KableExtra

Basic Tables

One can create an extremely basic table using just the kable() function and no formatting. This table feature no formatting and not incorporate any borders between cells.

You do not have to specify the format = argument, if you do not the table will be created regardless of your output type (word, html, etc.). Specifying this argument is mostly required for additional changes to the appearance of the table when using the kableExtra package. In the case below, we have specified a html output, if we attempted to knit this document to word or pdf the table would be created.

imdb_animation_avgs <- imdb %>%
  filter(type != "videoGame")%>%
  group_by(type, animation)%>%
  summarise(Mean.Votes = mean(numVotes, na.rm = TRUE),
            Mean.Rating = mean(averageRating, na.rm = TRUE),
            Number.Of.Entries = n())

knitr::kable(imdb_animation_avgs, "html") #basic table with no editing of appearance

type	animation	Mean.Votes	Mean.Rating	Number.Of.Entries
movie	FALSE	17337.709	6.254202	41085
movie	TRUE	36393.630	6.656642	1197
short	FALSE	1576.763	6.823500	1183
short	TRUE	1822.737	7.363987	933
tvMiniSeries	FALSE	5779.093	7.452980	906
tvMiniSeries	TRUE	2829.885	7.411458	96
tvMovie	FALSE	1857.997	6.093077	3019
tvMovie	TRUE	2162.584	7.100000	125
tvSeries	FALSE	12953.661	7.208266	4682
tvSeries	TRUE	7238.361	7.269250	1174
tvShort	FALSE	1929.167	7.422917	48
tvShort	TRUE	2902.484	7.179121	91
tvSpecial	FALSE	1830.817	7.427378	431
tvSpecial	TRUE	5492.600	7.820000	5
video	FALSE	2556.080	5.559368	1425
video	TRUE	5036.685	6.431738	397

include a title and change column names

Unlike other packages, you can set many formatting options within the call to kable() this includes arguments for the number of digits for numeric columns, row names, column names, column alignment and a caption. As well as many arguments which can be passed to the format.args = option.

Using the kableExtra package, we are then able to create a much nicer and cleaner looking output for our table. This package is mainly designed to improve the appearance for tables in html and pdf, with different options available for these. You can pipe in the kable_styling() function with no arguments which will provide a default sleeker looking appearance to the table. The appearance depends on the format specified in the kable function. The example below focuses on html.

top_20_movies <- imdb %>%
  filter(type == "movie" & numVotes > 99999)%>%
  arrange(desc(averageRating))%>%
  select(title, year, averageRating, numVotes)%>%
  slice(1:20)

knitr::kable(top_20_movies,
             "html",
             col.names = c("Movie", "Year", "Rating", "Total Votes"), #provide a string of column names
             caption = "IMDB Entries with at least 100,000 votes")%>% # provide a caption (title)
  kable_styling()

IMDB Entries with at least 100,000 votes

Movie	Year	Rating	Total Votes
The Shawshank Redemption	1994	9.3	2138866
The Godfather	1972	9.2	1468315
The Godfather: Part II	1974	9.0	1021652
The Dark Knight	2008	9.0	2102907
The Mountain II	2016	9.0	101445
12 Angry Men	1957	8.9	610262
Schindler’s List	1993	8.9	1109973
Pulp Fiction	1994	8.9	1678715
The Lord of the Rings: The Return of the King	2003	8.9	1520719
The Good, the Bad and the Ugly	1966	8.8	635197
Forrest Gump	1994	8.8	1646150
The Lord of the Rings: The Fellowship of the Ring	2001	8.8	1536315
Fight Club	1999	8.8	1709679
Inception	2010	8.8	1875664
One Flew Over the Cuckoo’s Nest	1975	8.7	845858
Star Wars: Episode V - The Empire Strikes Back	1980	8.7	1070168
Goodfellas	1990	8.7	924279
The Matrix	1999	8.7	1540221
The Lord of the Rings: The Two Towers	2002	8.7	1375568
It’s a Wonderful Life	1946	8.6	363682

change number formatting and collapse rows

Additionally within the kable() function you can change number formatting by using the digits argument. You can do this by providing vector equal to the number of columns with the number of digits for each column if you wish to have columns with different arguments. Alternatively you can just provide a single number for a global option applicable to the whole table.

