 01 03 1542 1.966837 1 21
## 4 06 01 04 3495 2.989735 1 476
## 5 06 01 05 17915 2.523950 1 64
## 6 06 01 06 1066 2.119284 1 18
## 7 06 01 07 1039 3.463333 1 44
## 8 06 01 08 8909 2.702973 1 100
## 9 06 01 09 671 1.525000 1 10
## 10 06 01 10 148 1.494949 1 6
## 11 06 01 11 2436 2.709677 1 30
## 12 06 01 12 1184 2.557235 1 187
## 13 06 02 01 828 1.527675 1 24
## 14 06 02 02 4069 2.118168 1 20
## 15 06 02 03 1622 1.670443 1 17
## 16 06 02 04 894 2.050459 1 15
## 17 06 03 01 1093 1.762903 1 12
## 18 06 03 02 587 2.111511 1 10
## 19 06 03 03 811 1.393471 1 4
## 20 06 03 04 4668 2.386503 1 35
## 21 06 03 05 292 2.703704 1 9
## 22 06 03 06 1821 2.575672 1 20
## 23 06 03 07 974 1.913556 1 100
## 24 06 03 08 1718 2.239896 1 14
## 25 06 03 09 5675 1.904362 1 30
## 26 06 03 10 5429 3.583498 1 38
## 27 06 03 11 132 1.552941 1 6
## 28 06 03 12 1416 1.724726 1 20
## 29 06 04 01 13756 1.879235 1 25
## 30 06 04 02 1307 1.516241 1 15
## 31 06 04 03 1364 2.098462 1 10
## 32 06 04 04 3265 1.675218 1 35
## 33 06 04 05 315 1.657895 1 8
## 34 06 04 06 447 1.960526 1 14
## 35 06 04 07 2052 1.770492 1 23
## 36 06 04 08 3589 1.976322 1 20
## 37 06 04 09 3660 1.960364 1 20
## 38 06 04 10 5383 1.790156 1 18
## 39 06 04 11 2418 2.782509 1 30
## 40 06 04 12 979 2.096360 0 10
## 41 06 04 13 1944 1.892892 1 20
## 42 06 04 14 246 1.720280 1 18
## 43 06 04 15 3679 2.378151 1 18
## 44 06 04 16 570 2.270916 1 20
## 45 06 04 17 4444 1.915517 1 15
## 46 06 04 18 435 2.558824 1 15
## 47 06 04 19 5752 1.780254 1 12
## 48 06 05 01 2729 2.924973 1 80
## 49 06 05 02 158 2.724138 1 24
## 50 06 05 03 745 2.450658 1 17
## 51 06 05 04 399 1.472325 1 6
## 52 06 05 05 693 2.125767 1 20
## 53 06 05 06 304 1.911950 1 11
## 54 06 05 07 553 2.333333 1 21
## 55 06 05 08 251 2.127119 1 11
## 56 06 06 01 13386 1.819492 0 24
## 57 06 06 02 1481 1.716107 1 12
## 58 06 06 03 885 2.418033 1 30
## 59 06 06 04 191 1.528000 1 5
## 60 06 06 05 805 1.750000 1 7
## 61 06 06 06 870 1.831579 1 11
## 62 06 06 07 5034 1.688129 1 31
## 63 06 06 08 2503 2.488072 1 15
## 64 06 06 09 214 1.963303 1 12
## 65 06 06 10 790 1.899038 1 14
## 66 06 06 11 1084 1.843537 1 10
## 67 06 06 12 660 1.709845 1 12
## 68 06 06 13 1623 1.953069 1 15
## 69 06 06 14 2702 1.934145 1 20
## 70 06 06 15 531 3.360759 1 94
## 71 06 07 01 2111 1.587218 1 10
## 72 06 07 02 3776 3.125828 1 45
## 73 06 07 03 4889 2.076890 0 40
## 74 06 08 01 2256 2.685714 1 64
## 75 06 08 02 1958 6.775087 1 458
## 76 06 08 03 1428 2.509666 1 30
## 77 06 08 04 1960 2.242563 1 40
## 78 06 08 05 1064 2.249471 1 20
## 79 06 08 06 465 3.000000 1 21
## 80 06 08 07 731 2.070822 1 28
## 81 06 08 08 246 2.176991 1 11
## 82 06 08 09 1442 1.909934 1 20
## 83 06 08 10 645 1.966463 1 14
## 84 06 08 11 839 2.581538 1 15
## 85 06 08 12 2118 3.228659 1 35
## 86 06 09 01 2108 3.340729 1 40
## 87 06 09 02 1485 3.817481 1 78
## 88 06 09 03 4251 3.134956 1 40
## 89 06 09 04 706 2.377104 1 30
## 90 06 09 05 1479 2.515306 1 20
## 91 06 09 06 3018 3.864277 0 86
## 92 06 09 07 1652 2.259918 1 15
## 93 06 10 01 1506 1.590285 1 11
## 94 06 10 02 454 1.459807 1 8
## 95 06 10 03 591 2.686364 1 15
## 96 06 10 04 3151 2.896140 1 130
## 97 06 10 05 259 1.550898 1 10
## 98 06 10 06 433 1.383387 1 10
## 99 06 10 07 3348 2.125714 1 31
## 100 06 11 01 3272 1.809735 1 19
## 101 06 11 02 548 2.647343 1 14
## 102 06 11 03 2701 2.477982 1 35
## 103 06 11 04 3804 4.517815 1 40
## 104 06 11 05 642 2.118812 1 12
## 105 06 11 06 241 1.746377 1 10
## 106 06 11 07 3343 2.274150 1 20
## 107 06 11 08 365 2.534722 1 25
## 108 06 11 09 2232 2.786517 1 20
## 109 06 11 10 706 2.485915 1 23
## 110 06 11 11 4740 3.342736 1 40
## 111 06 11 12 2746 3.113379 1 35
## 112 06 11 13 1026 2.658031 1 25
## 113 06 12 01 3008 2.029690 1 30
## 114 06 12 02 1097 1.990926 1 12
## 115 06 12 03 182 1.876289 1 8
## 116 06 12 04 2641 3.032147 1 33
## 117 06 13 01 1006 1.564541 0 9
## 118 06 13 02 550 1.612903 1 15
## 119 06 13 03 3991 2.503764 1 23
## 120 06 13 04 1198 1.439904 1 10
## 121 06 13 05 1562 1.481973 1 5
## 122 06 13 06 1209 1.695652 1 10
## 123 06 13 07 1838 2.269136 1 16
## 124 06 13 08 833 1.557009 1 10
## 125 06 13 09 259 4.177419 1 20
## 126 06 13 10 1022 1.747009 1 10
## 127 06 13 11 741 1.798544 1 8cajamarca_papas <- mutate(cajamarca_papas, ubigeo=paste(cajamarca_papas$P001,cajamarca_papas$P002,cajamarca_papas$P003))
Tcajamarca_papas <- group_by(cajamarca_papas, ubigeo)
papas <- data.frame(summarize(Tcajamarca_papas, SuperficieTotal= sum(P025),
Media=mean(P025),
Minimo=min(P025),
Maximo=max(P025)))
papas## ubigeo SuperficieTotal Media Minimo Maximo
## 1 06 01 01 713.0200 0.1757939 0.0003 200.00
## 2 06 01 02 175.5750 0.1894013 0.0050 2.00
## 3 06 01 03 132.2125 0.2523139 0.0010 2.00
## 4 06 01 04 155.5230 0.2549557 0.0010 2.50
## 5 06 01 05 3124.7061 0.3689581 0.0010 200.00
## 6 06 01 06 303.3260 0.3048503 0.0020 3.50
## 7 06 01 07 165.0919 0.2442188 0.0006 20.00
## 8 06 01 08 979.2109 0.2715504 0.0003 50.00
## 9 06 01 09 151.4700 0.3047686 0.0100 6.00
## 10 06 01 10 132.7508 0.2084000 0.0020 2.75
## 11 06 01 11 475.4649 0.3743818 0.0010 6.00
## 12 06 01 12 87.2260 0.2478011 0.0050 2.00
## 13 06 02 01 532.8461 0.2600518 0.0004 10.00
## 14 06 02 02 948.7900 0.4433598 0.0100 100.00
## 15 06 02 03 299.6541 0.2381988 0.0004 3.00
## 16 06 02 04 234.9500 0.3671094 0.0100 3.50
## 17 06 03 01 779.4652 0.3436795 0.0010 40.00
## 18 06 03 02 117.8800 0.2585088 0.0100 2.00
## 19 06 03 03 126.2750 0.2856900 0.0050 2.00
## 20 06 03 04 1461.6480 0.5587339 0.0040 200.00
## 21 06 03 05 22.2100 0.5552500 0.0050 2.20
## 22 06 03 06 428.3993 0.5659172 0.0020 100.00
## 23 06 03 07 322.5550 0.3435091 0.0050 10.00
## 24 06 03 08 498.2136 0.3904495 0.0040 5.00
## 25 06 03 09 1511.9204 0.3310533 0.0001 30.00
## 26 06 03 10 675.9588 0.3985606 0.0005 10.00
## 27 06 03 11 86.2400 0.4562963 0.0400 10.00
## 28 06 03 12 375.0200 0.2988207 0.0050 2.00
## 29 06 04 01 3821.6476 0.4902062 0.0003 250.00
## 30 06 04 02 121.6700 0.3227321 0.0500 1.00
## 31 06 04 03 94.2700 0.2271566 0.0100 2.50
## 32 06 04 04 406.7550 0.2753927 0.0100 2.02
## 33 06 04 05 6.1300 0.2357692 0.1000 0.50
## 34 06 04 06 32.7600 0.2848696 0.0100 2.00
## 35 06 04 07 328.2300 0.4533564 0.0200 4.00
## 36 06 04 08 403.6200 0.2692595 0.0100 3.00
## 37 06 04 09 228.1125 0.4580572 0.0300 3.00
## 38 06 04 10 857.1500 0.2859073 0.0100 4.00
## 39 06 04 11 13.2100 0.4403333 0.0100 2.00
## 40 06 04 12 208.2000 0.4248980 0.2000 4.00
## 41 06 04 13 129.4500 0.1912112 0.0050 2.50
## 42 06 04 14 5.6300 0.2680952 0.0700 0.75
## 43 06 04 15 176.4000 0.5600000 0.0100 3.00
## 44 06 04 16 10.1800 0.3084848 0.1000 1.00
## 45 06 04 17 803.1870 0.3708158 0.0020 3.00
## 46 06 04 18 4.7500 0.2638889 0.1200 0.50
## 47 06 04 19 1013.1570 0.3846458 0.0020 100.00
## 48 06 05 01 158.5700 0.4556609 0.0200 4.00
## 49 06 05 02 0.9600 0.1600000 0.0100 0.25
## 50 06 05 03 31.0100 0.4367606 0.0200 1.00
## 51 06 05 04 60.5700 0.2422800 0.0100 1.50
## 52 06 05 05 3.2500 0.3250000 0.2500 1.00
## 53 06 05 06 11.1600 0.1992857 0.0100 0.50
## 54 06 05 07 44.4100 0.3364394 0.1000 1.25
## 55 06 06 01 2828.1979 0.3599590 0.0010 13.00
## 56 06 06 02 3.5100 0.5014286 0.0100 1.00
## 57 06 06 03 0.2500 0.2500000 0.2500 0.25
## 58 06 06 04 0.0300 0.0300000 0.0300 0.03
## 59 06 06 05 17.6100 0.2174074 0.0100 1.00
## 60 06 06 06 0.7500 0.2500000 0.2500 0.25
## 61 06 06 07 70.8900 0.5100000 0.0200 4.00
## 62 06 06 08 65.7000 0.2628000 0.0100 1.50
## 63 06 06 09 1.1500 0.1642857 0.0500 0.25
## 64 06 06 10 53.1000 0.3179641 0.0500 2.00
## 65 06 06 11 1.2500 0.3125000 0.2500 0.50
## 66 06 06 12 4.4500 0.2966667 0.1000 1.00
## 67 06 06 13 30.9000 0.4478261 0.2000 1.50
## 68 06 06 14 276.0970 0.3590338 0.0100 3.00
## 69 06 07 01 1880.0133 1.0520500 0.0010 250.00
## 70 06 07 02 437.0700 0.3669773 0.0100 13.00
## 71 06 07 03 783.9300 0.3000115 0.0050 5.00
## 72 06 08 01 11.4700 0.4779167 0.0100 2.50
## 73 06 08 03 1.5800 0.2633333 0.0800 0.50
## 74 06 08 04 3.0000 0.5000000 0.2500 1.00
## 75 06 08 05 2.3500 0.2350000 0.1000 0.25
## 76 06 08 06 0.5000 0.5000000 0.5000 0.50
## 77 06 08 07 10.2500 0.3534483 0.2500 1.00
## 78 06 08 08 0.2500 0.2500000 0.2500 0.25
## 79 06 08 09 92.0500 0.4109375 0.0500 3.00
## 80 06 08 10 3.7500 0.3125000 0.1000 0.50
## 81 06 08 12 1.5000 0.7500000 0.5000 1.00
## 82 06 09 01 0.1000 0.1000000 0.1000 0.10
## 83 06 09 02 0.7500 0.7500000 0.7500 0.75
## 84 06 09 03 1.2500 0.4166667 0.2500 0.50
## 85 06 09 07 1.2500 0.4166667 0.2500 0.50
## 86 06 10 01 346.1774 0.2595033 0.0005 4.00
## 87 06 10 02 42.7400 0.3365354 0.0100 3.00
## 88 06 10 03 412.9345 7.6469352 0.0100 400.00
## 89 06 10 04 770.8196 0.3489450 0.0004 100.00
## 90 06 10 05 23.2637 0.1954933 0.0012 1.50
## 91 06 10 06 665.3992 1.3124245 0.0010 300.00
## 92 06 10 07 1485.7544 0.4713688 0.0020 10.00
## 93 06 11 01 460.7545 0.2729588 0.0010 5.00
## 94 06 11 02 10.0500 0.5583333 0.1500 1.50
## 95 06 11 03 219.0600 0.2289028 0.0100 2.00
## 96 06 11 04 252.2700 0.3917236 0.0100 5.00
## 97 06 11 05 45.6300 0.3380000 0.0100 1.00
## 98 06 11 07 210.2400 0.2608437 0.0100 4.00
## 99 06 11 09 133.2700 0.3671350 0.0200 8.00
## 100 06 11 10 10.0000 0.3030303 0.2500 1.00
## 101 06 11 11 370.6416 0.4211836 0.0050 9.00
## 102 06 11 12 198.3500 0.3361864 0.0100 4.00
## 103 06 11 13 46.3000 0.3645669 0.1000 2.00
## 104 06 12 01 264.4975 0.3249355 0.0100 4.00
## 105 06 12 02 76.5600 0.4374857 0.0100 3.00
## 106 06 12 03 1.0000 0.3333333 0.2500 0.50
## 107 06 12 04 172.9000 0.3632353 0.0100 5.00
## 108 06 13 01 57.2900 0.2715166 0.0300 1.90
## 109 06 13 02 45.4900 0.2825466 0.0500 2.00
## 110 06 13 03 338.9000 0.4567385 0.0100 5.99
## 111 06 13 04 85.0000 0.4381443 0.1000 2.00
## 112 06 13 05 109.7400 0.3682550 0.0500 1.10
## 113 06 13 06 165.9000 0.2472429 0.0300 1.50
## 114 06 13 07 103.1700 0.2939316 0.0100 10.00
## 115 06 13 08 92.7800 0.3092667 0.0200 1.00
## 116 06 13 09 2.0000 2.0000000 2.0000 2.00
## 117 06 13 10 180.8603 0.2511949 0.0003 1.95
## 118 06 13 11 179.7100 0.3476015 0.0100 2.00cajamarca_narmand <- mutate(cajamarca_narmand, ubigeo=paste(cajamarca_narmand$P001,cajamarca_narmand$P002,cajamarca_narmand$P003))
Tcajamarca_narmand <- group_by(cajamarca_narmand, ubigeo)
citricos <- data.frame(summarize(Tcajamarca_narmand, SuperficieTotal= sum(P025),
Media=mean(P025),
Minimo=min(P025),
Maximo=max(P025)))
citricos## ubigeo SuperficieTotal Media Minimo Maximo
