I am trying to create interaction variables for all 20 variables in a dataframe, so I would have in total 20 base variables and 380 interaction variables. For any single variable, I am able to create a dataframe of 19 variables by using:
in_sample[3:22] %>%
transmute(across(.cols = -c(frpm_frac_s), .fns = function(x){x*frpm_frac_s}))
But I am unable to iterate across the columns. I tried to use map over a vector of column names but am unable to get the function inside map to read as.symbol(character). Here is a sample of my data from dput:
structure(list(frpm_frac_s = c(0.870400011539459, 0.904699981212616,
0.98089998960495, 0.838800013065338, 0.919900000095367, 0.837700009346008,
0.84799998998642, 0.925999999046326, 0.963900029659271, 0.887899994850159
), enrollment_s = c(364, 608, 571, 705, 566, 838, 421, 757, 693,
535), ell_frac_s = c(0.46000000834465, 0.334000021219254, 0.300999999046326,
0.209999993443489, 0.706999957561493, 0.552999973297119, 0.412999987602234,
0.359000027179718, 0.726000010967255, 0.646999955177307), edi_s = c(8,
38, 39, 37, 11, 35, 15, 39, 9, 4), te_fte_s = c(23, 22, 20, 25,
24.5, 36, 18, 30.2999992370605, 24.3999996185303, 19)), row.names = c(NA,
10L), class = "data.frame")
When using:
in_sample[3:22] %>%
transmute(across(.cols = -c(frpm_frac_s), .fns = function(x){x*frpm_frac_s}))
I get:
structure(list(enrollment_s = c(316.825604200363, 550.057588577271,
560.093894064426, 591.354009211063, 520.663400053978, 701.992607831955,
357.007995784283, 700.981999278069, 667.982720553875, 475.026497244835
), ell_frac_s = c(0.400384012571335, 0.302169812922072, 0.295250895935631,
0.17614799724412, 0.650369261028242, 0.463248082799339, 0.350223985351086,
0.33243402482605, 0.699791432103968, 0.574471256869984), edi_s = c(6.96320009231567,
34.3785992860794, 38.255099594593, 31.0356004834175, 10.118900001049,
29.3195003271103, 12.7199998497963, 36.1139999628067, 8.67510026693344,
3.55159997940063), te_fte_s = c(20.0192002654076, 19.9033995866776,
19.617999792099, 20.9700003266335, 22.5375500023365, 30.1572003364563,
15.2639998197556, 28.0577992646217, 23.5191603559875, 16.870099902153
)), row.names = c(NA, 10L), class = "data.frame")
I would like to do this for all variables and then cbind them together. Thank you for your help.
CodePudding user response：
You can use model.matrix to create interaction terms. (This is what's done under the hood in most modeling functions.)
m = model.matrix(~ .^2 - . 0, data = df)
m
# frpm_frac_s:enrollment_s frpm_frac_s:ell_frac_s frpm_frac_s:edi_s frpm_frac_s:te_fte_s
# 1 316.8256 0.4003840 6.9632 20.01920
# 2 550.0576 0.3021698 34.3786 19.90340
# 3 560.0939 0.2952509 38.2551 19.61800
# 4 591.3540 0.1761480 31.0356 20.97000
# 5 520.6634 0.6503693 10.1189 22.53755
# 6 701.9926 0.4632481 29.3195 30.15720
# 7 357.0080 0.3502240 12.7200 15.26400
# 8 700.9820 0.3324340 36.1140 28.05780
# 9 667.9827 0.6997914 8.6751 23.51916
# 10 475.0265 0.5744713 3.5516 16.87010
# enrollment_s:ell_frac_s enrollment_s:edi_s enrollment_s:te_fte_s ell_frac_s:edi_s
# 1 167.440 2912 8372.0 3.680
# 2 203.072 23104 13376.0 12.692
# 3 171.871 22269 11420.0 11.739
# 4 148.050 26085 17625.0 7.770
# 5 400.162 6226 13867.0 7.777
# 6 463.414 29330 30168.0 19.355
# 7 173.873 6315 7578.0 6.195
# 8 271.763 29523 22937.1 14.001
# 9 503.118 6237 16909.2 6.534
# 10 346.145 2140 10165.0 2.588
# ell_frac_s:te_fte_s edi_s:te_fte_s
# 1 10.5800 184.0
# 2 7.3480 836.0
# 3 6.0200 780.0
# 4 5.2500 925.0
# 5 17.3215 269.5
# 6 19.9080 1260.0
# 7 7.4340 270.0
# 8 10.8777 1181.7
# 9 17.7144 219.6
# 10 12.2930 76.0
# attr(,"assign")
# [1] 1 2 3 4 5 6 7 8 9 10
Your math is a little off, because order doesn't matter in multiplication there are n * (n - 1) / 2 possibilities, (same as n choose 2), so you should expect 190 columns output for 20 columns input.
I made the formula to only include interaction terms, you can use ~ .^2 0 to include the first order terms too, or ~ .^2 to also include an intercept.