How can I add a legend to this graph?

How can I add a legend to this graph? - r

I have a graph that I would like to add a legend to:
So far I have been able to either add a legend which defaults to its own colours, or one which changes the colours to a gradient fill which isn't what I want.
What am I doing wrong and how can I fix it?
Plot code:
ggplot(df) +
geom_line(aes(date, actual, group = 1),
colour = "forestgreen") +
geom_line(aes(date, plan, group = 1),
colour = "red") +
geom_smooth(aes(date, difference),
method = "auto",
se = FALSE,
linetype = "dashed") +
scale_x_date(date_breaks = "4 days" , date_labels = "%d-%b-%y") +
facet_grid(sector ~ ., scales = "free")
dput:
structure(list(date = structure(c(17959, 17959, 17959, 17959,
17959, 17960, 17960, 17960, 17960, 17960, 17961, 17961, 17961,
17961, 17961, 17962, 17962, 17962, 17962, 17962, 17963, 17963,
17963, 17963, 17963, 17964, 17964, 17964, 17964, 17964, 17965,
17965, 17965, 17965, 17965, 17966, 17966, 17966, 17966, 17966,
17967, 17967, 17967, 17967, 17967, 17968, 17968, 17968, 17968,
17968, 17969, 17969, 17969, 17969, 17969, 17970, 17970, 17970,
17970, 17970, 17971, 17971, 17971, 17971, 17971, 17972, 17972,
17972, 17972, 17972, 17973, 17973, 17973, 17973, 17973, 17974,
17974, 17974, 17974, 17974, 17975, 17975, 17975, 17975, 17975,
17976, 17976, 17976, 17976, 17976, 17977, 17977, 17977, 17977,
17977, 17978, 17978, 17978, 17978, 17978, 17979, 17979, 17979,
17979, 17979, 17980, 17980, 17980, 17980, 17980, 17981, 17981,
17981, 17981, 17981, 17982, 17982, 17982, 17982, 17982, 17983,
17983, 17983, 17983, 17983, 17984, 17984, 17984, 17984, 17984,
17985, 17985, 17985, 17985, 17985, 17986, 17986, 17986, 17986,
17986, 17987, 17987, 17987, 17987, 17987, 17988, 17988, 17988,
17988, 17988, 17989, 17989, 17989, 17989, 17989, 17990, 17990,
17990, 17990, 17990, 17991, 17991, 17991, 17991, 17991, 17992,
17992, 17992, 17992, 17992, 17993, 17993, 17993, 17993, 17993
), class = "Date"), sector = structure(c(1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L,
2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L,
1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L), .Label = c("ADVANCE CLINICIAN",
"CALL HANDLERS", "CLINICIANS", "GP", "PHARMACIST"), class = "factor"),
actual = c(16L, 232L, 104L, 40L, 12L, 23L, 231L, 90L, 47L,
12L, 34L, 245L, 109L, 61L, 0L, 32L, 226L, 99L, 50L, 0L, 25L,
247L, 103L, 58L, 7L, 84L, 362L, 89L, 57L, 8L, 50L, 333L,
86L, 44L, 11L, 14L, 276L, 71L, 68L, 7L, 24L, 263L, 93L, 62L,
12L, 42L, 241L, 92L, 42L, 13L, 42L, 242L, 106L, 60L, 8L,
50L, 262L, 101L, 58L, 8L, 38L, 340L, 80L, 42L, 0L, 32L, 312L,
73L, 39L, 8L, 14L, 219L, 81L, 54L, 15L, 19L, 239L, 100L,
50L, 20L, 15L, 245L, 104L, 58L, 13L, 38L, 233L, 90L, 57L,
8L, 50L, 236L, 94L, 41L, 7L, 78L, 370L, 106L, 61L, 8L, 34L,
328L, 91L, 45L, 8L, 16L, 247L, 54L, 57L, 20L, 19L, 263L,
93L, 43L, 15L, 26L, 231L, 88L, 58L, 13L, 50L, 234L, 87L,
61L, 8L, 68L, 245L, 113L, 50L, 15L, 68L, 352L, 89L, 38L,
8L, 22L, 288L, 98L, 18L, 11L, 13L, 201L, 34L, 40L, 8L, 12L,
208L, 41L, 45L, 8L, 5L, 215L, 48L, 45L, 0L, 53L, 210L, 45L,
55L, 0L, 20L, 220L, 71L, 23L, 8L, 12L, 349L, 80L, 54L, 8L,
2L, 287L, 52L, 35L, 0L), plan = c(43L, 291L, 28L, 32L, 11L,
37L, 262L, 25L, 32L, 11L, 39L, 260L, 22L, 32L, 11L, 34L,
266L, 90L, 32L, 11L, 39L, 269L, 93L, 32L, 11L, 66L, 422L,
152L, 61L, 14L, 54L, 373L, 133L, 53L, 14L, 43L, 291L, 106L,
32L, 11L, 37L, 262L, 90L, 32L, 11L, 39L, 260L, 94L, 32L,
11L, 34L, 266L, 90L, 32L, 11L, 39L, 269L, 93L, 32L, 11L,
66L, 422L, 152L, 61L, 14L, 54L, 373L, 133L, 53L, 14L, 43L,
291L, 106L, 32L, 11L, 37L, 262L, 90L, 32L, 11L, 39L, 260L,
94L, 32L, 11L, 34L, 266L, 90L, 32L, 11L, 39L, 269L, 93L,
32L, 11L, 66L, 422L, 152L, 61L, 14L, 54L, 373L, 133L, 53L,
14L, 43L, 291L, 106L, 32L, 11L, 37L, 262L, 90L, 32L, 11L,
39L, 260L, 94L, 32L, 11L, 34L, 266L, 90L, 32L, 11L, 39L,
269L, 93L, 32L, 11L, 66L, 422L, 152L, 61L, 14L, 54L, 373L,
133L, 53L, 14L, 43L, 283L, 73L, 32L, 11L, 37L, 283L, 99L,
32L, 11L, 39L, 264L, 88L, 32L, 11L, 34L, 260L, 90L, 32L,
11L, 39L, 275L, 98L, 32L, 11L, 66L, 424L, 156L, 61L, 14L,
54L, 360L, 130L, 53L, 14L), difference = c(-27L, -59L, 76L,
8L, 1L, -14L, -31L, 65L, 15L, 1L, -5L, -15L, 87L, 29L, -11L,
-2L, -40L, 9L, 18L, -11L, -14L, -22L, 10L, 26L, -4L, 18L,
-60L, -63L, -4L, -6L, -4L, -40L, -47L, -9L, -3L, -29L, -15L,
-35L, 36L, -4L, -13L, 1L, 3L, 30L, 1L, 3L, -19L, -2L, 10L,
2L, 8L, -24L, 16L, 28L, -3L, 11L, -7L, 8L, 26L, -3L, -28L,
-82L, -72L, -19L, -14L, -22L, -61L, -60L, -14L, -6L, -29L,
-72L, -25L, 22L, 4L, -18L, -23L, 10L, 18L, 9L, -24L, -15L,
10L, 26L, 2L, 4L, -33L, 0L, 25L, -3L, 11L, -33L, 1L, 9L,
-4L, 12L, -52L, -46L, 0L, -6L, -20L, -45L, -42L, -8L, -6L,
-27L, -44L, -52L, 25L, 9L, -18L, 1L, 3L, 11L, 4L, -13L, -29L,
-6L, 26L, 2L, 16L, -32L, -3L, 29L, -3L, 29L, -24L, 20L, 18L,
4L, 2L, -70L, -63L, -23L, -6L, -32L, -85L, -35L, -35L, -3L,
-30L, -82L, -39L, 8L, -3L, -25L, -75L, -58L, 13L, -3L, -34L,
-49L, -40L, 13L, -11L, 19L, -50L, -45L, 23L, -11L, -19L,
-55L, -27L, -9L, -3L, -54L, -75L, -76L, -7L, -6L, -52L, -73L,
-78L, -18L, -14L)), row.names = c(NA, -175L), class = c("tbl_df",
"tbl", "data.frame"))

Place the colour argument in aes and just write the label for it there. You can rename the title with +labs(colour="Legend Title"), and manipulate the colours with scale_fill_manual(values)
ggplot(df) +
geom_line(aes(date, actual, group = 1,colour = "actual")
) +
geom_line(aes(date, plan, group = 1,colour ="plan")) +
geom_smooth(aes(date, difference,colour="difference"),
method = "auto",
se = FALSE,
linetype = "dashed") +
scale_x_date(date_breaks = "4 days" , date_labels = "%d-%b-%y")+
facet_grid(sector ~ ., scales = "free")+
labs(colour="Method")+
scale_colour_manual(values=c("forestgreen","red","blue"))
Hope this is helpful!

Related

How to plot coefficients with robust standard errors?

I have this LSDV model using the "lm()" function and adding the country dummy variables minus the intercept. Then I made robust standard errors in order to fix heteroskedasticity and autocorrelation:
msubv2 <- lm(subv ~ preelec + elec + postelec + ideo + ali +
crec_pib + pob + pob16 + pob64 + factor(ccaa)-1, data = datos)
rsecoef_msubv2 <- coeftest(msubv2, vcovHAC(msubv2))
This is the code I used in order to implement the new coefficients in a regression output with stargazer() by the way:
cov12 <- vcovHAC(msubv2)
rsesubv2 <- sqrt(diag(cov12))
Now I want to plot these new coefficients of the explanatory variables "preelec", "elec" and "postelec" using either ggplot2() or coefplot() from the namesake package. However, as my object which contains the new coefficients is not an "lm" object, when I use those functions I get an error.
Hence, I just want to know how can I convert the object rsecoef_msubv2 into an "lm" object, or just another way to plot the coefficients for those 3 variables.
P.S. Ok, so this is a subset of my data. It must be converted into a panel data
structure(list(ccaa = structure(c(1L, 1L, 2L, 2L, 3L, 3L, 4L,
4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L, 10L, 11L, 11L,
12L, 12L, 13L, 13L, 14L, 14L, 15L, 15L, 16L, 16L, 17L, 17L), .Label = c("ANDALUCIA",
"ARAGON", "ASTURIAS", "BALEARES", "CANARIAS", "CANTABRIA", "CASTILLA LA-MANCHA",
"CASTILLA Y LEÓN", "CATALUÑA", "EXTREMADURA", "GALICIA", "LA RIOJA",
"MADRID", "MURCIA", "NAVARRA", "PAIS VASCO", "VALENCIA"), class = "factor"),
year = structure(c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c("1986", "1987",
"1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995",
"1996", "1997", "1998", "1999", "2000", "2001", "2002", "2003",
"2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011",
"2012", "2013", "2014", "2015", "2016", "2017"), class = "factor"),
ccaa_year = structure(c("AND86", "AND87", "ARA86", "ARA87",
"AST86", "AST87", "BAL86", "BAL87", "ISC86", "ISC87", "CANT86",
"CANT87", "CLM86", "CLM87", "CYL86", "CYL87", "CAT86", "CAT87",
"EXT86", "EXT87", "GAL86", "GAL87", "RIO86", "RIO87", "MAD86",
"MAD87", "MUR86", "MUR87", "NAV86", "NAV87", "PAV86", "PAV87",
"VAL86", "VAL87"), index = structure(list(ccaa = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 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, 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, 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, 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,
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, 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, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L), .Label = c("ANDALUCIA", "ARAGON",
"ASTURIAS", "BALEARES", "CANARIAS", "CANTABRIA", "CASTILLA LA-MANCHA",
"CASTILLA Y LEÓN", "CATALUÑA", "EXTREMADURA", "GALICIA",
"LA RIOJA", "MADRID", "MURCIA", "NAVARRA", "PAIS VASCO",
"VALENCIA"), class = "factor"), year = structure(c(1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L,
28L, 29L, 30L, 31L, 32L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 31L, 32L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L,
32L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L,
26L, 27L, 28L, 29L, 30L, 31L, 32L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L,
31L, 32L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L,
25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L,
31L, 32L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L,
25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L,
31L, 32L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L,
25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L,
31L, 32L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L,
25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L,
31L, 32L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L,
25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L,
31L, 32L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L,
25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L), .Label = c("1986",
"1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994",
"1995", "1996", "1997", "1998", "1999", "2000", "2001", "2002",
"2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010",
"2011", "2012", "2013", "2014", "2015", "2016", "2017"), class = "factor")), row.names = c(1L,
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0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 1L)), class = c("grouped_df", "tbl_df", "tbl", "data.frame"
), row.names = c(NA, -34L), groups = structure(list(ccaa = structure(1:17, .Label = c("ANDALUCIA",
"ARAGON", "ASTURIAS", "BALEARES", "CANARIAS", "CANTABRIA", "CASTILLA LA-MANCHA",
"CASTILLA Y LEÓN", "CATALUÑA", "EXTREMADURA", "GALICIA", "LA RIOJA",
"MADRID", "MURCIA", "NAVARRA", "PAIS VASCO", "VALENCIA"), class = "factor"),
.rows = structure(list(1:2, 3:4, 5:6, 7:8, 9:10, 11:12, 13:14,
15:16, 17:18, 19:20, 21:22, 23:24, 25:26, 27:28, 29:30,
31:32, 33:34), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -17L), .drop = TRUE))
P.S. I just need something like this
P.S. Finally I think I found a solution. The coefficients plot can be performed with the fuction "ggcoef" from the "GGally" package, which enables us to include as an object the coeftest() argument. Then we can procede like this:
First we create an object for our coeftest():
matrix_coeftestmsubv2 <- coeftest(msubv2, vcovHAC(msubv2))
After that we just create the plot with "ggcoef()":
ggcoef(matrix_coefmsubv2) + coord_flip()
Nevertheless, I still have some doubts regarding how to keep certain variables from the model, how to order them in the X Axis and how to add a line to connect the coefficients points, but I think I'll make a new post in order to get an answer.

