Extracting overlaping pairs of rows from df with two columns - r

I would like to find out which pairs overlap between these two tables:
> dput(data1)
structure(list(Name.x = c("MDH1", "MDH1", "IDH2", "IDH2", "IDH2",
"IDH2", "IDH2", "IDH2", "IDH2", "SCOALB", "SCOALB", "CSY4", "CSY4",
"CSY4", "CSY4", "CSY4", "FUM1", "FUM1", "IDH6", "IDH6", "IDH6",
"ODC1-1", "ODC1-1", "ODC1-1", "ODC1-1", "ODC1-1", "ODC2-1", "ODC2-1",
"ODC2-1", "ACO2", "IDH1", "IDH1", "IDH1", "IDH1", "ODC2-2"),
Name.y = c("SCOALB", "SCOALA-1", "CSY4", "IDH6", "ODC1-1",
"ODC2-1", "IDH1", "ODC2-2", "ODC1-2", "SCOALA-1", "SCOALA-2",
"IDH6", "SDH2-1", "IDH1", "IDH5", "ICDH", "ODC1-1", "ODC1-2",
"ACO2", "IDH1", "IDH5", "ODC2-1", "IDH1", "IDH5", "ODC2-2",
"ODC1-2", "IDH1", "ODC2-2", "ODC1-2", "IDH1", "IDH5", "SCOALA-2",
"ODC2-2", "ODC1-2", "ODC1-2")), .Names = c("Name.x", "Name.y"
), class = "data.frame", row.names = c(NA, -35L))
> dput(data2)
structure(list(Protein1 = structure(c(3L, 7L, 18L, 19L, 7L, 19L,
6L, 18L, 6L, 18L, 18L, 19L, 9L, 8L, 19L, 18L, 9L, 7L, 18L, 12L,
8L, 19L, 5L, 29L, 12L, 29L, 12L, 18L, 7L, 17L, 6L, 5L, 9L, 19L,
12L, 3L, 19L, 16L, 18L, 17L, 16L, 17L, 9L, 29L, 12L, 7L, 29L,
18L, 16L, 18L, 29L, 8L, 17L, 16L, 17L, 12L, 6L, 8L, 17L, 29L,
9L, 17L, 29L, 19L, 8L, 17L, 29L, 9L, 9L, 16L, 29L, 29L, 19L,
19L, 19L, 29L, 12L, 19L, 17L, 29L, 17L, 16L, 16L, 19L, 16L, 4L,
1L, 5L, 17L, 9L, 18L, 18L, 6L, 4L, 8L, 16L, 16L, 29L, 7L, 12L,
8L, 4L, 29L, 12L, 5L), .Label = c("ACO2", "ACO3", "CSY4", "FUM1",
"ICDH", "IDH1", "IDH2", "IDH5", "IDH6", "LPD1", "LPD2", "MDH1",
"MDH2", "ME1", "ME2", "ODC1-1", "ODC1-2", "ODC2-1", "ODC2-2",
"PDC1a-1", "PDC1a-2", "PDC1b", "PDC2-1", "PDC2-2", "SCoALa-1",
"SCoALa-2", "SCoALb", "SDH1-1", "SDH2-1", "SDH2-2", "SDH2-3",
"SDH3-1", "SDH4", "SDH5", "SDH6", "SDH7a", "SDH7b", "SDH8"), class = "factor"),
Protein2 = structure(c(1L, 6L, 7L, 17L, 1L, 16L, 3L, 9L,
1L, 5L, 17L, 9L, 8L, 7L, 18L, 18L, 5L, 3L, 16L, 3L, 5L, 8L,
4L, 7L, 5L, 3L, 6L, 6L, 5L, 3L, 5L, 3L, 3L, 6L, 7L, 3L, 7L,
9L, 1L, 8L, 5L, 16L, 7L, 6L, 4L, 7L, 4L, 3L, 3L, 12L, 1L,
1L, 9L, 7L, 7L, 9L, 6L, 6L, 5L, 8L, 1L, 17L, 29L, 3L, 8L,
6L, 9L, 9L, 6L, 12L, 5L, 19L, 12L, 5L, 1L, 16L, 1L, 19L,
4L, 18L, 12L, 1L, 4L, 4L, 6L, 3L, 1L, 1L, 1L, 4L, 4L, 8L,
4L, 1L, 3L, 8L, 16L, 12L, 4L, 12L, 4L, 4L, 17L, 8L, 5L), .Label = c("ACO2",
"ACO3", "CSY4", "FUM1", "ICDH", "IDH1", "IDH2", "IDH5", "IDH6",
"LPD1", "LPD2", "MDH1", "MDH2", "ME1", "ME2", "ODC1-1", "ODC1-2",
"ODC2-1", "ODC2-2", "PDC1a-1", "PDC1a-2", "PDC1b", "PDC2-1",
"PDC2-2", "SCoALa-1", "SCoALa-2", "SCoALb", "SDH1-1", "SDH2-1",
"SDH2-2", "SDH2-3", "SDH3-1", "SDH4", "SDH5", "SDH6", "SDH7a",
"SDH7b", "SDH8"), class = "factor")), .Names = c("Protein1",
"Protein2"), class = "data.frame", row.names = c(1L, 4L, 6L,
12L, 22L, 25L, 28L, 33L, 44L, 48L, 51L, 52L, 53L, 60L, 68L, 70L,
72L, 76L, 86L, 109L, 110L, 119L, 133L, 144L, 146L, 158L, 170L,
197L, 202L, 206L, 211L, 213L, 226L, 227L, 237L, 271L, 272L, 286L,
290L, 297L, 304L, 305L, 306L, 319L, 323L, 327L, 347L, 348L, 351L,
357L, 370L, 372L, 373L, 378L, 379L, 392L, 406L, 410L, 414L, 417L,
419L, 437L, 442L, 445L, 448L, 455L, 457L, 462L, 471L, 479L, 482L,
483L, 488L, 503L, 509L, 522L, 536L, 563L, 618L, 620L, 623L, 628L,
630L, 644L, 647L, 666L, 668L, 673L, 676L, 678L, 679L, 690L, 691L,
694L, 698L, 703L, 709L, 714L, 715L, 722L, 723L, 724L, 727L, 739L,
740L))
In each of df there are two columns which store strings. Strings overlap between table. However, the order between pairs might be different. One string from the pair might be find in first column of data1 and in second column in data2. How to find what pairs and how many of them overlap between datasets ?