## 1 06 03 02 0.10 0.10 0.10 0.10
## 2 06 03 04 0.50 0.50 0.50 0.50
## 3 06 04 11 1.00 1.00 1.00 1.00
## 4 06 05 01 0.01 0.01 0.01 0.01
## 5 06 06 01 0.05 0.05 0.05 0.05
## 6 06 06 10 0.25 0.25 0.25 0.25
## 7 06 08 08 0.15 0.15 0.15 0.15
## 8 06 10 01 0.04 0.04 0.04 0.04cajamarca_pollos <- mutate(cajamarca_pollos, ubigeo=paste(cajamarca_pollos$P001,cajamarca_pollos$P002,cajamarca_pollos$P003))
Tcajamarca_pollos <- group_by(cajamarca_pollos, ubigeo)
pollos <- data.frame(summarize(Tcajamarca_pollos, SuperficieTotal= sum(P067_03),
Media=mean(P067_03),
Minimo=min(P067_03),
Maximo=max(P067_03)))
pollos## ubigeo SuperficieTotal Media Minimo Maximo
## 1 06 01 01 19480 6.006784 0 300
## 2 06 01 02 103 3.814815 1 15
## 3 06 01 03 1315 4.354305 1 30
## 4 06 01 04 136 7.555556 1 20
## 5 06 01 05 8724 4.118980 0 32
## 6 06 01 06 5351 5.135317 1 50
## 7 06 01 07 3260 6.626016 1 70
## 8 06 01 08 13437 6.258500 0 60
## 9 06 01 09 14756 24.190164 1 11000
## 10 06 01 10 1731 6.340659 1 38
## 11 06 01 11 4183 5.088808 1 30
## 12 06 01 12 3250 5.742049 1 80
## 13 06 02 01 13148 7.184699 1 150
## 14 06 02 02 11596 7.772118 1 600
## 15 06 02 03 5221 15.869301 0 2000
## 16 06 02 04 1626 6.182510 1 40
## 17 06 03 01 1049 5.763736 0 20
## 18 06 03 02 1617 5.426174 1 30
## 19 06 03 03 1053 4.808219 1 20
## 20 06 03 04 4393 4.801093 0 44
## 21 06 03 05 116 5.800000 2 10
## 22 06 03 06 617 6.049020 1 40
## 23 06 03 07 152 3.304348 1 20
## 24 06 03 08 408 4.000000 1 30
## 25 06 03 09 7777 3.931749 0 25
## 26 06 03 10 4141 5.302177 0 40
## 27 06 03 11 2151 8.638554 1 60
## 28 06 03 12 679 3.857955 1 15
## 29 06 04 01 23641 6.057136 1 200
## 30 06 04 02 2377 5.046709 1 25
## 31 06 04 03 2131 5.300995 1 24
## 32 06 04 04 2021 5.462162 0 30
## 33 06 04 05 201 6.483871 0 20
## 34 06 04 06 1885 8.303965 1 100
## 35 06 04 07 9071 7.368806 1 70
## 36 06 04 08 4093 5.622253 0 40
## 37 06 04 09 466 7.898305 2 24
## 38 06 04 10 8850 6.000000 0 50
## 39 06 04 11 684 8.444444 0 30
## 40 06 04 12 1629 7.472477 0 50
## 41 06 04 13 929 6.451389 1 100
## 42 06 04 14 74 5.285714 1 11
## 43 06 04 15 14236 13.072544 1 230
## 44 06 04 16 7 7.000000 7 7
## 45 06 04 17 2514 5.846512 0 100
## 46 06 04 18 15 3.000000 0 8
## 47 06 04 19 1056 4.821918 1 25
## 48 06 05 01 1161 8.352518 1 40
## 49 06 05 02 399 19.950000 10 51
## 50 06 05 03 2317 8.710526 1 50
## 51 06 05 04 1872 6.907749 1 40
## 52 06 05 05 2973 11.892000 1 700
## 53 06 05 06 58 29.000000 3 55
## 54 06 05 07 662 9.194444 2 40
## 55 06 05 08 5257 11.503282 1 60
## 56 06 06 01 46939 7.768785 0 3000
## 57 06 06 02 13125 7.892363 0 50
## 58 06 06 03 4406 11.473958 1 60
## 59 06 06 04 2800 9.210526 1 50
## 60 06 06 05 3997 7.671785 0 50
## 61 06 06 06 45 5.000000 1 15
## 62 06 06 07 7371 8.029412 0 50
## 63 06 06 08 386 8.577778 1 60
## 64 06 06 10 1723 8.927461 1 60
## 65 06 06 11 1115 8.991935 1 30
## 66 06 06 12 5160 8.087774 1 80
## 67 06 06 13 6415 9.060734 1 60
## 68 06 06 14 8525 7.374567 1 83
## 69 06 06 15 1690 9.388889 1 40
## 70 06 07 01 6877 5.147455 1 43
## 71 06 07 02 2110 6.963696 1 30
## 72 06 07 03 3979 4.127593 1 30
## 73 06 08 01 18371 16.580325 1 5000
## 74 06 08 02 28861 44.265337 0 20000
## 75 06 08 03 3145 10.080128 1 60
## 76 06 08 04 11608 10.738205 1 100
## 77 06 08 05 82 3.153846 0 12
## 78 06 08 06 2344 10.191304 1 50
## 79 06 08 07 5771 9.445172 1 50
## 80 06 08 08 3414 11.230263 1 50
## 81 06 08 09 5247 7.998476 1 40
## 82 06 08 10 1885 7.421260 1 50
## 83 06 08 11 1430 13.619048 1 100
## 84 06 08 12 7549 12.334967 1 120
## 85 06 09 01 4651 10.244493 1 50
## 86 06 09 02 5244 12.138889 1 300
## 87 06 09 03 9639 12.139798 0 70
## 88 06 09 04 11250 10.264599 1 100
## 89 06 09 05 4231 10.498759 0 50
## 90 06 09 06 12227 10.810787 0 100
## 91 06 09 07 11253 9.967228 0 100
## 92 06 10 01 7812 6.482988 0 150
## 93 06 10 02 1530 5.907336 1 21
## 94 06 10 03 274 9.448276 3 20
## 95 06 10 04 2423 4.750980 1 30
## 96 06 10 05 1533 6.813333 1 70
## 97 06 10 06 586 4.966102 1 20
## 98 06 10 07 1524 4.916129 0 40
## 99 06 11 01 5427 4.496272 0 30
## 100 06 11 03 1902 4.245536 1 22
## 101 06 11 04 13 3.250000 2 4
## 102 06 11 05 2692 9.478873 1 40
## 103 06 11 06 2154 8.685484 0 30
## 104 06 11 07 3020 3.381859 0 90
## 105 06 11 08 1341 9.860294 2 40
## 106 06 11 09 51 8.500000 2 24
## 107 06 11 10 1519 15.500000 2 500
## 108 06 11 11 1499 5.832685 0 30
## 109 06 11 12 2139 5.628947 1 25
## 110 06 11 13 2791 8.888535 1 50
## 111 06 12 01 63 4.846154 0 12
## 112 06 12 02 1804 10.802395 1 400
## 113 06 12 04 61 3.812500 1 15
## 114 06 13 01 3736 30.129032 0 2300
## 115 06 13 02 26 8.666667 7 11
## 116 06 13 03 7615 7.915800 0 100
## 117 06 13 04 4216 7.851024 1 35
## 118 06 13 05 4807 7.283333 1 50
## 119 06 13 06 6740 8.641026 1 60
## 120 06 13 07 2372 5.581176 0 20
## 121 06 13 08 919 5.967532 0 20
## 122 06 13 10 2372 8.123288 1 20
## 123 06 13 11 2406 6.701950 1 25lima_vacas <- mutate(lima_vacas, ubigeo=paste(lima_vacas$P001,lima_vacas$P002,lima_vacas$P003))
Tlima_vacas <- group_by(lima_vacas, ubigeo)
vacas <- data.frame(summarize(Tlima_vacas, SuperficieTotal= sum(P067_03),
Media=mean(P067_03),
Minimo=min(P067_03),
Maximo=max(P067_03)))
vacas## ubigeo SuperficieTotal Media Minimo Maximo
## 1 15 01 02 20 20.000000 20 20
## 2 15 01 03 110 55.000000 20 90
## 3 15 01 06 972 14.507463 1 130
## 4 15 01 08 23 7.666667 1 15
## 5 15 01 09 11 1.833333 1 3
## 6 15 01 10 127 12.700000 1 30
## 7 15 01 14 232 116.000000 114 118
## 8 15 01 18 209 8.708333 1 67
## 9 15 01 19 5809 27.530806 1 716
## 10 15 01 23 824 16.480000 1 340
## 11 15 01 25 1415 67.380952 2 690
## 12 15 01 33 1 1.000000 1 1
## 13 15 01 35 6 6.000000 6 6
## 14 15 01 42 100 16.666667 1 90
## 15 15 01 43 7 2.333333 1 4
## 16 15 02 01 223 4.847826 1 80
## 17 15 02 02 301 8.852941 1 200
## 18 15 02 03 578 8.257143 1 199
## 19 15 02 04 1991 5.545961 1 53
## 20 15 02 05 266 7.000000 1 63
## 21 15 03 01 2361 5.675481 1 67
## 22 15 03 02 551 3.422360 1 20
## 23 15 03 03 1817 4.819629 1 60
## 24 15 03 04 750 3.246753 1 20
## 25 15 03 05 712 4.263473 1 25
## 26 15 04 01 2453 9.051661 1 900
## 27 15 04 02 686 8.909091 1 30
## 28 15 04 03 1444 6.197425 1 31
## 29 15 04 04 1161 5.893401 1 40
## 30 15 04 05 1282 8.217949 1 30
## 31 15 04 06 474 4.601942 1 100
## 32 15 04 07 358 5.424242 1 20
## 33 15 05 01 2501 6.581579 1 223
## 34 15 05 02 266 6.650000 1 117
## 35 15 05 03 62 2.384615 1 6
## 36 15 05 04 1180 28.780488 1 200
## 37 15 05 05 762 190.500000 1 400
## 38 15 05 06 74 2.242424 1 10
## 39 15 05 07 3931 55.366197 1 1978
## 40 15 05 08 179 2.753846 1 18
## 41 15 05 09 686 7.221053 1 120
## 42 15 05 10 2257 7.075235 1 272
## 43 15 05 11 218 4.192308 1 22
## 44 15 05 12 3391 18.329730 1 1450
## 45 15 05 13 65 3.611111 1 16
## 46 15 05 14 1291 10.165354 1 100
## 47 15 05 15 21 1.750000 1 4
## 48 15 05 16 53 3.117647 1 7
## 49 15 06 01 1033 11.351648 1 324
## 50 15 06 02 1976 10.917127 1 250
## 51 15 06 03 538 5.546392 1 80
## 52 15 06 04 446 7.824561 1 80
## 53 15 06 05 3081 26.560345 1 568
## 54 15 06 06 1637 5.644828 1 40
## 55 15 06 07 410 7.735849 1 30
## 56 15 06 08 1276 7.733333 1 90
## 57 15 06 09 202 8.782609 2 25
## 58 15 06 10 544 7.157895 2 60
## 59 15 06 11 1162 7.354430 1 80
## 60 15 06 12 1087 12.077778 1 70
## 61 15 07 01 819 5.318182 1 20
## 62 15 07 02 27 5.400000 1 17
## 63 15 07 03 183 4.945946 1 18
## 64 15 07 04 781 10.012821 1 60
## 65 15 07 05 70 4.666667 1 11
## 66 15 07 06 94 2.764706 1 7
## 67 15 07 07 334 6.301887 1 22
## 68 15 07 08 674 8.986667 1 30
## 69 15 07 09 1136 5.259259 1 20
## 70 15 07 10 336 3.082569 1 20
## 71 15 07 11 339 2.947826 1 11
## 72 15 07 12 584 7.786667 1 25
## 73 15 07 13 543 4.379032 1 20
## 74 15 07 15 476 3.282759 1 15
## 75 15 07 16 651 6.925532 1 30
## 76 15 07 17 38 3.800000 1 8
## 77 15 07 18 1124 4.323077 1 22
## 78 15 07 19 607 7.493827 1 35
## 79 15 07 20 235 5.000000 1 17
## 80 15 07 21 1372 6.471698 1 30
## 81 15 07 22 1461 5.513208 1 90
## 82 15 07 23 401 6.573770 1 30
## 83 15 07 24 672 5.843478 1 24
## 84 15 07 25 151 4.081081 1 12
## 85 15 07 26 328 5.466667 1 20
## 86 15 07 27 1 1.000000 1 1
## 87 15 07 28 13 2.166667 1 4
## 88 15 07 29 377 3.886598 1 11
## 89 15 07 30 33 3.000000 1 10
## 90 15 07 31 159 7.571429 1 40
## 91 15 07 32 273 4.403226 1 15
## 92 15 08 01 1846 6.236486 1 40
## 93 15 08 02 2076 6.197015 1 60
## 94 15 08 03 39 3.545455 1 10
## 95 15 08 04 1642 5.822695 1 30
## 96 15 08 05 100 4.166667 1 15
## 97 15 08 06 161 3.926829 1 15
## 98 15 08 07 1102 6.638554 1 42
## 99 15 08 08 1422 4.787879 1 26
## 100 15 08 09 1708 9.333333 1 74
## 101 15 08 10 1724 11.808219 1 720
## 102 15 08 11 717 14.058824 1 270
## 103 15 08 12 7225 15.178571 1 1448
## 104 15 09 01 2453 7.322388 1 107
## 105 15 09 02 729 4.892617 1 34
## 106 15 09 03 460 5.287356 1 30
## 107 15 09 04 873 4.386935 1 38
## 108 15 09 05 704 4.165680 1 70
## 109 15 09 06 1524 5.729323 1 35
## 110 15 10 01 860 5.058824 1 40
## 111 15 10 02 224 9.333333 1 40
## 112 15 10 03 838 5.624161 1 20
## 113 15 10 04 721 8.792683 1 70
## 114 15 10 05 492 4.100000 1 15
## 115 15 10 06 313 4.287671 1 16
## 116 15 10 07 204 4.975610 2 20
## 117 15 10 08 277 6.441860 1 35
## 118 15 10 09 320 3.478261 1 20
## 119 15 10 10 72 5.538462 1 10
## 120 15 10 11 780 4.905660 1 25
## 121 15 10 12 679 6.345794 1 33
## 122 15 10 13 160 5.000000 1 12
## 123 15 10 14 945 10.053191 1 30
## 124 15 10 15 489 3.569343 1 50
## 125 15 10 16 756 8.042553 1 35
## 126 15 10 17 325 6.914894 1 20
## 127 15 10 18 457 5.313953 1 26
## 128 15 10 19 684 7.276596 1 45
## 129 15 10 20 848 5.542484 1 50
## 130 15 10 21 525 7.094595 3 18
## 131 15 10 22 433 4.811111 1 14
## 132 15 10 23 138 9.200000 3 40
## 133 15 10 24 758 6.890909 1 25
## 134 15 10 25 209 6.531250 1 19
## 135 15 10 26 139 9.266667 2 20
## 136 15 10 27 613 6.521277 1 17
## 137 15 10 28 785 13.534483 1 80
## 138 15 10 29 692 5.766667 1 30
## 139 15 10 30 639 6.454545 1 25
## 140 15 10 31 727 5.635659 1 20
## 141 15 10 32 1183 3.969799 1 20
## 142 15 10 33 418 7.333333 1 22lima_papas <- mutate(lima_papas, ubigeo=paste(lima_papas$P001,lima_papas$P002,lima_papas$P003))
Tlima_papas <- group_by(lima_papas, ubigeo)
papas <- data.frame(summarize(Tlima_papas, SuperficieTotal= sum(P025),
Media=mean(P025),
Minimo=min(P025),
Maximo=max(P025)))
papas## ubigeo SuperficieTotal Media Minimo Maximo