So I found a definitive solution, I'm going to share it with you all. The function we need is dwplot() which belongs to the "dotwhisker" package. This one allows us to include a "coeftest" object and uses "ggplot2" to custom the graph easily. However, I recommend to convert the coeftest object into a dataframe because it makes it easier to delete the variables we don't need.
First we need to convert the object rsecoef_msubv2 into a dataframe:
library(dotwhisker)
rsecoef_msubv2 <- as.data.frame(rsecoef_msubv2)
After that we delete the rows we don't need, in my case:
tidycoefisubv <- tidycoefisubv[-c(4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26), ]
Finally we just create the plot using "dwplot". In this example I flipped the position of the axis, changed the color of the background and the font and size of the text of both axis.
dwplot(tidycoefisubv, vars_order = c("Postelectoral", "Electoral", "Preelectoral")) +
coord_flip() + theme_bw() + theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), text = element_text(size = 10),
axis.text.y = element_text(size=10, color="black"), axis.text.x = element_text(size=10,
color="black"),legend.position = "none") + labs(x = "Transferencias per cápita", y = NULL)
And this is the result:

How to identify (not remove) SETS of data that are duplicated? Dplyr or other solution?

so I have data about Sites, nested in Class. In each Site there is a Time (timepoint) variable. The data of interest is Count1, Total1, Count2, Total2.
I know there are whole duplicate sets within Class, across Sites for the values of Count1, Total1, Count2, Total2 for Time.
Here's what I mean - Let's say we have Class 1, with the first Site:
Class Site Time Count1 Total1 Count2 Total2
1 a0QjvO281o1 1 8 64 4 34
1 a0QjvO281o1 2 16 64 8 34
1 a0QjvO281o1 3 16 64 8 34
1 a0QjvO281o1 4 16 64 8 34
1 a0QjvO281o1 6 8 64 4 34
And, I've noticed there are several other Sites with this EXACT pattern (or other repeated patterns).
Class Site Time Count1 Total1 Count2 Total2
1 zlG1VmpE6QQ 1 8 64 4 34
1 zlG1VmpE6QQ 2 16 64 8 34
1 zlG1VmpE6QQ 3 16 64 8 34
1 zlG1VmpE6QQ 4 16 64 8 34
1 zlG1VmpE6QQ 6 8 64 4 34
I want to identify within Class how many Sites have the same pattern. Either marking them or reducing the data sets to the first unique site pattern, but I would like to be able to say how many Sites fit each found pattern.
So, here's the partial data:
df <-
structure(list(Class = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), Site = structure(c(3L,
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 9L, 9L,
9L, 9L, 9L, 17L, 17L, 17L, 17L, 17L, 19L, 19L, 19L, 19L, 19L,
30L, 30L, 30L, 30L, 30L, 49L, 49L, 49L, 49L, 49L, 54L, 54L, 54L,
54L, 54L, 56L, 56L, 56L, 56L, 56L, 62L, 62L, 62L, 62L, 62L, 66L,
66L, 66L, 66L, 66L, 86L, 86L, 86L, 86L, 86L, 88L, 88L, 88L, 88L,
88L, 98L, 98L, 98L, 98L, 98L, 33L, 33L, 33L, 33L, 33L, 128L,
128L, 128L, 128L, 128L, 141L, 141L, 141L, 141L, 141L, 153L, 153L,
153L, 153L, 153L, 154L, 154L, 154L, 154L, 154L, 274L, 274L, 274L,
274L, 274L, 291L, 291L, 291L, 291L, 291L, 306L, 306L, 306L, 306L,
306L, 309L, 309L, 309L, 309L, 309L, 336L, 336L, 336L, 336L, 336L,
342L, 342L, 342L, 342L, 342L, 396L, 396L, 396L, 396L, 396L, 413L,
413L, 413L, 413L, 413L, 418L, 418L, 418L, 418L, 418L, 435L, 435L,
435L, 435L, 435L, 451L), .Label = c("~", "A0e3A15Lh1d", "a0QjvO281o1",
"A0R2gEqRbTv", "A4J3Jp6KNz2", "A757EHpLOya", "A8kkDgEvEZV", "ab5F7MfRxZW",
"AcjfpLUXjwt", "admxsO3fTtq", "aEBm7REs6XS", "AEZgWxwdbd9", "AezXCsZxd2U",
"AFjm1YmnfyO", "AFTwI0xBM6e", "aGw7PyLMEkl", "aHNXoYj7uNJ", "AibLRYCSE4P",
"aitNX6Qxkon", "ajEqsuhE9fV", "aJFDh98Iahb", "AKG4BvCUVsF", "AMtGkXGugJb",
"aNczAtKAJsv", "aoY0wrz6qBF", "aOz3ikxG7qM", "aPWuF0rDfuJ", "aQrGXlhzEJB",
"ARu0wnYDkam", "As7tGowP84e", "AsqolR3dfgv", "atj39UeK8N9", "atmjKVCRnzw",
"aUhP7zZ7LPU", "aUMEQzUKI0K", "AuP8NAgS7Th", "aUyy9i4fwhS", "AVFW2vlGxds",
"awoAlwC06Go", "awxCmxmeea2", "AWYFb5fwcYb", "Ax2Q16uPW55", "AXO6R085bth",
"Ay6W05BTgDV", "aZMeFIlkevS", "B08adcYOEl7", "b5MVFPi1inY", "B7fffQm5omx",
"ba3kFfcKXNk", "bCK7hWM4bnK", "BDlYKSCaOIG", "BE3TZDysXuQ", "bErpy9bSZAV",
"Beu6pmpSDJE", "BgfNJiJlDrF", "bGUeQEEpq7q", "bgWDDBsRLIL", "bHwo17fsILI",
"bifefa8JnfN", "bIQ3gsw51RH", "bisxDvmwluW", "biy6fHoOcZp", "bK7yQP8LNkJ",
"Bke0tWeJyBr", "bKMNhuIYaYW", "blkWvfFDVm6", "bnaDFC8EVAo", "BNDeQ6sJctI",
"Bokks2ESodd", "BoKlS77F7Il", "BqLRDDu69ic", "bqoZAzbsajz", "BRlA0HkkMGM",
"bT501IhkxV9", "BTliRZoJs4i", "bTTf1R7zgRn", "bTZAPQPXgI5", "BUtglXWCjkf",
"BvcJEyVWsGG", "bVHpRZguCL2", "BVymUZcbCuf", "BwkVolONMBn", "bWtq9NnOoCU",
"c2YR2oDyx7t", "c3dhvyZuPum", "c3LYcysugey", "c46Q9ExLocA", "C52gwcl9fmp",
"c5IYnQ3M7dj", "c6yCKEAemfr", "C8uv1qapHmC", "Ca2rjTu7g6A", "cAsHVMiIVHT",
"cB7mNM1MNm0", "Cbboq0XBHn1", "cbUfMWJl9sK", "ccixNtjWLkf", "ccL7Esacksn",
"CgmvbI2pkyK", "cGvhZR5kDxQ", "chFA8wLA953", "cIb00kbYPgm", "cjoj6MxgfxE",
"cJrxpXipqCm", "cMR1ECoHpE4", "CmRKRa25mZu", "cnCuI3VeJKt", "cNUlz8NllVu",
"CoySgwRgeRE", "CpZyeEzz39h", "CqIH5ytvqTS", "cRbK3weaIO6", "cs2MtDT1y17",
"CSVVXoe0xGC", "ctEZrxoEucg", "CxCDdfOd0Nj", "cXzO64qne5O", "CZq12nSSyn9",
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"DGDkUV76OgX", "Dgt3VcFh8rl", "DHdEugYqcEI", "Dhku9zrZoJe", "dHokR5oLiIl",
"DhPZWGceA1Q", "DiKXevYOYNB", "DJIgnE1QQbB", "dkR7YOB6UT6", "dKy3aHycCap",
"dl9g8UYxk20", "DLmEBtWqO9S", "DLza3NSQYUI", "dmUHnTHgfYg", "dnRXJOdEzdw",
"doRK8OhG0kd", "DQaEryfraV6", "dQk8ubXxXLX", "dQOwWKXxFeq", "DrHlSXIHalR",
"DrLeENdZwxX", "DRUaAOrybxb", "dSJcUkmJWvZ", "dSuHNzaRaSf", "dtDftsTowRA",
"DVF2BNdSzV9", "DW7NajJs9ry", "dw94DZyrpUZ", "Dxa8RiDlXB6", "dXBB3LIqhd8",
"dY1ATXbywBu", "DY3V0E6pUYD", "dYIdx3HoWbL", "DZMyvdZEDeB", "dZrjKdqCi1w",
"e2cMNKCnHOw", "E2g3H9rUdML", "e59NHDOFTWC", "E6KoR8hXk7P", "E6vLBntf9QE",
"E8PnLO9QRcE", "e9NQxtBNruk", "e9QjFd6fZ4I", "EAdX1JPb4Dm", "eCGBeD0uz0D",
"ECHaJeidpTR", "edLdPyMjbaz", "EefeXxr8yDS", "ef6tzAcpMeF", "eFB6BfJ2BTY",
"EjFYleP5G9K", "eLGdmsoRjWn", "ElmgbenqYn7", "EM5PauW0KWg", "EmhBF1JUw3i",
"enR40fiMtoo", "EpxhEmcMVXh", "EQpPsVwWvqz", "EQtHhnAYjJp", "erfgs35WGXU",
"eRNEYF9OfA8", "ERqjIjzKnNm", "EsdcJsyJTJG", "ESNgljw6VvC", "eSZjKIwHPYi",
"etyPfIkrlrM", "Eu1JrO8bBkB", "euFWewBZ5Xr", "EVaNkH5nz1s", "eXgA6Zfn6KQ",
"EXIi96SW1Bm", "eYPdhvwFirr", "eZ2NazTVbb6", "EzN8D82lOTp", "F03oK0VRgyk",
"f0WCSs2fwvv", "F3CHKWYM2Pb", "f3FoF8cpKiH", "F42k81lXXMO", "F8ZvmoAy2bh",
"fd5zuIbL3Qd", "fDN9KAuRv2o", "FdqK3U8rDRX", "fG2ws21A6Lj", "fgDQSAYp5pj",
"FGjbxwib4q5", "FgLXwaIGGbn", "FiqXUXkRHXr", "fiuesJ8f3xw", "fJAqAOFzB2b",
"fJmQ6P38mHh", "fJy2O3xh1fV", "FjZuMxKuYvb", "FKe5fQHbu8l", "FKuw35vjqRz",
"FmAQ159jI3w", "FMGmKkEOmV4", "FmuzZuFFMzD", "FMX7RNQIwYu", "FNUYBvpbWaA",
"fnzDrz05g0T", "FO80di9Jxuk", "FOKfyVchS21", "fP0XmUTTfks", "fpCA3TMnMA3",
"FPkj0JvlmyK", "FPSoejJAWSU", "FqkwtkM7eXB", "FqlQZiGKxpr", "FThJa71HEEs",
"FVaQ3fSHtT5", "FvQrsd2gVeu", "fx7bCRgdYic", "FxrH3E1ge0f", "fYtsyMj84LY",
"G0EID1cpxEB", "g4jJZ1SNP4I", "g7AYmMzlRL5", "G7hnxrBDXd2", "GCQVHCnV25O",
"GcYMteoIkw9", "GDCM1IWa7Zh", "gdsUTJnwdzb", "Ge7oZ5R4iBk", "gEff10Pq35y",
"GFPi9bpW3sN", "ggMEnqgD9kD", "gKR0a28tTp5", "gKRGyOXbpzj", "glEuzcZNWIM",
"GlWdTuycHxs", "glyDmwEFzrr", "gmmjFqs7MFB", "GMWgNQ8JB1r", "gqFwQOY1wSE",
"gqNh2d7WJva", "grKa7EwswRX", "gsIY3JD3iHh", "GSWPAgMxhy2", "gsX0auFXP9m",
