reorder geom_raster

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Alessandra Carioli

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Apr 24, 2018, 1:23:26 PM4/24/18
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Dear all,

I am trying to plot various raster panels and to stack them one on top of each other.

I would like to order the id by its value in year 2000, but have no idea how to do it… 

Bests,

Ale

ggplot ( dt3 ,  aes ( year, id ) ) + geom_raster ( aes ( fill = ydr ) ) + scale_fill_gradient ( low = “yellow”,     high = “blue”) +  scale_colour_gradient ( low = “yellow”,     high = “blue”)+
theme_bw() + facet_grid(iso ~ .)

DPUT:

structure(list(iso = c("AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
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"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "AFG", 
"AFG", "AFG", "AFG", "AFG", "AFG", "AFG", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", "GMB", 
"GMB", "GMB", "GMB"), id = c(14L, 14L, 14L, 14L, 14L, 14L, 14L, 
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 
14L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 28L, 28L, 28L, 28L, 
28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 
28L, 28L, 28L, 28L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 
33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 21L, 
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 
21L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 
22L, 22L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 27L, 27L, 27L, 
27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 
27L, 27L, 27L, 27L, 27L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 
25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 
26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 17L, 17L, 17L, 17L, 17L, 
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 
17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 24L, 24L, 
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 
24L, 24L, 24L, 24L, 24L, 24L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 31L, 31L, 
31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 
31L, 31L, 31L, 31L, 31L, 31L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 
19L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 
29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 32L, 32L, 32L, 32L, 32L, 
32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 
32L, 32L, 32L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 
34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 15L, 15L, 
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
15L, 15L, 15L, 15L, 15L, 15L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
13L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 
23L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L), year = 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, 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), ydr = c(0.580787885, 
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0.728000732, 0.729359291, 0.789900606, 0.788848371, 0.785556098, 
0.784645099, 0.781984016, 0.776740014, 0.771835421, 0.764784808, 
0.760230729, 0.754639456, 0.748038358, 0.740487439, 0.731997616
)), .Names = c("iso", "id", "year", "ydr"), row.names = c(NA, 
-882L), class = c("tbl_df", "tbl", "data.frame"), spec = structure(list(
    cols = structure(list(iso = structure(list(), class = c("collector_character", 
    "collector")), id = structure(list(), class = c("collector_integer", 
    "collector")), year = structure(list(), class = c("collector_integer", 
    "collector")), ydr = structure(list(), class = c("collector_double", 
    "collector"))), .Names = c("iso", "id", "year", "ydr")), 
    default = structure(list(), class = c("collector_guess", 
    "collector"))), .Names = c("cols", "default"), class = "col_spec"))

Roman Luštrik

unread,
Apr 24, 2018, 1:28:11 PM4/24/18
to Alessandra Carioli, ggplot2
You should reorder the levels of the factor you're trying to plot.

Cheers,
Roman

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Please provide a reproducible example: https://github.com/hadley/devtools/wiki/Reproducibility
 
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In God we trust, all others bring data.

Alessandra Carioli

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Apr 24, 2018, 3:56:41 PM4/24/18
to Roman Luštrik, ggplot2
I’ve tried creating a new variable mean.ydr playing around with summary statistics and ordering according to it but it doesn’t really seem to sort any effect…

dt4 <-  dt3 %>% rowwise() %>% group_by ( iso,  year )  %>% 
  mutate( mean.ydr = min ( ydr )) %>% select(iso,  adm_id,  id,  year,  ydr,  mean.ydr)

ggplot(dt4 , aes(year, reorder(id, mean.ydr))) + geom_raster(aes(fill = ydr)) + 

scale_fill_distiller ( palette =  "Spectral" ) + scale_colour_distiller ( palette = "Spectral" )+

  facet_grid (iso ~ . , scales = "free", space = "free", margins = F)+

  theme(panel.spacing = unit( 0, "lines"))
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