## 1 15 01 03 0.1410 0.02820000 0.0020 0.1000
## 2 15 01 06 555.7200 8.29432836 0.0100 250.7200
## 3 15 01 10 0.1000 0.10000000 0.1000 0.1000
## 4 15 01 14 1.4000 1.40000000 1.4000 1.4000
## 5 15 01 18 2.3565 0.06368919 0.0005 0.3000
## 6 15 01 19 0.9324 0.05827500 0.0012 0.2500
## 7 15 01 23 7.3520 0.49013333 0.0400 2.7500
## 8 15 01 26 0.1100 0.11000000 0.1100 0.1100
## 9 15 01 38 0.0100 0.01000000 0.0100 0.0100
## 10 15 01 42 0.0005 0.00050000 0.0005 0.0005
## 11 15 01 43 1.3420 0.01765789 0.0010 0.3000
## 12 15 02 01 2192.0500 3.53556452 0.0200 200.0000
## 13 15 02 02 0.6500 0.32500000 0.0500 0.6000
## 14 15 02 03 221.8500 2.26377551 0.0500 10.0000
## 15 15 02 04 151.3200 2.25850746 0.1000 8.0000
## 16 15 02 05 142.7818 1.98308056 0.1550 12.0000
## 17 15 03 01 53.7358 0.21408685 0.0020 3.0000
## 18 15 03 02 21.3525 0.12132102 0.0100 0.7500
## 19 15 03 03 896.2087 3.14459193 0.0100 800.0000
## 20 15 03 04 15.5625 0.26831897 0.0625 1.0000
## 21 15 03 05 21.8975 0.27718354 0.0050 2.0000
## 22 15 04 01 47.4700 0.59337500 0.0800 10.0000
## 23 15 04 02 203.3550 5.21423077 0.0040 200.0000
## 24 15 04 03 14.9740 0.26270175 0.0100 2.0000
## 25 15 04 04 7.2089 0.04119371 0.0009 0.5000
## 26 15 04 05 26.3500 0.29277778 0.0500 1.5000
## 27 15 04 06 21.1300 0.31073529 0.0300 1.5000
## 28 15 04 07 11.5100 1.15100000 0.0300 2.0000
## 29 15 05 01 95.2200 2.44153846 0.2500 9.0000
## 30 15 05 02 0.7800 0.78000000 0.7800 0.7800
## 31 15 05 04 4.5000 1.50000000 0.2500 3.0000
## 32 15 05 07 110.7800 2.57627907 0.7400 8.9000
## 33 15 05 08 0.0180 0.00600000 0.0030 0.0100
## 34 15 05 09 4.1900 0.83800000 0.0300 2.5000
## 35 15 05 10 406.7800 8.30163265 0.2400 300.0000
## 36 15 05 11 0.3200 0.16000000 0.0700 0.2500
## 37 15 05 12 386.2100 7.15203704 0.2000 250.0000
## 38 15 05 13 0.2668 0.03335000 0.0068 0.0900
## 39 15 05 14 47.1300 2.94562500 1.0000 5.0000
## 40 15 05 15 0.1798 0.03596000 0.0100 0.0798
## 41 15 06 01 821.0990 2.95359353 0.0100 30.0000
## 42 15 06 02 9.7450 0.08940367 0.0050 0.3000
## 43 15 06 03 2.1300 0.12529412 0.0050 0.6000
## 44 15 06 04 615.2200 2.67486957 0.1000 14.0000
## 45 15 06 05 567.0750 2.43379828 0.0050 11.0000
## 46 15 06 06 41.2900 0.33032000 0.0100 1.0000
## 47 15 06 07 2.0050 0.25062500 0.0050 0.5000
## 48 15 06 08 0.8855 0.00962500 0.0030 0.0300
## 49 15 06 09 1.5525 0.11089286 0.0010 0.5000
## 50 15 06 10 1.4600 0.05214286 0.0030 0.6000
## 51 15 06 11 22.5900 0.19643478 0.0100 0.7000
## 52 15 06 12 1.7450 0.19388889 0.0100 0.9000
## 53 15 07 01 187.4725 1.73585648 0.0020 150.0000
## 54 15 07 03 0.1100 0.01571429 0.0100 0.0300
## 55 15 07 04 0.7475 0.02265152 0.0010 0.3000
## 56 15 07 05 8.8720 0.03373384 0.0025 0.5000
## 57 15 07 06 303.9950 7.99986842 0.0030 150.0000
## 58 15 07 07 0.8292 0.02438824 0.0012 0.1500
## 59 15 07 08 0.6920 0.01472340 0.0020 0.2000
## 60 15 07 09 18.4627 0.09231350 0.0010 0.4000
## 61 15 07 10 4.6355 0.03069868 0.0030 0.2500
## 62 15 07 11 1551.1160 33.00246809 0.0080 250.0000
## 63 15 07 12 4.0685 0.13561667 0.0030 0.7500
## 64 15 07 13 4.2151 0.10280732 0.0021 0.5000
## 65 15 07 15 24.0200 0.11950249 0.0030 1.2000
## 66 15 07 16 3404.3700 45.39160000 0.0050 300.0000
## 67 15 07 18 11.8027 0.04817429 0.0005 1.8000
## 68 15 07 19 250.1838 22.74398182 0.0028 150.0000
## 69 15 07 20 4.5500 0.35000000 0.2500 0.5000
## 70 15 07 21 814.5750 8.85407609 0.0250 200.0000
## 71 15 07 22 16.4816 0.07665860 0.0001 2.0000
## 72 15 07 23 0.9660 0.08781818 0.0060 0.6000
## 73 15 07 24 5.8250 0.05443925 0.0010 0.5000
## 74 15 07 25 5.7525 0.16919118 0.0575 0.5750
## 75 15 07 26 208.0896 3.65069474 0.0025 200.0000
## 76 15 07 27 0.1000 0.10000000 0.1000 0.1000
## 77 15 07 28 0.0100 0.01000000 0.0100 0.0100
## 78 15 07 29 10.7797 0.10888586 0.0024 0.5000
## 79 15 07 30 0.0600 0.06000000 0.0600 0.0600
## 80 15 07 31 803.7060 29.76688889 0.0060 200.0000
## 81 15 07 32 10.1675 0.27479730 0.0100 0.7500
## 82 15 08 01 0.0130 0.00650000 0.0030 0.0100
## 83 15 08 02 106.4100 0.43256098 0.0100 3.0000
## 84 15 08 03 7.0000 2.33333333 2.0000 3.0000
## 85 15 08 04 596.4615 4.35373358 0.0050 250.0000
## 86 15 08 05 0.1300 0.03250000 0.0100 0.0500
## 87 15 08 06 590.4500 19.68166667 0.0500 300.0000
## 88 15 08 07 25.3501 0.25867449 0.0001 1.0000
## 89 15 08 08 9.8990 0.19798000 0.0040 1.0000
## 90 15 08 09 25.9782 0.24053889 0.0002 1.0000
## 91 15 08 10 1.7253 0.14377500 0.0060 1.0000
## 92 15 08 11 53.1909 3.79935000 0.1000 16.0000
## 93 15 08 12 96.1400 2.40350000 0.0700 14.0000
## 94 15 09 01 669.8569 1.85555928 0.0020 300.0000
## 95 15 09 02 17.6510 0.21013095 0.0050 1.5000
## 96 15 09 03 16.5100 0.25400000 0.0300 1.0000
## 97 15 09 04 29.2400 0.23967213 0.0300 0.7500
## 98 15 09 05 3.5000 0.38888889 0.0500 1.5000
## 99 15 09 06 43.7140 0.11657067 0.0010 16.5000
## 100 15 10 01 12.2110 0.08599296 0.0010 1.0000
## 101 15 10 02 180.6425 2.44111486 0.0020 100.0000
## 102 15 10 03 21.7100 0.35590164 0.0100 2.0000
## 103 15 10 04 12.8036 0.18290857 0.0030 1.5000
## 104 15 10 05 29.9100 0.17189655 0.0300 2.0000
## 105 15 10 06 6.8993 0.13267885 0.0003 2.0000
## 106 15 10 07 205.0889 4.18548776 0.0010 200.0000
## 107 15 10 08 3.6050 0.07510417 0.0100 0.3000
## 108 15 10 09 3.1400 0.08486486 0.0300 0.4000
## 109 15 10 10 1.3430 0.05372000 0.0050 0.5000
## 110 15 10 11 9.6100 0.24641026 0.0300 0.7500
## 111 15 10 12 17.7926 0.11553636 0.0007 1.0000
## 112 15 10 13 168.4989 3.82952045 0.0100 100.0000
## 113 15 10 14 300.6250 7.33231707 0.0020 300.0000
## 114 15 10 15 21.5900 0.17552846 0.0020 2.0000
## 115 15 10 16 5.8464 0.07692632 0.0015 0.5000
## 116 15 10 17 0.5136 0.01141333 0.0011 0.0400
## 117 15 10 18 5.8716 0.04414737 0.0005 0.9900
## 118 15 10 19 85.8400 0.52987654 0.0200 4.0000
## 119 15 10 20 34.2200 0.20993865 0.0100 10.0000
## 120 15 10 21 3.3510 0.03682418 0.0030 0.2500
## 121 15 10 22 5.4501 0.25952857 0.0001 0.5000
## 122 15 10 24 569.9074 6.70479294 0.0050 200.0000
## 123 15 10 25 1.2010 0.07506250 0.0040 0.2500
## 124 15 10 26 1.0189 0.03773704 0.0050 0.2500
## 125 15 10 27 0.3800 0.07600000 0.0050 0.2000
## 126 15 10 29 858.0140 5.50008974 0.0020 200.0000
## 127 15 10 30 1.7280 0.01280000 0.0010 0.0800
## 128 15 10 31 14.8161 0.15931290 0.0001 1.5000
## 129 15 10 32 508.4650 1.30042199 0.0020 200.0000
## 130 15 10 33 0.6952 0.01616744 0.0012 0.2000lima_narmand <- mutate(lima_narmand, ubigeo=paste(lima_narmand$P001,lima_narmand$P002,lima_narmand$P003))
Tlima_narmand <- group_by(lima_narmand, ubigeo)
citricos <- data.frame(summarize(Tlima_narmand, SuperficieTotal= sum(P025),
Media=mean(P025),
Minimo=min(P025),
Maximo=max(P025)))
citricos## ubigeo SuperficieTotal Media Minimo Maximo
## 1 15 01 09 0.1000 0.100000000 0.1000 0.100
## 2 15 01 14 1.0000 1.000000000 1.0000 1.000
## 3 15 01 18 0.0250 0.012500000 0.0050 0.020
## 4 15 01 19 86.0300 17.206000000 0.0300 38.000
## 5 15 01 23 4.7800 1.593333333 0.0300 4.000
## 6 15 01 26 96.4600 24.115000000 0.0600 96.000
## 7 15 01 27 199.9500 7.998000000 7.9500 8.000
## 8 15 01 42 0.0145 0.007250000 0.0025 0.012
## 9 15 01 43 0.0800 0.007272727 0.0020 0.018
## 10 15 02 02 2.8000 0.700000000 0.2500 1.800
## 11 15 02 03 9.1500 0.915000000 0.1000 3.000
## 12 15 02 04 30.0500 3.756250000 0.2500 8.000
## 13 15 02 05 10.0000 10.000000000 10.0000 10.000
## 14 15 03 05 0.0500 0.050000000 0.0500 0.050
## 15 15 05 01 447.7800 6.219166667 0.0100 80.000
## 16 15 05 04 1.0000 1.000000000 1.0000 1.000
## 17 15 05 06 0.1300 0.130000000 0.1300 0.130
## 18 15 05 07 295.9400 18.496250000 0.5000 67.000
## 19 15 05 08 22.8600 2.286000000 0.0700 6.500
## 20 15 05 09 0.2600 0.130000000 0.0600 0.200
## 21 15 05 10 119.5000 6.638888889 0.4500 50.300
## 22 15 05 12 197.3600 7.048571429 0.7500 34.500
## 23 15 05 13 0.5400 0.270000000 0.0400 0.500
## 24 15 05 14 363.0800 24.205333333 1.2500 80.500
## 25 15 05 15 7.7801 1.556020000 0.0144 6.880
## 26 15 06 01 2868.4743 3.141811939 0.0002 84.650
## 27 15 06 04 1372.1275 2.127329457 0.1000 16.500
## 28 15 06 05 253.9000 3.576056338 0.0500 44.960
## 29 15 07 28 0.1000 0.100000000 0.1000 0.100
## 30 15 08 01 264.5236 3.005950000 0.0100 73.000
## 31 15 08 06 82.2500 20.562500000 10.0000 32.250
## 32 15 08 10 74.6075 2.869519231 0.0275 23.000
## 33 15 08 11 1701.0900 3.507402062 0.0400 77.000
## 34 15 08 12 69.8400 3.880000000 0.1000 22.000
## 35 15 10 06 0.1000 0.100000000 0.1000 0.100lima_pollos <- mutate(lima_pollos, ubigeo=paste(lima_pollos$P001,lima_pollos$P002,lima_pollos$P003))
Tlima_pollos <- group_by(lima_pollos, ubigeo)
pollos <- data.frame(summarize(Tlima_pollos, SuperficieTotal= sum(P067_03),
Media=mean(P067_03),
Minimo=min(P067_03),
Maximo=max(P067_03)))
pollos## ubigeo SuperficieTotal Media Minimo Maximo
## 1 15 01 02 60020 2.000667e+04 10 60000
## 2 15 01 03 705 3.204545e+01 2 300
## 3 15 01 06 15627 5.444948e+01 1 10000
## 4 15 01 08 10352 7.963077e+02 2 5600
## 5 15 01 09 287 1.435000e+01 1 80
## 6 15 01 10 4 4.000000e+00 4 4
## 7 15 01 14 348 1.740000e+02 98 250
## 8 15 01 18 14404 8.891358e+01 2 9000
## 9 15 01 19 382756 1.428194e+03 1 166298
## 10 15 01 23 805641 7.067026e+03 1 135000
## 11 15 01 24 242000 2.420000e+05 242000 242000
## 12 15 01 25 82426 4.121300e+03 2 50000
## 13 15 01 26 1601738 8.430200e+04 2 768004
## 14 15 01 27 523136 2.615680e+05 140000 383136
## 15 15 01 29 2256870 2.507633e+05 20 570000
## 16 15 01 33 256 1.347368e+01 1 50
## 17 15 01 35 189 1.718182e+01 7 50
## 18 15 01 42 51064 4.220165e+02 1 40000
## 19 15 01 43 604 1.232653e+01 2 40
## 20 15 02 01 1506 9.354037e+00 1 40
## 21 15 02 02 3683829 1.400695e+04 1 225681
## 22 15 02 03 1236 8.351351e+00 1 33
## 23 15 02 04 3135 1.215116e+01 1 100
## 24 15 02 05 108 7.714286e+00 3 20
## 25 15 03 01 292 6.212766e+00 1 40
## 26 15 03 02 69 3.631579e+00 1 8
## 27 15 03 03 6 2.000000e+00 1 3
## 28 15 03 04 113 4.708333e+00 2 11
## 29 15 03 05 201 8.739130e+00 2 30
## 30 15 04 01 454 6.394366e+00 1 50
## 31 15 04 02 173 1.153333e+01 1 40
## 32 15 04 03 97 8.818182e+00 2 40
## 33 15 04 04 267 6.357143e+00 1 25
## 34 15 04 05 32 2.909091e+00 1 5
## 35 15 04 06 69 3.833333e+00 2 12
## 36 15 04 07 1805815 1.962842e+04 1 800000
## 37 15 05 01 474704 1.707568e+03 1 273540
## 38 15 05 02 315595 3.586307e+03 1 270000
## 39 15 05 03 595 8.150685e+00 1 71
## 40 15 05 04 1702307 2.210788e+04 2 778458
## 41 15 05 05 2138965 7.922093e+04 4 900000
## 42 15 05 06 176777 2.762141e+03 2 145911
## 43 15 05 07 1256 9.812500e+00 1 50
## 44 15 05 08 3484 9.340483e+00 1 200
## 45 15 05 09 234813 1.701543e+03 1 207176
## 46 15 05 10 43497 1.430822e+02 1 6000
## 47 15 05 11 1133 1.231522e+01 1 500
## 48 15 05 12 1498616 2.938463e+04 2 467000
## 49 15 05 13 245060 4.084333e+03 1 244200
## 50 15 05 14 902 9.204082e+00 1 40
## 51 15 05 15 332663 8.990892e+03 1 332303
## 52 15 05 16 64 5.818182e+00 3 18
## 53 15 06 01 7259390 1.581566e+04 0 388516
## 54 15 06 02 58 4.461538e+00 1 10
## 55 15 06 03 178 7.416667e+00 1 32
## 56 15 06 04 6916882 4.044960e+04 2 999998
## 57 15 06 05 6946150 2.651202e+04 1 850000
## 58 15 06 06 1293 8.979167e+00 