JKL
Pedro González
2024-11-10
setwd("C:/Users/VIP/Documents/JKL")
library(dplyr)##
## Adjuntando el paquete: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionVitamina C
df=read.csv("vitaminaC.csv",sep=",")
df## CODIGO CONC ESPECIE
## 1 L1MC1 10.147 CAPURI
## 2 L1MC1 10.869 CAPURI
## 3 L1MC1 10.704 CAPURI
## 4 L2MC2 10.674 CAPURI
## 5 L2MC2 10.548 CAPURI
## 6 L2MC2 10.506 CAPURI
## 7 L3MC3 10.638 CAPURI
## 8 L3MC3 10.579 CAPURI
## 9 L3MC3 10.566 CAPURI
## 10 L1MP1 10.055 PAPAYO
## 11 L1MP1 9.924 PAPAYO
## 12 L1MP1 10.059 PAPAYO
## 13 L2MP2 10.043 PAPAYO
## 14 L2MP2 10.155 PAPAYO
## 15 L2MP2 10.550 PAPAYO
## 16 L3MP3 10.677 PAPAYO
## 17 L3MP3 10.570 PAPAYO
## 18 L3MP3 10.462 PAPAYO
## 19 L1MHDT1 9.883 BTORO
## 20 L1MHDT1 9.648 BTORO
## 21 L1MHDT1 9.891 BTORO
## 22 L2MHDT2 10.604 BTORO
## 23 L2MHDT2 10.510 BTORO
## 24 L2MHDT2 10.190 BTORO
## 25 25.L3MHDT3 10.449 BTORO
## 26 26.L3MDHT3 10.578 BTORO
## 27 27.L3MDHT3 10.417 BTOROboxplot(CONC~ESPECIE,data=df,col=c("orange","red","lightblue"))
modelo<-aov(CONC~ESPECIE,data=df)
summary(modelo)## Df Sum Sq Mean Sq F value Pr(>F)
## ESPECIE 2 0.6282 0.31409 3.87 0.0349 *
## Residuals 24 1.9478 0.08116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1De acuerdo al p-valor=0.0359<0.05 hay diferencias significativas en las concentraciones de Vitamina C entre las especies estudiadas
df=read.csv("macroElementos.csv",sep=",")
df$ESPECIE<-factor(df$ESPECIE)
df## K Ca Mg Na ESPECIE
## 1 0.51 0.20 1.38 37.97 CAPURI
## 2 0.49 0.20 1.36 32.83 CAPURI
## 3 0.51 0.21 1.32 31.36 CAPURI
## 4 0.52 0.25 1.46 15.62 CAPURI
## 5 0.48 0.23 1.42 12.99 CAPURI
## 6 0.54 0.30 1.51 19.51 CAPURI
## 7 0.60 0.26 1.78 26.89 CAPURI
## 8 0.67 0.25 1.76 28.45 CAPURI
## 9 0.61 0.23 1.70 23.45 CAPURI
## 10 0.54 0.38 2.27 15.10 PAPAYO
## 11 0.50 0.40 2.37 20.53 PAPAYO
## 12 0.50 0.37 2.25 19.89 PAPAYO
## 13 0.50 0.44 2.22 5.30 PAPAYO
## 14 0.80 0.40 2.30 5.75 PAPAYO
## 15 0.51 0.42 2.20 6.18 PAPAYO
## 16 0.57 0.29 2.12 1.11 PAPAYO
## 17 0.60 0.31 2.08 1.73 PAPAYO
## 18 0.58 0.31 2.09 1.39 PAPAYO
## 19 0.86 0.35 2.40 33.42 BTORO
## 20 0.87 0.34 2.44 37.41 BTORO
## 21 0.87 0.31 2.36 31.05 BTORO
## 22 0.85 0.27 2.38 33.54 BTORO
## 23 0.83 0.30 2.41 30.84 BTORO
## 24 0.84 0.30 2.37 32.31 BTORO
## 25 0.92 0.29 2.35 24.13 BTORO
## 26 0.90 0.27 2.33 21.78 BTORO
## 27 0.90 0.27 2.29 22.72 BTOROPotasio K
boxplot(K~ESPECIE,data=df,col=c("orange","red","lightblue"))
modelo<-aov(K~ESPECIE,data=df)
summary(modelo)## Df Sum Sq Mean Sq F value Pr(>F)
## ESPECIE 2 0.5928 0.29638 62.7 2.95e-10 ***
## Residuals 24 0.1134 0.00473
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Existe diferencia significativa entre los niveles de K p-valor=2.95e-10
Calcio Ca
boxplot(Ca~ESPECIE,data=df,col=c("orange","red","lightblue"))
modelo<-aov(Ca~ESPECIE,data=df)
summary(modelo)## Df Sum Sq Mean Sq F value Pr(>F)
## ESPECIE 2 0.07872 0.03936 24.67 1.51e-06 ***
## Residuals 24 0.03829 0.00160
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Existe diferencia significativa entre los niveles de K p-valor=1.51e-06
Magnesio Mg
boxplot(Mg~ESPECIE,data=df,col=c("orange","red","lightblue"))
modelo<-aov(Mg~ESPECIE,data=df)
summary(modelo)## Df Sum Sq Mean Sq F value Pr(>F)
## ESPECIE 2 3.666 1.8329 125.3 1.98e-13 ***
## Residuals 24 0.351 0.0146
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Existe diferencia significativa entre los niveles de K p-valor=1.98e-13
Sodio
boxplot(Na~ESPECIE,data=df,col=c("orange","red","lightblue"))
modelo<-aov(Na~ESPECIE,data=df)
summary(modelo)## Df Sum Sq Mean Sq F value Pr(>F)