> data1$combine = as.character(interaction(data1$Name.x, data1$Name.y))
> data2$combine = as.character(interaction(data2$Protein1, data2$Protein2))
>
> dat.overlap = data1[complete.cases(match(data2$combine, data1$combine)),]
> dat.overlap
Name.x Name.y combine
2 MDH1 SCOALA-1 MDH1.SCOALA-1
4 IDH2 IDH6 IDH2.IDH6
11 SCOALB SCOALA-2 SCOALB.SCOALA-2
13 CSY4 SDH2-1 CSY4.SDH2-1
18 FUM1 ODC1-2 FUM1.ODC1-2
28 ODC2-1 ODC2-2 ODC2-1.ODC2-2
data1[complete.cases(match(data1$combine, data2$combine)),]
Name.x Name.y combine
3 IDH2 CSY4 IDH2.CSY4
7 IDH2 IDH1 IDH2.IDH1
19 IDH6 ACO2 IDH6.ACO2
20 IDH6 IDH1 IDH6.IDH1
21 IDH6 IDH5 IDH6.IDH5
23 ODC1-1 IDH1 ODC1-1.IDH1
24 ODC1-1 IDH5 ODC1-1.IDH5
27 ODC2-1 IDH1 ODC2-1.IDH1
29 ODC2-1 ODC1-2 ODC2-1.ODC1-2
35 ODC2-2 ODC1-2 ODC2-2.ODC1-2


Sort row-wise and make a key by pasting, then merge:
data1$key <- apply(data1, 1, function(i) paste(sort(i), collapse = "_"))
data2$key <- apply(data2, 1, function(i) paste(sort(i), collapse = "_"))
res <- merge(data1, data2, by = "key")
head(res)
# key Name.x Name.y Protein1 Protein2
# 1 ACO2_IDH1 ACO2 IDH1 IDH1 ACO2
# 2 ACO2_IDH6 IDH6 ACO2 IDH6 ACO2
# 3 CSY4_ICDH CSY4 ICDH ICDH CSY4
# 4 CSY4_IDH1 CSY4 IDH1 IDH1 CSY4
# 5 CSY4_IDH2 IDH2 CSY4 IDH2 CSY4
# 6 CSY4_IDH5 CSY4 IDH5 IDH5 CSY4

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,
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, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L,
87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L,
99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L,
109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L, 118L,
119L, 120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L,
129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 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,
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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:

