How do I add select function to select colmuns that I wanted in the glance function?

Question

How do I add "select()" after "glance()" to select values "r.squared" ?

Answer 1

There are several methods you can use; here is an example:

library(tidyverse)
library(broom)
#> Warning: package 'broom' was built under R version 4.1.2

data_adult <-read.csv("https://raw.githubusercontent.com/guru99-edu/R-Programming/master/adult.csv")
glimpse(data_adult)
#> Rows: 48,842
#> Columns: 10
#> $ x               <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,…
#> $ age             <int> 25, 38, 28, 44, 18, 34, 29, 63, 24, 55, 65, 36, 26, 58…
#> $ workclass       <chr> "Private", "Private", "Local-gov", "Private", "?", "Pr…
#> $ education       <chr> "11th", "HS-grad", "Assoc-acdm", "Some-college", "Some…
#> $ educational.num <int> 7, 9, 12, 10, 10, 6, 9, 15, 10, 4, 9, 13, 9, 9, 9, 14,…
#> $ marital.status  <chr> "Never-married", "Married-civ-spouse", "Married-civ-sp…
#> $ race            <chr> "Black", "White", "White", "Black", "White", "White", …
#> $ gender          <chr> "Male", "Male", "Male", "Male", "Female", "Male", "Mal…
#> $ hours.per.week  <int> 40, 50, 40, 40, 30, 30, 40, 32, 40, 10, 40, 40, 39, 35…
#> $ income          <chr> "<=50K", "<=50K", ">50K", ">50K", "<=50K", "<=50K", "<…

data_adult_rescale <- data_adult %>%
  mutate(across(where(is.numeric), ~scale(.x))) %>%
  mutate(
    education = factor(
      ifelse(
        education == "Preschool" |
          education == "10th" |
          education == "11th" |
          education == "12th" |
          education == "1st-4th" |
          education == "5th-6th" |
          education == "7th-8th" |
          education == "9th",
        "dropout",
        ifelse(
          education == "HS-grad",
          "HighGrad",
          ifelse(
            education == "Some-college" |
              education == "Assoc-acdm" | education == "Assoc-voc",
            "Community",
            ifelse(education == "Bachelors", "Bachelors", 
                   ifelse(education == "Masters" | education == "Prof-school", "Master", "PhD")
                   )
            )
          )
        )
      )
    ) %>%
  mutate(marital.status = factor(
    ifelse(
      marital.status == "Never-married" |
        marital.status == "Married-spouse-absent",
      "Not_married",
      ifelse(
        marital.status == "Married-AF-spouse" |
          marital.status == "Married-civ-spouse",
        "Married",
        ifelse(
          marital.status == "Separated" |
            marital.status == "Divorced",
          "Separated",
          "Widow"
        )
      )
    )
  )) %>%
  mutate(workclass = ifelse(workclass == "?", NA, workclass)) %>%
  mutate(income = factor(income)) %>%
  select(-x)

create_train_test <- function(data, size = 0.8, train = TRUE) {
  n_row = nrow(data)
  total_row = size * n_row
  train_sample <- 1: total_row
  if (train == TRUE) {
    return (data[train_sample, ])
  } else {
    return (data[-train_sample, ])
  }
}

data_train <- create_train_test(data_adult_rescale, 0.8, train = TRUE)
data_test <- create_train_test(data_adult_rescale, 0.8, train = FALSE)

logit <- glm(income ~ ., data = data_train, family = 'binomial')
summary(logit)
#> 
#> Call:
#> glm(formula = income ~ ., family = "binomial", data = data_train)
#> 
#> Deviance Residuals: 
#>     Min       1Q   Median       3Q      Max  
#> -2.6985  -0.5935  -0.2641  -0.0702   3.1709  
#> 
#> Coefficients:
#>                            Estimate Std. Error z value Pr(>|z|)    
#> (Intercept)                0.023398   0.215881   0.108  0.91369    
#> age                        0.410256   0.018996  21.597  < 2e-16 ***
#> workclassLocal-gov        -0.624603   0.093467  -6.683 2.35e-11 ***
#> workclassNever-worked     -6.956721  72.524856  -0.096  0.92358    
#> workclassPrivate          -0.515633   0.078443  -6.573 4.92e-11 ***
#> workclassSelf-emp-inc     -0.064390   0.102062  -0.631  0.52811    
#> workclassSelf-emp-not-inc -1.095943   0.090596 -12.097  < 2e-16 ***
#> workclassState-gov        -0.811879   0.105447  -7.699 1.37e-14 ***
#> workclassWithout-pay      -1.077321   0.857160  -1.257  0.20881    
#> educationCommunity        -0.454462   0.081759  -5.559 2.72e-08 ***
#> educationdropout          -1.057308   0.210564  -5.021 5.13e-07 ***
#> educationHighGrad         -0.691164   0.116868  -5.914 3.34e-09 ***
#> educationMaster            0.348331   0.067133   5.189 2.12e-07 ***
#> educationPhD               0.426371   0.154295   2.763  0.00572 ** 
#> educational.num            0.570351   0.070057   8.141 3.91e-16 ***
#> marital.statusNot_married -2.503252   0.050547 -49.524  < 2e-16 ***
#> marital.statusSeparated   -2.141204   0.053698 -39.875  < 2e-16 ***
#> marital.statusWidow       -2.202177   0.123572 -17.821  < 2e-16 ***
#> raceAsian-Pac-Islander     0.103824   0.201573   0.515  0.60651    
#> raceBlack                  0.092367   0.191678   0.482  0.62989    
#> raceOther                 -0.008654   0.275873  -0.031  0.97498    
#> raceWhite                  0.370077   0.182859   2.024  0.04299 *  
#> genderMale                 0.122433   0.042441   2.885  0.00392 ** 
#> hours.per.week             0.374525   0.017029  21.993  < 2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for binomial family taken to be 1)
#> 
#>     Null deviance: 41133  on 36834  degrees of freedom
#> Residual deviance: 27593  on 36811  degrees of freedom
#>   (2238 observations deleted due to missingness)
#> AIC: 27641
#> 
#> Number of Fisher Scoring iterations: 10
glance(logit)
#> # A tibble: 1 × 8
#>   null.deviance df.null  logLik    AIC    BIC deviance df.residual  nobs
#>           <dbl>   <int>   <dbl>  <dbl>  <dbl>    <dbl>       <int> <int>
#> 1        41133.   36834 -13797. 27641. 27846.   27593.       36811 36835

# To get AIC as a tibble
glance(logit) %>% select(AIC)
#> # A tibble: 1 × 1
#>      AIC
#>    <dbl>
#> 1 27641.

# To get AIC as a vector
glance(logit)$AIC
#> [1] 27641.18

glance(logit)[[4]]
#> [1] 27641.18

^{Created on 2022-02-28 by the reprex package (v2.0.1)}

Obviously you would replace "AIC" with "r.squared" in your case. Does that solve your problem?