Similarly you can provide a string to the align argument to specify the column alignments, with “l” for left, “r” for right and “c” for centre. For instance if you write a string of “clc” then the first column will be centre aligned, the second left aligned and the third centre aligned.

You can use the collapse_rows() function to group same values in columns into multiple row cells. The main argument here is to provide the column you wish to apply this to. You can also specify the valign = argument by providing a string saying either top, middle, or bottom. This specifies the vertical alignment of the text in the grouped cell.

knitr::kable(imdb_animation_avgs,
             "html",
             col.names = gsub("[.]", " ", names(imdb_animation_avgs)), # specify column names, can use a formatting function instead of a string if you so wish
             caption = "Popularity and Quality of Animation",
             digits = c(0,0,0,2,0), # specify the digits for all columns
             format.args = list(big.mark = ","), # include a big mark for numbers above 1000
             align = "llrrr")%>% #RULER OF OMICRON PERSEI 8
            #provide a character string to set column aligmnets
  collapse_rows(columns = 1, valign = "middle")%>% # group 1st coulumn by the same value and align text in the middle of the cell
  kable_styling()

Popularity and Quality of Animation

type	animation	Mean Votes	Mean Rating	Number Of Entries
movie	FALSE	17,338	6.25	41,085
movie	TRUE	36,394	6.66	1,197
short	FALSE	1,577	6.82	1,183
short	TRUE	1,823	7.36	933
tvMiniSeries	FALSE	5,779	7.45	906
tvMiniSeries	TRUE	2,830	7.41	96
tvMovie	FALSE	1,858	6.09	3,019
tvMovie	TRUE	2,163	7.10	125
tvSeries	FALSE	12,954	7.21	4,682
tvSeries	TRUE	7,238	7.27	1,174
tvShort	FALSE	1,929	7.42	48
tvShort	TRUE	2,902	7.18	91
tvSpecial	FALSE	1,831	7.43	431
tvSpecial	TRUE	5,493	7.82	5
video	FALSE	2,556	5.56	1,425
video	TRUE	5,037	6.43	397

add a footnote

You can also add footnotes using the footnote() function. You can create different types of footnotes with different symbols using different arguments. general = will create an overall footnote with no symbol attached, number = will create a numeric symbol footnote, alphabet = will create an alphabetic footnote and symbol = will create footnotes using special symbols. Special symbols can also be manually specified.

These footnotes will not specify to a particular cell/title in the table unfortunately, something you can do with gt. In order to do this you need to edit the particular cell beforehand, or if it is a column name then you can do so within the col.names argument. You specify which footnote this applies to by using the footnote_marker_***() function. This specifies which list of footnotes to use and which symbol in the list to use. For example, using footnote_marker_alphabet(1) will put an “a” after the value, footnote_marker_alphabet(2) will put a “b” after the value and so on and so on.

If you are ever using these footnote marker functions, then within your call to kable, you have to include the escape = FALSE argument. This allows the function escape special characters when converting to the table. This will avoid any syntax errors created by these symbols.

top_20_movies[5,3] <- paste(top_20_movies[5,3], footnote_marker_symbol(1)) #sepcify the first sybmbol (* default)

knitr::kable(top_20_movies,
             "html",
             col.names = c(paste("Movie", footnote_marker_alphabet(1)), "Year", "Rating", "Total Votes"), #include and "a" agter the column name
             caption = "IMDB Entries with at least 100,000 votes",
             escape = F)%>% # ensure that html code is correctly honoured
  kable_styling()%>%
    footnote(general = "Here is a general comments of the table. ", #use vector of character strings to write multiple footnotes at once 
           number = c("Footnote 1; ", "Footnote 2; "),
           alphabet = c("Footnote A; ", "Footnote B; "),
           symbol = c("Footnote Symbol 1; ", "Footnote Symbol 2"))

