Dynamic variable names in R regressions

Question

Being aware of the danger of using dynamic variable names, I am trying to loop over varios regression models where different variables specifications are choosen. Usually !!rlang::sym() solves this kind of problem for me just fine, but it somehow fails in regressions. A minimal example would be the following:

y= runif(1000) 
x1 = runif(1000) 
x2 = runif(1000) 

df2= data.frame(y,x1,x2)
summary(lm(y ~ x1+x2, data=df2)) ## works

var = "x1"
summary(lm(y ~ !!rlang::sym(var)) +x2, data=df2) # gives an error

My understanding was that !!rlang::sym(var)) takes the values of var (namely x1) and puts that in the code in a way that R thinks this is a variable (not a char). BUt I seem to be wrong. Can anyone enlighten me?

Answer 1

Personally, I like to do this with some computing on the language. For me, a combination of bquote with eval is easiest (to remember).

var <- as.symbol(var)
eval(bquote(summary(lm(y ~ .(var) + x2, data = df2))))
#Call:
#lm(formula = y ~ x1 + x2, data = df2)
#
#Residuals:
#     Min       1Q   Median       3Q      Max 
#-0.49298 -0.26248 -0.00046  0.24111  0.51988 
#
#Coefficients:
#            Estimate Std. Error t value Pr(>|t|)    
#(Intercept)  0.50244    0.02480  20.258   <2e-16 ***
#x1          -0.01468    0.03161  -0.464    0.643    
#x2          -0.01635    0.03227  -0.507    0.612    
#---
#Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
#Residual standard error: 0.2878 on 997 degrees of freedom
#Multiple R-squared:  0.0004708,    Adjusted R-squared:  -0.001534 
#F-statistic: 0.2348 on 2 and 997 DF,  p-value: 0.7908

I find this superior to any approach that doesn't show the same call as summary(lm(y ~ x1+x2, data=df2)).

Answer 2

The bang-bang operator !! only works with "tidy" functions. It's not a part of the core R language. A base R function like lm() has no idea how to expand such operators. Instead, you need to wrap those in functions that can do the expansion. rlang::expr is one such example

rlang::expr(summary(lm(y ~ !!rlang::sym(var) + x2, data=df2)))
# summary(lm(y ~ x1 + x2, data = df2))

Then you need to use rlang::eval_tidy to actually evaluate it

rlang::eval_tidy(rlang::expr(summary(lm(y ~ !!rlang::sym(var) + x2, data=df2))))

# Call:
# lm(formula = y ~ x1 + x2, data = df2)
# 
# Residuals:
#     Min       1Q   Median       3Q      Max 
# -0.49178 -0.25482  0.00027  0.24566  0.50730 
# 
# Coefficients:
#               Estimate Std. Error t value Pr(>|t|)    
# (Intercept)  0.4953683  0.0242949  20.390   <2e-16 ***
# x1          -0.0006298  0.0314389  -0.020    0.984    
# x2          -0.0052848  0.0318073  -0.166    0.868    
# ---
# Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# Residual standard error: 0.2882 on 997 degrees of freedom
# Multiple R-squared:  2.796e-05,   Adjusted R-squared:  -0.001978 
# F-statistic: 0.01394 on 2 and 997 DF,  p-value: 0.9862

You can see this version preserves the expanded formula in the model object.