The formula for Cronbach's alpha is:
$$
\alpha =\frac{K}{K-1} ( 1-\frac{\sum_{i=1}^{K}\sigma^2_{Y_i}}{\sigma^2_X})
$$
Here, K is the number of different items you administered to each subject. Sometimes items are different questionnaire items designed to measure the same underlying construct. In your case, it sounds like each item is a separate run of the experiment.
In order to calculate Cronbach's alpha, you need to put your data in "wide format" (as Michelle mentioned). This means that each of the 200 measurements needs to have its own column/variable. So your columns would be SubjectID, Answer1, Answer2, ... , Answer 200.
I'm not sure where your expected answer column comes in. Cronbach's alpha is used to test the consistency of answers to each other, not to some true value, because the true value being measured is latent (i.e., unknown). I suppose you could calculate the correlation between the mean of the answers and the expected answer to see how well people did. Or calculate the times they answered exactly the way you expected and call that their score.
As has been suggested in the comments, it doesn't seem to make sense to calculate alpha on the raw scores. If they are only meaningful in relation to the expected answer (i.e., as indicative of how well a subject does on this particular test), you need to use adjusted scores that actually quantify how well a subject did. If an answer of 50 with an expected answer of 25 doesn't mean the same thing as an answer of 50 with an expected answer of 45, then calculating alpha on the raw scores is meaningless.
To calculate Cronbach's alpha using R, read the CSV file into a dataframe, reformat into wide format, then run cronbach.alpha on only the answer columns (assuming your columns for subject and values are called SubjectID and Score):
x.long <- read.csv(file="myfile.csv")
library(reshape2)
x.wide <- dcast(x.long, SubjectID ~ Score)
library(ltm)
cronbach.alpha(x.wide[,-1]) # remove SubjectID in first column
First, alpha is a quantity for a scale (a set of items).
Ad 1: Strictly speaking, alpha only makes sense for metric items (which, I believe, you mean by numerical variables). However, it is often used on (sum) scales of ordinal items too (there the general rule of thumb is a sum of more than 7 items and more than 4 levels of the item). I believe this is bad practice though.
Ad 2: You can use alpha in this case. Generally, it is rarely a good idea to make a numeric variable ordinal.
Ad 3: I would not use it in this case (but mainly because they are ordinal).
Best Answer
Two main causes:
Remedies:
See also the references in Reverse scoring when question is stated in a negative fashion