Formatting x_scale in ggplot with weekly data - r

In my ggplot, I've managed to create a x_scale based on time (I have weekly data) but am not sure how to create year-month labels using scale_x_date
The following is my code - I have tried using (...breaks = "1 month") and (...minor_breaks = "1 month") but this does not produce the desired result. I am aiming for the labels to simply be Dec-16, Jan-17, Feb-17 and so on. What is the proper formatting to make the x_scale to show Month-Year in an abbreviated way?
ggplot(data=test, aes(x=as.Date(test$weekly), y=test$dist, group=1)) +
geom_path(col = "blue") +
scale_x_date(labels = date_format("%b-%Y"))
Here is a sample of the data
> dput(test)
structure(list(weekly = structure(list(sec = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), min = c(0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), hour = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), mday = c(18L, 25L, 1L,
8L, 15L, 22L, 29L, 5L, 12L, 19L, 26L, 3L, 10L, 17L, 24L, 31L,
7L, 14L, 21L, 28L, 5L, 12L, 19L, 26L, 2L, 9L, 16L, 23L, 30L,
6L, 13L, 20L, 27L, 6L, 13L, 20L, 27L, 3L, 10L, 17L, 24L, 1L,
8L, 15L, 22L, 29L, 5L, 12L, 19L, 26L, 3L, 10L, 17L, 24L, 31L),
mon = c(6L, 6L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L,
9L, 9L, 9L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 0L, 0L,
0L, 0L, 0L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L),
year = c(116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L,
116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L,
116L, 116L, 116L, 116L, 116L, 116L, 117L, 117L, 117L, 117L,
117L, 117L, 117L, 117L, 117L, 117L, 117L, 117L, 117L, 117L,
117L, 117L, 117L, 117L, 117L, 117L, 117L, 117L, 117L, 117L,
117L, 117L, 117L, 117L, 117L, 117L, 117L), wday = c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), yday = c(199L, 206L, 213L,
220L, 227L, 234L, 241L, 248L, 255L, 262L, 269L, 276L, 283L,
290L, 297L, 304L, 311L, 318L, 325L, 332L, 339L, 346L, 353L,
360L, 1L, 8L, 15L, 22L, 29L, 36L, 43L, 50L, 57L, 64L, 71L,
78L, 85L, 92L, 99L, 106L, 113L, 120L, 127L, 134L, 141L, 148L,
155L, 162L, 169L, 176L, 183L, 190L, 197L, 204L, 211L), isdst = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), zone = c("CEST", "CEST",
"CEST", "CEST", "CEST", "CEST", "CEST", "CEST", "CEST", "CEST",
"CEST", "CEST", "CEST", "CEST", "CEST", "CET", "CET", "CET",
"CET", "CET", "CET", "CET", "CET", "CET", "CET", "CET", "CET",
"CET", "CET", "CET", "CET", "CET", "CET", "CET", "CET", "CET",
"CEST", "CEST", "CEST", "CEST", "CEST", "CEST", "CEST", "CEST",
"CEST", "CEST", "CEST", "CEST", "CEST", "CEST", "CEST", "CEST",
"CEST", "CEST", "CEST"), gmtoff = c(NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_)), .Names = c("sec",
"min", "hour", "mday", "mon", "year", "wday", "yday", "isdst",
"zone", "gmtoff"), class = c("POSIXlt", "POSIXt")), dist = c(23621.19,
30400.01, 27458.66, 24511.07, 24908.37, 24413.81, 21096.52, 24557.51,
14833.71, 15513.72, 13516.23, 8102.02, 5881.44, 8370.01, 7339.34,
7703.79, 9297.52, 4542, 3555.56, 4438.33, 1968.65, 2259.06, 2729.89,
2876.66, 1767.86, 784.17, 2004.55, 4446.98, 2203.16, 3956.35,
4095.28, 3999.88, 3288.59, 4593.19, 6164.63, 6111.46, 8462.84,
7404.8, 9725.91, 9652.72, 9357.52, 15535.51, 11810.82, 17890.89,
23518.06, 18754.44, 16377.46, 15023.27, 23354.14, 23328.12, 27024.1,
23414.38, 28273.08, 24213.3, 19068.03)), .Names = c("weekly",
"dist"), row.names = c(NA, -55L), class = "data.frame")

Related

How to manually calculate structure coefficients for part R2?

