datos agropecuarios 2
juan camilo gil
30/11/2020
datos bovinos
ordenar los datos
- llamamos la base de datos que vamos a ordenar.
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.3 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.0## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(readxl)
library(janitor)## Warning: package 'janitor' was built under R version 4.0.3##
## Attaching package: 'janitor'## The following objects are masked from 'package:stats':
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## chisq.test, fisher.testlibrary(Hmisc)## Warning: package 'Hmisc' was built under R version 4.0.3## Loading required package: lattice## Loading required package: survival## Loading required package: Formula## Warning: package 'Formula' was built under R version 4.0.3##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:dplyr':
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## src, summarize## The following objects are masked from 'package:base':
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## format.pval, unitsporcinos <- read_excel("PORCINOS-CENSOS-2020.xlsx", skip = 4)
porcinos2 <- clean_names(porcinos) %>%
select(departamento,municipio,total_porcinos_2019) %>%
mutate(departamento = capitalize(tolower(departamento)),municipio = capitalize(tolower(municipio)))
porcinos2## # A tibble: 1,123 x 3
## departamento municipio total_porcinos_2019
## <chr> <chr> <dbl>
## 1 Antioquia Medellin 164598
## 2 Antioquia Abejorral 300
## 3 Antioquia Abriaqui 455
## 4 Antioquia Alejandria 507
## 5 Antioquia Amaga 10657
## 6 Antioquia Amalfi 3228
## 7 Antioquia Andes 15034
## 8 Antioquia Angelopolis 20708
## 9 Antioquia Angostura 26887
## 10 Antioquia Anori 220
## # ... with 1,113 more rowslibrary(readxl)
library(tidyverse)
library(janitor)
library(Hmisc)
bufalos <- read_excel("BUFALOS-CENSO-2020.xlsx", skip = 4)
bufalos2 <- clean_names(bufalos) %>%
select(departamento, municipio, total_bufalos) %>%
mutate(departamento = capitalize(tolower(departamento)),municipio=capitalize(tolower(municipio)))library(readxl)
library(tidyverse)
library(janitor)
library(Hmisc)
equinos <- read_excel("Equinos-Caprinos-Ovinos-CENSOS-2020.xlsx", skip = 4)
equinos2 <- clean_names(equinos) %>%
select(departamento, municipio, total_equinos) %>%
mutate(departamento = capitalize(tolower(departamento)),municipio=capitalize(tolower(municipio)))# union de los datos
porcinos_deptos <- porcinos2 %>%
group_by(departamento) %>%
summarise(total_cerdos = sum(total_porcinos_2019))## `summarise()` ungrouping output (override with `.groups` argument)porcinos_deptos## # A tibble: 34 x 2
## departamento total_cerdos
## <chr> <dbl>
## 1 Amazonas 270
## 2 Antioquia 1998407
## 3 Arauca 35675
## 4 Atlantico 198240
## 5 Bogota d.c. 1584
## 6 Bolivar 304261
## 7 Boyaca 165956
## 8 Caldas 132147
## 9 Caqueta 54370
## 10 Casanare 76850
## # ... with 24 more rowsbufalos_deptos <- bufalos2 %>%
group_by(departamento) %>%
summarise(total_bufalos = sum(total_bufalos))## `summarise()` ungrouping output (override with `.groups` argument)bufalos_deptos## # A tibble: 34 x 2
## departamento total_bufalos
## <chr> <dbl>
## 1 Amazonas 89
## 2 Antioquia 54638
## 3 Arauca 7347
## 4 Atlantico 509
## 5 Bogota d.c. 39
## 6 Bolivar 17620
## 7 Boyaca 6404
## 8 Caldas 2367
## 9 Caqueta 13417
## 10 Casanare 131
## # ... with 24 more rowsequinos_deptos <- equinos2 %>%
group_by(departamento) %>%
summarise(total_equinos = sum(total_equinos))## `summarise()` ungrouping output (override with `.groups` argument)equinos_deptos## # A tibble: 34 x 2
## departamento total_equinos
## <chr> <dbl>
## 1 Amazonas 55
## 2 Antioquia 253631
## 3 Arauca 53005
## 4 Atlantico 9775
## 5 Bogota d.c. 1689
## 6 Bolivar 61068
## 7 Boyaca 52279
## 8 Caldas 42118
## 9 Caqueta 64719
## 10 Casanare 92471
## # ... with 24 more rowsdatos unidos
animales <- inner_join(x = porcinos_deptos,
y = bufalos_deptos,
by = "departamento")
animales## # A tibble: 34 x 3
## departamento total_cerdos total_bufalos
## <chr> <dbl> <dbl>
## 1 Amazonas 270 89
## 2 Antioquia 1998407 54638
## 3 Arauca 35675 7347
## 4 Atlantico 198240 509
## 5 Bogota d.c. 1584 39
## 6 Bolivar 304261 17620
## 7 Boyaca 165956 6404
## 8 Caldas 132147 2367
## 9 Caqueta 54370 13417
## 10 Casanare 76850 131
## # ... with 24 more rowsbase de datos unida
total_animales <- inner_join(x =animales,
y =equinos_deptos,
by = "departamento")
total_animales## # A tibble: 34 x 4
## departamento total_cerdos total_bufalos total_equinos
## <chr> <dbl> <dbl> <dbl>
## 1 Amazonas 270 89 55
## 2 Antioquia 1998407 54638 253631
## 3 Arauca 35675 7347 53005
## 4 Atlantico 198240 509 9775
## 5 Bogota d.c. 1584 39 1689
## 6 Bolivar 304261 17620 61068
## 7 Boyaca 165956 6404 52279
## 8 Caldas 132147 2367 42118
## 9 Caqueta 54370 13417 64719
## 10 Casanare 76850 131 92471
## # ... with 24 more rows#ejemplo
ejemplo <- inner_join(x = porcinos_deptos,
y = bufalos_deptos,
by = "departamento") %>%
inner_join(x = equinos_deptos,
by = "departamento")
ejemplo## # A tibble: 34 x 4
## departamento total_equinos total_cerdos total_bufalos
## <chr> <dbl> <dbl> <dbl>
## 1 Amazonas 55 270 89
## 2 Antioquia 253631 1998407 54638
## 3 Arauca 53005 35675 7347
## 4 Atlantico 9775 198240 509
## 5 Bogota d.c. 1689 1584 39
## 6 Bolivar 61068 304261 17620
## 7 Boyaca 52279 165956 6404
## 8 Caldas 42118 132147 2367
## 9 Caqueta 64719 54370 13417
## 10 Casanare 92471 76850 131
## # ... with 24 more rowsconsulta
- como es la distribucion proporcional de bufalos equinos y porcinos por departamentos de colombia.¿ en un solo grafico es posible evidenciar esta informacion?
total_animales %>%
pivot_longer(names_to = "inventario", values_to = "animales", cols = -departamento) %>%
filter(!is.na(departamento)) %>%
ggplot(mapping = aes(x = reorder(departamento, animales),
y = animales,
fill = inventario)) +
geom_col(position = "fill")+
coord_flip()+
labs(x= "Departamento")