Useful Packages

This tutorial introduces a variety of methods to explore a dataset, including summary statistics, variable distributions, correlations, and handling missing data.

Note

This tutorial uses the same dataset as previous tutorials, including working with a predictive question, machine learning with a continuous outcome, and machine learning with a binary outcome.

TableOne

TableOne is an R package that provides a simple method to create the classic “Table 1” seen in health research papers, summarizing the characteristics of the dataset. It also offers functions like svyCreateTableOne for survey data, allowing users to account for strata and weights, and display counts and proportions for both weighted and unweighted data.

require(tableone)
#> Loading required package: tableone

CreateTableOne(vars = c("Disease.category", "Cancer", "Cardiovascular", "Congestive.HF", 
                        "Dementia", "Psychiatric", "Pulmonary", "Renal", "Hepatic", "GI.Bleed", "Tumor",
                        "Immunosuppression", "Transfer.hx", "MI", "age", "sex", "edu", "DASIndex",
                        "APACHE.score", "Glasgow.Coma.Score", "blood.pressure", "WBC", "Heart.rate",
                        "Respiratory.rate", "Temperature", "PaO2vs.FIO2", "Albumin", "Hematocrit",
                        "Bilirubin", "Creatinine", "Sodium", "Potassium", "PaCo2", "PH", "Weight",
                        "DNR.status", "Medical.insurance", "Respiratory.Diag", "Cardiovascular.Diag",
                        "Neurological.Diag", "Gastrointestinal.Diag", "Renal.Diag", "Metabolic.Diag",
                        "Hematologic.Diag", "Sepsis.Diag", "Trauma.Diag", "Orthopedic.Diag", "race", 
                        "income", "Length.of.Stay", "Death"),
               strata = "RHC.use",
               data = ObsData,
               includeNA = TRUE,
               test = TRUE)
#> Warning in ModuleReturnVarsExist(vars, data): The data frame does not have:
#> Immunosuppression Dropped
#>                                  Stratified by RHC.use
#>                                   0               1               p      test
#>   n                                 3551            2184                     
#>   Disease.category (%)                                            <0.001     
#>      ARF                            1581 (44.5)      909 (41.6)              
#>      CHF                             247 ( 7.0)      209 ( 9.6)              
#>      Other                           955 (26.9)      208 ( 9.5)              
#>      MOSF                            768 (21.6)      858 (39.3)              
#>   Cancer (%)                                                       0.001     
#>      None                           2652 (74.7)     1727 (79.1)              
#>      Localized (Yes)                 638 (18.0)      334 (15.3)              
#>      Metastatic                      261 ( 7.4)      123 ( 5.6)              
#>   Cardiovascular = 1 (%)             567 (16.0)      446 (20.4)   <0.001     
#>   Congestive.HF = 1 (%)              596 (16.8)      425 (19.5)    0.011     
#>   Dementia = 1 (%)                   413 (11.6)      151 ( 6.9)   <0.001     
#>   Psychiatric = 1 (%)                286 ( 8.1)      100 ( 4.6)   <0.001     
#>   Pulmonary = 1 (%)                  774 (21.8)      315 (14.4)   <0.001     
#>   Renal = 1 (%)                      149 ( 4.2)      106 ( 4.9)    0.268     
#>   Hepatic = 1 (%)                    265 ( 7.5)      136 ( 6.2)    0.084     
#>   GI.Bleed = 1 (%)                   131 ( 3.7)       54 ( 2.5)    0.014     
#>   Tumor = 1 (%)                      872 (24.6)      444 (20.3)   <0.001     
#>   Transfer.hx = 1 (%)                335 ( 9.4)      327 (15.0)   <0.001     
#>   MI = 1 (%)                         105 ( 3.0)       95 ( 4.3)    0.007     
#>   age (%)                                                         <0.001     
#>      [-Inf,50)                       884 (24.9)      540 (24.7)              
#>      [50,60)                         546 (15.4)      371 (17.0)              
#>      [60,70)                         812 (22.9)      577 (26.4)              
#>      [70,80)                         809 (22.8)      529 (24.2)              
#>      [80, Inf)                       500 (14.1)      167 ( 7.6)              
#>   sex = Female (%)                  1637 (46.1)      906 (41.5)    0.001     
#>   edu (mean (SD))                  11.57 (3.13)    11.86 (3.16)    0.001     
#>   DASIndex (mean (SD))             20.37 (5.48)    20.70 (5.03)    0.023     
#>   APACHE.score (mean (SD))         50.93 (18.81)   60.74 (20.27)  <0.001     
#>   Glasgow.Coma.Score (mean (SD))   22.25 (31.37)   18.97 (28.26)  <0.001     
#>   blood.pressure (mean (SD))       84.87 (38.87)   68.20 (34.24)  <0.001     
#>   WBC (mean (SD))                  15.26 (11.41)   16.27 (12.55)   0.002     
#>   Heart.rate (mean (SD))          112.87 (40.94)  118.93 (41.47)  <0.001     
#>   Respiratory.rate (mean (SD))     28.98 (13.95)   26.65 (14.17)  <0.001     
#>   Temperature (mean (SD))          37.63 (1.74)    37.59 (1.83)    0.429     
#>   PaO2vs.FIO2 (mean (SD))         240.63 (116.66) 192.43 (105.54) <0.001     
#>   Albumin (mean (SD))               3.16 (0.67)     2.98 (0.93)   <0.001     
#>   Hematocrit (mean (SD))           32.70 (8.79)    30.51 (7.42)   <0.001     
#>   Bilirubin (mean (SD))             2.00 (4.43)     2.71 (5.33)   <0.001     
#>   Creatinine (mean (SD))            1.92 (2.03)     2.47 (2.05)   <0.001     
#>   Sodium (mean (SD))              137.04 (7.68)   136.33 (7.60)    0.001     
#>   Potassium (mean (SD))             4.08 (1.04)     4.05 (1.01)    0.321     
#>   PaCo2 (mean (SD))                39.95 (14.24)   36.79 (10.97)  <0.001     
#>   PH (mean (SD))                    7.39 (0.11)     7.38 (0.11)   <0.001     
#>   Weight (mean (SD))               65.04 (29.50)   72.36 (27.73)  <0.001     
#>   DNR.status = Yes (%)               499 (14.1)      155 ( 7.1)   <0.001     
#>   Medical.insurance (%)                                           <0.001     
#>      Medicaid                        454 (12.8)      193 ( 8.8)              
#>      Medicare                        947 (26.7)      511 (23.4)              
#>      Medicare & Medicaid             251 ( 7.1)      123 ( 5.6)              
#>      No insurance                    186 ( 5.2)      136 ( 6.2)              
#>      Private                         967 (27.2)      731 (33.5)              
#>      Private & Medicare              746 (21.0)      490 (22.4)              
#>   Respiratory.Diag = Yes (%)        1481 (41.7)      632 (28.9)   <0.001     
#>   Cardiovascular.Diag = Yes (%)     1007 (28.4)      924 (42.3)   <0.001     
#>   Neurological.Diag = Yes (%)        575 (16.2)      118 ( 5.4)   <0.001     
#>   Gastrointestinal.Diag = Yes (%)    522 (14.7)      420 (19.2)   <0.001     
#>   Renal.Diag = Yes (%)               147 ( 4.1)      148 ( 6.8)   <0.001     
#>   Metabolic.Diag = Yes (%)           172 ( 4.8)       93 ( 4.3)    0.337     
#>   Hematologic.Diag = Yes (%)         239 ( 6.7)      115 ( 5.3)    0.029     
#>   Sepsis.Diag = Yes (%)              515 (14.5)      516 (23.6)   <0.001     
#>   Trauma.Diag = Yes (%)               18 ( 0.5)       34 ( 1.6)   <0.001     
#>   Orthopedic.Diag = Yes (%)            3 ( 0.1)        4 ( 0.2)    0.516     
#>   race (%)                                                         0.425     
#>      white                          2753 (77.5)     1707 (78.2)              
#>      black                           585 (16.5)      335 (15.3)              
#>      other                           213 ( 6.0)      142 ( 6.5)              
#>   income (%)                                                      <0.001     
#>      $11-$25k                        713 (20.1)      452 (20.7)              
#>      $25-$50k                        500 (14.1)      393 (18.0)              
#>      > $50k                          257 ( 7.2)      194 ( 8.9)              
#>      Under $11k                     2081 (58.6)     1145 (52.4)              
#>   Length.of.Stay (mean (SD))       19.53 (23.59)   24.86 (28.90)  <0.001     
#>   Death (mean (SD))                 0.63 (0.48)     0.68 (0.47)   <0.001

