library(report)

model <- lm(Sepal.Length ~ Species, data = iris)
model$terms
#> Sepal.Length ~ Species
#> attr(,"variables")
#> list(Sepal.Length, Species)
#> attr(,"factors")
#>              Species
#> Sepal.Length       0
#> Species            1
#> attr(,"term.labels")
#> [1] "Species"
#> attr(,"order")
#> [1] 1
#> attr(,"intercept")
#> [1] 1
#> attr(,"response")
#> [1] 1
#> attr(,".Environment")
#> <environment: R_GlobalEnv>
#> attr(,"predvars")
#> list(Sepal.Length, Species)
#> attr(,"dataClasses")
#> Sepal.Length      Species 
#>    "numeric"     "factor"
report(model)
#> We fitted a linear model (estimated using OLS) to predict Sepal.Length with
#> Species (formula: Sepal.Length ~ Species). The model explains a statistically
#> significant and substantial proportion of variance (R2 = 0.62, F(2, 147) =
#> 119.26, p < .001, adj. R2 = 0.61). The model's intercept, corresponding to
#> Species = setosa, is at 5.01 (95% CI [4.86, 5.15], t(147) = 68.76, p < .001).
#> Within this model:
#> 
#>   - The effect of Species [versicolor] is statistically significant and positive
#> (beta = 0.93, 95% CI [0.73, 1.13], t(147) = 9.03, p < .001; Std. beta = 1.12,
#> 95% CI [0.88, 1.37])
#>   - The effect of Species [virginica] is statistically significant and positive
#> (beta = 1.58, 95% CI [1.38, 1.79], t(147) = 15.37, p < .001; Std. beta = 1.91,
#> 95% CI [1.66, 2.16])
#> 
#> Standardized parameters were obtained by fitting the model on a standardized
#> version of the dataset. 95% Confidence Intervals (CIs) and p-values were
#> computed using a Wald t-distribution approximation.

x <- 'Species'
y <- 'Sepal.Length'
model <- lm(reformulate(x, response = y), data = iris)
model$terms
#> Sepal.Length ~ Species
#> attr(,"variables")
#> list(Sepal.Length, Species)
#> attr(,"factors")
#>              Species
#> Sepal.Length       0
#> Species            1
#> attr(,"term.labels")
#> [1] "Species"
#> attr(,"order")
#> [1] 1
#> attr(,"intercept")
#> [1] 1
#> attr(,"response")
#> [1] 1
#> attr(,".Environment")
#> <environment: R_GlobalEnv>
#> attr(,"predvars")
#> list(Sepal.Length, Species)
#> attr(,"dataClasses")
#> Sepal.Length      Species 
#>    "numeric"     "factor"
report(model)
#> Error in reformulate(x, response = y): 'termlabels' must be a character vector of length at least one
#> Error: Unable to refit the model with standardized data.
#>   Try instead to standardize the data (standardize(data)) and refit the
#>   model manually.

R: sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Arch Linux

Matrix products: default
BLAS:   /usr/lib/libblas.so.3.11.0 
LAPACK: /usr/lib/liblapack.so.3.11.0

locale:
 [1] LC_CTYPE=en_CA.UTF-8       LC_NUMERIC=C               LC_TIME=en_CA.UTF-8        LC_COLLATE=en_CA.UTF-8    
 [5] LC_MONETARY=en_CA.UTF-8    LC_MESSAGES=en_CA.UTF-8    LC_PAPER=en_CA.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C       

time zone: Canada/Atlantic
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] report_0.5.7

loaded via a namespace (and not attached):
 [1] vctrs_0.6.3        cli_3.6.1          knitr_1.43         rlang_1.1.1        xfun_0.39         
 [6] processx_3.8.2     targets_1.2.2      generics_0.1.3     data.table_1.14.8  glue_1.6.2        
[11] backports_1.4.1    performance_0.10.4 parameters_0.21.1  ps_1.7.5           datawizard_0.8.0  
[16] fansi_1.0.4        tibble_3.2.1       base64url_1.4      yaml_2.3.7         lifecycle_1.0.3   
[21] insight_0.19.3     compiler_4.3.1     dplyr_1.1.2        codetools_0.2-19   igraph_1.5.0      
[26] fs_1.6.2           pkgconfig_2.0.3    rstudioapi_0.14    digest_0.6.32      R6_2.5.1          
[31] effectsize_0.8.5   reprex_2.0.2       tidyselect_1.2.0   utf8_1.2.3         pillar_1.9.0      
[36] callr_3.7.3        magrittr_2.0.3     tools_4.3.1        withr_2.5.0        bayestestR_0.13.1