Libraries, Data, and LMER Model
I am using these three libraries for this inquiry:
#### Libraries ####
library(lmerTest)
library(performance)
library(partR2)
I have built a lmer model using this data:
structure(list(Mins_Work = c(435L, 350L, 145L, 135L, 15L, 60L,
60L, 390L, 395L, 395L, 315L, 80L, 580L, 175L, 545L, 230L, 435L,
370L, 255L, 515L, 330L, 65L, 115L, 550L, 420L, 45L, 266L, 196L,
198L, 220L, 17L, 382L, 0L, 180L, 343L, 207L, 263L, 332L, 0L,
0L, 259L, 417L, 282L, 685L, 517L, 111L, 64L, 466L, 499L, 460L,
269L, 300L, 427L, 301L, 436L, 342L, 229L, 379L, 102L, 146L, 94L,
345L, 73L, 204L, 512L, 113L, 135L, 458L, 493L, 552L, 108L, 335L,
395L, 508L, 546L, 396L, 159L, 325L, 747L, 650L, 377L, 461L, 669L,
186L, 220L, 410L, 708L, 409L, 515L, 413L, 166L, 451L, 660L, 177L,
192L, 191L, 461L, 637L, 297L, 601L, 586L, 270L, 479L, 0L, 480L,
397L, 174L, 111L, 0L, 610L, 332L, 345L, 423L, 160L, 611L, 0L,
345L, 550L, 324L, 427L, 505L, 632L, 560L, 230L, 495L, 235L, 522L,
654L, 465L, 377L, 260L, 572L, 612L, 594L, 624L, 237L, 0L, 38L,
409L, 634L, 292L, 706L, 399L, 568L, 0L, 694L, 298L, 616L, 553L,
581L, 423L, 636L, 623L, 338L, 345L, 521L, 438L, 504L, 600L, 616L,
656L, 285L, 474L, 688L, 278L, 383L, 535L, 363L, 470L, 457L, 303L,
123L, 363L, 329L, 513L, 636L, 421L, 220L, 430L, 428L, 536L, 156L,
615L, 429L, 103L, 332L, 250L, 281L, 248L, 435L, 589L, 515L, 158L,
0L, 649L, 427L, 193L, 225L, 0L, 280L, 163L, 536L, 301L, 406L,
230L, 519L, 0L, 303L, 472L, 392L, 326L, 368L, 405L, 515L, 308L,
259L, 769L, 93L, 517L, 261L, 420L, 248L, 265L, 834L, 313L, 131L,
298L, 134L, 385L, 648L, 529L, 487L, 533L, 641L, 429L, 339L, 508L,
560L, 439L, 381L, 397L, 692L, 534L, 148L, 366L, 167L, 425L, 476L,
384L, 498L, 502L, 308L, 360L, 203L, 410L, 626L, 593L, 409L, 531L,
157L, 0L, 357L, 443L, 615L, 564L, 341L, 352L, 609L, 686L, 386L,
323L, 362L, 597L, 325L, 51L, 570L, 579L, 284L, 0L), Coffee_Cups = c(3L,
0L, 2L, 6L, 4L, 5L, 3L, 3L, 2L, 2L, 3L, 1L, 1L, 3L, 2L, 2L, 0L,
1L, 1L, 4L, 4L, 3L, 0L, 1L, 3L, 0L, 0L, 0L, 0L, 2L, 0L, 1L, 2L,
3L, 2L, 2L, 4L, 3L, 6L, 6L, 3L, 4L, 6L, 8L, 3L, 5L, 0L, 2L, 2L,
8L, 6L, 4L, 6L, 4L, 4L, 2L, 6L, 6L, 5L, 1L, 1L, 5L, 4L, 6L, 5L,
0L, 6L, 6L, 4L, 4L, 2L, 2L, 6L, 6L, 7L, 3L, 3L, 0L, 5L, 7L, 6L,
