Given the following data for tool 1

# Full sample data
data <- c(
  31.6, 32.1, 31.9, 31.7, 32.2, 31.8, 32.0, 31.5, 31.9, 31.7,
  32.1, 32.0, 32.1, 32.3, 31.9, 31.9, 32.3, 32.2, 31.9, 32.1,
  31.9, 31.9, 32.1, 31.6, 31.6, 31.9, 31.8, 32.1, 31.8, 31.7,
  32.3, 32.0, 32.0, 31.7, 31.9, 32.0, 31.7, 32.1, 31.9, 31.9,
  31.9, 32.4, 32.0, 31.8, 32.2, 31.7, 31.9
)

# Summary statistics for Tool 1
n1 <- length(data)     
s1 <- sd(data)         
mean1 <- mean(data)    

list(n1 = n1, mean = mean1, sd = s1)
## $n1
## [1] 47
## 
## $mean
## [1] 31.93617
## 
## $sd
## [1] 0.2079146

I would like to put given parameter, formula of tool 2 and T-test

#parameter
alpha <- 0.05
power <- 0.90
delta <- 0.20
z_alpha <- qnorm(1 - alpha/2)   
z_beta  <- qnorm(power)

# Formula to solve for n2
rhs <- (delta / (s1 * (z_alpha + z_beta)))^2
n2 <- 1 / (rhs - 1/n1)
n2_required <- ceiling(n2)

# t-test against target CD = 32.0
target <- 32.0
t_result <- t.test(data, mu=target)
t_result
## 
##  One Sample t-test
## 
## data:  data
## t = -2.1047, df = 46, p-value = 0.04081
## alternative hypothesis: true mean is not equal to 32
## 95 percent confidence interval:
##  31.87512 31.99722
## sample estimates:
## mean of x 
##  31.93617