Context
I am learning tidy evaluation on this website, And I see an example:
x <- sym("height")
expr(transmute(starwars, bmi = mass / (!! x)^2))
#> transmute(starwars, bmi = mass/(height)^2)
transmute(starwars, bmi = mass / (!! x)^2)
#> # A tibble: 87 x 1
#> bmi
#> <dbl>
#> 1 26.0
#> 2 26.9
#> 3 34.7
#> 4 33.3
#> # ... with 83 more rows
Then, I imitated the above example in my own code using sym and !!, but it reported an error.
library(survival)
library(rms)
data(cancer)
x = sym('meal.cal')
expr(cph(Surv(time, status) ~ rcs(!! x), data = lung))
# cph(Surv(time, status) ~ rcs(meal.cal), data = lung)
cph(Surv(time, status) ~ rcs(!!x), data = lung)
# Error in !x : invalid argument type
Question
Why do I use sym and !! in my code will report an error and how to fix it.
Is it because of the rcs().
We can use rlang::inject() to splice arguments with !! into functions that usually don't support tidy evaluation. This saves us from using eval(expr(...)) and also answers your question why we don't need rlang::inject() with dplyr::transmute(). The latter already supports tidy evaluation while cph() does not.
library(survival)
library(rms)
data(cancer)
x = sym('meal.cal')
rlang::inject(cph(Surv(time, status) ~ rcs(!!x), data = lung))
#> Frequencies of Missing Values Due to Each Variable
#> Surv(time, status) meal.cal
#> 0 47
#>
#> Cox Proportional Hazards Model
#>
#> cph(formula = Surv(time, status) ~ rcs(meal.cal), data = lung)
#>
#>
#> Model Tests Discrimination
#> Indexes
#> Obs 181 LR chi2 0.72 R2 0.004
#> Events 134 d.f. 4 R2(4,181)0.000
#> Center -0.3714 Pr(> chi2) 0.9485 R2(4,134)0.000
#> Score chi2 0.76 Dxy 0.048
#> Pr(> chi2) 0.9443
#>
#> Coef S.E. Wald Z Pr(>|Z|)
#> meal.cal -0.0006 0.0013 -0.48 0.6299
#> meal.cal' 0.0007 0.0051 0.14 0.8860
#> meal.cal'' 0.0010 0.0261 0.04 0.9682
#> meal.cal''' -0.0132 0.0676 -0.19 0.8456
#>
Without tidy evaluation we can stay in base R and use eval(bquote(...)) and splice x with .(x).
library(survival)
library(rms)
data(cancer)
x = sym('meal.cal')
eval(bquote(cph(Surv(time, status) ~ rcs(.(x)), data = lung)))
#> Frequencies of Missing Values Due to Each Variable
#> Surv(time, status) meal.cal
#> 0 47
#>
#> Cox Proportional Hazards Model
#>
#> cph(formula = Surv(time, status) ~ rcs(meal.cal), data = lung)
#>
#>
#> Model Tests Discrimination
#> Indexes
#> Obs 181 LR chi2 0.72 R2 0.004
#> Events 134 d.f. 4 R2(4,181)0.000
#> Center -0.3714 Pr(> chi2) 0.9485 R2(4,134)0.000
#> Score chi2 0.76 Dxy 0.048
#> Pr(> chi2) 0.9443
#>
#> Coef S.E. Wald Z Pr(>|Z|)
#> meal.cal -0.0006 0.0013 -0.48 0.6299
#> meal.cal' 0.0007 0.0051 0.14 0.8860
#> meal.cal'' 0.0010 0.0261 0.04 0.9682
#> meal.cal''' -0.0132 0.0676 -0.19 0.8456
#>
Created on 2022-09-26 by the reprex package (v2.0.1)