table1

The table1 package is useful for generating descriptive summary tables commonly used in medical research. Below are examples of how to use it.

Basic usage of table1:

require(table1)
#> Loading required package: table1
#> 
#> Attaching package: 'table1'
#> The following objects are masked from 'package:base':
#> 
#>     units, units<-

ObsData$RHC.use.factor <- factor(ObsData$RHC.use, 
                          levels = c(0, 1), 
                          labels = c("No RHC", "Received RHC"))
# Generate a basic Table 1 summarizing characteristics of ObsData, grouped by 'RHC.use'
table1(~ age + sex + APACHE.score + Medical.insurance | RHC.use.factor, data = ObsData)

	No RHC (N=3551)	Received RHC (N=2184)	Overall (N=5735)
age
[-Inf,50)	884 (24.9%)	540 (24.7%)	1424 (24.8%)
[50,60)	546 (15.4%)	371 (17.0%)	917 (16.0%)
[60,70)	812 (22.9%)	577 (26.4%)	1389 (24.2%)
[70,80)	809 (22.8%)	529 (24.2%)	1338 (23.3%)
[80, Inf)	500 (14.1%)	167 (7.6%)	667 (11.6%)
sex
Male	1914 (53.9%)	1278 (58.5%)	3192 (55.7%)
Female	1637 (46.1%)	906 (41.5%)	2543 (44.3%)
APACHE.score
Mean (SD)	50.9 (18.8)	60.7 (20.3)	54.7 (20.0)
Median [Min, Max]	50.0 [3.00, 147]	60.0 [9.00, 135]	54.0 [3.00, 147]
Medical.insurance
Medicaid	454 (12.8%)	193 (8.8%)	647 (11.3%)
Medicare	947 (26.7%)	511 (23.4%)	1458 (25.4%)
Medicare & Medicaid	251 (7.1%)	123 (5.6%)	374 (6.5%)
No insurance	186 (5.2%)	136 (6.2%)	322 (5.6%)
Private	967 (27.2%)	731 (33.5%)	1698 (29.6%)
Private & Medicare	746 (21.0%)	490 (22.4%)	1236 (21.6%)

Customizing labels and formats

# Label variables and modify the format
labels <- list(
  age = "Age (years)",
  APACHE.score = "APACHE II Score",
  Medical.insurance = "Medical Insurance Status"
)

table1(~ age + sex + APACHE.score + Medical.insurance | RHC.use.factor, data = ObsData,
       label = labels, caption = "Table 1: Summary of patient characteristics")

Table 1: Summary of patient characteristics

	No RHC (N=3551)	Received RHC (N=2184)	Overall (N=5735)
age
[-Inf,50)	884 (24.9%)	540 (24.7%)	1424 (24.8%)
[50,60)	546 (15.4%)	371 (17.0%)	917 (16.0%)
[60,70)	812 (22.9%)	577 (26.4%)	1389 (24.2%)
[70,80)	809 (22.8%)	529 (24.2%)	1338 (23.3%)
[80, Inf)	500 (14.1%)	167 (7.6%)	667 (11.6%)
sex
Male	1914 (53.9%)	1278 (58.5%)	3192 (55.7%)
Female	1637 (46.1%)	906 (41.5%)	2543 (44.3%)
APACHE.score
Mean (SD)	50.9 (18.8)	60.7 (20.3)	54.7 (20.0)
Median [Min, Max]	50.0 [3.00, 147]	60.0 [9.00, 135]	54.0 [3.00, 147]
Medical.insurance
Medicaid	454 (12.8%)	193 (8.8%)	647 (11.3%)
Medicare	947 (26.7%)	511 (23.4%)	1458 (25.4%)
Medicare & Medicaid	251 (7.1%)	123 (5.6%)	374 (6.5%)
No insurance	186 (5.2%)	136 (6.2%)	322 (5.6%)
Private	967 (27.2%)	731 (33.5%)	1698 (29.6%)
Private & Medicare	746 (21.0%)	490 (22.4%)	1236 (21.6%)

Handling missing data

# Including missing values in the summary table
table1(~ age + sex + APACHE.score + Medical.insurance | RHC.use.factor, data = ObsData,
       overall = TRUE, render.missing = TRUE)

	No RHC (N=3551)	Received RHC (N=2184)	TRUE (N=5735)
age
[-Inf,50)	884 (24.9%)	540 (24.7%)	1424 (24.8%)
[50,60)	546 (15.4%)	371 (17.0%)	917 (16.0%)
[60,70)	812 (22.9%)	577 (26.4%)	1389 (24.2%)
[70,80)	809 (22.8%)	529 (24.2%)	1338 (23.3%)
[80, Inf)	500 (14.1%)	167 (7.6%)	667 (11.6%)
sex
Male	1914 (53.9%)	1278 (58.5%)	3192 (55.7%)
Female	1637 (46.1%)	906 (41.5%)	2543 (44.3%)
APACHE.score
Mean (SD)	50.9 (18.8)	60.7 (20.3)	54.7 (20.0)
Median [Min, Max]	50.0 [3.00, 147]	60.0 [9.00, 135]	54.0 [3.00, 147]
Medical.insurance
Medicaid	454 (12.8%)	193 (8.8%)	647 (11.3%)
Medicare	947 (26.7%)	511 (23.4%)	1458 (25.4%)
Medicare & Medicaid	251 (7.1%)	123 (5.6%)	374 (6.5%)
No insurance	186 (5.2%)	136 (6.2%)	322 (5.6%)
Private	967 (27.2%)	731 (33.5%)	1698 (29.6%)
Private & Medicare	746 (21.0%)	490 (22.4%)	1236 (21.6%)

ObsData$RHC.use.factor <- NULL

gtsummary

gtsummary provides highly customizable functions to construct tables. It allows renaming variables, adding captions, and selecting specific measures for variable types. It is particularly useful for creating clean, customized Table 1s and includes options for survey data.

require(gt)
#> Loading required package: gt
#> Warning: package 'gt' was built under R version 4.2.3
require(gtsummary)
#> Loading required package: gtsummary
#> Warning: package 'gtsummary' was built under R version 4.2.3

tbl_summary(ObsData)

