Caracteristicas suelos papa
Pedro Lizarazo, Geraldyne Rozo, Jimer Lozano
2023-11-22
Librerias usadas
library(readxl)
library(dplyr)
library(tidyverse)
library(stringi)
library(corrplot)
library(GGally)
library(textshape)
library(FactoMineR)
library(factoextra)
library(rlang)
library(tibble)
library(ggstatsplot)Cargar municipios con aptitud para el cultivo de papa
AP.TOT <- read_excel("K:/Articulos/Analisis suelos papa 2023/Bases de datos/MUNICIPIOS CON ALTO DESMPEÑO PRODUCTIVO PAPA 07.10.23.xlsx")
# Quitar las tildes a la columna de municipios
AP.TOT$MUN <- toupper(stri_trans_general(AP.TOT$MUN,"Latin-ASCII"))
AP.CUN <- AP.TOT %>% filter(., DEPARTAMENTO=="CUNDINAMARCA") %>%
mutate(., IDPM= pmin(IDPMi, IDPMii, na.rm = T) )
AP.CUN## # A tibble: 47 × 7
## DEPARTAMENTO MUN IDPMi IDPMii ALTITUD TEMPERATURA IDPM
## <chr> <chr> <dbl> <dbl> <chr> <chr> <dbl>
## 1 CUNDINAMARCA BOGOTA 3 3 2625 13.1 3
## 2 CUNDINAMARCA CAJICA 3 3 2558 14 3
## 3 CUNDINAMARCA CARMEN DE CARUPA 1 2 2600 12 1
## 4 CUNDINAMARCA CHIA 2 2 2600 14 2
## 5 CUNDINAMARCA CHIPAQUE 3 3 2400 13 3
## 6 CUNDINAMARCA CHOACHI 3 3 1923 18 3
## 7 CUNDINAMARCA CHOCONTA 1 2 2689 10 1
## 8 CUNDINAMARCA COGUA 1 2 2600 14 1
## 9 CUNDINAMARCA COTA 2 2 2566 14 2
## 10 CUNDINAMARCA CUCUNUBA 2 NA 2590 14 2
## # ℹ 37 more rowsCargar base de datos de productividad DATOS ABIERTOS
PRO.TOT <- read_excel("K:/Articulos/Analisis suelos papa 2023/Bases de datos/RENDIMIENTO DE CULTIVOS EN COLOMBIA POR AÑO 1.10.23.xlsx", sheet = "RENDIMIENTO DE CULTIVOS EN COLO")
# Quitar las tildes a la columna de municipios
PRO.TOT$MUN <- toupper(stri_trans_general(PRO.TOT$MUN,"Latin-ASCII"))
#
PRO.CUN1 <- PRO.TOT %>%
filter(., Depertamento==c("CUNDINAMARCA", "BOGOTÁ"), Cultivo=="PAPA") %>%
group_by(., MUN) %>%
dplyr::summarise(.,
Asem1=mean(`Area sembrada`, na.rm = TRUE),
Acos1=mean(`Area cosechada`, na.rm = TRUE),
Prod1=mean(`Produccion`, na.rm = TRUE),
Rmax1=max(`Rendimiento`, na.rm = TRUE),
Rmed1=median(`Rendimiento`, na.rm = TRUE),
Rmea1=mean(`Rendimiento`, na.rm = TRUE)) %>%
ungroup() %>%
right_join(., AP.CUN, by="MUN") %>%
dplyr::mutate(.,
ALT=as.numeric(ALTITUD),
TEM=as.numeric(TEMPERATURA),
IDPM=as.numeric(IDPM) ) %>%
select(., !c("IDPMi", "IDPMii", "DEPARTAMENTO", "ALTITUD", "TEMPERATURA") )
PRO.CUN1## # A tibble: 47 × 10
## MUN Asem1 Acos1 Prod1 Rmax1 Rmed1 Rmea1 IDPM ALT TEM
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CAJICA 20.1 12.2 251. 30 20 20.7 3 2558 14
## 2 CARMEN DE CARUPA 945. 855. 15280. 32.6 15 16.0 1 2600 12
## 3 CHIA 125. 118. 2035 18 17 17.1 2 2600 14
## 4 CHIPAQUE 118. 116. 1722. 20 14.5 14.0 3 2400 13
## 5 CHOACHI 41.8 41.5 631. 18 15 15.5 3 1923 18
## 6 CHOCONTA 923. 859. 21884. 30.7 20 22.1 1 2689 10
## 7 COGUA 792. 789 15641. 25 19.7 21.1 1 2600 14
## 8 COTA 71.4 71.1 1960. 40 23 23.3 2 2566 14
## 9 CUCUNUBA 158. 158. 2422. 18.1 15.2 15.5 2 2590 14
## 10 EL ROSAL 276. 259. 4601. 18 18 17.4 1 2685 12
## # ℹ 37 more rowsCargar base de datos de productividad PAPA TETRAPLOIDE papa de año (AGRONET)
PRO.CUN2 <- read_excel("K:/Articulos/Analisis suelos papa 2023/Bases de datos/agronet, 2022. Produccion y rendimiento cundinamarca.xlsx")
# Quitar las tildes a la columna de municipios
PRO.CUN2$MUN <- toupper(stri_trans_general(PRO.CUN2$MUN,"Latin-ASCII"))
#
PRO.CUN2 <- PRO.CUN2 %>%
group_by(., MUN) %>%
dplyr::summarise(.,
Asem2=mean(`Area sembrada`, na.rm = TRUE),
Acos2=mean(`Area cosechada`, na.rm = TRUE),
Prod2=mean(`Produccion`, na.rm = TRUE),
Rmax2=max(`Rendimiento`, na.rm = TRUE),
Rmed2=median(`Rendimiento`, na.rm = TRUE),
Rmea2=mean(`Rendimiento`, na.rm = TRUE)) %>%
ungroup() %>%
right_join(., AP.CUN, by="MUN") %>%
dplyr::mutate(.,
ALT=as.numeric(ALTITUD),
TEM=as.numeric(TEMPERATURA),
IDPM=as.numeric(IDPM) ) %>%
select(., !c("IDPMi", "IDPMii", "DEPARTAMENTO", "ALTITUD", "TEMPERATURA") )
PRO.CUN2## # A tibble: 47 × 10
## MUN Asem2 Acos2 Prod2 Rmax2 Rmed2 Rmea2 IDPM ALT TEM
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CAJICA 39.4 26.5 514. 30 19.5 19.5 3 2558 14
## 2 CARMEN DE CARUPA 2276. 2083. 39427 28.7 19.0 19.3 1 2600 12
## 3 CHIA 189. 195. 3465. 25 18 18.2 2 2600 14
## 4 CHIPAQUE 121. 156. 2366. 30 15.7 16.4 3 2400 13
## 5 CHOACHI 70.1 71.8 1080. 18 15.5 15.4 3 1923 18
## 6 CHOCONTA 3448. 3091. 84074. 30.7 28.9 27.2 1 2689 10
## 7 COGUA 1309. 1362. 27124. 25 20.0 20.8 1 2600 14
## 8 COTA 146. 144. 4054. 36.4 26 26.6 2 2566 14
## 9 CUCUNUBA 292. 290. 4856 30 15.7 17.3 2 2590 14
## 10 EL ROSAL 718. 675. 13333. 24 18 19.3 1 2685 12
## # ℹ 37 more rowsCargar base de datos de productividad PAPA DIPLOIDE
PRO.CUN3 <- read_excel("K:/Articulos/Analisis suelos papa 2023/Bases de datos/agronet, 2022. Produccion y rendimiento cundinamarca criolla.xlsx")
# Quitar las tildes a la columna de municipios
PRO.CUN3$MUN <- toupper(stri_trans_general(PRO.CUN3$MUN,"Latin-ASCII"))
#
PRO.CUN3 <- PRO.CUN3 %>%
group_by(., MUN) %>%
dplyr::summarise(.,
Asem3=mean(`Area sembrada`, na.rm = TRUE),
Acos3=mean(`Area cosechada`, na.rm = TRUE),
Prod3=mean(`Produccion`, na.rm = TRUE),
Rmax3=max(`Rendimiento`, na.rm = TRUE),
Rmed3=median(`Rendimiento`, na.rm = TRUE),
Rmea3=mean(`Rendimiento`, na.rm = TRUE)) %>%
ungroup() %>%
right_join(., AP.CUN, by="MUN") %>%
dplyr::mutate(.,
ALT=as.numeric(ALTITUD),
TEM=as.numeric(TEMPERATURA),
IDPM=as.numeric(IDPM) ) %>%
select(., !c("IDPMi", "IDPMii", "DEPARTAMENTO", "ALTITUD", "TEMPERATURA") )
PRO.CUN3## # A tibble: 47 × 10
## MUN Asem3 Acos3 Prod3 Rmax3 Rmed3 Rmea3 IDPM ALT TEM
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 CAJICA 10 12 140 11.7 11.7 11.7 3 2558 14
## 2 CARMEN DE CARUPA 77.6 69.4 956. 20 14.0 13.1 1 2600 12
## 3 CHIPAQUE 38.3 46.8 625. 20 11.7 12.5 3 2400 13
## 4 CHOACHI 76.5 70.1 987. 16 14.5 14.4 3 1923 18
## 5 CHOCONTA 150. 138. 2327. 25.6 17.9 18.4 1 2689 10
## 6 COGUA 453. 452. 8403. 19.8 17.9 12.6 1 2600 14
## 7 COTA 22 22 338. 16 15.7 15.6 2 2566 14
## 8 CUCUNUBA 21.2 21.2 353. 22 16.7 17.3 2 2590 14
## 9 EL ROSAL 459 436. 7616 18 18 17.3 1 2685 12
## 10 FACATATIVA 127. 112. 1988. 23.0 17 17.6 1 2586 19
## # ℹ 37 more rowsUnificamos las bases de datos de productividad
PRO.CUN <- PRO.CUN1 %>%
full_join(., PRO.CUN2) %>%
full_join(., PRO.CUN3) %>%
select(sort(names(.))); PRO.CUN## # A tibble: 47 × 22
## Acos1 Acos2 Acos3 ALT Asem1 Asem2 Asem3 IDPM MUN Prod1 Prod2 Prod3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 12.2 26.5 12 2558 20.1 39.4 10 3 CAJICA 251. 514. 140
## 2 855. 2083. 69.4 2600 945. 2276. 77.6 1 CARMEN… 15280. 39427 956.
## 3 118. 195. NA 2600 125. 189. NA 2 CHIA 2035 3465. NA
## 4 116. 156. 46.8 2400 118. 121. 38.3 3 CHIPAQ… 1722. 2366. 625.
## 5 41.5 71.8 70.1 1923 41.8 70.1 76.5 3 CHOACHI 631. 1080. 987.
## 6 859. 3091. 138. 2689 923. 3448. 150. 1 CHOCON… 21884. 84074. 2327.
## 7 789 1362. 452. 2600 792. 1309. 453. 1 COGUA 15641. 27124. 8403.
## 8 71.1 144. 22 2566 71.4 146. 22 2 COTA 1960. 4054. 338.
## 9 158. 290. 21.2 2590 158. 292. 21.2 2 CUCUNU… 2422. 4856 353.
## 10 259. 675. 436. 2685 276. 718. 459 1 EL ROS… 4601. 13333. 7616
## # ℹ 37 more rows
## # ℹ 10 more variables: Rmax1 <dbl>, Rmax2 <dbl>, Rmax3 <dbl>, Rmea1 <dbl>,
## # Rmea2 <dbl>, Rmea3 <dbl>, Rmed1 <dbl>, Rmed2 <dbl>, Rmed3 <dbl>, TEM <dbl>names(PRO.CUN)## [1] "Acos1" "Acos2" "Acos3" "ALT" "Asem1" "Asem2" "Asem3" "IDPM" "MUN"
## [10] "Prod1" "Prod2" "Prod3" "Rmax1" "Rmax2" "Rmax3" "Rmea1" "Rmea2" "Rmea3"
## [19] "Rmed1" "Rmed2" "Rmed3" "TEM"Cargar base de datos de analisis de suelos
RAS.TOT <- read_excel("K:/Articulos/Analisis suelos papa 2023/Bases de datos/RESULTADOS ANALISIS DE SUELOS 1.10.23.xlsx", sheet = "RAS")
# Quitar las tildes a la columna de municipios
RAS.TOT$MUN <- toupper(stri_trans_general(RAS.TOT$MUN,"Latin-ASCII"))
RAS.TOT## # A tibble: 46,745 × 31
## DEP MUN CUL EST TIE TOP DRE RIE FER FEC PH MAT FOS
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <dbl> <chr>
## 1 CUND… FUNZA Uchu… POR … NO A… Ondu… Bueno No i… No i… NA 5.66 9.71 5.62…
## 2 CUND… BITU… Citr… POR … NO A… Ondu… Bueno No c… No i… NA 8.08 3.42 7.57…
## 3 CUND… VILL… Past… ESTA… NO I… Ondu… Bueno No c… No i… NA 5.87 2.34 16.9…
## 4 CUND… VILL… Past… ESTA… NO I… Ondu… Bueno No c… No i… NA 5.56 6.85 32.0…
## 5 CUND… BOGO… Papa… POR … NO A… Pend… Bueno No c… 15-1… NA 4.87 16.0 64.1…
## 6 HUILA GIGA… Agua… POR … NO A… Pend… Bueno No c… No h… NA 5.82 2.34 7.09…
## 7 META ACAC… Citr… ESTA… mas … Plano Bueno No c… No i… NA 4.68 2.12 9.16…
## 8 META ACAC… Agua… ESTA… mas … Plano Regu… No c… Cal … NA 4.51… 1.51 36.1…
## 9 META ACAC… Cacao ESTA… mas … Plano Bueno No c… 18-1… NA 5.15 2.10 12.0…
## 10 META ACAC… Cacao ESTA… de 5… Plano Bueno No c… Calf… NA 4.79 1.22 7.81…
## # ℹ 46,735 more rows
## # ℹ 18 more variables: AZU <dbl>, ACI <chr>, ALU <chr>, CAL <chr>, MAG <chr>,
## # POT <chr>, SOD <chr>, CAP <dbl>, CON <dbl>, HIE <chr>, COB <chr>,
## # MAN <chr>, ZIN <chr>, BOR <dbl>, HIE2 <chr>, COB2 <chr>, MAN2 <chr>,
## # ZIN2 <chr>Extraemos los analisis de suelos para municipios con potencial de producción de papa
cultivos.calido <- c("Plátano", "Cacao", "Aguacate", "Pastos-Estrella", "Yuca", "Caña panelera/azucar", "Maracuyá", "Citricos",
"Arroz", "Pastos-brachiaria", "Ñame", "Piña", "Algodón", "Sacha Inchi", "Citricos-Limón", "Citricos-Naranjo", "Guayaba", "Mango", "Melón", "Palma de aceite", "Caucho", "Chirimoya", "Guadua", "Guanabana", "Sábila", "Papaya", "Ají", "Balu"); cultivos.calido## [1] "Plátano" "Cacao" "Aguacate"
## [4] "Pastos-Estrella" "Yuca" "Caña panelera/azucar"
## [7] "Maracuyá" "Citricos" "Arroz"
## [10] "Pastos-brachiaria" "Ñame" "Piña"
## [13] "Algodón" "Sacha Inchi" "Citricos-Limón"
## [16] "Citricos-Naranjo" "Guayaba" "Mango"
## [19] "Melón" "Palma de aceite" "Caucho"
## [22] "Chirimoya" "Guadua" "Guanabana"
## [25] "Sábila" "Papaya" "Ají"
## [28] "Balu"RAS.CUN <- RAS.TOT %>%
mutate_all(~ ifelse(. == "ND", 0.0, .)) %>%
mutate_all(~ ifelse(. == "<0,01", 0.005, .)) %>%
mutate_all(~ ifelse(. == "<0,06", 0.030, .)) %>%
mutate_all(~ ifelse(. == "<0,09", 0.045, .)) %>%
mutate_all(~ ifelse(. == "<0,14", 0.070, .)) %>%
mutate_all(~ ifelse(. == "<0,20", 0.100, .)) %>%
mutate_all(~ ifelse(. == "<0,50", 0.250, .)) %>%
mutate_all(~ ifelse(. == "<0,55", 0.275, .)) %>%
mutate_all(~ ifelse(. == "<0,59", 0.295, .)) %>%
mutate_all(~ ifelse(. == "<1,00", 0.500, .)) %>%
mutate_all(~ ifelse(. == "<3,87", 1.935, .)) %>%
mutate_all(~ ifelse(. == "<3,80", 1.900, .)) %>%
mutate_all(~ ifelse(. == "<5,00", 2.500, .)) %>%
mutate_all(~ ifelse(. == "<10,00", 5.000, .)) %>%
mutate_all(~ ifelse(. == ">12,88", 12.88, .)) %>%
select(., !c(DEP, EST, TIE, TOP, DRE, RIE, FER, FEC, HIE2, COB2, MAN2, ZIN2) ) %>%
dplyr::right_join(., PRO.CUN, by = "MUN") %>%
mutate(.,
MUN=as.factor(MUN),
CUL=as.factor(CUL),
IDPM=as.numeric(IDPM),
ALT=as.numeric(ALT),
TEM=as.numeric(TEM),
Asem1=as.numeric(Asem1),
Asem2=as.numeric(Asem2),
Asem3=as.numeric(Asem3),
Acos1=as.numeric(Acos1),
Acos2=as.numeric(Acos2),
Acos3=as.numeric(Acos3),
Prod1=as.numeric(Prod1),
Prod2=as.numeric(Prod2),
Prod3=as.numeric(Prod3),
Rmax1=as.numeric(Rmax1),
Rmax2=as.numeric(Rmax2),
Rmax3=as.numeric(Rmax3),
Rmea1=as.numeric(Rmea1),
Rmea2=as.numeric(Rmea2),
Rmea3=as.numeric(Rmea3),
Rmed1=as.numeric(Rmed1),
Rmed2=as.numeric(Rmed2),
Rmed3=as.numeric(Rmed3),
pH=as.numeric(PH),
CIC=as.numeric(CAP),
CE=as.numeric(CON),
MO=as.numeric(MAT),
P=as.numeric(FOS),
K=as.numeric(POT),
Ca=as.numeric(CAL),
S=as.numeric(AZU),
Mg=as.numeric(MAG),
Na=as.numeric(SOD),
Fe=as.numeric(HIE),
Cu=as.numeric(COB),
Mn=as.numeric(MAN),
Zn=as.numeric(ZIN),
B=as.numeric(BOR),
Aci=as.numeric(ACI),
Al=as.numeric(ALU) ) %>%
mutate(.,
P.dis=(P*2.29), # FOSFORO DISPONIBLE EN PPM P2O5
N.tot=(MO*0.05), # NITROGENO EN PORCENTAJE
N.dis=((MO*0.05)*0.015)*10000, # NITROGENO DISPONIBLE CLIMA FRÍO EN ppm
S.Bas=((Ca+Mg+K+Na)/CIC)*100, # SATURACION DE BASES
S.Al=(Al/CIC)*100, # SATURACION DE ALUMINIO
S.Ca=(Ca/CIC)*100, # SATURACION DE CALCIO
S.Mg=(Mg/CIC)*100, # SATURACION DE MAGNESIO
S.K=(K/CIC)*100, # SATURACION DE POTASIO
S.Na=(Na/CIC)*100, # SATURACION DE SODIO
Ca_Mg=(Ca/Mg), # RELACION CALCIO MAGNESIO
Mg_K=(Mg/K), # RELACION MAGNESIO POTASIO
Ca_K=(Ca/K), # RELACION CALCIO POTASIO
Ca.Mg_K=((Ca+Mg)/K), # RELACION CALCIO MAGNESIO CON POTASIO
Ca_B=(Ca/B), # RELACION CALCIO BORO
Fe_Mn=(Fe/Mn), # RELACION HIERRO MANGANESO
P_Zn=(P/Zn), # RELACION FOSFORO ZINC
Fe_Zn=(Fe/Zn) ) %>% # RELACION HIERRO ZINC
select(., !c(PH, MAT, FOS, AZU, ACI, ALU, CAL, MAG, POT, SOD, CAP, CON, HIE, COB, MAN, ZIN, BOR) ) %>%
filter(., !(CUL %in% cultivos.calido)
)