IMDB Entries with at least 100,000 votes

Movie a	Year	Rating	Total Votes
The Shawshank Redemption	1994	9.3	2138866
The Godfather	1972	9.2	1468315
The Godfather: Part II	1974	9	1021652
The Dark Knight	2008	9	2102907
The Mountain II	2016	9 *	101445
12 Angry Men	1957	8.9	610262
Schindler’s List	1993	8.9	1109973
Pulp Fiction	1994	8.9	1678715
The Lord of the Rings: The Return of the King	2003	8.9	1520719
The Good, the Bad and the Ugly	1966	8.8	635197
Forrest Gump	1994	8.8	1646150
The Lord of the Rings: The Fellowship of the Ring	2001	8.8	1536315
Fight Club	1999	8.8	1709679
Inception	2010	8.8	1875664
One Flew Over the Cuckoo’s Nest	1975	8.7	845858
Star Wars: Episode V - The Empire Strikes Back	1980	8.7	1070168
Goodfellas	1990	8.7	924279
The Matrix	1999	8.7	1540221
The Lord of the Rings: The Two Towers	2002	8.7	1375568
It’s a Wonderful Life	1946	8.6	363682
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

Special Features

text and cell colours

This is quite a complicated example but shows some of the extensions one can do within kable. This using cell_spec() to specify the format of a cell before sending the data frame to kable. The function will provide LaTex of html code formatting to the cells of your data frame including text colour, background colours, italic text, bold text etc. You can even use the spec_color() function which will apply a continuous colour gradient to your cells.

iris[1:10, ] %>%
mutate_if(is.numeric, function(x) {
cell_spec(x, "html", bold = T, color = spec_color(x, end = 0.9), # mutate the cells of the data frame directly, cell_spec will add in html or Latex coding which kable will be able to read
font_size = spec_font_size(x)) # specify font size, changes relative to range of values in the column in this case. A continous scale of font size and colour
}) %>%
mutate(Species = cell_spec(
Species, "html", color = "white", bold = T,
background = spec_color(1:10, end = 0.9, option = "C", direction = -1) #specify a background contninous colour gradient
)) %>%
kable("html", escape = F, booktabs = T, linesep = "", align = "c")%>% # remember the escape argument to make sure html code is read properly
  kable_styling()

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

scroll box

There are many html only features that can be used within kableExtra that could especially be useful if ever wanting to create tables in a shiny application such as one of our data monitoring platforms.

This is done using the scroll_box() function in which you specify a character string defining the width and height of the box using either pixels or a percentage.

top_100_movies <- imdb %>%
  filter(type == "movie" & numVotes > 99999)%>%
  arrange(desc(averageRating))%>%
  slice(1:100)

kable(top_100_movies,
      "html")%>%
  kable_styling()%>%
  scroll_box(width = "500px", height = "400px") # specify size of the scroll box

