Kable and Kable extra

kableExtra is to help you build common complex tables and manipulate table styles.

library(knitr)
library(kableExtra)
dt <- mtcars[1:5, 1:6]
dt
##                    mpg cyl disp  hp drat    wt
## Mazda RX4         21.0   6  160 110 3.90 2.620
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875
## Datsun 710        22.8   4  108  93 3.85 2.320
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215
## Hornet Sportabout 18.7   8  360 175 3.15 3.440

Basic HTML table

dt %>%
  kable() %>%
  kable_styling()
mpg cyl disp hp drat wt
Mazda RX4 21.0 6 160 110 3.90 2.620
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875
Datsun 710 22.8 4 108 93 3.85 2.320
Hornet 4 Drive 21.4 6 258 110 3.08 3.215
Hornet Sportabout 18.7 8 360 175 3.15 3.440

Bootstrap table classes

dt %>%
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
mpg cyl disp hp drat wt
Mazda RX4 21.0 6 160 110 3.90 2.620
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875
Datsun 710 22.8 4 108 93 3.85 2.320
Hornet 4 Drive 21.4 6 258 110 3.08 3.215
Hornet Sportabout 18.7 8 360 175 3.15 3.440

Bootstarap option:

  1. striped : include stripe style
  2. bordered : include border
  3. hover : highlights a row if you mouse over it
  4. condensed : slightly shorter row height
  5. responsive : response for different screen
kable(dt) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
mpg cyl disp hp drat wt
Mazda RX4 21.0 6 160 110 3.90 2.620
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875
Datsun 710 22.8 4 108 93 3.85 2.320
Hornet 4 Drive 21.4 6 258 110 3.08 3.215
Hornet Sportabout 18.7 8 360 175 3.15 3.440

Full Width & Position

kable(dt) %>%
  kable_styling(bootstrap_options = "striped", full_width = F)
mpg cyl disp hp drat wt
Mazda RX4 21.0 6 160 110 3.90 2.620
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875
Datsun 710 22.8 4 108 93 3.85 2.320
Hornet 4 Drive 21.4 6 258 110 3.08 3.215
Hornet Sportabout 18.7 8 360 175 3.15 3.440

Table position

# Position 
# Align the table to center, left or right side of the page
kable(dt) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
mpg cyl disp hp drat wt
Mazda RX4 21.0 6 160 110 3.90 2.620
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875
Datsun 710 22.8 4 108 93 3.85 2.320
Hornet 4 Drive 21.4 6 258 110 3.08 3.215
Hornet Sportabout 18.7 8 360 175 3.15 3.440

Font Size

# Front Size
kable(dt) %>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left", font_size = 18)
mpg cyl disp hp drat wt
Mazda RX4 21.0 6 160 110 3.90 2.620
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875
Datsun 710 22.8 4 108 93 3.85 2.320
Hornet 4 Drive 21.4 6 258 110 3.08 3.215
Hornet Sportabout 18.7 8 360 175 3.15 3.440

float-left or float-right

kable(dt) %>%
  kable_styling(bootstrap_options = "striped", full_width = F,  position = "float_right", font_size = 18)
mpg cyl disp hp drat wt
Mazda RX4 21.0 6 160 110 3.90 2.620
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875
Datsun 710 22.8 4 108 93 3.85 2.320
Hornet 4 Drive 21.4 6 258 110 3.08 3.215
Hornet Sportabout 18.7 8 360 175 3.15 3.440

wrap text around the table using the float-left or float-right options.

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Column / Row Specification

x <- data.frame(
  Items = c("Item 1", "Item 2", "Item 3"),
  Features = c(
    "one",
    "two", 
    "three"
  )
)

kable(x) %>%
  kable_styling(full_width = F) %>%
  column_spec(1, bold = T, border_right = T) %>%
  column_spec(2, width = "30em",color = "white", background = "blue")
Items Features
Item 1 one
Item 2 two
Item 3 three 
kable(dt) %>%
  kable_styling("striped", full_width = F) %>%
  column_spec(5:7, bold = T) %>%
  row_spec(3:5, bold = T, color = "white", background = "blue")
mpg cyl disp hp drat wt
Mazda RX4 21.0 6 160 110 3.90 2.620
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875
Datsun 710 22.8 4 108 93 3.85 2.320
Hornet 4 Drive 21.4 6 258 110 3.08 3.215
Hornet Sportabout 18.7 8 360 175 3.15 3.440

Header Rows

kable(dt) %>%
  kable_styling("striped", full_width = F) %>%
  row_spec(0, angle = -45)
mpg cyl disp hp drat wt
Mazda RX4 21.0 6 160 110 3.90 2.620
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875
Datsun 710 22.8 4 108 93 3.85 2.320
Hornet 4 Drive 21.4 6 258 110 3.08 3.215
Hornet Sportabout 18.7 8 360 175 3.15 3.440