RAS.CUN## # A tibble: 3,054 × 57
## MUN CUL Acos1 Acos2 Acos3 ALT Asem1 Asem2 Asem3 IDPM Prod1 Prod2
## <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FUNZA Uchu… 408. 586. NA 2548 492. 651. NA 1 8161. 11779.
## 2 BOGOTA Papa… NA NA NA 2625 NA NA NA 3 NA NA
## 3 CHIA Frut… 118. 195. NA 2600 125. 189. NA 2 2035 3465.
## 4 CARMEN D… Papa… 855. 2083. 69.4 2600 945. 2276. 77.6 1 15280. 39427
## 5 CARMEN D… Papa… 855. 2083. 69.4 2600 945. 2276. 77.6 1 15280. 39427
## 6 CARMEN D… Papa… 855. 2083. 69.4 2600 945. 2276. 77.6 1 15280. 39427
## 7 CARMEN D… Papa… 855. 2083. 69.4 2600 945. 2276. 77.6 1 15280. 39427
## 8 CARMEN D… Arve… 855. 2083. 69.4 2600 945. 2276. 77.6 1 15280. 39427
## 9 CARMEN D… Past… 855. 2083. 69.4 2600 945. 2276. 77.6 1 15280. 39427
## 10 CARMEN D… Past… 855. 2083. 69.4 2600 945. 2276. 77.6 1 15280. 39427
## # ℹ 3,044 more rows
## # ℹ 45 more variables: Prod3 <dbl>, Rmax1 <dbl>, Rmax2 <dbl>, Rmax3 <dbl>,
## # Rmea1 <dbl>, Rmea2 <dbl>, Rmea3 <dbl>, Rmed1 <dbl>, Rmed2 <dbl>,
## # Rmed3 <dbl>, TEM <dbl>, pH <dbl>, CIC <dbl>, CE <dbl>, MO <dbl>, P <dbl>,
## # K <dbl>, Ca <dbl>, S <dbl>, Mg <dbl>, Na <dbl>, Fe <dbl>, Cu <dbl>,
## # Mn <dbl>, Zn <dbl>, B <dbl>, Aci <dbl>, Al <dbl>, P.dis <dbl>, N.tot <dbl>,
## # N.dis <dbl>, S.Bas <dbl>, S.Al <dbl>, S.Ca <dbl>, S.Mg <dbl>, S.K <dbl>, …Conteo de observaciones por cultivo
head(sort(table(RAS.CUN$CUL), decreasing = T), n = 80)##
## Pastos Papa de año
## 999 427
## Hortalizas Frijol
## 153 116
## Papa Criolla Arveja
## 105 99
## Uchuva Café
## 96 95
## Maíz Pastos-raigrás
## 93 91
## Cebolla de bulbo Mora
## 72 65
## Tomate Fresa
## 63 59
## Quinua Pastos-kikuyo
## 51 44
## Zanahoria Forestal
## 39 34
## No indica Tomate de arbol
## 32 30
## Avena Cebolla de rama
## 29 24
## Gulupa Ajo
## 20 19
## Lechuga Cebolla
## 14 12
## Granadilla Frutales
## 12 11
## Lulo Arandanos
## 11 10
## Aromaticas Aromaticas-Hierbabuena
## 10 10
## Cannabis Feijoa
## 10 6
## Frambuesa Eucalipto
## 6 5
## Pastos-Kikuyo Soya
## 5 5
## Alfalfa Aromaticas-Calendula
## 4 4
## Caducifolios Calas
## 4 4
## Cebada Cebolla puerro
## 4 3
## flores Ornamentales Ahuyama
## 3 2
## Alcachofa Aromaticas-Laurel
## 2 2
## Arracacha Calabaza
## 2 2
## Citricos-Tangelo Coliflor
## 2 2
## Espinaca Follaje Ornamental
## 2 2
## Guisantes agrosilvopastoril
## 2 1
## Aromaticas-Estragón Aromaticas-Tomillo
## 1 1
## Banco de proteina forrajero Calabacín
## 1 1
## Cañamo cebolla
## 1 1
## Cebollin Cilantro
## 1 1
## Cubio Epifitas
## 1 1
## flores industrial-Rosa flores Ornamentales-Crisantemo
## 1 1
## flores Ornamentales-Hortencias flores Ornamentales-Snapdragon
## 1 1
## Follaje Ornamental-Brillantina follaje Ornamental-Ruscus
## 1 1
## Forestal-Pino Haba
## 1 1
## Helecho Huerta
## 1 1
## Pastos-Angleton Pastos-King grass
## 1 1
## Pimentón Solidago
## 1 1### NOTA ELIMINAR ANALISIS DE SUELOS RELACIONADOS CON CULTIVOS DE CLIMA CALIDO
# Plátano, Cacao, Aguacate, Pastos-Estrella, Yuca, Caña panelera/azucar, Maracuyá, Citricos, Arroz, Pastos-brachiaria, Ñame, Piña, Algodón, Sacha Inchi, Citricos-Limón,
# Citricos-Naranjo, Citricos-Tangelo, Guayaba, Mango, Melón, Palma de aceite, Caucho, Chirimoya, Guadua, GuanabanaConteo de observaciones por municipio
head(sort(table(RAS.CUN$MUN), decreasing = T), n = 50)##
## BOGOTA CHOCONTA GUASCA CARMEN DE CARUPA
## 322 156 144 128
## SIBATE GUATAVITA CUCUNUBA MANTA
## 109 106 102 102
## GRANADA TAUSA SIMIJACA GUTIERREZ
## 98 95 92 90
## LENGUAZAQUE SUESCA LA CALERA PASCA
## 90 89 84 83
## SAN CAYETANO CHOACHI SAN BERNARDO FACATATIVA
## 82 74 73 70
## SUSA GUACHETA UNE CHIPAQUE
## 65 63 63 59
## SUBACHOQUE FUQUENE FUNZA SESQUILE
## 55 54 50 49
## PACHO COGUA VILLAPINZON FOSCA
## 42 35 34 32
## JUNIN UBAQUE TENJO TABIO
## 32 28 26 22
## ZIPAQUIRA CHIA SUTATAUSA EL ROSAL
## 22 21 21 20
## ZIPACON MADRID SOACHA SOPO
## 20 17 14 6
## CAJICA COTA MACHETA
## 5 5 5Agrupar variables por municipio
p.truncado <- 0.05
GR.CUN <- RAS.CUN %>% group_by(., MUN) %>%
dplyr::summarise(.,
pH=mean(pH, na.rm = TRUE, trim = p.truncado),
MO=mean(MO, na.rm = TRUE, trim = p.truncado),
N.tot=mean(N.tot, na.rm = TRUE, trim = p.truncado),
N.dis=mean(N.dis, na.rm = TRUE, trim = p.truncado),
P=mean(P, na.rm = TRUE, trim = p.truncado),
P.dis=mean(P.dis, na.rm = TRUE, trim = p.truncado),
S=mean(S, na.rm = TRUE, trim = p.truncado),
Aci=mean(Aci, na.rm = TRUE, trim = p.truncado),
Al=mean(Al, na.rm = TRUE, trim = p.truncado),
Ca=mean(Ca, na.rm = TRUE, trim = p.truncado),
Mg=mean(Mg, na.rm = TRUE, trim = p.truncado),
K=mean(K, na.rm = TRUE, trim = p.truncado),
Na=mean(Na, na.rm = TRUE, trim = p.truncado),
CIC=mean(CIC, na.rm = TRUE, trim = p.truncado),
CE=mean(CE, na.rm = TRUE, trim = p.truncado),
Fe=mean(Fe, na.rm = TRUE, trim = p.truncado),
Cu=mean(Cu, na.rm = TRUE, trim = p.truncado),
Mn=mean(Mn, na.rm = TRUE, trim = p.truncado),
Zn=mean(Zn, na.rm = TRUE, trim = p.truncado),
B=mean(B, na.rm = TRUE, trim = p.truncado),
S.Bas=mean(S.Bas, na.rm = TRUE, trim = p.truncado),
S.Al=mean(S.Al, na.rm = TRUE, trim = p.truncado),
S.Ca=mean(S.Ca, na.rm = TRUE, trim = p.truncado),
S.Mg=mean(S.Mg, na.rm = TRUE, trim = p.truncado),
S.K=mean(S.K, na.rm = TRUE, trim = p.truncado),
S.Na=mean(S.Na, na.rm = TRUE, trim = p.truncado),
Ca_Mg=mean(Ca_Mg, na.rm = TRUE, trim = p.truncado),
Mg_K=mean(Mg_K, na.rm = TRUE, trim = p.truncado),
Ca_K=mean(Ca_K, na.rm = TRUE, trim = p.truncado),
Ca.Mg_K=mean(Ca.Mg_K, na.rm = TRUE, trim = p.truncado),
Ca_B=mean(Ca_B, na.rm = TRUE, trim = p.truncado),
Fe_Mn=mean(Fe_Mn, na.rm = TRUE, trim = p.truncado),
Fe_Zn=mean(Fe_Zn, na.rm = TRUE, trim = p.truncado)) %>%
right_join(., PRO.CUN, by="MUN") %>%
textshape::column_to_rownames(., 1) %>%
select(., c(Rmed1, Rmed2, Rmed3, Rmea1, Rmea2, Rmea3, Rmax1, Rmax2, Rmax3,
Asem1, Asem2, Asem3, Acos1, Acos2, Acos3, Prod1, Prod2, Prod3,
IDPM, ALT, TEM,
pH, Aci, Al, CE, CIC, MO,
N.tot, N.dis, P, P.dis, K, Ca, Mg, S, Na, Fe, Cu, Mn, Zn, B,
S.Bas, S.Al, S.Ca, S.Mg, S.K, S.Na,
Ca_Mg, Mg_K, Ca_K, Ca.Mg_K,
Ca_B, Fe_Mn, Fe_Zn) )
GR.CUN## Rmed1 Rmed2 Rmed3 Rmea1 Rmea2 Rmea3 Rmax1 Rmax2
## BOGOTA NA NA NA NA NA NA NA NA
## CAJICA 20.000 19.510 11.670 20.72727 19.52375 11.670000 30.00 30.00
## CARMEN DE CARUPA 15.000 19.040 13.950 15.99538 19.32875 13.072727 32.57 28.74
## CHIA 17.000 18.000 NA 17.10222 18.21562 NA 18.00 25.00
## CHIPAQUE 14.500 15.670 11.690 14.04067 16.39133 12.450667 20.00 30.00
## CHOACHI 15.000 15.460 14.470 15.53412 15.41750 14.402308 18.00 18.00
## CHOCONTA 20.000 28.945 17.885 22.10111 27.16437 18.355000 30.69 30.71
## COGUA 19.670 20.020 17.890 21.05467 20.84250 12.573333 25.00 25.00
## COTA 23.000 26.000 15.725 23.27875 26.62875 15.612500 40.00 36.37
## CUCUNUBA 15.240 15.685 16.660 15.49250 17.27750 17.330000 18.13 30.00
## EL ROSAL 18.000 18.000 18.000 17.41333 19.28063 17.262500 18.00 24.00
## FACATATIVA 17.220 18.710 17.000 17.77059 19.56312 17.563846 20.00 26.12
## FOSCA 18.000 20.000 15.055 18.87739 20.41937 16.553125 24.00 24.00
## FUNZA 20.000 20.000 NA 19.88385 20.38188 NA 22.00 29.08
## FUQUENE 14.750 17.290 12.000 15.83167 18.04375 12.917692 26.05 26.38
## GRANADA 17.500 19.115 15.000 16.81375 18.24750 15.741875 20.00 20.00
## GUACHETA 16.000 17.400 16.000 15.81579 17.52000 16.074286 18.00 22.00
## GUASCA 18.130 19.635 18.500 21.43800 20.47375 17.543333 34.30 34.30
## GUATAVITA 22.585 28.235 21.250 23.83821 26.98937 19.646250 45.59 38.97
## GUTIERREZ 20.840 20.810 NA 21.58167 19.56000 NA 24.00 24.00
## JUNIN 25.000 15.000 NA 21.17400 17.32500 NA 34.00 34.00
## LA CALERA 16.250 25.000 16.375 19.46875 25.12375 17.336250 30.00 30.00
## LENGUAZAQUE 18.000 17.700 13.710 16.95364 17.19063 13.552500 18.29 19.05
## MACHETA 14.000 14.270 8.810 13.48333 14.69125 10.264286 23.86 20.92
## MADRID 21.000 20.865 NA 20.97182 21.06875 NA 22.50 24.00
## MANTA 12.000 16.070 NA 13.30647 15.33812 NA 28.00 24.21
## PACHO 13.000 18.000 12.000 14.00000 17.40375 12.075625 22.00 24.25
## PASCA 20.000 22.680 10.000 19.39765 21.86812 6.666667 25.00 25.00
## SAN BERNARDO 12.000 16.150 NA 13.51600 15.67000 NA 19.00 25.60
## SAN CAYETANO 16.000 20.000 12.000 15.65789 20.64250 11.510000 20.00 39.15
## SESQUILE 25.000 25.480 17.070 23.17267 26.88812 15.642500 31.59 41.74
## SIBATE 20.000 21.150 18.000 19.87474 21.53063 19.135000 25.74 25.82
## SIMIJACA 14.500 16.775 NA 15.12500 19.41500 NA 18.00 35.00
## SOACHA 14.500 20.000 11.500 15.04167 18.20938 12.923750 20.00 22.00
## SOPO 20.000 20.000 15.000 18.58333 20.46750 13.778889 25.00 33.00
## SUBACHOQUE 21.040 21.535 18.000 21.39148 23.29250 17.883125 46.00 34.36
## SUESCA 23.120 27.105 21.250 23.22571 25.03250 20.425000 32.00 32.00
## SUSA 15.000 19.610 13.850 16.09833 17.41200 12.577778 24.07 22.22
## SUTATAUSA 15.000 15.310 NA 15.95000 17.11437 NA 23.20 26.45
## TABIO 22.000 21.920 17.200 21.98375 21.47250 16.216667 23.00 23.49
## TAUSA 18.000 18.120 14.500 19.33312 19.32000 13.078182 26.65 26.60
## TENJO 23.000 22.265 19.000 21.52727 21.48000 19.472500 23.00 30.00
## UBAQUE 15.000 15.690 12.475 16.18095 16.69625 13.875000 22.00 22.00
## UNE 29.075 29.075 21.670 27.10250 27.47188 21.333750 33.79 33.77
## VILLAPINZON 19.000 25.350 15.650 19.52000 25.34625 18.853636 32.69 31.48
## ZIPACON 12.000 16.960 11.750 15.01545 23.58000 12.394375 43.26 41.46
## ZIPAQUIRA 20.750 24.550 17.500 20.80556 23.01562 17.013000 27.00 25.00
## Rmax3 Asem1 Asem2 Asem3 Acos1 Acos2
## BOGOTA NA NA NA NA NA NA
## CAJICA 11.67 20.09091 39.35625 10.000000 12.18182 26.49375
## CARMEN DE CARUPA 20.00 945.23077 2276.43750 77.636364 854.69231 2082.68750
## CHIA NA 124.66667 189.31250 NA 118.33333 195.31250
## CHIPAQUE 20.00 118.33333 121.30000 38.333333 116.33333 155.50000
## CHOACHI 16.00 41.82353 70.06250 76.538462 41.47059 71.75000
## CHOCONTA 25.64 922.88889 3448.18750 149.562500 858.70370 3091.25000
## COGUA 19.83 792.26667 1308.68750 453.333333 789.00000 1361.68750
## COTA 16.00 71.37500 145.75438 22.000000 71.06250 144.26687
## CUCUNUBA 22.00 157.50000 291.50000 21.250000 157.50000 289.62500
## EL ROSAL 18.00 276.50000 717.56250 459.000000 259.16667 675.43750
## FACATATIVA 22.98 247.05882 703.37500 127.384615 240.76471 674.44375
## FOSCA 32.62 208.91304 448.63125 441.737500 172.39130 428.56875
## FUNZA NA 491.53846 651.37500 NA 408.46154 585.75000
## FUQUENE 18.33 70.00000 199.95375 20.483077 66.27778 188.57875
## GRANADA 25.00 65.00000 110.61250 100.021250 60.66667 107.45625
## GUACHETA 19.25 198.73684 480.00000 15.142857 163.00000 458.25000
## GUASCA 22.00 396.50000 677.50000 32.500000 333.40000 652.87500
## GUATAVITA 27.61 571.25000 1604.68750 140.437500 567.89286 1632.34375
## GUTIERREZ NA 44.16667 58.00000 NA 42.00000 50.92000
## JUNIN NA 188.80000 164.57143 NA 185.00000 152.35714
## LA CALERA 22.00 283.75000 1351.87500 244.312500 275.06250 1257.75000
## LENGUAZAQUE 15.00 528.18182 1002.93750 49.500000 495.54545 1001.12500
## MACHETA 14.00 307.55556 553.06250 46.821429 286.83333 512.18750
## MADRID NA 426.36364 883.46875 NA 418.27273 864.45625
## MANTA NA 20.70588 42.25000 NA 18.94118 40.62500
## PACHO 15.00 126.20000 188.43750 291.562500 110.28000 167.93750
## PASCA 10.00 792.94118 1740.62500 100.000000 658.82353 1459.18750
## SAN BERNARDO NA 172.26667 220.76875 NA 145.73333 198.46875
## SAN CAYETANO 18.00 850.68421 2725.37500 51.785714 838.89474 2616.68750
## SESQUILE 20.00 919.00000 2370.93750 102.500000 915.86667 2330.31250
## SIBATE 25.00 773.42105 1784.50000 725.166667 717.89474 1721.06250
## SIMIJACA NA 219.12500 347.75000 NA 191.75000 305.31250
## SOACHA 20.00 180.50000 666.43750 61.187500 165.75000 612.06250
## SOPO 20.00 48.75000 78.12500 9.111111 48.75000 86.25000
## SUBACHOQUE 18.00 290.22222 774.12500 193.937500 290.22222 764.37500
## SUESCA 32.00 503.92857 1485.13125 61.583333 478.92857 1458.38125
## SUSA 20.00 546.00000 1138.73333 46.555556 495.00000 1092.73333
## SUTATAUSA NA 138.23077 267.62500 NA 133.84615 267.18750
## TABIO 23.45 68.93750 143.93750 17.750000 62.31250 129.75000
## TAUSA 18.00 4182.18750 11944.33333 147.727273 4125.68750 11443.80000
## TENJO 24.89 526.18182 877.68750 21.250000 480.90909 784.06250
## UBAQUE 22.00 309.76190 398.00000 335.857143 295.47619 433.42500
## UNE 25.00 1314.16667 2632.78750 476.125000 1261.25000 2564.13750
## VILLAPINZON 50.00 2509.90476 8348.81250 72.336364 2390.28571 7824.12500
## ZIPACON 21.82 106.68182 221.43750 224.312500 103.95455 194.87500
## ZIPAQUIRA 18.91 1027.83333 2580.63125 173.100000 931.72222 2432.50000
## Acos3 Prod1 Prod2 Prod3 IDPM ALT TEM
## BOGOTA NA NA NA NA 3 2625 13.1
## CAJICA 12.000000 250.5455 513.6344 140.0000 3 2558 14.0
## CARMEN DE CARUPA 69.363636 15279.7692 39427.0000 956.2727 1 2600 12.0
## CHIA NA 2035.0000 3465.1875 NA 2 2600 14.0
## CHIPAQUE 46.800000 1722.1333 2366.3500 625.2013 3 2400 13.0
## CHOACHI 70.076923 630.7059 1079.5000 987.4615 3 1923 18.0
## CHOCONTA 138.437500 21884.0741 84073.7125 2326.6875 1 2689 10.0
## COGUA 452.333333 15640.6667 27124.3125 8403.3333 1 2600 14.0
## COTA 22.000000 1959.8750 4053.8387 338.5000 2 2566 14.0
## CUCUNUBA 21.250000 2422.5000 4856.0000 352.7500 2 2590 14.0
## EL ROSAL 436.312500 4601.3889 13332.8125 7616.0000 1 2685 12.0
## FACATATIVA 111.519231 4317.4118 13230.1938 1988.0577 1 2586 19.0
## FOSCA 379.300000 3286.2174 8797.2812 6584.6250 2 2713 14.0
## FUNZA NA 8160.7692 11779.3125 NA 1 2548 14.0
## FUQUENE 18.121538 1110.8889 3261.5637 231.3046 3 2750 13.0
## GRANADA 105.271250 998.6250 1943.7750 1654.6875 3 2500 11.0
## GUACHETA 14.800000 2740.2632 8267.7500 234.9429 2 2688 13.0
## GUASCA 27.333333 6832.3000 12334.5000 468.5833 2 2710 13.0
## GUATAVITA 136.281250 16392.9286 45677.7500 2889.7812 2 2680 14.0
## GUTIERREZ NA 917.5000 1060.7193 NA 3 2350 14.0
## JUNIN NA 5687.6000 3238.7336 NA 3 2300 16.0
## LA CALERA 219.625000 6261.8125 32573.9375 3992.1562 1 2780 14.0
## LENGUAZAQUE 47.375000 8445.8182 17084.3125 636.0500 1 2589 14.0
## MACHETA 42.892857 4152.7778 7453.4637 461.5000 2 2094 17.0
## MADRID NA 8765.1818 18206.2281 NA 1 2554 14.0
## MANTA NA 253.9412 627.3750 NA 3 1924 18.0
## PACHO 263.062500 1508.0400 3142.1250 3256.5000 3 2136 19.0
## PASCA 60.000000 13574.1176 31655.1562 600.0000 1 2180 15.0
## SAN BERNARDO NA 2132.3333 2739.8544 NA 3 1600 20.0
## SAN CAYETANO 43.285714 15864.2105 53816.1875 541.6429 1 2700 12.0
## SESQUILE 86.750000 22788.2667 61495.3125 1205.5000 1 2595 15.0
## SIBATE 669.750000 14651.4737 36672.3375 13957.1667 1 2700 14.0
## SIMIJACA NA 2786.5000 5558.2188 NA 2 2559 14.0
## SOACHA 53.437500 2935.4583 11210.9375 719.4375 3 2566 14.0
## SOPO 9.111111 1009.1667 1887.0000 147.3422 3 2650 14.0
## SUBACHOQUE 194.875000 6742.1481 17781.4375 3485.2500 1 2663 13.0
## SUESCA 59.833333 10641.1429 36001.4094 1416.3333 1 2584 14.0
## SUSA 42.444444 7097.4167 18418.8000 655.7778 2 2655 14.0
## SUTATAUSA NA 2100.3846 4408.2188 NA 2 2550 14.0
## TABIO 14.166667 1366.8750 2765.6250 230.9333 3 2569 14.0
## TAUSA 134.863636 93032.4375 238167.3333 1978.0455 1 2950 12.0
## TENJO 21.250000 10059.1818 16089.6250 440.0000 1 2587 13.0
## UBAQUE 304.464286 4316.0476 7120.9663 4265.2857 3 1867 20.0
## UNE 432.875000 35525.0000 68172.0813 9190.7500 1 2376 16.0
## VILLAPINZON 70.245455 60119.4762 198077.0200 1292.3136 1 2715 13.0
## ZIPACON 201.437500 2015.9545 5975.2500 2702.1250 2 2500 16.0
## ZIPAQUIRA 154.100000 21236.5556 55512.5000 2679.3000 1 2652 12.0
## pH Aci Al CE CIC MO
## BOGOTA 5.559103 1.48842672 1.14813493 0.4744674 12.685202 9.778200
## CAJICA 5.316000 1.10836635 0.87816355 0.4607279 10.384645 8.094967
## CARMEN DE CARUPA 5.062155 2.06117455 1.63019473 0.3139291 6.417927 12.586212
## CHIA 5.918421 0.08288328 0.05627741 0.8380108 14.984885 7.087336
## CHIPAQUE 5.771273 0.76391403 0.61289557 0.5017144 12.069595 6.234734
## CHOACHI 5.166176 2.69555878 2.17397142 0.2091443 7.574417 6.979696
## CHOCONTA 5.114296 1.57266228 1.31436399 0.3881219 7.185019 9.893829
## COGUA 5.452424 1.13583515 0.94955086 0.3825780 8.582600 10.649070
## COTA 5.014000 1.68571680 1.16897724 1.2191779 11.399998 8.561286
## CUCUNUBA 5.555217 0.50128910 0.36834854 0.3394613 7.801094 6.126479
## EL ROSAL 5.579444 0.47765807 0.34018440 0.6773613 12.920994 20.790809
## FACATATIVA 5.761250 0.32234394 0.21942756 1.2174820 16.797049 13.113757
## FOSCA 5.394667 0.86581904 0.66269228 0.3239092 6.237568 5.311936
## FUNZA 5.595870 0.28513281 0.18055710 1.1689460 13.619075 11.631361
## FUQUENE 5.097600 1.15825959 0.86576309 0.3985827 8.620903 8.077060
## GRANADA 5.335811 0.62158721 0.41788006 0.9952873 9.594231 13.810492
## GUACHETA 5.192105 0.97107427 0.71774410 0.4200620 8.585921 5.944584
## GUASCA 5.509538 0.47896412 0.31924071 0.2686508 5.720285 10.018231
## GUATAVITA 5.104896 2.05703561 1.35063107 0.2192190 5.516139 12.252528
## GUTIERREZ 5.304146 1.36450159 1.09273274 0.3135042 7.407471 7.487618
## JUNIN 5.245667 2.73902422 2.32933987 0.1621760 6.131229 8.237882
## LA CALERA 5.470921 1.46741721 1.15194301 0.4327048 9.397545 11.516739
## LENGUAZAQUE 4.933537 2.64988291 2.24520904 0.3815322 6.748871 7.177118
## MACHETA 5.224000 3.36183015 2.93779138 0.1341186 7.422145 4.311126
## MADRID 5.772353 0.34159585 0.26367691 1.4138369 17.466278 13.073099
## MANTA 5.731957 0.54565071 0.40491772 0.2626550 10.327537 4.042129
## PACHO 5.222105 1.68835696 1.32885948 0.2557147 4.981400 10.007553
## PASCA 5.687200 0.44322089 0.31324232 0.6390085 10.783257 10.289681
## SAN BERNARDO 5.237612 1.79245649 1.44831640 0.3380505 8.861645 6.340610
## SAN CAYETANO 5.159459 2.01875624 1.72941120 0.2280057 6.290645 7.793149
## SESQUILE 5.184222 2.03168523 1.32865996 0.3121670 6.733806 12.484487
## SIBATE 5.218485 1.35931642 0.95480135 0.6461802 8.908929 17.425073
## SIMIJACA 5.044405 1.79354525 1.47046480 0.9808050 11.942048 7.753307
## SOACHA 5.767143 0.60816555 0.46267176 2.1790074 14.701346 8.185902
## SOPO 5.533333 0.74140222 0.55051123 0.4027289 9.028509 5.764675
## SUBACHOQUE 5.296275 1.11352096 0.86840251 0.4407986 7.670090 20.534207
## SUESCA 5.359383 0.72004084 0.50577887 0.2968667 6.033355 6.763315
## SUSA 5.174068 1.93916819 1.51178281 0.2559407 6.598838 11.852425
## SUTATAUSA 5.585789 0.52922592 0.38475114 1.1215687 11.402752 5.880491
## TABIO 5.374000 1.22141038 0.99110769 0.4345830 9.137376 10.278675
## TAUSA 5.010575 3.25329783 2.59302652 0.4933287 7.664277 19.107391
## TENJO 5.531667 0.65449398 0.49054135 0.8992575 11.832478 13.849393
## UBAQUE 5.477308 0.67162119 0.50695938 0.4094572 7.157293 12.631683
## UNE 5.901754 0.56291347 0.45066803 0.4648777 11.402627 6.630656
## VILLAPINZON 5.264062 1.22915100 1.00330076 0.3099199 6.455969 11.234601
## ZIPACON 5.543889 0.25472939 0.16862078 0.3463251 7.790373 13.774041
## ZIPAQUIRA 5.420000 1.29654018 1.03122146 0.4560806 9.809536 13.956820
## N.tot N.dis P P.dis K Ca
## BOGOTA 0.4889100 73.33650 55.976267 128.18565 0.8372324 7.609837
## CAJICA 0.4047484 60.71225 29.651472 67.90187 0.6229017 6.828402
## CARMEN DE CARUPA 0.6293106 94.39659 50.340103 115.27883 0.6813839 2.703054
## CHIA 0.3543668 53.15502 59.404397 136.03607 1.3264158 10.384599
## CHIPAQUE 0.3117367 46.76050 106.190896 243.17715 0.6996663 8.785649
## CHOACHI 0.3489848 52.34772 20.152388 46.14897 0.4317104 3.117594
## CHOCONTA 0.4946914 74.20372 51.977796 119.02915 0.7592882 3.426372
## COGUA 0.5324535 79.86803 48.109959 110.17181 0.5279060 5.137754
## COTA 0.4280643 64.20965 37.849774 86.67598 1.3810860 5.196928
## CUCUNUBA 0.3063240 45.94859 21.269575 48.70733 0.8593057 4.451287
## EL ROSAL 1.0395405 155.93107 29.548854 67.66688 1.2002920 8.727308
## FACATATIVA 0.6556879 98.35318 52.841052 121.00601 1.1939526 11.861185
## FOSCA 