Calculating R squared from multiple columns - r

Question

I'm very new to R and have been trying to figure out how to calculate R^2 from a few columns within a large data set of approx 300+ columns.
Example:
rcalc <- data.frame('x1' = c(694, 702, 701), 'x2'=c(652, 659, 655),
'x3'=c(614, 612, 613), 'y1'= c(17.97, 17.95, 17.96), 'y2' = c(12.03, 12.0,
12.1), 'y3' = c(0.09, 0.1, 0.1))
From here I am stuck.
The formula in excel I can do, and looks like this:
RSQ(X1:X3, Y1:Y3) or RSQ(694:652:614, 17.97:12.03:0.09)
So, each row needs to be calculated for R^2. I was able to use the 'lm' command but was only able to do this for 1 row:
I had to take the value from each column of x (x1:x3) and stack them into 1 column, then each value from each column y (y1:y3) and stack into 1 column. Then performed the following:
rsqrd = lm(x~y, data=rcalc)
summary(rsqrd)$r.squared
This worked but again, only for 1 row. I'm not sure how to do this for thousands of rows. I hope this wasn't too confusing. Any help is greatly appreciated.
Troubleshooting:
with pivot_longer:
row col obs value
1 c 300_0 DUT Ip2_comp 784.9775
1 c 300_12 DUT Ip2_comp 864.4234
1 c 300_18 DUT Ip2_comp 919.3384
1 c 300_0 REF O2 0.09
1 c 300_12 REF O2 11.95
1 c 300_18 REF O2 17.98
2 c 300_0 DUT Ip2_comp 781.5785
2 c 300_12 DUT Ip2_comp 865.5541
2 c 300_18 DUT Ip2_comp 921.0646
2 c 300_0 REF O2 0.09
With Pivot_wider:
row obs c
1 300_0 DUT Ip2_comp 784.9775
1 300_12 DUT Ip2_comp 864.4234
1 300_18 DUT Ip2_comp 919.3384
1 300_0 REF O2 0.09
1 300_12 REF O2 11.95
1 300_18 REF O2 17.98
2 300_0 DUT Ip2_comp 781.5785
2 300_12 DUT Ip2_comp 865.5541
2 300_18 DUT Ip2_comp 921.0646

Answer 1

I'm sure this could be done more concisely, but here's one approach using tidyverse functions. First, I do some reshaping to add a row number and make it into a longer shape, with columns for row, observation # (1-3), x, and y.
Then I "nest" all the data except row number so that I can run a separate regression on each row's data, and then extract r squared (and a variety of other stats) from each regression.
library(tidyverse)
rcalc %>% # your data
# reshape to get matched columns for all x and for all y values
mutate(row = row_number()) %>%
pivot_longer(-row, names_to = c("col", "obs"), names_sep = 1) %>% # split column name into two fields after first character
pivot_wider(names_from = col, values_from = value) %>%
# nest data, regression, unnest
nest(-row) %>%
mutate(model = map(data, function(df) lm(y ~ x, data = df)),
tidied = map(model, broom::glance)) %>%
unnest(tidied)
Result
# A tibble: 3 x 15
row data model r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
<int> <list> <list> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
1 1 <tibble [3 × 3]> <lm> 0.952 0.905 2.81 20.0 0.140 1 -5.71 17.4 14.7 7.91 1 3
2 2 <tibble [3 × 3]> <lm> 0.973 0.946 2.10 36.3 0.105 1 -4.84 15.7 13.0 4.43 1 3
3 3 <tibble [3 × 3]> <lm> 0.951 0.903 2.84 19.6 0.141 1 -5.74 17.5 14.8 8.05 1 3
Edit: for troubleshooting, I am adding here the results I see at each stage:
after the pivot_longer step:
# A tibble: 18 x 4
row col obs value
<int> <chr> <chr> <dbl>
1 1 x 1 694
2 1 x 2 652
3 1 x 3 614
4 1 y 1 18.0
5 1 y 2 12.0
6 1 y 3 0.09
7 2 x 1 702
8 2 x 2 659
9 2 x 3 612
10 2 y 1 18.0
11 2 y 2 12
12 2 y 3 0.1
13 3 x 1 701
14 3 x 2 655
15 3 x 3 613
16 3 y 1 18.0
17 3 y 2 12.1
18 3 y 3 0.1
after the pivot_wider step:
# A tibble: 9 x 4
row obs x y
<int> <chr> <dbl> <dbl>
1 1 1 694 18.0
2 1 2 652 12.0
3 1 3 614 0.09
4 2 1 702 18.0
5 2 2 659 12
6 2 3 612 0.1
7 3 1 701 18.0
8 3 2 655 12.1
9 3 3 613 0.1