## ESPECIE 2 2251 1125.4 21.08 5.18e-06 ***
## Residuals 24 1281 53.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Existe diferencia significativa entre los niveles de K p-valor=5.18e-06
Micro Elements , P y Acidez
df=read.csv("microElementos.csv",sep=",")
df$ESPECIE<-factor(df$ESPECIE)
df## Fe Cu Mn Zn P Acidez ESPECIE
## 1 2.30 0.71 1.09 0 3.93 0.29 CAPURI
## 2 2.25 0.64 1.06 0 4.15 0.29 CAPURI
## 3 2.33 0.59 1.22 0 4.15 0.30 CAPURI
## 4 1.17 0.48 3.65 0 3.82 0.30 CAPURI
## 5 1.23 0.61 3.46 0 3.72 0.28 CAPURI
## 6 1.54 0.58 4.15 0 3.66 0.22 CAPURI
## 7 4.46 0.92 3.93 0 3.50 0.25 CAPURI
## 8 4.13 1.06 4.50 0 3.56 0.22 CAPURI
## 9 4.01 1.03 3.39 0 3.39 0.26 CAPURI
## 10 1.76 0.67 65.66 0 3.02 0.59 PAPAYO
## 11 1.59 0.65 63.01 0 3.23 0.47 PAPAYO
## 12 1.58 0.68 62.16 0 3.39 0.54 PAPAYO
## 13 2.80 0.30 89.23 0 2.97 0.44 PAPAYO
## 14 3.31 0.29 78.82 0 3.34 0.44 PAPAYO
## 15 2.47 0.32 83.48 0 3.45 0.46 PAPAYO
## 16 9.63 1.01 38.85 0 3.34 0.37 PAPAYO
## 17 10.24 0.96 34.31 0 3.45 0.37 PAPAYO
## 18 8.61 0.95 36.75 0 3.39 0.37 PAPAYO
## 19 17.24 0.31 24.83 0 3.02 0.44 BTORO
## 20 17.46 0.39 25.79 0 2.97 0.46 BTORO
## 21 13.03 0.30 23.48 0 2.80 0.45 BTORO
## 22 45.16 0.66 26.74 0 2.75 0.44 BTORO
## 23 41.28 0.65 26.31 0 2.70 0.44 BTORO
## 24 38.91 0.67 26.01 0 2.64 0.45 BTORO
## 25 9.31 0.37 31.40 0 2.59 0.44 BTORO
## 26 7.20 0.43 29.96 0 2.59 0.44 BTORO
## 27 8.48 0.34 30.76 0 2.48 0.47 BTOROHierro
boxplot(Fe~ESPECIE,data=df,col=c("orange","red","lightblue"))
modelo<-aov(Fe~ESPECIE,data=df)
summary(modelo)## Df Sum Sq Mean Sq F value Pr(>F)
## ESPECIE 2 2045 1022.4 12.25 0.000215 ***
## Residuals 24 2003 83.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Cobre
boxplot(Cu~ESPECIE,data=df,col=c("orange","red","lightblue"))
modelo<-aov(Cu~ESPECIE,data=df)
summary(modelo)## Df Sum Sq Mean Sq F value Pr(>F)
## ESPECIE 2 0.3629 0.18145 3.521 0.0456 *
## Residuals 24 1.2367 0.05153
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Manganeso
boxplot(Mn~ESPECIE,data=df,col=c("orange","red","lightblue"))
modelo<-aov(Mn~ESPECIE,data=df)
summary(modelo)## Df Sum Sq Mean Sq F value Pr(>F)
## ESPECIE 2 15504 7752 52.94 1.58e-09 ***
## Residuals 24 3514 146
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fósforo
boxplot(P~ESPECIE,data=df,col=c("orange","red","lightblue"))
modelo<-aov(P~ESPECIE,data=df)
summary(modelo)## Df Sum Sq Mean Sq F value Pr(>F)
## ESPECIE 2 4.857 2.4283 52.73 1.65e-09 ***
## Residuals 24 1.105 0.0461
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Acidez
boxplot(Acidez~ESPECIE,data=df,col=c("orange","red","lightblue"))
modelo<-aov(Acidez~ESPECIE,data=df)
summary(modelo)## Df Sum Sq Mean Sq F value Pr(>F)
## ESPECIE 2 0.19683 0.09841 41.65 1.57e-08 ***
## Residuals 24 0.05671 0.00236
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df2<- df %>% group_by(ESPECIE) %>% summarise(n=n(),promFe=mean(Fe),stdFe=sd(Fe),promMn=mean(Mn),stdMn=sd(Mn),promCu=mean(Cu),stdCu=sd(Cu))
df2## # A tibble: 3 × 8
## ESPECIE n promFe stdFe promMn stdMn promCu stdCu
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 BTORO 9 22.0 15.3 27.3 2.78 0.458 0.157
## 2 CAPURI 9 2.60 1.28 2.94 1.40 0.736 0.213
## 3 PAPAYO 9 4.67 3.69 61.4 20.7 0.648 0.291df2<- df %>% group_by(ESPECIE) %>% summarise(n=n(),promP=mean(P),stdP=sd(P),promAcidez=mean(Acidez),stdAcidez=sd(Acidez))
df2## # A tibble: 3 × 6
## ESPECIE n promP stdP promAcidez stdAcidez
## <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 BTORO 9 2.73 0.179 0.448 0.0109
## 2 CAPURI 9 3.76 0.272 0.268 0.0319
## 3 PAPAYO 9 3.29 0.179 0.45 0.0771