Characteristic	N = 5,7351
Disease.category
ARF	2,490 (43%)
CHF	456 (8.0%)
Other	1,163 (20%)
MOSF	1,626 (28%)
Cancer
None	4,379 (76%)
Localized (Yes)	972 (17%)
Metastatic	384 (6.7%)
Cardiovascular
0	4,722 (82%)
1	1,013 (18%)
Congestive.HF
0	4,714 (82%)
1	1,021 (18%)
Dementia
0	5,171 (90%)
1	564 (9.8%)
Psychiatric
0	5,349 (93%)
1	386 (6.7%)
Pulmonary
0	4,646 (81%)
1	1,089 (19%)
Renal
0	5,480 (96%)
1	255 (4.4%)
Hepatic
0	5,334 (93%)
1	401 (7.0%)
GI.Bleed
0	5,550 (97%)
1	185 (3.2%)
Tumor
0	4,419 (77%)
1	1,316 (23%)
Immunosupperssion
0	4,192 (73%)
1	1,543 (27%)
Transfer.hx
0	5,073 (88%)
1	662 (12%)
MI
0	5,535 (97%)
1	200 (3.5%)
age
[-Inf,50)	1,424 (25%)
[50,60)	917 (16%)
[60,70)	1,389 (24%)
[70,80)	1,338 (23%)
[80, Inf)	667 (12%)
sex
Male	3,192 (56%)
Female	2,543 (44%)
edu	12.0 (10.0, 13.0)
DASIndex	19.7 (16.1, 23.4)
APACHE.score	54 (41, 67)
Glasgow.Coma.Score	0 (0, 41)
blood.pressure	63 (50, 115)
WBC	14 (8, 20)
Heart.rate	124 (97, 141)
Respiratory.rate	30 (14, 38)
Temperature	38.09 (36.09, 39.00)
PaO2vs.FIO2	203 (133, 317)
Albumin	3.50 (2.60, 3.50)
Hematocrit	30 (26, 36)
Bilirubin	1.01 (0.80, 1.40)
Creatinine	1.50 (1.00, 2.40)
Sodium	136 (132, 142)
Potassium	3.80 (3.40, 4.60)
PaCo2	37 (31, 42)
PH	7.40 (7.34, 7.46)
Weight	70 (56, 84)
DNR.status	654 (11%)
Medical.insurance
Medicaid	647 (11%)
Medicare	1,458 (25%)
Medicare & Medicaid	374 (6.5%)
No insurance	322 (5.6%)
Private	1,698 (30%)
Private & Medicare	1,236 (22%)
Respiratory.Diag	2,113 (37%)
Cardiovascular.Diag	1,931 (34%)
Neurological.Diag	693 (12%)
Gastrointestinal.Diag	942 (16%)
Renal.Diag	295 (5.1%)
Metabolic.Diag	265 (4.6%)
Hematologic.Diag	354 (6.2%)
Sepsis.Diag	1,031 (18%)
Trauma.Diag	52 (0.9%)
Orthopedic.Diag	7 (0.1%)
race
white	4,460 (78%)
black	920 (16%)
other	355 (6.2%)
income
$11-$25k	1,165 (20%)
$25-$50k	893 (16%)
> $50k	451 (7.9%)
Under $11k	3,226 (56%)
Length.of.Stay	14 (7, 25)
Death	3,722 (65%)
RHC.use	2,184 (38%)
1 n (%); Median (IQR)

tbl_summary(ObsData, by = RHC.use,
            statistic = list(
                              all_continuous() ~ "{mean} ({sd})",
                              all_categorical() ~ "{n} ({p}%)"
                            ),
            digits = all_continuous() ~ 2) |> 
  as_gt() |>
  gt::tab_source_note(gt::md("*Add note here.*"))

Characteristic	0, N = 3,5511	1, N = 2,1841
Disease.category
ARF	1,581 (45%)	909 (42%)
CHF	247 (7.0%)	209 (9.6%)
Other	955 (27%)	208 (9.5%)
MOSF	768 (22%)	858 (39%)
Cancer
None	2,652 (75%)	1,727 (79%)
Localized (Yes)	638 (18%)	334 (15%)
Metastatic	261 (7.4%)	123 (5.6%)
Cardiovascular
0	2,984 (84%)	1,738 (80%)
1	567 (16%)	446 (20%)
Congestive.HF
0	2,955 (83%)	1,759 (81%)
1	596 (17%)	425 (19%)
Dementia
0	3,138 (88%)	2,033 (93%)
1	413 (12%)	151 (6.9%)
Psychiatric
0	3,265 (92%)	2,084 (95%)
1	286 (8.1%)	100 (4.6%)
Pulmonary
0	2,777 (78%)	1,869 (86%)
1	774 (22%)	315 (14%)
Renal
0	3,402 (96%)	2,078 (95%)
1	149 (4.2%)	106 (4.9%)
Hepatic
0	3,286 (93%)	2,048 (94%)
1	265 (7.5%)	136 (6.2%)
GI.Bleed
0	3,420 (96%)	2,130 (98%)
1	131 (3.7%)	54 (2.5%)
Tumor
0	2,679 (75%)	1,740 (80%)
1	872 (25%)	444 (20%)
Immunosupperssion
0	2,644 (74%)	1,548 (71%)
1	907 (26%)	636 (29%)
Transfer.hx
0	3,216 (91%)	1,857 (85%)
1	335 (9.4%)	327 (15%)
MI
0	3,446 (97%)	2,089 (96%)
1	105 (3.0%)	95 (4.3%)
age
[-Inf,50)	884 (25%)	540 (25%)
[50,60)	546 (15%)	371 (17%)
[60,70)	812 (23%)	577 (26%)
[70,80)	809 (23%)	529 (24%)
[80, Inf)	500 (14%)	167 (7.6%)
sex
Male	1,914 (54%)	1,278 (59%)
Female	1,637 (46%)	906 (41%)
edu	11.57 (3.13)	11.86 (3.16)
DASIndex	20.37 (5.48)	20.70 (5.03)
APACHE.score	50.93 (18.81)	60.74 (20.27)
Glasgow.Coma.Score	22.25 (31.37)	18.97 (28.26)
blood.pressure	84.87 (38.87)	68.20 (34.24)
WBC	15.26 (11.41)	16.27 (12.55)
Heart.rate	112.87 (40.94)	118.93 (41.47)
Respiratory.rate	28.98 (13.95)	26.65 (14.17)
Temperature	37.63 (1.74)	37.59 (1.83)
PaO2vs.FIO2	240.63 (116.66)	192.43 (105.54)
Albumin	3.16 (0.67)	2.98 (0.93)
Hematocrit	32.70 (8.79)	30.51 (7.42)
Bilirubin	2.00 (4.43)	2.71 (5.33)
Creatinine	1.92 (2.03)	2.47 (2.05)
Sodium	137.04 (7.68)	136.33 (7.60)
Potassium	4.08 (1.04)	4.05 (1.01)
PaCo2	39.95 (14.24)	36.79 (10.97)
PH	7.39 (0.11)	7.38 (0.11)
Weight	65.04 (29.50)	72.36 (27.73)
DNR.status	499 (14%)	155 (7.1%)
Medical.insurance
Medicaid	454 (13%)	193 (8.8%)
Medicare	947 (27%)	511 (23%)
Medicare & Medicaid	251 (7.1%)	123 (5.6%)
No insurance	186 (5.2%)	136 (6.2%)
Private	967 (27%)	731 (33%)
Private & Medicare	746 (21%)	490 (22%)
Respiratory.Diag	1,481 (42%)	632 (29%)
Cardiovascular.Diag	1,007 (28%)	924 (42%)
Neurological.Diag	575 (16%)	118 (5.4%)
Gastrointestinal.Diag	522 (15%)	420 (19%)
Renal.Diag	147 (4.1%)	148 (6.8%)
Metabolic.Diag	172 (4.8%)	93 (4.3%)
Hematologic.Diag	239 (6.7%)	115 (5.3%)
Sepsis.Diag	515 (15%)	516 (24%)
Trauma.Diag	18 (0.5%)	34 (1.6%)
Orthopedic.Diag	3 (<0.1%)	4 (0.2%)
race
white	2,753 (78%)	1,707 (78%)
black	585 (16%)	335 (15%)
other	213 (6.0%)	142 (6.5%)
income
$11-$25k	713 (20%)	452 (21%)
$25-$50k	500 (14%)	393 (18%)
> $50k	257 (7.2%)	194 (8.9%)
Under $11k	2,081 (59%)	1,145 (52%)
Length.of.Stay	19.53 (23.59)	24.86 (28.90)
Death	2,236 (63%)	1,486 (68%)
Add note here.
1 n (%); Mean (SD)

