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Furniture

The furniture R package contains table1 for publication-ready simple and stratified descriptive statistics, tableC for publication-ready correlation matrixes, and other tables #rstats

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/learn @TysonStanley/Furniture
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README

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furniture: v1.11.0 <img src="man/figures/furniture_hex_v2_full.png" align="right" width="40%" height="40%" />

The furniture R package contains functions to help with data cleaning/tidying (e.g., washer(), rowmeans(), rowsums()), exploratory data analysis and reporting (e.g., table1(), tableC(), tableF()). It currently contains eight main functions:

  1. table1() : gives a well-formatted table for academic publication of descriptive statistics. Very useful for quick analyses as well. Notably, table1() now works with dplyr::group_by().
  2. tableC() : gives a well-formatted table of correlations.
  3. tableF() : provides a thorough frequency table for quick checks of the levels of a variable.
  4. washer() : changes several values in a variable (very useful for changing place holder values to missing).
  5. long() : is a wrapper of stats::reshape(), takes the data from wide to long format (long is often the tidy version of the data), works well with the tidyverse, and can handle unbalanced multilevel data.
  6. wide() : also a wrapper of stats::reshape(), takes the data from long to wide, and like long(), works well with the tidyverse and can handle unbalanced multilevel data.
  7. rowmeans() and rowmeans.n() : tidyverse friendly versions of rowMeans(), where the rowmeans.n() function allows n number of missing
  8. rowsums() and rowsums.n() : tidyverse friendly versions of rowSums(), where the rowsums.n() function allows n number of missing

In conjunction with many other tidy tools, the package should be useful for health, behavioral, and social scientists working on quantitative research.

Installation

The latest stable build of the package can be downloaded from CRAN via:

install.packages("furniture")

You can download the developmental version via:

remotes::install_github("tysonstanley/furniture")

Using furniture

The main functions are the table*() functions (e.g., table1(), tableC(), tableF()).

library(furniture)

data("nhanes_2010")

table1(nhanes_2010,
       age, marijuana, illicit, rehab,
       splitby=~asthma,
       na.rm = FALSE)
#> 
#> ───────────────────────────────────
#>                   asthma 
#>            Yes         No         
#>            n = 251     n = 1164   
#>  age                              
#>            23.0 (3.9)  23.4 (4.0) 
#>  marijuana                        
#>     Yes    131 (52.2%) 584 (50.2%)
#>     No     97 (38.6%)  434 (37.3%)
#>     NA     23 (9.2%)   146 (12.5%)
#>  illicit                          
#>     Yes    23 (9.2%)   117 (10.1%)
#>     No     205 (81.7%) 901 (77.4%)
#>     NA     23 (9.2%)   146 (12.5%)
#>  rehab                            
#>     Yes    10 (4%)     37 (3.2%)  
#>     No     121 (48.2%) 547 (47%)  
#>     NA     120 (47.8%) 580 (49.8%)
#> ───────────────────────────────────

table1(nhanes_2010,
       age, marijuana, illicit, rehab,
       splitby=~asthma, 
       output = "text2",
       na.rm = FALSE)
#> 
#> ───────────────────────────────────
#>                   asthma 
#>            Yes         No         
#>            n = 251     n = 1164   
#>  --------- ----------- -----------
#>  age                              
#>            23.0 (3.9)  23.4 (4.0) 
#>  marijuana                        
#>     Yes    131 (52.2%) 584 (50.2%)
#>     No     97 (38.6%)  434 (37.3%)
#>     NA     23 (9.2%)   146 (12.5%)
#>  illicit                          
#>     Yes    23 (9.2%)   117 (10.1%)
#>     No     205 (81.7%) 901 (77.4%)
#>     NA     23 (9.2%)   146 (12.5%)
#>  rehab                            
#>     Yes    10 (4%)     37 (3.2%)  
#>     No     121 (48.2%) 547 (47%)  
#>     NA     120 (47.8%) 580 (49.8%)
#> ───────────────────────────────────

library(tidyverse)
nhanes_2010 %>%
  group_by(asthma) %>%
  table1(age, marijuana, illicit, rehab,
         output = "text2",
         na.rm = FALSE)
#> 
#> ───────────────────────────────────
#>                   asthma 
#>            Yes         No         
#>            n = 251     n = 1164   
#>  --------- ----------- -----------
#>  age                              
#>            23.0 (3.9)  23.4 (4.0) 
#>  marijuana                        
#>     Yes    131 (52.2%) 584 (50.2%)
#>     No     97 (38.6%)  434 (37.3%)
#>     NA     23 (9.2%)   146 (12.5%)
#>  illicit                          
#>     Yes    23 (9.2%)   117 (10.1%)
#>     No     205 (81.7%) 901 (77.4%)
#>     NA     23 (9.2%)   146 (12.5%)
#>  rehab                            
#>     Yes    10 (4%)     37 (3.2%)  
#>     No     121 (48.2%) 547 (47%)  
#>     NA     120 (47.8%) 580 (49.8%)
#> ───────────────────────────────────

table1() can do bivariate significance tests as well.