title	type	year	length	numVotes	averageRating	director	birthYear	animation	action	adventure	comedy	documentary	fantasy	romance	sci_fi	thriller
The Shawshank Redemption	movie	1994	142	2138866	9.3	Frank Darabont	1959	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
The Godfather	movie	1972	175	1468315	9.2	Francis Ford Coppola	1939	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
The Godfather: Part II	movie	1974	202	1021652	9.0	Francis Ford Coppola	1939	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
The Dark Knight	movie	2008	152	2102907	9.0	Christopher Nolan	1970	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
The Mountain II	movie	2016	135	101445	9.0	Alper Caglar	NA	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
12 Angry Men	movie	1957	96	610262	8.9	Sidney Lumet	1924	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Schindler’s List	movie	1993	195	1109973	8.9	Steven Spielberg	1946	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Pulp Fiction	movie	1994	154	1678715	8.9	Quentin Tarantino	1963	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
The Lord of the Rings: The Return of the King	movie	2003	201	1520719	8.9	Peter Jackson	1961	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE
The Good, the Bad and the Ugly	movie	1966	161	635197	8.8	Sergio Leone	1929	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Forrest Gump	movie	1994	142	1646150	8.8	Robert Zemeckis	1951	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE
The Lord of the Rings: The Fellowship of the Ring	movie	2001	178	1536315	8.8	Peter Jackson	1961	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE
Fight Club	movie	1999	139	1709679	8.8	David Fincher	1962	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Inception	movie	2010	148	1875664	8.8	Christopher Nolan	1970	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE
One Flew Over the Cuckoo’s Nest	movie	1975	133	845858	8.7	Milos Forman	1932	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Star Wars: Episode V - The Empire Strikes Back	movie	1980	124	1070168	8.7	Irvin Kershner	1923	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE
Goodfellas	movie	1990	146	924279	8.7	Martin Scorsese	1942	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
The Matrix	movie	1999	136	1540221	8.7	Lana Wachowski	1965	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE
The Lord of the Rings: The Two Towers	movie	2002	179	1375568	8.7	Peter Jackson	1961	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE
It’s a Wonderful Life	movie	1946	130	363682	8.6	Frank Capra	1897	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE
Seven Samurai	movie	1954	207	289616	8.6	Akira Kurosawa	1910	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Star Wars: Episode IV - A New Hope	movie	1977	121	1139020	8.6	George Lucas	1944	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE
The Silence of the Lambs	movie	1991	118	1158497	8.6	Jonathan Demme	1944	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE
Se7en	movie	1995	127	1314485	8.6	David Fincher	1962	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Life Is Beautiful	movie	1997	116	564954	8.6	Roberto Benigni	1952	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE
The Green Mile	movie	1999	189	1039125	8.6	Frank Darabont	1959	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE
Saving Private Ryan	movie	1998	169	1133391	8.6	Steven Spielberg	1946	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Spirited Away	movie	2001	125	571049	8.6	Hayao Miyazaki	1941	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
City of God	movie	2002	130	654621	8.6	Kátia Lund	1966	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Interstellar	movie	2014	169	1332041	8.6	Christopher Nolan	1970	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE
Avengers: Endgame	movie	2019	181	565586	8.6	Anthony Russo	1970	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE
City Lights	movie	1931	87	151102	8.5	Charles Chaplin	1889	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE
Modern Times	movie	1936	87	195573	8.5	Charles Chaplin	1889	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE
The Great Dictator	movie	1940	125	184860	8.5	Charles Chaplin	1889	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE
Casablanca	movie	1942	102	484406	8.5	Michael Curtiz	1886	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE
Rear Window	movie	1954	112	407082	8.5	Alfred Hitchcock	1899	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE
Psycho	movie	1960	109	549773	8.5	Alfred Hitchcock	1899	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE
Once Upon a Time in the West	movie	1968	165	276867	8.5	Sergio Leone	1929	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Back to the Future	movie	1985	116	956609	8.5	Robert Zemeckis	1951	FALSE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	TRUE	FALSE
Grave of the Fireflies	movie	1988	89	203114	8.5	Isao Takahata	1935	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Cinema Paradiso	movie	1988	155	206440	8.5	Giuseppe Tornatore	1956	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Terminator 2: Judgment Day	movie	1991	137	924116	8.5	James Cameron	1954	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE
The Lion King	movie	1994	88	862509	8.5	Roger Allers	1949	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Léon: The Professional	movie	1994	110	945821	8.5	Luc Besson	1959	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
The Usual Suspects	movie	1995	106	921947	8.5	Bryan Singer	1965	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE
American History X	movie	1998	119	966092	8.5	Tony Kaye	1952	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Gladiator	movie	2000	155	1234507	8.5	Ridley Scott	1937	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
The Pianist	movie	2002	150	658270	8.5	Roman Polanski	1933	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