"Gtef53Qyxrj", "GTQqEhUUV1F", "guGv3PY445Z", "gUve5bZAut8", "gVZ58EQOH6K",
"GwXv8OX78AT", "GXIQmznIdQe", "GxIVLRDNmVF", "GziA2Vc0HX4", "h0RMK448nhs",
"H0vjaO76Wg8", "H1G7wWYemSm", "H3mOm6sbODE", "h4IQGhyYAQp", "H6LR8zRVQLW",
"hAoSAyLR3I6", "HB6ZBS6kyJ8", "HcKIEHFgpDb", "hCuRPOStRLU", "HdTW2XJg3IO",
"HdxFUpXFp2O", "hFwwNnFm1B8", "hHMHykeQBua", "HI3Z2eSmWYl", "hiRGzSqrLx5",
"hjeei4JLTiF", "HjwC2LDSWHK", "HlElMRh1t6W", "hlIZJlEsd7B", "hLwLFwQgUdb",
"HmIC1eI4aEQ", "HmuBn2Tdutx", "HN6AdgqShbf", "hoSu28MRYPv", "hq6x4qBOYsg",
"HQHoA9YKMAI", "hqvimuJJhKL", "hrpWiEmnynY", "hsLoXTDJDib", "htJFOM9EYmH",
"HU4RdTNlezp", "hWWRAoV26mI", "HXA0U1WlIhx", "hxckGietsww", "Hy4Uo9AjrnA",
"hy52ywnDIAM", "Hy5stTfQzCG", "HZd0k5dqZ9h", "hZV0CekLNni", "i0rzEGmhViY",
"i0UbyVCIMMY", "I21MUYJoVMy", "I2G30Bxw2BX", "I2tQnsS7wn6", "I3n104WlitM",
"i3UCGccuhCZ", "i4KTQ0RGK3T", "i5GWQwiObW1", "I5NWo4ucWB3", "I6v4GYaXpQC",
"i7xMMyJ6A6E", "IAvpgvgrG0f", "iBB477oQopG", "IBhZ0h4Ap4D", "IbltT4i4TK6",
"Icts0NC4qAd", "IesVnrPQeSZ", "IFINQSPg4YM", "IFTZCzzniHQ", "IFvY9G1PHAV",
"igDf6uUnTYe", "iHIs3hIFf0i", "IHWMvXnrYmQ", "Ii0xFlLHHXz", "iI2i5pPbl5B",
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"iwrIeTg1XFz", "iWvqk82htTQ", "IxcUubx1fw5", "j2k93SJevE1", "j2X8kPMcchC",
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"jUtaZ7I8azt", "juWqrHQgdew", "jVb0CSg6sIR", "JVHpkK4exDw", "JVk9m9vVA1D",
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"KqyKD3POUbS", "KQYxmgQNUSD", "KRQ61nuKa1b", "KtDVkM6bDeW", "ktTYjYLEW3v",
"kubDpNzUTG7", "KujnNfVcY2N", "kVJ0jf7P7Wf", "kWBZ1e0JH5h", "Kwts2m2rUUp",
"KxEa3dXzAYv", "kyGz0JzX3Z0", "kzHnYcum1wX", "L3iJ4hZ2ypn", "l7dBO27dhA6",
"l7RKRoGgmlq", "L7xlpOoRnWm", "LaH8j5yWJZ1", "lawU1EpVZVc", "LBEkbl9SzHf",
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"lS1XvFsr6no", "lUDMkJxSxHL", "Lw70k8Wjzp4", "LXWKW1xwmoZ", "lYNYlzUvgos",
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"m8FGZ1tP0UE", "M8kI8XD6qF9", "Ma2YKDqULAr", "MA3CYGbUEaG", "MAk4KZRu1L9",
"MAtmMxsNpeZ", "mC01s0xdGEm", "MCE5Y33BYDN", "MCT0SGxhkuU", "MdmyzozNJ02",
"mDNJnXJ3Bap", "ME541MEplIz", "ME9FWjRMe4e", "mePQU0trYhJ", "MFT0CnzHbgk",
"MFy31o7euAb", "mfZwiJJpZcR", "MgptQftlksp", "mgUgOViogq7", "MI2vOsP8NSo",
"MjCkEceL336", "mJY0L6TiTId", "MkU5WMbgI4U", "mKYg307awDr", "MM5BhvP1qVK",
"MM5CMbf9hxl", "mnshKO7lVDt", "moicbsA41fH", "mOSub2ULY1O", "Mpi4Xzop4kw",
"mPQwmRVhsKK", "mpxTG4BSHvb", "mR9nchmQZXC", "mruhLKuBF86", "mVZB3R5M66F",
"MW1EtjyMl5d", "MXHQSQfyHl2", "My1mHzVMqV6", "Mynld4Vekod", "N1giIHXfzhb",
"N23VxXj21Wv", "N2gVM6xHjXX", "n33C6ztvpqu", "N3LQS3eat8p", "n46vbqoLchh",
"N4rlgJRGUs3", "n5H2FaL7kap", "N5PPLwwES0c", "N6CPQoLRnz6", "N8nfcWXZtit",
"NawPD8q2KC8", "nChFLgqqH0w", "NCqjtm01Y4E", "NdMiR2VVel6", "nfR5nCiNHMC",
"nfwoSSAiWjg", "nfWs6WgmRC2", "nG7qJqJR13Z", "NGHkoHvBwF0", "nH6JZBFhCXs",
"nhfdWznpsqJ", "nhnQpVPQ7zK", "nhsj9HCnhEs", "nHtTsUMZoVG", "nIhIdZmXLXS",
"NIsmtALRuS5", "nj2KML2oqvV", "NJKcpotvrAQ", "nkXtOreJnSJ", "NLBLC0uWFuB",
"nmdSUueCjti", "NP8pgYnty0q", "NQxDKw6jGTj", "NSZxDwLVCeC", "nUanptGavqT",
"Nv5WX50ktwr", "nvJQYEQIFFM", "nvXHNeXXvJ5", "nwbO0NqAg7S", "nWJFiQq1vDL",
"nx2J294i6hk", "nxgu0uT1tLT", "NxKCqlm0eTG", "NYEpdnELJ54", "nYIBsKHueFr",
"nYnOM20f4fb", "NZxaguajfAY", "O1U2KTQp7RW", "O2p0zdfIFmP", "o3nzTkLC1Pl",
"o3pKyi7ckFO", "o4gtcJidna5", "O4slz8eLLn6", "o79rSRM0UlM", "O7qGvpaAt2w",
"oByIGUGsrgx", "od9Sosf2Y0V", "oDTFc2FqImi", "OdyuvCVU9Hz", "oEFK7vjkTU0",
"oEXOZcbaHxA", "OgLIyzin181", "OHtxRBRAzYs", "oJNbeCd6bvb", "oJsgj7WMDkq",
"OLEt9ovMHrz", "OlkZe7ivV0p", "oN0anW8xCpq", "oNDzB1D5as4", "oNfV9ntBJ9u",
"oNttkuJFbwC", "ooElCfPc54o", "OpEVn6IiULE", "OQ3BQRswMx7", "oTB157EY3jY",
"otmVyzT3xRC", "oUWkMygGP2W", "owxf1XoQ3Lu", "oYgWYWUVt2h", "OYjhvD7DqIP",
"oZfnfo46pS4", "p1NV2hE2fCZ", "p25NocgpHkc", "P2eQdjxbuZo", "p3T3oB4tfNN",
"P3Uob5UKAoM", "p4hBFnI8WIp", "p5L7w9Tjay3", "p7C2DczQikw", "P8tFheT6TtS",
"P91Rf8wCj7Z", "p9J64kFu5Fd", "PDOfJJdpbob", "pdRTIO2JqPL", "PDWC7RxX4t9",
"pEAFBcOJIVF", "PEfq6d3TONP", "PeNS8yHqYH1", "pEvaEn24SR1", "pg9F69FU9fh",
"Pi6v7zcA26e", "PibIwh4xKHI", "PicYz4ZaEkF", "PIm96jtkVB5", "pIVjHCsQgJI",
"PJI3sARzQAG", "PK027w8aZ5K", "PKfz9RYfKzF", "pl8h1HdqpFW", "pl9IGnhmOJc",
"PlISiBPN3db", "pMiRPEvyleJ", "pMtEAU5iVTB", "PnB0GLiMdBm", "PPb3XMcCAf3",
"PSdLvfFlDRF", "pthlRKVLgNp", "PTZfXfOkUR1", "pWmPB9No5RJ", "PWXwPbUM2DB",
"pxPQCkuJZrl", "PxXh1I86blw", "Pz198xRjRHD", "q2UUKkPtvll", "q4hyZcb2pgA",
"q6ke2WlwbWr", "Q75pcfnDLwr", "Q86baYhZPOB", "q8fmqtJVDhh", "qBrBhSbFC0d",
"qc9eMgI8Y95", "QCY2lUMpt7f", "QDkCAOGVng6", "QdYKp8ivavV", "qeBFicifeNz",
"QeKGz2D6wNe", "qEt7nmwua6v", "QGJz6Rv3qHU", "Qgzh7S5pLc3", "qHaaYvuNGIB",
"qiBueINJbti", "qimfq5GL5mV", "qJsVouyMqE8", "qlnxDl1BOrw", "Qlt1DOyb7iP",
"qm0fcx7VGOQ", "QMT77ObrHQa", "QOyCdSRSUXL", "Qpj3LVa0kMf", "qQ84fCTxdGh",
"QRaKmOedEZx", "qs9EipoiiBD", "qsPQEZph59z", "QTFJClMfP8c", "QtJyTjN5faU",
"qU7z54bY9jA", "QvByLV2hsHo", "QVFUUfes7vc", "QvQ5bpVOJDj", "Qwzbgh4Flmx",
"qx2DdF2CKFL", "qXdueHJNqcv", "QxSfgx5QfT7", "qxuRrLWQmXL", "Qztk8cjmz1e",
"r0ehsy1jjxa", "r2w7bZu3FsL", "R3ac44RpwRG", "r4mXVpHUWC7", "r6p12UeHOyg",
"r9efDheFtk3", "rakWSnvNhWr", "rbBZoYFr4DM", "rBtlT7YCRKx", "RdbYAXOnm2S",
"RdM4hjZsFRg", "Re2M8SlCc98", "RfmkqgjDUPL", "rgAmPaAHmNU", "rGbQXTyOdmW",
"RHpQbDCZK5O", "rhxxSbYXZRR", "RiIZqF2hfqY", "rIR8cwAz0sf", "rJ3tipUjVQ4",
"rlAmYWNUTnR", "rLiYzJJRiBA", "rLOyzoOdZqC", "RMKAo2HcVkM", "rnGH1Q5IyIU",
"robJRJuEFfM", "RovRnV9RWFd", "rpmWXDmHjsq", "rPPdTvv1QoY", "RqLdtXwHdGO",
"rR1aDWav3z1", "RrjHJQJDQSr", "rrZEwHEjjy8", "rsM3sdDc3Lk", "RsmDQZSmpD7",
"RtK3aS9WP2H", "ru8BHTnYxI3", "RU8DlKBg48x", "rUysfjKrKqk", "Rv3o89GkqWH",
"rVC8KePJHu3", "RvCLp5qbvtz", "RvQqAbOcEfA", "RW617O0UjQJ", "RWvmueaioAl",
"RxADuUq1Ba1", "RxHTSbz8VN5", "RxND5KsxzvW", "RyRJf2UHJL1", "S1Rh4YnCAAZ",
"s28njgt1wYe", "s4eb8Spa5TC", "S6gaiIWGmh9", "S6X4d5WHA1H", "sAnH4cWV41G",
"SATZgjyfpdZ", "sAyk7hwXEbV", "sBu9GwU5IKe", "SdlDgZMNxqX", "SegMIAP4dhw",
"SfB5NwJXaot", "SfPGp94cYZa", "SG0QMcMgRRq", "sG2EfH7UYLQ", "SgK14sd0Fq1",
"sgNOxONNZIv", "sGXcrRdwzAk", "shkTq2LdpXw", "si6qmHhCV9F", "skR6XpFhu3u",
"skuXY545bae", "skVM9VC2v6H", "sLkylFDaonQ", "sLQ3GDMCRSz", "sMVuTESYbpd",
"soMCF3RbHqt", "sQhxc449PV4", "sRbFOoSk7qZ", "srTptJGYtcK", "SSS1hmwqHOR",
"StSjQheznIv", "SvLVieXqQT7", "SVR6pSBhbCb", "sWlH85siDIT", "SWTCBn32M8D",
"sYBdL54a73r", "t0V5NCdjdPi", "T15MpYA7f51", "t3snPDHuVBW", "T5LdflE3Peq",
"T6RUeMH9KP0", "T6VbSgxjG4o", "t9Fl7c8SJbm", "TafeAKXESCA", "TBMPJiR0PKA",
"tcjz9dmJW4y", "tDDh1EjIZkh", "TE62MxBLgne", "TE7dhvcKVwp", "tEiDKptkacd",
"tEr481bYdow", "tfEtbnUgkGv", "TgpNd1eUCH5", "tGV21Z1HgXN", "thQZhxRh887",
"TJp862VOKlS", "TK7P7QXIDOA", "TKb3FP8mXY0", "TL5cvVAN3cA", "TM31sX4CThP",
"tMpwPcDzIfU", "tNf8m963xKK", "tnR9XvFJ5d7", "tNu7AdZ5358", "tOEYJ1EgIkn",
"TqSXqCuyodR", "tRgUTgCKu4J", "troIuBzxemz", "TSQWaAvOer5", "TsSlV9eE7Mz",
"ttKnsfno2BN", "tvoTu4cpYbh", "TWJPFfCeHES", "twyDPmlDNjH", "TyjDUvHkCAx",
"TYYPCGssY7i", "tz00ETYw78Y", "TZ307ap3HvE", "TzPwGs1AcCL", "TZxEGcWjbdk",
"u0ezFwC4OLL", "U3DjjRVyEun", "u45lZujojLF", "U6Mo4GsQKwT", "U7jt55boMwC",
"U8feQBluEhj", "UBe2SLdSmxV", "uBjjsyieqtr", "UccWk7OAtZ2", "uDXFpf8Ko6P",
"uE4KejhmDyk", "uGfkThgxZsI", "Uih0KGtvZeo", "UIyI4hkq7Bx", "UjoXPWJKPXb",
"uKFFT93nPmp", "UKSoohp2vBC", "UL70316n0C2", "UlD5QNXAW40", "uLDFnAy4ro0",
