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Introduction to panelsummary

Source: vignettes/panelsummary.Rmd
panelsummary.Rmd

panelsummary consists of one main function, panelsummary::panelsummary, to provide a simple, yet customizable way to create a regression table with multiple panels. As of this writing, the package is intended for use with regressions of class lm and fixest, with an emphasis on the latter. There are more classes in the pipeline, and you can always check panelsummary::models_supported for an exhaustive list of models supported. However, it is likely that a regression package will work if modelsummary supports it, although some functionality may be limited (e.g., mean_dependent argument will only work with fixest regressions).

To begin, load in the panelsummary package.

Motivating Example

To motivate the power of panelsummary, consider a model using the mtcars dataset:

\[ Y_{i} = \beta_0 + \beta_1hp_i + \beta_2cyl_i + e_i\] Where \(Y_i\) is the dependent variable, which can be either \(mpg_i\) or \(disp\) of car \(i\), \(hp_i\) is the horsepower, \(cyl_i\) is the number of cylinders, and \(e_i\) is the error term. To estimate this model with both of the dependent variables, the following code is provided:

## estimating the model with two depedent variables:
## 1) mpg and 2) disp
mpg_1 <- lm(mpg ~ hp + cyl, data = mtcars)
disp_1 <- lm(disp ~ hp + cyl, data = mtcars)

While one option to present the regression coefficients in a table would be to create a separate regression table for each, there is a more concise, and cleaner way to present this information. This is where panelsummary is most useful. With a simple pass of panelsummary::panelsummary, a beautiful regression table is created without any additional editing, and can immediately be viewed in the RStudio Viewer panel.

## creating a beautiful regression table with panelsummary
panelsummary(mpg_1, disp_1, 
             panel_labels = c("Panel A: MPG", "Panel B: Displacement"))
Panel A: MPG
(Intercept) 36.908
(2.191)
hp -0.019
(0.015)
cyl -2.265
(0.576)
Num.Obs. 32
R2 0.741
R2 Adj. 0.723
AIC 169.6
BIC 175.4
Log.Lik. -80.781
RMSE 3.02
Panel B: Displacement
(Intercept) -144.569
(37.652)
hp 0.236
(0.258)
cyl 55.063
(9.898)
Num.Obs. 32
R2 0.819
R2 Adj. 0.806
AIC 351.6
BIC 357.4
Log.Lik. -171.793
RMSE 51.91

Note that the number of arguments passed into ... is how the delineation of panels is determined. Importantly, the length of the character vector passed into panel_labels must match the number of arguments passed into .... This ensures that each panel has its own label.

Cleaning Variable Names

Behind the scenes, panelsummary uses modelsummary to help create the regression tables. The benefit of this is the ability to use coef_map argument (which is passed through to modelsummary::modelsummary) to clean variable names. Recall that to use coef_map, the syntax is c("old_name" = "new_name"):

panelsummary(mpg_1, disp_1, 
             coef_map = c("hp" = "Horse Power",
                          "cyl" = "Cylinder"),
             panel_labels = c("Panel A: MPG", "Panel B: Displacement"))
Panel A: MPG
Horse Power -0.019
(0.015)
Cylinder -2.265
(0.576)
Num.Obs. 32
R2 0.741
R2 Adj. 0.723
AIC 169.6
BIC 175.4
Log.Lik. -80.781
RMSE 3.02
Panel B: Displacement
Horse Power 0.236
(0.258)
Cylinder 55.063
(9.898)
Num.Obs. 32
R2 0.819
R2 Adj. 0.806
AIC 351.6
BIC 357.4
Log.Lik. -171.793
RMSE 51.91

For more information on coef_map, see the modelsummary website.

Excluding Goodness-of-Fit Statistics

Similar to cleaning names, panelsummary also supports the gof_map and gof_omit arguments from modelsummary. In the following, gof_map will be used to change the name of “Num.Obs” to “Observations” and “F” to “F-stat”, while removing the other goodness-of-fit statistics:

## mapping the goodness of fit statistics to new names - see modelsummary for more details
gm <- tibble::tribble(
        ~raw,      ~clean,          ~fmt,  ~omit,
        "nobs",      "Observations",     0,  FALSE,
        "F", "F-stat",               3,  FALSE
)
panelsummary(mpg_1, disp_1, 
             coef_map = c("hp" = "Horse Power",
                          "cyl" = "Cylinder"),
             gof_map = gm,
             panel_labels = c("Panel A: MPG", "Panel B: Displacement"))
Panel A: MPG
Horse Power -0.019
(0.015)
Cylinder -2.265
(0.576)
Observations 32
Panel B: Displacement
Horse Power 0.236
(0.258)
Cylinder 55.063
(9.898)
Observations 32

For more information on how to use gof_map, see the modelsummary website.

