How can dplyr and conditional statements be used in R to summarize mean and confidence intervals?

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To summarize mean and confidence intervals using dplyr and conditional statements in R, we can use the group_by() and summarise() functions from the dplyr package along with the confint() function from the stats package. Here's an example:

library(dplyr)
data <- iris  # Load iris dataset

# Group data by Species
data_summary <- data %>% 
  group_by(Species) %>% 

  # Summarize mean and confidence intervals for Sepal.Length
  summarise(
    mean = mean(Sepal.Length),
    ci_lower = confint(lm(Sepal.Length ~ 1))["(Intercept)", "2.5 %"],
    ci_upper = confint(lm(Sepal.Length ~ 1))["(Intercept)", "97.5 %"]
  )

# View summary data
data_summary

In this example, we used the lm() function to fit a linear model for Sepal.Length, and the confint() function to obtain the confidence intervals. We then grouped the data by Species using groupby(), and calculated the mean and confidence intervals using summarise(). Finally, we saved the summary data in the datasummary object and printed it to the console.