0.2655968 39.83952 111.897412 256.24507 0.5307785 3.943650
## FUNZA 0.5815681 87.23521 72.892387 166.92357 0.7367824 9.049120
## FUQUENE 0.4038530 60.57795 20.479064 46.89706 0.4861748 4.639825
## GRANADA 0.6905246 103.57869 47.339680 108.40787 0.5472834 6.144561
## GUACHETA 0.2972292 44.58438 19.451621 44.54421 0.6005536 4.809975
## GUASCA 0.5009115 75.13673 30.326777 69.44832 0.4989278 3.367402
## GUATAVITA 0.6126264 91.89396 27.265881 62.43887 0.3936100 1.962767
## GUTIERREZ 0.3743809 56.15713 85.814510 196.51523 0.4522899 4.620345
## JUNIN 0.4118941 61.78411 10.967087 25.11463 0.2749652 2.092673
## LA CALERA 0.5758370 86.37555 36.456175 83.48464 0.5700326 5.960935
## LENGUAZAQUE 0.3588559 53.82839 70.195228 160.74707 0.5608173 2.382194
## MACHETA 0.2155563 32.33345 14.103114 32.29613 0.4614810 2.317661
## MADRID 0.6536549 98.04824 100.325374 229.74511 1.9763222 10.806383
## MANTA 0.2021065 30.31597 41.948427 96.06190 0.6681469 7.370387
## PACHO 0.5003776 75.05665 9.316242 21.33420 0.2527059 1.932011
## PASCA 0.5144841 77.17261 105.945881 242.61607 0.7231711 7.924545
## SAN BERNARDO 0.3170305 47.55458 78.964168 180.82794 0.3785195 5.036355
## SAN CAYETANO 0.3896575 58.44862 14.335644 32.82863 0.1826725 2.847023
## SESQUILE 0.6242243 93.63365 43.067545 98.62468 0.5565116 2.827452
## SIBATE 0.8712537 130.68805 60.314884 138.12108 0.6264456 5.325352
## SIMIJACA 0.3876653 58.14980 28.272022 64.74293 0.7921099 6.725803
## SOACHA 0.4092951 61.39426 43.015980 98.50659 0.8523538 9.432303
## SOPO 0.2882337 43.23506 18.998175 43.50582 0.5194166 5.295229
## SUBACHOQUE 1.0267104 154.00656 35.832079 82.05546 0.8484872 4.075166
## SUESCA 0.3381658 50.72486 28.990104 66.38734 0.7465279 3.135905
## SUSA 0.5926213 88.89319 41.254939 94.47381 0.6221803 2.962146
## SUTATAUSA 0.2940245 44.10368 79.172046 181.30399 0.6200010 8.159469
## TABIO 0.5139337 77.09006 21.824843 49.97889 0.8974949 4.789854
## TAUSA 0.9553696 143.30544 100.889183 231.03623 0.6459231 2.759601
## TENJO 0.6924697 103.87045 100.600645 230.37548 1.2233185 7.490951
## UBAQUE 0.6315842 94.73763 110.722670 253.55491 0.7351836 4.676258
## UNE 0.3315328 49.72992 170.414166 390.24844 0.7137648 8.240352
## VILLAPINZON 0.5617301 84.25951 73.303644 167.86534 0.6554738 3.531951
## ZIPACON 0.6887020 103.30530 9.995007 22.88857 0.4305246 5.514894
## ZIPAQUIRA 0.6978410 104.67615 45.832989 104.95754 0.5026438 6.082949
## Mg S Na Fe Cu Mn
## BOGOTA 1.7951393 10.507277 0.12319214 569.1982 2.3304314 7.651463
## CAJICA 1.5094093 13.102876 0.30956664 668.5700 2.6000000 7.291400
## CARMEN DE CARUPA 0.7181425 8.190727 0.08674512 532.3260 1.5063362 5.883974
## CHIA 2.5320848 31.323270 0.27498420 488.5985 3.5517887 6.985933
## CHIPAQUE 1.4130209 12.127141 0.10290401 669.0530 4.2071258 7.183408
## CHOACHI 0.8909178 6.645534 0.07732360 785.9876 1.3285659 6.952517
## CHOCONTA 0.9684186 8.993108 0.17511828 520.5033 2.5644189 9.027619
## COGUA 1.4431673 15.964870 0.13715575 763.5126 3.8724567 6.770941
## COTA 2.7790936 20.586415 0.35717320 795.6744 4.3288000 10.535400
## CUCUNUBA 1.5760503 11.428284 0.12308086 624.6912 2.7378376 5.479770
## EL ROSAL 2.1647076 17.861430 0.17910686 199.6696 2.7410294 4.981460
## FACATATIVA 2.4470635 39.566737 0.25014833 208.5844 2.4855832 6.010325
## FOSCA 0.7771380 8.819503 0.05958466 506.1218 7.1078333 8.329267
## FUNZA 2.6870710 49.734446 0.40554220 407.3529 3.7286610 6.303742
## FUQUENE 2.0289622 37.360956 0.13548251 1329.7090 3.6976594 14.772262
## GRANADA 1.8123246 46.235197 0.11806982 278.1683 5.1739505 6.424500
## GUACHETA 1.9723353 16.462326 0.15544412 783.8662 3.3075683 8.250854
## GUASCA 1.1208034 8.403813 0.09926487 537.0052 2.6483480 7.249797
## GUATAVITA 0.7815846 7.676379 0.05321787 747.4083 2.6560143 7.999875
## GUTIERREZ 0.7560211 8.590721 0.09168774 503.1262 2.5071049 14.009986
## JUNIN 0.6138866 5.284479 0.07137625 583.6759 1.5084053 6.825052
## LA CALERA 0.9350799 8.644076 0.09284027 625.3544 1.9973696 7.253078
## LENGUAZAQUE 0.7358587 14.960065 0.10778068 876.8909 1.7988788 5.502578
## MACHETA 1.1483899 2.926136 0.08478355 582.2499 0.8968649 6.037654
## MADRID 3.7989980 80.936942 0.52650822 463.1219 2.4339097 11.946925
## MANTA 1.3783723 6.815903 0.13806267 581.0128 4.6780870 7.229685
## PACHO 0.6736951 7.971077 0.06530890 244.4244 1.7057403 3.332639
## PASCA 1.1939198 14.293321 0.12028547 289.0372 8.7542173 4.517080
## SAN BERNARDO 1.0463044 9.607265 0.09865306 540.6916 3.2564606 8.762998
## SAN CAYETANO 0.7954409 5.257374 0.10541479 398.5048 0.8345135 4.030425
## SESQUILE 0.6986989 9.848105 0.05951783 695.0550 2.7688667 8.722667
## SIBATE 1.0166715 14.500787 0.18124861 511.8916 4.2744133 5.963710
## SIMIJACA 2.1156070 76.475056 0.23171513 1174.4268 1.1451890 11.168375
## SOACHA 3.0191177 73.127804 0.77869144 230.1939 3.2276655 4.586873
## SOPO 2.2712253 11.543862 0.19123587 693.6312 1.9216120 5.775189
## SUBACHOQUE 1.1870670 13.485496 0.10165910 424.5978 2.2254359 6.758521
## SUESCA 1.1041059 8.328884 0.08185894 522.3281 2.9441468 8.526384
## SUSA 0.8112063 17.523490 0.08375177 638.3813 2.0400633 5.709596
## SUTATAUSA 1.3669953 34.351701 0.50158465 351.2381 2.3745789 6.667158
## TABIO 1.6674089 13.560286 0.15346145 635.8791 2.8903118 9.193293
## TAUSA 0.6489134 14.953695 0.16554767 470.8931 1.3604383 6.920701
## TENJO 1.8092387 25.496444 0.23576234 380.8290 3.0664202 5.634095
## UBAQUE 0.8740120 11.937510 0.06705591 277.3353 3.3484615 4.772989
## UNE 1.2919375 12.952596 0.10805196 408.8872 7.4151957 5.939497
## VILLAPINZON 0.7974836 14.355854 0.07051152 619.1350 2.9893624 7.328563
## ZIPACON 1.4249624 9.759495 0.08402422 221.0799 2.0710813 7.739677
## ZIPAQUIRA 1.4028510 13.693114 0.14889878 832.1795 3.4091941 6.380285
## Zn B S.Bas S.Al S.Ca S.Mg
## BOGOTA 5.463535 0.3275570 80.05188 14.7452755 55.16836 13.568203
## CAJICA 6.398600 0.3558244 80.67935 15.4288655 57.30971 13.820678
## CARMEN DE CARUPA 1.920793 0.2525678 66.48783 26.3535645 41.91207 11.137922
## CHIA 11.472134 0.4177221 99.31008 0.4654782 68.81959 16.914227
## CHIPAQUE 5.334073 0.3241120 89.93421 7.9391507 69.18755 12.300763
## CHOACHI 3.222804 0.1310648 59.64343 31.5265113 39.99056 11.456064
## CHOCONTA 2.880188 0.2669020 76.23524 19.5441738 47.32400 13.265068
## COGUA 5.599856 0.2983707 85.37701 12.1882056 59.12271 17.296942
## COTA 28.979400 0.4398446 72.98395 19.4199499 39.26746 19.919034
## CUCUNUBA 2.904986 0.2615827 92.33921 5.3468453 56.74571 20.733591
## EL ROSAL 8.285466 0.3866722 92.95790 4.5533721 64.06779 16.293285
## FACATATIVA 7.419592 0.4082495 95.29772 2.8526592 67.91950 15.139390
## FOSCA 5.703100 0.2029506 83.60708 12.1007762 60.37720 12.313138
## FUNZA 16.565071 0.5769619 96.39223 1.9050203 67.71797 19.312456
## FUQUENE 8.067467 0.2827392 85.86268 10.1669683 52.84402 23.716778
## GRANADA 12.161302 0.5569017 89.18058 7.4281554 62.24793 16.988092
## GUACHETA 6.569300 0.2952467 87.07609 9.5278237 54.84609 21.949058
## GUASCA 2.698380 0.2513500 90.86439 5.9007369 59.06861 19.549113
## GUATAVITA 2.156927 0.1513918 62.78067 23.6880716 38.42958 14.473765
## GUTIERREZ 3.542726 0.3029657 78.26410 17.2662794 59.71984 9.937097
## JUNIN 3.153675 0.1574059 48.76613 41.3027097 32.48146 9.619547
## LA CALERA 3.357971 0.2263380 75.56990 19.0462365 54.81031 10.700277
## LENGUAZAQUE 2.437085 0.3167189 58.54142 34.4287355 35.49699 10.860087
## MACHETA 1.997232 0.1479174 53.18053 40.2219427 30.45249 15.246628
## MADRID 17.849911 0.5474817 94.83883 3.9872994 59.13032 21.197394
## MANTA 3.619380 0.3111185 91.89935 5.9956611 68.42633 13.517769
## PACHO 2.358481 0.1343537 63.66178 27.1144823 40.29819 14.711142
## PASCA 8.738744 0.3379247 91.86695 5.9067966 71.28184 11.334093
## SAN BERNARDO 5.423376 0.2715315 76.61698 18.8071387 57.63528 11.740923
## SAN CAYETANO 2.992689 0.1695861 61.76864 32.0957150 43.08813 12.069240
## SESQUILE 2.790444 0.1569658 65.19560 22.4494314 43.98237 10.160880
## SIBATE 7.047068 0.3672236 82.59789 12.1732038 59.01888 11.282632
## SIMIJACA 7.345060 0.3752028 81.78400 14.8497602 53.48437 17.339228
## SOACHA 7.209866 0.4015325 83.76125 12.2199911 54.18933 15.415347
## SOPO 3.416762 0.2216387 86.61411 9.8155378 59.60813 20.427077
## SUBACHOQUE 6.414248 0.3047218 81.68475 14.0101016 52.21020 15.119114
## SUESCA 2.383673 0.2140021 88.01743 8.2137716 53.25031 18.414799
## SUSA 2.601359 0.2305126 69.28075 23.8886218 42.97241 11.896822
## SUTATAUSA 4.634158 0.3312990 93.87815 4.6078133 66.15861 13.322642
## TABIO 6.293630 0.2634338 85.63516 11.4733695 53.89752 18.701918
## TAUSA 3.686032 0.3908919 55.09451 35.6446294 35.28947 8.315885
## TENJO 11.478407 0.4902641 92.25186 5.8883004 62.00234 15.627753
## UBAQUE 6.073077 0.2824154 88.07622 8.9062406 63.00816 12.359357
## UNE 6.844354 0.3007872 92.26753 6.1445993 71.06692 11.683787
## VILLAPINZON 3.149603 0.3706109 77.13117 18.5653449 50.78021 12.959840
## ZIPACON 6.268119 0.1585402 93.37620 4.1878722 68.12849 17.209582
## ZIPAQUIRA 4.810961 0.3252102 83.87497 12.9462299 60.13385 14.200342
## S.K S.Na Ca_Mg Mg_K Ca_K Ca.Mg_K
## BOGOTA 7.243108 1.3205465 4.207091 2.613154 10.679175 13.431593
## CAJICA 6.534641 3.0143130 4.650541 4.041579 23.226207 27.267786
## CARMEN DE CARUPA 10.871190 1.3922997 3.952252 1.244362 4.686444 5.946376
## CHIA 8.629590 1.9297157 4.150889 2.791340 13.325539 16.116879
## CHIPAQUE 6.428829 0.9590380 6.157040 2.107519 13.378390 15.714203
## CHOACHI 5.840890 1.1197015 3.475578 2.445354 8.259583 10.737634
## CHOCONTA 10.551657 2.5688854 4.155828 1.600995 5.741427 7.352359
## COGUA 6.084893 1.7734148 4.019314 3.544209 11.898310 15.463167
## COTA 11.360041 2.4374106 2.313750 1.995038 4.270404 6.265442
## CUCUNUBA 11.023580 1.5833355 3.124588 2.223052 5.858726 8.184432
## EL ROSAL 9.434368 1.4263307 3.967007 2.137465 8.895605 11.156403
## FACATATIVA 8.239967 1.8181514 4.796457 2.908115 12.660487 15.894671
## FOSCA 8.690495 0.9819731 5.507378 1.617921 8.436878 10.038924
## FUNZA 5.684595 2.6549998 3.934824 4.798546 16.256570 21.309211
## FUQUENE 6.365412 1.7528772 2.472948 4.510470 10.428636 14.978127
## GRANADA 6.036071 1.3746619 4.352253 4.018939 13.878298 18.540743
## GUACHETA 7.149819 1.7164996 2.674055 3.454300 8.380502 11.837539
## GUASCA 8.595033 1.7770319 3.356945 2.602765 8.178246 10.883756
## GUATAVITA 7.419013 1.0142213 2.997810 2.128698 5.570188 7.815801
## GUTIERREZ 6.166221 1.3169163 6.277940 1.685047 10.524148 12.280181
## JUNIN 4.494838 1.3230160 3.138291 2.259452 7.410669 9.725518
## LA CALERA 6.723682 1.1273306 5.885796 1.808105 11.602960 13.737836
## LENGUAZAQUE 8.791739 1.6002877 3.642946 1.488717 5.048062 6.572472
## MACHETA 6.331221 1.1501939 2.347562 2.590489 5.527744 8.118233
## MADRID 11.975529 2.5355867 3.083577 2.477477 6.803889 9.281366
## MANTA 6.811436 1.3810430 5.179842 2.343526 13.718844 16.265023
## PACHO 5.798944 1.5800767 2.882946 2.764143 7.418826 10.163052
## PASCA 6.807680 1.2746707 6.590371 1.941682 12.042624 13.986523
## SAN BERNARDO 4.405980 1.2651621 5.664800 3.541532 15.819488 19.560620
## SAN CAYETANO 3.248339 1.8336048 3.471815 5.853562 20.444469 26.282011
## SESQUILE 8.930915 0.8623909 4.888239 1.431617 6.061083 7.485572
## SIBATE 7.105849 2.0369629 6.183241 1.944085 11.218633 13.475778
## SIMIJACA 7.509215 1.7331667 3.883233 2.856109 10.373053 13.284027
## SOACHA 8.487468 5.6691042 4.073192 5.181092 16.955785 22.136876
## SOPO 4.901099 1.6778094 3.563062 4.761034 17.480546 22.241580
## SUBACHOQUE 11.150428 1.4575350 3.665545 1.744570 5.916611 7.660675
## SUESCA 12.970238 1.3573717 3.305623 1.729761 4.951949 6.757195
## SUSA 10.109411 1.2026532 3.925609 1.677665 6.286635 7.964299
## SUTATAUSA 5.929483 4.7011573 8.279938 3.162612 30.496932 33.715224
## TABIO 9.687765 1.7414743 2.844602 2.138461 5.969263 8.095845
## TAUSA 8.223951 2.3121699 4.329034 1.366540 5.999341 7.475064
## TENJO 10.758324 1.9886135 4.343637 1.692927 6.630484 8.314953
## UBAQUE 11.123505 0.9724024 5.550376 1.413229 7.516967 8.930196
## UNE 6.700554 1.0261327 6.525925 2.173082 12.742967 15.047349
## VILLAPINZON 10.549304 1.0902017 4.505706 1.602982 6.758757 8.402251
## ZIPACON 5.656838 1.2531240 4.024144 3.559467 16.478456 20.023376
## ZIPAQUIRA 5.070922 1.6184154 4.901222 3.492617 15.869630 19.480282
## Ca_B Fe_Mn Fe_Zn
## BOGOTA 22.911929 76.11250 159.48352
## CAJICA 22.103206 99.79597 224.82995
## CARMEN DE CARUPA 13.358083 101.88795 348.51124
## CHIA 28.283091 82.06782 44.59346
## CHIPAQUE 31.356669 106.15040 192.95755
## CHOACHI 44.860094 126.77638 463.21697
## CHOCONTA 15.303524 64.50734 279.89002
## COGUA 21.599593 127.99761 231.20179
## COTA 10.596020 137.53683 58.32030
## CUCUNUBA 23.369612 124.07392 283.95300
## EL ROSAL 24.313911 48.45113 31.38065
## FACATATIVA 36.037275 45.69982 39.99380
## FOSCA 20.342930 73.69159 121.38584
## FUNZA 21.154209 76.80013 37.19371
## FUQUENE 17.942565 119.74120 291.30422
## GRANADA 13.619364 57.26523 61.45104
## GUACHETA 18.141728 124.07656 235.46899
## GUASCA 16.317502 82.78848 271.42576
## GUATAVITA 18.490373 99.78590 406.21095
## GUTIERREZ 17.651530 62.19801 181.54670
## JUNIN 13.253069 120.11062 543.41485
## LA CALERA 25.690887 106.75522 252.41292
## LENGUAZAQUE 9.805060 194.46502 604.75564
## MACHETA 33.369561 105.21371 770.00062
## MADRID 23.038606 54.92473 30.35978
## MANTA 26.836958 93.91041 269.76120
## PACHO 15.783438 89.83600 155.79598
## PASCA 24.694176 81.70473 53.13072
## SAN BERNARDO 23.554260 90.54170 174.79516
## SAN CAYETANO 16.708943 151.18552 266.55311
## SESQUILE 22.908559 91.64701 328.82178
## SIBATE 16.078475 98.58501 104.90479
## SIMIJACA 20.374094 146.26137 291.09793
## SOACHA 26.551133 59.29421 60.45141
## SOPO 27.203337 130.78752 387.17109
## SUBACHOQUE 14.552539 74.09232 119.03846
## SUESCA 18.355345 66.46467 266.70547
## SUSA 13.797263 124.09790 355.08541
## SUTATAUSA 28.081424 65.38077 113.22886
## TABIO 19.664735 79.87481 175.23262
## TAUSA 7.764209 81.64860 164.86202
## TENJO 17.833142 71.50533 112.37959
## UBAQUE 17.187490 62.11595 70.69824
## UNE 31.925446 92.23563 85.98830
## VILLAPINZON 11.359261 100.98522 248.48556
## ZIPACON 35.109077 48.52643 74.34853
## ZIPAQUIRA 20.422276 124.80835 229.29622# Obtener los nombres de las variables
variables <- colnames(GR.CUN)
# Iterar a través de las columnas y crear los gráficos de caja
for (i in variables) {
# Crear un gráfico de caja para la columna i
boxplot(x=GR.CUN[, i], main = i, xlab = paste("Valores de", i), horizontal = T)
}
Correlaciones con variables numericas y agrupadas por municipio
names(GR.CUN)## [1] "Rmed1" "Rmed2" "Rmed3" "Rmea1" "Rmea2" "Rmea3" "Rmax1"
## [8] "Rmax2" "Rmax3" "Asem1" "Asem2" "Asem3" "Acos1" "Acos2"
## [15] "Acos3" "Prod1" "Prod2" "Prod3" "IDPM" "ALT" "TEM"
## [22] "pH" "Aci" "Al" "CE" "CIC" "MO" "N.tot"
## [29] "N.dis" "P" "P.dis" "K" "Ca" "Mg" "S"
## [36] "Na" "Fe" "Cu" "Mn" "Zn" "B" "S.Bas"
## [43] "S.Al" "S.Ca" "S.Mg" "S.K" "S.Na" "Ca_Mg" "Mg_K"
## [50] "Ca_K" "Ca.Mg_K" "Ca_B" "Fe_Mn" "Fe_Zn"# Mediana
Rmed = cor( data.frame(GR.CUN[1:3], GR.CUN[19:54]), method = "spearman", use = "complete.obs")
corrplot(Rmed, method = 'number', tl.cex = 0.5, number.cex = 0.5)
resumen.Rmed=colMeans(abs(Rmed)); resumen.Rmed## Rmed1 Rmed2 Rmed3 IDPM ALT TEM pH Aci
## 0.2199837 0.1919973 0.2162040 0.2271984 0.2007124 0.1704436 0.3778719 0.4024098
## Al CE CIC MO N.tot N.dis P P.dis
## 0.3995615 0.4237473 0.3941915 0.2626628 0.2626628 0.2626628 0.3036068 0.3036068
## K Ca Mg S Na Fe Cu Mn
## 0.3531328 0.4536474 0.3862067 0.3366827 0.3052603 0.2629259 0.3160893 0.1686968
## Zn B S.Bas S.Al S.Ca S.Mg S.K S.Na
## 0.4080836 0.3913679 0.3966931 0.3914814 0.4157206 0.2702435 0.2334159 0.2069863
## Ca_Mg Mg_K Ca_K Ca.Mg_K Ca_B Fe_Mn Fe_Zn
## 0.3286142 0.3229608 0.3209722 0.3443807 0.2893246 0.2746610 0.4091090# Media
Rmea = cor( data.frame(GR.CUN[4:6], GR.CUN[19:54]), method = "spearman", use = "complete.obs")
corrplot(Rmea, method = 'number', tl.cex = 0.5, number.cex = 0.5)
resumen.Rmea=colMeans(abs(Rmea)); resumen.Rmea## Rmea1 Rmea2 Rmea3 IDPM ALT TEM pH Aci
## 0.2264288 0.1897649 0.2320037 0.2260671 0.2031082 0.1685426 0.3795893 0.4042044
## Al CE CIC MO N.tot N.dis P P.dis
## 0.4021478 0.4232030 0.3918592 0.2654191 0.2654191 0.2654191 0.3045266 0.3045266
## K Ca Mg S Na Fe Cu Mn
## 0.3528693 0.4547813 0.3845148 0.3378942 0.3010633 0.2624935 0.3143282 0.1753677
## Zn B S.Bas S.Al S.Ca S.Mg S.K S.Na
## 0.4085785 0.3905314 0.3968086 0.3926008 0.4173584 0.2672896 0.2353335 0.2058950
## Ca_Mg Mg_K Ca_K Ca.Mg_K Ca_B Fe_Mn Fe_Zn
## 0.3317909 0.3255606 0.3210118 0.3443028 0.2880092 0.2816789 0.4113134# Maximo
Rmax = cor( data.frame(GR.CUN[7:9], GR.CUN[19:54]), method = "spearman", use = "complete.obs")
corrplot(Rmax, method = 'number', tl.cex = 0.5, number.cex = 0.5)
#
resumen.Rmax=colMeans(abs(Rmax)); resumen.Rmax## Rmax1 Rmax2 Rmax3 IDPM ALT TEM pH Aci
## 0.1963914 0.1327502 0.2045243 0.2095235 0.2005158 0.1656050 0.3854493 0.4093764
## Al CE CIC MO N.tot N.dis P P.dis
## 0.4057806 0.4182441 0.4044546 0.2569657 0.2569657 0.2569657 0.2979895 0.2979895
## K Ca Mg S Na Fe Cu Mn
## 0.3383829 0.4556518 0.3901028 0.3471701 0.3087825 0.2640397 0.3041996 0.1746429
## Zn B S.Bas S.Al S.Ca S.Mg S.K S.Na
## 0.4088791 0.3864069 0.4010284 0.3956295 0.4215491 0.2675150 0.2247683 0.2085259
## Ca_Mg Mg_K Ca_K Ca.Mg_K Ca_B Fe_Mn Fe_Zn
## 0.3269290 0.3182728 0.3142877 0.3362580 0.2851304 0.2798019 0.4077322#
head(rbind(resumen.Rmed, resumen.Rmea, resumen.Rmax))## Rmed1 Rmed2 Rmed3 IDPM ALT TEM
## resumen.Rmed 0.2199837 0.1919973 0.2162040 0.2271984 0.2007124 0.1704436
## resumen.Rmea 0.2264288 0.1897649 0.2320037 0.2260671 0.2031082 0.1685426
## resumen.Rmax 0.1963914 0.1327502 0.2045243 0.2095235 0.2005158 0.1656050
## pH Aci Al CE CIC MO
## resumen.Rmed 0.3778719 0.4024098 0.3995615 0.4237473 0.3941915 0.2626628
## resumen.Rmea 0.3795893 0.4042044 0.4021478 0.4232030 0.3918592 0.2654191
## resumen.Rmax 0.3854493 0.4093764 0.4057806 0.4182441 0.4044546 0.2569657
## N.tot N.dis P P.dis K Ca
## resumen.Rmed 0.2626628 0.2626628 0.3036068 0.3036068 0.3531328 0.4536474
## resumen.Rmea 0.2654191 0.2654191 0.3045266 0.3045266 0.3528693 0.4547813
## resumen.Rmax 0.2569657 0.2569657 0.2979895 0.2979895 0.3383829 0.4556518
## Mg S Na Fe Cu Mn
## resumen.Rmed 0.3862067 0.3366827 0.3052603 0.2629259 0.3160893 0.1686968
## resumen.Rmea 0.3845148 0.3378942 0.3010633 0.2624935 0.3143282 0.1753677
## resumen.Rmax 0.3901028 0.3471701 0.3087825 0.2640397 0.3041996 0.1746429
## Zn B S.Bas S.Al S.Ca S.Mg
## resumen.Rmed 0.4080836 0.3913679 0.3966931 0.3914814 0.4157206 0.2702435
## resumen.Rmea 0.4085785 0.3905314 0.3968086 0.3926008 0.4173584 0.2672896
## resumen.Rmax 0.4088791 0.3864069 0.4010284 0.3956295 0.4215491 0.2675150
## S.K S.Na Ca_Mg Mg_K Ca_K Ca.Mg_K
## resumen.Rmed 0.2334159 0.2069863 0.3286142 0.3229608 0.3209722 0.3443807
## resumen.Rmea 0.2353335 0.2058950 0.3317909 0.3255606 0.3210118 0.3443028
## resumen.Rmax 0.2247683 0.2085259 0.3269290 0.3182728 0.3142877 0.3362580
## Ca_B Fe_Mn Fe_Zn
## resumen.Rmed 0.2893246 0.2746610 0.4091090
## resumen.Rmea 0.2880092 0.2816789 0.4113134
## resumen.Rmax 0.2851304 0.2798019 0.4077322Base de datos con rendimiento de la base de datos 1
Boxplot exploratorio
A <- data.frame(GR.CUN[1], GR.CUN[19:54])
# Obtener los nombres de las variables
variables <- colnames(A)
# Iterar a través de las columnas y crear los gráficos de dispersion
for (i in variables) {
# Crear un gráfico de caja para la columna i
plot(y = A$Rmed1, x = A[, i], main = i, ylab = "Rendimiento", xlab = i)
}
## Correlogramas ### T O D A S L A S V A R I A B L E S
A.cor <- cor( A, method = "spearman", use = "complete.obs"); A.cor## Rmed1 IDPM ALT TEM pH