"UNxoCz1KXnW", "uOmh6keHjf6", "uormVxMEerw", "Upe0kYdbeUy", "UPSbASHNQmU",
"UQ1K5VqXqcZ", "uQvg5rWo87I", "usFB6MgBB6t", "uTeZmtXQzSN", "utgv86YyClH",
"UTmdWR44H5x", "uUmAJIXkmsO", "UUsAfkqIPhV", "Uv6Baj6YaG1", "UV9ZR51T6Ts",
"UvVxiC7b1jZ", "UW4ZNlm05Jq", "UxEq7311Xzd", "UXhcOzwv9o5", "UXSSmcXoWR8",
"v15yxuZyGjR", "V1MbBFGqwbB", "V5LD5oYeZys", "v6BprVsEEt2", "V7Hl62C5Wgz",
"Vah8YYh5HI5", "vbDOTEMQjfW", "vBjsjEqsmWL", "vBym1l507tA", "vf1kkxsjkB1",
"vFSbE4W5Kg6", "VfZPt9kXxL9", "vGLQ19KWuBv", "VHK1T5sygmw", "VLuN2iZ9oZp",
"VmwVU8HFDBn", "vnaUuR9C4FH", "vniKeY4S1Ru", "vOu023c0Snx", "vOuGO9bkEUa",
"vrQvRBzXiLv", "vRRoviRJVgX", "VS1mxlo1mVx", "VsFXXXagVmp", "VSHfWQyhzUu",
"vugElbQMtcL", "vvaX4oKLyKo", "vw87QIZ7dhk", "VWLVmvtDCSI", "vxXQe9jxSPE",
"vy0hyVTrTom", "Vy1JFQbsNBB", "vzGc2nPWraO", "vzVRv2jtJxL", "w0aCC4wNNzW",
"W1wtZLbWuY0", "w2yXiR4CyWt", "w539HzekPQh", "w55gRgLikEN", "WBhss2tvLa8",
"WcPEy9epMgd", "WCSGolF5yhy", "wdcS5ORWZte", "WDyq0ryAjpn", "webeuXrveDi",
"WeSJR8GDPmC", "WFApCUf18Lp", "wfFCmvMEGOQ", "WFiPvuGJf9O", "WggRnJplCQI",
"wgqFTVU7Iky", "wIMmZwl1gpX", "WjCGPzMzLVr", "WJfiDULf7ZC", "wkl1yyAzga3",
"wlspYUyDoQM", "wm060hpEM7g", "wMPB6u0GZDL", "Wn07Tbv74qp", "wNha3idA7l6",
"WnZVpXq5XCO", "wOe4JHkqbUm", "Wog7gclb7TJ", "wq4bmXnJK45", "Wq4O1nlYk1C",
"wqUwUpMD2mJ", "wrGYa8E94Yc", "WSAfRmiEJOF", "wSP90pEfCng", "wSW662GVwZP",
"wtoXU3G9YIy", "WtPSqPwjH2f", "wtV2TtEPCCZ", "Wtw2jbyaHz2", "WUChzooYWJ1",
"WUFgPdTN02g", "wUQiuRjZxiO", "Ww9Rq2KLlqV", "WWabB2sc4B7", "wxKEHpSLvib",
"wXnoTA2MDy9", "WYk4A1fVYD7", "WYMXHupBG7P", "wzD83xmvR3b", "WzemydwRD0R",
"X4ZVDdDd2xa", "X6efCWparbb", "X6uv3PName4", "X7deWPhTiIy", "X8TsrtMQFiu",
"X8UmaBiq1yy", "xbJCVaOZWp5", "XbjRzgMPN24", "XbubJh2yjOw", "XcqBCAaLcq5",
"xd8LIlN7N8h", "XdKVljaiZ9j", "xeEUMp35d5m", "XeUDpg1CTKf", "xf9Q4yYDlq5",
"XFEHZnnEGkT", "xFO9GKAXi1n", "xfxtwRZ7Ejp", "xhOpIbHQy8I", "XjBkSXvZLOZ",
"xjfIPJ04cET", "XLt8l1uPicg", "xlYle4v5GZ8", "xmJNiAbmSfe", "XnkRi1jTMKr",
"XPhxWI0fDyq", "XqDQsrhQ7W5", "Xsd3yzbnFOf", "XTF6vymtG8J", "xuovzIjWZUG",
"Xv1I8z1cK76", "XvVmyn071HT", "XxBMueAFsnk", "xxVZKlzMYJJ", "xyr4dO4G3tW",
"y4rr2PbfufS", "yaa2uBLsdRa", "YBG39jGSV17", "yDcnCB4aZEX", "YDuoFIKpONe",
"YdWxRCaQR2D", "yfgSogitBGX", "YFi06xiFHWs", "YFi2V7qfmJf", "yfpM2zJ3Zuc",
"ygTl7hih5qi", "YGtrgJxKWiU", "yIcfnuZhejK", "YIxt0WtezdT", "yJ014QFEqru",
"yJO8QTnBF3o", "yKfdWuLsdDx", "ylMgcLnwgce", "YNy9ymD2A8p", "yONz8gph9A7",
"YowwYq8CIXJ", "YPsxC0bl7T2", "YQP6diqjJAl", "YqR6LoSk2Ed", "yqwh11CvYXU",
"YRemZ3p9bFA", "ySxRSgTOeqD", "yTvx2IJ0w0z", "ytwga9hKjVj", "YtyO06HBaVr",
"YvEkkZlNeCK", "yVFdJkYsLK5", "yvoQHXHGvbT", "YVT9zsaVBzp", "YWbmL6VK8R6",
"Ywm8eA9tZHe", "yXady1QV27H", "yY7MHufA6C9", "yYG52aLO1GK", "yYgG4h097xR",
"YyhPAO5yx22", "Yz5yhyHf7Ul", "z2cGjpx37Mw", "Z42m6cWsI9m", "z4DptoHrJnb",
"z4kLOdnL1Op", "z5tZes2s49Z", "z5WklS85YjT", "z6bId6qlNk4", "Z6ZZLw50mAM",
"z8MwD6T43n2", "z8UkGdr2xNs", "Z90jET09ZrD", "zaeb1Zos2Mu", "ZBkpY2KdibX",
"Zc0BcScQDBU", "zCjn57zZQVN", "ZcrdEBruDka", "ZCT4YbaBFUb", "ZdVIx83rdI7",
"zEQXA689E4a", "ZfjQmCjVKRF", "zfutn6ulVcO", "zFzYdXMnPoP", "zG4JqtM8wHO",
"ZGyAErBl5PS", "ZifoCg4OvIj", "ZJ6MAab9PJE", "ZKVzRmYkKzQ", "zlG1VmpE6QQ",
"zN6xXPgmzqK", "zOfDRrZmbQO", "zOGa9wLHDFE", "zQmuipEUYbz", "zR7UekDUG3X",
"zrs6iFpEtF1", "ZrUjQFzR1gM", "zTnxsAMqHRP", "Zu7gpmcwfqY", "zvOkAI9ewwE",
"zvv07VAowTS", "ZWAdop7zYgJ", "ZWAEE8DrywN", "zxIlF5RwQFi", "ZXONCt7P01p"
), class = "factor"), Time = c(1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L,
4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L,
6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L,
1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L,
2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L,
3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L,
4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L,
6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L,
1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L,
2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L),
Count1 = c(8L, 16L, 16L, 16L, 8L, 12L, 24L, 24L, 24L, 12L,
8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L,
16L, 8L, 8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L, 12L,
24L, 24L, 24L, 12L, 8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L,
16L, 8L, 12L, 24L, 24L, 24L, 12L, 8L, 16L, 16L, 16L, 8L,
12L, 24L, 24L, 24L, 12L, 8L, 16L, 16L, 16L, 8L, 8L, 16L,
16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L,
8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L,
16L, 8L, 8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L, 8L,
16L, 16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L, 16L,
8L, 8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L, 8L, 16L,
16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L,
8L), Total1 = c(64L, 64L, 64L, 64L, 64L, 96L, 96L, 96L, 96L,
96L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 96L, 96L, 96L, 96L, 96L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 96L, 96L, 96L, 96L, 96L, 64L, 64L,
64L, 64L, 64L, 96L, 96L, 96L, 96L, 96L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L), Count2 = c(4L,
8L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 3L, 7L, 8L, 8L, 4L, 4L,
8L, 8L, 8L, 4L, 3L, 8L, 8L, 8L, 4L, 3L, 7L, 8L, 8L, 4L, 2L,
4L, 4L, 4L, 2L, 3L, 5L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 4L,
8L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 3L, 6L, 8L, 8L, 4L, 4L,
8L, 8L, 8L, 4L, 3L, 4L, 6L, 6L, 2L, 2L, 4L, 4L, 4L, 2L, 4L,
8L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 4L,
8L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 4L,
8L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 4L,
8L, 8L, 8L, 4L, 3L, 8L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 3L,
8L, 8L, 8L, 4L, 3L, 5L, 7L, 8L, 3L, 4L, 8L, 8L, 8L, 4L, 4L
), Total2 = c(34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L,
34L, 32L, 32L, 32L, 32L, 32L, 34L, 34L, 34L, 34L, 34L, 33L,
33L, 33L, 33L, 33L, 32L, 32L, 32L, 32L, 32L, 16L, 16L, 16L,
16L, 16L, 30L, 30L, 30L, 30L, 30L, 34L, 34L, 34L, 34L, 34L,
34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 31L, 31L,
31L, 31L, 31L, 34L, 34L, 34L, 34L, 34L, 22L, 22L, 22L, 22L,
22L, 16L, 16L, 16L, 16L, 16L, 34L, 34L, 34L, 34L, 34L, 34L,
34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L,
34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L,
34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L,
34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 33L, 33L, 33L, 33L,
33L, 34L, 34L, 34L, 34L, 34L, 33L, 33L, 33L, 33L, 33L, 28L,
28L, 28L, 28L, 28L, 34L, 34L, 34L, 34L, 34L, 34L)), row.names = c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L,
55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L,
68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 1041L, 1042L, 1043L,
1044L, 1045L, 1046L, 1047L, 1048L, 1049L, 1050L, 1051L, 1052L,
1053L, 1054L, 1055L, 1056L, 1057L, 1058L, 1059L, 1060L, 1061L,
1062L, 1063L, 1064L, 1065L, 1066L, 1067L, 1068L, 1069L, 1070L,
1071L, 1072L, 1073L, 1074L, 1075L, 1076L, 1077L, 1078L, 1079L,
1080L, 1081L, 1082L, 1083L, 1084L, 1085L, 1086L, 1087L, 1088L,
1089L, 1090L, 1091L, 1092L, 1093L, 1094L, 1095L, 1096L, 1097L,
1098L, 1099L, 1100L, 1101L, 1102L, 1103L, 1104L, 1105L, 1106L,
1107L, 1108L, 1109L, 1110L, 1111L, 1112L, 1113L, 1114L, 1115L,
1116L), class = "data.frame")