1 50
## 59 15 06 07 149 9.312500e+00 1 30
## 60 15 06 08 10 5.000000e+00 4 6
## 61 15 06 09 10 5.000000e+00 4 6
## 62 15 06 11 158 4.647059e+00 2 15
## 63 15 06 12 34 8.500000e+00 5 14
## 64 15 07 01 206 7.357143e+00 1 60
## 65 15 07 02 528 1.466667e+01 1 100
## 66 15 07 03 7 3.500000e+00 3 4
## 67 15 07 04 21 3.500000e+00 2 5
## 68 15 07 05 6 6.000000e+00 6 6
## 69 15 07 06 31 6.200000e+00 3 12
## 70 15 07 08 48 4.363636e+00 2 14
## 71 15 07 09 25 4.166667e+00 1 7
## 72 15 07 11 315 4.921875e+00 1 15
## 73 15 07 12 3 3.000000e+00 3 3
## 74 15 07 13 78 6.000000e+00 1 15
## 75 15 07 14 67 1.116667e+01 2 30
## 76 15 07 15 234 1.017391e+01 2 50
## 77 15 07 16 270 1.421053e+01 1 60
## 78 15 07 17 236 1.388235e+01 2 50
## 79 15 07 18 100 5.263158e+00 1 18
## 80 15 07 19 6 6.000000e+00 6 6
## 81 15 07 20 4 4.000000e+00 4 4
## 82 15 07 21 4 4.000000e+00 4 4
## 83 15 07 22 120 5.714286e+00 1 16
## 84 15 07 23 177 5.900000e+00 1 20
## 85 15 07 24 11 3.666667e+00 2 5
## 86 15 07 25 6 6.000000e+00 6 6
## 87 15 07 26 1 1.000000e+00 1 1
## 88 15 07 27 96 1.200000e+01 2 40
## 89 15 07 28 371 9.512821e+00 1 24
## 90 15 07 29 20 5.000000e+00 2 10
## 91 15 07 30 13 6.500000e+00 1 12
## 92 15 07 31 72 8.000000e+00 1 20
## 93 15 07 32 45 9.000000e+00 2 13
## 94 15 08 01 6856587 7.881134e+04 1 475000
## 95 15 08 02 667 7.847059e+00 1 40
## 96 15 08 03 2039 5.097500e+02 6 2000
## 97 15 08 04 120 5.714286e+00 1 21
## 98 15 08 05 430651 2.266584e+04 1 429000
## 99 15 08 06 707903 1.361352e+04 1 207000
## 100 15 08 07 293 6.369565e+00 1 60
## 101 15 08 08 130 5.200000e+00 1 16
## 102 15 08 09 100 5.000000e+00 1 20
## 103 15 08 10 3888 3.240000e+01 1 2500
## 104 15 08 11 2465459 1.867772e+04 1 620000
## 105 15 08 12 5330535 2.422970e+04 1 407176
## 106 15 09 01 513 5.288660e+00 1 33
## 107 15 09 02 5 5.000000e+00 5 5
## 108 15 09 03 115 5.750000e+00 1 15
## 109 15 09 04 10 3.333333e+00 2 5
## 110 15 09 05 55 4.230769e+00 1 7
## 111 15 10 01 96 4.800000e+00 1 24
## 112 15 10 02 121 6.368421e+00 2 15
## 113 15 10 03 30 3.750000e+00 1 10
## 114 15 10 04 60 4.285714e+00 1 10
## 115 15 10 05 25 3.125000e+00 1 6
## 116 15 10 06 111 4.826087e+00 1 15
## 117 15 10 07 30 5.000000e+00 2 8
## 118 15 10 08 208 5.073171e+00 1 30
## 119 15 10 09 78 7.800000e+00 2 20
## 120 15 10 10 116 8.285714e+00 1 27
## 121 15 10 11 22 2.444444e+00 1 4
## 122 15 10 12 10 2.000000e+00 1 4
## 123 15 10 13 29 4.142857e+00 1 10
## 124 15 10 15 14 4.666667e+00 1 12
## 125 15 10 16 73 4.055556e+00 2 7
## 126 15 10 17 25 5.000000e+00 3 10
## 127 15 10 18 7 3.500000e+00 3 4
## 128 15 10 19 4 4.000000e+00 4 4
## 129 15 10 22 110 7.857143e+00 2 18
## 130 15 10 23 3 3.000000e+00 3 3
## 131 15 10 24 60 6.000000e+00 1 15
## 132 15 10 25 32 4.571429e+00 2 10
## 133 15 10 26 20 3.333333e+00 1 6
## 134 15 10 27 126 6.300000e+00 1 20
## 135 15 10 28 4 2.000000e+00 1 3
## 136 15 10 29 6 6.000000e+00 6 6
## 137 15 10 30 12 6.000000e+00 4 8
## 138 15 10 31 158 8.777778e+00 1 20
## 139 15 10 32 184 4.717949e+00 1 20
## 140 15 10 33 4 4.000000e+00 4 4ancash_vacas <- mutate(ancash_vacas, ubigeo=paste(ancash_vacas$P001,ancash_vacas$P002,ancash_vacas$P003))
Tancash_vacas <- group_by(ancash_vacas, ubigeo)
vacas <- data.frame(summarize(Tancash_vacas, SuperficieTotal= sum(P067_03),
Media=mean(P067_03),
Minimo=min(P067_03),
Maximo=max(P067_03)))
vacas## ubigeo SuperficieTotal Media Minimo Maximo
## 1 02 01 01 1376 2.191083 1 24
## 2 02 01 02 639 3.227273 1 15
## 3 02 01 03 343 4.182927 1 20
## 4 02 01 04 343 2.766129 1 14
## 5 02 01 05 1897 2.383166 1 300
## 6 02 01 06 446 2.218905 1 9
## 7 02 01 07 422 3.149254 1 20
## 8 02 01 08 296 2.368000 1 10
## 9 02 01 09 465 2.924528 1 25
## 10 02 01 10 478 2.265403 1 8
## 11 02 01 11 1093 2.239754 1 10
## 12 02 01 12 378 1.928571 1 20
## 13 02 02 01 451 2.854430 1 50
## 14 02 02 02 440 2.391304 1 17
## 15 02 02 03 63 1.431818 1 3
## 16 02 02 04 458 1.892562 1 15
## 17 02 02 05 231 2.431579 1 9
## 18 02 03 01 208 1.434483 1 5
## 19 02 03 02 261 2.211864 1 30
## 20 02 03 03 86 1.592593 1 8
## 21 02 03 04 249 1.976190 1 23
## 22 02 03 05 371 1.460630 1 7
## 23 02 03 06 221 2.600000 1 25
## 24 02 04 01 940 2.338308 1 14
## 25 02 04 02 511 1.557927 1 11
## 26 02 05 01 1134 4.447059 1 25
## 27 02 05 02 20 3.333333 1 8
## 28 02 05 03 249 2.964286 1 13
## 29 02 05 04 1110 5.967742 1 30
## 30 02 05 05 635 3.713450 1 20
## 31 02 05 06 214 2.853333 1 9
## 32 02 05 07 8 2.666667 1 5
## 33 02 05 08 1971 6.198113 1 45
## 34 02 05 09 1440 5.829960 1 30
## 35 02 05 10 537 4.401639 1 21
## 36 02 05 11 93 3.720000 1 13
## 37 02 05 12 253 4.080645 1 15
## 38 02 05 13 982 6.819444 1 135
## 39 02 05 14 229 3.469697 1 14
## 40 02 05 15 343 4.083333 1 12
## 41 02 06 01 1091 2.090038 1 180
## 42 02 06 02 141 2.104478 1 18
## 43 02 06 03 166 1.551402 1 9
## 44 02 06 04 164 1.726316 1 6
## 45 02 06 05 13 1.444444 1 3
## 46 02 06 06 539 1.967153 1 20
## 47 02 06 07 217 1.682171 1 15
## 48 02 06 08 101 1.262500 1 5
## 49 02 06 09 246 1.473054 1 11
## 50 02 06 10 89 1.648148 1 10
## 51 02 06 11 448 2.650888 1 10
## 52 02 07 01 1238 1.691257 1 82
## 53 02 07 02 408 1.805310 1 12
## 54 02 07 03 728 1.453094 1 10
## 55 02 08 01 349 2.311258 1 15
## 56 02 08 02 78 1.560000 1 10
## 57 02 08 03 110 2.619048 1 8
## 58 02 08 04 367 2.184524 1 10
## 59 02 09 01 610 2.811060 1 22
## 60 02 09 02 304 3.494253 1 30
## 61 02 09 03 180 3.103448 1 15
## 62 02 09 04 1994 4.691765 1 40
## 63 02 09 05 161 2.639344 1 15
## 64 02 09 06 203 2.569620 1 8
## 65 02 09 07 125 2.272727 1 9
## 66 02 10 01 1202 2.119929 1 30
## 67 02 10 02 175 1.861702 1 30
## 68 02 10 03 332 1.765957 1 8
## 69 02 10 04 583 2.259690 1 20
## 70 02 10 05 245 1.788321 1 7
## 71 02 10 06 212 2.617284 1 14
## 72 02 10 07 435 1.883117 1 10
## 73 02 10 08 368 1.727700 1 10
## 74 02 10 09 64 1.523810 1 6
## 75 02 10 10 713 5.527132 1 384
## 76 02 10 11 162 1.486239 1 6
## 77 02 10 12 20 2.000000 1 7
## 78 02 10 13 143 2.508772 1 9
## 79 02 10 14 1302 3.321429 1 100
## 80 02 10 15 257 1.875912 1 8
## 81 02 10 16 161 2.824561 1 18
## 82 02 11 01 268 3.228916 1 40
## 83 02 11 02 394 3.030769 1 13
## 84 02 11 03 45 2.500000 1 8
## 85 02 11 04 262 2.847826 1 9
## 86 02 11 05 634 4.116883 1 23
## 87 02 12 01 370 1.737089 1 10
## 88 02 12 02 92 2.875000 1 15
## 89 02 12 03 763 2.577703 1 15
## 90 02 12 04 374 2.066298 1 9
## 91 02 12 05 466 2.505376 1 14
## 92 02 12 06 1854 2.449141 1 20
## 93 02 12 07 677 1.655257 1 21
## 94 02 12 08 488 1.621262 1 15
## 95 02 12 09 471 2.012821 1 9
## 96 02 12 10 397 3.675926 1 15
## 97 02 13 01 596 1.712644 1 9
## 98 02 13 02 986 1.635158 1 8
## 99 02 13 03 450 2.393617 1 12
## 100 02 13 04 465 1.937500 1 10
## 101 02 13 05 398 1.800905 1 5
## 102 02 13 06 874 1.677543 1 10
## 103 02 13 07 376 1.634783 1 17
## 104 02 13 08 284 1.786164 1 10
## 105 02 14 01 1072 6.056497 1 40
## 106 02 14 02 289 6.148936 1 20
## 107 02 14 03 187 3.740000 1 7
## 108 02 14 04 137 4.892857 1 30
## 109 02 14 05 326 10.516129 1 40
## 110 02 14 06 900 4.545455 1 16
## 111 02 14 07 50 2.631579 1 6
## 112 02 14 08 470 5.595238 1 146
## 113 02 14 09 330 6.875000 1 40
## 114 02 14 10 267 4.377049 1 12
## 115 02 15 01 1104 3.172414 1 30
## 116 02 15 02 608 3.070707 1 20
## 117 02 15 03 2070 4.394904 0 35
## 118 02 15 04 70 1.320755 1 4
## 119 02 15 05 703 4.750000 1 25
## 120 02 15 06 276 3.325301 1 20
## 121 02 15 07 164 1.782609 1 8
## 122 02 15 08 551 2.135659 1 20
## 123 02 15 09 708 3.323944 1 22
## 124 02 15 10 204 1.871560 1 10
## 125 02 15 11 798 2.162602 1 8
## 126 02 16 01 2579 2.115669 1 30
## 127 02 16 02 525 1.557864 1 8
## 128 02 16 03 1065 1.871705 1 12
## 129 02 16 04 575 1.903974 1 9
## 130 02 17 01 691 2.531136 1 15
## 131 02 17 02 2126 6.216374 1 100
## 132 02 17 03 987 10.846154 1 50
## 133 02 17 04 320 3.298969 1 30
## 134 02 17 05 226 3.323529 1 15
## 135 02 17 06 741 4.602484 1 30
## 136 02 17 07 1034 6.228916 1 30
## 137 02 17 08 896 7.225806 1 80
## 138 02 17 09 377 3.927083 1 13
## 139 02 17 10 571 4.198529 1 25
## 140 02 18 01 2218 3.340361 1 35
## 141 02 18 02 1383 3.019651 1 20
## 142 02 18 04 1324 2.718686 1 21
## 143 02 18 05 245 2.578947 1 9
## 144 02 18 06 92 2.705882 1 15
## 145 02 18 07 56 1.750000 1 5
## 146 02 18 08 521 5.920455 1 20
## 147 02 18 09 509 3.416107 1 25
## 148 02 19 01 317 2.264286 1 15
## 149 02 19 02 453 3.040268 1 12
## 150 02 19 03 239 2.685393 1 14
## 151 02 19 04 307 1.774566 1 10
## 152 02 19 05 706 5.515625 1 40
## 153 02 19 06 1548 2.632653 1 30
## 154 02 19 07 761 2.439103 1 10
## 155 02 19 08 867 2.675926 1 21
## 156 02 19 09 1783 2.486750 1 21
## 157 02 19 10 406 2.103627 1 12
## 158 02 20 01 952 1.621806 1 25
## 159 02 20 02 253 1.781690 1 9
## 160 02 20 03 717 1.707143 1 74
## 161 02 20 04 60 1.428571 1 5
## 162 02 20 05 734 1.699074 1 10
## 163 02 20 06 235 1.477987 1 10
## 164 02 20 07 480 2.105263 1 10
## 165 02 20 08 796 1.796840 1 64ancash_papas <- mutate(ancash_papas, ubigeo=paste(ancash_papas$P001,ancash_papas$P002,ancash_papas$P003))
Tancash_papas <- group_by(ancash_papas, ubigeo)
papas <- data.frame(summarize(Tancash_papas, SuperficieTotal= sum(P025),
Media=mean(P025),
Minimo=min(P025),
Maximo=max(P025)))
papas## ubigeo SuperficieTotal Media Minimo Maximo
