tableHTML

Here we are exploring the different themes that can be used with tableHTML

library(tableHTML)

Specifying a theme

Although the package has been designed so that it gives the utmost freedom to the user to style the HTML table as they please, some themes have been included for those who need something quick and nice. The package offers three themes for now: scientific, rshiny-blue, and colorize. To use them, you need to use the add_theme function.

Notice: When working with themes you can still add extra css (using the add_css_* family from below) but you will not be able to overwrite the styling that is there e.g. to change the width of the lines.

scientific

This is the scientifc theme where the table design resembles scientific tables for publishing.

tableHTML(mtcars, widths = c(140, rep(50, 11))) %>%
 add_theme('scientific')

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
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

tableHTML(mtcars,
          rownames = FALSE,
          widths = c(140, 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'))) %>%
  add_theme('scientific')

col1			col2				col3
	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
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

rshiny-blue

This theme matches the color of the standard shiny apps.

tableHTML(mtcars, widths = c(140, rep(50, 11))) %>%
 add_theme('rshiny-blue')

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
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

tableHTML(mtcars,
          rownames = FALSE,
          widths = c(140, 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'))) %>%
  add_theme('rshiny-blue')

col1			col2				col3
	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
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

colorize

This theme is used to create MS Excel-like tables (with any color you like). You can also use it to highlight specific rows (usually corresponding to some aggregation function, for example totals or averages). The arguments that can be used with the colorize theme are color, total_rows, and id_column. The default color is steelblue and by default no rows are chosen to be highlighted. You can also highlight the first column with the id_column argument. The documentation can be found at ?add_theme_colorize.

df <- mtcars[, 1:6]
df %>%
  tableHTML(widths = c(150, rep(70, ncol(df))), rownames = TRUE) %>%
  add_theme('colorize')

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21	6	160	110	3.9	2.62
Mazda RX4 Wag	21	6	160	110	3.9	2.875
Datsun 710	22.8	4	108	93	3.85	2.32
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.44
Valiant	18.1	6	225	105	2.76	3.46
Duster 360	14.3	8	360	245	3.21	3.57
Merc 240D	24.4	4	146.7	62	3.69	3.19
Merc 230	22.8	4	140.8	95	3.92	3.15
Merc 280	19.2	6	167.6	123	3.92	3.44
Merc 280C	17.8	6	167.6	123	3.92	3.44
Merc 450SE	16.4	8	275.8	180	3.07	4.07
Merc 450SL	17.3	8	275.8	180	3.07	3.73
Merc 450SLC	15.2	8	275.8	180	3.07	3.78
Cadillac Fleetwood	10.4	8	472	205	2.93	5.25
Lincoln Continental	10.4	8	460	215	3	5.424
Chrysler Imperial	14.7	8	440	230	3.23	5.345
Fiat 128	32.4	4	78.7	66	4.08	2.2
Honda Civic	30.4	4	75.7	52	4.93	1.615
Toyota Corolla	33.9	4	71.1	65	4.22	1.835
Toyota Corona	21.5	4	120.1	97	3.7	2.465
Dodge Challenger	15.5	8	318	150	2.76	3.52
AMC Javelin	15.2	8	304	150	3.15	3.435
Camaro Z28	13.3	8	350	245	3.73	3.84
Pontiac Firebird	19.2	8	400	175	3.08	3.845
Fiat X1-9	27.3	4	79	66	4.08	1.935
Porsche 914-2	26	4	120.3	91	4.43	2.14
Lotus Europa	30.4	4	95.1	113	3.77	1.513
Ford Pantera L	15.8	8	351	264	4.22	3.17
Ferrari Dino	19.7	6	145	175	3.62	2.77
Maserati Bora	15	8	301	335	3.54	3.57
Volvo 142E	21.4	4	121	109	4.11	2.78

df <- mtcars[, 1:6]
df %>%
  tableHTML(widths = c(150, rep(70, ncol(df))), rownames = TRUE) %>%
  add_theme('colorize', color = 'darkgreen')