The Departed	movie	2006	151	1094918	8.5	Martin Scorsese	1942	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE
The Prestige	movie	2006	130	1085577	8.5	Christopher Nolan	1970	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE
The Intouchables	movie	2011	112	691362	8.5	Olivier Nakache	1973	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE
Whiplash	movie	2014	106	633344	8.5	Damien Chazelle	1985	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Avengers: Infinity War	movie	2018	149	710517	8.5	Anthony Russo	1970	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE
Sunset Blvd.	movie	1950	110	183192	8.4	Billy Wilder	1906	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Paths of Glory	movie	1957	88	160363	8.4	Stanley Kubrick	1928	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb	movie	1964	95	421090	8.4	Stanley Kubrick	1928	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE
Alien	movie	1979	117	727260	8.4	Ridley Scott	1937	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE
Apocalypse Now	movie	1979	147	558918	8.4	Francis Ford Coppola	1939	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
The Shining	movie	1980	146	800013	8.4	Stanley Kubrick	1928	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Raiders of the Lost Ark	movie	1981	115	825793	8.4	Steven Spielberg	1946	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Once Upon a Time in America	movie	1984	229	283447	8.4	Sergio Leone	1929	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Aliens	movie	1986	137	610070	8.4	James Cameron	1954	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE
Princess Mononoke	movie	1997	134	301169	8.4	Hayao Miyazaki	1941	TRUE	FALSE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE
Memento	movie	2000	113	1045575	8.4	Christopher Nolan	1970	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE
Oldboy	movie	2003	120	467919	8.4	Chan-wook Park	1963	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
The Lives of Others	movie	2006	137	328872	8.4	Florian Henckel von Donnersmarck	1973	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE
WALL·E	movie	2008	98	923571	8.4	Andrew Stanton	1965	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Like Stars on Earth	movie	2007	165	146744	8.4	Amole Gupte	NA	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
3 Idiots	movie	2009	170	307626	8.4	Rajkumar Hirani	1962	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE
The Dark Knight Rises	movie	2012	164	1408845	8.4	Christopher Nolan	1970	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE
Django Unchained	movie	2012	165	1238051	8.4	Quentin Tarantino	1963	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Coco	movie	2017	105	296439	8.4	Lee Unkrich	1967	TRUE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Spider-Man: Into the Spider-Verse	movie	2018	117	261452	8.4	Bob Persichetti	NA	TRUE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Dangal	movie	2016	161	129035	8.4	Nitesh Tiwari	NA	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Your Name.	movie	2016	106	144067	8.4	Makoto Shinkai	1973	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE
The Kid	movie	1921	68	100324	8.3	Charles Chaplin	1889	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE
Metropolis	movie	1927	153	147033	8.3	Fritz Lang	1890	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE
M	movie	1931	117	130845	8.3	Fritz Lang	1890	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE
Citizen Kane	movie	1941	119	369257	8.3	Orson Welles	1915	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Double Indemnity	movie	1944	107	130903	8.3	Billy Wilder	1906	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Bicycle Thieves	movie	1948	89	130810	8.3	Vittorio De Sica	1901	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Singin’ in the Rain	movie	1952	103	200992	8.3	Stanley Donen	1924	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE
Vertigo	movie	1958	128	327671	8.3	Alfred Hitchcock	1899	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE	TRUE
North by Northwest	movie	1959	136	276715	8.3	Alfred Hitchcock	1899	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE
The Apartment	movie	1960	125	147551	8.3	Billy Wilder	1906	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE
Lawrence of Arabia	movie	1962	228	247178	8.3	David Lean	1908	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
To Kill a Mockingbird	movie	1962	129	276491	8.3	Robert Mulligan	1925	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
For a Few Dollars More	movie	1965	132	211177	8.3	Sergio Leone	1929	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
2001: A Space Odyssey	movie	1968	149	553153	8.3	Stanley Kubrick	1928	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE
A Clockwork Orange	movie	1971	136	701839	8.3	Stanley Kubrick	1928	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE	FALSE
The Sting	movie	1973	129	224060	8.3	George Roy Hill	1921	FALSE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE
Taxi Driver	movie	1976	114	650939	8.3	Martin Scorsese	1942	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Das Boot	movie	1981	149	214202	8.3	Wolfgang Petersen	1941	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE
Star Wars: Episode VI - Return of the Jedi	movie	1983	131	876077	8.3	Richard Marquand	1937	FALSE	TRUE	TRUE	FALSE	FALSE	TRUE	FALSE	FALSE	FALSE
Scarface	movie	1983	170	676833	8.3	Brian De Palma	1940	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Amadeus	movie	1984	160	342609	8.3	Milos Forman	1932	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Full Metal Jacket	movie	1987	116	621426	8.3	Stanley Kubrick	1928	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Reservoir Dogs	movie	1992	99	845824	8.3	Quentin Tarantino	1963	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	TRUE
Braveheart	movie	1995	178	903150	8.3	Mel Gibson	1956	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE	FALSE
Toy Story	movie	1995	81	815123	8.3	John Lasseter	1957	TRUE	FALSE	TRUE	TRUE	FALSE	FALSE	FALSE	FALSE	FALSE

