The following are a set of more complex examples of the tableHTML package. The goal of these examples is to show you what you can do with the package and all the different ways you can use it to make the HTML tables they way you want them to look. The point of the examples is to demonstrate what can be achieved and not to show nice looking HTML tables (apart from the demonstration of the themes).
Row groups and Second Headerslibrary(tableHTML)
tableHTML(mtcars,
rownames = FALSE,
widths = c(120, rep(50, 11)),
row_groups = list(c(10, 10, 12), c('Group 1', 'Group 2', 'Group 3')),
second_headers = list(c(3, 4, 5), c('col1', 'col2', 'col3')))
Group 1 21 6 160 110 3.9 2.62 16.46 0 1 4 4 21 6 160 110 3.9 2.875 17.02 0 1 4 4 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4 Group 2 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4 10.4 8 460 215 3 5.424 17.82 0 0 3 4 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 33.9 4 71.1 65 4.22 1.835 19.9 1 1 4 1 Group 3 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 15 8 301 335 3.54 3.57 14.6 0 1 5 8 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2 Add row css and column css
tableHTML(mtcars,
border = 5,
rownames = TRUE,
widths = c(100, 140, rep(50, 11)),
row_groups = list(c(10, 10, 12), c('Group 1', 'Group 2', 'Group 3')),
second_headers = list(c(3, 4, 6), c('col1', 'col2', 'col3'))) %>%
add_css_row(css = list('background-color', 'lightgray'), rows = odd(3:34)) %>%
add_css_row(css = list('background-color', 'lightblue'), rows = even(3:34)) %>%
add_css_column(css = list('background-color', 'white'), columns = 'row_groups')
Group 1 Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4 Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1 Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2 Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4 Group 2 Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3 Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4 Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4 Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1 Honda Civic 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 19.9 1 1 4 1 Group 3 Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1 Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2 AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2 Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4 Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2 Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1 Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2 Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2 Add row, column, second header and header css
tableHTML(mtcars,
border = 1,
rownames = TRUE,
widths = c(110, 140, rep(50, 11)),
row_groups = list(c(10, 10, 12), c('Group 1', 'Group 2', 'Group 3')),
second_headers = list(c(2, 5, 6), c('', 'col2', 'col3'))) %>%
add_css_row(css = list('background-color', 'lightgray'), rows = odd(3:34)) %>%
add_css_row(css = list('background-color', 'lightblue'), rows = even(3:34)) %>%
add_css_column(css = list('background-color', 'white'), columns = 'row_groups') %>%
add_css_second_header(css = list(c('border-top', 'border-left'), c('1px solid white', '1px solid white')),
second_headers = 1) %>%
add_css_header(css = list(c('border-top', 'border-left', 'border-right'),
c('1px solid white', '1px solid white', '1px solid white')),
headers = 1) %>%
add_css_header(css = list('background-color', 'lightgreen'), headers = 3:13)
Group 1 Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4 Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1 Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2 Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4 Group 2 Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3 Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4 Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4 Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1 Honda Civic 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 19.9 1 1 4 1 Group 3 Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1 Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2 AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2 Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4 Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2 Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1 Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2 Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2 scientific theme
tableHTML(mtcars,
rownames = TRUE,
widths = c(110, 140, rep(50, 11)),
row_groups = list(c(10, 10, 12), c('Group 1', 'Group 2', 'Group 3')),
second_headers = list(c(2, 5, 6), c('col1', '', 'col3'))) %>%
add_theme('scientific')
Group 1 Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4 Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1 Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2 Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4 Group 2 Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3 Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4 Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4 Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1 Honda Civic 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 19.9 1 1 4 1 Group 3 Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1 Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2 AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2 Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4 Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2 Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1 Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2 Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2 rshiny-blue theme
tableHTML(mtcars,
rownames = TRUE,
widths = c(110, 140, rep(50, 11)),
row_groups = list(c(10, 10, 12), c('Group 1', 'Group 2', 'Group 3')),
second_headers = list(c(2, 5, 6), c('', 'col2', 'col3'))) %>%
add_theme('rshiny-blue')
Group 1 Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4 Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1 Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2 Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4 Group 2 Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3 Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4 Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4 Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1 Honda Civic 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 19.9 1 1 4 1 Group 3 Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1 Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2 AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2 Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4 Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2 Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1 Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2 Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2 colorize theme
mtcars %>%
tableHTML(rownames = TRUE,