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21	6	160	110	3.9	2.62
Mazda RX4 Wag	21	6	160	110	3.9	2.875
Datsun 710	22.8	4	108	93	3.85	2.32
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.44
Valiant	18.1	6	225	105	2.76	3.46
Duster 360	14.3	8	360	245	3.21	3.57
Merc 240D	24.4	4	146.7	62	3.69	3.19
Merc 230	22.8	4	140.8	95	3.92	3.15
Merc 280	19.2	6	167.6	123	3.92	3.44
Merc 280C	17.8	6	167.6	123	3.92	3.44
Merc 450SE	16.4	8	275.8	180	3.07	4.07
Merc 450SL	17.3	8	275.8	180	3.07	3.73
Merc 450SLC	15.2	8	275.8	180	3.07	3.78
Cadillac Fleetwood	10.4	8	472	205	2.93	5.25
Lincoln Continental	10.4	8	460	215	3	5.424
Chrysler Imperial	14.7	8	440	230	3.23	5.345
Fiat 128	32.4	4	78.7	66	4.08	2.2
Honda Civic	30.4	4	75.7	52	4.93	1.615
Toyota Corolla	33.9	4	71.1	65	4.22	1.835
Toyota Corona	21.5	4	120.1	97	3.7	2.465
Dodge Challenger	15.5	8	318	150	2.76	3.52
AMC Javelin	15.2	8	304	150	3.15	3.435
Camaro Z28	13.3	8	350	245	3.73	3.84
Pontiac Firebird	19.2	8	400	175	3.08	3.845
Fiat X1-9	27.3	4	79	66	4.08	1.935
Porsche 914-2	26	4	120.3	91	4.43	2.14
Lotus Europa	30.4	4	95.1	113	3.77	1.513
Ford Pantera L	15.8	8	351	264	4.22	3.17
Ferrari Dino	19.7	6	145	175	3.62	2.77
Maserati Bora	15	8	301	335	3.54	3.57
Volvo 142E	21.4	4	121	109	4.11	2.78

df <- mtcars[, 1:6]
df['Mean', ] <- (df %>% apply(2, mean))
df %>%
  tableHTML(widths = c(150, rep(70, ncol(df))), rownames = TRUE) %>%
  add_theme('colorize', color = c('steelblue', 'red'))

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21	6	160	110	3.9	2.62
Mazda RX4 Wag	21	6	160	110	3.9	2.875
Datsun 710	22.8	4	108	93	3.85	2.32
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.44
Valiant	18.1	6	225	105	2.76	3.46
Duster 360	14.3	8	360	245	3.21	3.57
Merc 240D	24.4	4	146.7	62	3.69	3.19
Merc 230	22.8	4	140.8	95	3.92	3.15
Merc 280	19.2	6	167.6	123	3.92	3.44
Merc 280C	17.8	6	167.6	123	3.92	3.44
Merc 450SE	16.4	8	275.8	180	3.07	4.07
Merc 450SL	17.3	8	275.8	180	3.07	3.73
Merc 450SLC	15.2	8	275.8	180	3.07	3.78
Cadillac Fleetwood	10.4	8	472	205	2.93	5.25
Lincoln Continental	10.4	8	460	215	3	5.424
Chrysler Imperial	14.7	8	440	230	3.23	5.345
Fiat 128	32.4	4	78.7	66	4.08	2.2
Honda Civic	30.4	4	75.7	52	4.93	1.615
Toyota Corolla	33.9	4	71.1	65	4.22	1.835
Toyota Corona	21.5	4	120.1	97	3.7	2.465
Dodge Challenger	15.5	8	318	150	2.76	3.52
AMC Javelin	15.2	8	304	150	3.15	3.435
Camaro Z28	13.3	8	350	245	3.73	3.84
Pontiac Firebird	19.2	8	400	175	3.08	3.845
Fiat X1-9	27.3	4	79	66	4.08	1.935
Porsche 914-2	26	4	120.3	91	4.43	2.14
Lotus Europa	30.4	4	95.1	113	3.77	1.513
Ford Pantera L	15.8	8	351	264	4.22	3.17
Ferrari Dino	19.7	6	145	175	3.62	2.77
Maserati Bora	15	8	301	335	3.54	3.57
Volvo 142E	21.4	4	121	109	4.11	2.78
Mean	20.090625	6.1875	230.721875	146.6875	3.5965625	3.21725

Here we are hightlighting the last row which contains the average of the columns.