library(tidyverse)
nhanes_2010 %>%
  group_by(asthma) %>%
  table1(age, marijuana, illicit, rehab,
         output = "text2",
         na.rm = FALSE,
         test = TRUE)
#> 
#> ───────────────────────────────────────────
#>                   asthma 
#>            Yes         No          P-Value
#>            n = 251     n = 1164           
#>  --------- ----------- ----------- -------
#>  age                               0.201  
#>            23.0 (3.9)  23.4 (4.0)         
#>  marijuana                         1      
#>     Yes    131 (52.2%) 584 (50.2%)        
#>     No     97 (38.6%)  434 (37.3%)        
#>     NA     23 (9.2%)   146 (12.5%)        
#>  illicit                           0.623  
#>     Yes    23 (9.2%)   117 (10.1%)        
#>     No     205 (81.7%) 901 (77.4%)        
#>     NA     23 (9.2%)   146 (12.5%)        
#>  rehab                             0.729  
#>     Yes    10 (4%)     37 (3.2%)          
#>     No     121 (48.2%) 547 (47%)          
#>     NA     120 (47.8%) 580 (49.8%)        
#> ───────────────────────────────────────────

By default it does the appropriate parametric tests. However, you can change that by setting param = FALSE (new with v 1.9.1).

library(tidyverse)
nhanes_2010 %>%
  group_by(asthma) %>%
  table1(age, marijuana, illicit, rehab,
         output = "text2",
         na.rm = FALSE,
         test = TRUE,
         param = FALSE,
         type = "condense")
#> 
#> ───────────────────────────────────────────────
#>                       asthma 
#>                Yes         No          P-Value
#>                n = 251     n = 1164           
#>  ------------- ----------- ----------- -------
#>  age           23.0 (3.9)  23.4 (4.0)  0.235  
#>  marijuana: No 97 (38.6%)  434 (37.3%) 1      
#>  illicit: No   205 (81.7%) 901 (77.4%) 0.623  
#>  rehab: No     121 (48.2%) 547 (47%)   0.729  
#> ───────────────────────────────────────────────

It can also do a total column with the stratified columns (new with v 1.9.0) with the total = TRUE argument.

nhanes_2010 %>%
  group_by(asthma) %>%
  table1(age, marijuana, illicit, rehab,
         output = "text2",
         na.rm = FALSE,
         test = TRUE,
         type = "condense",
         total = TRUE)
#> 
#> ────────────────────────────────────────────────────────────
#>                                   asthma 
#>                Total        Yes         No          P-Value
#>                n = 1417     n = 251     n = 1164           
#>  ------------- ------------ ----------- ----------- -------
#>  age           23.3 (4.0)   23.0 (3.9)  23.4 (4.0)  0.201  
#>  marijuana: No 532 (37.5%)  97 (38.6%)  434 (37.3%) 1      
#>  illicit: No   1107 (78.1%) 205 (81.7%) 901 (77.4%) 0.623  
#>  rehab: No     668 (47.1%)  121 (48.2%) 547 (47%)   0.729  
#> ────────────────────────────────────────────────────────────

It can also report the statistics in addition to the p-values.

nhanes_2010 %>%
  group_by(asthma) %>%
  table1(age, marijuana, illicit, rehab,
         output = "text2",
         na.rm = FALSE,
         test = TRUE,
         total = TRUE,
         type = "full")
#> 
#> ─────────────────────────────────────────────────────────────────────────
#>                                       asthma 
#>            Total        Yes         No          Test             P-Value
#>            n = 1417     n = 251     n = 1164                            
#>  --------- ------------ ----------- ----------- ---------------- -------
#>  age                                            T-Test: -1.28    0.201  
#>            23.3 (4.0)   23.0 (3.9)  23.4 (4.0)                          
#>  marijuana                                      Chi Square: 0    1      
#>     Yes    716 (50.5%)  131 (52.2%) 584 (50.2%)                         
#>     No     532 (37.5%)  97 (38.6%)  434 (37.3%)                         
#>     NA     169 (11.9%)  23 (9.2%)   146 (12.5%)                         
#>  illicit                                        Chi Square: 0.24 0.623  
#>     Yes    141 (10%)    23 (9.2%)   117 (10.1%)                         
#>     No     1107 (78.1%) 205 (81.7%) 901 (77.4%)                         
#>     NA     169 (11.9%)  23 (9.2%)   146 (12.5%)                         
#>  rehab                                          Chi Square: 0.12 0.729  
#>     Yes    48 (3.4%)    10 (4%)     37 (3.2%)                           
#>     No     668 (47.1%)  121 (48.2%) 547 (47%)                           
#>     NA     701 (49.5%)  120 (47.8%) 580 (49.8%)                         
#> ─────────────────────────────────────────────────────────────────────────

`table1(

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