An option is to group by 'Class', 'Site', paste (str_c) the columns except 'Time' to a single string, then grouped by 'Class', 'Count1', ..., 'Total2', columns, get the group indices to create the 'ind' column and do a left_join with original dataset
library(dplyr)
library(stringr)
df %>%
group_by(Class, Site) %>%
summarise_at(vars(-Time), str_c, collapse="") %>%
group_by(Class, Count1, Total1, Count2, Total2) %>%
mutate(ind = group_indices()) %>%
ungroup %>%
select(Class, Site, ind) %>%
left_join(df)
Or a similar logic with data.table
library(data.table)
setDT(df)[df[, lapply(.SD, paste, collapse=""),
.(Class, Site), .SDcols = patterns('Count|Total')][,
ind := .GRP, by = c('Class', 'Count1', 'Total1', 'Count2', 'Total2')
][, .(Class, Site, ind)], on = .(Class, Site)]

ggplot2 legend (guide) formatting (spacing or grouping that is aesthetically pleasing)

I want to do is improve this ggplot's legend in terms of its ascetics. I have tried to plot multiple plots and then glue them back together, but it didn't really work. I am wondering if anyone has any ideas as to how to space out the legend as I have drawn in the picture below. I have put the code I have currently used except I changed the titles to bogus ones to make this presentable outside my organization.
Anyone have any ideas as to improve the clarity of the legend so that the different groups are clearer to a viewer of this chart?
code:
MemberGroup <- structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L,
25L, 25L, 25L, 25L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L, 37L,
37L, 37L, 37L, 49L, 49L, 49L, 49L, 49L, 49L, 49L, 49L, 49L, 49L,
49L, 49L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L,
61L), .Label = c("Bogus1_Group1",
"Bogus1_Group1", "Bogus1_Group1",
"Bogus1_Group1", "Bogus1_Group1",
"Bogus1_Group1", "Bogus1_Group1",
"Bogus1_Group1", "Bogus1_Group1",
"Bogus1_Group1", "Bogus1_Group1",
"Bogus1_Group1", "Bogus2_Group1",
"Bogus2_Group1", "Bogus2_Group1",
"Bogus2_Group1", "Bogus2_Group1",
"Bogus2_Group1", "Bogus2_Group1",
"Bogus2_Group1", "Bogus2_Group1",
"Bogus2_Group1", "Bogus2_Group1",
"Bogus2_Group1", "Bogus3_Group1",
"Bogus3_Group1", "Bogus3_Group1",
"Bogus3_Group1", "Bogus3_Group1",
"Bogus3_Group1", "Bogus3_Group1",
"Bogus3_Group1", "Bogus3_Group1",
"Bogus3_Group1", "Bogus3_Group1",
"Bogus3_Group1", "Bogus4_Group2",
"Bogus4_Group2", "Bogus4_Group2",
"Bogus4_Group2", "Bogus4_Group2",
"Bogus4_Group2", "Bogus4_Group2",
"Bogus4_Group2", "Bogus4_Group2",
"Bogus4_Group2", "Bogus4_Group2",
"Bogus4_Group2", "Bogus5_Group2",
"Bogus5_Group2", "Bogus5_Group2",
"Bogus5_Group2", "Bogus5_Group2",
"Bogus5_Group2", "Bogus5_Group2",
"Bogus5_Group2", "Bogus5_Group2",
"Bogus5_Group2", "Bogus5_Group2",
"Bogus5_Group2", "Bogus6_Group3",
"Bogus6_Group3", "Bogus6_Group3",
"Bogus6_Group3", "Bogus6_Group3",
"Bogus6_Group3", "Bogus6_Group3",
"Bogus6_Group3", "Bogus6_Group3",
"Bogus6_Group3", "Bogus6_Group3",
"Bogus6_Group3"), class = "factor");
Display <- c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L);
Oid <- c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L);
AgeBucket <- structure(c(1L, 7L, 13L, 19L, 25L, 31L, 37L,
43L, 49L, 55L, 61L, 67L, 1L, 7L, 13L, 19L, 25L, 31L, 37L,
43L, 49L, 55L, 61L, 67L, 1L, 7L, 13L, 19L, 25L, 31L, 37L,
43L, 49L, 55L, 61L, 67L, 1L, 7L, 13L, 19L, 25L, 31L, 37L,
43L, 49L, 55L, 61L, 67L, 1L, 7L, 13L, 19L, 25L, 31L, 37L,
43L, 49L, 55L, 61L, 67L, 1L, 7L, 13L, 19L, 25L, 31L, 37L,
43L, 49L, 55L, 61L, 67L), .Label = c("10-14", "10-14", "10-14",
"10-14", "10-14", "10-14", "15-19", "15-19", "15-19", "15-19",
"15-19", "15-19", "20-24", "20-24", "20-24", "20-24", "20-24",
"20-24", "25-29", "25-29", "25-29", "25-29", "25-29", "25-29",
"30-34", "30-34", "30-34", "30-34", "30-34", "30-34", "35-39",
"35-39", "35-39", "35-39", "35-39", "35-39", "40-44", "40-44",
"40-44", "40-44", "40-44", "40-44", "45-49", "45-49", "45-49",
"45-49", "45-49", "45-49", "50-54", "50-54", "50-54", "50-54",
"50-54", "50-54", "55-59", "55-59", "55-59", "55-59", "55-59",
"55-59", "60-64", "60-64", "60-64", "60-64", "60-64", "60-64",
"65-69", "65-69", "65-69", "65-69", "65-69", "65-69"), class = "factor");
BinMin <- c(10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
60L, 65L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
60L, 65L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
60L, 65L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
60L, 65L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
60L, 65L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
60L, 65L);
per <- c(0.387832699619772, 0.317991631799163,
0.123521681997372, 0.0947368421052632, 0.0972359328726555,
0.0937873357228196, 0.103455162772119, 0.108816521048451,
0.114578206516389, 0.116530727610434, 0.12681131040965, 0.110502997133177,
0.20532319391635, 0.259414225941423, 0.258869908015769, 0.232456140350877,
0.16436327739388, 0.0949820788530466, 0.0728979428799681,
0.0557320624834525, 0.0484299635790924, 0.0429621254946297,
0.0460562387405029, 0.0406567630961689, 0.0342205323193916,
0.0502092050209205, 0.0801576872536137, 0.171929824561404,
0.262092793682132, 0.328853046594982, 0.387257839025364,
0.42176330420969, 0.450241165469042, 0.462731163692159, 0.479439179133704,
0.445660672400313, 0.0418250950570342, 0.0167364016736402,
0.00919842312746386, 0.00701754385964912, 0.00740375123395854,
0.00627240143369176, 0.00279608548032754, 0.00238284352660842,
0.0016733930504971, 0.00121133812484858, 0.00109657711286912,
0.000260620276257493, 0.102661596958175, 0.0711297071129707,
0.0315374507227332, 0.0254385964912281, 0.0108588351431392,
0.00686977299880526, 0.00499300978629918, 0.00357426528991263,
0.00246087213308397, 0.00185738512476783, 0.00117490404950262,
0.00130310138128746, 0.608365019011407, 0.54602510460251,
0.314060446780552, 0.292982456140351, 0.285291214215202,
0.253584229390681, 0.226682644297983, 0.231665342864707,
0.237621813170588, 0.246305418719212, 0.265058353567792,
0.252541047693511);
Ct <- c(102L, 152L, 94L, 108L, 197L,
314L, 518L, 822L, 1164L, 1443L, 1619L, 424L, 54L, 124L, 197L,
265L, 333L, 318L, 365L, 421L, 492L, 532L, 588L, 156L, 9L,
24L, 61L, 196L, 531L, 1101L, 1939L, 3186L, 4574L, 5730L,
6121L, 1710L, 11L, 8L, 7L, 8L, 15L, 21L, 14L, 18L, 17L, 15L,
14L, 1L, 27L, 34L, 24L, 29L, 22L, 23L, 25L, 27L, 25L, 23L,
15L, 5L, 160L, 261L, 239L, 334L, 578L, 849L, 1135L, 1750L,
2414L, 3050L, 3384L, 969L);
InRange <- c(263L, 478L, 761L,
1140L, 2026L, 3348L, 5007L, 7554L, 10159L, 12383L, 12767L,
3837L, 263L, 478L, 761L, 1140L, 2026L, 3348L, 5007L, 7554L,
10159L, 12383L, 12767L, 3837L, 263L, 478L, 761L, 1140L, 2026L,
3348L, 5007L, 7554L, 10159L, 12383L, 12767L, 3837L, 263L,
478L, 761L, 1140L, 2026L, 3348L, 5007L, 7554L, 10159L, 12383L,
12767L, 3837L, 263L, 478L, 761L, 1140L, 2026L, 3348L, 5007L,
7554L, 10159L, 12383L, 12767L, 3837L, 263L, 478L, 761L, 1140L,
2026L, 3348L, 5007L, 7554L, 10159L, 12383L, 12767L, 3837L);
df <- as.data.frame(list(MemberGroup,Display,Oid,AgeBucket,BinMin,per,Ct,InRange))
library(ggplot2)
p <- ggplot(data=df, aes(x=BinMin, y=per, color = MemberGroup))#, order = as.numeric(Oid))
p <- p + geom_line(aes(linetype=MemberGroup, color=MemberGroup, size=1))
p <- p + theme(legend.position=c(.4,.8)) + guides(size = "none")
p <- p + theme(text = element_text(size = 30))
p <- p + scale_y_continuous(labels = scales::percent, breaks=seq(0, .80, .05))
p <- p + scale_x_continuous(breaks=seq(10,65,5), labels = unique(all_labs) )
p <- p + theme(axis.text.x=element_text(angle=50,vjust=0.5))
p <- p + scale_color_manual(values=c("#ff1919", "#ff4c4c", "#ff9999","#76ee00","#32cd32","#1874cd"))
p <- p + scale_linetype_manual(values = c("dashed","dashed","dashed","dotted","dotted","dotted"))
p <- p + xlab("XXX Range")
p <- p + ylab("%")
p <- p + theme(legend.background = element_rect(fill = "transparent"),legend.key = element_rect(fill = "transparent",color ="transparent"))
p <- p + guides(color = guide_legend(override.aes = list(size=1)))
p <- p + theme(legend.key.width = unit(3,"cm"))
p <- p + theme(legend.title=element_blank())
p

