Dynamic variable names in R regressions

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?


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)).

How can I loop through a list of strings as variables in a model?, The code below gives an example of how to loop through a list of variable names as strings and use the variable name in a model. A single string is generated  in other words i want to create separate variable name each time the for loop executes. NOTE: it is preferred to have variable names to be in the name of "project"columns values. r variables dynamic-data


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.

to: Create a Sequence of Numbered Variable Names with a , Generates sequentially numbered variable names, all starting with the same prefix, equivalent to standard R function paste0("m", 1:10) # generate a 10 x 10 data by2 6. reg(Y ~ X, Rmd="eg") Regression + R markdown file that, when knit​,  For each list of variable arguments, we want to group using the first variable and then summarise the grouped data frame by calculating the mean of the second variable. Here, dynamic argument construction really comes into account, because we programmatically construct the arguments of summarise_() , e.g. mean_mpg = mean(mpg) using string


1) Just use lm(df2) or if lm has additional columns beyond what is shown in the question but we just want to regress on x1 and x2 then

df3 <- df2[c("y", var, "x2")]
lm(df3)

The following are optional and only apply if it is important that the formula appear in the output as if it had been explicitly given. Compute the formula fo using the first line below and then run lm as in the second line:

fo <- formula(model.frame(df3))
fm <- do.call("lm", list(fo, quote(df3)))

or just run lm as in the first line below and then write the formula into it as in the second line:

fm <- lm(df3)
fm$call <- formula(model.frame(df3))

Either one gives this:

> fm
Call:
lm(formula = y ~ x1 + x2, data = df3)

Coefficients:
(Intercept)           x1           x2  
    0.44752      0.04278      0.05011  

2) character string lm accepts a character string for the formula so this also works. The fn$ causes substitution to occur in the character arguments.

library(gsubfn)

fn$lm("y ~ $var + x2", quote(df2))

or at the expense of more involved code, without gsubfn:

do.call("lm", list(sprintf("y ~ %s + x2", var), quote(df2)))

or if you don't care that the formula displays without var substituted then just:

lm(sprintf("y ~ %s + x2", var), df2)

Dynamic Documents with R and knitr, For example, linear regressions of mpg against two variables in the mtcars tags in a template, and dynamically evaluate them in the current environment. We write a template file as shown in Figure 12.10 and name it as mtcarstemplate. Variables in a data frame in R always need to have a name. To access the variable names, you can again treat a data frame like a matrix and use the function colnames () like this: > colnames (employ.data) "employee" "salary" "startdate" But, in fact, this is taking the long way around.


Parametric variable names and dplyr – Win Vector LLC, What is Chapter 8 of Practical Data Science with R? Site re-Org · Linear and Logistic Regression in Practical Data Science with R 2nd Edition  $\begingroup$ @mpiktas In R, it is more natural to make a list, set its names parameter and later either just use it, attach it or convert it into an environment with list2env and eval inside it. With no loops, parse or other ugly stuff. $\endgroup$ – user88 May 16 '11 at 10:38


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