Good evening everyone! I'm a student working on a project and I am hitting a wall with one of the questions.

I don't imagine it is a difficult problem, I just have no experience in either RStudio or statistics in general, so I've managed to struggle.

I have a dataset that contains information about a lexical decision task in which people were timed in their responses to certain words. Frequency in this data set represents the number of times a word occurs.

The task at hand is : Calculate the correlation coefficient, t-value, and p-value for the relationship between frequency and reaction time in this dataset.

The issue I am having is, due to my inexperience across the board, I do not know where to start working towards this.

I apologise for my question formatting, this is my first post into this community and there may be significant information missing. Let me know if more information is needed and I can provide it

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 built-in 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 < 2e-16 ***
#> wt -5.3445 0.5591 -9.559 1.29e-10 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 3.046 on 30 degrees of freedom
#> Multiple R-squared: 0.7528, Adjusted R-squared: 0.7446
#> F-statistic: 91.38 on 1 and 30 DF, p-value: 1.294e-10

^{Created on 2020-03-30 by the reprex package (v0.3.0)}