## Rmed1 1.000000000 -0.403015305 0.198689666 -0.1764740770 0.001669041
## IDPM -0.403015305 1.000000000 -0.470778752 0.3235934753 0.021130286
## ALT 0.198689666 -0.470778752 1.000000000 -0.6733691958 -0.282638874
## TEM -0.176474077 0.323593475 -0.673369196 1.0000000000 0.211788427
## pH 0.001669041 0.021130286 -0.282638874 0.2117884269 1.000000000
## Aci 0.004636225 0.040279608 0.113499679 -0.0319522314 -0.855565834
## Al -0.026210124 0.043713279 0.095364404 -0.0309839820 -0.837804502
## CE 0.129196128 -0.190700832 0.004256238 -0.2126921264 0.412149245
## CIC -0.036657083 -0.039619286 -0.208000498 -0.0006454996 0.586925686
## MO 0.292020340 -0.457998952 0.279431274 -0.2941541790 -0.068270120
## N.tot 0.292020340 -0.457998952 0.279431274 -0.2941541790 -0.068270120
## N.dis 0.292020340 -0.457998952 0.279431274 -0.2941541790 -0.068270120
## P 0.178587375 -0.297804970 -0.059278908 -0.0560939174 0.310391613
## P.dis 0.178587375 -0.297804970 -0.059278908 -0.0560939174 0.310391613
## K 0.224825990 -0.313916813 0.045338187 -0.1598902571 0.376626580
## Ca -0.029919103 -0.039619286 -0.192455977 0.0163311405 0.751218008
## Mg -0.033813532 0.079502702 -0.081485367 -0.0930164959 0.513166821
## S 0.048031288 -0.176702018 0.131634954 -0.2472263563 0.225655257
## Na 0.068059779 -0.118593731 0.059710701 -0.2476782060 0.324576010
## Fe 0.083204779 0.148704389 0.204422791 -0.0833340016 -0.494542091
## Cu 0.197935886 0.028790015 -0.062239770 -0.0121353929 0.412519272
## Mn 0.173951151 0.164816232 0.098510319 -0.0545447183 -0.227258711
## Zn 0.129567026 -0.017564550 -0.106159210 -0.0604833148 0.409312365
## B 0.133955986 -0.223188647 0.010363014 -0.3059022722 0.323712612
## S.Bas -0.013290511 -0.062466408 -0.125959970 0.0034856980 0.817946346
## S.Al -0.010014245 0.061277830 0.099065481 -0.0061322464 -0.803885291
## S.Ca -0.052976594 0.006603214 -0.239459648 0.0954693945 0.830527290
## S.Mg -0.033009920 0.136554474 0.076612283 -0.1580828581 0.247486895
## S.K 0.273660890 -0.336631871 0.284427728 -0.2052688807 -0.040271354
## S.Na 0.087841004 -0.140780531 0.211578206 -0.3309476576 0.044465002
## Ca_Mg -0.021759348 -0.132064288 -0.133115384 0.0984386928 0.387974098
## Mg_K -0.289918584 0.337556321 -0.180612532 0.0767499054 0.285723096
## Ca_K -0.287878646 0.245243383 -0.190975547 0.0949529948 0.516743756
## Ca.Mg_K -0.321753994 0.282485513 -0.212503475 0.1174163818 0.520690718
## Ca_B -0.277988033 0.143025624 -0.367701949 0.4601766825 0.649337034
## Fe_Mn -0.074550493 0.051769201 0.165993280 -0.0437648746 -0.478507555
## Fe_Zn -0.091179086 0.155703796 0.156000374 -0.0182676394 -0.569904409
## Aci Al CE CIC MO
## Rmed1 0.004636225 -0.02621012 0.129196128 -0.0366570833 0.29202034
## IDPM 0.040279608 0.04371328 -0.190700832 -0.0396192865 -0.45799895
## ALT 0.113499679 0.09536440 0.004256238 -0.2080004983 0.27943127
## TEM -0.031952231 -0.03098398 -0.212692126 -0.0006454996 -0.29415418
## pH -0.855565834 -0.83780450 0.412149245 0.5869256861 -0.06827012
## Aci 1.000000000 0.99358619 -0.531421523 -0.5708911502 -0.04064138
## Al 0.993586186 1.00000000 -0.523774283 -0.5530064755 -0.06148628
## CE -0.531421523 -0.52377428 1.000000000 0.8560592044 0.29423373
## CIC -0.570891150 -0.55300648 0.856059204 1.0000000000 0.05408572
## MO -0.040641381 -0.06148628 0.294233734 0.0540857231 1.00000000
## N.tot -0.040641381 -0.06148628 0.294233734 0.0540857231 1.00000000
## N.dis -0.040641381 -0.06148628 0.294233734 0.0540857231 1.00000000
## P -0.250200432 -0.24452667 0.440518039 0.3066913352 0.13166821
## P.dis -0.250200432 -0.24452667 0.440518039 0.3066913352 0.13166821
## K -0.476657416 -0.46456984 0.681282763 0.6048103608 0.20752390
## Ca -0.762565526 -0.74270737 0.814246068 0.9340117175 0.05901943
## Mg -0.658464385 -0.64798027 0.709034844 0.8180696886 -0.02263336
## S -0.429417206 -0.42238668 0.840518039 0.7006475486 0.30348443
## Na -0.442614863 -0.41880974 0.808202282 0.8121492445 0.03990133
## Fe 0.520444033 0.50107925 -0.291026827 -0.1840888067 -0.32309590
## Cu -0.498612396 -0.52291088 0.366265803 0.3491211841 -0.02954055
## Mn 0.118593895 0.08812828 -0.040764724 -0.0233734197 -0.16127043
## Zn -0.575208141 -0.57039778 0.830157262 0.7952513105 0.24773358
## B -0.462843047 -0.44791859 0.855195806 0.7573851372 0.30681468
## S.Bas -0.959050262 -0.95621338 0.569164354 0.6366327475 0.02843047
## S.Al 0.962380512 0.96682084 -0.558310207 -0.6127042862 -0.04804194
## S.Ca -0.847795251 -0.83533765 0.499105766 0.6104841196 -0.01165587
## S.Mg -0.506753006 -0.50909652 0.297440641 0.3127351218 -0.07838421
## S.K -0.114646932 -0.12821462 0.176318224 -0.0281837805 0.24440333
## S.Na -0.234165896 -0.21270429 0.547456059 0.4269503546 0.11082331
## Ca_Mg -0.247116867 -0.23786617 0.276842430 0.2823928461 0.08307123
## Mg_K -0.326919519 -0.30545791 0.197533148 0.3839037928 -0.22417515
## Ca_K -0.456429232 -0.42584027 0.322232501 0.4894850447 -0.15066297
## Ca.Mg_K -0.467653407 -0.43780450 0.299784150 0.4984890533 -0.20222017
## Ca_B -0.454455751 -0.45050879 0.161270429 0.5208140611 -0.34542091
## Fe_Mn 0.561023743 0.57113784 -0.351341351 -0.2238051187 -0.35726179
## Fe_Zn 0.675238976 0.66043787 -0.745914277 -0.6185013876 -0.37354302
## N.tot N.dis P P.dis K
## Rmed1 0.29202034 0.29202034 0.178587375 0.178587375 0.22482599
## IDPM -0.45799895 -0.45799895 -0.297804970 -0.297804970 -0.31391681
## ALT 0.27943127 0.27943127 -0.059278908 -0.059278908 0.04533819
## TEM -0.29415418 -0.29415418 -0.056093917 -0.056093917 -0.15989026
## pH -0.06827012 -0.06827012 0.310391613 0.310391613 0.37662658
## Aci -0.04064138 -0.04064138 -0.250200432 -0.250200432 -0.47665742
## Al -0.06148628 -0.06148628 -0.244526673 -0.244526673 -0.46456984
## CE 0.29423373 0.29423373 0.440518039 0.440518039 0.68128276
## CIC 0.05408572 0.05408572 0.306691335 0.306691335 0.60481036
## MO 1.00000000 1.00000000 0.131668208 0.131668208 0.20752390
## N.tot 1.00000000 1.00000000 0.131668208 0.131668208 0.20752390
## N.dis 1.00000000 1.00000000 0.131668208 0.131668208 0.20752390
## P 0.13166821 0.13166821 1.000000000 1.000000000 0.36577243
## P.dis 0.13166821 0.13166821 1.000000000 1.000000000 0.36577243
## K 0.20752390 0.20752390 0.365772433 0.365772433 1.00000000
## Ca 0.05901943 0.05901943 0.385383904 0.385383904 0.55757015
## Mg -0.02263336 -0.02263336 -0.046808511 -0.046808511 0.55794018
## S 0.30348443 0.30348443 0.342460685 0.342460685 0.57644157
## Na 0.03990133 0.03990133 0.165340734 0.165340734 0.58310207
## Fe -0.32309590 -0.32309590 -0.275362319 -0.275362319 -0.22602529
## Cu -0.02954055 -0.02954055 0.476534073 0.476534073 0.28103608
## Mn -0.16127043 -0.16127043 -0.027937095 -0.027937095 -0.02682701
## Zn 0.24773358 0.24773358 0.312118409 0.312118409 0.52673451
## B 0.30681468 0.30681468 0.536971940 0.536971940 0.65451742
## S.Bas 0.02843047 0.02843047 0.287203207 0.287203207 0.50823312
## S.Al -0.04804194 -0.04804194 -0.271908726 -0.271908726 -0.49811903
## S.Ca -0.01165587 -0.01165587 0.468023435 0.468023435 0.29781067
## S.Mg -0.07838421 -0.07838421 -0.368115942 -0.368115942 0.31162504
## S.K 0.24440333 0.24440333 0.244279988 0.244279988 0.72482270
## S.Na 0.11082331 0.11082331 -0.022510022 -0.022510022 0.36083873
## Ca_Mg 0.08307123 0.08307123 0.714831946 0.714831946 0.05408572
## Mg_K -0.22417515 -0.22417515 -0.415726179 -0.415726179 -0.24477336
## Ca_K -0.15066297 -0.15066297 0.094048720 0.094048720 -0.20629047
## Ca.Mg_K -0.20222017 -0.20222017 0.001171755 0.001171755 -0.21985816
## Ca_B -0.34542091 -0.34542091 -0.028800493 -0.028800493 0.06074622
## Fe_Mn -0.35726179 -0.35726179 -0.353931545 -0.353931545 -0.33876041
## Fe_Zn -0.37354302 -0.37354302 -0.471230342 -0.471230342 -0.51316682
## Ca Mg S Na Fe
## Rmed1 -0.02991910 -0.03381353 0.048031288 0.06805978 0.08320478
## IDPM -0.03961929 0.07950270 -0.176702018 -0.11859373 0.14870439
## ALT -0.19245598 -0.08148537 0.131634954 0.05971070 0.20442279
## TEM 0.01633114 -0.09301650 -0.247226356 -0.24767821 -0.08333400
## pH 0.75121801 0.51316682 0.225655257 0.32457601 -0.49454209
## Aci -0.76256553 -0.65846438 -0.429417206 -0.44261486 0.52044403
## Al -0.74270737 -0.64798027 -0.422386679 -0.41880974 0.50107925
## CE 0.81424607 0.70903484 0.840518039 0.80820228 -0.29102683
## CIC 0.93401172 0.81806969 0.700647549 0.81214924 -0.18408881
## MO 0.05901943 -0.02263336 0.303484428 0.03990133 -0.32309590
## N.tot 0.05901943 -0.02263336 0.303484428 0.03990133 -0.32309590
## N.dis 0.05901943 -0.02263336 0.303484428 0.03990133 -0.32309590
## P 0.38538390 -0.04680851 0.342460685 0.16534073 -0.27536232
## P.dis 0.38538390 -0.04680851 0.342460685 0.16534073 -0.27536232
## K 0.55757015 0.55794018 0.576441566 0.58310207 -0.22602529
## Ca 1.00000000 0.78834413 0.642183164 0.71557200 -0.31951896
## Mg 0.78834413 1.00000000 0.674375578 0.77008942 -0.06530990
## S 0.64218316 0.67437558 1.000000000 0.70767808 -0.10465618
## Na 0.71557200 0.77008942 0.707678076 1.00000000 -0.11551033
## Fe -0.31951896 -0.06530990 -0.104656183 -0.11551033 1.00000000
## Cu 0.48356460 0.35195806 0.311995066 0.17422140 -0.05642923
## Mn -0.02781375 0.07332717 -0.001541782 -0.02917052 0.44286155
## Zn 0.79253777 0.74344743 0.746901018 0.65895776 -0.27634906
## B 0.72050570 0.59728646 0.822386679 0.75750848 -0.24822695
## S.Bas 0.79303114 0.69608387 0.490225100 0.48775825 -0.41991983
## S.Al -0.77391304 -0.68720321 -0.476164046 -0.46382979 0.42078323
## S.Ca 0.81079248 0.49034844 0.323219241 0.34098057 -0.43953130
## S.Mg 0.31779217 0.77674992 0.401665125 0.43213074 0.06308973
## S.K -0.04039470 0.06604995 0.220844897 0.05001542 -0.02954055
## S.Na 0.34295405 0.50046253 0.519457293 0.84656183 -0.12883133
## Ca_Mg 0.43324083 -0.15349985 0.076287388 0.03361085 -0.32728955
## Mg_K 0.39821153 0.58347209 0.219118101 0.41634289 -0.03287080
## Ca_K 0.62368178 0.36046870 0.205303731 0.35627505 -0.25932778
## Ca.Mg_K 0.61788467 0.42621030 0.200493370 0.38020352 -0.22627197
## Ca_B 0.55251311 0.43089732 -0.007832254 0.18889917 -0.12143077
## Fe_Mn -0.35997533 -0.15942029 -0.202960222 -0.12352760 0.79895159
## Fe_Zn -0.71039161 -0.49244527 -0.555473327 -0.47715079 0.69164354
## Cu Mn Zn B S.Bas
## Rmed1 0.19793589 0.173951151 0.12956703 0.13395599 -0.013290511
## IDPM 0.02879001 0.164816232 -0.01756455 -0.22318865 -0.062466408
## ALT -0.06223977 0.098510319 -0.10615921 0.01036301 -0.125959970
## TEM -0.01213539 -0.054544718 -0.06048331 -0.30590227 0.003485698
## pH 0.41251927 -0.227258711 0.40931237 0.32371261 0.817946346
## Aci -0.49861240 0.118593895 -0.57520814 -0.46284305 -0.959050262
## Al -0.52291088 0.088128276 -0.57039778 -0.44791859 -0.956213383
## CE 0.36626580 -0.040764724 0.83015726 0.85519581 0.569164354
## CIC 0.34912118 -0.023373420 0.79525131 0.75738514 0.636632747
## MO -0.02954055 -0.161270429 0.24773358 0.30681468 0.028430466
## N.tot -0.02954055 -0.161270429 0.24773358 0.30681468 0.028430466
## N.dis -0.02954055 -0.161270429 0.24773358 0.30681468 0.028430466
## P 0.47653407 -0.027937095 0.31211841 0.53697194 0.287203207
## P.dis 0.47653407 -0.027937095 0.31211841 0.53697194 0.287203207
## K 0.28103608 -0.026827012 0.52673451 0.65451742 0.508233117
## Ca 0.48356460 -0.027813753 0.79253777 0.72050570 0.793031144
## Mg 0.35195806 0.073327166 0.74344743 0.59728646 0.696083873
## S 0.31199507 -0.001541782 0.74690102 0.82238668 0.490225100
## Na 0.17422140 -0.029170521 0.65895776 0.75750848 0.487758249
## Fe -0.05642923 0.442861548 -0.27634906 -0.24822695 -0.419919827
## Cu 1.00000000 0.092568609 0.52944804 0.40807894 0.504656183
## Mn 0.09256861 1.000000000 0.08701819 -0.03274746 -0.056305890
## Zn 0.52944804 0.087018193 1.00000000 0.75874191 0.598396546
## B 0.40807894 -0.032747456 0.75874191 1.00000000 0.494295405
## S.Bas 0.50465618 -0.056305890 0.59839655 0.49429541 1.000000000
## S.Al -0.51267345 0.029663891 -0.60505705 -0.48208449 -0.993709528
## S.Ca 0.59198273 -0.111440025 0.56275054 0.43681776 0.871723713
## S.Mg 0.18754240 0.179155103 0.38402714 0.21406105 0.532161579
## S.K 0.09392538 0.107123034 0.04323158 0.21751465 0.105889608
## S.Na -0.05359235 -0.032254086 0.39290780 0.54412581 0.232439100
## Ca_Mg 0.37193956 -0.113783534 0.17385137 0.28831329 0.249953747
## Mg_K 0.04150478 -0.049892075 0.32864632 0.10638298 0.313598520
## Ca_K 0.23687943 -0.154609929 0.38809744 0.24637681 0.429417206
## Ca.Mg_K 0.21813136 -0.148196115 0.38193031 0.22972556 0.446191798
## Ca_B 0.10206599 -0.031144002 0.18778908 -0.05815603 0.477890842
## Fe_Mn -0.19654641 -0.028677151 -0.33123651 -0.29435708 -0.497502313
## Fe_Zn -0.48183780 0.126487820 -0.77835338 -0.66882516 -0.628862165
## S.Al S.Ca S.Mg S.K S.Na
## Rmed1 -0.010014245 -0.052976594 -0.03300992 0.27366089 0.08784100
## IDPM 0.061277830 0.006603214 0.13655447 -0.33663187 -0.14078053
## ALT 0.099065481 -0.239459648 0.07661228 0.28442773 0.21157821
## TEM -0.006132246 0.095469395 -0.15808286 -0.20526888 -0.33094766
## pH -0.803885291 0.830527290 0.24748689 -0.04027135 0.04446500
## Aci 0.962380512 -0.847795251 -0.50675301 -0.11464693 -0.23416590
## Al 0.966820845 -0.835337650 -0.50909652 -0.12821462 -0.21270429
## CE -0.558310207 0.499105766 0.29744064 0.17631822 0.54745606
## CIC -0.612704286 0.610484120 0.31273512 -0.02818378 0.42695035
## MO -0.048041936 -0.011655874 -0.07838421 0.24440333 0.11082331
## N.tot -0.048041936 -0.011655874 -0.07838421 0.24440333 0.11082331
## N.dis -0.048041936 -0.011655874 -0.07838421 0.24440333 0.11082331
## P -0.271908726 0.468023435 -0.36811594 0.24427999 -0.02251002
## P.dis -0.271908726 0.468023435 -0.36811594 0.24427999 -0.02251002
## K -0.498119026 0.297810669 0.31162504 0.72482270 0.36083873
## Ca -0.773913043 0.810792476 0.31779217 -0.04039470 0.34295405
## Mg -0.687203207 0.490348443 0.77674992 0.06604995 0.50046253
## S -0.476164046 0.323219241 0.40166512 0.22084490 0.51945729
## Na -0.463829787 0.340980574 0.43213074 0.05001542 0.84656183
## Fe 0.420783225 -0.439531298 0.06308973 -0.02954055 -0.12883133
## Cu -0.512673451 0.591982732 0.18754240 0.09392538 -0.05359235
## Mn 0.029663891 -0.111440025 0.17915510 0.10712303 -0.03225409
## Zn -0.605057046 0.562750540 0.38402714 0.04323158 0.39290780
## B -0.482084490 0.436817761 0.21406105 0.21751465 0.54412581
## S.Bas -0.993709528 0.871723713 0.53216158 0.10588961 0.23243910
## S.Al 1.000000000 -0.860006167 -0.54079556 -0.12500771 -0.21776133
## S.Ca -0.860006167 1.000000000 0.18248535 -0.13709528 0.01473944
## S.Mg -0.540795560 0.182485353 1.00000000 0.15880358 0.40585877
## S.K -0.125007709 -0.137095282 0.15880358 1.00000000 0.04717854
## S.Na -0.217761332 0.014739439 0.40585877 0.04717854 1.00000000
## Ca_Mg -0.233302498 0.605057046 -0.56941104 -0.15596670 -0.19358619
## Mg_K -0.296330558 0.254147394 0.52944804 -0.64366327 0.39932162
## Ca_K -0.404872032 0.643909960 0.03102066 -0.68732655 0.18815911
## Ca.Mg_K -0.422510022 0.614431082 0.13697194 -0.71125501 0.22577860
## Ca_B -0.467036694 0.541042245 0.15374653 -0.31310515 -0.13228492
## Fe_Mn 0.517730496 -0.454332408 -0.07246377 -0.24020968 -0.09182855
## Fe_Zn 0.632439100 -0.634659266 -0.19740981 -0.11662041 -0.31174838
## Ca_Mg Mg_K Ca_K Ca.Mg_K Ca_B
## Rmed1 -0.02175935 -0.28991858 -0.28787865 -0.321753994 -0.277988033
## IDPM -0.13206429 0.33755632 0.24524338 0.282485513 0.143025624
## ALT -0.13311538 -0.18061253 -0.19097555 -0.212503475 -0.367701949
## TEM 0.09843869 0.07674991 0.09495299 0.117416382 0.460176683
## pH 0.38797410 0.28572310 0.51674376 0.520690718 0.649337034
## Aci -0.24711687 -0.32691952 -0.45642923 -0.467653407 -0.454455751
## Al -0.23786617 -0.30545791 -0.42584027 -0.437804502 -0.450508788
## CE 0.27684243 0.19753315 0.32223250 0.299784150 0.161270429
## CIC 0.28239285 0.38390379 0.48948504 0.498489053 0.520814061
## MO 0.08307123 -0.22417515 -0.15066297 -0.202220167 -0.345420907
## N.tot 0.08307123 -0.22417515 -0.15066297 -0.202220167 -0.345420907
## N.dis 0.08307123 -0.22417515 -0.15066297 -0.202220167 -0.345420907
## P 0.71483195 -0.41572618 0.09404872 0.001171755 -0.028800493
## P.dis 0.71483195 -0.41572618 0.09404872 0.001171755 -0.028800493
## K 0.05408572 -0.24477336 -0.20629047 -0.219858156 0.060746223
## Ca 0.43324083 0.39821153 0.62368178 0.617884675 0.552513105
## Mg -0.15349985 0.58347209 0.36046870 0.426210299 0.430897317
## S 0.07628739 0.21911810 0.20530373 0.200493370 -0.007832254
## Na 0.03361085 0.41634289 0.35627505 0.380203515 0.188899167
## Fe -0.32728955 -0.03287080 -0.25932778 -0.226271970 -0.121430774
## Cu 0.37193956 0.04150478 0.23687943 0.218131360 0.102065988
## Mn -0.11378353 -0.04989208 -0.15460993 -0.148196115 -0.031144002
## Zn 0.17385137 0.32864632 0.38809744 0.381930311 0.187789084
## B 0.28831329 0.10638298 0.24637681 0.229725563 -0.058156028
## S.Bas 0.24995375 0.31359852 0.42941721 0.446191798 0.477890842
## S.Al -0.23330250 -0.29633056 -0.40487203 -0.422510022 -0.467036694
## S.Ca 0.60505705 0.25414739 0.64390996 0.614431082 0.541042245
## S.Mg -0.56941104 0.52944804 0.03102066 0.136971940 0.153746531
## S.K -0.15596670 -0.64366327 -0.68732655 -0.711255011 -0.313105150
## S.Na -0.19358619 0.39932162 0.18815911 0.225778600 -0.132284921
## Ca_Mg 1.00000000 -0.20419365 0.49491212 0.388837496 0.252913969
## Mg_K -0.20419365 1.00000000 0.70669134 0.796361394 0.426827012
## Ca_K 0.49491212 0.70669134 1.00000000 0.985198890 0.516990441
## Ca.Mg_K 0.38883750 0.79636139 0.98519889 1.000000000 0.549059513
## Ca_B 0.25291397 0.42682701 0.51699044 0.549059513 1.000000000
## Fe_Mn -0.32062905 0.09417206 -0.10021585 -0.063089732 -0.148936170
## Fe_Zn -0.35417823 -0.14979957 -0.35898859 -0.324699352 -0.172494604
## Fe_Mn Fe_Zn
## Rmed1 -0.07455049 -0.09117909
## IDPM 0.05176920 0.15570380
## ALT 0.16599328 0.15600037
## TEM -0.04376487 -0.01826764
## pH -0.47850755 -0.56990441
## Aci 0.56102374 0.67523898
## Al 0.57113784 0.66043787
## CE -0.35134135 -0.74591428
## CIC -0.22380512 -0.61850139
## MO -0.35726179 -0.37354302
## N.tot -0.35726179 -0.37354302
## N.dis -0.35726179 -0.37354302
## P -0.35393154 -0.47123034
## P.dis -0.35393154 -0.47123034
## K -0.33876041 -0.51316682
## Ca -0.35997533 -0.71039161
## Mg -0.15942029 -0.49244527
## S -0.20296022 -0.55547333
## Na -0.12352760 -0.47715079
## Fe 0.79895159 0.69164354
## Cu -0.19654641 -0.48183780
## Mn -0.02867715 0.12648782
## Zn -0.33123651 -0.77835338
## B -0.29435708 -0.66882516
## S.Bas -0.49750231 -0.62886216
## S.Al 0.51773050 0.63243910
## S.Ca -0.45433241 -0.63465927
## S.Mg -0.07246377 -0.19740981
## S.K -0.24020968 -0.11662041
## S.Na -0.09182855 -0.31174838
## Ca_Mg -0.32062905 -0.35417823
## Mg_K 0.09417206 -0.14979957
## Ca_K -0.10021585 -0.35898859
## Ca.Mg_K -0.06308973 -0.32469935
## Ca_B -0.14893617 -0.17249460
## Fe_Mn 1.00000000 0.65957447
## Fe_Zn 0.65957447 1.00000000# Variables que mas se correlacionan con otras
sort(colMeans(abs(A.cor)))## Mn Rmed1 TEM ALT IDPM S.K MO N.tot
## 0.1219523 0.1667262 0.1796174 0.2010750 0.2136756 0.2390535 0.2449938 0.2449938
## N.dis S.Na Ca_Mg Cu Fe S.Mg P P.dis
## 0.2449938 0.2647851 0.2850079 0.2912108 0.2931143 0.2980425 0.3031235 0.3031235
## Fe_Mn Ca_B Mg_K Ca_K Ca.Mg_K Na K S
## 0.3080173 0.3100796 0.3276790 0.3582400 0.3643480 0.3745913 0.3792259 0.3866161
## Mg pH B Zn S.Ca CIC Al S.Al
## 0.4119308 0.4131707 0.4269159 0.4361608 0.4380561 0.4392280 0.4406023 0.4440413
## Aci S.Bas Fe_Zn CE Ca
## 0.4465732 0.4495176 0.4538240 0.4557674 0.4879267#
corrplot(A.cor, method = 'number', tl.cex = 0.5, number.cex = 0.4)
#
corrplot.mixed(A.cor, lower.col = "black", tl.cex = 0.5, number.cex = 0.4)