3L, 5L, 3L, 3L, 1L, 9L, 9L, 3L, 3L, 6L, 6L, 6L, 3L, 0L, 7L, 6L,
6L, 3L, 9L, 3L, 8L, 8L, 3L, 3L, 7L, 6L, 3L, 3L, 3L, 6L, 6L, 6L,
1L, 9L, 3L, 3L, 2L, 6L, 3L, 6L, 9L, 6L, 8L, 9L, 6L, 6L, 6L, 0L,
3L, 0L, 3L, 3L, 6L, 3L, 0L, 9L, 3L, 0L, 2L, 0L, 6L, 6L, 6L, 3L,
6L, 3L, 9L, 3L, 0L, 0L, 6L, 3L, 3L, 3L, 3L, 6L, 0L, 6L, 3L, 3L,
5L, 5L, 3L, 0L, 6L, 4L, 2L, 0L, 2L, 4L, 0L, 6L, 4L, 4L, 2L, 2L,
0L, 9L, 6L, 3L, 6L, 6L, 9L, 0L, 6L, 6L, 6L, 6L, 6L, 6L, 3L, 3L,
0L, 9L, 6L, 3L, 6L, 3L, 6L, 1L, 6L, 6L, 6L, 6L, 6L, 1L, 3L, 9L,
6L, 3L, 6L, 9L, 3L, 5L, 6L, 3L, 0L, 6L, 3L, 3L, 5L, 0L, 6L, 3L,
5L, 3L, 0L, 6L, 7L, 3L, 6L, 6L, 6L, 6L, 3L, 5L, 6L, 7L, 6L, 6L,
4L, 6L, 4L, 5L, 5L, 6L, 8L, 6L, 6L, 6L, 9L, 3L, 3L, 9L, 7L, 8L,
4L, 3L, 3L, 3L, 6L, 6L, 6L, 3L, 4L, 3L, 3L, 6L, 4L, 3L, 3L, 4L,
6L, 0L, 3L, 6L, 4L, 3L), Tea_Cups = c(2L, 4L, 2L, 0L, 0L, 2L,
0L, 2L, 4L, 0L, 0L, 0L, 2L, 6L, 5L, 0L, 2L, 0L, 2L, 4L, 0L, 0L,
0L, 2L, 1L, 0L, 4L, 4L, 4L, 2L, 1L, 0L, 2L, 0L, 0L, 4L, 2L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 1L, 0L, 2L, 0L, 0L, 2L, 0L, 3L,
0L, 2L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 2L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
6L, 3L, 0L, 3L, 3L, 0L, 0L, 0L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
3L, 0L, 0L, 0L, 3L, 0L, 0L, 6L, 0L, 0L, 0L, 0L, 3L, 0L, 6L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 3L, 5L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 3L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L,
0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 1L, 0L, 0L, 0L, 0L, 0L,
0L)), class = "data.frame", row.names = c(NA, -279L))
This is the model itself:
model.work <- lmer(Mins_Work ~ Coffee_Cups + Tea_Cups +
(1|Month_Name),
data = work)
Solution So Far
Based off the instructions here for the partR2 package, I have calculated beta weights, inclusive R2, and part R2. First, by automatically calculating it with this code:
#### Obtain Part R2 ####
part.work <- partR2(model.work,
partvars = c("Coffee_Cups",
"Tea_Cups"),
data = work,
nboot = 100)
summary(part.work)
I obtain the numbers for each:
R2 (marginal) and 95% CI for the full model:
R2 CI_lower CI_upper ndf
0.105 0.0557 0.1771 3
----------
Part (semi-partial) R2:
Predictor(s) R2 CI_lower CI_upper ndf
Model 0.1050 0.0557 0.1771 3