You might need to delve into the structure of the legend.
(There are some problems with your code. For instance, check these lines: scale_x_continuous(....., and geom_line(....
After you obtain plot p, try this:
library(gtable)
library(grid)
# Get the ggplot grob
g = ggplotGrob(p)
# Get the legend
leg = g$grobs[[which(g$layout$name == "guide-box")]]$grobs[[1]]
# Add rown to the legend gtable
leg = gtable_add_rows(leg, unit(1, "lines"), pos = 8) # Space below "Group 2"
leg = gtable_add_rows(leg, unit(1, "lines"), pos = 6) # Space below "Group 1"
# Put the legned back into the plot
g$grobs[[which(g$layout$name == "guide-box")]]$grobs[[1]] = leg
# Draw it
grid.newpage()
grid.draw(g)

Bubble Plot of Negative and Positive values in space ggplot2 R

I would like to make a bubble plot using ggplot2 in R. My code and data are found below.
Please leave the colors as they are. I am having difficulties in scaling positive and negative values equally. For example, -3 is scaled smaller than +3. I would like negatives and positives to be scaled proportionately irrespective of sign.
Identify negative from positive values using some kind of outline linetype for bubbles and include it in the legend.
Also remove the "Mean" part of the legend.
Thanks very much for your great help.
#=====================================================================
library(ggplot2)
if (dev.cur() == 1) x11(width=8,height=6)
par(mfcol=c(1,1))
p<-ggplot(site.resiudal, aes(x=Eastings, y=Northings, size=Mean,label=site.resiudal$Site,legend = FALSE))+
#theme(legend.position="none")+
geom_point(shape=21)+
geom_point(aes(colour = factor(Region)))+
scale_area(range=c(1,15))+
scale_alpha(guide = 'none')+
scale_x_continuous(name="Longitude", limits=c(-120,-95))+
scale_y_continuous(name="Latitude", limits=c(48,61))+
geom_text(size=4)+
scale_colour_manual(name="Region",labels = c("A", "B","C","D", "E"),values = c("1" = "firebrick3","2" = "palegreen4","3" = "sandybrown","4" = "red","5" = "gray0"))+
theme(legend.title = element_text(colour="black", size=16, face="plain"))+
theme(legend.text = element_text(colour="black", size = 16, face = "plain"))
p
#Data[["sign"]] = ifelse(Data[["Mean"]] >= 0, "positive", "negative")
#=================================================
structure(list(Site = structure(c(101L, 102L, 105L, 107L, 108L,
110L, 111L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 16L, 20L, 47L, 52L, 53L, 55L, 91L, 92L, 93L, 94L, 95L,
96L, 99L, 15L, 17L, 18L, 19L, 21L, 114L, 23L, 26L, 36L, 59L,
60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L,
73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L,
86L, 87L, 88L, 89L, 98L, 100L, 103L, 104L, 106L, 109L, 112L,
113L, 115L, 116L, 117L, 119L, 42L, 44L, 46L, 48L, 49L, 50L, 51L,
54L, 56L, 57L, 58L, 90L, 97L, 118L, 120L, 22L, 24L, 25L, 27L,
28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 37L, 38L, 39L, 40L, 41L,
43L, 45L), .Label = c("G100", "G101", "G102", "G103", "G104",
"G105", "G106", "G107", "G108", "G109", "G110", "G111", "G112",
"G113", "G114", "G115", "G116", "G117", "G118", "G119", "G120",
"GG10", "GG11", "GG12", "GG13", "GG14", "GG15", "GG16", "GG17",
"GG18", "GG19", "GG20", "GG21", "GG22", "GG23", "GG24", "GG25",
"GG26", "GG27", "GG28", "GG29", "GG30", "GG31", "GG32", "GG33",
"GG34", "GG35", "GG36", "GG37", "GG38", "GG39", "GG40", "GG41",
"GG42", "GG43", "GG44", "GG45", "GG46", "GG47", "GG48", "GG49",
"GG50", "GG51", "GG52", "GG53", "GG54", "GG55", "GG56", "GG57",
"GG58", "GG59", "GG60", "GG61", "GG62", "GG63", "GG64", "GG65",
"GG66", "GG67", "GG68", "GG69", "GG70", "GG71", "GG72", "GG73",
"GG74", "GG75", "GG76", "GG77", "GG78", "GG79", "GG80", "GG81",
"GG82", "GG83", "GG84", "GG85", "GG86", "GG87", "GG88", "GG89",
"GG90", "GG91", "GG92", "GG93", "GG94", "GG95", "GG96", "GG97",
"GG98", "GG99", "GGG1", "GGG2", "GGG3", "GGG4", "GGG5", "GGG6",
"GGG7", "GGG8", "GGG9"), class = "factor"), Name = structure(c(53L,
87L, 29L, 92L, 36L, 76L, 102L, 103L, 119L, 2L, 9L, 11L, 45L,
47L, 49L, 54L, 90L, 30L, 105L, 66L, 78L, 107L, 81L, 42L, 41L,
43L, 59L, 110L, 24L, 27L, 56L, 61L, 64L, 118L, 40L, 21L, 44L,
70L, 108L, 25L, 58L, 98L, 83L, 5L, 19L, 26L, 31L, 38L, 55L, 60L,
71L, 74L, 75L, 85L, 95L, 120L, 109L, 1L, 67L, 20L, 50L, 63L,
106L, 111L, 116L, 62L, 6L, 99L, 114L, 73L, 84L, 89L, 93L, 97L,
115L, 80L, 10L, 12L, 88L, 79L, 15L, 17L, 33L, 35L, 94L, 100L,
3L, 16L, 37L, 101L, 117L, 8L, 39L, 48L, 86L, 113L, 23L, 13L,
69L, 96L, 104L, 32L, 65L, 82L, 14L, 22L, 18L, 46L, 68L, 72L,
77L, 91L, 112L, 4L, 7L, 28L, 51L, 57L, 52L, 34L), .Label = c("ANEROID",
"ARBORG", "ATHABASCA", "BANFF", "BANGOR", "BATTLEFORD", "BEAVER MINES",
"BEAVERLODGE", "BERENS RIVER", "BIRTLE", "BISSETT", "BRANDON",
"BUFFALO NARROWS", "CALGARY", "CALMAR", "CAMPSIE", "CAMROSE",
"CARWAY", "CEYLON", "CHAPLIN", "CHURCHILL", "CLARESHOLM", "COLD LAKE",
"COLLINS BAY", "CORONATION", "COTE", "CREE LAKE", "CROWSNEST",
"CYPRESS RIVER", "DAUPHIN", "DAVIDSON", "DRUMHELLER", "EDMONTON",
"EDSON", "ELK POINT", "EMERSON AUT", "ENILDA-BERG", "ESTEVAN",
"FAIRVIEW", "FLIN FLON", "FORT CHIPEWYAN", "FORT MCMURRAY", "FORT VERMILION",
"GILLAM", "GIMLI", "GLEICHEN", "GRAND RAPIDS", "GRANDE PRAIRIE",
"GREAT FALLS", "HIGH POINT", "HIGHWOOD", "HINTON VALLEY", "HUDSON BAY",
"INDIAN BAY", "INDIAN HEAD", "ISLAND FALLS", "JASPER WARDEN",
"JENNER", "KEG RIVER RS", "KELLIHER", "KEY LAKE", "KINDERSLEY",
"KLINTONEL", "LA RONGE", "LACOMBE 2", "LANGRUTH WEST", "LEADER",
"LETHBRIDGE", "LOON LAKE", "LYNN LAKE", "MANOR", "MEDICINE HAT",
"MELFORT", "MOOSE JAW", "MOOSOMIN", "MORDEN", "MOUNTAIN VIEW",
"NEEPAWA MURRAY", "NINETTE", "NIPAWIN", "NORWAY HOUSE", "OLDS",
"ONEFOUR", "OUTLOOK", "PASWEGIN", "PEACE RIVER", "PELLY", "PIERSON",
"PILGER", "PINAWA WNRE", "PINCHER CREEK ", "PORTAGE PRAIRIE",
"PRINCE ALBERT", "RANFURLY", "REGINA", "ROCKY MT HOUSE ", "SASKATOON",
"SCOTFIELD", "SCOTT", "SION", "SLAVE LAKE", "SPRAGUE", "STEINBACH",
"STETTLER NORTH", "SWAN RIVER", "SWIFT CURRENT", "THE PAS", "THOMPSON",
"TONKIN", "URANIUM CITY ", "VAL-MARIE", "VAUXHALL", "WABASCA RS",
"WASECA", "WASKESIU LAKE", "WEST POPLAR", "WHITECOURT", "WHITESAND DAM",
"WINNIPEG", "YELLOW GRASS"), class = "factor"), Mean = c(-0.020525899,
0.333863493, 0.210353772, NA, NA, 0.093520458, 0.341295298, NA,
-0.175074657, 0.09834825, 0.075610648, NA, -0.117503802, 0.18309367,
0.25246942, 0.221329766, 0.072167004, -0.094766032, NA, NA, 0.19783711,
-0.166351357, -0.0996169, -0.038555432, -0.028092042, 0.297855371,
0.108263891, 0.002057761, 0.327731415, NA, 0.180100638, 0.193837736,
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0.036412958, 0.055218077, NA, -0.236229087, 0.265211081, 0.271625885,
-0.293179359, 0.113744571, -0.207770026, 0.100471248, -0.071569464,
NA, NA, NA, -0.052716493, 0.057385851, 0.090340517, -0.30456625,
-0.234420722, 0.082287977, 0.009973663, NA, -0.06405062, 0.074703356,
-0.208329196, -0.272401078, 0.217991554, -0.043619919, -0.208901155,
-0.020022401, 0.111495318, NA, 0.38239749, 0.199136959, -0.177740258,
NA, 0.147515615, 0.309306538, 0.298741467, 0.068170296, NA, -0.02102765,
0.001754313, -0.010196512, 0.108254156, -0.228183063, -0.196261239,
NA, -0.167054722, 0.039949534, 0.154337034, -0.020855461, 0.136010278,
NA, 0.096997744, NA, -0.241963754, 0.660176529, 0.423554314,
0.190305726, -0.210778787, -0.261148915, NA, 0.054264129, -0.098706619,
-0.138776994, NA, NA, NA, -0.113823745, 0.373292721, -0.047060083
), Eastings = c(-102.5800018, -101.8700027, -99.08000183, -98.26999664,
-97.23000336, -98.08000183, -95.59999847, -96.76999664, -97.23000336,
-97.08000183, -97.02999878, -95.69999695, -97.01999664, -99.27999878,
-96, -95.19999695, -96.06999969, -100.0500031, -101.2300034,
-98.80000305, -99.56999969, -101.0999985, -97.84999847, -111.2200012,
-111.1200027, -116.0299988, -117.6200027, -108.4800034, -103.6999969,
-107.1299973, -102.3499985, -105.6200027, -105.2699966, -103.1500015,
-101.8799973, -94.06999969, -94.72000122, -101.0800018, -97.87000275,
-111.4499969, -111.1999969, -111.3499985, -110.4700012, -102.2799988,
-104.6500015, -101.7799988, -105.9800034, -102.9700012, -103.6500015,
-103.75, -102.0999985, -105.5500031, -101.6699982, -103.9199982,
-104.6699982, -104.1800003, -102.2300034, -107.3000031, -109.5,
-106.6500015, -107.9300003, -108.9199982, -107.7300034, -107.8499985,
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-114.7799988, -115.7799988, -119.4000015, -118.5299988, -118.8799973,
-117.4499969, -113.8300018, -110.2799988, -108.4300003, -109.0999985,
-114.9199982, -112.7200012, -112.8700027, -113.75, -114.0999985,
-114.0199966, -113.7300034, -113.3799973, -113.0500031, -112.8000031,
-110.7200012, -113.6299973, -113.9800034, -112.1299973, -115.5500031,
-114.1800003, -114.4800034, -114.3700027, -118.0299988, -117.5299988,
-116.4499969), Northings = c(52.88000107, 52.08000183, 49.54999924,
49.95000076, 49, 49.18000031, 49.02000046, 49.52999878, 49.91999817,
50.93000031, 52.34999847, 51.02999878, 50.63000107, 53.15000153,
50.47000122, 49.61999893, 50.18000031, 51.09999847, 52.11999893,
50.41999817, 50.15000153, 53.97000122, 53.97000122, 56.65000153,
58.77000046, 58.38000107, 57.75, 59.56999969, 58.18000031, 57.34999847,
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51.58000183, 49.11999893, 50.90000153, 49.38000107, 51.52000046,
51.27000046, 49.22000122, 50.54999924, 51.25, 49.61999893, 50.33000183,
50.13000107, 51.97999954, 50.43000031, 49.81999969, 51.20000076,
49.72000122, 50.90000153, 50.47000122, 50.97999954, 49.68000031,
50.27000046, 49.36999893, 49, 51.52000046, 52.77000046, 52.36999893,
53.13000107, 52.81999969, 51.47999954, 52.41999817, 53.22000122,
52.16999817, 53.91999817, 53.33000183, 50.43000031, 49.91999817,
49.18000031, 49.41999817, 53.27999878, 53.02999878, 53.31999969,
53.88000107, 53.41999817, 53.88000107, 54.72000122, 54.13000107,
55.41999817, 55.27999878, 54.15000153, 55.20000076, 56.08000183,
55.18000031, 56.22999954, 55.97000122, 54.41999817, 55.83000183,
54.04999924, 52.41999817, 52.33000183, 51.47000122, 52.45000076,
51.77999878, 51.11999893, 49.93000031, 49, 50.88000107, 49.63000107,
50.02000046, 49.13000107, 49.52000046, 50.04999924, 51.20000076,
49.47000122, 49.63000107, 50.54999924, 52.93000031, 53.40000153,
53.58000183), Region = c(1L, 1L, 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, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 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, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L)), .Names = c("Site",
"Name", "Mean", "Eastings", "Northings", "Region"), class = "data.frame", row.names = c(NA,
-120L))
#============================================

