Introduction

The tidycat package includes the tidy_categorical() function to expand broom::tidy() outputs for categorical parameter estimates.

Installation

You can install the released version of tidycat from CRAN with:

install.packages("tidycat")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("guyabel/tidycat")

Additional columns for categorical parameter estimates

The tidy() function in the broom package takes the messy output of built-in functions in R, such as lm(), and turns them into tidy data frames.

library(dplyr)
library(broom)
m0 <- esoph %>%
   mutate_if(is.factor, ~factor(., ordered = FALSE)) %>%
   glm(cbind(ncases, ncontrols) ~ agegp + tobgp + alcgp, data = ., family = binomial())
# tidy
tidy(m0)
#> # A tibble: 12 x 5
#>    term        estimate std.error statistic  p.value
#>    <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#>  1 (Intercept)   -6.90      1.09      -6.35 2.16e-10
#>  2 agegp35-44     1.98      1.10       1.79 7.28e- 2
#>  3 agegp45-54     3.78      1.07       3.54 4.07e- 4
#>  4 agegp55-64     4.34      1.07       4.07 4.69e- 5
#>  5 agegp65-74     4.90      1.08       4.55 5.39e- 6
#>  6 agegp75+       4.83      1.12       4.30 1.67e- 5
#>  7 tobgp10-19     0.438     0.228      1.92 5.50e- 2
#>  8 tobgp20-29     0.513     0.273      1.88 6.04e- 2
#>  9 tobgp30+       1.64      0.344      4.77 1.85e- 6
#> 10 alcgp40-79     1.43      0.250      5.74 9.63e- 9
#> 11 alcgp80-119    1.98      0.285      6.96 3.51e-12
#> 12 alcgp120+      3.60      0.385      9.36 8.19e-21

Note: Currently ordered factor not supported in tidycat, hence their removal in mutate_if() above

The tidy_categorical() function adds further columns (variable, level and effect) to the broom::tidy() output to help manage categorical variables

library(tidycat)
m0 %>%
  tidy() %>%
  tidy_categorical(m = m0, include_reference =  FALSE)
#> # A tibble: 12 x 8
#>    term        estimate std.error statistic  p.value variable    level    effect
#>    <chr>          <dbl>     <dbl>     <dbl>    <dbl> <chr>       <fct>    <chr> 
#>  1 (Intercept)   -6.90      1.09      -6.35 2.16e-10 (Intercept) (Interc~ main  
#>  2 agegp35-44     1.98      1.10       1.79 7.28e- 2 agegp       35-44    main  
#>  3 agegp45-54     3.78      1.07       3.54 4.07e- 4 agegp       45-54    main  
#>  4 agegp55-64     4.34      1.07       4.07 4.69e- 5 agegp       55-64    main  
#>  5 agegp65-74     4.90      1.08       4.55 5.39e- 6 agegp       65-74    main  
#>  6 agegp75+       4.83      1.12       4.30 1.67e- 5 agegp       75+      main  
#>  7 tobgp10-19     0.438     0.228      1.92 5.50e- 2 tobgp       10-19    main  
#>  8 tobgp20-29     0.513     0.273      1.88 6.04e- 2 tobgp       20-29    main  
#>  9 tobgp30+       1.64      0.344      4.77 1.85e- 6 tobgp       30+      main  
#> 10 alcgp40-79     1.43      0.250      5.74 9.63e- 9 alcgp       40-79    main  
#> 11 alcgp80-119    1.98      0.285      6.96 3.51e-12 alcgp       80-119   main  
#> 12 alcgp120+      3.60      0.385      9.36 8.19e-21 alcgp       120+     main

Additional rows for reference categories

Include additional rows for reference category terms and a column to indicate their location by setting include_reference = TRUE (default). Setting exponentiate = TRUE ensures the parameter estimates in the reference group are set to one instead of zero (even odds in the logistic regression example below).

