Predict values for each group in gamm4

Predict values for each group in gamm4 - r

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.

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,
199L, 200L, 201L, 202L, 203L, 204L, 205L, 206L, 207L, 208L,
209L, 210L, 211L, 212L, 213L, 214L, 215L, 216L, 217L, 218L,
219L, 220L, 221L, 222L, 223L, 224L, 225L, 226L, 227L, 228L,
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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",
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"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",
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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:

Adding a year to a time series with only months and days

I have a df like this
month day x
1 1 1 84
2 1 2 43
3 1 3 49
4 1 4 67
5 1 5 59
......
366 12 31 97
The year should be 2019 from Oct-Dec and 2020 from Jan-Sep
I tried to use
df$year<-as.date(df,origin='2019-01-01')
But I am not sure how to make an argument.
I want the year column to get a date column and try then
df$date<-as.date(with(paste("???",month,day,sep="-"), %Y-%m-%d,origin ="2019-01-01")
but again I don't know how to make an argument for year
Please any help would save me a lot of time because doing it manually seems impossible

We could use an ifelse statement with the make_date function from lubridate:
library(dplyr)
library(lubridate)
df %>%
mutate(year= ifelse(month %in% c(10,11,12), 2019, 2020),
date = make_date(year, month, day))
output:
month day x year date
1 1 1 84 2020 2020-01-01
2 1 2 43 2020 2020-01-02
3 11 3 49 2019 2019-11-03
4 1 4 67 2020 2020-01-04
5 1 5 59 2020 2020-01-05
366 12 31 97 2019 2019-12-31

You could use something like below. If you need a fixed variable instead of 2019/2020 you can use something like var-1 when it is oct-dec and var when it is jan - sep.
library(dplyr)
library(lubridate)
df1 %>%
mutate(date = if_else(month %in% c(10:12),
ymd(paste(2019, df1$month, df1$day, sep = "-")),
ymd(paste(2020, df1$month, df1$day, sep = "-"))))
data:
df1 <- data.frame(month = c(1:12), day = 1, x = 5)