Arranging time stamped data in chronological order

An output table of one of my codes looks like this:
> head(act.byHour_corr)
hour date activity
1: 0 Activity on 6/20/2018 59
2: 1 Activity on 6/20/2018 74
3: 2 Activity on 6/20/2018 2683
4: 3 Activity on 6/20/2018 4341
5: 4 Activity on 6/20/2018 3676
6: 5 Activity on 6/20/2018 2143
The column hour represents the hours of the day from 0 to 23 and the data in date is chronologically organized. Unfortunately, when the data comes to the point where the next month 7/dd/2018 is reached, date is not chronologically organized anymore:
> head(act.byHour_corr[287:293])
hour date activity
1: 22 Activity on 7/1/2018 400
2: 23 Activity on 7/1/2018 201
3: 0 Activity on 7/10/2018 705
4: 1 Activity on 7/10/2018 47
5: 2 Activity on 7/10/2018 605
6: 3 Activity on 7/10/2018 257
You can see that 7/10/2018 and its associated values come after 7/1/2018 instead of that being 7/2/2018.
If that helps I can provide my dataset below:
> dput(act.byHour_corr)
structure(list(hour = c(0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L,
23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
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18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
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15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
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1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
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18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L), date = 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, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
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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, 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, 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,
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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,
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714L, 177L, 132L, 29L, 22L, 43L, 0L, 90L, 1094L, 1655L, 2643L,
2108L, 2249L, 2453L, 2857L, 915L, 437L, 1142L, 2193L, 2993L,
1139L, 1549L, 652L, 580L, 970L, 674L, 211L, 206L, 167L, 63L,
1L, 786L, 617L, 1575L, 2237L, 1302L, 1149L, 2009L, 2234L, 1263L,
1259L, 2017L, 1641L, 2683L, 1184L, 449L, 65L, 956L, 1538L, 1287L,
593L, 362L, 594L, 1172L, 25L, 445L, 921L, 1812L, 2235L, 1153L,
422L, 1084L, 2158L, 1610L, 845L, 1187L, 2528L, 2161L, 976L, 19L,
747L, 570L, 576L, 19L, 304L, 2L, 301L, 7L, 399L, 494L, 723L,
1088L, 771L, 85L, 1338L, 866L, 384L, 1356L, 2862L, 3805L, 2142L,
1655L, 249L, 235L, 3L, 0L, 283L, 981L, 634L, 1370L, 9L, 137L,
33L, 975L, 1690L, 1639L, 985L, 210L, 1266L, 2135L, 2080L, 1704L,
2449L, 3133L, 1055L, 3222L, 1152L, 173L, 858L, 188L, 700L, 330L,
905L, 1232L, 1006L, 5L, 21L, 520L, 1162L, 1771L, 2463L, 1403L,
1353L, 1938L, 2388L, 4133L, 900L, 2660L, 3504L, 3946L, 1956L,
818L, 604L, 937L, 373L, 48L, 400L, 201L, 705L, 47L, 605L, 257L,
1359L, 41L, 1019L, 1426L, 2219L, 1179L, 1624L, 537L, 421L, 1747L,
2941L, 2921L, 1046L, 283L, 476L, 218L, 59L, 389L, 657L, 1293L,
24L, 455L, 6L, 1232L, 2264L, 1152L, 600L, 11L, 980L, 1519L, 2004L,
1933L, 2161L, 1386L, 1883L, 2978L, 1385L, 104L, 1309L, 2L, 364L,
550L, 0L, 1433L, 1634L, 27L, 860L, 1095L, 1102L, 132L, 582L,
710L, 1368L, 2470L, 2944L, 1030L, 1286L, 387L, 2590L, 2449L,
743L, 134L, 274L, 205L, 360L, 627L, 1357L, 591L, 216L, 143L,
70L, 2L, 477L, 42L, 81L, 304L, 2827L, 2437L, 2002L, 688L, 935L,
812L, 404L, 1098L, 1157L, 857L, 466L, 215L, 714L, 269L, 1223L,
8L, 1L, 635L, 6L, 1797L, 1363L, 246L, 704L, 1089L, 943L, 2251L,
813L, 2643L, 1657L, 18L, 1132L, 2884L, 1044L, 149L, 1146L, 68L,
1227L, 1189L, 129L, 1291L, 7L, 9L, 1299L, 389L, 288L, 157L, 0L,
324L, 248L, 915L, 795L, 598L, 733L, 308L, 2760L, 2874L, 1903L,
499L, 73L, 31L, 1146L, 920L, 852L, 2L, 104L, 564L, 16L, 1903L,
675L, 1859L, 720L, 1017L, 4L, 2114L, 2264L, 1152L, 935L, 1691L,
1031L, 2568L, 2035L, 226L, 18L, 1716L, 249L, 717L, 635L, 919L,
1436L, 16L, 17L, 1891L, 1175L, 74L, 435L, 377L, 718L, 619L, 439L,