V A R I A B L E S M A S I M P O R T A N T E S
# Retiro variables que poco aportan al PCA.
names(A)## [1] "Rmed1" "IDPM" "ALT" "TEM" "pH" "Aci" "Al"
## [8] "CE" "CIC" "MO" "N.tot" "N.dis" "P" "P.dis"
## [15] "K" "Ca" "Mg" "S" "Na" "Fe" "Cu"
## [22] "Mn" "Zn" "B" "S.Bas" "S.Al" "S.Ca" "S.Mg"
## [29] "S.K" "S.Na" "Ca_Mg" "Mg_K" "Ca_K" "Ca.Mg_K" "Ca_B"
## [36] "Fe_Mn" "Fe_Zn"B <- A[,-c(1,2,3,4,6,7,11,12,13,20,21,22,28,29,30,31,32,33)];B## pH CE CIC MO P.dis K
## BOGOTA 5.559103 0.4744674 12.685202 9.778200 128.18565 0.8372324
## CAJICA 5.316000 0.4607279 10.384645 8.094967 67.90187 0.6229017
## CARMEN DE CARUPA 5.062155 0.3139291 6.417927 12.586212 115.27883 0.6813839
## CHIA 5.918421 0.8380108 14.984885 7.087336 136.03607 1.3264158
## CHIPAQUE 5.771273 0.5017144 12.069595 6.234734 243.17715 0.6996663
## CHOACHI 5.166176 0.2091443 7.574417 6.979696 46.14897 0.4317104
## CHOCONTA 5.114296 0.3881219 7.185019 9.893829 119.02915 0.7592882
## COGUA 5.452424 0.3825780 8.582600 10.649070 110.17181 0.5279060
## COTA 5.014000 1.2191779 11.399998 8.561286 86.67598 1.3810860
## CUCUNUBA 5.555217 0.3394613 7.801094 6.126479 48.70733 0.8593057
## EL ROSAL 5.579444 0.6773613 12.920994 20.790809 67.66688 1.2002920
## FACATATIVA 5.761250 1.2174820 16.797049 13.113757 121.00601 1.1939526
## FOSCA 5.394667 0.3239092 6.237568 5.311936 256.24507 0.5307785
## FUNZA 5.595870 1.1689460 13.619075 11.631361 166.92357 0.7367824
## FUQUENE 5.097600 0.3985827 8.620903 8.077060 46.89706 0.4861748
## GRANADA 5.335811 0.9952873 9.594231 13.810492 108.40787 0.5472834
## GUACHETA 5.192105 0.4200620 8.585921 5.944584 44.54421 0.6005536
## GUASCA 5.509538 0.2686508 5.720285 10.018231 69.44832 0.4989278
## GUATAVITA 5.104896 0.2192190 5.516139 12.252528 62.43887 0.3936100
## GUTIERREZ 5.304146 0.3135042 7.407471 7.487618 196.51523 0.4522899
## JUNIN 5.245667 0.1621760 6.131229 8.237882 25.11463 0.2749652
## LA CALERA 5.470921 0.4327048 9.397545 11.516739 83.48464 0.5700326
## LENGUAZAQUE 4.933537 0.3815322 6.748871 7.177118 160.74707 0.5608173
## MACHETA 5.224000 0.1341186 7.422145 4.311126 32.29613 0.4614810
## MADRID 5.772353 1.4138369 17.466278 13.073099 229.74511 1.9763222
## MANTA 5.731957 0.2626550 10.327537 4.042129 96.06190 0.6681469
## PACHO 5.222105 0.2557147 4.981400 10.007553 21.33420 0.2527059
## PASCA 5.687200 0.6390085 10.783257 10.289681 242.61607 0.7231711
## SAN BERNARDO 5.237612 0.3380505 8.861645 6.340610 180.82794 0.3785195
## SAN CAYETANO 5.159459 0.2280057 6.290645 7.793149 32.82863 0.1826725
## SESQUILE 5.184222 0.3121670 6.733806 12.484487 98.62468 0.5565116
## SIBATE 5.218485 0.6461802 8.908929 17.425073 138.12108 0.6264456
## SIMIJACA 5.044405 0.9808050 11.942048 7.753307 64.74293 0.7921099
## SOACHA 5.767143 2.1790074 14.701346 8.185902 98.50659 0.8523538
## SOPO 5.533333 0.4027289 9.028509 5.764675 43.50582 0.5194166
## SUBACHOQUE 5.296275 0.4407986 7.670090 20.534207 82.05546 0.8484872
## SUESCA 5.359383 0.2968667 6.033355 6.763315 66.38734 0.7465279
## SUSA 5.174068 0.2559407 6.598838 11.852425 94.47381 0.6221803
## SUTATAUSA 5.585789 1.1215687 11.402752 5.880491 181.30399 0.6200010
## TABIO 5.374000 0.4345830 9.137376 10.278675 49.97889 0.8974949
## TAUSA 5.010575 0.4933287 7.664277 19.107391 231.03623 0.6459231
## TENJO 5.531667 0.8992575 11.832478 13.849393 230.37548 1.2233185
## UBAQUE 5.477308 0.4094572 7.157293 12.631683 253.55491 0.7351836
## UNE 5.901754 0.4648777 11.402627 6.630656 390.24844 0.7137648
## VILLAPINZON 5.264062 0.3099199 6.455969 11.234601 167.86534 0.6554738
## ZIPACON 5.543889 0.3463251 7.790373 13.774041 22.88857 0.4305246
## ZIPAQUIRA 5.420000 0.4560806 9.809536 13.956820 104.95754 0.5026438
## Ca Mg S Na Zn B
## BOGOTA 7.609837 1.7951393 10.507277 0.12319214 5.463535 0.3275570
## CAJICA 6.828402 1.5094093 13.102876 0.30956664 6.398600 0.3558244
## CARMEN DE CARUPA 2.703054 0.7181425 8.190727 0.08674512 1.920793 0.2525678
## CHIA 10.384599 2.5320848 31.323270 0.27498420 11.472134 0.4177221
## CHIPAQUE 8.785649 1.4130209 12.127141 0.10290401 5.334073 0.3241120
## CHOACHI 3.117594 0.8909178 6.645534 0.07732360 3.222804 0.1310648
## CHOCONTA 3.426372 0.9684186 8.993108 0.17511828 2.880188 0.2669020
## COGUA 5.137754 1.4431673 15.964870 0.13715575 5.599856 0.2983707
## COTA 5.196928 2.7790936 20.586415 0.35717320 28.979400 0.4398446
## CUCUNUBA 4.451287 1.5760503 11.428284 0.12308086 2.904986 0.2615827
## EL ROSAL 8.727308 2.1647076 17.861430 0.17910686 8.285466 0.3866722
## FACATATIVA 11.861185 2.4470635 39.566737 0.25014833 7.419592 0.4082495
## FOSCA 3.943650 0.7771380 8.819503 0.05958466 5.703100 0.2029506
## FUNZA 9.049120 2.6870710 49.734446 0.40554220 16.565071 0.5769619
## FUQUENE 4.639825 2.0289622 37.360956 0.13548251 8.067467 0.2827392
## GRANADA 6.144561 1.8123246 46.235197 0.11806982 12.161302 0.5569017
## GUACHETA 4.809975 1.9723353 16.462326 0.15544412 6.569300 0.2952467
## GUASCA 3.367402 1.1208034 8.403813 0.09926487 2.698380 0.2513500
## GUATAVITA 1.962767 0.7815846 7.676379 0.05321787 2.156927 0.1513918
## GUTIERREZ 4.620345 0.7560211 8.590721 0.09168774 3.542726 0.3029657
## JUNIN 2.092673 0.6138866 5.284479 0.07137625 3.153675 0.1574059
## LA CALERA 5.960935 0.9350799 8.644076 0.09284027 3.357971 0.2263380
## LENGUAZAQUE 2.382194 0.7358587 14.960065 0.10778068 2.437085 0.3167189
## MACHETA 2.317661 1.1483899 2.926136 0.08478355 1.997232 0.1479174
## MADRID 10.806383 3.7989980 80.936942 0.52650822 17.849911 0.5474817
## MANTA 7.370387 1.3783723 6.815903 0.13806267 3.619380 0.3111185
## PACHO 1.932011 0.6736951 7.971077 0.06530890 2.358481 0.1343537
## PASCA 7.924545 1.1939198 14.293321 0.12028547 8.738744 0.3379247
## SAN BERNARDO 5.036355 1.0463044 9.607265 0.09865306 5.423376 0.2715315
## SAN CAYETANO 2.847023 0.7954409 5.257374 0.10541479 2.992689 0.1695861
## SESQUILE 2.827452 0.6986989 9.848105 0.05951783 2.790444 0.1569658
## SIBATE 5.325352 1.0166715 14.500787 0.18124861 7.047068 0.3672236
## SIMIJACA 6.725803 2.1156070 76.475056 0.23171513 7.345060 0.3752028
## SOACHA 9.432303 3.0191177 73.127804 0.77869144 7.209866 0.4015325
## SOPO 5.295229 2.2712253 11.543862 0.19123587 3.416762 0.2216387
## SUBACHOQUE 4.075166 1.1870670 13.485496 0.10165910 6.414248 0.3047218
## SUESCA 3.135905 1.1041059 8.328884 0.08185894 2.383673 0.2140021
## SUSA 2.962146 0.8112063 17.523490 0.08375177 2.601359 0.2305126
## SUTATAUSA 8.159469 1.3669953 34.351701 0.50158465 4.634158 0.3312990
## TABIO 4.789854 1.6674089 13.560286 0.15346145 6.293630 0.2634338
## TAUSA 2.759601 0.6489134 14.953695 0.16554767 3.686032 0.3908919
## TENJO 7.490951 1.8092387 25.496444 0.23576234 11.478407 0.4902641
## UBAQUE 4.676258 0.8740120 11.937510 0.06705591 6.073077 0.2824154
## UNE 8.240352 1.2919375 12.952596 0.10805196 6.844354 0.3007872
## VILLAPINZON 3.531951 0.7974836 14.355854 0.07051152 3.149603 0.3706109
## ZIPACON 5.514894 1.4249624 9.759495 0.08402422 6.268119 0.1585402
## ZIPAQUIRA 6.082949 1.4028510 13.693114 0.14889878 4.810961 0.3252102
## S.Bas S.Al S.Ca Ca.Mg_K Ca_B Fe_Mn
## BOGOTA 80.05188 14.7452755 55.16836 13.431593 22.911929 76.11250
## CAJICA 80.67935 15.4288655 57.30971 27.267786 22.103206 99.79597
## CARMEN DE CARUPA 66.48783 26.3535645 41.91207 5.946376 13.358083 101.88795
## CHIA 99.31008 0.4654782 68.81959 16.116879 28.283091 82.06782
## CHIPAQUE 89.93421 7.9391507 69.18755 15.714203 31.356669 106.15040
## CHOACHI 59.64343 31.5265113 39.99056 10.737634 44.860094 126.77638
## CHOCONTA 76.23524 19.5441738 47.32400 7.352359 15.303524 64.50734
## COGUA 85.37701 12.1882056 59.12271 15.463167 21.599593 127.99761
## COTA 72.98395 19.4199499 39.26746 6.265442 10.596020 137.53683
## CUCUNUBA 92.33921 5.3468453 56.74571 8.184432 23.369612 124.07392
## EL ROSAL 92.95790 4.5533721 64.06779 11.156403 24.313911 48.45113
## FACATATIVA 95.29772 2.8526592 67.91950 15.894671 36.037275 45.69982
## FOSCA 83.60708 12.1007762 60.37720 10.038924 20.342930 73.69159
## FUNZA 96.39223 1.9050203 67.71797 21.309211 21.154209 76.80013
## FUQUENE 85.86268 10.1669683 52.84402 14.978127 17.942565 119.74120
## GRANADA 89.18058 7.4281554 62.24793 18.540743 13.619364 57.26523
## GUACHETA 87.07609 9.5278237 54.84609 11.837539 18.141728 124.07656
## GUASCA 90.86439 5.9007369 59.06861 10.883756 16.317502 82.78848
## GUATAVITA 62.78067 23.6880716 38.42958 7.815801 18.490373 99.78590
## GUTIERREZ 78.26410 17.2662794 59.71984 12.280181 17.651530 62.19801
## JUNIN 48.76613 41.3027097 32.48146 9.725518 13.253069 120.11062
## LA CALERA 75.56990 19.0462365 54.81031 13.737836 25.690887 106.75522
## LENGUAZAQUE 58.54142 34.4287355 35.49699 6.572472 9.805060 194.46502
## MACHETA 53.18053 40.2219427 30.45249 8.118233 33.369561 105.21371
## MADRID 94.83883 3.9872994 59.13032 9.281366 23.038606 54.92473
## MANTA 91.89935 5.9956611 68.42633 16.265023 26.836958 93.91041
## PACHO 63.66178 27.1144823 40.29819 10.163052 15.783438 89.83600
## PASCA 91.86695 5.9067966 71.28184 13.986523 24.694176 81.70473
## SAN BERNARDO 76.61698 18.8071387 57.63528 19.560620 23.554260 90.54170
## SAN CAYETANO 61.76864 32.0957150 43.08813 26.282011 16.708943 151.18552
## SESQUILE 65.19560 22.4494314 43.98237 7.485572 22.908559 91.64701
## SIBATE 82.59789 12.1732038 59.01888 13.475778 16.078475 98.58501
## SIMIJACA 81.78400 14.8497602 53.48437 13.284027 20.374094 146.26137
## SOACHA 83.76125 12.2199911 54.18933 22.136876 26.551133 59.29421
## SOPO 86.61411 9.8155378 59.60813 22.241580 27.203337 130.78752
## SUBACHOQUE 81.68475 14.0101016 52.21020 7.660675 14.552539 74.09232
## SUESCA 88.01743 8.2137716 53.25031 6.757195 18.355345 66.46467
## SUSA 69.28075 23.8886218 42.97241 7.964299 13.797263 124.09790
## SUTATAUSA 93.87815 4.6078133 66.15861 33.715224 28.081424 65.38077
## TABIO 85.63516 11.4733695 53.89752 8.095845 19.664735 79.87481
## TAUSA 55.09451 35.6446294 35.28947 7.475064 7.764209 81.64860
## TENJO 92.25186 5.8883004 62.00234 8.314953 17.833142 71.50533
## UBAQUE 88.07622 8.9062406 63.00816 8.930196 17.187490 62.11595
## UNE 92.26753 6.1445993 71.06692 15.047349 31.925446 92.23563
## VILLAPINZON 77.13117 18.5653449 50.78021 8.402251 11.359261 100.98522
## ZIPACON 93.37620 4.1878722 68.12849 20.023376 35.109077 48.52643
## ZIPAQUIRA 83.87497 12.9462299 60.13385 19.480282 20.422276 124.80835
## Fe_Zn
## BOGOTA 159.48352
## CAJICA 224.82995
## CARMEN DE CARUPA 348.51124
## CHIA 44.59346
## CHIPAQUE 192.95755
## CHOACHI 463.21697
## CHOCONTA 279.89002
## COGUA 231.20179
## COTA 58.32030
## CUCUNUBA 283.95300
## EL ROSAL 31.38065
## FACATATIVA 39.99380
## FOSCA 121.38584
## FUNZA 37.19371
## FUQUENE 291.30422
## GRANADA 61.45104
## GUACHETA 235.46899
## GUASCA 271.42576
## GUATAVITA 406.21095
## GUTIERREZ 181.54670
## JUNIN 543.41485
## LA CALERA 252.41292
## LENGUAZAQUE 604.75564
## MACHETA 770.00062
## MADRID 30.35978
## MANTA 269.76120
## PACHO 155.79598
## PASCA 53.13072
## SAN BERNARDO 174.79516
## SAN CAYETANO 266.55311
## SESQUILE 328.82178
## SIBATE 104.90479
## SIMIJACA 291.09793
## SOACHA 60.45141
## SOPO 387.17109
## SUBACHOQUE 119.03846
## SUESCA 266.70547
## SUSA 355.08541
## SUTATAUSA 113.22886
## TABIO 175.23262
## TAUSA 164.86202
## TENJO 112.37959
## UBAQUE 70.69824
## UNE 85.98830
## VILLAPINZON 248.48556
## ZIPACON 74.34853
## ZIPAQUIRA 229.29622# Mariam: [,-c(1,2,3,4,6,7,11,12,13,20,21,22,28,29,30,31,32,33)]
# Geraldine y Jimer: [,-c(1,2,4,6,7,10,11,13,20,21,22,28,30,31,32,33,36,37)]
# Pedro: [,-c(1,2,4,6,10,11,12,13,20,22,29,30,32,35,36,37)]; B
B.cor <- cor( B, method = "spearman", use = "complete.obs"); B.cor## pH CE CIC MO P.dis K
## pH 1.00000000 0.4248381 0.60811748 -0.067414431 0.32227105 0.39639223
## CE 0.42483811 1.0000000 0.85534228 0.288390379 0.44357077 0.68466698
## CIC 0.60811748 0.8553423 1.00000000 0.051110083 0.31278908 0.61991212
## MO -0.06741443 0.2883904 0.05111008 1.000000000 0.12685014 0.20501850
## P.dis 0.32227105 0.4435708 0.31278908 0.126850139 1.00000000 0.35996762
## K 0.39639223 0.6846670 0.61991212 0.205018501 0.35996762 1.00000000
## Ca 0.76607308 0.8154487 0.93605458 0.052960222 0.39396392 0.56868640
## Mg 0.51630435 0.7123034 0.82065217 -0.023473636 -0.04047179 0.56949584
## S 0.22421369 0.8311748 0.67865402 0.298103608 0.33568455 0.56371415
## Na 0.32747456 0.8038853 0.80053191 0.037118409 0.16177151 0.58024977
## Zn 0.41003700 0.8255088 0.77913969 0.243871415 0.30908881 0.52705828
## B 0.33672525 0.8604302 0.75809436 0.302150786 0.53503700 0.65575856
## S.Bas 0.80076318 0.5615171 0.61574931 0.030527290 0.27763645 0.49294635
## S.Al -0.79209066 -0.5509944 -0.59447271 -0.050763182 -0.26711378 -0.48554579
## S.Ca 0.82192414 0.4925994 0.59481961 -0.008672525 0.46773821 0.28596207
## Ca.Mg_K 0.52197040 0.3036540 0.49757169 -0.201896392 0.01075393 -0.20975948
## Ca_B 0.65633673 0.1680157 0.52636448 -0.344703978 -0.02058279 0.07539315
## Fe_Mn -0.48369565 -0.3598520 -0.24387142 -0.355342276 -0.35615171 -0.35106383
## Fe_Zn -0.57539315 -0.7472248 -0.61933395 -0.370721554 -0.47236355 -0.51757632
## Ca Mg S Na Zn B
## pH 0.76607308 0.51630435 0.22421369 0.32747456 0.4100370 0.33672525
## CE 0.81544866 0.71230342 0.83117484 0.80388529 0.8255088 0.86043016
## CIC 0.93605458 0.82065217 0.67865402 0.80053191 0.7791397 0.75809436
## MO 0.05296022 -0.02347364 0.29810361 0.03711841 0.2438714 0.30215079
## P.dis 0.39396392 -0.04047179 0.33568455 0.16177151 0.3090888 0.53503700
## K 0.56868640 0.56949584 0.56371415 0.58024977 0.5270583 0.65575856
## Ca 1.00000000 0.78630897 0.62615634 0.70733117 0.7800648 0.72120722
## Mg 0.78630897 1.00000000 0.66824699 0.76815449 0.7430620 0.60372340
## S 0.62615634 0.66824699 1.00000000 0.70582794 0.7441027 0.81544866
## Na 0.70733117 0.76815449 0.70582794 1.00000000 0.6546022 0.75555042
## Zn 0.78006475 0.74306198 0.74410268 0.65460222 1.0000000 0.75370028
## B 0.72120722 0.60372340 0.81544866 0.75555042 0.7537003 1.00000000
## S.Bas 0.77624884 0.68316374 0.49572155 0.48566142 0.6000231 0.48496762
## S.Al -0.75844126 -0.67483811 -0.47768270 -0.45929695 -0.6046485 -0.47294172
## S.Ca 0.79856614 0.47918594 0.32238668 0.33811286 0.5588575 0.43119796
## Ca.Mg_K 0.61667438 0.42391304 0.19946809 0.38031915 0.3792784 0.23485199
## Ca_B 0.56047641 0.42726642 -0.01179463 0.19021739 0.1864015 -0.04752544
## Fe_Mn -0.37268733 -0.17090657 -0.19067993 -0.12453747 -0.3331406 -0.30238205
## Fe_Zn -0.71045328 -0.49364015 -0.54833488 -0.47097595 -0.7775208 -0.66894080
## S.Bas S.Al S.Ca Ca.Mg_K Ca_B
## pH 0.80076318 -0.79209066 0.821924144 0.52197040 0.65633673
## CE 0.56151711 -0.55099445 0.492599445 0.30365402 0.16801573
## CIC 0.61574931 -0.59447271 0.594819611 0.49757169 0.52636448
## MO 0.03052729 -0.05076318 -0.008672525 -0.20189639 -0.34470398
## P.dis 0.27763645 -0.26711378 0.467738205 0.01075393 -0.02058279
## K 0.49294635 -0.48554579 0.285962072 -0.20975948 0.07539315
## Ca 0.77624884 -0.75844126 0.798566142 0.61667438 0.56047641
## Mg 0.68316374 -0.67483811 0.479185939 0.42391304 0.42726642
## S 0.49572155 -0.47768270 0.322386679 0.19946809 -0.01179463
## Na 0.48566142 -0.45929695 0.338112858 0.38031915 0.19021739
## Zn 0.60002313 -0.60464847 0.558857539 0.37927845 0.18640148
## B 0.48496762 -0.47294172 0.431197965 0.23485199 -0.04752544
## S.Bas 1.00000000 -0.99364015 0.869565217 0.44634598 0.47143848
## S.Al -0.99364015 1.00000000 -0.858926920 -0.42113784 -0.46172525
## S.Ca 0.86956522 -0.85892692 1.000000000 0.61644311 0.53746531
## Ca.Mg_K 0.44634598 -0.42113784 0.616443108 1.00000000 0.54891304
## Ca_B 0.47143848 -0.46172525 0.537465310 0.54891304 1.00000000
## Fe_Mn -0.48392692 0.50832562 -0.448543016 -0.06255782 -0.15379278
## Fe_Zn -0.62129972 0.62789084 -0.630666050 -0.32435245 -0.17819149
## Fe_Mn Fe_Zn
## pH -0.48369565 -0.5753932
## CE -0.35985199 -0.7472248
## CIC -0.24387142 -0.6193340
## MO -0.35534228 -0.3707216
## P.dis -0.35615171 -0.4723636
## K -0.35106383 -0.5175763
## Ca -0.37268733 -0.7104533
## Mg -0.17090657 -0.4936401
## S -0.19067993 -0.5483349
## Na -0.12453747 -0.4709759
## Zn -0.33314061 -0.7775208
## B -0.30238205 -0.6689408
## S.Bas -0.48392692 -0.6212997
## S.Al 0.50832562 0.6278908
## S.Ca -0.44854302 -0.6306660
## Ca.Mg_K -0.06255782 -0.3243525
## Ca_B -0.15379278 -0.1781915
## Fe_Mn 1.00000000 0.6607308
## Fe_Zn 0.66073080 1.0000000# Variables que mas se correlacionan con otras
sort(colMeans(abs(B.cor)))## MO P.dis Ca_B Fe_Mn Ca.Mg_K K S Na