Coffee_Cups 0.1011 0.0518 0.1734 2
Tea_Cups 0.0015 0.0000 0.0773 2
Coffee_Cups+Tea_Cups 0.1050 0.0557 0.1771 1
----------
Inclusive R2 (SC^2 * R2):
Predictor IR2 CI_lower CI_upper
Coffee_Cups 0.0999 0.0491 0.1707
Tea_Cups 0.0012 0.0000 0.0171
----------
Structure coefficients r(Yhat,x):
Predictor SC CI_lower CI_upper
Coffee_Cups 0.9753 0.8541 0.9993
Tea_Cups 0.1077 -0.2130 0.4190
----------
Beta weights (standardised estimates)
Predictor BW CI_lower CI_upper
Coffee_Cups 0.3262 0.2362 0.4283
Tea_Cups 0.0724 -0.0322 0.1700
----------
I have manually obtained all these measures with the following code:
#### Manually Calculate Pseudo R Square ####
var.ran <- get_variance_random(model.work, verbose = TRUE)
var.fix <- get_variance_fixed(model.work, verbose = TRUE)
var.res <- get_variance_residual(model.work, verbose = TRUE)
var.ran # random effect variance
var.res # residual variance
var.fix # fixed effect variance
mar.r.square <- (var.fix/(var.fix+var.ran+var.res))
con.r.square <- ((var.fix+var.ran)/(var.fix+var.ran+var.res))
mar.r.square # effect of fixed factors
con.r.square # effect of fixed and random
mar.r.square-con.r.square # # effect of random
#### Manually Calculate Part R2 ####
reduced <- lmer(Mins_Work ~ Tea_Cups +
(1|Month_Name),
data = work)
reduced.fix <- get_variance_fixed(reduced)
(var.fix-reduced.fix)/(var.fix+var.ran+var.res) # coffee part R2
#### Manually Calculate Beta Weights ####
25.45*sd(work$Coffee_Cups)/sd(work$Mins_Work) # coffee predictor
10.39*sd(work$Tea_Cups)/sd(work$Mins_Work) # tea predictor
#### Manually Calculate Inclusive R2 ####
.9753^2*.105 # coffee predictor
.1077^2*.105 # tea predictor
Problem
The single value I am still having issues with obtaining is the structure coefficient for each predictor. This is supposedly obtained by measuring the correlation between predicted y and a single predictor x. I tried doing this with the following code:
cor(predict(model.work),work$Coffee_Cups)
Which gives me this value:
1] 0.8222992
This however doesn't match the value in the summarized output from the partR2 function:
Structure coefficients r(Yhat,x):
Predictor SC CI_lower CI_upper
Coffee_Cups 0.9753 0.8541 0.9993
What am I missing here? Am I reading something wrong?