widths = c(140, rep(50, 11))) %>%
add_theme('colorize', color = 'navyblue')
Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4 Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1 Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2 Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4 Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3 Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4 Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4 Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1 Honda Civic 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 19.9 1 1 4 1 Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1 Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2 AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2 Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4 Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2 Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1 Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2 Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2 colorize theme with Two Colors
mtcars %>%
tableHTML(rownames = TRUE,
widths = c(140, rep(50, 11))) %>%
add_theme_colorize(color=c('steelblue', 'green'))
Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4 Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1 Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2 Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4 Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3 Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4 Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4 Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1 Honda Civic 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 19.9 1 1 4 1 Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1 Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2 AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2 Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4 Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2 Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1 Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2 Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2 colorize theme with total rows and an id_column
ts_to_df <- data.frame(year = trunc(time(AirPassengers)),
Month = month.abb[cycle(AirPassengers)],
AirPassengers,
stringsAsFactors = F)
ts_to_df <- ts_to_df[ts_to_df$year < 1951, ]
rbind(ts_to_df[1:12, 2:3],
c('AVG', round(mean(ts_to_df[1:12, ]$AirPassengers), 2)),
ts_to_df[13:24, 2:3],
c('AVG', round(mean(ts_to_df[13:24, ]$AirPassengers), 2))) %>%
tableHTML(rownames = FALSE,
widths = rep(75, 3),
row_groups = list(c(13, 13),
unique(ts_to_df$year))) %>%
add_theme_colorize(id_column = TRUE,
total_rows = c(13, 26),
color = c('#009999', 'yellow2'))
1949 Jan 112 Feb 118 Mar 132 Apr 129 May 121 Jun 135 Jul 148 Aug 148 Sep 136 Oct 119 Nov 104 Dec 118 AVG 126.67 1950 Jan 115 Feb 126 Mar 141 Apr 135 May 125 Jun 149 Jul 170 Aug 170 Sep 158 Oct 133 Nov 114 Dec 140 AVG 139.67 Coloring a Row Group
tableHTML(mtcars,
border = 5,
rownames = TRUE,
widths = c(100, 140, rep(50, 11)),
row_groups = list(c(10, 10, 12), c('Group 1', 'Group 2', 'Group 3')),
second_headers = list(c(3, 4, 6), c('col1', 'col2', 'col3'))) %>%
add_css_row(css = list('background-color', 'lightgray'), rows = odd(3:34)) %>%
add_css_row(css = list('background-color', 'lightblue'), rows = even(3:34)) %>%
add_css_column(css = list('background-color', 'white'), columns = 'row_groups') %>%
replace_html(pattern = '<td id="tableHTML_row_groups" style="background-color:white;" rowspan="10">Group 1',
replacement = '<td id="tableHTML_row_groups" style="background-color:lightyellow;" rowspan="10">Group 1')
Group 1 Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4 Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1 Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2 Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4 Group 2 Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3 Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4 Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4 Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1 Honda Civic 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 19.9 1 1 4 1 Group 3 Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1 Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2 AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2 Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4 Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2 Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1 Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2 Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2 Collapsed table
tableHTML(mtcars,
border = 5,
rownames = TRUE,
collapse = 'separate',
widths = c(100, 140, rep(50, 11)),
row_groups = list(c(10, 10, 12), c('Group 1', 'Group 2', 'Group 3')),
second_headers = list(c(3, 4, 6), c('col1', 'col2', 'col3'))) %>%
add_css_row(css = list('background-color', 'lightgray'), rows = odd(3:34)) %>%
add_css_row(css = list('background-color', 'lightblue'), rows = even(3:34)) %>%
add_css_column(css = list('background-color', 'white'), columns = 'row_groups')
add_css_column overwrites add_css_row
tableHTML(mtcars,
border = 5,
rownames = TRUE,
collapse = 'collapse',
widths = c(140, rep(50, 11)),
second_headers = list(c(3, 4, 5), c('col1', 'col2', 'col3'))) %>%
add_css_row(css = list('background-color', 'lightgray'), rows = odd(3:34)) %>%
add_css_row(css = list('background-color', 'lightblue'), rows = even(3:34)) %>%
add_css_column(css = list('background-color', 'lightyellow'), columns = 'mpg') %>%
add_css_thead(css = list('background-color', 'lightblue'))
Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4 Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1 Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2 Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4 Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3 Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4 Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4 Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1 Honda Civic 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 19.9 1 1 4 1 Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1 Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2 AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2 Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4 Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2 Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1 Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2 Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2 Table with thead and tbody
tableHTML(mtcars,
border = 5,
rownames = TRUE,
widths = c(140, rep(50, 11)),
second_headers = list(c(3, 4, 5), c('col1', 'col2', 'col3'))) %>%
add_css_thead(css = list('background-color', 'lightgray')) %>%
add_css_tbody(css = list('background-color', 'lightblue'))
Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4 Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1 Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2 Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4 Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3 Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4 Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4 Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1 Honda Civic 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 19.9 1 1 4 1 Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1 Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2 AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2 Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4 Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2 Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1 Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2 Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2 Column Overwrites Row Overwrites tbody / thead Overwrites table
tableHTML(mtcars,
rownames = TRUE,
widths = c(140, rep(50, 11)),
second_headers = list(c(3, 4, 5), c('col1', 'col2', 'col3'))) %>%
add_css_table(css = list('background-color', 'lightgray')) %>%
add_css_tbody(css = list('background-color', 'lightblue')) %>%
add_css_row(css = list('background-color', 'red'), row = 5) %>%
add_css_column(css = list('background-color', 'lightgreen'), columns = 'mpg')
Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4 Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1 Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2 Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4 Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3 Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4 Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4 Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1 Honda Civic 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 19.9 1 1 4 1 Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1 Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2 AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2 Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4 Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2 Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1 Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2 Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2 Using non-collapsed tables in shiny
tableHTML(mtcars, collapse = 'separate_shiny', spacing = '5px 2px') %>%
add_css_table(css = list(c('background-color'), c('lightgray'))) %>%
add_css_table(css = list('color', 'blue'))
Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4 Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1 Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2 Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4 Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3 Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4 Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4 Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1 Honda Civic 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 19.9 1 1 4 1 Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1 Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2 AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2 Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4 Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2 Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1 Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2 Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2 Using conditional formatting with other tableHTML functions
tableHTML(mtcars,
widths = c(140, rep(50, 11))) %>%
add_theme('scientific') %>%
add_css_row(css = list('background-color', '#E0E0E0'), rows = odd(3:34)) %>%
add_css_conditional_column(conditional = 'between',
between = c(3.5, 4.22),
css = list(c('background-color'), c('gray')),
columns = c('drat', 'wt')) %>%
add_css_header(css = list(c('transform', 'height'),
c('rotate(-45deg)', '40px')),
headers = 1:12)
Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4 Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1 Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2 Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4 Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3 Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4 Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4 Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1 Honda Civic 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 19.9 1 1 4 1 Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1 Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2 AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2 Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4 Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2 Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1 Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2 Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2 Iris dataset with second headers, rowgroups, and conditional css
Note: the <div>
container is introduced, because the css property 'transform:rotate(...deg);'
would rotate the whole <td>
element would be rotated and background color would be out of place
tableHTML(iris[c(1:5, 51:55, 101:105), 1:4],
rownames = FALSE,
headers = rep(c('Length', 'Width'), 2),
second_headers = list(c(1, 2, 2), c('Species', 'Sepal', 'Petal')),
row_groups = list(rep(5, 3), paste0('<div style="width:100%; height:100%; ',
'transform:rotate(-90deg); font-size:16px; ',
'font-weight:bold; color:white; align:center">',
c('setosa', 'versicolor', 'virginica'),
'</div>'))) %>%
add_css_column(css = list(c('background-color', 'border'),
c('gray', 'white')),
columns = 'row_groups') %>%
add_css_second_header(css = list(c('color', 'background-color'),
c('white', 'gray')),
second_headers = 1:3) %>%
add_css_header(css = list(c('color', 'background-color'),
c('white', 'gray')),
headers = 1:5) %>%
add_css_conditional_column('color_rank',
color_rank_theme = 'White-Green',
columns = 1:2,
same_scale = FALSE) %>%
add_css_conditional_column('color_rank',
color_rank_theme = 'White-Blue',
columns = 3:4,
same_scale = FALSE)
setosa
5.1 3.5 1.4 0.2 4.9 3 1.4 0.2 4.7 3.2 1.3 0.2 4.6 3.1 1.5 0.2 5 3.6 1.4 0.2versicolor
7 3.2 4.7 1.4 6.4 3.2 4.5 1.5 6.9 3.1 4.9 1.5 5.5 2.3 4 1.3 6.5 2.8 4.6 1.5virginica
6.3 3.3 6 2.5 5.8 2.7 5.1 1.9 7.1 3 5.9 2.1 6.3 2.9 5.6 1.8 6.5 3 5.8 2.2 Table added as an image (not as html)This works perfectly with rmarkdown when you want to add the table as an image in a pdf, word or html document.
mtcars %>%
tableHTML(widths = c(140, rep(50, 11))) %>%
add_theme('rshiny-blue') %>%
tableHTML_to_image(type = 'png')
Table added as an image (changing size of image)
To increase the size of the image, you can use the rmarkdown chunk options. Here, I am using fig.height=7
and fig.width=7
.
mtcars %>%
tableHTML(widths = c(140, rep(50, 11))) %>%
add_theme('rshiny-blue') %>%
tableHTML_to_image(type = 'png')
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