1373L, 2154L, 2481L, 763L, 2084L, 910L, 641L, 669L, 737L, 793L,
1471L, 12L, 96L, 6L, 13L, 81L, 1227L, 1685L, 260L, 238L, 575L,
930L, 330L, 1139L, 785L, 1110L, 1007L, 1770L, 2824L, 729L, 776L,
602L, 550L, 1432L, 567L, 197L, 107L, 38L, 648L, 264L, 911L, 2239L,
1063L, 9L, 1336L, 1235L, 628L, 1722L, 1028L, 1393L, 44L, 2110L,
1719L, 666L, 127L, 885L, 788L, 1274L, 765L, 1094L, 38L, 876L,
505L, 162L, 775L, 1567L, 896L, 1648L, 995L, 2574L, 1080L, 997L,
1881L, 1375L, 1283L, 2156L, 2384L, 982L, 33L, 20L, 761L, 241L,
696L, 133L, 915L, 514L, 14L, 59L, 1081L, 1266L, 359L, 1055L,
280L, 123L, 2251L, 2302L, 1116L, 2750L, 764L, 1377L, 2776L, 970L,
814L, 10L, 1364L, 1137L, 279L, 10L, 605L, 279L, 596L, 12L, 1443L,
1463L, 1426L, 132L, 924L, 379L, 693L, 137L, 219L, 884L, 194L,
450L, 1204L, 487L, 578L, 445L, 9L, 823L, 2L, 1212L, 12L, 200L,
9L, 152L, 1062L, 1926L, 1156L, 1951L, 1735L, 753L, 570L, 362L,
813L, 756L, 1403L, 308L, 1895L, 325L, 768L, 666L, 33L, 634L,
1294L, 819L, 39L, 579L, 8L, 657L, 438L, 521L, 896L, 2560L, 1383L,
819L, 1293L, 2257L, 476L, 1850L, 759L, 2482L, 1513L, 789L, 78L,
329L, 43L, 50L, 1583L, 342L, 0L, 495L, 13L, 127L, 1415L, 1534L,
939L, 2315L, 649L, 154L, 2838L, 1462L, 2255L, 1058L, 316L, 1825L,
2391L, 324L, 185L, 813L, 997L, 830L, 407L, 796L, 624L, 1002L,
6L, 86L, 1091L, 1951L, 8L, 1863L, 2555L, 799L, 749L, 2386L, 1893L,
524L, 846L, 2263L, 2266L, 779L, 88L, 380L, 1495L, 1985L, 3L,
1462L, 1450L, 1L, 19L, 967L, 1565L, 1066L, 9L, 99L, 4L, 1889L,
1848L, 1924L, 471L, 1357L, 626L, 1465L, 1787L, 1437L, 115L, 322L,
717L, 1639L, 990L, 1029L, 1112L, 372L, 8L, 256L, 1679L, 1209L,
2246L, 2153L, 1762L, 1883L, 1551L, 998L, 728L, 1274L, 888L, 508L,
2357L, 452L, 1167L, 2385L, 3280L, 320L, 1130L, 878L, 583L, 799L,
4L, 61L, 394L, 1237L, 854L, 68L, 379L, 2910L, 3088L, 1011L, 840L,
1024L, 2496L, 3079L, 2830L, 1841L, 1772L, 595L, 65L, 584L, 2110L,
1966L, 473L, 21L, 847L, 293L, 881L, 840L, 1912L, 683L, 1362L,
1276L, 3131L, 3110L, 1773L, 1077L, 1437L, 769L, 2311L, 1623L,
562L, 42L, 1791L, 1318L, 1230L, 202L, 2630L, 623L, 918L, 48L,
523L, 721L, 1624L, 1047L, 1783L, 313L, 1042L, 2211L, 2430L, 1770L,
1610L, 2814L, 2460L, 1770L, 25L, 709L, 416L, 709L, 998L, 921L,
89L, 1174L, 396L, 52L, 2261L, 1237L, 56L, 927L, 2491L, 3180L,
352L, 81L, 2072L, 3207L, 2394L, 600L, 3280L, 1745L, 147L, 1L,
1544L, 350L, 2198L, 1833L, 55L, 0L, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
258L, 1242L, 75L, 1131L, 893L, 402L, 381L, 51L, 15L, 47L, 762L,
777L, 479L, 2416L, 3639L, 1991L, 202L, 1054L, 917L, 1565L, 503L,
61L, 44L, 2103L, 2212L, 352L, 1L, 666L, 351L, 1321L, 7L, 1010L,
1222L, 1080L, 1643L, 1101L, 188L, 2793L, 1548L, 1811L, 1807L,
51L, 788L, 1108L, 1157L, 1038L, 225L, 454L, 441L, 376L, 444L,
5L, 501L, 579L, 1253L, 1600L, 1051L, 498L, 2217L, 2362L, 2425L,
1220L, 2037L, 2684L, 799L, 471L, 139L, 545L, 1117L, 177L, 487L,
1420L, 692L, 303L, 736L, 750L, 1386L, 926L, 30L, 862L, 1912L,
2731L, 1123L, 1160L, 2892L, 1634L, 585L, 3473L, 2243L, 441L,
399L, 1482L, 111L, 455L, 1315L, 691L, 1428L, 96L, 52L, 258L,
1135L, 1727L, 448L, 2148L, 358L, 2180L, 1519L, 2634L, 828L, 1212L,
1052L, 2851L, 902L, 171L, 236L, 3L, 727L, 1366L, 637L, 43L, 0L,
1320L, 146L, 664L, 862L, 663L, 227L, 227L, 995L, 743L, 1793L,
2421L, 1346L, 1874L, 2182L, 1333L, 1967L, 1023L, 297L, 340L,
1469L, 10L, 213L, 805L)), row.names = c(NA, -1008L), class = c("data.table",
"data.frame"), .internal.selfref = <pointer: 0x0000000002641ef0>)
Hope I can get some help to organize the data chronologically for the full dataset. Any input is appreciated.

This should help!
act.byHour_corr$date <- as.Date(gsub('Activity on ', '', act.byHour_corr$date),
format = '%m/%d/%Y')
act.byHour_corr <- act.byHour_corr[order(act.byHour_corr$date),]
It removes the 'Activity on' portion of the column. Does that work, or do you need to keep the 'Activity on' part?

Add hour to you data:
library(data.table)
library(lubridate)
library(stringr)
act.byHour_corr[, data_hour:=(paste0(date," ", str_pad(hour, 2, "left",0),":00"))]
act.byHour_corr[, data_hour:=mdy_hm(data_hour)]
act.byHour_corr[order(data_hour)]

Straight-forward AND open-source alternatives to asreml-r for spatial models?

