t.test.R

# Data in two numeric vectors
parcela1_AGB <- c(38.9, 61.2, 73.3, 21.8, 63.4, 64.6, 48.4, 48.8, 48.5)
parcela2_AGB <- c(67.8, 60, 63.4, 76, 89.4, 73.3, 67.3, 61.3, 62.4) 
# Create a data frame
my_data <- data.frame( 
  parcela = rep(c("uno", "dos"), each = 9),
  AGB = c(parcela1_AGB,  parcela2_AGB)
)
#Presente un resumen estadistico de los datos. Use el paquete dplyr
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

group_by(my_data, parcela) %>%
  summarise(
    count = n(),
    mean = mean(AGB, na.rm = TRUE),
    sd = sd(AGB, na.rm = TRUE)
  )

## # A tibble: 2 x 4
##   parcela count  mean    sd
##   <fct>   <int> <dbl> <dbl>
## 1 dos         9  69.0  9.38
## 2 uno         9  52.1 15.6

# Visualice los datos usando box plots
library("ggpubr")

## Loading required package: ggplot2

## Loading required package: magrittr

ggboxplot(my_data, x = "parcela", y = "AGB", 
          color = "parcela", palette = c("#00AFBB", "#E7B800"),
          ylab = "AGB", xlab = "parcela")

# Realice la prueba de t
res <- t.test(AGB ~ parcela, data = my_data, var.equal = TRUE)
res

## 
##  Two Sample t-test
## 
## data:  AGB by parcela
## t = 2.7842, df = 16, p-value = 0.01327
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   4.029759 29.748019
## sample estimates:
## mean in group dos mean in group uno 
##          68.98889          52.10000

# Compruebe normalidad
# Shapiro-Wilk normality test for Men's weights
with(my_data, shapiro.test(AGB[parcela == "uno"]))# p = 0.1

## 
##  Shapiro-Wilk normality test
## 
## data:  AGB[parcela == "uno"]
## W = 0.94266, p-value = 0.6101

# Shapiro-Wilk normality test for Women's weights
with(my_data, shapiro.test(AGB[parcela == "dos"])) # p = 0.6

## 
##  Shapiro-Wilk normality test
## 
## data:  AGB[parcela == "dos"]
## W = 0.86425, p-value = 0.1066

# Homegeneidad de varianzas con F-test
res.ftest <- var.test(AGB ~ parcela, data = my_data)
res.ftest

## 
##  F test to compare two variances
## 
## data:  AGB by parcela
## F = 0.36134, num df = 8, denom df = 8, p-value = 0.1714
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
##  0.08150656 1.60191315
## sample estimates:
## ratio of variances 
##          0.3613398