Adding Additional Models to the Table

It is simple to add additional models to each panel in the table. This is most useful when presenting robustness of estimates with a variety of different explanatory variables. As an example, consider three more models with mpg as the dependent variable:

## creating two additional models for the first panel 
mpg_2 <- lm(mpg ~ hp + cyl + drat, data = mtcars)
mpg_3 <- lm(mpg ~hp + cyl + drat + wt, data = mtcars)

To add these models to Panel A, simply replace mpg_1 with list(mpg_1, mpg_2, mpg_3) in the original code:

panelsummary(list(mpg_1, mpg_2, mpg_3), disp_1, 
             coef_map = c("hp" = "Horse Power",
                          "cyl" = "Cylinder",
                          "drat" = "Rear Axle Ratio",
                          "wt" = "Weight (1000lbs)"),
             gof_map = gm,
             panel_labels = c("Panel A: MPG", "Panel B: Displacement"))
Panel A: MPG
Horse Power -0.019 -0.029 -0.021
(0.015) (0.015) (0.013)
Cylinder -2.265 -1.361 -0.762
(0.576) (0.735) (0.635)
Rear Axle Ratio 2.841 0.818
(1.522) (1.387)
Weight (1000lbs) -2.973
(0.818)
Observations 32 32 32
Panel B: Displacement
Horse Power 0.236
(0.258)
Cylinder 55.063
(9.898)
Observations 32

Adding Additional Panels to the Table

As alluded to, the number of arguments passed into ... will determine the number of panels created in the table. Hence, simply add another argument to ..., and a corresponding label to panel_labels:

panelsummary(list(mpg_1, mpg_2, mpg_3), disp_1, list(mpg_1, mpg_2),
             coef_map = c("hp" = "Horse Power",
                          "cyl" = "Cylinder",
                          "drat" = "Rear Axle Ratio",
                          "wt" = "Weight (1000lbs)"),
             gof_map = gm,
             panel_labels = c("Panel A: MPG", "Panel B: Displacement", "Panel C: Demonstration of Additional Panel"))
Panel A: MPG
Horse Power -0.019 -0.029 -0.021
(0.015) (0.015) (0.013)
Cylinder -2.265 -1.361 -0.762
(0.576) (0.735) (0.635)
Rear Axle Ratio 2.841 0.818
(1.522) (1.387)
Weight (1000lbs) -2.973
(0.818)
Observations 32 32 32
Panel B: Displacement
Horse Power 0.236
(0.258)
Cylinder 55.063
(9.898)
Observations 32
Panel C: Demonstration of Additional Panel
Horse Power -0.019 -0.029
(0.015) (0.015)
Cylinder -2.265 -1.361
(0.576) (0.735)
Rear Axle Ratio 2.841
(1.522)
Observations 32 32

At this time, panelsummary will only support up to five panels. This is done intentionally to discourage an overly long table.

Adding Significance-Stars

To add significance stars, simply set the stars argument to TRUE. By default, panelsummary uses the following convention (symbol=pvalue): +=.1, *=.05, **=.01, ***=0.001.

However, you can also change the significance stars by passing in a vector of corresponding significance:

## change the significance stars to match economic convention
table_stars <- panelsummary(list(mpg_1, mpg_2, mpg_3), disp_1, 
             coef_map = c("hp" = "Horse Power",
                          "cyl" = "Cylinder",
                          "drat" = "Rear Axle Ratio",
                          "wt" = "Weight (1000lbs)"),
             gof_map = gm,
             stars = c('*' = .1, '**' = .05, '***' = .01),
             panel_labels = c("Panel A: MPG", "Panel B: Displacement"))
table_stars
Panel A: MPG
Horse Power -0.019 -0.029* -0.021
(0.015) (0.015) (0.013)
Cylinder -2.265*** -1.361* -0.762
(0.576) (0.735) (0.635)
Rear Axle Ratio 2.841* 0.818
(1.522) (1.387)
Weight (1000lbs) -2.973***
(0.818)
Observations 32 32 32
Panel B: Displacement
Horse Power 0.236
(0.258)
Cylinder 55.063***
(9.898)
Observations 32

However, you if you plan on using the economics convention of *=.1, **=.05, ***=.01, you can simply pass in the word “econ”.