Conditional logic

library(dplyr)

mtcars[1:10, 1:2] %>%
  mutate(
    car = row.names(.),
    mpg = cell_spec(mpg, "html", color = ifelse(mpg > 20, "red", "blue")),
    cyl = cell_spec(cyl, "html", color = "white", align = "c", angle = 45, 
                    background = factor(cyl, c(4, 6, 8), 
                                        c("#666666", "#999999", "#BBBBBB")))
  ) %>%
  select(car, mpg, cyl) %>%
  kable(format = "html", escape = F) %>%
  kable_styling("striped", full_width = F)
car mpg cyl
Mazda RX4 21 6
Mazda RX4 Wag 21 6
Datsun 710 22.8 4
Hornet 4 Drive 21.4 6
Hornet Sportabout 18.7 8
Valiant 18.1 6
Duster 360 14.3 8
Merc 240D 24.4 4
Merc 230 22.8 4
Merc 280 19.2 6

Visualize data with Viridis Color

iris[1:10, ] %>%
  mutate_if(is.numeric, function(x) {
    cell_spec(x, bold = T, 
              color = spec_color(x, end = 0.9),
              font_size = spec_font_size(x))
  }) %>%
  mutate(Species = cell_spec(
    Species, color = "white", bold = T,
    background = spec_color(1:10, end = 0.9, option = "A", direction = -1)
  )) %>%
  kable(escape = F, align = "c") %>%
  kable_styling(c("striped", "condensed"), full_width = F)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
5.1 3.5 1.4 0.2 setosa
4.9 3 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
4.6 3.1 1.5 0.2 setosa
5 3.6 1.4 0.2 setosa
5.4 3.9 1.7 0.4 setosa
4.6 3.4 1.4 0.3 setosa
5 3.4 1.5 0.2 setosa
4.4 2.9 1.4 0.2 setosa
4.9 3.1 1.5 0.1 setosa

Add header rows to group columns

# Header rows 
kable(dt) %>%
  kable_styling("striped") %>%
  add_header_above(c("Row " = 1, "Group 1" = 2, "Group 2" = 2, "Group 3" = 2))
Row
Group 1
Group 2
Group 3
mpg cyl disp hp drat wt
Mazda RX4 21.0 6 160 110 3.90 2.620
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875
Datsun 710 22.8 4 108 93 3.85 2.320
Hornet 4 Drive 21.4 6 258 110 3.08 3.215
Hornet Sportabout 18.7 8 360 175 3.15 3.440 
kable(dt) %>%
  kable_styling(c("striped", "bordered")) %>%
  add_header_above(c(" ", "Group 1" = 2, "Group 2" = 2, "Group 3" = 2)) %>%
  add_header_above(c(" ", "Group 4" = 4, "Group 5" = 2)) %>%
  add_header_above(c(" ", "Group 6" = 6))
Group 6
Group 4
Group 5
Group 1
Group 2
Group 3
mpg cyl disp hp drat wt
Mazda RX4 21.0 6 160 110 3.90 2.620
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875
Datsun 710 22.8 4 108 93 3.85 2.320
Hornet 4 Drive 21.4 6 258 110 3.08 3.215
Hornet Sportabout 18.7 8 360 175 3.15 3.440

Group via leveling

kable(mtcars[1:10, 1:6], caption = "Group Rows") %>%
  kable_styling("striped", full_width = F) %>%
  pack_rows("Group 1", 4, 7) %>%
  pack_rows("Group 2", 8, 10)
Group Rows
mpg cyl disp hp drat wt
Mazda RX4 21.0 6 160.0 110 3.90 2.620
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875
Datsun 710 22.8 4 108.0 93 3.85 2.320
Group 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440
Valiant 18.1 6 225.0 105 2.76 3.460
Duster 360 14.3 8 360.0 245 3.21 3.570
Group 2
Merc 240D 24.4 4 146.7 62 3.69 3.190
Merc 230 22.8 4 140.8 95 3.92 3.150
Merc 280 19.2 6 167.6 123 3.92 3.440

Group labeling

kable(dt) %>%
  kable_styling("striped", full_width = F) %>%
  pack_rows("Group 1", 3, 5, label_row_css = "background-color: #666; color: #fff;")
mpg cyl disp hp drat wt
Mazda RX4 21.0 6 160 110 3.90 2.620
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875
Group 1
Datsun 710 22.8 4 108 93 3.85 2.320
Hornet 4 Drive 21.4 6 258 110 3.08 3.215
Hornet Sportabout 18.7 8 360 175 3.15 3.440