## 1 02 01 01 1014.4250 0.46490605 0.0005 300.000
## 2 02 01 02 54.6500 0.27325000 0.1000 4.000
## 3 02 01 03 26.3800 0.82437500 0.0100 20.000
## 4 02 01 04 63.7600 0.26238683 0.0600 1.000
## 5 02 01 05 692.4056 0.16466245 0.0007 50.000
## 6 02 01 06 93.1768 0.11915192 0.0005 2.000
## 7 02 01 07 62.4700 0.45598540 0.0400 2.000
## 8 02 01 08 1341.5968 2.03272242 0.0030 400.000
## 9 02 01 09 29.4600 0.26781818 0.1000 1.000
## 10 02 01 10 128.4200 0.44590278 0.0100 3.000
## 11 02 01 11 509.6745 0.84383195 0.0100 248.000
## 12 02 01 12 125.3625 0.12399852 0.0010 2.000
## 13 02 02 01 78.9225 0.36708140 0.0025 8.000
## 14 02 02 02 45.8814 0.30792886 0.0500 1.500
## 15 02 02 03 12.2500 0.36029412 0.2500 0.750
## 16 02 02 04 171.8500 0.35071429 0.0200 3.000
## 17 02 02 05 23.9750 0.41336207 0.1000 1.000
## 18 02 03 01 70.0625 0.46093750 0.0625 4.000
## 19 02 03 02 24.6250 0.29668675 0.0625 1.000
## 20 02 03 03 82.4750 0.35095745 0.0625 2.000
## 21 02 03 04 73.5950 0.31997826 0.0325 3.000
## 22 02 03 05 494.6300 0.46928843 0.0625 3.500
## 23 02 03 06 211.2225 0.29336458 0.0600 2.000
## 24 02 04 01 696.3537 0.67607155 0.0004 200.000
## 25 02 04 02 239.2977 0.22553977 0.0080 3.000
## 26 02 05 01 34.3699 0.19418023 0.0050 2.000
## 27 02 05 03 4.4000 0.15714286 0.0050 0.500
## 28 02 05 04 19.1512 0.10698994 0.0100 2.000
## 29 02 05 05 27.0500 0.28473684 0.0100 3.000
## 30 02 05 06 9.8300 0.42739130 0.0300 0.900
## 31 02 05 08 243.4700 0.62912145 0.0500 7.500
## 32 02 05 09 2106.1200 8.00806084 0.0030 200.000
## 33 02 05 10 20.0520 0.21331915 0.0120 1.000
## 34 02 05 11 301.0102 5.37518214 0.0050 300.000
## 35 02 05 12 1367.8076 59.46989565 0.0200 400.000
## 36 02 05 13 508.5340 5.13670707 0.0020 300.000
## 37 02 05 14 12.8960 0.32240000 0.0100 2.000
## 38 02 05 15 27.3900 0.37013514 0.0100 3.000
## 39 02 06 01 1616.8324 0.96068473 0.0001 250.000
## 40 02 06 02 249.3480 1.14379817 0.0020 200.000
## 41 02 06 03 38.7695 0.07832222 0.0010 1.250
## 42 02 06 04 102.2208 0.34651119 0.0020 4.000
## 43 02 06 05 3.0381 0.04161781 0.0020 0.500
## 44 02 06 06 397.9488 0.34246885 0.0004 200.000
## 45 02 06 07 39.0337 0.09567083 0.0020 1.000
## 46 02 06 08 86.9510 0.12601594 0.0015 4.500
## 47 02 06 09 681.4634 0.53743170 0.0003 300.000
## 48 02 06 10 9.2961 0.08687944 0.0010 0.500
## 49 02 06 11 22.1971 0.08221148 0.0020 2.000
## 50 02 07 01 674.3041 0.25264297 0.0100 5.000
## 51 02 07 02 234.7875 0.42304054 0.0250 2.500
## 52 02 07 03 843.8725 0.45937534 0.0100 150.000
## 53 02 08 04 4.0000 0.30769231 0.0500 1.000
## 54 02 09 01 69.3750 0.29149160 0.0625 6.000
## 55 02 09 02 23.8750 0.39139344 0.1250 1.000
## 56 02 09 03 7.6875 0.26508621 0.1250 1.000
## 57 02 09 04 329.3300 1.00712538 0.0100 200.000
## 58 02 09 05 16.2750 0.49318182 0.1250 1.000
## 59 02 09 06 13.0500 0.21048387 0.0600 0.750
## 60 02 09 07 8.8750 0.22756410 0.1250 0.750
## 61 02 10 01 259.6480 0.22211121 0.0030 2.000
## 62 02 10 02 590.1250 1.50927110 0.0625 500.000
## 63 02 10 03 197.1450 0.20096330 0.0250 2.750
## 64 02 10 04 552.2125 0.24652344 0.0050 4.000
## 65 02 10 05 38.0425 0.08806134 0.0050 1.500
## 66 02 10 06 38.9375 0.26669521 0.0625 7.000
## 67 02 10 07 352.6875 0.32565789 0.0625 2.000
## 68 02 10 08 259.0225 0.27467922 0.0625 2.000
## 69 02 10 09 59.0975 0.22385417 0.0200 1.500
## 70 02 10 10 45.3125 0.24361559 0.0625 1.750
## 71 02 10 11 239.0250 0.29692547 0.0375 3.000
## 72 02 10 12 4.4027 0.23172105 0.0625 0.625
## 73 02 10 13 103.9450 1.02915842 0.0500 50.000
## 74 02 10 14 1028.3265 0.26192728 0.0020 10.000
## 75 02 10 15 255.2250 0.29201945 0.0500 3.750
## 76 02 10 16 50.2700 0.28562500 0.0625 2.000
## 77 02 11 02 54.2575 0.49777523 0.0010 30.000
## 78 02 11 04 24.6650 0.34256944 0.0250 1.000
## 79 02 11 05 24.3300 0.22321101 0.0500 1.500
## 80 02 12 01 105.3939 0.20911488 0.0020 3.000
## 81 02 12 02 3.0413 0.20275333 0.0100 2.000
## 82 02 12 03 110.3500 0.89715447 0.1200 70.000
## 83 02 12 04 266.9913 2.36275487 0.0030 250.000
## 84 02 12 05 14.5611 0.18431772 0.0040 1.500
## 85 02 12 06 673.9832 0.65626407 0.0150 5.000
## 86 02 12 07 805.5022 0.50948906 0.0003 500.000
## 87 02 12 08 1567.5640 4.00911509 0.0020 300.000
## 88 02 12 09 15.0375 0.13795872 0.0500 0.500
## 89 02 12 10 49.8200 0.34597222 0.0300 2.000
## 90 02 13 01 136.2925 0.20464339 0.0150 1.500
## 91 02 13 02 372.9729 0.26358509 0.0020 3.000
## 92 02 13 03 76.0425 0.39400259 0.0625 1.000
## 93 02 13 04 144.0625 0.31942905 0.0625 2.000
## 94 02 13 05 72.9500 0.28385214 0.0625 2.000
## 95 02 13 06 447.1000 0.31419536 0.0150 5.000
## 96 02 13 07 191.9250 0.26582410 0.0125 3.000
## 97 02 13 08 27.7700 0.13099057 0.0100 0.500
## 98 02 14 01 18.9900 0.70333333 0.0400 4.000
## 99 02 14 02 4.2500 0.32692308 0.2500 1.000
## 100 02 14 03 8.2500 0.37500000 0.1000 1.500
## 101 02 14 04 0.0700 0.01400000 0.0030 0.032
## 102 02 14 05 4.0000 4.00000000 4.0000 4.000
## 103 02 14 06 464.7900 3.29638298 0.0050 200.000
## 104 02 14 07 1.7600 0.35200000 0.0300 1.000
## 105 02 14 08 8.9010 0.52358824 0.0010 3.500
## 106 02 14 09 3.2500 0.54166667 0.2500 1.000
## 107 02 14 10 1012.0300 24.68365854 0.0250 250.000
## 108 02 15 01 96.0605 0.37819094 0.0030 10.000
## 109 02 15 02 16.5200 0.24294118 0.0500 1.000
## 110 02 15 03 237.9500 0.25313830 0.0100 4.000
## 111 02 15 04 11.6705 0.25934444 0.0030 0.500
## 112 02 15 05 26.8500 0.31964286 0.0500 1.000
## 113 02 15 06 34.3728 0.22465882 0.0008 2.000
## 114 02 15 07 15.4175 0.22344203 0.0625 1.000
## 115 02 15 08 27.5225 0.21009542 0.0175 1.000
## 116 02 15 09 112.7900 0.20432971 0.0100 2.000
## 117 02 15 10 7.6675 0.28398148 0.0625 1.000
## 118 02 15 11 78.0225 0.23571752 0.0300 2.130
## 119 02 16 01 1359.2914 0.41760104 0.0010 300.000
## 120 02 16 02 319.3268 0.46822111 0.0010 100.000
## 121 02 16 03 248.0484 0.24129222 0.0010 2.000
## 122 02 16 04 191.6453 0.26506957 0.0001 2.000
## 123 02 17 01 210.8625 0.27781621 0.0015 4.000
## 124 02 17 02 19.8752 0.14095887 0.0015 3.000
## 125 02 17 03 14.9250 0.29264706 0.0150 1.000
## 126 02 17 04 29.7300 0.34172414 0.1300 1.000
## 127 02 17 05 7.5755 0.22956061 0.0261 2.500
## 128 02 17 06 27.1526 0.26108269 0.0006 2.000
## 129 02 17 07 73.6620 0.46621519 0.0020 3.000
## 130 02 17 08 2.0000 0.33333333 0.1200 0.750
## 131 02 17 09 58.3750 0.65589888 0.0625 3.000
## 132 02 17 10 32.0116 0.29101455 0.0030 2.000
## 133 02 18 01 10.3300 1.03300000 0.0300 3.000
## 134 02 18 02 128.7900 0.39506135 0.0400 2.000
## 135 02 18 04 72.6450 0.27517045 0.0300 2.000
## 136 02 18 05 0.5000 0.50000000 0.5000 0.500
## 137 02 18 06 0.5000 0.50000000 0.5000 0.500
## 138 02 18 07 2.0000 2.00000000 2.0000 2.000
## 139 02 18 09 2.0000 0.40000000 0.2500 0.500
## 140 02 19 01 230.9190 0.47030346 0.0025 4.500
## 141 02 19 02 87.2663 0.40030413 0.0001 10.000
## 142 02 19 03 209.1500 1.91880734 0.2500 9.000
## 143 02 19 04 218.6493 0.23235845 0.0010 4.000
## 144 02 19 05 70.6117 0.47710608 0.0100 2.500
## 145 02 19 06 338.8700 0.30337511 0.0100 2.900
## 146 02 19 07 105.1200 0.35040000 0.0200 2.500
## 147 02 19 08 261.4601 0.29115824 0.0050 2.000
## 148 02 19 09 610.6100 0.39987557 0.0100 6.000
## 149 02 19 10 97.6960 0.25050256 0.0020 3.000
## 150 02 20 01 1266.3262 0.57875969 0.0015 250.000
## 151 02 20 02 542.7180 1.56854913 0.0020 200.000
## 152 02 20 03 192.0030 0.12886107 0.0016 4.000
## 153 02 20 04 108.0765 2.57325000 0.0050 100.000
## 154 02 20 05 357.2400 0.25737752 0.0100 2.000
## 155 02 20 06 63.6709 0.11209665 0.0050 1.000
## 156 02 20 07 66.7058 0.12586000 0.0058 2.000
## 157 02 20 08 795.7435 0.57620818 0.0040 200.000ancash_narmand <- mutate(ancash_narmand, ubigeo=paste(ancash_narmand$P001,ancash_narmand$P002,ancash_narmand$P003))
Tancash_narmand <- group_by(ancash_narmand, ubigeo)
citricos <- data.frame(summarize(Tancash_narmand, SuperficieTotal= sum(P025),
Media=mean(P025),
Minimo=min(P025),
Maximo=max(P025)))
citricos## ubigeo SuperficieTotal Media Minimo Maximo
## 1 02 06 04 0.1200 0.060000 0.0400 0.0800
## 2 02 06 07 0.0120 0.012000 0.0120 0.0120
## 3 02 07 01 0.1375 0.068750 0.0125 0.1250
## 4 02 10 09 0.4375 0.437500 0.4375 0.4375
## 5 02 10 10 0.5000 0.500000 0.5000 0.5000
## 6 02 12 10 0.0300 0.015000 0.0100 0.0200
## 7 02 15 03 0.0100 0.010000 0.0100 0.0100
## 8 02 15 08 0.0100 0.010000 0.0100 0.0100
## 9 02 15 10 0.5000 0.500000 0.5000 0.5000
## 10 02 17 05 0.1000 0.100000 0.1000 0.1000
## 11 02 17 06 0.6000 0.600000 0.6000 0.6000
## 12 02 18 01 0.2500 0.250000 0.2500 0.2500
## 13 02 18 06 1.0000 1.000000 1.0000 1.0000
## 14 02 18 09 14.9000 2.128571 0.5000 4.9000ancash_pollos <- mutate(ancash_pollos, ubigeo=paste(ancash_pollos$P001,ancash_pollos$P002,ancash_pollos$P003))
Tancash_pollos <- group_by(ancash_pollos, ubigeo)
pollos <- data.frame(summarize(Tancash_pollos, SuperficieTotal= sum(P067_03),
Media=mean(P067_03),
Minimo=min(P067_03),
Maximo=max(P067_03)))
pollos## ubigeo SuperficieTotal Media Minimo Maximo
## 1 02 01 01 1916 4.950904 1 30
## 2 02 01 04 113 6.277778 1 17
## 3 02 01 05 3968 5.126615 1 50
## 4 02 01 06 697 4.840278 1 20
## 5 02 01 07 51 5.100000 2 8
## 6 02 01 08 90 3.600000 1 10
## 7 02 01 10 327 6.055556 1 20
## 8 02 01 11 47 3.916667 1 12
## 9 02 01 12 754 6.732143 1 22
## 10 02 02 01 316 5.180328 1 12
## 11 02 02 02 70 3.684211 1 7
## 12 02 02 04 376 5.150685 1 30
## 13 02 02 05 10 3.333333 3 4
## 14 02 03 01 361 5.730159 1 15
## 15 02 03 02 135 4.090909 1 10
## 16 02 03 03 10 5.000000 4 6
## 17 02 03 04 61 7.625000 4 12
## 18 02 03 05 75 3.260870 1 8
## 19 02 03 06 2 2.000000 2 2
## 20 02 04 01 474 3.853659 1 31
## 21 02 04 02 172 4.300000 1 18
## 22 02 05 01 440 4.680851 1 25
## 23 02 05 02 25 12.500000 5 20
## 24 02 05 03 70 7.777778 2 12
## 25 02 05 04 34 4.250000 2 8
## 26 02 05 05 69 5.307692 1 15
## 27 02 05 06 29 9.666667 4 18
## 28 02 05 07 42 6.000000 2 10
## 29 02 05 08 52 4.727273 2 8
## 30 02 05 09 250 5.434783 1 20
## 31 02 05 10 228 4.750000 1 30
## 32 02 05 11 72 4.235294 1 14
## 33 02 05 12 109 4.192308 1 10
## 34 02 05 13 46 4.181818 2 9
## 35 02 05 14 101 5.050000 2 10
## 36 02 05 15 86 5.058824 2 14
## 37 02 06 01 1407 5.742857 1 28
## 38 02 06 02 856 6.635659 1 20
## 39 02 06 03 1036 6.556962 1 30
## 40 02 06 04 1046 5.909605 1 35
## 41 02 06 05 312 4.952381 1 15
## 42 02 06 06 1305 5.904977 1 30
## 43 02 06 07 552 4.312500 1 35
## 44 02 06 08 5 5.000000 5 5
## 45 02 06 09 58 4.461538 1 10
## 46 02 06 10 489 6.698630 1 30
## 47 02 06 11 36 5.142857 1 12
## 48 02 07 01 180 3.333333 1 12
## 49 02 07 02 11 5.500000 3 8
## 50 02 07 03 26 2.363636 1 6
## 51 02 08 01 551590 4881.327434 1 550200
## 52 02 08 02 87 5.800000 1 20
## 53 02 08 03 649 12.980000 2 40
## 54 02 08 04 1881 10.627119 1 90
## 55 02 09 01 827 6.563492 1 30
## 56 02 09 03 101 5.315789 2 10
## 57 02 09 04 125 4.464286 1 12
## 58 02 09 05 38 9.500000 3 20
## 59 02 09 07 119 7.933333 2 20
## 60 02 10 01 14 2.000000 1 4
## 61 02 10 02 2 2.000000 2 2
## 62 02 10 03 25 5.000000 1 11
## 63 02 10 04 635 4.123377 1 15
## 64 02 10 05 28 3.111111 1 6
## 65 02 10 07 24 4.800000 2 12
## 66 02 10 08 222 7.928571 1 27
## 67 02 10 09 24 4.000000 1 6
## 68 02 10 10 11 2.750000 1 5
## 69 02 10 11 11 5.500000 1 10
## 70 02 10 12 12 6.000000 2 10
## 71 02 10 14 1049 4.229839 1 80
## 72 02 10 16 2 2.000000 2 2
## 73 02 11 01 2246 11.118812 1 50
## 74 02 11 02 982 7.732283 1 60
## 75 02 11 03 262 7.081081 1 49
## 76 02 11 04 499 7.796875 1 30
## 77 02 11 05 165 4.125000 1 10
## 78 02 12 01 238 7.437500 1 45
## 79 02 12 02 99 16.500000 2 50
## 80 02 12 03 830 5.646259 1 22
## 81 02 12 04 553 6.076923 1 22
## 82 02 12 05 32 5.333333 2 8
## 83 02 12 06 80 3.333333 1 10
## 84 02 12 07 276 4.312500 1 18
## 85 02 12 08 221 4.911111 1 20
## 86 02 12 09 74 4.625000 3 7
## 87 02 12 10 290 8.529412 2 26
## 88 02 13 01 252 4.344828 1 18
## 89 02 13 02 229 3.271429 0 25
## 90 02 13 03 69 3.631579 1 8
## 91 02 13 04 16 8.000000 4 12
## 92 02 13 05 47 5.875000 1 12
## 93 02 13 06 249 5.081633 1 20
## 94 02 13 07 