Using base features you could use rowSums to identify 31th of October, then ISOdate.
w <- which.max(rowSums(d[1:2]) == 31 + 10)
d$year <- c(rep(2020, w), rep(2019, 365 - w))
d$date <- do.call(\(year, month, day, ...) as.Date(ISOdate(year, month, day)), d)
Result
head(d, 3)
# month day x year date
# 1 1 1 58 2020 2020-01-01
# 2 1 2 74 2020 2020-01-02
# 3 1 3 43 2020 2020-01-03
tail(d, 3)
# month day x year date
# 363 12 29 46 2019 2019-12-29
# 364 12 30 82 2019 2019-12-30
# 365 12 31 63 2019 2019-12-31
Note:
R.version.string
# [1] "R version 4.1.1 (2021-08-10)"
Data:
d <- structure(list(month = 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, 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, 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, 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, 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, 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, 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), day = 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, 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, 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, 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, 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,
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, 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, 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, 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, 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, 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, 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),
x = c(72L, 95L, 95L, 76L, 84L, 64L, 85L, 84L, 70L, 95L, 75L,
64L, 72L, 48L, 68L, 68L, 44L, 53L, 46L, 49L, 62L, 53L, 74L,
86L, 58L, 63L, 85L, 85L, 81L, 44L, 66L, 82L, 86L, 90L, 75L,
54L, 53L, 52L, 47L, 48L, 61L, 95L, 96L, 73L, 59L, 57L, 94L,
70L, 81L, 68L, 83L, 83L, 95L, 55L, 73L, 51L, 50L, 83L, 58L,
45L, 74L, 64L, 54L, 60L, 77L, 94L, 90L, 47L, 44L, 50L, 70L,
69L, 76L, 69L, 62L, 63L, 62L, 55L, 47L, 43L, 71L, 47L, 66L,
69L, 74L, 53L, 85L, 62L, 53L, 57L, 52L, 65L, 85L, 68L, 62L,
43L, 72L, 69L, 79L, 71L, 95L, 45L, 96L, 70L, 96L, 51L, 48L,
67L, 52L, 48L, 72L, 54L, 64L, 79L, 49L, 55L, 90L, 57L, 51L,
63L, 79L, 69L, 48L, 52L, 89L, 70L, 95L, 64L, 75L, 95L, 70L,
94L, 95L, 43L, 87L, 56L, 46L, 53L, 60L, 91L, 61L, 88L, 83L,
89L, 45L, 87L, 69L, 83L, 71L, 44L, 93L, 96L, 80L, 46L, 80L,
66L, 80L, 59L, 86L, 51L, 48L, 80L, 81L, 79L, 65L, 80L, 72L,
84L, 61L, 55L, 49L, 54L, 60L, 44L, 44L, 84L, 49L, 94L, 45L,
80L, 79L, 51L, 70L, 48L, 66L, 89L, 60L, 57L, 76L, 86L, 88L,
71L, 79L, 94L, 74L, 93L, 80L, 75L, 90L, 91L, 77L, 95L, 48L,
90L, 77L, 50L, 49L, 56L, 71L, 73L, 62L, 85L, 90L, 76L, 67L,
44L, 96L, 52L, 73L, 85L, 44L, 44L, 79L, 89L, 93L, 58L, 57L,
75L, 48L, 58L, 59L, 51L, 64L, 89L, 82L, 76L, 51L, 56L, 46L,
82L, 48L, 76L, 93L, 60L, 52L, 75L, 77L, 53L, 52L, 56L, 50L,
66L, 70L, 67L, 87L, 90L, 50L, 80L, 54L, 81L, 54L, 73L, 88L,
64L, 52L, 64L, 73L, 79L, 68L, 53L, 86L, 94L, 56L, 62L, 65L,
85L, 61L, 54L, 93L, 60L, 69L, 82L, 83L, 56L, 51L, 82L, 71L,
76L, 77L, 60L, 79L, 61L, 83L, 87L, 43L, 74L, 76L, 63L, 59L,
54L, 93L, 82L, 65L, 89L, 68L, 62L, 61L, 91L, 89L, 79L, 59L,
52L, 80L, 71L, 96L, 46L, 84L, 47L, 92L, 80L, 86L, 64L, 88L,
56L, 93L, 94L, 66L, 46L, 87L, 63L, 89L, 92L, 88L, 65L, 90L,
71L, 53L, 91L, 61L, 91L, 62L, 62L, 48L, 80L, 73L, 62L, 75L,
59L, 72L, 61L, 90L, 51L, 66L, 74L, 58L, 73L, 89L, 50L, 79L,
90L, 94L, 59L, 47L, 88L, 83L)), row.names = c(NA, -365L), class = "data.frame")

Base R option -
transform(df, date = as.Date(paste(ifelse(month %in% 10:12, 2019, 2020), month, day, sep = '-')))
# month day x date
#1 1 1 84 2020-01-01
#2 1 2 43 2020-01-02
#3 11 3 49 2019-11-03
#4 1 4 67 2020-01-04
#5 1 5 59 2020-01-05

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,
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, 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, 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,
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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)]

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))