DataExplorer

DataExplorer offers functions for initial data exploration, including various visualizations. Below are some examples using the RHC dataset.

require(DataExplorer)
#> Loading required package: DataExplorer

The introduce function provides an overview of the dataset dimensions, variable types, and missingness.

introduce(ObsData)

Plot the amount of missing data per variable:

plot_missing(ObsData)

Visualize categorical variable distributions with the plot_bar function:

plot_bar(ObsData)

Visualize the distribution of numerical variables with histograms:

plot_histogram(ObsData)

Quantile-quantile plots can be used to assess whether numerical variables are normally distributed:

plot_qq(ObsData)

Generate a correlation plot to show relationships between variables:

plot_correlation(ObsData)

Boxplots can visualize variable distributions based on treatment or outcome:

plot_boxplot(ObsData, by="RHC.use")

Automatically generate a full PDF report with the create_report function:

create_report(ObsData)

GGally

GGally provides methods to combine multiple ggplot2 plots, enabling visualization of several variables at once.

require(GGally)
#> Loading required package: GGally
#> Warning: package 'GGally' was built under R version 4.2.3
#> Loading required package: ggplot2
#> Warning: package 'ggplot2' was built under R version 4.2.3
#> Registered S3 method overwritten by 'GGally':
#>   method from   
#>   +.gg   ggplot2

ggpairs(ObsData, 
        columns = c('age', 'sex', 'edu', 'blood.pressure', 'Medical.insurance'),
        ggplot2::aes(color=as.factor(RHC.use)))
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

modelsummary

modelsummary provides functions to visualize data, including summaries, correlations, and Table 1-style tables.

require(modelsummary)
#> Loading required package: modelsummary
#> Warning: package 'modelsummary' was built under R version 4.2.3
require(rmarkdown)
#> Loading required package: rmarkdown
#> Warning: package 'rmarkdown' was built under R version 4.2.3
require(markdown)
#> Loading required package: markdown
#> Warning: package 'markdown' was built under R version 4.2.3

Overview of each variable:

datasummary_skim(ObsData)

	Unique (#)	Missing (%)	Mean	SD	Min	Median	Max
edu	42	0	11.7	3.1	0.0	12.0	30.0
DASIndex	1023	0	20.5	5.3	11.0	19.7	33.0
APACHE.score	123	0	54.7	20.0	3.0	54.0	147.0
Glasgow.Coma.Score	11	0	21.0	30.3	0.0	0.0	100.0
blood.pressure	178	0	78.5	38.0	0.0	63.0	259.0
WBC	520	0	15.6	11.9	0.0	14.1	192.0
Heart.rate	189	0	115.2	41.2	0.0	124.0	250.0
Respiratory.rate	72	0	28.1	14.1	0.0	30.0	100.0
Temperature	118	0	37.6	1.8	27.0	38.1	43.0
PaO2vs.FIO2	1342	0	222.3	115.0	11.6	202.5	937.5
Albumin	57	0	3.1	0.8	0.3	3.5	29.0
Hematocrit	450	0	31.9	8.4	2.0	30.0	66.2
Bilirubin	266	0	2.3	4.8	0.1	1.0	58.2
Creatinine	148	0	2.1	2.1	0.1	1.5	25.1
Sodium	73	0	136.8	7.7	101.0	136.0	178.0
Potassium	81	0	4.1	1.0	1.1	3.8	11.9
PaCo2	266	0	38.7	13.2	1.0	37.0	156.0
PH	96	0	7.4	0.1	6.6	7.4	7.8
Weight	922	0	67.8	29.1	0.0	70.0	244.0
Length.of.Stay	164	0	21.6	25.9	2.0	14.0	394.0
Death	2	0	0.6	0.5	0.0	1.0	1.0
RHC.use	2	0	0.4	0.5	0.0	0.0	1.0

Create a Table 1 using datasummary_balance:

datasummary_balance(~ RHC.use, ObsData)