4 4.000000 4 4
## 95 02 13 08 20 10.000000 5 15
## 96 02 14 01 8 4.000000 3 5
## 97 02 14 02 10 10.000000 10 10
## 98 02 14 03 61 4.692308 1 12
## 99 02 14 04 92 3.833333 1 18
## 100 02 14 05 4044 674.000000 2 4000
## 101 02 14 06 126 6.631579 1 30
## 102 02 14 07 51 5.666667 2 9
## 103 02 14 08 2 2.000000 2 2
## 104 02 14 09 73 9.125000 4 12
## 105 02 15 01 526 6.493827 1 30
## 106 02 15 02 625 9.920635 1 60
## 107 02 15 03 330 4.074074 1 15
## 108 02 15 04 68 5.230769 1 18
## 109 02 15 05 293 5.232143 1 25
## 110 02 15 06 38 2.714286 1 6
## 111 02 15 07 232 5.155556 2 24
## 112 02 15 08 620 5.438596 1 28
## 113 02 15 09 304 4.000000 1 10
## 114 02 15 10 357 8.302326 1 30
## 115 02 15 11 40 3.636364 1 9
## 116 02 16 01 245 3.602941 0 10
## 117 02 16 02 201 4.102041 1 20
## 118 02 16 03 1029 5.813559 1 20
## 119 02 16 04 246 3.153846 0 25
## 120 02 17 01 30 5.000000 0 10
## 121 02 17 02 16 2.285714 1 4
## 122 02 17 03 4 2.000000 0 4
## 123 02 17 05 5 2.500000 2 3
## 124 02 17 06 66 4.125000 1 12
## 125 02 17 07 29 5.800000 2 13
## 126 02 17 08 1129 7.680272 1 30
## 127 02 17 09 13 2.600000 2 3
## 128 02 17 10 249 4.698113 0 11
## 129 02 18 01 9719 12.993316 1 200
## 130 02 18 02 1118 8.534351 1 52
## 131 02 18 04 383 6.719298 1 24
## 132 02 18 05 71244 800.494382 2 65000
## 133 02 18 06 813900 8567.368421 2 800000
## 134 02 18 07 311 15.550000 2 150
## 135 02 18 08 1795 26.014493 1 700
## 136 02 18 09 2121 14.627586 1 100
## 137 02 19 01 152 12.666667 4 60
## 138 02 19 02 225 6.818182 2 15
## 139 02 19 04 50 2.631579 0 6
## 140 02 19 05 254 7.470588 2 22
## 141 02 19 06 390 5.270270 1 20
## 142 02 19 07 300 4.109589 1 14
## 143 02 19 08 89 6.357143 1 12
## 144 02 19 09 491 5.168421 1 20
## 145 02 19 10 62 5.166667 2 10
## 146 02 20 01 2695 7.656250 1 500
## 147 02 20 02 104 2.971429 1 10
## 148 02 20 03 1467 5.354015 1 30
## 149 02 20 04 124 6.200000 1 23
## 150 02 20 05 557 3.978571 1 30
## 151 02 20 06 632 7.022222 1 30
## 152 02 20 07 542 5.827957 2 24
## 153 02 20 08 278 2.438596 1 10ica_vacas <- mutate(ica_vacas, ubigeo=paste(ica_vacas$P001,ica_vacas$P002,ica_vacas$P003))
Tica_vacas <- group_by(ica_vacas, ubigeo)
vacas <- data.frame(summarize(Tica_vacas, SuperficieTotal= sum(P067_03),
Media=mean(P067_03),
Minimo=min(P067_03),
Maximo=max(P067_03)))
vacas## ubigeo SuperficieTotal Media Minimo Maximo
## 1 11 01 01 303 3.189474 1 17
## 2 11 01 02 134 3.941176 1 30
## 3 11 01 03 289 5.780000 1 60
## 4 11 01 04 166 2.441176 1 12
## 5 11 01 05 267 8.612903 1 150
## 6 11 01 06 20 1.818182 1 5
## 7 11 01 07 221 7.129032 1 167
## 8 11 01 08 167 7.260870 1 68
## 9 11 01 09 89 3.296296 1 12
## 10 11 01 10 206 10.300000 1 150
## 11 11 01 11 607 3.096939 1 85
## 12 11 01 12 150 5.555556 2 20
## 13 11 01 13 28 1.647059 1 5
## 14 11 01 14 228 2.814815 1 19
## 15 11 02 01 146 4.709677 1 20
## 16 11 02 02 439 5.701299 1 120
## 17 11 02 03 346 4.435897 1 12
## 18 11 02 04 914 14.507937 1 596
## 19 11 02 05 190 3.518519 1 15
## 20 11 02 06 590 4.370370 1 30
## 21 11 02 07 98 5.157895 1 15
## 22 11 02 08 630 4.666667 1 16
## 23 11 02 09 1131 4.812766 1 25
## 24 11 02 10 112 8.000000 2 50
## 25 11 02 11 45 3.461538 1 7
## 26 11 03 01 500 3.703704 1 38
## 27 11 03 02 67 2.161290 1 6
## 28 11 03 03 256 4.571429 1 14
## 29 11 03 05 200 2.941176 1 15
## 30 11 04 01 116 3.135135 1 13
## 31 11 04 02 64 3.047619 1 14
## 32 11 04 03 333 5.741379 1 43
## 33 11 04 04 26 1.733333 1 4
## 34 11 04 05 280 5.384615 1 30
## 35 11 05 01 127 3.097561 1 11
## 36 11 05 02 294 4.900000 1 25
## 37 11 05 03 471 4.131579 1 95
## 38 11 05 04 1577 3.893827 1 30
## 39 11 05 05 4 1.000000 1 1
## 40 11 05 06 1426 12.084746 1 472
## 41 11 05 07 218 4.844444 1 20
## 42 11 05 08 154 4.529412 1 13ica_papas <- mutate(ica_papas, ubigeo=paste(ica_papas$P001,ica_papas$P002,ica_papas$P003))
Tica_papas <- group_by(ica_papas, ubigeo)
papas <- data.frame(summarize(Tica_papas, SuperficieTotal= sum(P025),
Media=mean(P025),
Minimo=min(P025),
Maximo=max(P025)))
papas## ubigeo SuperficieTotal Media Minimo Maximo
## 1 11 01 01 3.000 3.0000000 3.00 3.00
## 2 11 01 02 60.030 3.5311765 0.04 9.20
## 3 11 01 08 4.960 1.6533333 0.01 4.80
## 4 11 01 09 43.400 3.6166667 1.00 9.00
## 5 11 01 10 7.500 1.8750000 1.00 3.00
## 6 11 01 11 3.950 1.9750000 1.25 2.70
## 7 11 01 12 0.960 0.2400000 0.02 0.71
## 8 11 01 14 31.718 0.3079417 0.01 1.50
## 9 11 02 03 38.220 0.2749640 0.02 1.00
## 10 11 02 04 4.000 4.0000000 4.00 4.00
## 11 11 02 05 4.000 4.0000000 4.00 4.00
## 12 11 02 08 9.500 0.1862745 0.05 0.50
## 13 11 02 09 49.175 0.2092553 0.01 2.00
## 14 11 03 01 51.950 2.7342105 1.00 12.00
## 15 11 03 02 6.500 6.5000000 6.50 6.50
## 16 11 03 03 5.000 2.5000000 1.00 4.00
## 17 11 03 05 61.250 4.7115385 0.50 12.00
## 18 11 04 01 9.000 1.0000000 0.50 1.20
## 19 11 04 02 2.500 1.2500000 0.50 2.00
## 20 11 04 03 5.000 5.0000000 5.00 5.00
## 21 11 04 05 15.500 0.4843750 0.15 1.50
## 22 11 05 02 306.250 38.2812500 0.25 300.00
## 23 11 05 03 4.850 1.2125000 0.10 3.00
## 24 11 05 04 4.420 4.4200000 4.42 4.42
## 25 11 05 05 0.200 0.2000000 0.20 0.20ica_narmand <- mutate(ica_narmand, ubigeo=paste(ica_narmand$P001,ica_narmand$P002,ica_narmand$P003))
Tica_narmand <- group_by(ica_narmand, ubigeo)
citricos <- data.frame(summarize(Tica_narmand, SuperficieTotal= sum(P025),
Media=mean(P025),
Minimo=min(P025),
Maximo=max(P025)))
citricos## ubigeo SuperficieTotal Media Minimo Maximo
## 1 11 01 01 2.60 1.300000 0.10 2.50
## 2 11 01 03 0.02 0.020000 0.02 0.02
## 3 11 01 05 68.30 34.150000 1.10 67.20
## 4 11 01 07 0.02 0.020000 0.02 0.02
## 5 11 01 08 216.73 19.702727 0.50 90.00
## 6 11 01 13 0.05 0.025000 0.02 0.03
## 7 11 02 02 597.40 49.783333 0.90 300.00
## 8 11 02 04 526.21 15.476765 0.26 93.00
## 9 11 02 05 821.22 20.029756 1.00 179.52
## 10 11 02 06 29.85 9.950000 0.10 29.00
## 11 11 02 10 89.04 22.260000 0.04 50.00
## 12 11 02 11 16.33 5.443333 3.93 7.40
## 13 11 03 01 0.50 0.500000 0.50 0.50
## 14 11 03 05 27.00 27.000000 27.00 27.00
## 15 11 04 01 65.35 13.070000 0.25 36.00
## 16 11 04 02 2.00 2.000000 2.00 2.00
## 17 11 05 02 6.50 6.500000 6.50 6.50
## 18 11 05 03 11.00 3.666667 1.00 5.00
## 19 11 05 05 4.50 4.500000 4.50 4.50
## 20 11 05 06 3.75 3.750000 3.75 3.75
## 21 11 05 07 1.00 1.000000 1.00 1.00ica_pollos <- mutate(ica_pollos, ubigeo=paste(ica_pollos$P001,ica_pollos$P002,ica_pollos$P003))
Tica_pollos <- group_by(ica_pollos, ubigeo)
pollos <- data.frame(summarize(Tica_pollos, SuperficieTotal= sum(P067_03),
Media=mean(P067_03),
Minimo=min(P067_03),
Maximo=max(P067_03)))
pollos## ubigeo SuperficieTotal Media Minimo Maximo
## 1 11 01 01 365048 3016.925620 1 299690
## 2 11 01 02 334 13.360000 2 50
## 3 11 01 03 1113 7.623288 1 50
## 4 11 01 04 71788 396.618785 1 70000
## 5 11 01 05 434 7.614035 1 40
## 6 11 01 06 172 8.190476 2 20
## 7 11 01 07 1501 7.357843 1 100
## 8 11 01 08 136612 1050.861538 1 104000
## 9 11 01 09 666 9.514286 1 50
## 10 11 01 10 1407 11.628099 1 80
## 11 11 01 11 2969 9.485623 1 130
## 12 11 01 12 1483 18.772152 1 400
## 13 11 01 13 1320 9.361702 1 60
## 14 11 01 14 219 7.551724 1 20
## 15 11 02 01 22132 2459.111111 1 18000
## 16 11 02 02 21072 540.307692 1 20090
## 17 11 02 04 5068 46.925926 1 2000
## 18 11 02 05 1875 11.645963 1 200
## 19 11 02 06 299932 2205.382353 0 40000
## 20 11 02 07 242650 18665.384615 6 119957
## 21 11 02 08 2 2.000000 2 2
## 22 11 02 09 4 4.000000 4 4
## 23 11 02 10 63013 388.969136 1 60000
## 24 11 02 11 7807 169.717391 2 2500
## 25 11 03 01 725 12.288136 2 100
## 26 11 03 02 249 9.576923 1 28
## 27 11 03 03 811 11.109589 2 50
## 28 11 03 05 1080 9.642857 1 50
## 29 11 04 01 1605 26.750000 1 800
## 30 11 04 02 380 11.176471 2 50
## 31 11 04 03 486 8.237288 1 30
## 32 11 04 04 50 50.000000 50 50
## 33 11 04 05 299 9.645161 1 40
## 34 11 05 01 350 11.666667 4 28
## 35 11 05 02 65 13.000000 3 30
## 36 11 05 03 753 8.755814 1 50
## 37 11 05 04 2096 9.794393 1 61
## 38 11 05 05 60264 4635.692308 4 40000
## 39 11 05 06 227621 2995.013158 1 80000
## 40 11 05 07 606370 14789.512195 2 205000
## 41 11 05 08 51 8.500000 3 17junin_vacas <- mutate(junin_vacas, ubigeo=paste(junin_vacas$P001,junin_vacas$P002,junin_vacas$P003))
Tjunin_vacas <- group_by(junin_vacas, ubigeo)
vacas <- data.frame(summarize(Tjunin_vacas, SuperficieTotal= sum(P067_03),
Media=mean(P067_03),
Minimo=min(P067_03),
Maximo=max(P067_03)))
vacas## ubigeo SuperficieTotal Media Minimo Maximo
## 1 12 01 01 1109 4.453815 1 45
## 2 12 01 04 8 1.600000 1 2
## 3 12 01 05 140 1.772152 1 11
## 4 12 01 06 243 2.150442 1 16
## 5 12 01 07 186 2.113636 1 13
## 6 12 01 08 639 3.530387 1 64
## 7 12 01 11 70 2.500000 1 10
## 8 12 01 12 162 1.653061 1 5
## 9 12 01 13 51 1.307692 1 4
## 10 12 01 14 973 2.756374 1 32
## 11 12 01 16 10 1.000000 1 1
## 12 12 01 17 251 2.510000 1 34
## 13 12 01 19 244 2.301887 1 80
## 14 12 01 20 467 3.335714 1 25
## 15 12 01 21 233 1.751880 1 12
## 16 12 01 22 102 1.924528 1 5
## 17 12 01 24 1931 2.571238 1 27
## 18 12 01 25 51 1.700000 1 6
## 19 12 01 26 134 1.696203 1 15
## 20 12 01 27 110 1.641791 1 11
## 21 12 01 28 277 3.794521 1 40
## 22 12 01 29 521 4.102362 1 100
## 23 12 01 30 155 1.890244 1 10
## 24 12 01 32 60 1.666667 1 4
## 25 12 01 33 394 1.576000 1 15
## 26 12 01 34 632 2.332103 1 23
## 27 12 01 35 3218 2.755137 1 46
## 28 12 01 36 31 1.409091 1 4
## 29 12 02 01 680 3.300971 1 20
## 30 12 02 02 100 1.265823 1 4
## 31 12 02 03 1639 2.727121 1 20
## 32 12 02 04 759 4.216667 1 35
## 33 12 02 05 225 2.122642 1 13
## 34 12 02 06 1924 2.796512 1 15
## 35 12 02 07 467 2.245192 1 8
## 36 12 02 08 155 1.597938 1 8
## 37 12 02 09 176 2.146341 1 7
## 38 12 02 10 2631 4.407035 1 38
## 39 12 02 11 444 2.611765 1 15
## 40 12 02 12 749 3.671569 1 68
## 41 12 02 13 1019 3.041791 1 30
## 42 12 02 14 4032 3.334988 1 35
## 43 12 02 15 559 2.852041 1 14
## 44 12 03 01 138 4.451613 1 17
## 45 12 03 02 347 3.212963 1 20
## 46 12 03 03 249 6.073171 1 70
## 47 12 03 04 58 2.320000 1 8
## 48 12 03 05 112 3.111111 1 25
## 49 12 03 06 89 2.870968 1 12
## 50 12 04 01 193 2.443038 1 7
## 51 12 04 02 907 1.628366 1 11
## 52 12 04 03 1496 2.700361 1 17
## 53 12 04 04 150 1.923077 1 5
## 54 12 04 05 1708 10.948718 1 1100
## 55 12 04 06 169 2.086420 1 10
## 56 12 04 07 863 3.452000 1 113
## 57 12 04 08 379 2.325153 1 20
## 58 12 04 09 121 1.890625 1 11
## 59 12 04 10 547 2.604762 1 24
## 60 12 04 11 272 1.581395 1 7
## 61 12 04 12 155 1.761364 1 7
## 62 12 04 13 319 2.511811 1 14
## 63 12 04 14 307 2.558333 1 10
## 64 12 04 15 166 1.537037 1 7
## 65 12 04 16 404 1.779736 1 7
## 66 12 04 17 180 1.636364 1 6
## 67 12 04 18 623 2.522267 