Creating decision tree

Question 1

The issue is almost certainly that you're including the name variable in your model, as it has too many factor levels. I would also remove it a methodological standpoint but this probably isn't the place for that discussion. Try:

train <- data
train$Name <- NULL
model<-tree(salary~.,train)

Question 2

It seems that your salary is a factor vector, while you are trying to perform a regression, so it should be a numbers vector. Simply convert you salary to numeric, and it should work just fine. For more details read the library's help:

http://cran.r-project.org/web/packages/tree/tree.pdf

Usage

tree(formula, data, weights, subset, na.action = na.pass, control = tree.control(nobs, ...), method = "recursive.partition", split = c("deviance", "gini"), model = FALSE, x = FALSE, y = TRUE, wts = TRUE, ...)

Arguments

formula A formula expression. The left-hand-side (response) should be either a numerical vector when a regression tree will be ﬁtted or a factor, when a classiﬁcation tree is produced. The right-hand-side should be a series of numeric or factor variables separated by +; there should be no interaction terms. Both . and - are allowed: regression trees can have offset terms. (...)

Depending on what exactly is stored in your salary variable, the conversion can be less or more tricky, but this should generaly work:

salary = as.numeric(levels(salary))[salary]

EDIT

As pointed out in the comment, the actual error corresponds to the data variable, so if it is a numerical data, it could also be converted to numeric to solve the issue, if it has to be a factor you will need another model or reduce the number of levels. You can also convert these factors to the numerical format by hand (by for example defining as many binary features as you have levels), but this can lead to the exponential growth of your input space.

EDIT2

It seems that you have to first decide what you are trying to model. You are trying to predict salary, but based on what? It seems that your data consists of players' records, then their names are for sure wrong type of data to use for this prediction (in particular - it is probably causing the 32 levels error). You should remove all the columns from the data variable which should not be used for building a prediction. I do not know what is the exact aim here (as there is no information regarding it in the question), so I can only guess that you are trying to predict the person's salary based on his/her stats, so you should remove from the input data: players' names, players' teams and obviously salaries (as predicting X using X is not a good idea ;)).