How can I use a two sample t-test when there are two groups in R?

Question

I have a data frame with the categories fruits, ripeness, and mean. How can I create a for loop that runs a ttest to determine the mean difference for the ripeness for EACH fruit? In other words, for apples, the ttest would produce a result of the mean difference between ripe and unripe apples. An example of this would look like the following table.

Answer 1

Something like this could work for returning p-values of the t-test comparing "Ripeness" as you loop through the unique "Fruits" that appear in your data.

## create a vector of the unique fruit in the data; vector of fruit to be tested
fruit<-unique(data$Fruits)
## iterate through your list of unique fruit, testing as you go
for(i in 1:length(fruit)){
  ## subset your data to include only the current fruit to be tested
  df<-filter(data, Fruits==fruit[i])
  ## let the user know which fruit is being tested
  message(fruit[i])
  ## create a vector of the unique ripeness states of the current fruit to be tested
  ripe<-unique(df$Ripeness)
  ## make sure two means exist; ensure there are both ripe and non-ripe values
  if(length(ripe) < 2){
    ## if only one ripeness, let user know and skip to next unique fruit
    message("only one ripeness")
    next
  }
  ## try testing the fruit and return p-value if success
  tryCatch(
    {
      message(t.test(Mean ~ Ripeness, data = df)$p.value)
    },
    ## if error in t-testing return message that there are "not enough observations"
    error=function(cond) {
      message("not enough observations")
    }
  )    
}

I hope this helps!

Answer 2

Assuming fruits is coded as a categorical variable (i.e. factor as it should be), you could use sapply to iteratively subset data by each fruit. In t.test we use alternative="two.sided", just to emphasize although its the default setting.

However, your data is very small and Bananas are only ripe. I therefore a larger sample data set to demonstrate.

res <- sapply(levels(dat$fruits), function(x) 
  t.test(mean ~ ripeness, dat[dat$fruits %in% x, ], alternative="two.sided")
)
res
#             Apple                     Banana                    Orange                   
# statistic   0.948231                  0.3432062                 0.4421971                
# parameter   23.38387                  30.86684                  16.47366                 
# p.value     0.3527092                 0.7337699                 0.664097                 
# conf.int    Numeric,2                 Numeric,2                 Numeric,2                
# estimate    Numeric,2                 Numeric,2                 Numeric,2                
# null.value  0                         0                         0                        
# stderr      0.8893453                 1.16548                   1.043739                 
# alternative "two.sided"               "two.sided"               "two.sided"              
# method      "Welch Two Sample t-test" "Welch Two Sample t-test" "Welch Two Sample t-test"
# data.name   "mean by ripeness"        "mean by ripeness"        "mean by ripeness"     

---

Data:

set.seed(42)
n <- 1e2
dat <- data.frame(fruits=factor(sample(1:3, n, replace=T),
                                labels=c("Apple", "Banana", "Orange")),
                  ripeness=factor(rbinom(n, 1, .4), labels=c("yes", "no")),
                  mean=round(runif(n)*10))

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