1 11
## 68 12 04 19 135 3.461538 1 18
## 69 12 04 20 351 2.140244 1 8
## 70 12 04 21 301 2.090278 1 15
## 71 12 04 22 165 1.617647 1 10
## 72 12 04 23 391 1.898058 1 21
## 73 12 04 24 217 2.009259 1 7
## 74 12 04 25 160 1.632653 1 7
## 75 12 04 26 1310 3.579235 1 40
## 76 12 04 27 488 2.726257 1 25
## 77 12 04 28 961 3.585821 1 113
## 78 12 04 29 337 2.717742 1 13
## 79 12 04 30 98 2.000000 1 6
## 80 12 04 31 1930 3.265651 1 30
## 81 12 04 32 173 1.679612 1 6
## 82 12 04 33 651 2.806034 1 29
## 83 12 04 34 76 1.652174 1 4
## 84 12 05 01 3710 9.611399 1 320
## 85 12 05 02 333 5.550000 1 24
## 86 12 05 03 3190 10.598007 1 300
## 87 12 05 04 1067 3.966543 1 70
## 88 12 06 01 501 4.318966 1 60
## 89 12 06 02 166 2.441176 1 11
## 90 12 06 03 529 3.550336 1 28
## 91 12 06 05 477 2.981250 1 50
## 92 12 06 07 426 3.435484 1 30
## 93 12 06 08 544 4.771930 1 85
## 94 12 06 99 1207 3.094872 1 30
## 95 12 07 01 844 2.226913 1 45
## 96 12 07 02 385 1.558704 0 9
## 97 12 07 03 309 2.491935 1 15
## 98 12 07 04 2183 2.675245 1 45
## 99 12 07 05 128 1.753425 1 4
## 100 12 07 06 782 3.370690 1 30
## 101 12 07 07 766 8.417582 1 560
## 102 12 07 08 1023 4.506608 1 118
## 103 12 07 09 840 2.234043 1 15
## 104 12 08 01 311 5.759259 1 35
## 105 12 08 02 599 5.650943 1 30
## 106 12 08 03 130 3.714286 1 8
## 107 12 08 04 2446 33.506849 1 1982
## 108 12 08 05 30 4.285714 2 10
## 109 12 08 06 764 6.314050 1 80
## 110 12 08 07 1487 13.897196 1 400
## 111 12 08 08 45 4.090909 1 12
## 112 12 08 09 291 4.770492 1 70
## 113 12 08 10 478 6.638889 1 98
## 114 12 09 01 1268 2.051780 1 15
## 115 12 09 02 819 1.481013 1 5
## 116 12 09 03 470 1.821705 1 23
## 117 12 09 04 897 2.270886 1 30
## 118 12 09 05 427 1.648649 1 12
## 119 12 09 06 497 1.988000 1 27
## 120 12 09 07 1274 2.211806 1 15
## 121 12 09 08 271 1.489011 1 6
## 122 12 09 09 3301 4.292588 1 75junin_papas <- mutate(junin_papas, ubigeo=paste(junin_papas$P001,junin_papas$P002,junin_papas$P003))
Tjunin_papas <- group_by(junin_papas, ubigeo)
papas <- data.frame(summarize(Tjunin_papas, SuperficieTotal= sum(P025),
Media=mean(P025),
Minimo=min(P025),
Maximo=max(P025)))
papas## ubigeo SuperficieTotal Media Minimo Maximo
## 1 12 01 01 208.2931 0.71825207 0.0020 150.0000
## 2 12 01 04 6.9830 0.14547917 0.0060 0.6600
## 3 12 01 05 24.4453 0.07591708 0.0015 1.0000
## 4 12 01 06 324.1202 1.78088022 0.0025 200.0000
## 5 12 01 07 21.6031 0.28055974 0.0050 4.0000
## 6 12 01 08 6.6623 0.02787573 0.0020 1.5000
## 7 12 01 11 103.3350 0.44540948 0.0010 40.0000
## 8 12 01 12 265.6747 0.61498773 0.0010 66.0000
## 9 12 01 13 193.6285 0.21538209 0.0040 3.0000
## 10 12 01 14 253.0639 0.36255573 0.0015 30.0000
## 11 12 01 16 291.5820 0.42753959 0.0010 150.0000
## 12 12 01 17 21.3496 0.07707437 0.0020 1.5000
## 13 12 01 19 47.1681 0.24695340 0.0020 20.0000
## 14 12 01 20 15.7420 0.06531950 0.0015 5.0000
## 15 12 01 21 198.6577 0.38057031 0.0004 150.0000
## 16 12 01 22 59.2002 0.11003755 0.0030 1.5000
## 17 12 01 24 831.9233 0.91722525 0.0020 300.0000
## 18 12 01 25 16.4219 0.11986788 0.0050 2.3000
## 19 12 01 26 497.3597 0.59139084 0.0010 142.0000
## 20 12 01 27 35.7400 0.34699029 0.0900 6.0000
## 21 12 01 28 28.6275 0.09940104 0.0030 1.0000
## 22 12 01 29 833.0359 6.77264959 0.0020 400.0000
## 23 12 01 30 31.7976 0.27177436 0.0080 2.0000
## 24 12 01 32 425.4069 5.06436786 0.0021 211.0000
## 25 12 01 33 171.8806 0.14077035 0.0020 7.0000
## 26 12 01 34 305.6740 0.48986218 0.0020 7.0000
## 27 12 01 35 267.0197 0.26622104 0.0040 3.4650
## 28 12 01 36 23.8137 0.08444574 0.0005 1.0000
## 29 12 02 01 45.8522 0.36977581 0.0050 3.0000
## 30 12 02 02 57.6441 0.14818535 0.0014 2.1650
## 31 12 02 03 420.4885 0.36788145 0.0010 5.0000
## 32 12 02 04 132.2759 0.20929731 0.0010 60.0000
## 33 12 02 05 222.0630 0.25177211 0.0200 7.0000
## 34 12 02 06 1440.4049 0.58648408 0.0065 693.0000
## 35 12 02 07 130.6013 0.26928103 0.0040 4.5000
## 36 12 02 08 39.5309 0.19864774 0.0025 16.5000
## 37 12 02 09 136.1557 0.33127908 0.0080 5.0000
## 38 12 02 10 102.9426 0.39291069 0.0010 5.5000
## 39 12 02 11 56.7068 0.51551636 0.0040 5.0000
## 40 12 02 12 50.9339 0.25986684 0.0020 2.0000
## 41 12 02 13 264.4044 0.39880000 0.0020 5.0000
## 42 12 02 14 78.5401 0.13024892 0.0035 3.0000
## 43 12 02 15 24.3999 0.16486419 0.0050 2.0000
## 44 12 03 01 0.2500 0.25000000 0.2500 0.2500
## 45 12 03 02 0.5000 0.50000000 0.5000 0.5000
## 46 12 03 03 197.0000 197.00000000 197.0000 197.0000
## 47 12 03 04 0.1500 0.15000000 0.1500 0.1500
## 48 12 03 05 2.5000 0.50000000 0.2500 1.0000
## 49 12 03 06 0.5300 0.17666667 0.1200 0.2500
## 50 12 04 01 393.6705 3.45325000 0.0030 320.0000
## 51 12 04 02 778.3211 0.40921193 0.0020 100.0000
## 52 12 04 03 1274.1144 1.39705526 0.0025 200.0000
## 53 12 04 04 21.8128 0.14738378 0.0020 2.0000
## 54 12 04 05 110.5168 1.64950448 0.0030 100.0000
## 55 12 04 06 50.6544 0.24709463 0.0050 3.0000
## 56 12 04 07 56.2311 0.20447673 0.0020 15.0000
## 57 12 04 08 43.5501 0.15017276 0.0040 1.3300
## 58 12 04 09 65.1120 0.34450794 0.0100 20.2100
## 59 12 04 10 65.0199 0.37802267 0.0050 2.0000
## 60 12 04 11 119.4690 0.30322081 0.0495 2.0000
## 61 12 04 12 24.4080 0.10342373 0.0016 2.0000
## 62 12 04 13 121.5773 0.31910052 0.0006 4.0000
## 63 12 04 14 22.5219 0.13649636 0.0010 1.0000
## 64 12 04 15 23.6932 0.16228219 0.0035 4.0000
## 65 12 04 16 62.3645 0.12779611 0.0025 3.6000
## 66 12 04 17 46.2221 0.14310248 0.0050 2.0000
## 67 12 04 18 71.6744 0.08837781 0.0020 3.0000
## 68 12 04 19 3.5800 0.44750000 0.1000 2.0000
## 69 12 04 20 137.4726 0.74713370 0.0030 100.0000
## 70 12 04 21 85.8026 0.17058171 0.0020 2.2098
## 71 12 04 22 53.1478 0.20760859 0.0018 4.0000
## 72 12 04 23 390.0421 0.75443346 0.0014 100.0000
## 73 12 04 24 46.7823 0.36548672 0.0020 7.0000
## 74 12 04 25 55.8980 0.25179279 0.0002 3.0000
## 75 12 04 26 161.2588 0.52527296 0.0008 15.0000
## 76 12 04 27 162.8925 0.24831174 0.0100 12.0000
## 77 12 04 28 106.9521 0.37136146 0.0100 15.0000
## 78 12 04 29 100.5931 0.63666519 0.0004 75.0000
## 79 12 04 30 53.2309 0.30592471 0.0005 6.0000
## 80 12 04 31 410.1896 0.38086314 0.0010 15.0000
## 81 12 04 32 64.7046 0.25079302 0.0009 4.0000
## 82 12 04 33 89.6999 0.22039287 0.0010 10.0000
## 83 12 04 34 282.9220 0.99620423 0.0060 10.0000
## 84 12 05 01 29.5025 0.27572430 0.0300 2.0000
## 85 12 05 02 843.2980 0.89522081 0.0400 4.5000
## 86 12 05 03 40.9375 0.47054598 0.0300 2.0000
## 87 12 05 04 19137.0850 2.98039013 0.0100 90.0000
## 88 12 06 03 86.8600 0.87737374 0.0200 5.0000
## 89 12 06 05 94.9550 0.39237603 0.0500 6.0000
## 90 12 06 99 45.9500 0.56728395 0.1000 1.5000
## 91 12 07 01 325.9052 0.28143800 0.0040 70.0000
## 92 12 07 02 235.4290 0.33680830 0.0050 6.7600
## 93 12 07 03 122.2128 0.25095031 0.0064 4.0000
## 94 12 07 04 3597.1200 1.04143602 0.0100 400.0000
## 95 12 07 05 25.3400 0.08121795 0.0200 1.0000
## 96 12 07 06 940.0128 0.94473648 0.0100 60.0000
## 97 12 07 07 113.4160 0.13172590 0.0160 2.4000
## 98 12 07 08 3525.4657 1.22369514 0.0050 200.0000
## 99 12 07 09 1598.9250 0.59728241 0.0200 9.6000
## 100 12 08 01 7.3554 0.03211965 0.0008 1.0000
## 101 12 08 02 29.8300 0.20572414 0.0100 1.6000
## 102 12 08 03 0.2350 0.01807692 0.0050 0.0500
## 103 12 08 06 18.8085 0.36879412 0.0009 9.0000
## 104 12 08 08 3.2086 0.03525934 0.0030 0.5000
## 105 12 08 10 0.0150 0.00750000 0.0050 0.0100
## 106 12 09 01 300.7812 0.26665000 0.0010 6.0000
## 107 12 09 02 260.9759 0.12510829 0.0010 33.0000
## 108 12 09 03 100.2792 0.15993493 0.0020 2.5000
## 109 12 09 04 150.7699 0.23266960 0.0010 4.0000
## 110 12 09 05 108.5378 0.13061107 0.0030 4.3000
## 111 12 09 06 626.2835 0.92372198 0.0003 500.0000
## 112 12 09 07 31.3637 0.08453827 0.0008 5.0000
## 113 12 09 08 106.6294 0.16229741 0.0085 2.0000
## 114 12 09 09 1055.5584 1.78908203 0.0020 200.0000junin_narmand <- mutate(junin_narmand, ubigeo=paste(junin_narmand$P001,junin_narmand$P002,junin_narmand$P003))
Tjunin_narmand <- group_by(junin_narmand, ubigeo)
citricos <- data.frame(summarize(Tjunin_narmand, SuperficieTotal= sum(P025),
Media=mean(P025),
Minimo=min(P025),
Maximo=max(P025)))
citricos## ubigeo SuperficieTotal Media Minimo Maximo
## 1 12 03 01 57.9400 1.3795238 0.150 6.00
## 2 12 03 02 80.1500 1.2722222 0.250 4.00
## 3 12 03 03 47.7300 1.4463636 0.220 8.00
## 4 12 03 04 14.5500 1.4550000 0.250 6.00
## 5 12 03 05 237.2073 1.2753081 0.062 9.75
## 6 12 03 06 1.2700 0.4233333 0.020 0.75
## 7 12 06 01 5.0000 1.2500000 0.500 3.00
## 8 12 06 02 9.3000 1.5500000 0.500 3.50
## 9 12 06 07 13.4500 1.9214286 0.200 4.75
## 10 12 06 99 33.5900 1.2440741 0.250 5.00junin_pollos <- mutate(junin_pollos, ubigeo=paste(junin_pollos$P001,junin_pollos$P002,junin_pollos$P003))
Tjunin_pollos <- group_by(junin_pollos, ubigeo)
pollos <- data.frame(summarize(Tjunin_pollos, SuperficieTotal= sum(P067_03),
Media=mean(P067_03),
Minimo=min(P067_03),
Maximo=max(P067_03)))
pollos## ubigeo SuperficieTotal Media Minimo Maximo
## 1 12 01 01 332 7.720930 1 30
## 2 12 01 04 4 4.000000 4 4
## 3 12 01 06 62 4.769231 3 13
## 4 12 01 07 599 8.808824 1 50
## 5 12 01 08 106 3.655172 1 10
## 6 12 01 11 317 5.981132 2 12
## 7 12 01 12 222 6.000000 1 32
## 8 12 01 13 331 3.987952 1 46
## 9 12 01 14 3928 12.275000 1 1500
## 10 12 01 16 752 6.064516 1 30
## 11 12 01 17 709 5.672000 1 24
## 12 12 01 19 1300 8.125000 1 40
## 13 12 01 20 133 3.694444 1 10
## 14 12 01 21 934 7.915254 1 60
## 15 12 01 22 202 4.926829 1 20
## 16 12 01 24 847 5.841379 1 40
## 17 12 01 25 311 10.032258 1 50
## 18 12 01 26 847 5.535948 1 33
## 19 12 01 28 168 8.400000 2 20
## 20 12 01 29 351 7.312500 1 36
## 21 12 01 30 336 6.000000 1 20
## 22 12 01 32 122 3.812500 1 12
## 23 12 01 33 1964 7.328358 1 60
## 24 12 01 34 1411 13.833333 1 300
## 25 12 01 35 1056 6.812903 1 80
## 26 12 01 36 928 8.592593 1 47
## 27 12 02 01 547 7.197368 1 50
## 28 12 02 02 305 5.083333 2 18
## 29 12 02 03 257 4.355932 1 14
## 30 12 02 04 544 4.945455 1 40
## 31 12 02 05 263 4.174603 1 24
## 32 12 02 06 453 3.511628 1 30
## 33 12 02 07 476 4.068376 1 20
## 34 12 02 08 643 5.741071 1 24
## 35 12 02 09 320 7.804878 1 45
## 36 12 02 10 626 7.364706 1 48
## 37 12 02 11 406 10.410256 1 52
## 38 12 02 12 696 6.105263 1 28
## 39 12 02 13 859 6.763780 1 30
## 40 12 02 14 1555 5.654545 1 39
## 41 12 02 15 284 5.259259 1 30
## 42 12 03 01 81915 112.675378 1 75000
## 43 12 03 02 5004 11.295711 1 100
## 44 12 03 03 4739 11.730198 1 100
## 45 12 03 04 8748 138.857143 2 8000
## 46 12 03 05 28338 111.566929 1 25000
## 47 12 03 06 1139 10.449541 1 50
## 48 12 04 01 711 9.115385 1 52
## 49 12 04 02 4638 7.730000 1 36
## 50 12 