R Notebook
#1.datos multianuales 2007-2018 dinamicas de produccion de platano y cereales en el departamento del meta.
##2.instalacion de libreria
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(readr)
library(ggplot2)###3.carga de archivos
list.files(".", pattern=c('csv'))## [1] "co (1).csv" "co.csv"
## [3] "meta_cereales_2020 (1).csv" "meta_platanos_2020 (1).csv"(eva = read_csv("C:\\Users\\PERSONAL\\Documents\\GB\\Evaluaciones_Agropecuarias_Municipales_EVA.csv", col_names = TRUE))## Rows: 5241 Columns: 17
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (10): DEPARTAMENTO, MUNICIPIO, GRUPO
## DE CULTIVO, SUBGRUPO
## DE CULTIVO, ...
## dbl (7): CÓD.
## DEP., CÓD. MUN., AÑO, Área Sembrada
## (ha), Área Cosechada
## (ha...
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.names(eva)## [1] "CÓD. \nDEP."
## [2] "DEPARTAMENTO"
## [3] "CÓD. MUN."
## [4] "MUNICIPIO"
## [5] "GRUPO \nDE CULTIVO"
## [6] "SUBGRUPO \nDE CULTIVO"
## [7] "CULTIVO"
## [8] "DESAGREGACIÓN REGIONAL Y/O SISTEMA PRODUCTIVO"
## [9] "AÑO"
## [10] "PERIODO"
## [11] "Área Sembrada\n(ha)"
## [12] "Área Cosechada\n(ha)"
## [13] "Producción\n(t)"
## [14] "Rendimiento\n(t/ha)"
## [15] "ESTADO FISICO PRODUCCION"
## [16] "NOMBRE \nCIENTIFICO"
## [17] "CICLO DE CULTIVO"####4.limpiar datos
eva %>% dplyr::select('CÓD. MUN.':'ESTADO FISICO PRODUCCION') -> eva.tmpeva.tmpeva.tmp %>% dplyr::select('Cod_Mun' = 'CÓD. MUN.',
'municipio'= 'MUNICIPIO',
'Grupo' = 'GRUPO \nDE CULTIVO',
'Subgrupo' = 'SUBGRUPO \nDE CULTIVO',
'cultivo' = 'CULTIVO',
'Year' = 'AÑO',
'AreaSembrada' = 'Área Sembrada\n(ha)',
'AreaCosechada' = 'Área Sembrada\n(ha)',
'Produccion' = 'Producción\n(t)', 'Rendimiento' = 'Rendimiento\n(t/ha)',
'Sistema' = 'DESAGREGACIÓN REGIONAL Y/O SISTEMA PRODUCTIVO',
'Estado' = 'ESTADO FISICO PRODUCCION') -> new_evanew_eva<
new_eva %>%
##filter(Produccion > 0) %>%
group_by(Grupo) %>%
summarize(total_produccion = sum(Produccion)) %>%
arrange(desc(total_produccion)) #####5.Total produccion.
new_eva %>%
group_by(Grupo) %>%
summarize(total_produccion = sum(Produccion)) -> PT PT %>%
filter(total_produccion > 1000000) -> main.groups(value = sum(main.groups$total_produccion))## [1] 27629576main.groups$percent = main.groups$total_produccion/value#5.1.diagrama produccion cultivos mas importantes.
library(ggplot2)
# Barplot
bp<- ggplot(main.groups, aes(x="", y=percent, fill=Grupo))+
geom_bar(width = 1, stat = "identity")
# Piechart
pie <- bp + coord_polar("y", start=0)
pie
new_eva %>%
group_by(Grupo,municipio) %>%
summarize(total_prod = sum(Produccion, na.rm = TRUE)) %>%
slice(which.max(total_prod)) %>%
arrange(desc(total_prod))## `summarise()` has grouped output by 'Grupo'. You can override using the
## `.groups` argument.new_eva %>%
group_by(Grupo,municipio) %>%
summarize(total_prod = sum(Produccion, na.rm = TRUE)) %>%
slice(which.max(total_prod)) -> leaders## `summarise()` has grouped output by 'Grupo'. You can override using the
## `.groups` argument.leadersleaders %>%
filter(total_prod > 50000) -> main.leaders##5.2.Tabla total produccion anual municipios.
# Basic barplot
p<-ggplot(data=main.leaders, aes(x=municipio, y=total_prod)) +
geom_bar(stat="identity")
p
#5.3.estadistica 2007 a 2018, comportamiento cultivo de platano toneladas anuales en Puerto lopez.
new_eva %>%
filter(municipio=="PUERTO LOPEZ" & cultivo=="PLATANO") %>%
group_by(Year, cultivo) %>%
select(municipio, cultivo, Produccion, Year) -> PUERTOLOPEZ_PLATANOPUERTOLOPEZ_PLATANOg <- ggplot(aes(x=Year, y=Produccion/1000), data =PUERTOLOPEZ_PLATANO) + geom_bar(stat='identity') + labs(y='Produccion de PLATANO [Ton x 1000]')g + ggtitle("Evolution of PLATANO Crop Production in PUERTO LOPEZ from 2007 to 2018") + labs(caption= "Based on EVA data (Minagricultura, 2020)")
https://rpubs.com/gundap0411/892327
sessionInfo()## R version 4.1.3 (2022-03-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 8.1 x64 (build 9600)
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## Matrix products: default
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## locale:
## [1] LC_COLLATE=Spanish_Colombia.1252 LC_CTYPE=Spanish_Colombia.1252
## [3] LC_MONETARY=Spanish_Colombia.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Colombia.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] ggplot2_3.3.5 readr_2.1.2 dplyr_1.0.8
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## loaded via a namespace (and not attached):
## [1] highr_0.9 pillar_1.7.0 bslib_0.3.1 compiler_4.1.3
## [5] jquerylib_0.1.4 tools_4.1.3 bit_4.0.4 digest_0.6.29
## [9] gtable_0.3.0 jsonlite_1.8.0 evaluate_0.15 lifecycle_1.0.1
## [13] tibble_3.1.6 pkgconfig_2.0.3 rlang_1.0.2 cli_3.2.0
## [17] DBI_1.1.2 rstudioapi_0.13 parallel_4.1.3 yaml_2.3.5
## [21] xfun_0.30 fastmap_1.1.0 withr_2.5.0 stringr_1.4.0
## [25] knitr_1.38 generics_0.1.2 vctrs_0.3.8 sass_0.4.1
## [29] hms_1.1.1 bit64_4.0.5 grid_4.1.3 tidyselect_1.1.2
## [33] glue_1.6.2 R6_2.5.1 fansi_1.0.2 vroom_1.5.7
## [37] rmarkdown_2.13 farver_2.1.0 purrr_0.3.4 tzdb_0.2.0
## [41] magrittr_2.0.2 scales_1.1.1 ellipsis_0.3.2 htmltools_0.5.2
## [45] assertthat_0.2.1 colorspace_2.0-3 labeling_0.4.2 utf8_1.2.2
## [49] stringi_1.7.6 munsell_0.5.0 crayon_1.5.1