Hi, and welcome!
Please see the FAQ: What's a reproducible example (`reprex`) and how do I do one? Using a reprex, complete with representative data will attract quicker and more answers. Also, please see homework policy
Coming up to speed on R
at the same time as assimilating the mysteries of statistics is challenging.
For the R
part, two suggestions.

R for Data Science is a great introduction to R
. In addition to the free linked version, it's well worth the cost to buy the physical version.

Think of R
as school algebra writ large: f(x) = y. R
presents to the user as functions, f that take one or more arguments, x, and return values, y. (This is also key to understanding how to read the help
pages.) And everything in R
is an object
, meaning that g(f(x)) is possible.
The dataset is an object that contains the two other objects, frequency
and reaction time
.
The first question is how to invoke them:
dataset$frequency
I'll call the two of them x and y. In fact you might even want to create temporary variables
x < dataset$frequency
(Think of temporary variables as training wheels while coming up to speed.)
Given x and y, what functions are available to produce the three results called for? There are a lot of choices, but here's one using the builtin mtcars
dataset.
fit < lm(mpg ~ wt, data = mtcars)
summary(fit)
#>
#> Call:
#> lm(formula = mpg ~ wt, data = mtcars)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> 4.5432 2.3647 0.1252 1.4096 6.8727
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>t)
#> (Intercept) 37.2851 1.8776 19.858 < 2e16 ***
#> wt 5.3445 0.5591 9.559 1.29e10 ***
#> 
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 3.046 on 30 degrees of freedom
#> Multiple Rsquared: 0.7528, Adjusted Rsquared: 0.7446
#> Fstatistic: 91.38 on 1 and 30 DF, pvalue: 1.294e10
^{Created on 20200330 by the reprex package (v0.3.0)}
I have a brief explainer here