04 03 2874 7.543307 1 60
## 51 12 04 04 448 8.960000 1 35
## 52 12 04 05 281 6.244444 2 25
## 53 12 04 06 474 8.172414 1 66
## 54 12 04 07 760 6.080000 1 66
## 55 12 04 08 829 8.290000 1 50
## 56 12 04 09 693 10.500000 1 40
## 57 12 04 10 826 10.589744 1 40
## 58 12 04 11 585 5.679612 2 24
## 59 12 04 12 317 5.660714 2 18
## 60 12 04 13 2876 10.893939 1 62
## 61 12 04 14 151 7.947368 2 24
## 62 12 04 15 24 4.000000 1 6
## 63 12 04 16 545 5.450000 1 25
## 64 12 04 17 81 3.681818 1 7
## 65 12 04 18 336 6.339623 1 18
## 66 12 04 19 264 11.000000 2 40
## 67 12 04 20 371 6.625000 1 25
## 68 12 04 21 2218 11.552083 1 48
## 69 12 04 22 506 6.170732 1 24
## 70 12 04 23 608 6.831461 2 30
## 71 12 04 24 936 10.064516 2 40
## 72 12 04 25 277 9.233333 3 29
## 73 12 04 26 596 6.696629 1 24
## 74 12 04 27 944 7.044776 1 50
## 75 12 04 28 661 7.184783 1 50
## 76 12 04 29 437 7.283333 1 24
## 77 12 04 30 794 8.921348 1 60
## 78 12 04 31 1622 7.440367 1 28
## 79 12 04 32 16 5.333333 4 8
## 80 12 04 33 729 6.942857 1 30
## 81 12 04 34 152 8.941176 2 22
## 82 12 05 01 32 4.000000 1 8
## 83 12 05 02 92 3.680000 1 10
## 84 12 05 03 57 3.352941 1 8
## 85 12 05 04 364 5.687500 1 100
## 86 12 06 01 56370 103.241758 1 50000
## 87 12 06 02 8 2.666667 1 4
## 88 12 06 03 39 4.875000 1 15
## 89 12 06 05 2531 10.205645 1 50
## 90 12 06 07 121122 467.652510 1 118000
## 91 12 06 08 747 11.857143 1 100
## 92 12 06 99 33145 48.671072 1 25000
## 93 12 07 01 1026 4.862559 1 30
## 94 12 07 02 1318 5.608511 0 40
## 95 12 07 03 261 4.745455 1 15
## 96 12 07 04 877 4.385000 1 30
## 97 12 07 05 307 4.514706 1 15
## 98 12 07 06 40 5.714286 1 12
## 99 12 07 07 78 3.900000 2 12
## 100 12 07 08 3 3.000000 3 3
## 101 12 07 09 782 4.227027 1 15
## 102 12 08 01 36 5.142857 2 16
## 103 12 08 02 2 2.000000 2 2
## 104 12 08 03 19 6.333333 3 10
## 105 12 08 04 3 3.000000 3 3
## 106 12 08 05 45 7.500000 3 15
## 107 12 08 06 248 22.545455 2 100
## 108 12 08 07 5 2.500000 2 3
## 109 12 08 09 2 2.000000 2 2
## 110 12 08 10 27 5.400000 1 12
## 111 12 09 01 1104 5.359223 1 25
## 112 12 09 02 1327 4.656140 1 20
## 113 12 09 03 976 6.379085 1 40
## 114 12 09 04 1294 6.919786 1 24
## 115 12 09 05 1730 6.947791 1 30
## 116 12 09 06 736 5.492537 1 33
## 117 12 09 07 275 3.481013 1 12
## 118 12 09 08 1018 8.209677 1 50
## 119 12 09 09 490 4.579439 1 30#Lima
df_lima_vacas <-data.frame(table(Tlima_vacas$ubigeo))
colnames(df_lima_vacas) <- c("Ubigeo","Freq")
df_lima_papas <-data.frame(table(Tlima_papas$ubigeo))
colnames(df_lima_papas) <- c("Ubigeo","Freq")
df_lima_narmand <-data.frame(table(Tlima_narmand$ubigeo))
colnames(df_lima_narmand) <- c("Ubigeo","Freq")
df_lima_pollos <-data.frame(table(Tlima_pollos$ubigeo))
colnames(df_lima_pollos) <- c("Ubigeo","Freq")
a1<-merge.data.frame(df_lima_vacas,df_lima_papas,by.x="Ubigeo",by.y="Ubigeo",all=TRUE)
a2<-merge.data.frame(df_lima_narmand,df_lima_pollos,by.x="Ubigeo",by.y="Ubigeo",all=TRUE)
lima<-merge.data.frame(a1,a2,by.x="Ubigeo",by.y="Ubigeo",all=TRUE)#cajamarca
df_cajamarca_vacas <-data.frame(table(Tcajamarca_vacas$ubigeo))
colnames(df_cajamarca_vacas) <- c("Ubigeo","Freq")
df_cajamarca_papas <-data.frame(table(Tcajamarca_papas$ubigeo))
colnames(df_cajamarca_papas) <- c("Ubigeo","Freq")
df_cajamarca_narmand <-data.frame(table(Tcajamarca_narmand$ubigeo))
colnames(df_cajamarca_narmand) <- c("Ubigeo","Freq")
df_cajamarca_pollos <-data.frame(table(Tcajamarca_pollos$ubigeo))
colnames(df_cajamarca_pollos) <- c("Ubigeo","Freq")
a1<-merge.data.frame(df_cajamarca_vacas,df_cajamarca_papas,by.x="Ubigeo",by.y="Ubigeo",all=TRUE)
a2<-merge.data.frame(df_cajamarca_narmand,df_cajamarca_pollos,by.x="Ubigeo",by.y="Ubigeo",all=TRUE)
cajamarca<-merge.data.frame(a1,a2,by.x="Ubigeo",by.y="Ubigeo",all=TRUE)#lalibertad
df_lalibertad_vacas <-data.frame(table(Tlalibertad_vacas$ubigeo))
colnames(df_lalibertad_vacas) <- c("Ubigeo","Freq")
df_lalibertad_papas <-data.frame(table(Tlalibertad_papas$ubigeo))
colnames(df_lalibertad_papas) <- c("Ubigeo","Freq")
df_lalibertad_narmand <-data.frame(table(Tlalibertad_narmand$ubigeo))
colnames(df_lalibertad_narmand) <- c("Ubigeo","Freq")
df_lalibertad_pollos <-data.frame(table(Tlalibertad_pollos$ubigeo))
colnames(df_lalibertad_pollos) <- c("Ubigeo","Freq")
a1<-merge.data.frame(df_lalibertad_vacas,df_lalibertad_papas,by.x="Ubigeo",by.y="Ubigeo",all=TRUE)
a2<-merge.data.frame(df_lalibertad_narmand,df_lalibertad_pollos,by.x="Ubigeo",by.y="Ubigeo",all=TRUE)
lalibertad<-merge.data.frame(a1,a2,by.x="Ubigeo",by.y="Ubigeo",all=TRUE)#ancash
df_ancash_vacas <-data.frame(table(Tancash_vacas$ubigeo))
colnames(df_ancash_vacas) <- c("Ubigeo","Freq")
df_ancash_papas <-data.frame(table(Tancash_papas$ubigeo))
colnames(df_ancash_papas) <- c("Ubigeo","Freq")
df_ancash_narmand <-data.frame(table(Tancash_narmand$ubigeo))
colnames(df_ancash_narmand) <- c("Ubigeo","Freq")
df_ancash_pollos <-data.frame(table(Tancash_pollos$ubigeo))
colnames(df_ancash_pollos) <- c("Ubigeo","Freq")
a1<-merge.data.frame(df_ancash_vacas,df_ancash_papas,by.x="Ubigeo",by.y="Ubigeo",all=TRUE)
a2<-merge.data.frame(df_ancash_narmand,df_ancash_pollos,by.x="Ubigeo",by.y="Ubigeo",all=TRUE)
ancash<-merge.data.frame(a1,a2,by.x="Ubigeo",by.y="Ubigeo",all=TRUE)#ica
df_ica_vacas <-data.frame(table(Tica_vacas$ubigeo))
colnames(df_ica_vacas) <- c("Ubigeo","Freq")
df_ica_papas <-data.frame(table(Tica_papas$ubigeo))
colnames(df_ica_papas) <- c("Ubigeo","Freq")
df_ica_narmand <-data.frame(table(Tica_narmand$ubigeo))
colnames(df_ica_narmand) <- c("Ubigeo","Freq")
df_ica_pollos <-data.frame(table(Tica_pollos$ubigeo))
colnames(df_ica_pollos) <- c("Ubigeo","Freq")
a1<-merge.data.frame(df_ica_vacas,df_ica_papas,by.x="Ubigeo",by.y="Ubigeo",all=TRUE)
a2<-merge.data.frame(df_ica_narmand,df_ica_pollos,by.x="Ubigeo",by.y="Ubigeo",all=TRUE)
ica<-merge.data.frame(a1,a2,by.x="Ubigeo",by.y="Ubigeo",all=TRUE)#junin
df_junin_vacas <-data.frame(table(Tjunin_vacas$ubigeo))
colnames(df_junin_vacas) <- c("Ubigeo","Freq")
df_junin_papas <-data.frame(table(Tjunin_papas$ubigeo))
colnames(df_junin_papas) <- c("Ubigeo","Freq")
df_junin_narmand <-data.frame(table(Tjunin_narmand$ubigeo))
colnames(df_junin_narmand) <- c("Ubigeo","Freq")
df_junin_pollos <-data.frame(table(Tjunin_pollos$ubigeo))
colnames(df_junin_pollos) <- c("Ubigeo","Freq")
a1<-merge.data.frame(df_junin_vacas,df_junin_papas,by.x="Ubigeo",by.y="Ubigeo",all=TRUE)
a2<-merge.data.frame(df_junin_narmand,df_junin_pollos,by.x="Ubigeo",by.y="Ubigeo",all=TRUE)
junin<-merge.data.frame(a1,a2,by.x="Ubigeo",by.y="Ubigeo",all=TRUE)#Bases de datos por distritos
library(DT)
datatable(cajamarca, colnames = c("Ubigeo","Ganado lechero","Papa","Cítricos","Pollos"), extensions = 'Buttons', options = list(pageLength = 25, dom='Bfrtip', buttons=c('copy','csv','pdf')), filter = 'top', caption = "Nro. productores por distrito, cajamarca")library(DT)
datatable(lalibertad, colnames = c("Ubigeo","Ganado lechero","Papa","Cítricos","Pollos"), extensions = 'Buttons', options = list(pageLength = 25, dom='Bfrtip', buttons=c('copy','csv','pdf')), filter = 'top', caption = "Nro. productores por distrito, lalibertad")library(DT)
datatable(lima, colnames = c("Ubigeo","Ganado lechero","Papa","Cítricos","Pollos"), extensions = 'Buttons', options = list(pageLength = 25, dom='Bfrtip', buttons=c('copy','csv','pdf')), filter = 'top', caption = "Nro. productores por distrito, Lima")library(DT)
datatable(ancash, colnames = c("Ubigeo","Ganado lechero","Papa","Cítricos","Pollos"), extensions = 'Buttons', options = list(pageLength = 25, dom='Bfrtip', buttons=c('copy','csv','pdf')), filter = 'top', caption = "Nro. productores por distrito, ancash")library(DT)
datatable(ica, colnames = c("Ubigeo","Ganado lechero","Papa","Cítricos","Pollos"), extensions = 'Buttons', options = list(pageLength = 25, dom='Bfrtip', buttons=c('copy','csv','pdf')), filter = 'top', caption = "Nro. productores por distrito, ica")library(DT)
datatable(junin, colnames = c("Ubigeo","Ganado lechero","Papa","Cítricos","Pollos"), extensions = 'Buttons', options = list(pageLength = 25, dom='Bfrtip', buttons=c('copy','csv','pdf')), filter = 'top', caption = "Nro. productores por distrito, junin")El criterio consistirá en la participación de unidades agropecuarias. La tabla a continuación muestra los resultados.
#Reportes generales
prop.table(Nro_productores, 2)## Ganado lechero Papas Cítricos Pollos
## Cajamarca 0.52818570 0.340554206 0.002622091 0.55015539
## La Libertad 0.13342626 0.156149308 0.002622091 0.18766731
## Lima 0.06680591 0.035344272 0.819403474 0.05442475
## Ancash 0.14972101 0.258841025 0.007538512 0.07611773
## Ica 0.01233758 0.002200962 0.042936742 0.02492384
## Junín 0.10952354 0.206910227 0.124877089 0.10671098Con dicha participación, definimos la distribución del tamaño de muestra entre departamentos con cada producto. Considerar escenarios.
Tomar una muestra de 145 ccpp por cada producto
round(prop.table(Nro_productores, 2)*145)## Ganado lechero Papas Cítricos Pollos
## Cajamarca 77 49 0 80
## La Libertad 19 23 0 27
## Lima 10 5 119 8
## Ancash 22 38 1 11
## Ica 2 0 6 4
## Junín 16 30 18 15Tomar la muestra de 145 ccpp y dividirlo en partes iguales para cada producto
round(prop.table(Nro_productores, 2)*(145/4))## Ganado lechero Papas Cítricos Pollos
## Cajamarca 19 12 0 20
## La Libertad 5 6 0 7
## Lima 2 1 30 2
## Ancash 5 9 0 3
## Ica 0 0 2 1
## Junín 4 8 5 4Tomar la muestra de 145 ccpp y dividirla en partes proporcionales para cada producto
colSums(Nro_productores)## Ganado lechero Papas Cítricos Pollos
## 231731 302595 3051 129996colSums(Nro_productores)/sum(colSums(Nro_productores))*100## Ganado lechero Papas Cítricos Pollos
## 34.7228611 45.3412110 0.4571656 19.4787623Con ello, la muestra final para cada producto en el escenario 3 sería:
part <- round(145*colSums(Nro_productores)/sum(colSums(Nro_productores)))
part## Ganado lechero Papas Cítricos Pollos
## 50 66 1 28a <- prop.table(Nro_productores,2)
print("Muestra Ganado lechero")## [1] "Muestra Ganado lechero"cbind(round(a[,1]*part[1]))## [,1]
## Cajamarca 26
## La Libertad 7
## Lima 3
## Ancash 7
## Ica 1
## Junín 5print("Muestra Papas")## [1] "Muestra Papas"cbind(round(a[,2]*part[2]))## [,1]
## Cajamarca 22
## La Libertad 10
## Lima 2
## Ancash 17
## Ica 0
## Junín 14print("Muestra Cítricos")## [1] "Muestra Cítricos"cbind(round(a[,3]*part[3]))## [,1]
## Cajamarca 0
## La Libertad 0
## Lima 1
## Ancash 0
## Ica 0
## Junín 0print("Muestra Pollos")## [1] "Muestra Pollos"cbind(round(a[,4]*part[4]))## [,1]
## Cajamarca 15
## La Libertad 5
## Lima 2
## Ancash 2
## Ica 1
## Junín 3