m0 %>%
  tidy(exponentiate = TRUE) %>%
  tidy_categorical(m = m0, exponentiate = TRUE, reference_label = "Baseline") %>%
  select(-statistic, -p.value)
#> # A tibble: 15 x 7
#>    term         estimate std.error variable    level       effect reference   
#>    <chr>           <dbl>     <dbl> <chr>       <fct>       <chr>  <chr>       
#>  1 (Intercept)   0.00101     1.09  (Intercept) (Intercept) main   Non-Baseline
#>  2 <NA>          1           1     agegp       25-34       main   Baseline    
#>  3 agegp35-44    7.25        1.10  agegp       35-44       main   Non-Baseline
#>  4 agegp45-54   43.7         1.07  agegp       45-54       main   Non-Baseline
#>  5 agegp55-64   76.3         1.07  agegp       55-64       main   Non-Baseline
#>  6 agegp65-74  134.          1.08  agegp       65-74       main   Non-Baseline
#>  7 agegp75+    125.          1.12  agegp       75+         main   Non-Baseline
#>  8 <NA>          1           1     tobgp       0-9g/day    main   Baseline    
#>  9 tobgp10-19    1.55        0.228 tobgp       10-19       main   Non-Baseline
#> 10 tobgp20-29    1.67        0.273 tobgp       20-29       main   Non-Baseline
#> 11 tobgp30+      5.16        0.344 tobgp       30+         main   Non-Baseline
#> 12 <NA>          1           1     alcgp       0-39g/day   main   Baseline    
#> 13 alcgp40-79    4.20        0.250 alcgp       40-79       main   Non-Baseline
#> 14 alcgp80-119   7.25        0.285 alcgp       80-119      main   Non-Baseline
#> 15 alcgp120+    36.7         0.385 alcgp       120+        main   Non-Baseline

Standard coefficient plots

The results from broom::tidy() can be used to quickly plot estimated coefficients and their confidence intervals.

# store parameter estimates and confidence intervals (except for the intercept)
d0 <- m0 %>%
  tidy(conf.int = TRUE) %>%
  slice(-1)
d0
#> # A tibble: 11 x 7
#>    term        estimate std.error statistic  p.value conf.low conf.high
#>    <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
#>  1 agegp35-44     1.98      1.10       1.79 7.28e- 2   0.184      4.95 
#>  2 agegp45-54     3.78      1.07       3.54 4.07e- 4   2.10       6.71 
#>  3 agegp55-64     4.34      1.07       4.07 4.69e- 5   2.67       7.27 
#>  4 agegp65-74     4.90      1.08       4.55 5.39e- 6   3.21       7.84 
#>  5 agegp75+       4.83      1.12       4.30 1.67e- 5   3.00       7.82 
#>  6 tobgp10-19     0.438     0.228      1.92 5.50e- 2  -0.0116     0.885
#>  7 tobgp20-29     0.513     0.273      1.88 6.04e- 2  -0.0290     1.04 
#>  8 tobgp30+       1.64      0.344      4.77 1.85e- 6   0.967      2.32 
#>  9 alcgp40-79     1.43      0.250      5.74 9.63e- 9   0.955      1.94 
#> 10 alcgp80-119    1.98      0.285      6.96 3.51e-12   1.43       2.55 
#> 11 alcgp120+      3.60      0.385      9.36 8.19e-21   2.87       4.39

library(ggplot2)
library(tidyr)
ggplot(data = d0,
        mapping = aes(x = term, y = estimate, ymin = conf.low, ymax = conf.high)) +
   coord_flip() +
   geom_hline(yintercept = 0, linetype = "dashed") +
   geom_pointrange()

Enhanced coefficient plots

The additional columns from tidy_categorical() can be used to group together terms from the same categorical variable by setting colour = variable

d0 <- m0 %>%
  tidy(conf.int = TRUE) %>%
  tidy_categorical(m = m0, include_reference = FALSE) %>%
  slice(-1)

