I have this object in R:

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")

I made this into a proper table in R (using the following answer: ):

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")

I have the following question: In R, is it possible to do "conditional color formatting" as is done in Microsoft Excel?

For example, is it possible to shade individual cells as darker colors of red if the number in the cell is higher ... and remain whiter if the number is lower?

Thank you!

I have this object in R:

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")

I made this into a proper table in R (using the following answer: https://stackoverflow/a/79450212/28702910):

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")

I have the following question: In R, is it possible to do "conditional color formatting" as is done in Microsoft Excel?

For example, is it possible to shade individual cells as darker colors of red if the number in the cell is higher ... and remain whiter if the number is lower?

Thank you!

• r
• excel
• kableextra

• 3 Sure it is! Have a look here r-bloggers/2016/11/conditional-formatting-of-a-table-in-r or even with gt stackoverflow/a/74311810/28479453. This is how to do it in kableExtra: forum.posit.co/t/… – Tim G Commented 3 hours ago
• 2 or treat it as heatmap with numbers and colours – Friede Commented 3 hours ago
• This question is similar to: Conditionally formatting cells in one column by comparing it with the value from another column using Kable. If you believe it’s different, please edit the question, make it clear how it’s different and/or how the answers on that question are not helpful for your problem. – Limey Commented 2 hours ago
• @limey I dont think this is a dupe since the OP is asking for a gradient based on the value. The excellent answer in the link you send only assigns constant color conditional on category (though please correct me if I'm wrong!) – jpsmith Commented 2 hours ago

2 Answers 2

This is a tricky question and the comments give great advice, but none of the resources seemed to tackle your exact question regarding a gradient (please correct me if I'm wrong).

There may be more elegant solutions, but I've come up with a multi-step way to color the background in a gradient based on the value while keeping the display as a kable table (i.e., not a heat map figure). I've broken it up into a few steps for ease of reading:

library(dplyr)
library(tidyr)
library(kableExtra)
library(scales)

# Wide just for ease of reading the answer
z_wide <- z %>%
  pivot_wider(names_from = "year_2", 
              values_from = name_count)

minmax <- range(z_wide[-1]) # get min and max of all data, for gradient

# helper function to define gradient
bg_color <- function(x, min, max){
  norm <- (x - min) / (max - min)
  colorRampPalette(c("white", "red"))(100)[ceiling(norm * 99) + 1]
}

# assign data for ease of reading this answer
z_wide_bg <- z_wide %>%
  mutate(across(-year_1, ~ cell_spec(., 
                                     background = bg_color(as.numeric(.), minmax[1], minmax[2]),
                                     format = "html")))

# code to make table
z_wide_bg %>%
  kable("html", escape = FALSE, align = "c") %>%
  kable_styling("striped") %>%
  add_header_above(c(" " = 1, "year_2" = 21), align = "l")

Output:

In R, is it possible to do "conditional color formatting" as is done in Microsoft Excel?

An Image with R vanilla

I had quite some fun an image with additional text() added to each cell. Wrapped in an optional local() to prevent the environment from getting cluttered.

The approach requires data which can be represented as square matrix!

Note. Was a long day in Germany. Would be nice if someone suggests a simplified approach to rotate M--probably by reverting s somewhere? Thank you!

local({
  # own re-format routine 
  values = unlist(z$name_coun)
  row_names = unique(z$year_1)
  col_names = unique(z$year_2)
  nr = nc = length(row_names)
  M = matrix(values, nrow = nr, byrow = TRUE, dimnames = list(row_names, col_names))

  # reverting magic that harmonises col-argument of image and text 
  s = seq(nr)
  M = t(M[rev(s), ])
  image(s, s, M, xlab='', ylab = '', axes = FALSE, 
        col = hcl.colors(1e3, 'Reds 3', rev = TRUE))
  with(expand.grid(x = s, y = s), text(x, y, labels = M, col = "black"))

  # add custome axes
  axis(2, at = s, labels = rev(row_names), las = 2, col = NA) # left 
  axis(3, at = s, labels = col_names, las = 2, col = NA) # top
})

Data

> dput(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, 
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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, 
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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")