use abs()
eg
instead of size=Mean use size=abs(Mean)
Then, you can track the sign using shape (or some other aesthetic, but color and size are already taken)
replace:
geom_point(shape=21)+
geom_point(aes(colour = factor(Region))) +
with the single line
geom_point(aes(shape=factor(sign(Mean)), colour = factor(Region))) +
If you'd like, you can also add lines such as
scale_shape_discrete(name="Mean Is", breaks=c(-1, 1), labels=c("Negative", "Positive"))
guides(size=FALSE)

Internal ordering of facets ggplot2

I'm trying to plot a facets in ggplot2 but I struggle to get the internal ordering of the different facets right. The data looks like this:
head(THAT_EXT)
ID FILE GENRE NODE
1 CKC_1823_01 CKC Novels better
2 CKC_1824_01 CKC Novels better
3 EW9_192_03 EW9 Popular Science better
4 H0B_265_01 H0B Popular Science sad
5 CS2_231_03 CS2 Academic Prose desirable
6 FED_8_05 FED Academic Prose certain
str(THAT_EXT)
'data.frame': 851 obs. of 4 variables:
$ ID : Factor w/ 851 levels "A05_122_01","A05_277_07",..: 345 346 439 608 402 484 319 395 228 5 ...
$ FILE : Factor w/ 241 levels "A05","A06","A0K",..: 110 110 127 169 120 135 105 119 79 2 ...
$ GENRE: Factor w/ 5 levels "Academic Prose",..: 4 4 5 5 1 1 1 5 1 5 ...
$ NODE : Factor w/ 115 levels "absurd","accepted",..: 14 14 14 89 23 16 59 59 18 66 ...
Part of the problem is that can't get the sorting right. Here is the code for the sorting of NODE that I use:
THAT_EXT <- within(THAT_EXT,
NODE <- factor(NODE,
levels=names(sort(table(NODE),
decreasing=TRUE))))
When I plot this with the code below I get a graphs in which the NODE is not correctly sorted in the individual GENREs since different NODEs are more frequent in different GENREs:
p1 <-
ggplot(THAT_EXT, aes(x=NODE)) +
geom_bar() +
scale_x_discrete("THAT_EXT", breaks=NULL) + # supress tick marks on x axis
facet_wrap(~GENRE)
What I want is for every facet to have NODE sorted in decreasing order for that particular GENRE. Can anyone help with this?
structure(list(ID = structure(c(1L, 2L, 3L, 4L, 10L, 133L, 137L,
138L, 139L, 140L, 141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L,
149L, 150L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L,
160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L,
171L, 172L, 173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L,
182L, 183L, 184L, 185L, 186L, 187L, 188L, 189L, 190L, 191L, 192L,
193L, 194L, 195L, 196L, 197L, 198L, 199L, 200L, 201L, 202L, 203L,
204L, 205L, 206L, 207L, 208L, 212L, 213L, 214L, 215L, 216L, 217L,
218L, 219L, 220L, 221L, 222L, 223L, 224L, 225L, 226L, 227L, 228L,
229L, 230L, 231L, 232L, 233L, 234L, 235L, 236L, 237L, 238L, 239L,
240L, 241L, 267L, 268L, 269L, 270L, 271L, 272L, 273L, 274L, 275L,
276L, 277L, 278L, 279L, 280L, 281L, 282L, 283L, 284L, 290L, 291L,
298L, 299L, 300L, 303L, 304L, 305L, 306L, 307L, 308L, 309L, 310L,
313L, 314L, 315L, 316L, 317L, 318L, 319L, 327L, 328L, 329L, 330L,
331L, 332L, 333L, 334L, 335L, 336L, 337L, 338L, 339L, 340L, 341L,
342L, 343L, 344L, 345L, 346L, 347L, 348L, 352L, 353L, 354L, 355L,
356L, 357L, 358L, 359L, 360L, 349L, 350L, 351L, 361L, 362L, 363L,
364L, 365L, 366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L, 374L,
375L, 376L, 377L, 378L, 379L, 380L, 381L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 41L, 42L, 43L, 44L, 45L,
46L, 50L, 54L, 72L, 73L, 74L, 75L, 76L, 90L, 91L, 92L, 97L, 98L,
102L, 115L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 209L,
210L, 211L, 242L, 243L, 244L, 245L, 246L, 289L, 292L, 293L, 294L,
295L, 296L, 297L, 301L, 302L, 311L, 312L, 320L, 321L, 322L, 323L,
324L, 325L, 326L, 382L, 383L, 384L, 385L, 386L, 387L, 388L, 5L,
6L, 7L, 8L, 9L, 11L, 37L, 38L, 39L, 40L, 47L, 48L, 49L, 51L,
52L, 53L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L,
66L, 67L, 68L, 69L, 70L, 71L, 77L, 78L, 79L, 80L, 81L, 82L, 83L,
84L, 85L, 86L, 87L, 88L, 89L, 93L, 94L, 95L, 96L, 99L, 100L,
101L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L,
113L, 114L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L,
134L, 135L, 136L, 247L, 248L, 249L, 250L, 251L, 252L, 253L, 254L,
255L, 256L, 257L, 258L, 259L, 260L, 261L, 262L, 263L, 264L, 265L,
266L, 285L, 286L, 287L, 288L), .Label = c("A05_122_01", "A05_277_07",
"A05_400_01", "A05_99_01", "A06_1283_02", "A06_1389_01", "A06_1390_01",
"A06_1441_02", "A06_884_03", "A0K_1190_03", "A77_1684_01", "A8K_525_03",
"A8K_582_01", "A8K_645_01", "A8K_799_01", "A90_341_02", "A90_496_01",
"A94_217_01", "A94_472_01", "A94_477_03", "A9M_164_01", "A9M_259_03",
"A9N_199_01", "A9N_489_01", "A9N_591_01", "A9R_173_01", "A9R_425_02",
"A9W_536_02", "AA5_121_01", "AAE_203_01", "AAE_243_01", "AAE_412_01",
"AAW_14_03", "AAW_244_02", "AAW_297_04", "AAW_365_04", "ADG_1398_01",
"ADG_1500_01", "ADG_1507_01", "ADG_1516_01", "AHB_336_01", "AHB_421_01",
"AHJ_1090_02", "AHJ_619_01", "AR3_340_01", "AR3_91_03", "ARF_879_01",
"ARF_985_01", "ARF_991_02", "ARK_1891_01", "ASL_33_04", "ASL_43_01",
"ASL_9_01", "AT7_1031_01", "B09_1162_01", "B09_1475_01", "B09_1493_01",
"B09_1539_01", "B0G_197_01", "B0G_320_01", "B0N_1037_01", "B0N_624_01",
"B0N_645_02", "B0N_683_01", "B3G_313_04", "B3G_320_03", "B3G_398_02",
"B7M_1630_01", "B7M_1913_01", "BNN_746_02", "BNN_895_01", "BP7_2426_01",
"BP7_2777_01", "BP7_2898_01", "BP9_410_01", "BP9_599_01", "BPK_829_01",
"C93_1407_02", "C9A_181_01", "C9A_196_01", "C9A_365_01", "C9A_82_02",
"C9A_9_01", "CB9_306_02", "CB9_63_04", "CB9_86_01", "CBJ_439_01",
"CBJ_702_02", "CBJ_705_01", "CCM_320_01", "CCM_665_01", "CCM_669_02",
"CCN_1036_02", "CCN_1078_01", "CCN_1119_01", "CCN_784_01", "CCW_2284_02",
"CCW_2349_03", "CE7_242_02", "CE7_284_01", "CE7_39_01", "CEB_1675_01",
"CER_145_03", "CER_23_01", "CER_235_02", "CER_378_10", "CET_1056_02",
"CET_680_01", "CET_705_01", "CET_797_01", "CET_838_01", "CET_879_05",
"CET_946_03", "CET_986_01", "CEY_2977_01", "CJ3_107_02", "CJ3_114_03",
"CJ3_20_01", "CJ3_81_01", "CK2_112_01", "CK2_22_01", "CK2_392_01",
"CK2_42_01", "CK2_75_01", "CKC_1776_01", "CKC_1777_01", "CKC_1823_01",
"CKC_1824_01", "CKC_1860_01", "CKC_1883_01", "CKC_1883_02", "CKC_2127_01",
"CMN_1439_02", "CRM_5767_01", "CRM_5770_03", "CRM_5789_01", "CS2_110_01",
"CS2_131_01", "CS2_139_01", "CS2_187_01", "CS2_187_03", "CS2_231_03",
"CS2_249_02", "CS2_301_01", "CS2_35_01", "CS2_58_02", "EV6_16_01",
"EV6_206_02", "EV6_240_01", "EV6_244_02", "EV6_28_01", "EV6_30_01",
"EV6_32_01", "EV6_450_01", "EV6_69_01", "EV6_80_01", "EV6_91_01",
"FAC_1019_01", "FAC_1026_01", "FAC_1027_01", "FAC_1235_01", "FAC_1269_05",
"FAC_1270_05", "FAC_1393_01", "FAC_1406_03", "FAC_933_01", "FAC_950_01",
"FAC_960_01", "FED_105_01", "FED_120_02", "FED_21_02", "FED_281_02",
"FED_302_02", "FED_53_01", "FED_8_05", "FEF_498_03", "FEF_674_03",
"FR2_410_01", "FR2_557_02", "FR2_593_01", "FR2_691_01", "FR4_232_01",
"FR4_331_01", "FR4_346_01", "FS7_818_01", "FS7_919_01", "FU0_368_02",
"FYT_1138_01", "FYT_1183_01", "FYT_901_05", "G08_1336_01", "G1E_385_01",
"G1N_824_01", "G1N_860_01", "G1N_868_01", "G1N_975_01", "GU5_854_01",
"GUJ_423_01", "GUJ_501_01", "GUJ_611_01", "GUJ_629_03", "GUJ_700_01",