df <- mtcars[, 1:6]
df['Mean', ] <- (df %>% apply(2, mean))
df %>%
  tableHTML(widths = c(150, rep(70, ncol(df))), rownames = TRUE) %>%
  add_theme('colorize', color = c('steelblue', 'red'), total_rows = nrow(df))

	mpg	cyl	disp	hp	drat	wt
Mazda RX4	21	6	160	110	3.9	2.62
Mazda RX4 Wag	21	6	160	110	3.9	2.875
Datsun 710	22.8	4	108	93	3.85	2.32
Hornet 4 Drive	21.4	6	258	110	3.08	3.215
Hornet Sportabout	18.7	8	360	175	3.15	3.44
Valiant	18.1	6	225	105	2.76	3.46
Duster 360	14.3	8	360	245	3.21	3.57
Merc 240D	24.4	4	146.7	62	3.69	3.19
Merc 230	22.8	4	140.8	95	3.92	3.15
Merc 280	19.2	6	167.6	123	3.92	3.44
Merc 280C	17.8	6	167.6	123	3.92	3.44
Merc 450SE	16.4	8	275.8	180	3.07	4.07
Merc 450SL	17.3	8	275.8	180	3.07	3.73
Merc 450SLC	15.2	8	275.8	180	3.07	3.78
Cadillac Fleetwood	10.4	8	472	205	2.93	5.25
Lincoln Continental	10.4	8	460	215	3	5.424
Chrysler Imperial	14.7	8	440	230	3.23	5.345
Fiat 128	32.4	4	78.7	66	4.08	2.2
Honda Civic	30.4	4	75.7	52	4.93	1.615
Toyota Corolla	33.9	4	71.1	65	4.22	1.835
Toyota Corona	21.5	4	120.1	97	3.7	2.465
Dodge Challenger	15.5	8	318	150	2.76	3.52
AMC Javelin	15.2	8	304	150	3.15	3.435
Camaro Z28	13.3	8	350	245	3.73	3.84
Pontiac Firebird	19.2	8	400	175	3.08	3.845
Fiat X1-9	27.3	4	79	66	4.08	1.935
Porsche 914-2	26	4	120.3	91	4.43	2.14
Lotus Europa	30.4	4	95.1	113	3.77	1.513
Ford Pantera L	15.8	8	351	264	4.22	3.17
Ferrari Dino	19.7	6	145	175	3.62	2.77
Maserati Bora	15	8	301	335	3.54	3.57
Volvo 142E	21.4	4	121	109	4.11	2.78
Mean	20.090625	6.1875	230.721875	146.6875	3.5965625	3.21725

Totals with add_theme_colorize

Instead of using the umbrella function add_theme, you could also explicitly use add_theme_colorize. The two functions are identical in terms of the output. To see the documentation visit ?add_theme_colorize.

# one total row
x1 <- sample(1:100, 12)
x2 <- sample(1:100, 12)
x3 <- sample(1:100, 12)

df <- data.frame(Month = month.abb, x1, x2, x3,
                 stringsAsFactors = FALSE)

df[nrow(df) + 1, ] <- c('Total', sum(x1), sum(x2), sum(x3))

df %>%
  tableHTML(widths = rep(50, 4), rownames = FALSE) %>%
  add_theme_colorize(color = 'darkred', total_rows = nrow(df))

Month	x1	x2	x3
Jan	75	42	19
Feb	84	21	36
Mar	68	37	15
Apr	95	56	51
May	32	52	5
Jun	86	81	41
Jul	3	33	98
Aug	34	93	52
Sep	44	84	60
Oct	70	70	43
Nov	24	4	76
Dec	38	78	66
Total	653	651	562

df_q <- rbind(
  df[1:3, ],
  c('Sum1', sum(x1[1:3]), sum(x2[1:3]), sum(x3[1:3])),
  df[4:6, ],
  c('Sum2', sum(x1[4:6]), sum(x2[4:6]), sum(x3[4:6])),
  df[7:9, ],
  c('Sum3', sum(x1[7:9]), sum(x2[7:9]), sum(x3[7:9])),
  df[10:12, ],
  c('Sum4', sum(x1[10:12]), sum(x2[10:12]), sum(x3[10:12])))

# Two colors and an id_column
df_q %>%
  tableHTML(widths = rep(50, 5),
            rownames = FALSE,
            row_groups = list(c(4, 4, 4, 4),
                              c('Q1', 'Q2', 'Q3', 'Q4'))) %>%
  add_theme_colorize(color = c('pink3', 'yellow2'),
                     total_rows = c(4, 8, 12, 16), id_column = TRUE)

	Month	x1	x2	x3
Q1	Jan	75	42	19
	Feb	84	21	36
	Mar	68	37	15
	Sum1	227	100	70
Q2	Apr	95	56	51
	May	32	52	5
	Jun	86	81	41
	Sum2	213	189	97
Q3	Jul	3	33	98
	Aug	34	93	52
	Sep	44	84	60
	Sum3	81	210	210
Q4	Oct	70	70	43
	Nov	24	4	76
	Dec	38	78	66
	Sum4	132	152	185