In the past, I have used asreml-r to account for spatial auto-correlation in agricultural field trials that were laid out in a “row and range” design. It is relatively easy to use the asreml package to specify a spatial model (i.e. rcov=~at(LOCATION):ar1(ROW):ar1(RANGE))
Unfortunately, asreml-r is expensive and difficult to learn. My research group also prefers to rely on nlme and lmer for the majority of it’s analytical needs. So they are reluctant to either pay for asreml-r or consider using.
Several years ago a question was posted asking if an open-source alternative to asreml-r was available that could be used to construct a two-dimensional spatial model with error structure in both direction. The consensus at the time seemed to be that it wasn’t straight forward to do this in either lmer or nlme.
After spending a few hours searching, it’s not totally clear to me whether there has been any progress on addressing this. Can anyone refer me to a recent discussion regarding this type of analysis? Or can they offer advice on how to construct a mixed effects models that accounts for spatial correlation in nlme or lmer?
Please note that neither myself nor other members of our group are exactly statisticians or high-level r coders. It is also not practical to contract an outside group to analyze our data. We just want to apply the best methods we can to routine annual analyses of data.
An example of the data being analyzed:
my.data <- structure(list(ENTRY = structure(c(23L, 23L, 23L, 40L, 12L, 8L,
1L, 15L, 30L, 1L, 24L, 8L, 1L, 8L, 30L, 33L, 12L, 38L, 41L, 36L,
43L, 32L, 44L, 31L, 26L, 11L, 13L, 34L, 5L, 22L, 4L, 14L, 11L,
20L, 25L, 11L, 21L, 43L, 44L, 4L, 42L, 45L, 42L, 41L, 42L, 4L,
44L, 20L, 40L, 29L, 29L, 24L, 2L, 3L, 28L, 24L, 34L, 27L, 41L,
28L, 29L, 5L, 3L, 25L, 14L, 20L, 15L, 21L, 31L, 22L, 40L, 21L,
6L, 38L, 43L, 12L, 6L, 14L, 5L, 3L, 30L, 45L, 31L, 7L, 9L, 39L,
22L, 15L, 26L, 28L, 34L, 10L, 25L, 27L, 16L, 45L, 10L, 18L, 32L,
10L, 6L, 18L, 33L, 16L, 37L, 9L, 32L, 38L, 39L, 2L, 2L, 39L,
36L, 36L, 7L, 27L, 7L, 26L, 17L, 9L, 33L, 13L, 17L, 17L, 35L,
37L, 37L, 18L, 16L, 19L, 13L, 19L, 35L, 19L, 35L, 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, 52L, 54L, 52L, 54L, 49L, 51L, 50L, 54L, 49L, 46L, 51L,
50L, 53L, 49L, 50L, 51L, 53L, 52L, 53L, 48L, 47L, 46L, 46L, 47L,
48L, 48L, 47L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L), .Label = c("20",
"112", "1478", "1495", "1521", "1522", "1590", "1608", "1657",
"1658", "1660", "1667", "1680", "1688", "1723", "1728", "1730",
"1731", "1743", "1745", "1748", "1751", "1766", "1778", "1802",
"1815", "1817", "1819", "1828", "1830", "1831", "1834", "1835",
"1836", "1837", "1838", "1839", "1840", "1841", "1842", "1843",
"1844", "1845", "1846", "1847", "3097", "3164", "3168", "3169",
"3170", "3178", "3180", "3181", "3182"), class = "factor"), BLOCK = structure(c(12L,
77L, 163L, 67L, 28L, 170L, 90L, 36L, 52L, 2L, 15L, 19L, 168L,
103L, 188L, 31L, 203L, 66L, 29L, 46L, 34L, 32L, 27L, 16L, 83L,
48L, 82L, 30L, 171L, 14L, 115L, 54L, 93L, 65L, 50L, 187L, 58L,
91L, 200L, 6L, 169L, 135L, 99L, 148L, 101L, 104L, 107L, 128L,
153L, 146L, 41L, 22L, 53L, 87L, 131L, 151L, 110L, 10L, 44L, 11L,
13L, 20L, 42L, 202L, 111L, 38L, 183L, 51L, 199L, 109L, 75L, 134L,
92L, 166L, 182L, 97L, 100L, 1L, 86L, 181L, 25L, 108L, 94L, 116L,
72L, 18L, 23L, 76L, 185L, 81L, 62L, 63L, 56L, 204L, 85L, 95L,
129L, 49L, 147L, 106L, 145L, 205L, 73L, 207L, 105L, 24L, 43L,
8L, 167L, 164L, 3L, 96L, 184L, 45L, 74L, 39L, 89L, 4L, 152L,
130L, 165L, 40L, 57L, 70L, 206L, 186L, 7L, 37L, 9L, 102L, 132L,
127L, 88L, 80L, 98L, 139L, 196L, 174L, 118L, 215L, 194L, 193L,
208L, 172L, 122L, 143L, 141L, 123L, 161L, 209L, 213L, 178L, 159L,
160L, 191L, 177L, 192L, 144L, 175L, 211L, 140L, 180L, 173L, 125L,
119L, 120L, 210L, 214L, 136L, 154L, 162L, 190L, 158L, 216L, 142L,
124L, 212L, 195L, 155L, 121L, 64L, 68L, 117L, 59L, 71L, 35L,
69L, 201L, 21L, 84L, 61L, 114L, 17L, 112L, 55L, 150L, 113L, 79L,
78L, 47L, 33L, 149L, 60L, 189L, 5L, 133L, 26L, 137L, 197L, 179L,
126L, 198L, 157L, 176L, 138L, 156L), .Label = c("101", "102",
"103", "104", "105", "106", "107", "108", "109", "110", "111",
"112", "113", "114", "115", "116", "117", "118", "201", "202",
"203", "204", "205", "206", "207", "208", "209", "210", "211",
"212", "213", "214", "215", "216", "217", "218", "301", "302",
"303", "304", "305", "306", "307", "308", "309", "310", "311",
"312", "313", "314", "315", "316", "317", "318", "401", "402",
"403", "404", "405", "406", "407", "408", "409", "410", "411",
"412", "413", "414", "415", "416", "417", "418", "501", "502",
"503", "504", "505", "506", "507", "508", "509", "510", "511",
"512", "513", "514", "515", "516", "517", "518", "601", "602",
"603", "604", "605", "606", "607", "608", "609", "610", "611",
"612", "613", "614", "615", "616", "617", "618", "701", "702",
"703", "704", "705", "706", "707", "708", "709", "710", "711",
"712", "713", "714", "715", "716", "717", "718", "801", "802",
"803", "804", "805", "806", "807", "808", "809", "810", "811",
"812", "813", "814", "815", "816", "817", "818", "901", "902",
"903", "904", "905", "906", "907", "908", "909", "910", "911",
"912", "913", "914", "915", "916", "917", "918", "1001", "1002",
"1003", "1004", "1005", "1006", "1007", "1008", "1009", "1010",
"1011", "1012", "1013", "1014", "1015", "1016", "1017", "1018",
"1101", "1102", "1103", "1104", "1105", "1106", "1107", "1108",
"1109", "1110", "1111", "1112", "1113", "1114", "1115", "1116",
"1117", "1118", "1201", "1202", "1203", "1204", "1205", "1206",
"1207", "1208", "1209", "1210", "1211", "1212", "1213", "1214",
"1215", "1216", "1217", "1218"), class = "factor"), PLOT = structure(c(3L,
1L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 1L, 2L, 3L, 2L, 3L, 2L,
3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 3L, 1L,
3L, 3L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 3L, 3L, 3L, 2L, 2L,
2L, 1L, 1L, 3L, 3L, 2L, 2L, 2L, 3L, 1L, 3L, 3L, 1L, 1L, 2L, 2L,