## 0.2136363 0.3270425 0.3456108 0.3664309 0.3894664 0.4815351 0.5124945 0.5132431
## pH S.Ca Mg B Fe_Zn S.Al S.Bas Zn
## 0.5290545 0.5558754 0.5581637 0.5652965 0.5797690 0.5821303 0.5890075 0.5900056
## CE CIC Ca
## 0.6173377 0.6269779 0.6709370#
corrplot(B.cor, method = 'number', tl.cex = 0.6, number.cex = 0.5)
#
corrplot.mixed(B.cor, lower.col = "black", tl.cex = 0.6, number.cex = 0.5)
#Correlograma variables PCA
names(B)## [1] "pH" "CE" "CIC" "MO" "P.dis" "K" "Ca"
## [8] "Mg" "S" "Na" "Zn" "B" "S.Bas" "S.Al"
## [15] "S.Ca" "Ca.Mg_K" "Ca_B" "Fe_Mn" "Fe_Zn"ggpairs(B, title="Correlograma")
# ANALISIS DE COMPONENTES PRINCIPALES ## PCA
names(B)## [1] "pH" "CE" "CIC" "MO" "P.dis" "K" "Ca"
## [8] "Mg" "S" "Na" "Zn" "B" "S.Bas" "S.Al"
## [15] "S.Ca" "Ca.Mg_K" "Ca_B" "Fe_Mn" "Fe_Zn"# 1,2,4,10,11,18,19
res.pca <- PCA(B, graph = TRUE)
## Extract eigenvalues/variances
get_eig(res.pca)## eigenvalue variance.percent cumulative.variance.percent
## Dim.1 9.326873703 49.08880896 49.08881
## Dim.2 2.702901505 14.22579739 63.31461
## Dim.3 2.012378503 10.59146580 73.90607
## Dim.4 1.049145896 5.52182051 79.42789
## Dim.5 0.984925577 5.18381883 84.61171
## Dim.6 0.884028482 4.65278148 89.26449
## Dim.7 0.531048395 2.79499155 92.05948
## Dim.8 0.468531507 2.46595530 94.52544
## Dim.9 0.289340903 1.52284686 96.04829
## Dim.10 0.221145282 1.16392253 97.21221
## Dim.11 0.148829835 0.78331492 97.99552
## Dim.12 0.136489484 0.71836571 98.71389
## Dim.13 0.101570067 0.53457930 99.24847
## Dim.14 0.053699083 0.28262675 99.53110
## Dim.15 0.044651139 0.23500599 99.76610
## Dim.16 0.025557020 0.13451063 99.90061
## Dim.17 0.012377952 0.06514712 99.96576
## Dim.18 0.004391232 0.02311175 99.98887
## Dim.19 0.002114437 0.01112862 100.00000# Visualize eigenvalues/variances
factoextra::fviz_screeplot(res.pca, addlabels = TRUE, ylim = c(0, 80))
# Extract the results for variables
var <- get_pca_var(res.pca)
var## Principal Component Analysis Results for variables
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for the variables"
## 2 "$cor" "Correlations between variables and dimensions"
## 3 "$cos2" "Cos2 for the variables"
## 4 "$contrib" "contributions of the variables"# Coordinates of variables
var$coord## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## pH 0.7312956 -0.52506300 0.05290544 0.186083052 -0.0634069748
## CE 0.8181439 0.40756387 0.18592662 -0.162106510 -0.0267239091
## CIC 0.9087339 0.11042047 0.20027652 0.160457788 -0.0492266033
## MO 0.1304721 0.28035762 -0.67886000 -0.274508393 -0.4099349443
## P.dis 0.3732286 -0.15927830 -0.40223526 0.193104030 0.6090742446
## K 0.7298916 0.36208886 -0.18833872 0.454128937 -0.1359704539
## Ca 0.9413509 -0.14309347 0.12155547 0.078868757 0.0006198796
## Mg 0.8339062 0.30424558 0.28045264 0.134943824 -0.1430879576
## S 0.7063383 0.44856320 0.23867077 -0.128524575 -0.0049718627
## Na 0.7196520 0.37233582 0.37528150 -0.207762663 -0.0250547577
## Zn 0.6551702 0.47189942 -0.07359911 0.124037056 0.1384138724
## B 0.7874600 0.38086142 -0.19204859 -0.079294605 0.2499556168
## S.Bas 0.8146526 -0.40357730 -0.14540494 -0.001307045 0.0338664431
## S.Al -0.8036492 0.39573452 0.17690168 0.012799949 0.0029970207
## S.Ca 0.7455631 -0.59559378 -0.14668631 -0.070704839 0.1359500202
## Ca.Mg_K 0.3851693 -0.36055354 0.50678952 -0.602539569 0.1471373326
## Ca_B 0.3022399 -0.55959373 0.48378999 0.286469180 -0.3222035197
## Fe_Mn -0.5100111 0.25841091 0.44152978 0.142888411 0.4146354322
## Fe_Zn -0.7713451 0.09523629 0.42832828 0.259093577 -0.0217499331# Contribution of variables
var$contrib## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## pH 5.7338963 10.1998223 0.1390884 3.300485e+00 4.081978e-01
## CE 7.1766758 6.1455553 1.7178035 2.504754e+00 7.250977e-02
## CIC 8.8539564 0.4510960 1.9931977 2.454063e+00 2.460347e-01
## MO 0.1825152 2.9080007 22.9008061 7.182496e+00 1.706186e+01
## P.dis 1.4935292 0.9386054 8.0398991 3.554240e+00 3.766492e+01
## K 5.7119010 4.8506519 1.7626641 1.965724e+01 1.877093e+00
## Ca 9.5009496 0.7575467 0.7342422 5.928900e-01 3.901318e-05
## Mg 7.4558701 3.4246668 3.9084935 1.735682e+00 2.078752e+00
## S 5.3492066 7.4441833 2.8306671 1.574478e+00 2.509775e-03
## Na 5.5527610 5.1290794 6.9984948 4.114330e+00 6.373485e-02
## Zn 4.6022707 8.2388894 0.2691755 1.466449e+00 1.945162e+00
## B 6.6484572 5.3666558 1.8327894 5.993098e-01 6.343404e+00
## S.Bas 7.1155545 6.0259183 1.0506272 1.628341e-04 1.164490e-01
## S.Al 6.9246364 5.7939887 1.5550853 1.561639e-02 9.119606e-04
## S.Ca 5.9598141 13.1241167 1.0692260 4.764994e-01 1.876528e+00
## Ca.Mg_K 1.5906231 4.8096038 12.7627889 3.460471e+01 2.198074e+00
## Ca_B 0.9794166 11.5855183 11.6306526 7.822038e+00 1.054040e+01
## Fe_Mn 2.7888371 2.4705377 9.6874690 1.946069e+00 1.745538e+01
## Fe_Zn 6.3791289 0.3355635 9.1168294 6.398489e+00 4.802998e-02# Graph of variables: default plot
fviz_pca_var(res.pca, col.var = "black",
repel = TRUE) # Avoid text overlapping)
# Control variable colors using their contributions
fviz_pca_var(res.pca, col.var="contrib",
gradient.cols = c("darkred", "#E7B800", "darkgreen"),
repel = TRUE # Avoid text overlapping
)
# Contributions of variables to PC1
fviz_contrib(res.pca, choice = "var", axes = 1, top = 20)
# Contributions of variables to PC2
fviz_contrib(res.pca, choice = "var", axes = 2, top = 20)
# Extract the results for individuals
ind <- get_pca_ind(res.pca)
ind## Principal Component Analysis Results for individuals
## ===================================================
## Name Description
## 1 "$coord" "Coordinates for the individuals"
## 2 "$cos2" "Cos2 for the individuals"
## 3 "$contrib" "contributions of the individuals"# Coordinates of individuals
ind$coord## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## BOGOTA 1.01772946 -0.516508702 -0.12748165 0.65535617 -0.38937405
## CAJICA 0.60951915 -0.380939247 1.56460970 -1.46947871 0.33350119
## CARMEN DE CARUPA -3.31756664 1.200810890 -1.07342807 0.15639112 -0.16628864
## CHIA 5.14182261 -0.788375662 0.63286494 1.39170961 -0.06649251
## CHIPAQUE 1.81643055 -2.443695706 0.46587356 1.26287240 1.23718286
## CHOACHI -3.79549832 -0.614636265 2.47836306 1.43297178 -1.30440058
## CHOCONTA -1.82657205 0.536307323 -1.03527185 0.06795718 -0.38187429
## COGUA -0.31571992 -0.653171223 0.25781454 -0.22342458 0.47605227
## COTA 2.01160704 5.341691806 0.08829582 1.27561954 0.97870850
## CUCUNUBA -0.46491561 -1.027126691 0.47587027 1.29046983 -0.19174801
## EL ROSAL 3.27304191 -0.012116979 -1.99870832 0.08301658 -2.37718211
## FACATATIVA 5.51878689 -0.743120210 0.35062511 0.65923016 -1.74230279
## FOSCA -0.97880966 -1.710371643 -1.20627173 0.45940584 1.36585426
## FUNZA 5.46159071 1.001322336 0.33451880 -1.19565352 0.83299631
## FUQUENE -0.48312954 0.660724147 0.96457200 -0.49354896 0.22217076
## GRANADA 2.53186927 1.021472443 -1.24681274 -2.00406128 0.34272977
## GUACHETA -0.50064686 0.180446267 0.76566857 0.14635855 0.36172736
## GUASCA -1.16275901 -1.387138287 -0.87571096 -0.33486669 -0.32330994
## GUATAVITA -4.15010714 0.531706722 -0.28204762 -0.08615468 -0.91051374
## GUTIERREZ -1.09727180 -1.106984317 -1.09288202 -0.34946474 0.87018340
## JUNIN -5.40181192 1.337546873 1.08556804 0.06748114 -0.27689075
## LA CALERA -1.08700820 -0.890904158 0.25566963 0.08277641 -0.56680619
## LENGUAZAQUE -4.63409165 2.617567472 1.01334755 0.93164833 2.26160830
## MACHETA -5.03149198 0.458611554 2.77047009 1.96614071 -1.23228016
## MADRID 8.06727022 3.340936940 -0.10762632 1.88299341 -0.54550166
## MANTA 0.83701163 -2.535562030 0.91353710 0.74527459 0.34744771
## PACHO -3.86981999 0.121524731 -0.34136377 -0.98842844 -0.87130733
## PASCA 2.18489215 -1.787598761 -1.12700221 0.36918126 0.97325015
## SAN BERNARDO -0.93183840 -1.174005574 0.34851617 -0.61429833 1.08360637
## SAN CAYETANO -3.84606245 -0.009094151 1.87350769 -1.92832802 0.73368784
## SESQUILE -3.13252094 0.116784993 -0.51596290 0.29132497 -0.94893412
## SIBATE 0.19326043 0.490348124 -1.65643715 -1.22828077 0.06549932
## SIMIJACA 1.23910630 2.436412697 2.04629668 -0.14331413 0.54067858
## SOACHA 5.94897950 2.124574727 3.21507402 -1.74531540 -0.98735328
## SOPO -0.20839152 -1.450534609 2.47379440 -0.05848120 -0.02944852
## SUBACHOQUE -0.53865585 0.802637643 -2.63934716 -0.59315837 -1.44692494
## SUESCA -1.48686548 -0.992164200 -0.78256984 0.57762848 -0.65929097
## SUSA -3.09787153 1.000102638 -0.43454872 0.11164170 0.04289014
## SUTATAUSA 3.44950779 -1.646857428 2.11444363 -2.30054950 0.77984205
## TABIO -0.05290202 0.065282147 -0.57632818 0.53939670 -1.00918398
## TAUSA -2.55240733 2.811240186 -2.60381798 -1.01544149 0.33774631
## TENJO 3.26328566 0.904889426 -1.89589858 0.83430376 0.48567925
## UBAQUE 0.17059865 -1.174317898 -2.60861595 0.10822587 0.63323393
## UNE 2.38615024 -3.013945664 -0.57500128 1.51759045 2.13570387
## VILLAPINZON -1.75607536 0.469888457 -1.55436679 -0.08595649 0.91894365
## ZIPACON 0.58653726 -2.966659952 -0.24895155 -1.09814771 -2.17898625
## ZIPAQUIRA 0.01181375 -0.547001186 0.11715198 -0.95061353 0.24547070# Graph of individuals
# 1. Use repel = TRUE to avoid overplotting
# 2. Control automatically the color of individuals using the cos2
# cos2 = the quality of the individuals on the factor map
# Use points only
# 3. Use gradient color
fviz_pca_ind(res.pca, col.ind = "cos2",
gradient.cols = c("#FC4E07", "#E7B800", "#00AFBB"),
repel = TRUE # Avoid text overlapping (slow if many points)
)
# Biplot of individuals and variables
fviz_pca_biplot(res.pca, repel = TRUE )
# Analisis de componentes principales usando la clasificacion IDPM
GR.CUN$IDPM <- as.factor(GR.CUN$IDPM)
str(GR.CUN)## 'data.frame': 47 obs. of 54 variables:
## $ Rmed1 : num NA 20 15 17 14.5 ...
## $ Rmed2 : num NA 19.5 19 18 15.7 ...
## $ Rmed3 : num NA 11.7 13.9 NA 11.7 ...
## $ Rmea1 : num NA 20.7 16 17.1 14 ...
## $ Rmea2 : num NA 19.5 19.3 18.2 16.4 ...
## $ Rmea3 : num NA 11.7 13.1 NA 12.5 ...
## $ Rmax1 : num NA 30 32.6 18 20 ...
## $ Rmax2 : num NA 30 28.7 25 30 ...
## $ Rmax3 : num NA 11.7 20 NA 20 ...
## $ Asem1 : num NA 20.1 945.2 124.7 118.3 ...
## $ Asem2 : num NA 39.4 2276.4 189.3 121.3 ...
## $ Asem3 : num NA 10 77.6 NA 38.3 ...
## $ Acos1 : num NA 12.2 854.7 118.3 116.3 ...
## $ Acos2 : num NA 26.5 2082.7 195.3 155.5 ...
## $ Acos3 : num NA 12 69.4 NA 46.8 ...
## $ Prod1 : num NA 251 15280 2035 1722 ...
## $ Prod2 : num NA 514 39427 3465 2366 ...
## $ Prod3 : num NA 140 956 NA 625 ...
## $ IDPM : Factor w/ 3 levels "1","2","3": 3 3 1 2 3 3 1 1 2 2 ...
## $ ALT : num 2625 2558 2600 2600 2400 ...
## $ TEM : num 13.1 14 12 14 13 18 10 14 14 14 ...
## $ pH : num 5.56 5.32 5.06 5.92 5.77 ...
## $ Aci : num 1.4884 1.1084 2.0612 0.0829 0.7639 ...
## $ Al : num 1.1481 0.8782 1.6302 0.0563 0.6129 ...
## $ CE : num 0.474 0.461 0.314 0.838 0.502 ...
## $ CIC : num 12.69 10.38 6.42 14.98 12.07 ...
## $ MO : num 9.78 8.09 12.59 7.09 6.23 ...
## $ N.tot : num 0.489 0.405 0.629 0.354 0.312 ...
## $ N.dis : num 73.3 60.7 94.4 53.2 46.8 ...
## $ P : num 56 29.7 50.3 59.4 106.2 ...
## $ P.dis : num 128.2 67.9 115.3 136 243.2 ...
## $ K : num 0.837 0.623 0.681 1.326 0.7 ...
## $ Ca : num 7.61 6.83 2.7 10.38 8.79 ...
## $ Mg : num 1.795 1.509 0.718 2.532 1.413 ...
## $ S : num 10.51 13.1 8.19 31.32 12.13 ...
## $ Na : num 0.1232 0.3096 0.0867 0.275 0.1029 ...
## $ Fe : num 569 669 532 489 669 ...
## $ Cu : num 2.33 2.6 1.51 3.55 4.21 ...
## $ Mn : num 7.65 7.29 5.88 6.99 7.18 ...
## $ Zn : num 5.46 6.4 1.92 11.47 5.33 ...
## $ B : num 0.328 0.356 0.253 0.418 0.324 ...
## $ S.Bas : num 80.1 80.7 66.5 99.3 89.9 ...
## $ S.Al : num 14.745 15.429 26.354 0.465 7.939 ...
## $ S.Ca : num 55.2 57.3 41.9 68.8 69.2 ...
## $ S.Mg : num 13.6 13.8 11.1 16.9 12.3 ...
## $ S.K : num 7.24 6.53 10.87 8.63 6.43 ...
## $ S.Na : num 1.321 3.014 1.392 1.93 0.959 ...
## $ Ca_Mg : num 4.21 4.65 3.95 4.15 6.16 ...
## $ Mg_K : num 2.61 4.04 1.24 2.79 2.11 ...
## $ Ca_K : num 10.68 23.23 4.69 13.33 13.38 ...
## $ Ca.Mg_K: num 13.43 27.27 5.95 16.12 15.71 ...
## $ Ca_B : num 22.9 22.1 13.4 28.3 31.4 ...
## $ Fe_Mn : num 76.1 99.8 101.9 82.1 106.2 ...
## $ Fe_Zn : num 159.5 224.8 348.5 44.6 193 ...names(GR.CUN)## [1] "Rmed1" "Rmed2" "Rmed3" "Rmea1" "Rmea2" "Rmea3" "Rmax1"
## [8] "Rmax2" "Rmax3" "Asem1" "Asem2" "Asem3" "Acos1" "Acos2"
## [15] "Acos3" "Prod1" "Prod2" "Prod3" "IDPM" "ALT" "TEM"
## [22] "pH" "Aci" "Al" "CE" "CIC" "MO" "N.tot"
## [29] "N.dis" "P" "P.dis" "K" "Ca" "Mg" "S"
## [36] "Na" "Fe" "Cu" "Mn" "Zn" "B" "S.Bas"
## [43] "S.Al" "S.Ca" "S.Mg" "S.K" "S.Na" "Ca_Mg" "Mg_K"
## [50] "Ca_K" "Ca.Mg_K" "Ca_B" "Fe_Mn" "Fe_Zn"# Visualize
# Use habillage to specify groups for coloring
fviz_pca_ind(res.pca,
label = "none", # hide individual labels
habillage = GR.CUN$IDPM, # color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
addEllipses = TRUE # Concentration ellipses
)
# Grafico de codo
es <- scale(B); es## pH CE CIC MO P.dis
## BOGOTA 0.68036621 -0.1881421 1.12337439 -0.08344961 0.095413550
## CAJICA -0.27908607 -0.2225784 0.36144570 -0.50258889 -0.656286960
## CARMEN DE CARUPA -1.28093116 -0.5905123 -0.95230461 0.61576896 -0.065526271
## CHIA 2.09847893 0.7230361 1.88501400 -0.75349767 0.193303277
## CHIPAQUE 1.51773112 -0.1198508 0.91948977 -0.96580267 1.529284652
## CHOACHI -0.87039206 -0.8531429 -0.56928298 -0.78030077 -0.927531863
## CHOCONTA -1.07514873 -0.4045566 -0.69824909 -0.05465716 -0.018762182
## COGUA 0.25933719 -0.4184518 -0.23537964 0.13340437 -0.129207684
## COTA -1.47098437 1.6783861 0.69772360 -0.38647150 -0.422185704
## CUCUNUBA 0.66502917 -0.5265186 -0.49420904 -0.99275902 -0.895630758
## EL ROSAL 0.76064567 0.3203875 1.20146729 2.65878301 -0.659217212
## FACATATIVA 1.47817459 1.6741356 2.48519057 0.74713221 0.005887965
## FOSCA 0.03138634 -0.5654982 -1.01203817 -1.19558717 1.692233325
## FUNZA 0.82547042 1.5524858 1.43266684 0.37800309 0.578450783
## FUQUENE -1.14104166 -0.3783380 -0.22269384 -0.50704795 -0.918203676
## GRANADA -0.20089789 1.1172310 0.09966569 0.92062504 -0.151202869
## GUACHETA -0.76805933 -0.3245026 -0.23427963 -1.03805251 -0.947542155
## GUASCA 0.48474891 -0.7039967 -1.18335904 -0.02367999 -0.637003734
## GUATAVITA -1.11224732 -0.8278918 -1.25097064 0.53267888 -0.724407153
## GUTIERREZ -0.32586870 -0.5915770 -0.62457423 -0.65382399 0.947440029
## JUNIN -0.55666944 -0.9708633 -1.04725701 -0.46700197 -1.189816747
## LA CALERA 0.33233824 -0.2928151 0.03452473 0.34946128 -0.461979714
## LENGUAZAQUE -1.78854797 -0.4210731 -0.84269833 -0.73114100 0.501433826
## MACHETA -0.64218091 -1.0411859 -0.61971437 -1.44479718 -1.100267972
## MADRID 1.52199438 2.1662756 2.70683452 0.73700789 1.361795562
## MANTA 1.36256251 -0.7190246 0.34253186 -1.51177971 -0.305149304
## PACHO -0.64965884 -0.7364196 -1.42807259 -0.02633890 -1.236956360
## PASCA 1.18592271 0.2242606 0.49346303 0.04391347 1.522288271
## SAN BERNARDO -0.58845890 -0.5300547 -0.14296175 -0.93943854 0.751829560
## SAN CAYETANO -0.89690197 -0.8058690 -0.99445964 -0.57774412 -1.093628105
## SESQUILE -0.79917120 -0.5949287 -0.84768754 0.59043854 -0.273193060
## SIBATE -0.66394747 0.2422357 -0.12730156 1.82068631 0.219302095
## SIMIJACA -1.35098641 1.0809327 0.87724699 -0.58766521 -0.695676959
## SOACHA 1.50143183 4.0840844 1.79110778 -0.47994537 -0.274665474
## SOPO 0.57865973 -0.3679461 -0.08769742 -1.08285132 -0.960490245
## SUBACHOQUE -0.35693632 -0.2725288 -0.53759659 2.59488694 -0.479800677
## SUESCA -0.10786823 -0.6332772 -1.07967244 -0.83418149 -0.675172242
## SUSA -0.83924750 -0.7358532 -0.89238808 0.43304991 -0.324951739
## SUTATAUSA 0.78568749 1.4337403 0.69863577 -1.05401223 0.757765493
## TABIO -0.05017845 -0.2881076 -0.05164165 0.04117270 -0.879775167
## TAUSA -1.48450289 -0.1408685 -0.53952201 2.23959771 1.377895025
## TENJO 0.57208193 0.8765438 0.84095819 0.93031177 1.369655877
## UBAQUE 0.35754428 -0.3510823 -0.70743146 0.62709176 1.658688761
## UNE 2.03270088 -0.2121774 0.69859446 -0.86721462 3.363170341
## VILLAPINZON -0.48406692 -0.6005606 -0.93970546 0.27920655 0.590194153
## ZIPACON 0.62031916 -0.5093153 -0.49775981 0.91154832 -1.217574334
## ZIPAQUIRA 0.13136898 -0.2342263 0.17097347 0.95706186 -0.194226225
## K Ca Mg S Na
## BOGOTA 0.429154680 0.83736291 0.479152756 -0.48172324 -0.32706552
## CAJICA -0.231347396 0.53346413 0.086089895 -0.34124332 