[r] Function to calculate R2 (R-squared) in R

I have a dataframe with observed and modelled data, and I would like to calculate the R2 value. I expected there to be a function I could call for this, but can't locate one. I know I can write my own and apply it, but am I missing something obvious? I want something like

obs <- 1:5
mod <- c(0.8,2.4,2,3,4.8)
df <- data.frame(obs, mod)

R2 <- rsq(df)
# 0.85

You can also use the summary for linear models:

summary(lm(obs ~ mod, data=df))$r.squared 

Not sure why this isn't implemented directly in R, but this answer is essentially the same as Andrii's and Wordsforthewise, I just turned into a function for the sake of convenience if somebody uses it a lot like me.

r2_general <-function(preds,actual){ 
  return(1- sum((preds - actual) ^ 2)/sum((actual - mean(actual))^2))
}

Here is the simplest solution based on [https://en.wikipedia.org/wiki/Coefficient_of_determination]

# 1. 'Actual' and 'Predicted' data
df <- data.frame(
  y_actual = c(1:5),
  y_predicted  = c(0.8, 2.4, 2, 3, 4.8))

# 2. R2 Score components

# 2.1. Average of actual data
avr_y_actual <- mean(df$y_actual)

# 2.2. Total sum of squares
ss_total <- sum((df$y_actual - avr_y_actual)^2)

# 2.3. Regression sum of squares
ss_regression <- sum((df$y_predicted - avr_y_actual)^2)

# 2.4. Residual sum of squares
ss_residuals <- sum((df$y_actual - df$y_predicted)^2)

# 3. R2 Score
r2 <- 1 - ss_residuals / ss_total

Why not this:

rsq <- function(x, y) summary(lm(y~x))$r.squared
rsq(obs, mod)
#[1] 0.8560185

It is not something obvious, but the caret package has a function postResample() that will calculate "A vector of performance estimates" according to the documentation. The "performance estimates" are

• RMSE
• Rsquared
• mean absolute error (MAE)

and have to be accessed from the vector like this

library(caret)
vect1 <- c(1, 2, 3)
vect2 <- c(3, 2, 2)
res <- caret::postResample(vect1, vect2)
rsq <- res[2]

However, this is using the correlation squared approximation for r-squared as mentioned in another answer. I'm not sure why Max Kuhn didn't just use the conventional 1-SSE/SST.

caret also has an R2() method, although it's hard to find in the documentation.

The way to implement the normal coefficient of determination equation is:

preds <- c(1, 2, 3)
actual <- c(2, 2, 4)
rss <- sum((preds - actual) ^ 2)
tss <- sum((actual - mean(actual)) ^ 2)
rsq <- 1 - rss/tss

Not too bad to code by hand of course, but why isn't there a function for it in a language primarily made for statistics? I'm thinking I must be missing the implementation of R^2 somewhere, or no one cares enough about it to implement it. Most of the implementations, like this one, seem to be for generalized linear models.