"GV0_10_01", "GV0_104_01", "GV0_111_01", "GV0_122_01", "GV0_160_01",
"GV0_232_02", "GV2_1465_01", "GV2_1899_01", "GV6_2683_01", "GW6_297_01",
"GW6_306_05", "GW6_307_01", "GW6_322_01", "GW6_330_02", "GW6_335_01",
"GW6_338_01", "GW6_367_02", "GW6_373_01", "GW6_407_01", "GW6_411_01",
"GW6_413_01", "GW6_421_01", "GW6_423_01", "GW6_424_01", "GW6_428_01",
"GW6_447_01", "GWM_480_01", "GWM_533_02", "GWM_554_02", "GWM_554_03",
"GWM_609_01", "GWM_609_04", "GWM_610_01", "GWM_730_01", "GWM_731_01",
"GWM_738_01", "GWM_804_06", "GWM_815_01", "GWM_832_03", "GVP_179_01",
"GVP_211_01", "GVP_393_02", "GVP_443_02", "GVP_710_01", "H0B_171_04",
"H0B_216_01", "H0B_265_01", "H0B_32_01", "H0B_361_03", "H0B_365_01",
"H0B_369_01", "H0B_74_01", "H0B_93_01", "H10_1002_01", "H10_1032_04",
"H10_653_01", "H10_803_01", "H10_824_01", "H10_825_03", "H10_881_01",
"H10_986_01", "H78_851_04", "H78_891_01", "H78_946_04", "H79_1959_19",
"H7S_110_05", "H7S_130_06", "H7S_131_03", "H7S_131_04", "H7S_146_01",
"H7S_148_01", "H7S_164_01", "H7S_179_01", "H7S_54_01", "H7S_56_05",
"H7S_62_03", "H7S_79_01", "H7S_8_01", "H7S_81_01", "H7S_83_01",
"H7S_87_01", "H7S_92_03", "H7X_1028_02", "H7X_1091_01", "H7X_691_01",
"H7X_695_01", "H8H_2917_01", "H8K_153_01", "H8K_55_01", "H8M_1897_01",
"H8M_2104_02", "H8T_3316_03", "H98_3204_01", "H98_3410_01", "H98_3490_02",
"H9R_130_02", "H9R_39_01", "H9S_1297_01", "HA2_3107_02", "HA2_3284_01",
"HPY_754_04", "HPY_785_09", "HPY_799_03", "HPY_807_04", "HPY_830_04",
"HPY_838_02", "HPY_843_01", "HPY_869_11", "HR7_190_01", "HR7_440_01",
"HTP_540_01", "HTP_585_01", "HTP_588_05", "HTP_593_01", "HTP_601_01",
"HTP_613_01", "HTP_648_02", "HTW_197_01", "HTW_494_01", "HTW_750_01",
"HWL_2770_01", "HWL_2919_01", "HWM_45_01", "HWM_45_02", "HXY_1047_03",
"HXY_701_01", "HXY_781_01", "HXY_783_01", "HXY_784_01", "HXY_836_01",
"HXY_931_01", "HXY_963_01", "HXY_972_01", "HXY_985_03", "HY6_1024_01",
"HY6_1025_01", "HY6_1164_01", "HY6_1223_01", "HY6_988_03", "HY6_989_01",
"HY8_160_01", "HY8_164_01", "HY8_292_03", "HY8_316_01", "HY9_778_03",
"HY9_845_02", "HYX_235_08", "HYX_245_01", "HYX_88_01", "J12_1474_02",
"J12_1492_01", "J12_1571_01", "J12_1845_01", "J14_341_01", "J18_597_04",
"J18_698_02", "J18_759_01", "J18_828_01", "J3R_197_01", "J3R_219_02",
"J3R_277_04", "J3T_267_01", "J3T_269_02", "J3T_57_02", "J41_41_02",
"J41_58_03", "J9B_133_03", "J9B_341_02", "J9B_341_03", "J9D_147_05",
"J9D_218_01", "J9D_411_01", "J9D_616_01", "J9D_616_02", "JNB_563_02",
"JT7_118_01", "JT7_129_02", "JT7_218_02", "JT7_344_02", "JXS_3663_01",
"JXU_407_01", "JXU_468_02", "JXU_559_01", "JXV_1439_04", "JXV_1592_01",
"JY1_100_01"), class = "factor"), GENRE = structure(c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 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, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L), .Label = c("Academic Prose", "Conversation", "News",
"Novels", "Popular Science"), class = "factor"), NODE = structure(c(9L,
10L, 10L, 10L, 4L, 10L, 71L, 35L, 49L, 6L, 5L, 15L, 28L, 44L,
64L, 64L, 28L, 28L, 18L, 18L, 32L, 18L, 58L, 10L, 72L, 28L, 18L,
10L, 64L, 10L, 35L, 64L, 64L, 69L, 8L, 10L, 50L, 69L, 49L, 49L,
15L, 69L, 10L, 49L, 8L, 64L, 49L, 10L, 69L, 18L, 61L, 67L, 67L,
61L, 57L, 69L, 11L, 10L, 64L, 10L, 59L, 61L, 49L, 10L, 59L, 1L,
61L, 35L, 54L, 54L, 39L, 44L, 61L, 64L, 69L, 1L, 23L, 49L, 49L,
8L, 69L, 49L, 69L, 49L, 49L, 69L, 35L, 49L, 49L, 49L, 35L, 10L,
49L, 48L, 10L, 49L, 11L, 44L, 50L, 11L, 50L, 69L, 49L, 10L, 59L,
68L, 47L, 69L, 49L, 35L, 29L, 8L, 49L, 50L, 35L, 10L, 35L, 8L,
35L, 8L, 10L, 35L, 10L, 10L, 10L, 35L, 44L, 61L, 35L, 44L, 28L,
47L, 39L, 39L, 49L, 61L, 43L, 60L, 19L, 10L, 10L, 10L, 44L, 44L,
62L, 44L, 10L, 59L, 10L, 61L, 1L, 53L, 33L, 10L, 8L, 8L, 64L,
64L, 10L, 57L, 61L, 64L, 66L, 19L, 61L, 64L, 10L, 10L, 8L, 19L,
35L, 28L, 10L, 61L, 35L, 42L, 35L, 28L, 32L, 64L, 10L, 18L, 28L,
25L, 35L, 35L, 10L, 18L, 10L, 22L, 55L, 28L, 10L, 1L, 55L, 51L,
1L, 38L, 28L, 28L, 33L, 10L, 44L, 29L, 16L, 8L, 28L, 69L, 32L,
10L, 61L, 20L, 35L, 10L, 28L, 10L, 32L, 10L, 46L, 59L, 64L, 35L,
66L, 2L, 35L, 28L, 30L, 18L, 69L, 32L, 10L, 28L, 17L, 36L, 64L,
61L, 10L, 64L, 33L, 3L, 37L, 26L, 28L, 64L, 44L, 28L, 64L, 64L,
6L, 6L, 64L, 50L, 32L, 8L, 64L, 50L, 28L, 24L, 18L, 47L, 35L,
40L, 24L, 55L, 44L, 22L, 1L, 49L, 44L, 18L, 45L, 63L, 64L, 35L,
12L, 35L, 10L, 35L, 10L, 10L, 10L, 44L, 44L, 44L, 65L, 44L, 55L,
32L, 49L, 64L, 39L, 69L, 1L, 60L, 7L, 14L, 44L, 33L, 10L, 19L,
10L, 70L, 53L, 8L, 61L, 61L, 44L, 61L, 65L, 28L, 68L, 69L, 27L,
61L, 28L, 72L, 34L, 61L, 32L, 10L, 49L, 35L, 49L, 10L, 10L, 69L,
39L, 40L, 19L, 59L, 53L, 49L, 49L, 44L, 49L, 35L, 49L, 61L, 61L,
1L, 10L, 28L, 49L, 35L, 49L, 61L, 50L, 69L, 35L, 61L, 35L, 50L,
10L, 28L, 69L, 61L, 21L, 69L, 29L, 35L, 35L, 35L, 11L, 69L, 8L,
41L, 56L, 35L, 61L, 69L, 49L, 49L, 49L, 1L, 13L, 64L, 64L, 52L,
44L, 64L, 64L, 50L, 49L, 69L, 11L, 59L, 49L, 31L), .Label = c("apparent",
"appropriate", "awful", "axiomatic", "best", "better", "breathtaking",
"certain", "characteristic", "clear", "conceivable", "convenient",
"crucial", "cruel", "desirable", "disappointing", "emphatic",
"essential", "evident", "expected", "extraordinary", "fair",
"fortunate", "Funny", "good", "great", "imperative", "important",
"impossible", "incredible", "inescapable", "inevitable", "interesting",
"ironic", "likely", "Likely", "lucky", "ludicrous", "natural",
"necessary", "needful", "notable", "noteworthy", "obvious", "odd",
"paradoxical", "plain", "plausible", "possible", "probable",
"proper", "relevant", "remarkable", "revealing", "right", "Sad",
"self-evident", "sensible", "significant", "striking", "surprising",
"symptomatic", "terrible", "true", "typical", "understandable",
"unexpected", "unfortunate", "unlikely", "unreasonable", "untrue",
"vital"), class = "factor")), .Names = c("ID", "GENRE", "NODE"
), class = "data.frame", row.names = c(NA, -388L))

As I mentioned already: facet_wrap is not intended for having individual scales. At least I didn't find a solution. Hence, setting the labels in scale_x_discrete did not bring the desired result.
But this my workaround:
library(plyr)
library(ggplot2)
nodeCount <- ddply( df, c("GENRE", "NODE"), nrow )
nodeCount$factors <- paste( nodeCount$GENRE, nodeCount$NODE, sep ="." )
nodeCount <- nodeCount[ order( nodeCount$GENRE, nodeCount$V1, decreasing=TRUE ), ]
nodeCount$factors <- factor( nodeCount$factors, levels=nodeCount$factors )
head(nodeCount)
GENRE NODE V1 factors
121 Popular Science possible 14 Popular Science.possible
128 Popular Science surprising 11 Popular Science.surprising
116 Popular Science likely 9 Popular Science.likely
132 Popular Science unlikely 9 Popular Science.unlikely
103 Popular Science clear 7 Popular Science.clear
129 Popular Science true 5 Popular Science.true
g <- ggplot( nodeCount, aes( y=V1, x = factors ) ) +
geom_bar() +
scale_x_discrete( breaks=NULL ) + # supress tick marks on x axis
facet_wrap( ~GENRE, scale="free_x" ) +
geom_text( aes( label = NODE, y = V1+2 ), angle = 45, vjust = 0, hjust=0, size=3 )
Which gives:

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