0.98692514
## CARMEN DE CARUPA -0.051122819 -1.07087483 -1.002411528 -0.60710032 -0.58402693
## CHIA 1.936668999 1.91646165 1.492927391 0.64488720 0.74310957
## CHIPAQUE 0.005217857 1.29463339 -0.046506212 -0.39405239 -0.47010233
## CHOACHI -0.820540645 -0.90966122 -0.764734238 -0.69072984 -0.65045119
## CHOCONTA 0.188954297 -0.78957810 -0.658120631 -0.56367361 0.03902779
## COGUA -0.524095119 -0.12402502 -0.005035424 -0.18634550 -0.22861833
## COTA 2.105145886 -0.10101237 1.832723633 0.06378341 1.32256427
## CUCUNUBA 0.497177999 -0.39099081 0.177764252 -0.43187615 -0.32785010
## EL ROSAL 1.547993803 1.27194469 0.987547184 -0.08369920 0.06714839
## FACATATIVA 1.528457756 2.49070285 1.375968422 1.09104299 0.56800996
## FOSCA -0.515242936 -0.58840970 -0.921254692 -0.57306953 -0.77551550
## FUNZA 0.119598456 1.39709703 1.706133212 1.64134329 1.66357881
## FUQUENE -0.652697835 -0.31766852 0.800809794 0.97166096 -0.24041514
## GRANADA -0.464379800 0.26752012 0.502793565 1.45195570 -0.36317932
## GUACHETA -0.300217224 -0.25149751 0.722911341 -0.15942199 -0.09968038
## GUASCA -0.613397152 -0.81251132 -0.448493469 -0.59556764 -0.49575931
## GUATAVITA -0.937954299 -1.35877093 -0.915137777 -0.63493809 -0.82040309
## GUTIERREZ -0.757120753 -0.32524426 -0.950304045 -0.58545173 -0.54918008
## JUNIN -1.303581665 -1.30825071 -1.145830456 -0.76439331 -0.69238161
## LA CALERA -0.394273839 0.19610828 -0.703982907 -0.58256404 -0.54105442
## LENGUAZAQUE -0.422672612 -1.19565679 -0.978040253 -0.24072790 -0.43572052
## MACHETA -0.728796527 -1.22075353 -0.410544249 -0.89203235 -0.59785649
## MADRID 3.939482385 2.08049273 3.235749033 3.33009564 2.51642202
## MANTA -0.091915203 0.74424132 -0.094170365 -0.68150906 -0.22222429
## PACHO -1.372178011 -1.37073185 -1.063555277 -0.61898832 -0.73515803
## PASCA 0.077652343 0.95975192 -0.347911345 -0.27681364 -0.34755837
## SAN BERNARDO -0.984458743 -0.16345885 -0.550977537 -0.53043396 -0.50007271
## SAN CAYETANO -1.587999446 -1.01488568 -0.896076464 -0.76586031 -0.45240067
## SESQUILE -0.435941396 -1.02249662 -1.029158973 -0.51739916 -0.77598666
## SIBATE -0.220426025 -0.05106857 -0.591741897 -0.26558509 0.08224827
## SIMIJACA 0.290100944 0.49356376 0.920002217 3.08860787 0.43805091
## SOACHA 0.475754168 1.54611602 2.162911332 2.90744677 4.29438200
## SOPO -0.550256669 -0.06278332 1.134077546 -0.42562081 0.15266119
## SUBACHOQUE 0.463838545 -0.53726323 -0.357338368 -0.32053501 -0.47887929
## SUESCA 0.149631143 -0.90254009 -0.471463214 -0.59962299 -0.61847582
## SUSA -0.233570337 -0.97011467 -0.874388800 -0.10198929 -0.60513083
## SUTATAUSA -0.240286222 1.05111328 -0.109821021 0.80879300 2.34070408
## TABIO 0.614865194 -0.25932254 0.303441184 -0.31648722 -0.11365872
## TAUSA -0.160402394 -1.04888401 -1.097646043 -0.24107266 -0.02844759
## TENJO 1.618954433 0.79112803 0.498548429 0.32952572 0.46658481
## UBAQUE 0.114671453 -0.30349999 -0.787990552 -0.40431567 -0.72284114
## UNE 0.048665088 1.08256856 -0.213073848 -0.34937682 -0.43380792
## VILLAPINZON -0.130970045 -0.74851872 -0.893266362 -0.27342922 -0.69847814
## ZIPACON -0.824194948 0.02264370 -0.030078938 -0.52219494 -0.60320998
## ZIPAQUIRA -0.601945370 0.24355939 -0.060496302 -0.30929826 -0.14582678
## Zn B S.Bas S.Al S.Ca
## BOGOTA -0.15146889 0.212636691 -0.03299549 -0.02067705 0.05475604
## CAJICA 0.03907862 0.469790311 0.01536918 0.04437992 0.24782114
## CARMEN DE CARUPA -0.87340839 -0.469553953 -1.07849583 1.08407881 -1.14043789
## CHIA 1.07296288 1.032885505 1.45140308 -1.37967921 1.28555675
## CHIPAQUE -0.17785063 0.181297259 0.72872280 -0.66841301 1.31873244
## CHOACHI -0.60808470 -1.574890229 -1.60605377 1.57638593 -1.31368197
## CHOCONTA -0.67790305 -0.339153582 -0.32717733 0.43603204 -0.65249502
## COGUA -0.12368937 -0.052876341 0.37745941 -0.26403229 0.41128129
## COTA 4.64059161 1.234138577 -0.57778264 0.42420970 -1.37887648
## CUCUNUBA -0.67284970 -0.387544150 0.91409734 -0.91512160 0.19697055
## EL ROSAL 0.42358392 0.750419126 0.96178506 -0.99063610 0.85713234
## FACATATIVA 0.24713634 0.946711595 1.14213525 -1.15249222 1.20440447
## FOSCA -0.10265029 -0.920931360 0.24103522 -0.27235291 0.52438686
## FUNZA 2.11080102 2.481520532 1.22649929 -1.24267861 1.18623466
## FUQUENE 0.37916014 -0.195079628 0.41489418 -0.45639257 -0.15480828
## GRANADA 1.21340135 2.299028719 0.67063407 -0.71704421 0.69305298
## GUACHETA 0.07386385 -0.081296598 0.50842209 -0.51721969 0.02569983
## GUASCA -0.71495187 -0.480632848 0.80041978 -0.86240798 0.40640417
## GUATAVITA -0.82528903 -1.389971259 -1.36423878 0.83040500 -1.45442006
## GUTIERREZ -0.54289108 -0.011075293 -0.17079543 0.21924580 0.46511948
## JUNIN -0.62217190 -1.335259898 -2.44446281 2.50678252 -1.99070521
## LA CALERA -0.58054053 -0.708172170 -0.37846064 0.38864356 0.02247362
## LENGUAZAQUE -0.76819841 0.114040814 -1.69099483 1.85258935 -1.71882390
## MACHETA -0.85783159 -1.421579150 -2.10420613 2.40392639 -2.17363880
## MADRID 2.37262561 2.213333160 1.10676466 -1.04450900 0.41196750
## MANTA -0.52727044 0.063092203 0.88019372 -0.85337408 1.25010016
## PACHO -0.78421642 -1.544970780 -1.29632372 1.15649503 -1.28594530
## PASCA 0.51595301 0.306953552 0.87769596 -0.86183127 1.50755392
## SAN BERNARDO -0.15965245 -0.297038150 -0.29775332 0.36588872 0.27717456
## SAN CAYETANO -0.65497750 -1.224454622 -1.44224534 1.63055680 -1.03440319
## SESQUILE -0.69619093 -1.339263596 -1.17809889 0.71252415 -0.95377799
## SIBATE 0.17122343 0.573491200 0.16324812 -0.26546001 0.40192033
## SIMIJACA 0.23194809 0.646079832 0.10051445 -0.01073328 -0.09707363
## SOACHA 0.20439841 0.885605828 0.25291843 -0.26100728 -0.03351424
## SOPO -0.56855995 -0.750921963 0.47281389 -0.48983806 0.45504728
## SUBACHOQUE 0.04226739 0.004900947 0.09286422 -0.09064323 -0.21195325
## SUESCA -0.77908275 -0.820394116 0.58097940 -0.64227746 -0.11817667
## SUSA -0.73472271 -0.670194609 -0.86322040 0.84949127 -1.04483669
## SUTATAUSA -0.32047920 0.246677950 1.03271689 -0.98545496 1.04564126
## TABIO 0.01768794 -0.370704638 0.39735731 -0.33206293 -0.05982394
## TAUSA -0.51368816 0.788806049 -1.95667915 1.96830546 -1.73753457
## TENJO 1.07424121 1.692814279 0.90736472 -0.86359155 0.67091069
## UBAQUE -0.02725644 -0.198024685 0.58551153 -0.57637549 0.76159597
## UNE 0.12991424 -0.030893108 0.90857250 -0.83919970 1.48817722
## VILLAPINZON -0.62300160 0.604306103 -0.25811965 0.34287731 -0.34088182
## ZIPACON 0.01248925 -1.324941326 0.99402732 -1.02542057 1.22324693
## ZIPAQUIRA -0.28445036 0.191287820 0.26168429 -0.19189144 0.50244648
## Ca.Mg_K Ca_B Fe_Mn Fe_Zn
## BOGOTA 0.02004431 0.21803742 -0.59001040 -0.37205131
## CAJICA 2.23442806 0.11069168 0.16742528 0.03937189
## CARMEN DE CARUPA -1.17791112 -1.05009160 0.23433007 0.81807321
## CHIA 0.44980515 0.93097813 -0.39954958 -1.09540279
## CHIPAQUE 0.38535974 1.33894930 0.37064998 -0.16129775
## CHOACHI -0.41110449 3.13132540 1.03030218 1.54026416
## CHOCONTA -0.95289362 -0.79186366 -0.96116227 0.38603184
## COGUA 0.34518318 0.04384462 1.06935911 0.07948919
## COTA -1.12684691 -1.41671381 1.37443891 -1.00897816
## CUCUNUBA -0.81972625 0.27878797 0.94387308 0.41161248
## EL ROSAL -0.34408352 0.40412935 -1.47466560 -1.17859109
## FACATATIVA 0.41424244 1.96022920 -1.56265701 -1.12436239
## FOSCA -0.52292810 -0.12295846 -0.66743506 -0.61191551
## FUNZA 1.28080073 -0.01527334 -0.56801909 -1.14199186
## FUQUENE 0.26755599 -0.44157074 0.80530573 0.45789600
## GRANADA 0.83772729 -1.01541047 -1.19277663 -0.98926696
## GUACHETA -0.23507258 -0.41513482 0.94395770 0.10635565
## GUASCA -0.38771872 -0.65727330 -0.37650189 0.33274057
## GUATAVITA -0.87872299 -0.36885749 0.16710318 1.18135247
## GUTIERREZ -0.16423088 -0.48020123 -1.03501848 -0.23314084
## JUNIN -0.57308645 -1.06403064 0.81712046 2.04519258
## LA CALERA 0.06905635 0.58690214 0.38999319 0.21303517
## LENGUAZAQUE -1.07770894 -1.52170182 3.19509428 2.43139616
## MACHETA -0.83032091 1.60613041 0.34069324 3.47178379
## MADRID -0.64416980 0.23485188 -1.26762952 -1.18501852
## MANTA 0.47351456 0.73902587 -0.02080442 0.32226049
## PACHO -0.50306231 -0.72816231 -0.15111086 -0.39526816
## PASCA 0.10885695 0.45460385 -0.41116188 -1.04165194
## SAN BERNARDO 1.00095128 0.30329713 -0.12854150 -0.27564874
## SAN CAYETANO 2.07666189 -0.60531545 1.81094588 0.30206222
## SESQUILE -0.93157385 0.21759008 -0.09319185 0.69410779
## SIBATE 0.02711576 -0.68900054 0.12869665 -0.71568074
## SIMIJACA -0.00357249 -0.11882189 1.65346378 0.45659721
## SOACHA 1.41326273 0.70108689 -1.12788648 -0.99556066
## SOPO 1.43001976 0.78765718 1.15858485 1.06147689
## SUBACHOQUE -0.90354987 -0.89154549 -0.65461907 -0.62669472
## SUESCA -1.04814530 -0.38678042 -0.89856357 0.30302151
## SUSA -0.85495693 -0.99179709 0.94464012 0.85946444
## SUTATAUSA 3.26629440 0.90420998 -0.93322839 -0.66327210
## TABIO -0.83390405 -0.21297860 -0.46968558 -0.27289448
## TAUSA -0.93325553 -1.79259404 -0.41295717 -0.33818812
## TENJO -0.79883737 -0.45609502 -0.73735516 -0.66861915
## UBAQUE -0.70037210 -0.54179552 -1.03764263 -0.93104626
## UNE 0.27863454 1.41444588 -0.07436677 -0.83477957
## VILLAPINZON -0.78486586 -1.31540511 0.20545941 0.18830837
## ZIPACON 1.07501203 1.83702496 -1.47225758 -0.90806394
## ZIPAQUIRA 0.98809377 -0.11242644 0.96736135 0.06749169
## attr(,"scaled:center")
## pH CE CIC MO P.dis K
## 5.3867141 0.5495326 9.2933016 10.1133280 120.5338152 0.6979731
## Ca Mg S Na Zn B
## 5.4566684 1.4468277 19.4079112 0.1695826 6.2068312 0.3041831
## S.Bas S.Al S.Ca Ca.Mg_K Ca_B Fe_Mn
## 80.4799505 14.9625408 54.5610466 13.3063494 21.2692759 94.5609261
## Fe_Zn
## 218.5765045
## attr(,"scaled:scale")
## pH CE CIC MO P.dis K
## 0.2533773 0.3989817 3.0193853 4.0159281 80.1965399 0.3244967
## Ca Mg S Na Zn B
## 2.5713690 0.7269322 18.4766558 0.1418385 4.9072555 0.1099241
## S.Bas S.Al S.Ca Ca.Mg_K Ca_B Fe_Mn
## 12.9737394 10.5075605 11.0913369 6.2483266 7.5338124 31.2679642
## Fe_Zn
## 158.8302005fviz_nbclust(x=es, FUNcluster=kmeans, method="wss", k.max=15, diss=get_dist(es, method="euclidean") )
# Optimal number of clusters for k-means
library("factoextra")
fviz_nbclust(es, kmeans, method = "gap_stat")
fviz_nbclust(es, FUN = hcut, method = "silhouette")
# Compute hierarchical clustering and cut into 4 clusters
res <- hcut(es, k = 4, stand = TRUE)
res$cluster## BOGOTA CAJICA CARMEN DE CARUPA CHIA
## 1 1 2 3
## CHIPAQUE CHOACHI CHOCONTA COGUA
## 1 4 2 1
## COTA CUCUNUBA EL ROSAL FACATATIVA
## 3 1 3 3
## FOSCA FUNZA FUQUENE GRANADA
## 1 3 1 3
## GUACHETA GUASCA GUATAVITA GUTIERREZ
## 1 1 2 1
## JUNIN LA CALERA LENGUAZAQUE MACHETA
## 4 1 4 4
## MADRID MANTA PACHO PASCA
## 3 1 2 1
## SAN BERNARDO SAN CAYETANO SESQUILE SIBATE
## 1 4 2 2
## SIMIJACA SOACHA SOPO SUBACHOQUE
## 3 3 1 2
## SUESCA SUSA SUTATAUSA TABIO
## 1 2 3 1
## TAUSA TENJO UBAQUE UNE
## 2 3 1 1
## VILLAPINZON ZIPACON ZIPAQUIRA
## 2 1 1# Visualize
fviz_dend(res, rect = TRUE, cex = 0.5,
k_colors = c("#00AFBB","#E7B800", "#FC4E07","#9E1FDE", "#9D9FDE"))
# Agrupamiento por
# 2. Compute k-means
set.seed(123)
km.res <- kmeans(es, 4, nstart = 10)
# 3. Visualize
library("factoextra")
fviz_cluster(km.res, data = es,
palette = c("#00AFBB","#E7B800", "#FC4E07","#9E1FDE", "#9D9FDE"),
ggtheme = theme_classic(),
main = "Partitioning Clustering Plot",
repel = TRUE
)
km.res$cluster## BOGOTA CAJICA CARMEN DE CARUPA CHIA
## 2 2 3 4
## CHIPAQUE CHOACHI CHOCONTA COGUA
## 4 3 2 2
## COTA CUCUNUBA EL ROSAL FACATATIVA
## 1 2 4 4
## FOSCA FUNZA FUQUENE GRANADA
## 2 1 2 4
## GUACHETA GUASCA GUATAVITA GUTIERREZ
## 2 2 3 2
## JUNIN LA CALERA LENGUAZAQUE MACHETA
## 3 2 3 3
## MADRID MANTA PACHO PASCA
## 1 2 3 4
## SAN BERNARDO SAN CAYETANO SESQUILE SIBATE
## 2 3 3 2
## SIMIJACA SOACHA SOPO SUBACHOQUE
## 1 1 2 2
## SUESCA SUSA SUTATAUSA TABIO
## 2 3 4 2
## TAUSA TENJO UBAQUE UNE
## 3 4 2 4
## VILLAPINZON ZIPACON ZIPAQUIRA
## 2 2 2#
Gr.clus=data.frame(Grupo=km.res$cluster, MUN=row.names( data.frame(km.res$cluster) ) ); Gr.clus## Grupo MUN
## BOGOTA 2 BOGOTA
## CAJICA 2 CAJICA
## CARMEN DE CARUPA 3 CARMEN DE CARUPA
## CHIA 4 CHIA
## CHIPAQUE 4 CHIPAQUE
## CHOACHI 3 CHOACHI
## CHOCONTA 2 CHOCONTA
## COGUA 2 COGUA
## COTA 1 COTA
## CUCUNUBA 2 CUCUNUBA
## EL ROSAL 4 EL ROSAL
## FACATATIVA 4 FACATATIVA
## FOSCA 2 FOSCA
## FUNZA 1 FUNZA
## FUQUENE 2 FUQUENE
## GRANADA 4 GRANADA
## GUACHETA 2 GUACHETA
## GUASCA 2 GUASCA
## GUATAVITA 3 GUATAVITA
## GUTIERREZ 2 GUTIERREZ
## JUNIN 3 JUNIN
## LA CALERA 2 LA CALERA
## LENGUAZAQUE 3 LENGUAZAQUE
## MACHETA 3 MACHETA
## MADRID 1 MADRID
## MANTA 2 MANTA
## PACHO 3 PACHO
## PASCA 4 PASCA
## SAN BERNARDO 2 SAN BERNARDO
## SAN CAYETANO 3 SAN CAYETANO
## SESQUILE 3 SESQUILE
## SIBATE 2 SIBATE
## SIMIJACA 1 SIMIJACA
## SOACHA 1 SOACHA
## SOPO 2 SOPO
## SUBACHOQUE 2 SUBACHOQUE
## SUESCA 2 SUESCA
## SUSA 3 SUSA
## SUTATAUSA 4 SUTATAUSA
## TABIO 2 TABIO
## TAUSA 3 TAUSA
## TENJO 4 TENJO
## UBAQUE 2 UBAQUE
## UNE 4 UNE
## VILLAPINZON 2 VILLAPINZON
## ZIPACON 2 ZIPACON
## ZIPAQUIRA 2 ZIPAQUIRA#
str(RAS.CUN)## tibble [3,054 × 57] (S3: tbl_df/tbl/data.frame)
## $ MUN : Factor w/ 47 levels "BOGOTA","CAJICA",..: 14 1 4 3 3 3 3 3 3 3 ...
## $ CUL : Factor w/ 109 levels "agrosilvopastoril",..: 107 86 64 86 86 86 86 18 88 88 ...
## $ Acos1 : num [1:3054] 408 NA 118 855 855 ...
## $ Acos2 : num [1:3054] 586 NA 195 2083 2083 ...
## $ Acos3 : num [1:3054] NA NA NA 69.4 69.4 ...
## $ ALT : num [1:3054] 2548 2625 2600 2600 2600 ...
## $ Asem1 : num [1:3054] 492 NA 125 945 945 ...
## $ Asem2 : num [1:3054] 651 NA 189 2276 2276 ...
## $ Asem3 : num [1:3054] NA NA NA 77.6 77.6 ...
## $ IDPM : num [1:3054] 1 3 2 1 1 1 1 1 1 1 ...
## $ Prod1 : num [1:3054] 8161 NA 2035 15280 15280 ...
## $ Prod2 : num [1:3054] 11779 NA 3465 39427 39427 ...
## $ Prod3 : num [1:3054] NA NA NA 956 956 ...
## $ Rmax1 : num [1:3054] 22 NA 18 32.6 32.6 ...
## $ Rmax2 : num [1:3054] 29.1 NA 25 28.7 28.7 ...
## $ Rmax3 : num [1:3054] NA NA NA 20 20 20 20 20 20 20 ...
## $ Rmea1 : num [1:3054] 19.9 NA 17.1 16 16 ...
## $ Rmea2 : num [1:3054] 20.4 NA 18.2 19.3 19.3 ...
## $ Rmea3 : num [1:3054] NA NA NA 13.1 13.1 ...
## $ Rmed1 : num [1:3054] 20 NA 17 15 15 15 15 15 15 15 ...
## $ Rmed2 : num [1:3054] 20 NA 18 19 19 ...
## $ Rmed3 : num [1:3054] NA NA NA 13.9 13.9 ...
## $ TEM : num [1:3054] 14 13.1 14 12 12 12 12 12 12 12 ...
## $ pH : num [1:3054] 5.66 4.87 6.42 4.78 4.88 4.63 4.3 5.39 5.05 5.16 ...
## $ CIC : num [1:3054] 6.29 8.08 22.47 4.9 5.46 ...
## $ CE : num [1:3054] 0.224 0.374 1.418 0.197 0.386 ...
## $ MO : num [1:3054] 9.71 16.04 6.83 8.9 14.82 ...
## $ P : num [1:3054] 5.62 64.2 61.74 19.16 9.09 ...
## $ K : num [1:3054] 0.205 0.313 0.659 0.148 0.332 ...
## $ Ca : num [1:3054] 4.97 2.23 19.49 1.22 1.88 ...
## $ S : num [1:3054] 4.35 5.52 81.94 11.93 6.67 ...
## $ Mg : num [1:3054] 0.979 0.347 1.819 0.285 0.455 ...
## $ Na : num [1:3054] 0.146 0.156 0.502 0.11 0.17 ...
## $ Fe : num [1:3054] 184.1 1685.4 53.8 1030.1 495.7 ...
## $ Cu : num [1:3054] 0.5 4.12 2.07 0.5 0.5 ...
## $ Mn : num [1:3054] 2.88 17.62 2.64 1.18 2.01 ...
## $ Zn : num [1:3054] 3.46 8.57 7.03 0.5 0.5 ...
## $ B : num [1:3054] 0.109 0.303 0.496 0.195 0.281 ...
## $ Aci : num [1:3054] 0 5.03 0 3.13 2.62 ...
## $ Al : num [1:3054] 0 4.64 0 2.8 2.36 ...
## $ P.dis : num [1:3054] 12.9 147 141.4 43.9 20.8 ...
## $ N.tot : num [1:3054] 0.485 0.802 0.342 0.445 0.741 ...
## $ N.dis : num [1:3054] 72.8 120.3 51.2 66.7 111.1 ...
## $ S.Bas : num [1:3054] 100 37.8 100 36.1 52 ...
## $ S.Al : num [1:3054] 0 57.5 0 57 43.2 ...
## $ S.Ca : num [1:3054] 78.9 27.7 86.7 25 34.4 ...
## $ S.Mg : num [1:3054] 15.55 4.29 8.1 5.82 8.34 ...
## $ S.K : num [1:3054] 3.25 3.88 2.93 3.02 6.08 ...
## $ S.Na : num [1:3054] 2.32 1.94 2.23 2.25 3.11 ...
## $ Ca_Mg : num [1:3054] 5.07 6.45 10.71 4.29 4.13 ...
## $ Mg_K : num [1:3054] 4.79 1.11 2.76 1.93 1.37 ...
## $ Ca_K : num [1:3054] 24.28 7.14 29.58 8.28 5.67 ...
## $ Ca.Mg_K: num [1:3054] 29.06 8.24 32.34 10.2 7.04 ...
## $ Ca_B : num [1:3054] 45.37 7.39 39.32 6.27 6.68 ...
## $ Fe_Mn : num [1:3054] 64 95.7 20.3 870.8 246.7 ...
## $ P_Zn : num [1:3054] 1.62 7.49 8.78 38.33 18.17 ...
## $ Fe_Zn : num [1:3054] 53.18 196.73 7.65 2060.27 991.34 ...# # Dataframe sin agrupar por municipios con grupos
DF.cluster.tot <- RAS.CUN %>% left_join(., y=Gr.clus) ; DF.cluster.tot## Joining with `by = join_by(MUN)`## # A tibble: 3,054 × 58