1L, 2L, 3L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 3L, 1L, 3L, 2L, 1L,
3L, 1L, 2L, 3L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 2L,
3L, 2L, 3L, 2L, 2L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 3L, 1L, 2L, 2L, 2L, 1L, 1L, 3L, 2L, 2L, 1L, 1L, 1L,
3L, 2L, 2L, 3L, 1L, 1L, 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, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 2L, 1L, 3L, 1L, 2L, 3L,
2L, 1L, 2L, 2L, 1L, 1L, 3L, 3L, 2L, 1L, 2L, 1L, 2L, 1L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("1", "2", "3", "4"), class = "factor"),
RANGE = structure(c(1L, 5L, 10L, 4L, 2L, 10L, 5L, 2L, 3L,
1L, 1L, 2L, 10L, 6L, 11L, 2L, 12L, 4L, 2L, 3L, 2L, 2L, 2L,
1L, 5L, 3L, 5L, 2L, 10L, 1L, 7L, 3L, 6L, 4L, 3L, 11L, 4L,
6L, 12L, 1L, 10L, 8L, 6L, 9L, 6L, 6L, 6L, 8L, 9L, 9L, 3L,
2L, 3L, 5L, 8L, 9L, 7L, 1L, 3L, 1L, 1L, 2L, 3L, 12L, 7L,
3L, 11L, 3L, 12L, 7L, 5L, 8L, 6L, 10L, 11L, 6L, 6L, 1L, 5L,
11L, 2L, 6L, 6L, 7L, 4L, 1L, 2L, 5L, 11L, 5L, 4L, 4L, 4L,
12L, 5L, 6L, 8L, 3L, 9L, 6L, 9L, 12L, 5L, 12L, 6L, 2L, 3L,
1L, 10L, 10L, 1L, 6L, 11L, 3L, 5L, 3L, 5L, 1L, 9L, 8L, 10L,
3L, 4L, 4L, 12L, 11L, 1L, 3L, 1L, 6L, 8L, 8L, 5L, 5L, 6L,
8L, 11L, 10L, 7L, 12L, 11L, 11L, 12L, 10L, 7L, 8L, 8L, 7L,
9L, 12L, 12L, 10L, 9L, 9L, 11L, 10L, 11L, 8L, 10L, 12L, 8L,
10L, 10L, 7L, 7L, 7L, 12L, 12L, 8L, 9L, 9L, 11L, 9L, 12L,
8L, 7L, 12L, 11L, 9L, 7L, 4L, 4L, 7L, 4L, 4L, 2L, 4L, 12L,
2L, 5L, 4L, 7L, 1L, 7L, 4L, 9L, 7L, 5L, 5L, 3L, 2L, 9L, 4L,
11L, 1L, 8L, 2L, 8L, 11L, 10L, 7L, 11L, 9L, 10L, 8L, 9L), .Label = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"), class = "factor"),
ROW = structure(c(12L, 5L, 1L, 13L, 10L, 8L, 18L, 18L, 16L,
2L, 15L, 1L, 6L, 13L, 8L, 13L, 5L, 12L, 11L, 10L, 16L, 14L,
9L, 16L, 11L, 12L, 10L, 12L, 9L, 14L, 7L, 18L, 3L, 11L, 14L,
7L, 4L, 1L, 2L, 6L, 7L, 9L, 9L, 4L, 11L, 14L, 17L, 2L, 9L,
2L, 5L, 4L, 17L, 15L, 5L, 7L, 2L, 10L, 8L, 11L, 13L, 2L,
6L, 4L, 3L, 2L, 3L, 15L, 1L, 1L, 3L, 8L, 2L, 4L, 2L, 7L,
10L, 1L, 14L, 1L, 7L, 18L, 4L, 8L, 18L, 18L, 5L, 4L, 5L,
9L, 8L, 9L, 2L, 6L, 13L, 5L, 3L, 13L, 3L, 16L, 1L, 7L, 1L,
9L, 15L, 6L, 7L, 8L, 5L, 2L, 3L, 6L, 4L, 9L, 2L, 3L, 17L,
4L, 8L, 4L, 3L, 4L, 3L, 16L, 8L, 6L, 7L, 1L, 9L, 12L, 6L,
1L, 16L, 8L, 8L, 13L, 16L, 12L, 10L, 17L, 14L, 13L, 10L,
10L, 14L, 17L, 15L, 15L, 17L, 11L, 15L, 16L, 15L, 16L, 11L,
15L, 12L, 18L, 13L, 13L, 14L, 18L, 11L, 17L, 11L, 12L, 12L,
16L, 10L, 10L, 18L, 10L, 14L, 18L, 16L, 16L, 14L, 15L, 11L,
13L, 10L, 14L, 9L, 5L, 17L, 17L, 15L, 3L, 3L, 12L, 7L, 6L,
17L, 4L, 1L, 6L, 5L, 7L, 6L, 11L, 15L, 5L, 6L, 9L, 5L, 7L,
8L, 11L, 17L, 17L, 18L, 18L, 13L, 14L, 12L, 12L), .Label = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12",
"13", "14", "15", "16", "17", "18"), class = "factor"), YIELD = c(7882.814724,
7641.976671, 7535.187491, 8462.821158, 6470.762695, 7086.39647,
7260.626003, 8374.363239, 8225.545799, 6870.562479, 7260.303179,
6472.786879, 6535.801894, 7335.468082, 8101.853381, 7544.810974,
5597.940891, 8144.903193, 8489.541356, 7420.247609, 8267.229308,
7388.809243, 8753.922873, 7675.2452, 7540.083649, 7459.719121,
7614.590404, 6910.577593, 7655.161236, 8086.00529, 6754.554032,
9141.060314, 7728.70075, 7210.881432, 8872.660416, 7341.942246,
8211.265337, 9030.218757, 8957.01212, 7134.079145, 8580.60533,
8901.807114, 9009.635596, 8972.04225, 8850.07798, 7244.08863,
9357.355395, 7693.962907, 9059.604638, 8115.135788, 8073.220877,
7694.865425, 7168.389384, 7931.776306, 8310.054831, 7743.358631,
7241.417998, 7887.710882, 8671.335868, 7900.074562, 7089.929401,
8252.964285, 8038.601576, 8749.99335, 7880.418003, 7227.593551,
9733.562528, 7715.095262, 6926.775409, 7770.203085, 9000.211927,
7808.710708, 8239.82626, 8252.964285, 9546.314331, 2801.654022,
7865.302917, 6472.037973, 11286.93314, 7698.702989, 8239.164252,
8391.871173, 7817.085477, 7987.7324, 8517.420004, 8286.027753,
8021.268999, 8605.836444, 8360.390812, 8408.648702, 6980.52271,
8484.391646, 7604.489488, 8047.32564, 6859.736888, 8211.744547,
8338.224508, 7549.875965, 7831.170315, 8002.372075, 8092.398475,
7233.303386, 7880.198456, 6431.676768, 8146.454012, 9012.217125,
7696.760712, 7916.314754, 8372.430545, 4552.305881, 4744.119616,
8072.706265, 8038.601576, 8070.612573, 7631.800415, 8124.412039,
7958.686488, 8565.578204, 7204.2532, 7782.851494, 8195.743097,
8075.444598, 7468.681342, 7376.4572, 7019.132415, 7450.186973,
7900.853201, 7077.396698, 6781.366002, 8195.304822, 7581.211378,
8155.600681, 7446.611537, 7887.710882, 6849.690117, 6384.206298,
6965.647058, 7732.576444, 7687.296996, 7887.710882, 8061.034883,
7861.831189, 6690.298381, 7982.777954, 8310.054831, 7476.530867,
5840.137517, 8012.816166, 9211.484507, 8906.076566, 7227.155276,
6795.608201, 6926.023806, 8026.998142, 7388.809243, 7700.812705,
7493.134187, 7397.470718, 6794.411986, 8475.249868, 8387.892097,
8503.435859, 7890.106874, 7631.800415, 8349.757061, 7852.912013,
7758.848165, 7580.919692, 6402.21648, 6920.804051, 8628.194894,
7489.137138, 7866.037678, 7311.596266, 8746.497033, 9147.374207,
9022.033508, 8475.348448, 8911.007949, 8961.95446, 8476.003123,
8932.837953, 8661.336305, 8949.625535, 9048.100379, 10684.87284,
8845.185424, 8182.999872, 8986.675848, 8136.137692, 10504.2443,
8848.254372, 7233.813327, 8707.732966, 8381.547529, 10471.33626,
7682.888263, 8071.666541, 7428.171461, 9736.360333, 9378.789551,
8294.552055, 8225.545799, 8874.930993, 8459.226077, 8749.99335,
9192.455984, 7875.820212, 8982.410256, 8642.199262, 8935.14394,
8480.821358, 10240.80452, 8746.68483, 7619.897735, 8417.475201
)), .Names = c("ENTRY", "BLOCK", "PLOT", "RANGE", "ROW",
"YIELD"), row.names = 372:587, class = "data.frame")
The spatial arrangement of the data:
library(reshape2)
dcast(my.data, RANGE ~ ROW, value.var ="YIELD")
Possible examples of models to analyze the data:
library(nlme)
fit1 = lme(fixed = YIELD ~ ENTRY, data = my.data,
random= ~1 | BLOCK,
method = "ML")
fit2 = lme(fixed = YIELD ~ ENTRY, data = my.data,
random= ~1 | BLOCK,
corr = corSpatial(form = ~RANGE+ROW),
method = "ML")