Table Footnote

kable(dt, align = "c") %>%
  kable_styling(full_width = F) %>%
  footnote(general = "Here is a general comments of the table. ",
           number = c("Footnote 1; ", "Footnote 2; "),
           alphabet = c("Footnote A; ", "Footnote B; "),
           symbol = c("Footnote Symbol 1; ", "Footnote Symbol 2")
           )
mpg cyl disp hp drat wt
Mazda RX4 21.0 6 160 110 3.90 2.620
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875
Datsun 710 22.8 4 108 93 3.85 2.320
Hornet 4 Drive 21.4 6 258 110 3.08 3.215
Hornet Sportabout 18.7 8 360 175 3.15 3.440
Note:
Here is a general comments of the table.
1 Footnote 1;
2 Footnote 2;
a Footnote A;
b Footnote B;
* Footnote Symbol 1;
† Footnote Symbol 2 
kable(dt, align = "c") %>%
  kable_styling(full_width = F) %>%
  footnote(general = "Here is a general comments of the table. ",
           number = c("Footnote 1; ", "Footnote 2; "),
           alphabet = c("Footnote A; ", "Footnote B; "),
           symbol = c("Footnote Symbol 1; ", "Footnote Symbol 2"),
           general_title = "General: ", number_title = "Type I: ",
           alphabet_title = "Type II: ", symbol_title = "Type III: ",
           footnote_as_chunk = T, title_format = c("italic", "underline")
           )
mpg cyl disp hp drat wt
Mazda RX4 21.0 6 160 110 3.90 2.620
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875
Datsun 710 22.8 4 108 93 3.85 2.320
Hornet 4 Drive 21.4 6 258 110 3.08 3.215
Hornet Sportabout 18.7 8 360 175 3.15 3.440
General: Here is a general comments of the table.
Type I: 1 Footnote 1; 2 Footnote 2;
Type II: a Footnote A; b Footnote B;
Type III: * Footnote Symbol 1; † Footnote Symbol 2

Scroll box

kable(cbind(mtcars, mtcars)) %>%
  kable_styling() %>%
  scroll_box(width = "500px", height = "200px")
mpg cyl disp hp drat wt qsec vs am gear carb mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2

xtable

Use this to convert model summaries to data.frames.

ANOVA Table

library("xtable")
data("tli")
fm1 <- aov(tlimth ~ sex + ethnicty + grade + disadvg, data=tli)
fm1.table <- xtable(fm1) #convert model summaries to data.frames.  

kable(fm1.table, caption = "Annova table")%>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left", font_size = 18)
Annova table
Df Sum Sq Mean Sq F value Pr(>F)
sex 1 75.37255 75.37255 0.3751912 0.5416830
ethnicty 3 2572.14918 857.38306 4.2679008 0.0071831
grade 1 36.30740 36.30740 0.1807318 0.6717271
disadvg 1 59.30338 59.30338 0.2952017 0.5882062
Residuals 93 18682.86749 200.89105 NA NA 
data("attitude")

## 2 OLS models
linear.1 <- lm(rating ~ complaints + privileges + learning + raises + critical, data=attitude)
linear.2 <- lm(rating ~ complaints + privileges + learning, data=attitude)
 
## create an indicator dependent variable, and run a probit model
attitude$high.rating <- (attitude$rating > 70)
probit.model <- glm(high.rating ~ learning + critical + advance, data=attitude, family = binomial(link = "probit"))
kable(xtable(linear.1))%>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left", font_size = 18)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.0111304 11.7039358 0.9408058 0.3561782
complaints 0.6920526 0.1488643 4.6488827 0.0001014
privileges -0.1035620 0.1347297 -0.7686650 0.4495906
learning 0.2490613 0.1596189 1.5603496 0.1317680
raises -0.0334606 0.2022762 -0.1654205 0.8699986
critical 0.0154883 0.1472500 0.1051840 0.9171040 
kable(xtable(linear.2))%>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left", font_size = 18)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.2583051 7.3183404 1.5383686 0.1360411
complaints 0.6824165 0.1288445 5.2964340 0.0000154
privileges -0.1032843 0.1293454 -0.7985149 0.4318056
learning 0.2379762 0.1394103 1.7070208 0.0997355 
kable(xtable(probit.model))%>%
  kable_styling(bootstrap_options = "striped", full_width = F, position = "left", font_size = 18)
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.4763927 3.5701890 -2.0941168 0.0362496
learning 0.1643745 0.0533782 3.0794322 0.0020740
critical -0.0005717 0.0439015 -0.0130228 0.9896096
advance -0.0618792 0.0420359 -1.4720567 0.1410056