'data.frame': $303$ obs. of $8$ variables:

$\{\begin{array}{ccc}\text{:}[\text{\$},& \text{:}\text{i}\text{n}\text{t},63,37,41,56,57,57,56,44,52,& 57]\end{array}$dots

$ sex : int $1110101010111$dots

$ trestbps: int $145130130120120140140120172150...$

$ chol : int $233,250,204,236,354,192,294,263,199,168,$dots

$$fbs$ : int $10000000010$dots...

$ thalach : int $150187172178163148153173162174$dots

$ exang : int $0000100000$dots

$ oldpeak : num $2\mathrm{.}3,3\mathrm{.}51\mathrm{.}4,0\mathrm{.}8,0\mathrm{.}6,0\mathrm{.}41\mathrm{.}3,0,0\mathrm{.}51\mathrm{.}6$dots

Here is what the output means using line $1$ and $2$ as examples.

The first line of the output tells us about the overall structure: $R$ imported the data from an excel spreadsheet as a "data. frame" with $303$ observations $($rows$)$ and $8$ variables $($columns$)$

The second line starts with a $"$ $ $"$ letting you know that it is a variable inside the data.frame $($remember data set$variable name$).$ Then the name of the variable in this case "age". The variable name is followed by a $"$: $"$ and then a three letter abbreviation for the variable type, which, for the age variable is "int" for integer. So when the data was imported R looked at the data and saw only whole numbers and applied the integer label. The numbers following the variable type are the first $10$ values for that variable.

So now that you know that the output means you can answer the following question.

So now that you know that the output means you can answer the following question.

$$ Which variables need to have their variable types changed

age

sex

trestbps

chol

fbs

thalach

exang

oldpeak