geom_path with discrete boxplot data

Finally run out of ideas and links I could find to try and explain this so I need some help!
I'm trying to add a step-function to a ggplot chart using the cumSeg package. I did this successfully in this previous question, so I'm used to the usage of the function etc.
When I made the plot in that thread, it was fairly simple, just using an x vs y barplot for the mean values of x, and I added on error bars myself afterwards (thus it was a 16 x 2 dataframe).
I want to re-create this plot, but using sequential boxplots instead of bars, which I have done, using the raw data this time, which is ~250 observations in 16 factors (same factors as before).
Now when I try to add a geom_line,path or step it's complaining about the dimensions of the data not matching, because even though there are 16 factors/boxplots, there are now no longer 16 observations (Error: Aesthetics must be either length 1 or the same as the data (249): x, y, colour, group, fill)
To calculate the step function, I give it the means of each of the 16, which returns a 16-member vector, not ~250 (obviously).
How can I add the step function on to the box plot so that it understands it should pertain to the 16 factor values? I can't work out if it's a problem with the dataframe or how I'm giving it to ggplot.
I tried specifying it in a second dataframe, and passing it as geom_path(data=df2) instead of inheriting the main plots data, as in this question, but it still complains (Error: Aesthetics must be either length 1 or the same as the data (16): x, y, colour, group (the code below is in this form still)
data.melt <- melt(t(infile)
operon_gc <- 0.408891366
opgc_stdev <- 0.015712091
genome_gc <- 0.425031611
gengc_stdev <- 0.007587437
stepfunc <- jumpoints(y=aggregate(melted_data$value~melted_data$Var1, simplify=TRUE, FUN="mean")$`melted_data$value`, k=1, output="1")
func_data <- data.frame(x = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16), y = stepfunc$fitted.values)
# Make boxplot
bp <- ggplot(melted_data, aes(x=Var1, y=value*100, fill=Var1)) + theme_bw()
#bp <- bp + scale_x_discrete(name = "Locus") + scale_y_continuous(name="GC Content (%)")
bp <- bp + geom_rect(xmin=0, xmax=17,
ymin=(operon_gc-opgc_stdev)*100,
ymax=(operon_gc+opgc_stdev)*100,
fill = "grey79", alpha=0.05)
bp <- bp + geom_rect(xmin=0, xmax=17,
ymin=(genome_gc-gengc_stdev)*100,
ymax=(genome_gc+gengc_stdev)*100,
fill = "beige", alpha=.08)
bp <- bp + geom_abline(intercept=genome_gc*100, slope=0,
colour="gray14", linetype=3)
bp <- bp + geom_abline(intercept=operon_gc*100, slope=0,
colour="gray14", linetype=3)
bp <- bp + geom_boxplot(alpha = 0.7)
bp <- bp + scale_color_manual(values = c("GC Step Fit"="red"), guides(color="Regression"))
bp <- bp + geom_path(linetype=4, size=0.9, aes(x=func_data$x,
y=func_data$y,
color="GC Step Fit",
group=1))
bp <- bp + theme(legend.position="bottom",
legend.direction="horizontal",
axis.text.x = element_text(angle=45, hjust=1)) + guides(fill=guide_legend(title="", nrow = 1))
bp
Data
> dput(func_data)
structure(list(x = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16), y = c(0.452456815737206, 0.452456815737206, 0.452456815737206,
0.452456815737206, 0.452456815737206, 0.452456815737206, 0.452456815737206,
0.452456815737206, 0.452456815737206, 0.452456815737206, 0.452456815737206,
0.375047391939972, 0.375047391939972, 0.375047391939972, 0.375047391939972,
0.375047391939972)), .Names = c("x", "y"), row.names = c(NA,
-16L), class = "data.frame")
> dput(melted_data)
structure(list(Var1 = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 1L, 2L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 14L, 15L, 16L, 1L, 2L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 15L, 16L, 1L, 2L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 15L, 16L, 11L), .Label = c("PVC1", "PVC2", "PVC3", "PVC4",
"PVC5", "PVC6", "PVC7", "PVC8", "PVC9", "PVC10", "PVC11", "PVC12",
"PVC13", "PVC14", "PVC15", "PVC16"), class = "factor"), Var2 = c(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, 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, 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, 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, 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, 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, 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, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 17L
), value = c(0.404444444, 0.436329588, 0.46031746, 0.479318735,
0.466230937, 0.480874317, 0.476811594, 0.441558442, 0.449172577,
0.476525822, 0.452674897, 0.460918332, 0.368041912, 0.339160839,
0.415355269, 0.408163265, 0.401826484, 0.45411985, 0.468609865,
0.479735318, 0.464052288, 0.469945355, 0.476811594, 0.444032158,
0.453900709, 0.494004796, 0.467315716, 0.457805907, 0.387071651,
0.390737117, 0.408679065, 0.425170068, 0.355555556, 0.438069217,
0.423076923, 0.466666667, 0.450980392, 0.422222222, 0.469298246,
0.43196005, 0.416666667, 0.496402878, 0.428676201, 0.382113821,
0.349765258, 0.332280147, 0.373371925, 0.346448087, 0.415555556,
0.440508629, 0.435222672, 0.455833333, 0.446623094, 0.422222222,
0.463450292, 0.43258427, 0.425675676, 0.497584541, 0.422524565,
0.392592593, 0.362779741, 0.337552743, 0.379856115, 0.348888889,
0.391111111, 0.421004566, 0.426439232, 0.480367586, 0.472766885,
0.455555556, 0.495726496, 0.447565543, 0.424460432, 0.48441247,
0.435164835, 0.39600551, 0.3858393, 0.323655914, 0.383693046,
0.329988852, 0.395555556, 0.452380952, 0.454756381, 0.448129252,
0.496732026, 0.423728814, 0.502923977, 0.433832709, 0.41607565,
0.498800959, 0.399161736, 0.368421053, 0.386568387, 0.369901547,
0.398550725, 0.34006734, 0.406392694, 0.455840456, 0.458598726,
0.43792517, 0.501089325, 0.427777778, 0.49122807, 0.435081149,
0.416020672, 0.48441247, 0.40617284, 0.379298942, 0.402298851,
0.361462729, 0.396135266, 0.356666667, 0.353333333, 0.439182916,
0.469316597, 0.461868038, 0.490196078, 0.405555556, 0.505847953,
0.430529595, 0.406619385, 0.470023981, 0.395262768, 0.355072464,
0.373677249, 0.348008386, 0.382804995, 0.355481728, 0.415555556,
0.481481481, 0.4550036, 0.485074627, 0.501089325, 0.5, 0.51754386,
0.465043695, 0.438478747, 0.501199041, 0.457733481, 0.416815742,
0.360672976, 0.388285024, 0.397509579, 0.356589147, 0.384444444,
0.482917821, 0.452525253, 0.487864078, 0.501089325, 0.488888889,
0.513157895, 0.47627965, 0.475609756, 0.513189448, 0.471391657,
0.419797257, 0.38467433, 0.376081425, 0.396666667, 0.370985604,
0.42, 0.477777778, 0.436063218, 0.476782753, 0.490196078, 0.466666667,
0.51754386, 0.45505618, 0.44295302, 0.532374101, 0.460707635,
0.426019548, 0.35755814, 0.389842632, 0.388489209, 0.358730159,
0.422222222, 0.459610028, 0.473304473, 0.502487562, 0.509803922,
0.438888889, 0.516081871, 0.480024969, 0.457317073, 0.527577938,
0.460969293, 0.424148607, 0.386850153, 0.369161868, 0.397677794,
0.357696567, 0.433333333, 0.450704225, 0.429118774, 0.497031383,
0.505446623, 0.455555556, 0.492690058, 0.444444444, 0.409722222,
0.501199041, 0.444812362, 0.414860681, 0.361111111, 0.390096618,
0.394724221, 0.358803987, 0.426666667, 0.471837488, 0.495748299,
0.511982571, 0.45, 0.513157895, 0.465043695, 0.438478747, 0.498800959,
0.453200148, 0.409375, 0.329166667, 0.384172662, 0.38961039,
0.413333333, 0.406113537, 0.450728363, 0.435244161, 0.431693989,
0.441520468, 0.427745665, 0.378076063, 0.389671362, 0.427222222,
0.397905759, 0.423295455, 0.375268817, 0.391111111, 0.39893617,
0.461538462, 0.437367304, 0.448087432, 0.454678363, 0.421323057,
0.384787472, 0.394366197, 0.419141914, 0.401331931, 0.423768939,
0.368817204, 0.42680776)), .Names = c("Var1", "Var2", "value"
), 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, 76L,
77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L,
90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L,
102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L,
113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L,
124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L,
135L, 136L, 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, 209L, 210L, 212L,
213L, 214L, 215L, 216L, 217L, 218L, 219L, 220L, 222L, 223L, 224L,
225L, 226L, 228L, 229L, 230L, 231L, 232L, 233L, 234L, 235L, 236L,
239L, 240L, 241L, 242L, 244L, 245L, 246L, 247L, 248L, 249L, 250L,
251L, 252L, 255L, 256L, 267L), class = "data.frame")

