r
Adding labels to a table in R
I have this dataframe in R which I am later planning on creating a contingency table with:
z = structure(list(year_1 = c(2000L, 2000L, 2000L, 2000L, 2000L,
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L,
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2001L, 2001L,
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L,
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L,
2001L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L,
2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L,
2002L, 2002L, 2002L, 2002L, 2003L, 2003L, 2003L, 2003L, 2003L,
2003L, 2003L, 2003L, 2003L, 2003L, 2003L, 2003L, 2003L, 2003L,
2003L, 2003L, 2003L, 2003L, 2003L, 2003L, 2003L, 2004L, 2004L,
2004L, 2004L, 2004L, 2004L, 2004L, 2004L, 2004L, 2004L, 2004L,
2004L, 2004L, 2004L, 2004L, 2004L, 2004L, 2004L, 2004L, 2004L,
2004L, 2005L, 2005L, 2005L, 2005L, 2005L, 2005L, 2005L, 2005L,
2005L, 2005L, 2005L, 2005L, 2005L, 2005L, 2005L, 2005L, 2005L,
2005L, 2005L, 2005L, 2005L, 2006L, 2006L, 2006L, 2006L, 2006L,
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L,
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L,
2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L,
2008L, 2008L, 2008L, 2008L, 2009L, 2009L, 2009L, 2009L, 2009L,
2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L,
2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2010L, 2010L,
2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L,
2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L,
2010L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L,
2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L,
2011L, 2011L, 2011L, 2011L, 2012L, 2012L, 2012L, 2012L, 2012L,
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2013L, 2013L,
2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L,
2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L,
2013L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L,
2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L,
2014L, 2014L, 2014L, 2014L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
2016L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2017L, 2018L, 2018L, 2018L, 2018L, 2018L,
2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L,
2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L, 2019L,
2019L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L,
2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L, 2020L,
2020L, 2020L, 2020L, 2020L), year_2 = c(2000L, 2001L, 2002L,
2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L,
2012L, 2013L, 2014L, 2015L, 2016L, 2017L, 2018L, 2019L, 2020L,
2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L,
2009L, 2010L, 2011L, 2012L, 2013L, 2014L, 2015L, 2016L, 2017L,
2018L, 2019L, 2020L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L,
2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 2014L,
2015L, 2016L, 2017L, 2018L, 2019L, 2020L, 2000L, 2001L, 2002L,
2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L,
2012L, 2013L, 2014L, 2015L, 2016L, 2017L, 2018L, 2019L, 2020L,
2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L,
2009L, 2010L, 2011L, 2012L, 2013L, 2014L, 2015L, 2016L, 2017L,
2018L, 2019L, 2020L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L,
2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 2014L,
2015L, 2016L, 2017L, 2018L, 2019L, 2020L, 2000L, 2001L, 2002L,
2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L,
2012L, 2013L, 2014L, 2015L, 2016L, 2017L, 2018L, 2019L, 2020L,
2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L,
2009L, 2010L, 2011L, 2012L, 2013L, 2014L, 2015L, 2016L, 2017L,
2018L, 2019L, 2020L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L,
2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 2014L,
2015L, 2016L, 2017L, 2018L, 2019L, 2020L, 2000L, 2001L, 2002L,
2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L,
2012L, 2013L, 2014L, 2015L, 2016L, 2017L, 2018L, 2019L, 2020L,
2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L,
2009L, 2010L, 2011L, 2012L, 2013L, 2014L, 2015L, 2016L, 2017L,
2018L, 2019L, 2020L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L,
2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 2014L,
2015L, 2016L, 2017L, 2018L, 2019L, 2020L, 2000L, 2001L, 2002L,
2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L,
2012L, 2013L, 2014L, 2015L, 2016L, 2017L, 2018L, 2019L, 2020L,
2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L,