Show difference between two matrices as lines through a 3d surface

I have a surface that I have represented as a matrix that has observed values. Some of these values are controls, and form a grid throughout my surface. I extracted JUST the controls, and interpolated/extrapolated in between to create a "control surface". Then, I can subtract the control surface from the observed surface to get the normalized values.
(sub)Question 1: The interpolation looks unnecessarily irregular... is my interpolation method correct? If not, is there a better method?
(main)Question 2: How can I visualize the result as lines poking through the control surface with the ids at the top? I would like something like this:
Code follows (sorry for ugly dput):
library(dplyr)
library(tidyr)
library(akima)
b <- structure(list(Entr = 1:931, Row = 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, 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, 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, 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, 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, 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, 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,
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, 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, 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, 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, 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, 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,
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, 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, 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, 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, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L),
Col = 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, 49L, 48L, 47L, 46L, 45L, 44L, 43L, 42L, 41L, 40L, 39L,
38L, 37L, 36L, 35L, 34L, 33L, 32L, 31L, 30L, 29L, 28L, 27L,
26L, 25L, 24L, 23L, 22L, 21L, 20L, 19L, 18L, 17L, 16L, 15L,
14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L,
1L, 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,
49L, 48L, 47L, 46L, 45L, 44L, 43L, 42L, 41L, 40L, 39L, 38L,
37L, 36L, 35L, 34L, 33L, 32L, 31L, 30L, 29L, 28L, 27L, 26L,
25L, 24L, 23L, 22L, 21L, 20L, 19L, 18L, 17L, 16L, 15L, 14L,
13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 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, 49L,
48L, 47L, 46L, 45L, 44L, 43L, 42L, 41L, 40L, 39L, 38L, 37L,
36L, 35L, 34L, 33L, 32L, 31L, 30L, 29L, 28L, 27L, 26L, 25L,
24L, 23L, 22L, 21L, 20L, 19L, 18L, 17L, 16L, 15L, 14L, 13L,
12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 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, 49L, 48L,
47L, 46L, 45L, 44L, 43L, 42L, 41L, 40L, 39L, 38L, 37L, 36L,
35L, 34L, 33L, 32L, 31L, 30L, 29L, 28L, 27L, 26L, 25L, 24L,
23L, 22L, 21L, 20L, 19L, 18L, 17L, 16L, 15L, 14L, 13L, 12L,
11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 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, 49L, 48L, 47L,