d0 %>%
  select(-(3:5))
#> # A tibble: 11 x 7
#>    term        estimate conf.low conf.high variable level  effect
#>    <chr>          <dbl>    <dbl>     <dbl> <chr>    <fct>  <chr> 
#>  1 agegp35-44     1.98    0.184      4.95  agegp    35-44  main  
#>  2 agegp45-54     3.78    2.10       6.71  agegp    45-54  main  
#>  3 agegp55-64     4.34    2.67       7.27  agegp    55-64  main  
#>  4 agegp65-74     4.90    3.21       7.84  agegp    65-74  main  
#>  5 agegp75+       4.83    3.00       7.82  agegp    75+    main  
#>  6 tobgp10-19     0.438  -0.0116     0.885 tobgp    10-19  main  
#>  7 tobgp20-29     0.513  -0.0290     1.04  tobgp    20-29  main  
#>  8 tobgp30+       1.64    0.967      2.32  tobgp    30+    main  
#>  9 alcgp40-79     1.43    0.955      1.94  alcgp    40-79  main  
#> 10 alcgp80-119    1.98    1.43       2.55  alcgp    80-119 main  
#> 11 alcgp120+      3.60    2.87       4.39  alcgp    120+   main

ggplot(data = d0,
        mapping = aes(x = term, y = estimate, ymin = conf.low, ymax = conf.high,
                      colour = variable)) +
   coord_flip() +
   geom_hline(yintercept = 0, linetype = "dashed") +
   geom_pointrange()

The additional rows from tidy_categorical() can be used to include the reference categories in a coefficient plot, allowing the reader to better grasp the meaning of the parameter estimates in each categorical variable. Using ggforce::facet_col() the terms of each variable can be separated to further improve the presentation of the coefficient plot.

d0 <- m0 %>%
  tidy(conf.int = TRUE) %>%
  tidy_categorical(m = m0) %>%
  slice(-1)

d0 %>%
  select(-(3:5))
#> # A tibble: 14 x 8
#>    term        estimate conf.low conf.high variable level     effect reference  
#>    <chr>          <dbl>    <dbl>     <dbl> <chr>    <fct>     <chr>  <chr>      
#>  1 <NA>           0       0          0     agegp    25-34     main   Baseline C~
#>  2 agegp35-44     1.98    0.184      4.95  agegp    35-44     main   Non-Baseli~
#>  3 agegp45-54     3.78    2.10       6.71  agegp    45-54     main   Non-Baseli~
#>  4 agegp55-64     4.34    2.67       7.27  agegp    55-64     main   Non-Baseli~
#>  5 agegp65-74     4.90    3.21       7.84  agegp    65-74     main   Non-Baseli~
#>  6 agegp75+       4.83    3.00       7.82  agegp    75+       main   Non-Baseli~
#>  7 <NA>           0       0          0     tobgp    0-9g/day  main   Baseline C~
#>  8 tobgp10-19     0.438  -0.0116     0.885 tobgp    10-19     main   Non-Baseli~
#>  9 tobgp20-29     0.513  -0.0290     1.04  tobgp    20-29     main   Non-Baseli~
#> 10 tobgp30+       1.64    0.967      2.32  tobgp    30+       main   Non-Baseli~
#> 11 <NA>           0       0          0     alcgp    0-39g/day main   Baseline C~
#> 12 alcgp40-79     1.43    0.955      1.94  alcgp    40-79     main   Non-Baseli~
#> 13 alcgp80-119    1.98    1.43       2.55  alcgp    80-119    main   Non-Baseli~
#> 14 alcgp120+      3.60    2.87       4.39  alcgp    120+      main   Non-Baseli~

library(ggforce)
ggplot(data = d0,
        mapping = aes(x = level, y = estimate, colour = reference,
                      ymin = conf.low, ymax = conf.high)) +
   facet_col(facets = vars(variable), scales = "free_y", space = "free") +
   coord_flip() +
   geom_hline(yintercept = 0, linetype = "dashed") +
   geom_pointrange()

Note the switch of the x aesthetic to the level column rather than term.

Alternatively, horizontal plots can be obtained using ggforce::facet_row() and loosing coord_flip();

ggplot(data = d0,
      mapping = aes(x = level, y = estimate,
                    ymin = conf.low, ymax = conf.high,
                    colour = reference)) +
 facet_row(facets = vars(variable), scales = "free_x", space = "free") +
 geom_hline(yintercept = 0, linetype = "dashed") +
 geom_pointrange() +
 theme(axis.text.x = element_text(angle = 45, hjust = 1))