2009L, 2010L, 2011L, 2012L, 2013L, 2014L, 2015L, 2016L, 2017L,
2018L, 2019L, 2020L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L,
2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 2014L,
2015L, 2016L, 2017L, 2018L, 2019L, 2020L, 2000L, 2001L, 2002L,
2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L,
2012L, 2013L, 2014L, 2015L, 2016L, 2017L, 2018L, 2019L, 2020L,
2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L,
2009L, 2010L, 2011L, 2012L, 2013L, 2014L, 2015L, 2016L, 2017L,
2018L, 2019L, 2020L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L,
2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 2014L,
2015L, 2016L, 2017L, 2018L, 2019L, 2020L, 2000L, 2001L, 2002L,
2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L,
2012L, 2013L, 2014L, 2015L, 2016L, 2017L, 2018L, 2019L, 2020L,
2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L,
2009L, 2010L, 2011L, 2012L, 2013L, 2014L, 2015L, 2016L, 2017L,
2018L, 2019L, 2020L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L,
2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 2014L,
2015L, 2016L, 2017L, 2018L, 2019L, 2020L), name_count = c(0L,
0L, 1L, 3L, 1L, 1L, 4L, 1L, 3L, 2L, 3L, 3L, 1L, 2L, 4L, 0L, 4L,
4L, 3L, 1L, 1L, 3L, 1L, 2L, 5L, 2L, 2L, 1L, 0L, 3L, 2L, 1L, 4L,
0L, 2L, 4L, 2L, 3L, 4L, 2L, 2L, 1L, 2L, 2L, 1L, 4L, 3L, 2L, 2L,
0L, 0L, 0L, 3L, 2L, 2L, 2L, 1L, 3L, 3L, 2L, 3L, 6L, 1L, 1L, 1L,
1L, 3L, 2L, 1L, 2L, 4L, 2L, 3L, 1L, 5L, 1L, 3L, 1L, 1L, 0L, 0L,
1L, 1L, 2L, 3L, 6L, 3L, 1L, 3L, 1L, 1L, 2L, 3L, 1L, 2L, 4L, 1L,
1L, 3L, 2L, 5L, 4L, 3L, 2L, 5L, 4L, 4L, 2L, 6L, 3L, 5L, 1L, 1L,
4L, 2L, 2L, 2L, 2L, 2L, 0L, 2L, 4L, 3L, 0L, 3L, 0L, 2L, 2L, 2L,
3L, 5L, 0L, 1L, 4L, 2L, 2L, 2L, 4L, 7L, 1L, 1L, 1L, 2L, 2L, 0L,
1L, 2L, 1L, 1L, 2L, 4L, 3L, 2L, 1L, 5L, 3L, 4L, 3L, 5L, 0L, 4L,
2L, 3L, 1L, 5L, 2L, 3L, 2L, 0L, 5L, 3L, 5L, 2L, 9L, 1L, 3L, 2L,
2L, 1L, 0L, 1L, 3L, 1L, 3L, 1L, 2L, 4L, 3L, 3L, 1L, 3L, 1L, 2L,
1L, 3L, 1L, 5L, 2L, 4L, 1L, 2L, 5L, 1L, 3L, 3L, 1L, 5L, 1L, 3L,
3L, 2L, 2L, 0L, 0L, 5L, 1L, 6L, 6L, 3L, 5L, 3L, 3L, 4L, 1L, 4L,
1L, 0L, 6L, 3L, 1L, 4L, 1L, 1L, 2L, 5L, 2L, 3L, 2L, 2L, 2L, 4L,
0L, 1L, 3L, 0L, 3L, 2L, 1L, 4L, 1L, 8L, 4L, 6L, 1L, 3L, 3L, 3L,
1L, 2L, 1L, 1L, 0L, 1L, 4L, 1L, 1L, 1L, 2L, 3L, 3L, 3L, 0L, 1L,
2L, 4L, 2L, 2L, 3L, 0L, 2L, 4L, 2L, 2L, 1L, 2L, 2L, 1L, 3L, 3L,
1L, 3L, 2L, 4L, 1L, 1L, 4L, 3L, 5L, 1L, 6L, 1L, 4L, 0L, 4L, 2L,
0L, 1L, 4L, 2L, 1L, 1L, 3L, 2L, 1L, 2L, 3L, 2L, 3L, 3L, 1L, 2L,
3L, 1L, 0L, 4L, 2L, 2L, 1L, 3L, 3L, 2L, 1L, 1L, 0L, 1L, 3L, 2L,
2L, 5L, 0L, 3L, 3L, 3L, 3L, 1L, 1L, 6L, 2L, 2L, 4L, 2L, 6L, 1L,
5L, 2L, 2L, 1L, 2L, 2L, 0L, 0L, 1L, 2L, 3L, 2L, 4L, 0L, 6L, 1L,
0L, 0L, 2L, 3L, 7L, 2L, 1L, 2L, 2L, 0L, 1L, 2L, 1L, 1L, 3L, 1L,
1L, 4L, 2L, 6L, 2L, 1L, 4L, 5L, 2L, 3L, 4L, 3L, 2L, 3L, 7L, 2L,
3L, 4L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 0L, 0L, 2L, 0L, 0L, 0L, 1L,
0L, 2L, 0L, 2L, 2L, 2L, 1L, 0L, 0L, 2L, 3L, 4L, 7L, 3L, 3L, 1L,
1L, 1L, 3L, 2L, 2L, 1L, 4L, 2L)), row.names = c(NA, -441L), class = "data.frame")
Using the following code, I made a contingency table and tried to add labels:
library(reshape2)
contingency_table <- dcast(z, year_1 ~ year_2, value.var = "name_count")
row_names <- paste("Year1:", contingency_table$year_1)
row.names(contingency_table) <- row_names
contingency_table <- contingency_table[,-1]
colnames(contingency_table) <- paste("Year2:", colnames(contingency_table))
contingency_table <- as.matrix(contingency_table)
The output looks like this:
Year2: 2000 Year2: 2001 Year2: 2002 Year2: 2003 Year2: 2004 Year2: 2005 Year2: 2006 Year2: 2007 Year2: 2008 Year2: 2009
Year1: 2000 0 0 1 3 1 1 4 1 3 2
Year1: 2001 3 1 2 5 2 2 1 0 3 2
Year1: 2002 2 2 1 4 3 2 2 0 0 0
Year1: 2003 1 1 1 3 2 1 2 4 2 3
Year1: 2004 3 6 3 1 3 1 1 2 3 1
Year1: 2005 4 4 2 6 3 5 1 1 4 2
Year1: 2006 2 2 2 3 5 0 1 4 2 2
Year1: 2007 1 1 2 4 3 2 1 5 3 4
Year1: 2008 0 5 3 5 2 9 1 3 2 2
Year1: 2009 1 3 1 2 1 3 1 5 2 4
I am wondering : Is it possible to add a single overarching label for the vertical and horizontal dimensions so that there is no repetition?
Thanks!
Solution
Here is another approach using kableExtra package:
- We use
pivot_wider()to widen the data - transform to kable format and
- apply kableExtra arguments
library(tidyr)
library(kableExtra)
z |>
pivot_wider(names_from = "year_2", values_from = name_count) |>
kable() |>
kable_styling(c("striped")) |>
add_header_above(c(" ", "year_2" = 21), align = "l")
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