46L, 45L, 44L, 43L, 42L, 41L, 40L, 39L, 38L, 37L, 36L, 35L,
34L, 33L, 32L, 31L, 30L, 29L, 28L, 27L, 26L, 25L, 24L, 23L,
22L, 21L, 20L, 19L, 18L, 17L, 16L, 15L, 14L, 13L, 12L, 11L,
10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 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, 49L, 48L, 47L, 46L,
45L, 44L, 43L, 42L, 41L, 40L, 39L, 38L, 37L, 36L, 35L, 34L,
33L, 32L, 31L, 30L, 29L, 28L, 27L, 26L, 25L, 24L, 23L, 22L,
21L, 20L, 19L, 18L, 17L, 16L, 15L, 14L, 13L, 12L, 11L, 10L,
9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 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, 49L, 48L, 47L, 46L, 45L,
44L, 43L, 42L, 41L, 40L, 39L, 38L, 37L, 36L, 35L, 34L, 33L,
32L, 31L, 30L, 29L, 28L, 27L, 26L, 25L, 24L, 23L, 22L, 21L,
20L, 19L, 18L, 17L, 16L, 15L, 14L, 13L, 12L, 11L, 10L, 9L,
8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 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, 49L, 48L, 47L, 46L, 45L, 44L,
43L, 42L, 41L, 40L, 39L, 38L, 37L, 36L, 35L, 34L, 33L, 32L,
31L, 30L, 29L, 28L, 27L, 26L, 25L, 24L, 23L, 22L, 21L, 20L,
19L, 18L, 17L, 16L, 15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L,
7L, 6L, 5L, 4L, 3L, 2L, 1L, 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, 49L, 48L, 47L, 46L, 45L, 44L, 43L,
42L, 41L, 40L, 39L, 38L, 37L, 36L, 35L, 34L, 33L, 32L, 31L,
30L, 29L, 28L, 27L, 26L, 25L, 24L, 23L, 22L, 21L, 20L, 19L,
18L, 17L, 16L, 15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L,
6L, 5L, 4L, 3L, 2L, 1L, 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), Value = c(3597L, 3519L, 2974L, 3499L,
3437L, 3669L, 2972L, 2953L, 3088L, 3224L, 2739L, 2762L, 3238L,
2838L, 2821L, 2765L, 3487L, 3696L, 3708L, 3369L, 3702L, 3362L,
3275L, 3073L, 3313L, 3316L, 3656L, 3898L, 3999L, 4074L, 3768L,
3846L, 3630L, 4130L, 3787L, 3418L, 3053L, 3764L, 3745L, 3187L,
3628L, 3147L, 3441L, 3465L, 3953L, 3288L, 3122L, 2208L, 3008L,
3487L, 3248L, 3411L, 3402L, 3627L, 3232L, 3713L, 3432L, 3657L,
3282L, 3859L, 3464L, 3161L, 3297L, 3308L, 3392L, 3334L, 3187L,
3396L, 3213L, 4019L, 3516L, 3578L, 3670L, 3484L, 3552L, 3365L,
3441L, 4022L, 3881L, 3343L, 3466L, 3128L, 3477L, 3398L, 3761L,
3540L, 3627L, 2864L, 3630L, 3320L, 3849L, 3939L, 3658L, 3424L,
3524L, 3626L, 3177L, 2923L, 3655L, 3750L, 4447L, 4426L, 4891L,
4705L, 4274L, 4398L, 4448L, 4148L, 4210L, 4141L, 4255L, 4083L,
4295L, 3190L, 3939L, 3258L, 3855L, 4181L, 3930L, 3871L, 3977L,
3594L, 4107L, 3416L, 4057L, 3692L, 3967L, 3943L, 4012L, 3852L,
4065L, 4019L, 3687L, 3663L, 4081L, 4015L, 3847L, 3690L, 3994L,
3447L, 3559L, 3636L, 3409L, 3121L, 2958L, 2899L, 3017L, 3158L,
3053L, 3572L, 2975L, 3431L, 3725L, 3702L, 3363L, 3487L, 3562L,
3190L, 3219L, 3923L, 3585L, 3989L, 4005L, 3658L, 3810L, 3983L,
3555L, 3712L, 3699L, 3774L, 3471L, 3428L, 3552L, 3468L, 3099L,
3069L, 3303L, 3470L, 3637L, 3624L, 3813L, 4344L, 3866L, 4044L,
3490L, 3809L, 3428L, 3839L, 2540L, 4349L, 3584L, 3627L, 3799L,
3800L, 2887L, 3523L, 3389L, 3411L, 3193L, 3111L, 3112L, 3222L,
3363L, 3551L, 3430L, 3483L, 3049L, 3340L, 4034L, 3447L, 3865L,
3626L, 3699L, 3758L, 4002L, 3500L, 3650L, 3354L, 3321L, 4088L,