Interactions

Models with interactions can also be handled in tidy_categorical(). Using the mtcars data we can create three types of interactions (between two numeric variables, between a numeric variable and categorical variable and between two categorical variables)

m1 <- mtcars %>%
  mutate(engine = recode_factor(vs, `0` = "straight", `1` = "V-shaped"),
         transmission = recode_factor(am, `0` = "automatic", `1` = "manual")) %>%
  lm(mpg ~ as.factor(cyl) + wt * hp + wt * transmission + engine * transmission , data = .)

tidy(m1)
#> # A tibble: 10 x 5
#>    term                              estimate std.error statistic p.value
#>    <chr>                                <dbl>     <dbl>     <dbl>   <dbl>
#>  1 (Intercept)                        35.5      12.3        2.89  0.00843
#>  2 as.factor(cyl)6                    -1.03      1.76      -0.585 0.565  
#>  3 as.factor(cyl)8                     2.01      4.09       0.492 0.628  
#>  4 wt                                 -4.65      3.55      -1.31  0.203  
#>  5 hp                                 -0.0731    0.0577    -1.27  0.218  
#>  6 transmissionmanual                 10.7      10.0        1.07  0.296  
#>  7 engineV-shaped                      3.74      3.21       1.16  0.257  
#>  8 wt:hp                               0.0134    0.0162     0.828 0.416  
#>  9 wt:transmissionmanual              -2.63      2.83      -0.930 0.362  
#> 10 transmissionmanual:engineV-shaped  -3.16      3.76      -0.842 0.409

Setting n_level = TRUE creates an additional column to monitor the number of observations in each of level of the categorical variables, including interaction terms in the model:

d1 <- m1 %>%
  tidy(conf.int = TRUE) %>%
  tidy_categorical(m = m1, n_level = TRUE) %>%
  slice(-1)

d1 %>%
  select(-(2:7))
#> # A tibble: 16 x 6
#>    term                              variable   level  effect reference  n_level
#>    <chr>                             <chr>      <fct>  <chr>  <chr>        <dbl>
#>  1 <NA>                              as.factor~ 4      main   Baseline ~      11
#>  2 as.factor(cyl)6                   as.factor~ 6      main   Non-Basel~       7
#>  3 as.factor(cyl)8                   as.factor~ 8      main   Non-Basel~      14
#>  4 wt                                wt         wt     main   Non-Basel~      NA
#>  5 hp                                hp         hp     main   Non-Basel~      NA
#>  6 <NA>                              transmiss~ autom~ main   Baseline ~      19
#>  7 transmissionmanual                transmiss~ manual main   Non-Basel~      13
#>  8 <NA>                              engine     strai~ main   Baseline ~      18
#>  9 engineV-shaped                    engine     V-sha~ main   Non-Basel~      14
#> 10 wt:hp                             wt:hp      wt:hp  inter~ Non-Basel~      NA
#> 11 <NA>                              wt:transm~ autom~ inter~ Baseline ~      19
#> 12 wt:transmissionmanual             wt:transm~ manual inter~ Non-Basel~      13
#> 13 <NA>                              transmiss~ autom~ inter~ Baseline ~      25
#> 14 <NA>                              transmiss~ manua~ inter~ Non-Basel~       0
#> 15 <NA>                              transmiss~ autom~ inter~ Non-Basel~       0
#> 16 transmissionmanual:engineV-shaped transmiss~ manua~ inter~ Non-Basel~       7

We can use similar plotting code as above to plot the interactions:

ggplot(data = d1,
        mapping = aes(x = level, y = estimate, colour = reference,
                      ymin = conf.low, ymax = conf.high)) +
   facet_col(facets = "variable", scales = "free_y", space = "free") +
   coord_flip() +
   geom_hline(yintercept = 0, linetype = "dashed") +
   geom_pointrange()

The empty levels can be dropped by filtering on the n_level column for categories with more than zero observations and not NA in term column.

d1 %>%
  dplyr::filter(n_level > 0 | !is.na(term)) %>%
  ggplot(mapping = aes(x = level, y = estimate, colour = reference,
                       ymin = conf.low, ymax = conf.high)) +
  facet_col(facets = "variable", scales = "free_y", space = "free") +
  coord_flip() +
  geom_hline(yintercept = 0, linetype = "dashed") +
  geom_pointrange()

Issues

If you have any trouble or suggestions please let me know by creating an issue on the tidycat Github