		0		1
		Mean	Std. Dev.	Mean	Std. Dev.
edu		11.6	3.1	11.9	3.2
DASIndex		20.4	5.5	20.7	5.0
APACHE.score		50.9	18.8	60.7	20.3
Glasgow.Coma.Score		22.3	31.4	19.0	28.3
blood.pressure		84.9	38.9	68.2	34.2
WBC		15.3	11.4	16.3	12.5
Heart.rate		112.9	40.9	118.9	41.5
Respiratory.rate		29.0	13.9	26.7	14.2
Temperature		37.6	1.7	37.6	1.8
PaO2vs.FIO2		240.6	116.7	192.4	105.5
Albumin		3.2	0.7	3.0	0.9
Hematocrit		32.7	8.8	30.5	7.4
Bilirubin		2.0	4.4	2.7	5.3
Creatinine		1.9	2.0	2.5	2.1
Sodium		137.0	7.7	136.3	7.6
Potassium		4.1	1.0	4.0	1.0
PaCo2		40.0	14.2	36.8	11.0
PH		7.4	0.1	7.4	0.1
Weight		65.0	29.5	72.4	27.7
Length.of.Stay		19.5	23.6	24.9	28.9
Death		0.6	0.5	0.7	0.5
		N	Pct.	N	Pct.
Disease.category	ARF	1581	44.5	909	41.6
	CHF	247	7.0	209	9.6
	Other	955	26.9	208	9.5
	MOSF	768	21.6	858	39.3
Cancer	None	2652	74.7	1727	79.1
	Localized (Yes)	638	18.0	334	15.3
	Metastatic	261	7.4	123	5.6
Cardiovascular	0	2984	84.0	1738	79.6
	1	567	16.0	446	20.4
Congestive.HF	0	2955	83.2	1759	80.5
	1	596	16.8	425	19.5
Dementia	0	3138	88.4	2033	93.1
	1	413	11.6	151	6.9
Psychiatric	0	3265	91.9	2084	95.4
	1	286	8.1	100	4.6
Pulmonary	0	2777	78.2	1869	85.6
	1	774	21.8	315	14.4
Renal	0	3402	95.8	2078	95.1
	1	149	4.2	106	4.9
Hepatic	0	3286	92.5	2048	93.8
	1	265	7.5	136	6.2
GI.Bleed	0	3420	96.3	2130	97.5
	1	131	3.7	54	2.5
Tumor	0	2679	75.4	1740	79.7
	1	872	24.6	444	20.3
Immunosupperssion	0	2644	74.5	1548	70.9
	1	907	25.5	636	29.1
Transfer.hx	0	3216	90.6	1857	85.0
	1	335	9.4	327	15.0
MI	0	3446	97.0	2089	95.7
	1	105	3.0	95	4.3
age	[-Inf,50)	884	24.9	540	24.7
	[50,60)	546	15.4	371	17.0
	[60,70)	812	22.9	577	26.4
	[70,80)	809	22.8	529	24.2
	[80, Inf)	500	14.1	167	7.6
sex	Male	1914	53.9	1278	58.5
	Female	1637	46.1	906	41.5
DNR.status	No	3052	85.9	2029	92.9
	Yes	499	14.1	155	7.1
Medical.insurance	Medicaid	454	12.8	193	8.8
	Medicare	947	26.7	511	23.4
	Medicare & Medicaid	251	7.1	123	5.6
	No insurance	186	5.2	136	6.2
	Private	967	27.2	731	33.5
	Private & Medicare	746	21.0	490	22.4
Respiratory.Diag	No	2070	58.3	1552	71.1
	Yes	1481	41.7	632	28.9
Cardiovascular.Diag	No	2544	71.6	1260	57.7
	Yes	1007	28.4	924	42.3
Neurological.Diag	No	2976	83.8	2066	94.6
	Yes	575	16.2	118	5.4
Gastrointestinal.Diag	No	3029	85.3	1764	80.8
	Yes	522	14.7	420	19.2
Renal.Diag	No	3404	95.9	2036	93.2
	Yes	147	4.1	148	6.8
Metabolic.Diag	No	3379	95.2	2091	95.7
	Yes	172	4.8	93	4.3
Hematologic.Diag	No	3312	93.3	2069	94.7
	Yes	239	6.7	115	5.3
Sepsis.Diag	No	3036	85.5	1668	76.4
	Yes	515	14.5	516	23.6
Trauma.Diag	No	3533	99.5	2150	98.4
	Yes	18	0.5	34	1.6
Orthopedic.Diag	No	3548	99.9	2180	99.8
	Yes	3	0.1	4	0.2
race	white	2753	77.5	1707	78.2
	black	585	16.5	335	15.3
	other	213	6.0	142	6.5
income	$11-$25k	713	20.1	452	20.7
	$25-$50k	500	14.1	393	18.0
	> $50k	257	7.2	194	8.9
	Under $11k	2081	58.6	1145	52.4

You can also customize the appearance of tables generated with modelsummary. For example, you can adjust the number of digits displayed in the summaries.

datasummary_balance(~ RHC.use, ObsData, 
                    fmt="%.2f", 
                    output="markdown")
#> Warning: Please install the `estimatr` package or set `dinm=FALSE` to suppress
#> this warning.

		0 / Mean	0 / Std. Dev.	1 / Mean	1 / Std. Dev.
edu		11.57	3.13	11.86	3.16
DASIndex		20.37	5.48	20.70	5.03
APACHE.score		50.93	18.81	60.74	20.27
Glasgow.Coma.Score		22.25	31.37	18.97	28.26
blood.pressure		84.87	38.87	68.20	34.24
WBC		15.26	11.41	16.27	12.55
Heart.rate		112.87	40.94	118.93	41.47
Respiratory.rate		28.98	13.95	26.65	14.17
Temperature		37.63	1.74	37.59	1.83
PaO2vs.FIO2		240.63	116.66	192.43	105.54
Albumin		3.16	0.67	2.98	0.93
Hematocrit		32.70	8.79	30.51	7.42
Bilirubin		2.00	4.43	2.71	5.33
Creatinine		1.92	2.03	2.47	2.05
Sodium		137.04	7.68	136.33	7.60
Potassium		4.08	1.04	4.05	1.01
PaCo2		39.95	14.24	36.79	10.97
PH		7.39	0.11	7.38	0.11
Weight		65.04	29.50	72.36	27.73
Length.of.Stay		19.53	23.59	24.86	28.90
Death		0.63	0.48	0.68	0.47
		N	Pct.	N	Pct.
Disease.category	ARF	1581	44.5	909	41.6
	CHF	247	7.0	209	9.6
	Other	955	26.9	208	9.5
	MOSF	768	21.6	858	39.3
Cancer	None	2652	74.7	1727	79.1
	Localized (Yes)	638	18.0	334	15.3
	Metastatic	261	7.4	123	5.6
Cardiovascular	0	2984	84.0	1738	79.6
	1	567	16.0	446	20.4
Congestive.HF	0	2955	83.2	1759	80.5
	1	596	16.8	425	19.5
Dementia	0	3138	88.4	2033	93.1
	1	413	11.6	151	6.9
Psychiatric	0	3265	91.9	2084	95.4
	1	286	8.1	100	4.6
Pulmonary	0	2777	78.2	1869	85.6
	1	774	21.8	315	14.4
Renal	0	3402	95.8	2078	95.1
	1	149	4.2	106	4.9
Hepatic	0	3286	92.5	2048	93.8
	1	265	7.5	136	6.2
GI.Bleed	0	3420	96.3	2130	97.5
	1	131	3.7	54	2.5
Tumor	0	2679	75.4	1740	79.7
	1	872	24.6	444	20.3
Immunosupperssion	0	2644	74.5	1548	70.9
	1	907	25.5	636	29.1
Transfer.hx	0	3216	90.6	1857	85.0
	1	335	9.4	327	15.0
MI	0	3446	97.0	2089	95.7
	1	105	3.0	95	4.3
age	[-Inf,50)	884	24.9	540	24.7
	[50,60)	546	15.4	371	17.0
	[60,70)	812	22.9	577	26.4
	[70,80)	809	22.8	529	24.2
	[80, Inf)	500	14.1	167	7.6
sex	Male	1914	53.9	1278	58.5
	Female	1637	46.1	906	41.5
DNR.status	No	3052	85.9	2029	92.9
	Yes	499	14.1	155	7.1
Medical.insurance	Medicaid	454	12.8	193	8.8
	Medicare	947	26.7	511	23.4
	Medicare & Medicaid	251	7.1	123	5.6
	No insurance	186	5.2	136	6.2
	Private	967	27.2	731	33.5
	Private & Medicare	746	21.0	490	22.4
Respiratory.Diag	No	2070	58.3	1552	71.1
	Yes	1481	41.7	632	28.9
Cardiovascular.Diag	No	2544	71.6	1260	57.7
	Yes	1007	28.4	924	42.3
Neurological.Diag	No	2976	83.8	2066	94.6
	Yes	575	16.2	118	5.4
Gastrointestinal.Diag	No	3029	85.3	1764	80.8
	Yes	522	14.7	420	19.2
Renal.Diag	No	3404	95.9	2036	93.2
	Yes	147	4.1	148	6.8
Metabolic.Diag	No	3379	95.2	2091	95.7
	Yes	172	4.8	93	4.3
Hematologic.Diag	No	3312	93.3	2069	94.7
	Yes	239	6.7	115	5.3
Sepsis.Diag	No	3036	85.5	1668	76.4
	Yes	515	14.5	516	23.6
Trauma.Diag	No	3533	99.5	2150	98.4
	Yes	18	0.5	34	1.6
Orthopedic.Diag	No	3548	99.9	2180	99.8
	Yes	3	0.1	4	0.2
race	white	2753	77.5	1707	78.2
	black	585	16.5	335	15.3
	other	213	6.0	142	6.5
income	$11-$25k	713	20.1	452	20.7
	$25-$50k	500	14.1	393	18.0
	> $50k	257	7.2	194	8.9
	Under $11k	2081	58.6	1145	52.4