Cotton Farming example

The cotton farming example shown within the seminar is fairly straightforward in comparison to the other packages despite kable being a more technical option. This is partly due to us being able to manually create our row groupings using pack_rows() rather than requiring an existing grouping column. Therefore in this case we did not need to create an additional column beforehand or use an additional function to directly group the data before we even made a table.

This function requires you to provide a kable for the grouping and then to specify the first and last rows within this new grouping.

knitr::kable(A,
             "html",
             col.names = c("Sub-group", 
                           "Farm Area (Mean)",
                           "Farm Area (Median)", 
                           "Cotton Area (Mean)",
                           "Cotton Area (Median)",
                           "Cotton Area as % of Farm Area (Mean)",
                           "Cotton Area (Median)",
                           "N"),
             caption = "Farm Area (Acre), Cotton Area (Acre), Cotton Area as % of Farm Area")%>%
  kable_styling(font_size = 9)%>%
  pack_rows(group_label = "State", 1, 4)%>% # include a label, the first and last rows of the grouping
  pack_rows(group_label = "Partner",5,11)%>%
  pack_rows(group_label = "Overall",12,12)

Farm Area (Acre), Cotton Area (Acre), Cotton Area as % of Farm Area

Sub-group	Farm Area (Mean)	Farm Area (Median)	Cotton Area (Mean)	Cotton Area (Median)	Cotton Area as % of Farm Area (Mean)	Cotton Area (Median)	N
State
Gujarat	4.97	5.00	4.72	4.50	95.3	100.0	100
Madhya Pradesh	4.24	4.00	3.32	3.00	77.9	83.8	100
Maharashtra	2.12	1.00	2.04	1.00	98.5	100.0	100
Rajasthan	4.80	4.30	2.01	1.50	43.1	48.2	50
Partner
1	5.31	5.05	4.87	5.00	91.6	100.0	50
2	4.64	4.05	4.57	4.00	99.0	100.0	50
3	1.00	1.00	1.00	1.00	100.0	100.0	50
4	3.24	3.00	3.08	2.25	97.1	100.0	50
5	4.69	5.05	3.91	3.00	82.9	100.0	50
6	3.79	4.00	2.72	3.00	72.9	75.0	50
7	4.80	4.30	2.01	1.50	43.1	48.2	50
Overall
Total	3.92	4.00	3.16	3.00	83.8	100.0	350

Modelling Examples

For creating model tables, again the solution is to create a tidy model data frame using the broom package and then using kable on this data frame. You can edit this table in any way you wish. One extra thing to note is that you can use the code opts <- options(knitr.kable.NA = "") to specify an empty string for missing data instead of “NA”.

movies <- imdb%>%
  filter(type == "movie")%>%
  mutate(num_Votes_10000 = numVotes/10000)

model1 <- aov(averageRating ~ length + num_Votes_10000 + animation, movies)

m1 <- broom::tidy(model1) # create a tidy model table

opts <- options(knitr.kable.NA = "") # set NA values to an empty string

kable(m1,
      "html",
      digits = c(0,0,0,0,1,5))%>%
  kable_styling()

term	df	sumsq	meansq	statistic	p.value
length	1	3710	3710	2834.6	0
num_Votes_10000	1	1170	1170	893.8	0
animation	1	339	339	259.0	0
Residuals	42278	55331	1

lm(averageRating ~ length + num_Votes_10000 + animation, movies) %>%
  broom::tidy()%>%
  kable("html", 
        digits = c(0,3,3,5))%>%
  kable_styling()

term	estimate	std.error	statistic	p.value
(Intercept)	4.925	0.026	187.77456	0
length	0.012	0.000	49.99840	0
num_Votes_10000	0.023	0.001	28.85666	0
animationTRUE	0.544	0.034	16.09431	0