R - Error T-test For loop command between variables - r

Question

Currently in the process of writing a For Loop that'll calculate and print t-test results, I'm testing for the difference in means of all variables (faminc, fatheduc, motheduc, white, cigtax, cigprice) between smokers and non-smokers ("smoke"; 0=non, 1=smoker)
Current code:
type <- c("faminc", "fatheduc", "motheduc", "white", "cigtax", "cigprice")
count <- 1
for(name in type){
temp <- subset(data, data[name]==1)
cat("For", name, "between smokers and non, the difference in means is: \n")
print(t.test(temp$smoke))
count <- count + 1
}
However, I feel that 'temp' doesn't belong here and when running the code I get:
For faminc between smokers and non, the difference in means is:
Error in t.test.default(temp$smoke) : not enough 'x' observations
The simple code of
t.test(faminc~smoke,data=data)
does what I need, but I'd like to get some practice/better understanding of for loops.

Answer 1

Here is a solution that generates the output requested in the OP, using lapply() with the mtcars data set.
data(mtcars)
varList <- c("wt","disp","mpg")
results <- lapply(varList,function(x){
t.test(mtcars[[x]] ~ mtcars$am)
})
names(results) <- varList
for(i in 1:length(results)){
message(paste("for variable:",names(results[i]),"difference between manual and automatic transmissions is:"))
print(results[[i]])
}
...and the output:
> for(i in 1:length(results)){
+ message(paste("for variable:",names(results[i]),"difference between manual and automatic transmissions is:"))
+ print(results[[i]])
+ }
for variable: wt difference between manual and automatic transmissions is:
Welch Two Sample t-test
data: mtcars[[x]] by mtcars$am
t = 5.4939, df = 29.234, p-value = 6.272e-06
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.8525632 1.8632262
sample estimates:
mean in group 0 mean in group 1
3.768895 2.411000
for variable: disp difference between manual and automatic transmissions is:
Welch Two Sample t-test
data: mtcars[[x]] by mtcars$am
t = 4.1977, df = 29.258, p-value = 0.00023
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
75.32779 218.36857
sample estimates:
mean in group 0 mean in group 1
290.3789 143.5308
for variable: mpg difference between manual and automatic transmissions is:
Welch Two Sample t-test
data: mtcars[[x]] by mtcars$am
t = -3.7671, df = 18.332, p-value = 0.001374
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-11.280194 -3.209684
sample estimates:
mean in group 0 mean in group 1
17.14737 24.39231
>

Answer 2

Compare your code that works...
t.test(faminc~smoke,data=data)
You are specifying a relationship between variables (faminc~smoke) which means that you think the mean of faminc is different between the values of smoke and you wish to use the data dataset.
The equivalent line in your loop...
print(t.test(temp$smoke))
...only gives the single column of temp$smoke after having selected those who have the value 1 for each of faminc, fatheduc etc. So even if you wrote...
print(t.test(faminc~smoke, data=data))
Further your count is doing nothing.
If you want to perform a range of testes in this manner you could
type <- c("faminc", "fatheduc", "motheduc", "white", "cigtax", "cigprice")
for(name in type){
cat("For", name, "between smokers and non, the difference in means is: \n")
print(t.test(name~smoke, data=data))
}
Whether this is what you want to do though isn't clear to me, your variables suggest family (faminc), father (fatheduc), mother (motheduc), ethnicity (white), tax (cigtax) and price (cigprice).
I can't think why you would want to compare the mean cigarette price or tax between smokers and non-smokers, because the later are not going to have any value for this since they don't smoke!
You're code suggests these are perhaps binary variables though (since you are filtering on each value being 1) which to me suggests this isn't even what you want to do.
If you wish to look in subsets of data then a tidier approach to performing regression rather than loops is to use purrr.
In future when asking consider providing a sample of data along with the full copy & pasted output as advised in How to create a Minimal, Complete, and Verifiable example - Help Center - Stack Overflow. Because this allows people to see in greater detail what you are doing (e.g. I've only guessed about your variables). With statistics its also useful to state what your hypothesis is too to help people understand what it is you are trying to achieve overall.