Extract correlations between variables with datasummary_correlation:

datasummary_correlation(ObsData)

	edu	DASIndex	APACHE.score	Glasgow.Coma.Score	blood.pressure	WBC	Heart.rate	Respiratory.rate	Temperature	PaO2vs.FIO2	Albumin	Hematocrit	Bilirubin	Creatinine	Sodium	Potassium	PaCo2	PH	Weight	Length.of.Stay	Death	RHC.use
edu	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
DASIndex	.10	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
APACHE.score	.02	−.06	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
Glasgow.Coma.Score	−.02	.04	.03	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
blood.pressure	−.04	.06	−.40	.02	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
WBC	−.02	.03	.13	.04	−.03	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
Heart.rate	.05	.02	.22	−.11	.06	.03	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
Respiratory.rate	.03	.00	.26	−.14	.03	.01	.28	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.
Temperature	.07	.15	−.04	.05	.01	−.01	.22	.14	1	.	.	.	.	.	.	.	.	.	.	.	.	.
PaO2vs.FIO2	−.01	−.07	−.23	.09	.07	−.06	−.08	−.10	−.10	1	.	.	.	.	.	.	.	.	.	.	.	.
Albumin	.00	.00	−.29	.02	.13	−.06	−.08	.00	−.01	.07	1	.	.	.	.	.	.	.	.	.	.	.
Hematocrit	−.05	.03	−.24	.03	.16	−.03	−.03	.01	−.02	−.01	.29	1	.	.	.	.	.	.	.	.	.	.
Bilirubin	.07	−.01	.28	.05	−.05	.01	.03	.01	−.03	.03	−.09	−.15	1	.	.	.	.	.	.	.	.	.
Creatinine	−.02	−.05	.38	.01	−.09	.05	−.06	−.04	−.11	.06	.00	−.20	.12	1	.	.	.	.	.	.	.	.
Sodium	−.01	.03	.02	.13	.02	−.04	.03	.02	.05	−.02	.00	.06	.02	−.01	1	.	.	.	.	.	.	.
Potassium	−.02	−.06	.15	−.01	−.07	.08	−.11	−.01	−.13	.04	.03	−.01	.00	.30	−.10	1	.	.	.	.	.	.
PaCo2	−.05	−.09	−.09	−.12	.04	−.05	−.01	.00	−.04	−.17	.09	.24	−.08	−.13	.06	.04	1	.	.	.	.	.
PH	.04	.05	−.33	.03	.13	−.06	.03	−.01	.14	.11	.04	−.03	.02	−.16	−.02	−.20	−.47	1	.	.	.	.
Weight	−.06	.05	.08	−.08	−.02	−.01	.02	−.01	.02	−.05	−.05	.05	.01	.10	.00	.06	.03	−.05	1	.	.	.
Length.of.Stay	.02	.04	.02	.00	−.02	.03	.07	−.01	.09	−.08	−.11	−.09	.00	.01	.04	−.02	.00	.02	.02	1	.	.
Death	−.03	−.18	.19	.12	−.10	.03	−.02	.01	−.10	.02	−.03	−.09	.08	.08	.00	.05	−.04	−.04	−.05	−.08	1	.
RHC.use	.04	.03	.24	−.05	−.21	.04	.07	−.08	−.01	−.20	−.12	−.13	.07	.13	−.04	−.01	−.12	−.06	.12	.10	.05	1

Generate a contingency table using datasummary_crosstab:

datasummary_crosstab(age ~ RHC.use, data=ObsData)

age		0	1	All
[-Inf,50)	N	884	540	1424
	% row	62.1	37.9	100.0
[50,60)	N	546	371	917
	% row	59.5	40.5	100.0
[60,70)	N	812	577	1389
	% row	58.5	41.5	100.0
[70,80)	N	809	529	1338
	% row	60.5	39.5	100.0
[80, Inf)	N	500	167	667
	% row	75.0	25.0	100.0
All	N	3551	2184	5735
	% row	61.9	38.1	100.0

The modelsummary package also allows for professional display of regression models, combining results from multiple models into one table for easy comparison.

# Example: Fit two regression models
model1 <- lm(Length.of.Stay ~ age + sex + APACHE.score, data = ObsData)
model2 <- lm(Length.of.Stay ~ age + sex + APACHE.score + Medical.insurance, data = ObsData)

# Display both models side by side
modelsummary(list(model1, model2), 
             statistic = "p.value", 
             stars = TRUE)

	(1)	(2)
(Intercept)	21.312***	21.200***
	(<0.001)	(<0.001)
age[50,60)	−1.065	−1.116
	(0.330)	(0.310)
age[60,70)	−1.420	−1.201
	(0.145)	(0.255)
age[70,80)	−2.763**	−2.334+
	(0.005)	(0.056)
age[80, Inf)	−6.762***	−6.530***
	(<0.001)	(<0.001)
sexFemale	0.932	1.021
	(0.176)	(0.140)
APACHE.score	0.033+	0.032+
	(0.057)	(0.063)
Medical.insuranceMedicare		0.561
		(0.687)
Medical.insuranceMedicare & Medicaid		−2.545
		(0.145)
Medical.insuranceNo insurance		0.475
		(0.788)
Medical.insurancePrivate		0.385
		(0.748)
Medical.insurancePrivate & Medicare		−0.817
		(0.569)
Num.Obs.	5735	5735
R2	0.007	0.008
R2 Adj.	0.006	0.006
AIC	53563.6	53567.9
BIC	53616.8	53654.4
Log.Lik.	−26773.805	−26770.932
F	6.388	4.006
RMSE	25.78	25.77
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

All tables generated by modelsummary can be saved in various formats such as LaTeX, HTML, or Word.