3259L, 3520L, 3444L, 3191L, 3578L, 3369L, 2479L, 4070L, 4171L,
4093L, 4184L, 4295L, 2681L, 3597L, 3901L, 2720L, 2700L, 2717L,
3483L, 3311L, 3223L, 3046L, 3310L, 2531L, 3317L, 3233L, 2134L,
3020L, 3360L, 3679L, 2773L, 3665L, 3124L, 4042L, 3713L, 3862L,
3961L, 4109L, 3794L, 4062L, 4078L, 4181L, 3940L, 4602L, 4149L,
3849L, 3582L, 4035L, 3431L, 3954L, 4244L, 3353L, 3519L, 3496L,
3408L, 2988L, 3327L, 3086L, 3180L, 4583L, 3742L, 4580L, 4707L,
4247L, 4422L, 4426L, 4100L, 4042L, 4096L, 3703L, 4001L, 4002L,
4265L, 3249L, 4765L, 4280L, 4628L, 4905L, 4611L, 4010L, 4125L,
4452L, 5044L, 4932L, 4613L, 4768L, 5033L, 4199L, 3944L, 3951L,
4179L, 4192L, 4195L, 3889L, 3928L, 3301L, 3764L, 3537L, 3843L,
4342L, 3792L, 3973L, 4251L, 4169L, 4374L, 4172L, 4028L, 3050L,
4488L, 4068L, 4697L, 4824L, 4184L, 3930L, 4012L, 3219L, 3519L,
3663L, 3493L, 2939L, 3363L, 3383L, 3464L, 2789L, 2927L, 3059L,
2884L, 2782L, 3090L, 3158L, 3132L, 3644L, 3803L, 3895L, 3885L,
3265L, 3682L, 3464L, 3171L, 3539L, 3474L, 3265L, 3666L, 3549L,
3591L, 3249L, 3173L, 3088L, 2563L, 3530L, 3234L, 3453L, 3200L,
3405L, 3471L, 3750L, 2906L, 3241L, 3186L, 3789L, 3174L, 2977L,
3281L, 3479L, 3241L, 3783L, 3339L, 3503L, 3591L, 3379L, 3392L,
3399L, 3675L, 3624L, 3772L, 3873L, 3477L, 3950L, 3538L, 4347L,
3818L, 4332L, 3727L, 4028L, 3679L, 3737L, 3444L, 3258L, 3535L,
3555L, 3474L, 3447L, 3748L, 3423L, 3577L, 3725L, 3227L, 2903L,
3526L, 3670L, 3256L, 3282L, 3396L, 3719L, 3598L, 3608L, 3259L,
3610L, 3373L, 3432L, 3393L, 3001L, 2867L, 2982L, 3345L, 3311L,
2727L, 3106L, 3108L, 2950L, 2714L, 3520L, 3016L, 2939L, 3435L,
3020L, 3175L, 3805L, 2779L, 3895L, 3308L, 2995L, 3083L, 3080L,
3432L, 3318L, 4486L, 3876L, 3588L, 3742L, 3986L, 3765L, 3758L,
3523L, 3696L, 3040L, 3448L, 2687L, 3282L, 4166L, 4169L, 3742L,
4032L, 3986L, 4306L, 4371L, 4231L, 4260L, 3585L, 4342L, 4188L,
3220L, 3464L, 3536L, 3595L, 4045L, 3937L, 3886L, 4774L, 3696L,
4214L, 4250L, 4543L, 4550L, 4517L, 4691L, 5042L, 3956L, 3953L,
3986L, 4032L, 3643L, 3562L, 3833L, 3803L, 3634L, 3895L, 4299L,
3862L, 3403L, 3272L, 3406L, 3253L, 3233L, 3344L, 3481L, 3363L,
2646L, 3631L, 3869L, 3246L, 3357L, 3696L, 3859L, 4296L, 3438L,
4000L, 3703L, 3960L, 3477L, 4247L, 3791L, 3853L, 3696L, 3835L,
3742L, 3588L, 3276L, 3093L, 3360L, 3207L, 3576L, 3025L, 3305L,
3295L, 3788L, 3963L, 3999L, 3294L, 3931L, 3448L, 2959L, 3304L,
3131L, 2685L, 3314L, 2887L, 3653L, 3141L, 3425L, 3542L, 3282L,
3478L, 3191L, 2639L, 3027L, 3504L, 3578L, 2806L, 4163L, 3735L,
3203L, 3419L, 3588L, 3545L, 3535L, 3333L, 3392L, 3806L, 3587L,
3134L, 2971L, 3069L, 3316L, 4520L, 3562L, 3665L, 3744L, 3207L,
3409L, 3744L, 3181L, 3096L, 3576L, 3572L, 3363L, 3386L, 3318L,
3791L, 3582L, 4032L, 4394L, 4087L, 4248L, 4342L, 4189L, 4656L,
3781L, 4335L, 3504L, 4267L, 4032L, 3824L, 3609L, 4012L, 3992L,
4417L, 4003L, 3846L, 4140L, 3683L, 3302L, 3859L, 4100L, 3847L,
3621L, 4176L, 4359L, 4081L, 3654L, 4062L, 3442L, 3560L, 3780L,
3295L, 3487L, 3409L, 3439L, 2362L, 3364L, 3412L, 3266L, 4051L,
3990L, 4068L, 3971L, 3197L, 3677L, 3765L, 3638L, 3439L, 4004L,
3648L, 3628L, 3475L, 3945L, 4167L, 3942L, 3929L, 4013L, 3906L,