I'm not exactly sure how I solved this. I can only assume I was making a really stupid mistake before, but here's the code that finally produced the desired outcome:
bp_gc <- ggplot(melted_data, aes(x=Var1, y=value*100)) + theme_bw()
bp_gc <- bp_gc + geom_rect(xmin=0, xmax=17,
ymin=(operon_gc-opgc_stdev)*100,
ymax=(operon_gc+opgc_stdev)*100,
fill = "grey79", alpha=0.05)
bp_gc <- bp_gc + geom_rect(xmin=0, xmax=17,
ymin=(genome_gc-gengc_stdev)*100,
ymax=(genome_gc+gengc_stdev)*100,
fill = "beige", alpha=.08)
bp_gc <- bp_gc + geom_abline(intercept=genome_gc*100, slope=0,
colour="gray14", linetype=3)
bp_gc <- bp_gc + geom_abline(intercept=operon_gc*100, slope=0,
colour="gray14", linetype=3)
bp_gc <- bp_gc + geom_boxplot(alpha = 0.7, fill="dodgerblue", color="gray11")
bp_gc <- bp_gc + ylab("GC Content (%)")
bp_gc <- bp_gc + xlab("Locus")
bp_gc <- bp_gc + theme(legend.position = "none",
axis.text.x = element_text(angle=45, hjust=1))
bp_gc <- bp_gc + coord_cartesian(ylim=c(30,60))
bp_gc <- bp_gc + geom_path(data=func_data, linetype=4, size=0.9, aes(x=x,y=y*100))
bp_gc

I'm not 100% clear on what you're trying to achieve. Is it like this?
ggplot(melted_df, aes(Var1, value)) +
geom_boxplot()
ggplot(df, aes(Var1, value)) +
stat_summary(fun.y = median, geom = "path", aes(group = 1)) +
geom_boxplot()
If you really want to compute your statistics outside the main dataframe, it's usually best to do it something like this:
ggplot(df1, aes(x, y)) + geom_point() +
geom_path(data = summarydf, aes(xmean, ymean))

Predict values for each group in gamm4

I have a data set like this:
dat <- structure(list(Y = c(152.75, 167.7, 169.7, 173.2, 174.4, 177.1,
196, 200.45, 206.1, 206.65, 203, 186.65, 208.9, 192.95, 201.05,
203.45, 200.3, 197.55, 205.1, 198.1, 205.15, 189.35, 201.25,
194.55, 204.15, 200.95, 166.6, 165.1, 175.2, 168.4, 153, 168.4,
161, 170.1, 168.15, 167.3, 169.2, 169.25, 185.35, 185.9, 178.55,
193.2, 210.25, 203.75, 203.25, 203.7, 200.15, 204, 204, 206.3,
197.7, 190.5, 185.95, 199, 185.1, 194.35, 186.2, 190.95, 191.55,
177.8, 182.95, 186.3, 177.25, 186.35, 177.1, 183.9, 188.55, 184.05,
188.55, 187.25, 185.25, 174.8, 180.9, 171.4, 169.6, 176.7, 178.35,
191.3, 180.45, 187.5, 183.85, 187.7, 176.45, 188.7, 179.15, 183.25,
180.1, 184.35, 185.35, 184.25, 182.55, 185.15, 181.2, 184.6,
183.05, 182.35, 177.55, 179.85, 176.1, 175.9, 173.7, 180.7, 194.55,
190.3, 200.5, 193.05, 191.55, 190.65, 194.9, 192.8, 202.65, 200.35,
181.95, 194.85, 198.3, 199.7, 185.7, 195.9, 195.15, 191.85, 198.65,
188.9, 192.25, 197.8, 185.75, 193.5, 178.2, 170.15, 175.4, 176.25,
176.6, 179.8, 182, 173.35, 181.75, 188.05, 198.05, 204.75, 190.75,
196.15, 193.15, 195.4, 192.35, 165.55, 187.15, 191.35, 200.4,
200.4, 204.85, 211.3, 206.45, 205.95, 201, 198.6, 202.45, 192.95,
198.25, 190.85, 182.9, 184.5, 175.75, 174.95, 178.8, 173.2, 174,
176.75, 167.2, 161.1, 155.6, 178.6, 187.8, 194.05), X1 = c(4L,
6L, 7L, 8L, 9L, 10L, 4L, 6L, 7L, 8L, 9L, 10L, 11L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 12L, 13L, 14L, 15L, 4L, 5L, 6L, 7L, 8L, 4L, 5L,
6L, 7L, 11L, 14L, 15L, 16L, 19L, 20L, 21L, 22L, 23L, 24L, 25L,
26L, 27L, 4L, 5L, 9L, 13L, 16L, 17L, 18L, 19L, 20L, 21L, 22L,
23L, 24L, 25L, 26L, 27L, 28L, 4L, 5L, 7L, 8L, 9L, 10L, 11L, 12L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 4L, 5L, 8L, 9L, 10L, 11L, 4L, 5L, 6L, 7L, 8L,
10L, 11L, 12L, 13L, 4L, 6L, 7L, 8L, 9L, 12L, 13L, 14L, 15L, 16L,
17L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 12L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 4L, 6L, 7L, 4L, 5L, 7L, 9L, 11L, 12L, 15L, 16L, 17L,
20L, 21L, 22L, 4L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 16L,
18L, 4L, 5L, 6L), X2 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 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, 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, 10L,
10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 18L, 18L, 18L), .Label = c("bec", "bi", "ebk", "ele",
"eli", "ian", "isy", "ith", "lda", "lli", "na", "nja", "ra",
"rda", "ria", "rik", "tje", "tri"), class = "factor")), .Names = c("Y",
"X1", "X2"), row.names = c(142L, 143L, 144L, 145L, 146L, 147L,
87L, 88L, 89L, 90L, 91L, 92L, 93L, 160L, 161L, 162L, 163L, 164L,
165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 82L, 83L, 84L, 85L, 86L, 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, 74L, 75L, 76L, 77L, 78L, 79L, 80L,
81L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L,
112L, 113L, 114L, 115L, 116L, 117L, 118L, 133L, 134L, 135L, 136L,
137L, 138L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 119L,
120L, 121L, 122L, 123L, 124L, 125L, 126L, 127L, 128L, 129L, 66L,
67L, 68L, 69L, 70L, 71L, 72L, 73L, 94L, 95L, 96L, 97L, 98L, 99L,
100L, 101L, 130L, 131L, 132L, 148L, 149L, 150L, 151L, 152L, 153L,
154L, 155L, 156L, 157L, 158L, 159L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 21L, 22L, 139L, 140L, 141L), class = "data.frame")
and I applied a gamm4-model from gamm4-package on it:
library(gamm4)
gamm.1 <- gamm4(Y ~ s(X1),random = ~(1+X1|X2),data = dat)
I also predicted and plotted the smoothed values using:
newDat <- data.frame(X1 = min(dat$X1):max(dat$X1))
p0 <- predict(gamm.1$gam,newDat,se=T)
plot(dat$X1,dat$Y)
lines(newDat$X1,p0$fit,lwd=3)
My question is: how can I predict the smoothed lines for each of the groups (X2)?
I know that I can get the random effects via ranef(gamm.1$mer) but I don't know how to use them correctly.

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