# Save as a LaTeX file
modelsummary(model1, output = "table.tex")

# Save as an HTML file
modelsummary(model1, output = "table.html")

stargazer

The stargazer package is widely used for outputting regression results in LaTeX, HTML, or plain text formats. It allows you to include various statistics, such as standard errors, t-values, p-values, and confidence intervals.

require(stargazer)
#> Loading required package: stargazer
#> 
#> Please cite as:
#>  Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
#>  R package version 5.2.3. https://CRAN.R-project.org/package=stargazer

# Example: Fit two regression models
model1 <- lm(Length.of.Stay ~ age + sex + APACHE.score, data = ObsData)
model2 <- lm(Length.of.Stay ~ age + sex + APACHE.score + Medical.insurance, data = ObsData)

# Display the regression results using stargazer
stargazer(model1, model2, type = "text", 
          title = "Regression Results",
          dep.var.labels = "Length of Stay",
          covariate.labels = c("Age", "Sex", "APACHE Score", "Medical Insurance"),
          out = "regression_table.txt")
#> 
#> Regression Results
#> ==============================================================================
#>                                             Dependent variable:               
#>                               ------------------------------------------------
#>                                                Length of Stay                 
#>                                         (1)                     (2)           
#> ------------------------------------------------------------------------------
#> Age                                   -1.065                   -1.116         
#>                                       (1.093)                 (1.099)         
#>                                                                               
#> Sex                                   -1.420                   -1.201         
#>                                       (0.974)                 (1.055)         
#>                                                                               
#> APACHE Score                         -2.763***                -2.334*         
#>                                       (0.982)                 (1.221)         
#>                                                                               
#> Medical Insurance                    -6.762***               -6.530***        
#>                                       (1.213)                 (1.424)         
#>                                                                               
#> sexFemale                              0.932                   1.021          
#>                                       (0.688)                 (0.691)         
#>                                                                               
#> APACHE.score                          0.033*                   0.032*         
#>                                       (0.017)                 (0.017)         
#>                                                                               
#> Medical.insuranceMedicare                                      0.561          
#>                                                               (1.391)         
#>                                                                               
#> Medical.insuranceMedicare            Medicaid                                 
#>                                                               (1.744)         
#>                                                                               
#> Medical.insuranceNo insurance                                  0.475          
#>                                                               (1.763)         
#>                                                                               
#> Medical.insurancePrivate                                       0.385          
#>                                                               (1.199)         
#>                                                                               
#> Medical.insurancePrivate             Medicare                                 
#>                                                               (1.436)         
#>                                                                               
#> Constant                             21.312***               21.200***        
#>                                       (1.220)                 (1.495)         
#>                                                                               
#> ------------------------------------------------------------------------------
#> Observations                           5,735                   5,735          
#> R2                                     0.007                   0.008          
#> Adjusted R2                            0.006                   0.006          
#> Residual Std. Error             25.795 (df = 5728)       25.793 (df = 5723)   
#> F Statistic                   6.388*** (df = 6; 5728) 4.006*** (df = 11; 5723)
#> ==============================================================================
#> Note:                                              *p<0.1; **p<0.05; ***p<0.01

broom

The broom package converts regression model outputs into tidy data frames, making it easy to extract and manipulate specific parts of the model for custom summaries.

require(broom)
#> Loading required package: broom

# Tidy the model results for model1
tidy(model1)
#> # A tibble: 7 × 5
#>   term         estimate std.error statistic  p.value
#>   <chr>           <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   21.3       1.22      17.5   1.21e-66
#> 2 age[50,60)    -1.07      1.09      -0.975 3.30e- 1
#> 3 age[60,70)    -1.42      0.974     -1.46  1.45e- 1
#> 4 age[70,80)    -2.76      0.982     -2.81  4.94e- 3
#> 5 age[80, Inf)  -6.76      1.21      -5.58  2.57e- 8
#> 6 sexFemale      0.932     0.688      1.35  1.76e- 1
#> 7 APACHE.score   0.0325    0.0171     1.90  5.71e- 2

# Glance at model1 for a quick summary of goodness-of-fit statistics
glance(model1)
#> # A tibble: 1 × 12
#>   r.squared adj.r.squared sigma statistic    p.value    df  logLik    AIC    BIC
#>       <dbl>         <dbl> <dbl>     <dbl>      <dbl> <dbl>   <dbl>  <dbl>  <dbl>
#> 1   0.00665       0.00561  25.8      6.39 0.00000102     6 -26774. 53564. 53617.
#> # ℹ 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>

texreg

The texreg package allows you to export regression tables into LaTeX, HTML, or Word. It supports outputting multiple regression models side by side for easy comparison.

require(texreg)
#> Loading required package: texreg
#> Version:  1.39.4
#> Date:     2024-07-23
#> Author:   Philip Leifeld (University of Manchester)
#> 
#> Consider submitting praise using the praise or praise_interactive functions.
#> Please cite the JSS article in your publications -- see citation("texreg").

# Export models into a LaTeX table
texreg(list(model1, model2), 
       file = "regression_table.tex", 
       label = "tab:regression", 
       caption = "Regression Models for Length of Stay")
#> The table was written to the file 'regression_table.tex'.

You can also customize the output from these packages by specifying different statistics to display, such as including robust standard errors, changing the number of decimal places, or adding significance stars.

stargazer(model1, model2, type = "text", 
          se = list(coef(summary(model1))[ , "Std. Error"], 
                    coef(summary(model2))[ , "Std. Error"]), 
          star.cutoffs = c(0.05, 0.01, 0.001))
#> 
#> ==============================================================================
#>                                             Dependent variable:               
#>                               ------------------------------------------------
#>                                                Length.of.Stay                 
#>                                         (1)                     (2)           
#> ------------------------------------------------------------------------------
#> age[50,60)                            -1.065                   -1.116         
#>                                       (1.093)                 (1.099)         
#>                                                                               
#> age[60,70)                            -1.420                   -1.201         
#>                                       (0.974)                 (1.055)         
#>                                                                               
#> age[70,80)                           -2.763**                  -2.334         
#>                                       (0.982)                 (1.221)         
#>                                                                               
#> age[80, Inf)                         -6.762***               -6.530***        
#>                                       (1.213)                 (1.424)         
#>                                                                               
#> sexFemale                              0.932                   1.021          
#>                                       (0.688)                 (0.691)         
#>                                                                               
#> APACHE.score                           0.033                   0.032          
#>                                       (0.017)                 (0.017)         
#>                                                                               
#> Medical.insuranceMedicare                                      0.561          
#>                                                               (1.391)         
#>                                                                               
#> Medical.insuranceMedicare            Medicaid                                 
#>                                                               (1.744)         
#>                                                                               
#> Medical.insuranceNo insurance                                  0.475          
#>                                                               (1.763)         
#>                                                                               
#> Medical.insurancePrivate                                       0.385          
#>                                                               (1.199)         
#>                                                                               
#> Medical.insurancePrivate             Medicare                                 
#>                                                               (1.436)         
#>                                                                               
#> Constant                             21.312***               21.200***        
#>                                       (1.220)                 (1.495)         
#>                                                                               
#> ------------------------------------------------------------------------------
#> Observations                           5,735                   5,735          
#> R2                                     0.007                   0.008          
#> Adjusted R2                            0.006                   0.006          
#> Residual Std. Error             25.795 (df = 5728)       25.793 (df = 5723)   
#> F Statistic                   6.388*** (df = 6; 5728) 4.006*** (df = 11; 5723)
#> ==============================================================================
#> Note:                                            *p<0.05; **p<0.01; ***p<0.001

janitor

The janitor package simplifies data cleaning tasks, such as checking for duplicate records, cleaning column names, and generating cross-tabulations.

require(janitor)
#> Loading required package: janitor
#> Warning: package 'janitor' was built under R version 4.2.3
#> 
#> Attaching package: 'janitor'
#> The following objects are masked from 'package:stats':
#> 
#>     chisq.test, fisher.test

# Clean column names to make them syntactically valid
ObsData_clean <- clean_names(ObsData)

# Method 1: Using the data frame name in the tabyl function
tabyl(ObsData, Disease.category, RHC.use)
#>  Disease.category    0   1
#>               ARF 1581 909
#>               CHF  247 209
#>             Other  955 208
#>              MOSF  768 858