3168L, 3606L, 4012L, 4317L, 4000L, 3781L, 4199L, 3997L, 4576L,
3997L, 4273L, 3891L, 3543L, 3294L, 3911L, 3715L, 4276L, 3660L,
4090L, 3921L, 3595L, 3513L, 3301L, 3470L, 3363L, 3989L, 3307L,
3565L, 3301L, 3738L, 3907L, 3653L, 3819L, 3232L, 3695L, 3435L,
2906L, 3620L, 3686L, 3284L, 4237L, 4100L, 4420L, 3654L, 2503L,
1680L, 3614L, 3314L, 4302L, 3114L, 2840L, 3036L, 1144L, 4153L,
3416L, 4484L, 3159L, 3839L, 3961L, 3373L, 3722L, 3605L, 3116L,
3818L, 3977L, 3527L, 3562L, 3794L, 4162L, 3800L, 3680L, 3578L,
3924L, 3484L, 3204L, 3200L, 3223L, 3536L, 3187L, 3171L, 3057L,
3268L, 3099L, 3517L, 3477L, 3751L, 3174L, 3569L, 3295L, 3229L,
3451L, 3200L, 3530L, 3798L, 3562L, 3484L, 2718L, 3980L, 3746L,
3576L, 3464L, 3302L, 4107L, 3452L, 3315L, 3680L, 3383L, 3462L,
3478L, 3888L, 3634L, 3445L, 3092L, 3445L, 2923L, 3040L, 2623L,
2874L, 3552L, 2336L, 3011L, 2671L, 2029L, 4002L, 3379L, 3779L,
3763L, 3496L, 3454L, 3613L, 3901L, 3727L, 3365L, 3836L, 2750L,
3763L, 3389L, 3542L, 3699L, 3904L, 3836L, 3399L, 3634L, 4162L,
3545L, 4182L, 3506L, 3849L, 3755L, 3770L, 2936L, 3670L, 3758L,
3487L, 3807L, 2868L, 3523L, 3148L, 3774L, 2851L, 2903L, 3181L,
3067L, 2695L, 3389L, 3670L, 2554L, 3494L, 4162L, 3533L, 2780L,
2822L, 2946L, 3324L, 1791L, 3530L, 3872L, 3676L, 3252L, 3395L,
3370L, 2662L, 2567L, 2786L, 2714L, 2479L, 1465L, 2000L, 3663L,
4375L, 3758L, 3742L, 3259L, 2985L, 3784L, 3373L, 2978L, 3487L,
3379L, 2953L, 3478L, 2890L, 2597L, 3001L, 2861L, 3988L, 3455L,
2950L, 3771L, 3550L, 2998L, 2991L, 3219L, 3073L, 3458L, 3585L,
3546L, 3637L, 4198L, 2903L, 3144L, 2825L, 2806L, 3409L, 1846L,
2564L, 3005L, 2675L, 2936L, 2124L, 2900L, 2388L, 2531L, 2916L,
778L, 2812L, 2577L, 2401L, 2868L, 3041L, 2039L, 2408L, 2104L,
3142L, 2610L, 3748L, 3370L, 2754L, 3546L, 2962L, 2453L, 3014L,
2626L, 2864L, 3399L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L)), class = "data.frame", row.names = c(NA,
-931L), .Names = c("Entr", "Row", "Col", "Value"))
## Control every 10
b$Control[b$Entr%%10==0] <- 1
b$Control[is.na(b$Control)] <- 0
## Isolate controls
b$Yield2[b$Control==1] <- b$Value[b$Control==1]
b$Yield2[is.na(b$Yield2)] <- 0
## -------------------- GENERATE MATRIX FOR SURFACE CALCULATION----------------
## ALL DATA
MaxCol <- max(b$Col)
MaxRow <- max(b$Row)
RealSurf <- matrix(b$Value,MaxRow,MaxCol)
## Matrix of just controls, first emptyish
ControlSurf <- matrix(b$Yield2,MaxRow,MaxCol)
## Interpolate empty data...
idx <- which(ControlSurf > 0, arr.ind=TRUE)
RealSurf.nz <- ControlSurf[idx]
## ... into a fullish matrix
InterpolatedControls <- interp.new(idx[,1], idx[,2], RealSurf.nz, xo=1:MaxRow, yo=1:MaxCol, extrap=TRUE)
ControlSurf <- matrix(InterpolatedControls$z,MaxRow,MaxCol)
##################### ???????????? IS THIS EVEN CORRECT??????? ##################
## Plot surfaces
par(mfrow=c(1,2))
persp( z=ControlSurf, theta = 50, phi = 30, expand = 0.1, col = "lightblue")
persp( z=RealSurf, theta = 50, phi = 30, expand = 0.1, col = "red")
## How to make the real surface "poke" through the Control Surface?

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