Standard error and CI in R

uncertainty.r

# Dealing with uncertainty in R
library(palmerpenguins)
data(penguins)
penguins <- na.omit(penguins)

# If we want to calculate 95% CI using the 2SE convention:
species <- NULL
means <- NULL
std_devs <- NULL
sizes <- NULL
sems <- NULL
cis <- NULL

# Counter
k <- 1

for (i in levels(penguins$species)) {
    x <- penguins$bill_length_mm[penguins$species == i]
    means[k] <- mean(x)
    std_devs[k] <- sd(x)
    sizes[k] <- length(x)
    sems[k] <- std_devs[k] / sqrt(sizes[k])
    cis[k] <- paste(means[k] - 2 * sems[k], means[k] + 2 * sems[k], sep = ",")
    species[k] <- paste(i)
    k <- k + 1
}

results <- data.frame(species, sizes, means, std_devs, sems, cis)
names(results) <- c("species", "n", "mean", "sd", "se", "ci")
print(results)

# Mean, standard deviation, standard error, and 95% confidence intervals using **t-distribution**
library(Rmisc)
rmisc_results <- summarySE(penguins, measurevar = "bill_length_mm", groupvars = "species")

ci_lower <- rmisc_results$bill_length_mm - rmisc_results$ci
ci_upper <- rmisc_results$bill_length_mm + rmisc_results$ci

print(paste(ci_lower, ci_upper, sep = ","))