# Method 2: Using the pipe operator
ObsData %>% tabyl(Disease.category, RHC.use)
#>  Disease.category    0   1
#>               ARF 1581 909
#>               CHF  247 209
#>             Other  955 208
#>              MOSF  768 858

skimr

The skimr package provides a more compact and readable summary compared to the default summary() function. It tailors its output for each variable type.

require(skimr)
#> Loading required package: skimr
#> Warning: package 'skimr' was built under R version 4.2.3

# Quick summary of the entire dataset
skim(ObsData)

Data summary

Name	ObsData
Number of rows	5735
Number of columns	52
_______________________
Column type frequency:
factor	30
numeric	22
________________________
Group variables	None

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
Disease.category	0	1	FALSE	4	ARF: 2490, MOS: 1626, Oth: 1163, CHF: 456
Cancer	0	1	FALSE	3	Non: 4379, Loc: 972, Met: 384
Cardiovascular	0	1	FALSE	2	0: 4722, 1: 1013
Congestive.HF	0	1	FALSE	2	0: 4714, 1: 1021
Dementia	0	1	FALSE	2	0: 5171, 1: 564
Psychiatric	0	1	FALSE	2	0: 5349, 1: 386
Pulmonary	0	1	FALSE	2	0: 4646, 1: 1089
Renal	0	1	FALSE	2	0: 5480, 1: 255
Hepatic	0	1	FALSE	2	0: 5334, 1: 401
GI.Bleed	0	1	FALSE	2	0: 5550, 1: 185
Tumor	0	1	FALSE	2	0: 4419, 1: 1316
Immunosupperssion	0	1	FALSE	2	0: 4192, 1: 1543
Transfer.hx	0	1	FALSE	2	0: 5073, 1: 662
MI	0	1	FALSE	2	0: 5535, 1: 200
age	0	1	FALSE	5	[-I: 1424, [60: 1389, [70: 1338, [50: 917
sex	0	1	FALSE	2	Mal: 3192, Fem: 2543
DNR.status	0	1	FALSE	2	No: 5081, Yes: 654
Medical.insurance	0	1	FALSE	6	Pri: 1698, Med: 1458, Pri: 1236, Med: 647
Respiratory.Diag	0	1	FALSE	2	No: 3622, Yes: 2113
Cardiovascular.Diag	0	1	FALSE	2	No: 3804, Yes: 1931
Neurological.Diag	0	1	FALSE	2	No: 5042, Yes: 693
Gastrointestinal.Diag	0	1	FALSE	2	No: 4793, Yes: 942
Renal.Diag	0	1	FALSE	2	No: 5440, Yes: 295
Metabolic.Diag	0	1	FALSE	2	No: 5470, Yes: 265
Hematologic.Diag	0	1	FALSE	2	No: 5381, Yes: 354
Sepsis.Diag	0	1	FALSE	2	No: 4704, Yes: 1031
Trauma.Diag	0	1	FALSE	2	No: 5683, Yes: 52
Orthopedic.Diag	0	1	FALSE	2	No: 5728, Yes: 7
race	0	1	FALSE	3	whi: 4460, bla: 920, oth: 355
income	0	1	FALSE	4	Und: 3226, $11: 1165, $25: 893, > $: 451

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
edu	0	1	11.68	3.15	0.00	10.00	12.00	13.00	30.00	▁▇▃▁▁
DASIndex	0	1	20.50	5.32	11.00	16.06	19.75	23.43	33.00	▃▇▆▂▃
APACHE.score	0	1	54.67	19.96	3.00	41.00	54.00	67.00	147.00	▂▇▅▁▁
Glasgow.Coma.Score	0	1	21.00	30.27	0.00	0.00	0.00	41.00	100.00	▇▂▂▁▁
blood.pressure	0	1	78.52	38.05	0.00	50.00	63.00	115.00	259.00	▆▇▆▁▁
WBC	0	1	15.65	11.87	0.00	8.40	14.10	20.05	192.00	▇▁▁▁▁
Heart.rate	0	1	115.18	41.24	0.00	97.00	124.00	141.00	250.00	▁▂▇▂▁
Respiratory.rate	0	1	28.09	14.08	0.00	14.00	30.00	38.00	100.00	▅▇▂▁▁
Temperature	0	1	37.62	1.77	27.00	36.09	38.09	39.00	43.00	▁▁▅▇▁
PaO2vs.FIO2	0	1	222.27	114.95	11.60	133.31	202.50	316.62	937.50	▇▇▁▁▁
Albumin	0	1	3.09	0.78	0.30	2.60	3.50	3.50	29.00	▇▁▁▁▁
Hematocrit	0	1	31.87	8.36	2.00	26.10	30.00	36.30	66.19	▁▆▇▃▁
Bilirubin	0	1	2.27	4.80	0.10	0.80	1.01	1.40	58.20	▇▁▁▁▁
Creatinine	0	1	2.13	2.05	0.10	1.00	1.50	2.40	25.10	▇▁▁▁▁
Sodium	0	1	136.77	7.66	101.00	132.00	136.00	142.00	178.00	▁▂▇▁▁
Potassium	0	1	4.07	1.03	1.10	3.40	3.80	4.60	11.90	▂▇▁▁▁
PaCo2	0	1	38.75	13.18	1.00	31.00	37.00	42.00	156.00	▃▇▁▁▁
PH	0	1	7.39	0.11	6.58	7.34	7.40	7.46	7.77	▁▁▂▇▁
Weight	0	1	67.83	29.06	0.00	56.30	70.00	83.70	244.00	▂▇▁▁▁
Length.of.Stay	0	1	21.56	25.87	2.00	7.00	14.00	25.00	394.00	▇▁▁▁▁
Death	0	1	0.65	0.48	0.00	0.00	1.00	1.00	1.00	▅▁▁▁▇
RHC.use	0	1	0.38	0.49	0.00	0.00	0.00	1.00	1.00	▇▁▁▁▅

corrplot

The corrplot package is useful for visualizing correlation matrices with different styles (circle, color, etc.), making correlations easier to interpret.

require(corrplot)
#> Loading required package: corrplot
#> corrplot 0.92 loaded

# Step 1: Select only numerical variables from ObsData
ObsData_num <- dplyr::select(ObsData, where(is.numeric))

# Step 2: Compute the correlation matrix
corr_matrix <- cor(ObsData_num, use = "complete.obs")

# Step 3: Plot the correlation matrix using corrplot
corrplot::corrplot(corr_matrix, method = "circle")

visdat

The visdat package helps visualize missing data patterns, data types, and distributions.

require(visdat)
#> Loading required package: visdat
#> Warning: package 'visdat' was built under R version 4.2.3

# Visualize missing data patterns
vis_miss(ObsData)

# Visualize data types and missingness
vis_dat(ObsData)

naniar

The naniar package provides tools to handle and visualize missing data, helping to explore missingness patterns in the data.

require(naniar)
#> Loading required package: naniar
#> Warning: package 'naniar' was built under R version 4.2.3
#> 
#> Attaching package: 'naniar'
#> The following object is masked from 'package:skimr':
#> 
#>     n_complete

# Introduce some missing values for demonstration
ObsData_with_NA <- ObsData
ObsData_with_NA$age[sample(1:nrow(ObsData), 10)] <- NA
ObsData_with_NA$sex[sample(1:nrow(ObsData), 10)] <- NA

# Visualize missing data across variables
gg_miss_var(ObsData_with_NA)

# Plot the missing data upset plot
naniar::gg_miss_upset(ObsData_with_NA)
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?