Customizing With kableExtra

When panelsummary::panelsummary is executed, a kableExtra object is created. The benefit of this feature is that all of kableExtra’s customizing functions are ready to pipe into. For instance, suppose a table calls for a new theme, a header above the model numbers, a footnote denoting significance:

library(kableExtra)

## customizing the table with kableExtra
table_stars |> 
  kable_classic(full_width = F, html_font = "Cambria") |> 
  add_header_above(c(" " = 1, "Models Using mtcars" = 3)) |> 
  footnote(list("* p < 0.1, ** p < 0.05, *** p < 0.01",
                "Customizations done using kableExtra package"))
Models Using mtcars
Panel A: MPG
Horse Power -0.019 -0.029* -0.021
(0.015) (0.015) (0.013)
Cylinder -2.265*** -1.361* -0.762
(0.576) (0.735) (0.635)
Rear Axle Ratio 2.841* 0.818
(1.522) (1.387)
Weight (1000lbs) -2.973***
(0.818)
Observations 32 32 32
Panel B: Displacement
Horse Power 0.236
(0.258)
Cylinder 55.063***
(9.898)
Observations 32
Note:
* p < 0.1, ** p < 0.05, *** p < 0.01
Customizations done using kableExtra package

Renaming Colnames

By default, panelsummary names the first column with an empty space, and each subsequent column with the numbers (1), (2), (3)…etc. To rename the columns, use the colnames argument and pass in a character vector which matches the length of the columns in the table. In this particular example, the column names are set to “Column 1”, “Column 2”, “Column 3”, and “Column 4”. Note that no element in the vector may be empty and hence, a single whitespace character is needed to give a blank look (not shown here):

panelsummary(list(mpg_1, mpg_2, mpg_3), disp_1, 
             coef_map = c("hp" = "Horse Power",
                          "cyl" = "Cylinder",
                          "drat" = "Rear Axle Ratio",
                          "wt" = "Weight (1000lbs)"),
             gof_map = gm,
             stars =c('*' = .1, '**' = .05, '***' = .01),
             panel_labels = c("Panel A: MPG", "Panel B: Displacement"),
             colnames = c("Column 1", "Column 2", "Column 3", "Column 4")) |> 
  kable_classic(full_width = F, html_font = "Cambria")
Column 1 Column 2 Column 3 Column 4
Panel A: MPG
Horse Power -0.019 -0.029* -0.021
(0.015) (0.015) (0.013)
Cylinder -2.265*** -1.361* -0.762
(0.576) (0.735) (0.635)
Rear Axle Ratio 2.841* 0.818
(1.522) (1.387)
Weight (1000lbs) -2.973***
(0.818)
Observations 32 32 32
Panel B: Displacement
Horse Power 0.236
(0.258)
Cylinder 55.063***
(9.898)
Observations 32

Italics/Bolds/Horizontal Lines

The labels passed into panel_labels can be changed to bold and/or italics using the bold and italic arguments. Furthermore, horizontal lines can be added below each label using the hline_after argument.

Pretty Numbers

Setting the pretty_num argument to TRUE will give comma-separated numbers. For instance, default printing of the number one-thousand is “1000”, while with pretty_num = TRUE, this would be “1,000”.

Output

The output is controlled using the format argument. By default, a kableExtra object is created which works seamlessly with Rmarkdown and knits automatically to LaTeX and HTML. For users that prefer to use their own LaTeX software over Rmarkdown, the format option can be switched to “latex” to give the corresponding LaTeX code for the table. Note: the tables are automatically set to booktabs style and therefore the booktabs LaTeX package is necessary (usepackage{booktabs}) in your tex file if the LaTeX output is copy and pasted.

Use with fixest

The panelsummary package works best with the fixest package to fully take advantage of its offerings. See the Using panelsummary with fixest vignette. Some of the capabilities include:

  • Collapsing fixed effects
  • Including the mean of the dependent variable.

Adding rows to a panelsummary table

Adding custom rows requires a different workflow rather than simply calling panelsummary::panelsummary, although it is still quite simple. See the Adding Rows to a panelsummary Table vignette for a detailed example.