What tests can I apply to following data in R? We're doing our first project in R as our final grade, which is to find a dataset and test some hypotheses relating to the dataset. Our R knowledge is pretty basic and stuff we know are pretty much limited to linear modeling/regression, t-tests, ANOVA tests and plotting graphs.
We found ourselves a dataset of alcohol consumption in students with the following variables:
    'data.frame':   395 obs. of  8 variables:
 $ sex      : Factor w/ 2 levels "F","M": 1 1 1 1 1 2 2 1 2 2 ...
 $ studytime: int  2 2 2 3 2 2 2 2 2 2 ...
 $ romantic : Factor w/ 2 levels "no","yes": 1 1 1 2 1 1 1 1 1 1 ...
 $ freetime : int  3 3 3 2 3 4 4 1 2 5 ...
 $ goout    : int  4 3 2 2 2 2 4 4 2 1 ...
 $ Walc     : int  1 1 3 1 2 2 1 1 1 1 ...
 $ absences : int  6 4 10 2 4 10 0 6 0 0 ...
 $ G3       : int  6 6 10 15 10 15 11 6 19 15 ...

Where G3 is students final grades, graded from 0-20,
Abscences are the days the students were absent,
Walc is the weekend alcohol consumption ratio on levels from 1 to 5
Go out, study time and free time are also on levels from 1 to 5
We are lost at how to apply tests to the leveled data. For example, if we want to test whether or not weekend alcohol consumption has an effect on the final grades, or whether free time has an effect on alcohol consumption, the p-values are always too little or too big. The linear regression functions do not make sense at all. Is this because everything is ranked from 1 to 5? How do we get past this problem?
 A: You probably want to use non-parametric methods to handle your ordinal data - for example, you could calculate the Spearman correlation (instead of Pearson), and test if the correlation between free time and Walc is zero or not.
There are also nonparametric analogues of the t-test, ANOVA and others - many of them have examples in R on Choosing the correct test from UCLA.
Be careful about jumping to causal claims - it seems just as plausible to argue that hangovers reduce free time as it would be to argue that extra free time causes more alcohol consumption.
A: I like @NeilFultz answer, and I more than encourage you to learn, practise and expand your knowledge. However, if time is of the essence for you, I will provide the following solution.
I guess if you are not completely comfortable with going beyond OLS-type questions, how about a practical solution of choosing a dataset, in which you can model a continuous response variable (DV)? If you deal with modeling ordinal (ordered categories) or nominal (discreet categories) data you need to use a bit more complicated models. For example, proportional-odds regression or multinominal regression for the ordered and discreet response variable accordingly. 
Now, practically speaking: How do you go about choosing a dataset with normally distributed continuous DV quickly. Here's a simple R code for you that gives you access to a wealth of datasets, so you can quickly scan through some of them.
### Access all available datasets


# check available datasets in all packages
datasets.load::datasets() 


# check datasets within a specific package
datasets.load::getDatasetInfo("lattice") 
datasets.load::getDatasetInfo("dplyr")


# Selecting a specific dataset
datasets::ChickWeight # type package name::name of dataset

One example of a dataset, where you may like to predict a normally-distributed variable (Chicken Weight) based on a few predictors such as type of diet is the datasets::ChickWeight. I know this may sound unexciting but you are more than welcome to browse through all datasets to find the one you like