## MUN CUL Acos1 Acos2 Acos3 ALT Asem1 Asem2 Asem3 IDPM Prod1 Prod2
## <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FUNZA Uchu… 408. 586. NA 2548 492. 651. NA 1 8161. 11779.
## 2 BOGOTA Papa… NA NA NA 2625 NA NA NA 3 NA NA
## 3 CHIA Frut… 118. 195. NA 2600 125. 189. NA 2 2035 3465.
## 4 CARMEN D… Papa… 855. 2083. 69.4 2600 945. 2276. 77.6 1 15280. 39427
## 5 CARMEN D… Papa… 855. 2083. 69.4 2600 945. 2276. 77.6 1 15280. 39427
## 6 CARMEN D… Papa… 855. 2083. 69.4 2600 945. 2276. 77.6 1 15280. 39427
## 7 CARMEN D… Papa… 855. 2083. 69.4 2600 945. 2276. 77.6 1 15280. 39427
## 8 CARMEN D… Arve… 855. 2083. 69.4 2600 945. 2276. 77.6 1 15280. 39427
## 9 CARMEN D… Past… 855. 2083. 69.4 2600 945. 2276. 77.6 1 15280. 39427
## 10 CARMEN D… Past… 855. 2083. 69.4 2600 945. 2276. 77.6 1 15280. 39427
## # ℹ 3,044 more rows
## # ℹ 46 more variables: Prod3 <dbl>, Rmax1 <dbl>, Rmax2 <dbl>, Rmax3 <dbl>,
## # Rmea1 <dbl>, Rmea2 <dbl>, Rmea3 <dbl>, Rmed1 <dbl>, Rmed2 <dbl>,
## # Rmed3 <dbl>, TEM <dbl>, pH <dbl>, CIC <dbl>, CE <dbl>, MO <dbl>, P <dbl>,
## # K <dbl>, Ca <dbl>, S <dbl>, Mg <dbl>, Na <dbl>, Fe <dbl>, Cu <dbl>,
## # Mn <dbl>, Zn <dbl>, B <dbl>, Aci <dbl>, Al <dbl>, P.dis <dbl>, N.tot <dbl>,
## # N.dis <dbl>, S.Bas <dbl>, S.Al <dbl>, S.Ca <dbl>, S.Mg <dbl>, S.K <dbl>, …# Dataframe agrupado por municipios con grupos
A1 <- tibble::rownames_to_column(A, "MUN")
DF.cluster.agru <- A1 %>% left_join(., y=Gr.clus) ; DF.cluster.agru## Joining with `by = join_by(MUN)`## MUN Rmed1 IDPM ALT TEM pH Aci Al
## 1 BOGOTA NA 3 2625 13.1 5.559103 1.48842672 1.14813493
## 2 CAJICA 20.000 3 2558 14.0 5.316000 1.10836635 0.87816355
## 3 CARMEN DE CARUPA 15.000 1 2600 12.0 5.062155 2.06117455 1.63019473
## 4 CHIA 17.000 2 2600 14.0 5.918421 0.08288328 0.05627741
## 5 CHIPAQUE 14.500 3 2400 13.0 5.771273 0.76391403 0.61289557
## 6 CHOACHI 15.000 3 1923 18.0 5.166176 2.69555878 2.17397142
## 7 CHOCONTA 20.000 1 2689 10.0 5.114296 1.57266228 1.31436399
## 8 COGUA 19.670 1 2600 14.0 5.452424 1.13583515 0.94955086
## 9 COTA 23.000 2 2566 14.0 5.014000 1.68571680 1.16897724
## 10 CUCUNUBA 15.240 2 2590 14.0 5.555217 0.50128910 0.36834854
## 11 EL ROSAL 18.000 1 2685 12.0 5.579444 0.47765807 0.34018440
## 12 FACATATIVA 17.220 1 2586 19.0 5.761250 0.32234394 0.21942756
## 13 FOSCA 18.000 2 2713 14.0 5.394667 0.86581904 0.66269228
## 14 FUNZA 20.000 1 2548 14.0 5.595870 0.28513281 0.18055710
## 15 FUQUENE 14.750 3 2750 13.0 5.097600 1.15825959 0.86576309
## 16 GRANADA 17.500 3 2500 11.0 5.335811 0.62158721 0.41788006
## 17 GUACHETA 16.000 2 2688 13.0 5.192105 0.97107427 0.71774410
## 18 GUASCA 18.130 2 2710 13.0 5.509538 0.47896412 0.31924071
## 19 GUATAVITA 22.585 2 2680 14.0 5.104896 2.05703561 1.35063107
## 20 GUTIERREZ 20.840 3 2350 14.0 5.304146 1.36450159 1.09273274
## 21 JUNIN 25.000 3 2300 16.0 5.245667 2.73902422 2.32933987
## 22 LA CALERA 16.250 1 2780 14.0 5.470921 1.46741721 1.15194301
## 23 LENGUAZAQUE 18.000 1 2589 14.0 4.933537 2.64988291 2.24520904
## 24 MACHETA 14.000 2 2094 17.0 5.224000 3.36183015 2.93779138
## 25 MADRID 21.000 1 2554 14.0 5.772353 0.34159585 0.26367691
## 26 MANTA 12.000 3 1924 18.0 5.731957 0.54565071 0.40491772
## 27 PACHO 13.000 3 2136 19.0 5.222105 1.68835696 1.32885948
## 28 PASCA 20.000 1 2180 15.0 5.687200 0.44322089 0.31324232
## 29 SAN BERNARDO 12.000 3 1600 20.0 5.237612 1.79245649 1.44831640
## 30 SAN CAYETANO 16.000 1 2700 12.0 5.159459 2.01875624 1.72941120
## 31 SESQUILE 25.000 1 2595 15.0 5.184222 2.03168523 1.32865996
## 32 SIBATE 20.000 1 2700 14.0 5.218485 1.35931642 0.95480135
## 33 SIMIJACA 14.500 2 2559 14.0 5.044405 1.79354525 1.47046480
## 34 SOACHA 14.500 3 2566 14.0 5.767143 0.60816555 0.46267176
## 35 SOPO 20.000 3 2650 14.0 5.533333 0.74140222 0.55051123
## 36 SUBACHOQUE 21.040 1 2663 13.0 5.296275 1.11352096 0.86840251
## 37 SUESCA 23.120 1 2584 14.0 5.359383 0.72004084 0.50577887
## 38 SUSA 15.000 2 2655 14.0 5.174068 1.93916819 1.51178281
## 39 SUTATAUSA 15.000 2 2550 14.0 5.585789 0.52922592 0.38475114
## 40 TABIO 22.000 3 2569 14.0 5.374000 1.22141038 0.99110769
## 41 TAUSA 18.000 1 2950 12.0 5.010575 3.25329783 2.59302652
## 42 TENJO 23.000 1 2587 13.0 5.531667 0.65449398 0.49054135
## 43 UBAQUE 15.000 3 1867 20.0 5.477308 0.67162119 0.50695938
## 44 UNE 29.075 1 2376 16.0 5.901754 0.56291347 0.45066803
## 45 VILLAPINZON 19.000 1 2715 13.0 5.264062 1.22915100 1.00330076
## 46 ZIPACON 12.000 2 2500 16.0 5.543889 0.25472939 0.16862078
## 47 ZIPAQUIRA 20.750 1 2652 12.0 5.420000 1.29654018 1.03122146
## CE CIC MO N.tot N.dis P P.dis
## 1 0.4744674 12.685202 9.778200 0.4889100 73.33650 55.976267 128.18565
## 2 0.4607279 10.384645 8.094967 0.4047484 60.71225 29.651472 67.90187
## 3 0.3139291 6.417927 12.586212 0.6293106 94.39659 50.340103 115.27883
## 4 0.8380108 14.984885 7.087336 0.3543668 53.15502 59.404397 136.03607
## 5 0.5017144 12.069595 6.234734 0.3117367 46.76050 106.190896 243.17715
## 6 0.2091443 7.574417 6.979696 0.3489848 52.34772 20.152388 46.14897
## 7 0.3881219 7.185019 9.893829 0.4946914 74.20372 51.977796 119.02915
## 8 0.3825780 8.582600 10.649070 0.5324535 79.86803 48.109959 110.17181
## 9 1.2191779 11.399998 8.561286 0.4280643 64.20965 37.849774 86.67598
## 10 0.3394613 7.801094 6.126479 0.3063240 45.94859 21.269575 48.70733
## 11 0.6773613 12.920994 20.790809 1.0395405 155.93107 29.548854 67.66688
## 12 1.2174820 16.797049 13.113757 0.6556879 98.35318 52.841052 121.00601
## 13 0.3239092 6.237568 5.311936 0.2655968 39.83952 111.897412 256.24507
## 14 1.1689460 13.619075 11.631361 0.5815681 87.23521 72.892387 166.92357
## 15 0.3985827 8.620903 8.077060 0.4038530 60.57795 20.479064 46.89706
## 16 0.9952873 9.594231 13.810492 0.6905246 103.57869 47.339680 108.40787
## 17 0.4200620 8.585921 5.944584 0.2972292 44.58438 19.451621 44.54421
## 18 0.2686508 5.720285 10.018231 0.5009115 75.13673 30.326777 69.44832
## 19 0.2192190 5.516139 12.252528 0.6126264 91.89396 27.265881 62.43887
## 20 0.3135042 7.407471 7.487618 0.3743809 56.15713 85.814510 196.51523
## 21 0.1621760 6.131229 8.237882 0.4118941 61.78411 10.967087 25.11463
## 22 0.4327048 9.397545 11.516739 0.5758370 86.37555 36.456175 83.48464
## 23 0.3815322 6.748871 7.177118 0.3588559 53.82839 70.195228 160.74707
## 24 0.1341186 7.422145 4.311126 0.2155563 32.33345 14.103114 32.29613
## 25 1.4138369 17.466278 13.073099 0.6536549 98.04824 100.325374 229.74511
## 26 0.2626550 10.327537 4.042129 0.2021065 30.31597 41.948427 96.06190
## 27 0.2557147 4.981400 10.007553 0.5003776 75.05665 9.316242 21.33420
## 28 0.6390085 10.783257 10.289681 0.5144841 77.17261 105.945881 242.61607
## 29 0.3380505 8.861645 6.340610 0.3170305 47.55458 78.964168 180.82794
## 30 0.2280057 6.290645 7.793149 0.3896575 58.44862 14.335644 32.82863
## 31 0.3121670 6.733806 12.484487 0.6242243 93.63365 43.067545 98.62468
## 32 0.6461802 8.908929 17.425073 0.8712537 130.68805 60.314884 138.12108
## 33 0.9808050 11.942048 7.753307 0.3876653 58.14980 28.272022 64.74293
## 34 2.1790074 14.701346 8.185902 0.4092951 61.39426 43.015980 98.50659
## 35 0.4027289 9.028509 5.764675 0.2882337 43.23506 18.998175 43.50582
## 36 0.4407986 7.670090 20.534207 1.0267104 154.00656 35.832079 82.05546
## 37 0.2968667 6.033355 6.763315 0.3381658 50.72486 28.990104 66.38734
## 38 0.2559407 6.598838 11.852425 0.5926213 88.89319 41.254939 94.47381
## 39 1.1215687 11.402752 5.880491 0.2940245 44.10368 79.172046 181.30399
## 40 0.4345830 9.137376 10.278675 0.5139337 77.09006 21.824843 49.97889
## 41 0.4933287 7.664277 19.107391 0.9553696 143.30544 100.889183 231.03623
## 42 0.8992575 11.832478 13.849393 0.6924697 103.87045 100.600645 230.37548
## 43 0.4094572 7.157293 12.631683 0.6315842 94.73763 110.722670 253.55491
## 44 0.4648777 11.402627 6.630656 0.3315328 49.72992 170.414166 390.24844
## 45 0.3099199 6.455969 11.234601 0.5617301 84.25951 73.303644 167.86534
## 46 0.3463251 7.790373 13.774041 0.6887020 103.30530 9.995007 22.88857
## 47 0.4560806 9.809536 13.956820 0.6978410 104.67615 45.832989 104.95754
## K Ca Mg S Na Fe Cu
## 1 0.8372324 7.609837 1.7951393 10.507277 0.12319214 569.1982 2.3304314
## 2 0.6229017 6.828402 1.5094093 13.102876 0.30956664 668.5700 2.6000000
## 3 0.6813839 2.703054 0.7181425 8.190727 0.08674512 532.3260 1.5063362
## 4 1.3264158 10.384599 2.5320848 31.323270 0.27498420 488.5985 3.5517887
## 5 0.6996663 8.785649 1.4130209 12.127141 0.10290401 669.0530 4.2071258
## 6 0.4317104 3.117594 0.8909178 6.645534 0.07732360 785.9876 1.3285659
## 7 0.7592882 3.426372 0.9684186 8.993108 0.17511828 520.5033 2.5644189
## 8 0.5279060 5.137754 1.4431673 15.964870 0.13715575 763.5126 3.8724567
## 9 1.3810860 5.196928 2.7790936 20.586415 0.35717320 795.6744 4.3288000
## 10 0.8593057 4.451287 1.5760503 11.428284 0.12308086 624.6912 2.7378376
## 11 1.2002920 8.727308 2.1647076 17.861430 0.17910686 199.6696 2.7410294
## 12 1.1939526 11.861185 2.4470635 39.566737 0.25014833 208.5844 2.4855832
## 13 0.5307785 3.943650 0.7771380 8.819503 0.05958466 506.1218 7.1078333
## 14 0.7367824 9.049120 2.6870710 49.734446 0.40554220 407.3529 3.7286610
## 15 0.4861748 4.639825 2.0289622 37.360956 0.13548251 1329.7090 3.6976594
## 16 0.5472834 6.144561 1.8123246 46.235197 0.11806982 278.1683 5.1739505
## 17 0.6005536 4.809975 1.9723353 16.462326 0.15544412 783.8662 3.3075683
## 18 0.4989278 3.367402 1.1208034 8.403813 0.09926487 537.0052 2.6483480
## 19 0.3936100 1.962767 0.7815846 7.676379 0.05321787 747.4083 2.6560143
## 20 0.4522899 4.620345 0.7560211 8.590721 0.09168774 503.1262 2.5071049
## 21 0.2749652 2.092673 0.6138866 5.284479 0.07137625 583.6759 1.5084053
## 22 0.5700326 5.960935 0.9350799 8.644076 0.09284027 625.3544 1.9973696
## 23 0.5608173 2.382194 0.7358587 14.960065 0.10778068 876.8909 1.7988788
## 24 0.4614810 2.317661 1.1483899 2.926136 0.08478355 582.2499 0.8968649
## 25 1.9763222 10.806383 3.7989980 80.936942 0.52650822 463.1219 2.4339097
## 26 0.6681469 7.370387 1.3783723 6.815903 0.13806267 581.0128 4.6780870
## 27 0.2527059 1.932011 0.6736951 7.971077 0.06530890 244.4244 1.7057403
## 28 0.7231711 7.924545 1.1939198 14.293321 0.12028547 289.0372 8.7542173
## 29 0.3785195 5.036355 1.0463044 9.607265 0.09865306 540.6916 3.2564606
## 30 0.1826725 2.847023 0.7954409 5.257374 0.10541479 398.5048 0.8345135
## 31 0.5565116 2.827452 0.6986989 9.848105 0.05951783 695.0550 2.7688667
## 32 0.6264456 5.325352 1.0166715 14.500787 0.18124861 511.8916 4.2744133
## 33 0.7921099 6.725803 2.1156070 76.475056 0.23171513 1174.4268 1.1451890
## 34 0.8523538 9.432303 3.0191177 73.127804 0.77869144 230.1939 3.2276655
## 35 0.5194166 5.295229 2.2712253 11.543862 0.19123587 693.6312 1.9216120
## 36 0.8484872 4.075166 1.1870670 13.485496 0.10165910 424.5978 2.2254359
## 37 0.7465279 3.135905 1.1041059 8.328884 0.08185894 522.3281 2.9441468
## 38 0.6221803 2.962146 0.8112063 17.523490 0.08375177 638.3813 2.0400633
## 39 0.6200010 8.159469 1.3669953 34.351701 0.50158465 351.2381 2.3745789
## 40 0.8974949 4.789854 1.6674089 13.560286 0.15346145 635.8791 2.8903118
## 41 0.6459231 2.759601 0.6489134 14.953695 0.16554767 470.8931 1.3604383
## 42 1.2233185 7.490951 1.8092387 25.496444 0.23576234 380.8290 3.0664202
## 43 0.7351836 4.676258 0.8740120 11.937510 0.06705591 277.3353 3.3484615
## 44 0.7137648 8.240352 1.2919375 12.952596 0.10805196 408.8872 7.4151957
## 45 0.6554738 3.531951 0.7974836 14.355854 0.07051152 619.1350 2.9893624
## 46 0.4305246 5.514894 1.4249624 9.759495 0.08402422 221.0799 2.0710813
## 47 0.5026438 6.082949 1.4028510 13.693114 0.14889878 832.1795 3.4091941
## Mn Zn B S.Bas S.Al S.Ca S.Mg
## 1 7.651463 5.463535 0.3275570 80.05188 14.7452755 55.16836 13.568203
## 2 7.291400 6.398600 0.3558244 80.67935 15.4288655 57.30971 13.820678
## 3 5.883974 1.920793 0.2525678 66.48783 26.3535645 41.91207 11.137922
## 4 6.985933 11.472134 0.4177221 99.31008 0.4654782 68.81959 16.914227
## 5 7.183408 5.334073 0.3241120 89.93421 7.9391507 69.18755 12.300763
## 6 6.952517 3.222804 0.1310648 59.64343 31.5265113 39.99056 11.456064
## 7 9.027619 2.880188 0.2669020 76.23524 19.5441738 47.32400 13.265068
## 8 6.770941 5.599856 0.2983707 85.37701 12.1882056 59.12271 17.296942
## 9 10.535400 28.979400 0.4398446 72.98395 19.4199499 39.26746 19.919034
## 10 5.479770 2.904986 0.2615827 92.33921 5.3468453 56.74571 20.733591
## 11 4.981460 8.285466 0.3866722 92.95790 4.5533721 64.06779 16.293285
## 12 6.010325 7.419592 0.4082495 95.29772 2.8526592 67.91950 15.139390
## 13 8.329267 5.703100 0.2029506 83.60708 12.1007762 60.37720 12.313138
## 14 6.303742 16.565071 0.5769619 96.39223 1.9050203 67.71797 19.312456
## 15 14.772262 8.067467 0.2827392 85.86268 10.1669683 52.84402 23.716778
## 16 6.424500 12.161302 0.5569017 89.18058 7.4281554 62.24793 16.988092
## 17 8.250854 6.569300 0.2952467 87.07609 9.5278237 54.84609 21.949058
## 18 7.249797 2.698380 0.2513500 90.86439 5.9007369 59.06861 19.549113
## 19 7.999875 2.156927 0.1513918 62.78067 23.6880716 38.42958 14.473765
## 20 14.009986 3.542726 0.3029657 78.26410 17.2662794 59.71984 9.937097
## 21 6.825052 3.153675 0.1574059 48.76613 41.3027097 32.48146 9.619547
## 22 7.253078 3.357971 0.2263380 75.56990 19.0462365 54.81031 10.700277
## 23 5.502578 2.437085 0.3167189 58.54142 34.4287355 35.49699 10.860087
## 24 6.037654 1.997232 0.1479174 53.18053 40.2219427 30.45249 15.246628
## 25 11.946925 17.849911 0.5474817 94.83883 3.9872994 59.13032 21.197394
## 26 7.229685 3.619380 0.3111185 91.89935 5.9956611 68.42633 13.517769
## 27 3.332639 2.358481 0.1343537 63.66178 27.1144823 40.29819 14.711142
## 28 4.517080 8.738744 0.3379247 91.86695 5.9067966 71.28184 11.334093
## 29 8.762998 5.423376 0.2715315 76.61698 18.8071387 57.63528 11.740923
## 30 4.030425 2.992689 0.1695861 61.76864 32.0957150 43.08813 12.069240
## 31 8.722667 2.790444 0.1569658 65.19560 22.4494314 43.98237 10.160880
## 32 5.963710 7.047068 0.3672236 82.59789 12.1732038 59.01888 11.282632
## 33 11.168375 7.345060 0.3752028 81.78400 14.8497602 53.48437 17.339228
## 34 4.586873 7.209866 0.4015325 83.76125 12.2199911 54.18933 15.415347
## 35 5.775189 3.416762 0.2216387 86.61411 9.8155378 59.60813 20.427077
## 36 6.758521 6.414248 0.3047218 81.68475 14.0101016 52.21020 15.119114
## 37 8.526384 2.383673 0.2140021 88.01743 8.2137716 53.25031 18.414799
## 38 5.709596 2.601359 0.2305126 69.28075 23.8886218 42.97241 11.896822
## 39 6.667158 4.634158 0.3312990 93.87815 4.6078133 66.15861 13.322642
## 40 9.193293 6.293630 0.2634338 85.63516 11.4733695 53.89752 18.701918
## 41 6.920701 3.686032 0.3908919 55.09451 35.6446294 35.28947 8.315885
## 42 5.634095 11.478407 0.4902641 92.25186 5.8883004 62.00234 15.627753
## 43 4.772989 6.073077 0.2824154 88.07622 8.9062406 63.00816 12.359357
## 44 5.939497 6.844354 0.3007872 92.26753 6.1445993 71.06692 11.683787
## 45 7.328563 3.149603 0.3706109 77.13117 18.5653449 50.78021 12.959840
## 46 7.739677 6.268119 0.1585402 93.37620 4.1878722 68.12849 17.209582
## 47 6.380285 4.810961 0.3252102 83.87497 12.9462299 60.13385 14.200342
## S.K S.Na Ca_Mg Mg_K Ca_K Ca.Mg_K Ca_B
## 1 7.243108 1.3205465 4.207091 2.613154 10.679175 13.431593 22.911929
## 2 6.534641 3.0143130 4.650541 4.041579 23.226207 27.267786 22.103206
## 3 10.871190 1.3922997 3.952252 1.244362 4.686444 5.946376 13.358083
## 4 8.629590 1.9297157 4.150889 2.791340 13.325539 16.116879 28.283091
## 5 6.428829 0.9590380 6.157040 2.107519 13.378390 15.714203 31.356669
## 6 5.840890 1.1197015 3.475578 2.445354 8.259583 10.737634 44.860094
## 7 10.551657 2.5688854 4.155828 1.600995 5.741427 7.352359 15.303524
## 8 6.084893 1.7734148 4.019314 3.544209 11.898310 15.463167 21.599593
## 9 11.360041 2.4374106 2.313750 1.995038 4.270404 6.265442 10.596020
## 10 11.023580 1.5833355 3.124588 2.223052 5.858726 8.184432 23.369612
## 11 9.434368 1.4263307 3.967007 2.137465 8.895605 11.156403 24.313911
## 12 8.239967 1.8181514 4.796457 2.908115 12.660487 15.894671 36.037275
## 13 8.690495 0.9819731 5.507378 1.617921 8.436878 10.038924 20.342930
## 14 5.684595 2.6549998 3.934824 4.798546 16.256570 21.309211 21.154209
## 15 6.365412 1.7528772 2.472948 4.510470 10.428636 14.978127 17.942565
## 16 6.036071 1.3746619 4.352253 4.018939 13.878298 18.540743 13.619364
## 17 7.149819 1.7164996 2.674055 3.454300 8.380502 11.837539 18.141728
## 18 8.595033 1.7770319 3.356945 2.602765 8.178246 10.883756 16.317502
## 19 7.419013 1.0142213 2.997810 2.128698 5.570188 7.815801 18.490373
## 20 6.166221 1.3169163 6.277940 1.685047 10.524148 12.280181 17.651530
## 21 4.494838 1.3230160 3.138291 2.259452 7.410669 9.725518 13.253069
## 22 6.723682 1.1273306 5.885796 1.808105 11.602960 13.737836 25.690887
## 23 8.791739 1.6002877 3.642946 1.488717 5.048062 6.572472 9.805060
## 24 6.331221 1.1501939 2.347562 2.590489 5.527744 8.118233 33.369561
## 25 11.975529 2.5355867 3.083577 2.477477 6.803889 9.281366 23.038606
## 26 6.811436 1.3810430 5.179842 2.343526 13.718844 16.265023 26.836958
## 27 5.798944 1.5800767 2.882946 2.764143 7.418826 10.163052 15.783438
## 28 6.807680 1.2746707 6.590371 1.941682 12.042624 13.986523 24.694176
## 29 4.405980 1.2651621 5.664800 3.541532 15.819488 19.560620 23.554260
## 30 3.248339 1.8336048 3.471815 5.853562 20.444469 26.282011 16.708943
## 31 8.930915 0.8623909 4.888239 1.431617 6.061083 7.485572 22.908559
## 32 7.105849 2.0369629 6.183241 1.944085 11.218633 13.475778 16.078475
## 33 7.509215 1.7331667 3.883233 2.856109 10.373053 13.284027 20.374094
## 34 8.487468 5.6691042 4.073192 5.181092 16.955785 22.136876 26.551133
## 35 4.901099 1.6778094 3.563062 4.761034 17.480546 22.241580 27.203337
## 36 11.150428 1.4575350 3.665545 1.744570 5.916611 7.660675 14.552539
## 37 12.970238 1.3573717 3.305623 1.729761 4.951949 6.757195 18.355345
## 38 10.109411 1.2026532 3.925609 1.677665 6.286635 7.964299 13.797263
## 39 5.929483 4.7011573 8.279938 3.162612 30.496932 33.715224 28.081424
## 40 9.687765 1.7414743 2.844602 2.138461 5.969263 8.095845 19.664735
## 41 8.223951 2.3121699 4.329034 1.366540 5.999341 7.475064 7.764209
## 42 10.758324 1.9886135 4.343637 1.692927 6.630484 8.314953 17.833142
## 43 11.123505 0.9724024 5.550376 1.413229 7.516967 8.930196 17.187490
## 44 6.700554 1.0261327 6.525925 2.173082 12.742967 15.047349 31.925446
## 45 10.549304 1.0902017 4.505706 1.602982 6.758757 8.402251 11.359261
## 46 5.656838 1.2531240 4.024144 3.559467 16.478456 20.023376 35.109077
## 47 5.070922 1.6184154 4.901222 3.492617 15.869630 19.480282 20.422276
## Fe_Mn Fe_Zn Grupo
## 1 76.11250 159.48352 2
## 2 99.79597 224.82995 2
## 3 101.88795 348.51124 3
## 4 82.06782 44.59346 4
## 5 106.15040 192.95755 4
## 6 126.77638 463.21697 3
## 7 64.50734 279.89002 2
## 8 127.99761 231.20179 2
## 9 137.53683 58.32030 1
## 10 124.07392 283.95300 2
## 11 48.45113 31.38065 4
## 12 45.69982 39.99380 4
## 13 73.69159 121.38584 2
## 14 76.80013 37.19371 1
## 15 119.74120 291.30422 2
## 16 57.26523 61.45104 4
## 17 124.07656 235.46899 2
## 18 82.78848 271.42576 2
## 19 99.78590 406.21095 3
## 20 62.19801 181.54670 2
## 21 120.11062 543.41485 3
## 22 106.75522 252.41292 2
## 23 194.46502 604.75564 3
## 24 105.21371 770.00062 3
## 25 54.92473 30.35978 1
## 26 93.91041 269.76120 2
## 27 89.83600 155.79598 3
## 28 81.70473 53.13072 4
## 29 90.54170 174.79516 2
## 30 151.18552 266.55311 3
## 31 91.64701 328.82178 3
## 32 98.58501 104.90479 2
## 33 146.26137 291.09793 1
## 34 59.29421 60.45141 1
## 35 130.78752 387.17109 2
## 36 74.09232 119.03846 2
## 37 66.46467 266.70547 2
## 38 124.09790 355.08541 3
## 39 65.38077 113.22886 4
## 40 79.87481 175.23262 2
## 41 81.64860 164.86202 3
## 42 71.50533 112.37959 4
## 43 62.11595 70.69824 2
## 44 92.23563 85.98830 4
## 45 100.98522 248.48556 2
## 46 48.52643 74.34853 2
## 47 124.80835 229.29622 2col=c("#00AFBB","#E7B800", "#FC4E07","#9E1FDE", "#9D9FDE")
library(ggplot2)
#
DF.cluster.tot$Grupo <- as.factor(DF.cluster.tot$Grupo)
ggplot(DF.cluster.tot, aes(x=Grupo, y=pH, colour=Grupo) ) +
geom_jitter(alpha=0.1)+
geom_boxplot(outlier.shape = NA, alpha=0.8)+
scale_fill_manual(values = col) +
scale_color_manual(values = col)+
theme_classic()+
theme(legend.position = "none")
#
DF.cluster.agru$Grupo <- as.factor(DF.cluster.agru$Grupo)
ggplot(DF.cluster.agru, aes(x=Grupo, y=pH, colour=Grupo) ) +
geom_point(alpha=0.4)+
geom_boxplot(outlier.shape = NA, alpha=0.4)+
scale_fill_manual(values = col) +
scale_color_manual(values = col)+
theme_classic()+
theme(legend.position = "none")
#
col <- c("#00AFBB", "#E7B800", "#FC4E07", "#9E1FDE", "#9D9FDE")
# Obtener los nombres de las variables
variables <- colnames(A)[-c(1:2)]
variables## [1] "ALT" "TEM" "pH" "Aci" "Al" "CE" "CIC"
## [8] "MO" "N.tot" "N.dis" "P" "P.dis" "K" "Ca"
## [15] "Mg" "S" "Na" "Fe" "Cu" "Mn" "Zn"
## [22] "B" "S.Bas" "S.Al" "S.Ca" "S.Mg" "S.K" "S.Na"
## [29] "Ca_Mg" "Mg_K" "Ca_K" "Ca.Mg_K" "Ca_B" "Fe_Mn" "Fe_Zn"# Iterar a través de las columnas y crear los gráficos de caja
for (i in variables) {
# Crear un gráfico de caja para la columna i
p <- ggplot(DF.cluster.agru, aes(x = Grupo, y = .data[[i]], colour = Grupo)) +
geom_jitter(alpha = 0.1) +
geom_boxplot(alpha = 0.8)
print(p) # Agregar esta línea para imprimir el gráfico en cada iteración
}
#
names(DF.cluster.agru)## [1] "MUN" "Rmed1" "IDPM" "ALT" "TEM" "pH" "Aci"
## [8] "Al" "CE" "CIC" "MO" "N.tot" "N.dis" "P"
## [15] "P.dis" "K" "Ca" "Mg" "S" "Na" "Fe"
## [22] "Cu" "Mn" "Zn" "B" "S.Bas" "S.Al" "S.Ca"
## [29] "S.Mg" "S.K" "S.Na" "Ca_Mg" "Mg_K" "Ca_K" "Ca.Mg_K"
## [36] "Ca_B" "Fe_Mn" "Fe_Zn" "Grupo"# Datos agrupados
ggbetweenstats(
data = DF.cluster.agru,
x = Grupo,
y = Ca,
type = "p",
var.equal = F,
mean.ci = F,
pairwise.comparisons = TRUE,
p.adjust.method = "bonferroni",
messages = F,
notch = F
)
# Datos totales
ggbetweenstats(
data = DF.cluster.tot,
x = Grupo,
y = Ca,
type = "p",
var.equal = F,
mean.ci = F,
pairwise.comparisons = TRUE,
p.adjust.method = "bonferroni",
messages = F,
notch = F
)