Data entry for t-test
Your experiment has: 1) two independent groups (e.g., wild-type vs mutant) and you are collecting continuous data at one time point (e.g., escape latency), OR 2) continuous data from one group of subjects, collected at two time points (e.g., reaction time before and after administration of a drug) OR 3) continuous data from one group of subjects, or at one time point, that is being compared against a population when we have limited information about the population (e.g., we may wish to test our sample's IQ score against the population mean [100]).
Two Independent Groups:
First, write the information about your group in column 1. You can do this initially in Excel, or whatever spreadsheet programme you use, and once the data is entered, simply save the file as ".txt". Here, I have called the groups 'a' and 'b', but you can call them whatever you like (e.g., 'wt' and 'mut' for wild-type and mutant). Word of warning to R newbies: R is case sensitive (i.e., hi ≠ Hi ≠ hI ≠ HI) and does not like gaps. I would suggest using all lower-case letters for variable names. If you need a gap for some reason, use underscore (a_b). Also, if you enter your groups as simply '1' and '2', R will treat the variable 'group' as an integer value. This is easy to rectify in R once you start working with the datasheet, but if you can avoid numbers alone as nominal variables, this will make life a little easier.
First, write the information about your group in column 1. You can do this initially in Excel, or whatever spreadsheet programme you use, and once the data is entered, simply save the file as ".txt". Here, I have called the groups 'a' and 'b', but you can call them whatever you like (e.g., 'wt' and 'mut' for wild-type and mutant). Word of warning to R newbies: R is case sensitive (i.e., hi ≠ Hi ≠ hI ≠ HI) and does not like gaps. I would suggest using all lower-case letters for variable names. If you need a gap for some reason, use underscore (a_b). Also, if you enter your groups as simply '1' and '2', R will treat the variable 'group' as an integer value. This is easy to rectify in R once you start working with the datasheet, but if you can avoid numbers alone as nominal variables, this will make life a little easier.
One group of subjects, two time points/two treatments:
The data entry for this type of data is similar to that above. This time, however, in the first column enter the subject ID (this is not necessary for the analysis, but will help you to keep track of who-did-what), time (or whatever your treatment is) in the second column and the response in the third column.
The data entry for this type of data is similar to that above. This time, however, in the first column enter the subject ID (this is not necessary for the analysis, but will help you to keep track of who-did-what), time (or whatever your treatment is) in the second column and the response in the third column.
One group of subjects, compared against a population:
The data entry for this is simpler, as you only need one column of data; the column containing your response variable. You will enter the population mean (mu) in the stats test, so it is not necessary to have this in the sheet.
The data entry for this is simpler, as you only need one column of data; the column containing your response variable. You will enter the population mean (mu) in the stats test, so it is not necessary to have this in the sheet.