ggplot + boxplot with time series value by group

A variation of this question -- I can't quite get the dimensions right in the data structure to make a boxplot with the right values.
what I'm looking to do: hours would be on the x-axis, region would be on the y-axis, and for every region there will be a boxplot showing the distribution of income by hour.
The closest I can get is the following, but it's not right. How do I create the boxplot with two factors (one a time series) as axes, populated by the value distribution?
data:
regions <- structure(list(location = structure(c(2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("east",
"north", "west"), class = "factor"), hour = structure(list(sec = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), min = c(0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), hour = c(0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 0L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L), mday = c(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), mon = c(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
), year = c(115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L,
115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L,
115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L,
115L, 115L), wday = c(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), yday = c(224L, 224L, 224L, 224L,
224L, 224L, 224L, 224L, 224L, 224L, 224L, 224L, 224L, 224L, 224L,
224L, 224L, 224L, 224L, 224L, 224L, 224L, 224L, 224L, 224L, 224L,
224L, 224L, 224L, 224L, 224L, 224L, 224L), isdst = 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),
zone = c("CDT", "CDT", "CDT", "CDT", "CDT", "CDT", "CDT",
"CDT", "CDT", "CDT", "CDT", "CDT", "CDT", "CDT", "CDT", "CDT",
"CDT", "CDT", "CDT", "CDT", "CDT", "CDT", "CDT", "CDT", "CDT",
"CDT", "CDT", "CDT", "CDT", "CDT", "CDT", "CDT", "CDT"),
gmtoff = c(NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_)), .Names = c("sec",
"min", "hour", "mday", "mon", "year", "wday", "yday", "isdst",
"zone", "gmtoff"), class = c("POSIXlt", "POSIXt")), hour_income = c(67L,
98L, 89L, 75L, 75L, 89L, 70L, 97L, 52L, 94L, 80L, 84L, 52L, 82L,
81L, 93L, 85L, 94L, 64L, 90L, 54L, 60L, 97L, 100L, 57L, 63L,
90L, 58L, 86L, 68L, 52L, 78L, 61L)), .Names = c("location", "hour",
"hour_income"), row.names = c(NA, -33L), class = "data.frame")
And the boxplot
ggplot(regions) +
geom_boxplot(aes(x=hour, y=hour_income, group=location))

First we convert datetimes to character format, then create the boxplots.
regions$hour <- strftime(regions$hour, format="%H:%M:%S")
ggplot(data = regions, aes(x = hour, y = hour_income)) + geom_boxplot()
But because you only have an observation for each region and hour when you try to create a boxplot to visualise the regions you can only obtain lines instead of boxplots, which are not very meaningful:
ggplot(data = regions, aes(x = hour, y = hour_income)) + geom_boxplot(aes(fill= location))

Merge two legends (size and color) into one [duplicate]

This question already has answers here:
How to combine scales for colour and size into one legend?
(2 answers)
Closed 7 years ago.
What is the code to make the two legends into one: A circles legend with color?
I think, a single legend with circles colored according to "size" and "# total number of crimes" is the best way to show the legend.
Desired output:
1) There should be one legend: the circles, instead of black should be colored: 0 circle = "yellow" to 800 circle = "red".
My code:
library(maps)
library(ggmap)
Get map from Google Maps
lima <- get_map(location = "lima", zoom = 11, maptype = c("terrain"))
Plot
ggmap(lima) + geom_point(data = limanov2, aes(x = LONGITUD , y = LATITUD, color = TOTALES,
size = TOTALES)) +
scale_size_continuous(name = "Cantidad\ndelitos",range = c(2,12)) +
scale_color_gradient(name = "Cantidad\ndelitos", low = "yellow", high = "red") +
theme(legend.text= element_text(size=14)) +
ggtitle("TOTAL DELITOS - LIMA NOV 2012") +
theme(plot.title = element_text(size = 12, vjust=2, family="Verdana", face="italic"),
legend.position = 'left')
My data:
structure(list(DISTRITO = c("SAN JUAN DE LURIGANCHO", "CALLAO",
"LOS OLIVOS", "ATE", "LIMA", "SAN MARTIN DE PORRES", "SANTIAGO DE SURCO",
"CHORILLOS", "COMAS", "INDEPENDENCIA", "EL AGUSTINO", "LA VICTORIA",
"SAN JUAN DE MIRAFLORES", "VILLA EL SALVADOR", "SAN MIGUEL",
"CARABAYLLO", "MIRAFLORES", "SAN BORJA", "VENTANILLA", "SURQUILLO",
"BREÑA", "ANCON", "PTE. PIEDRA", "RIMAC", "BARRANCO", "LA MOLINA",
"SAN LUIS", "SANTA ANITA", "LURIGANCHO", "P. LIBRE", "MAGDALENA DEL MAR",
"LA PERLA", "CHACLACAYO", "PUENTE PIEDRA", "SAN ISIDRO", "JESUS MARIA",
"BELLAVISTA", "LINCE", "CARMEN DE LA LEGUA REYNOSO", "CIENEGUILLA",
"SANTA ROSA", "LURIN", "PUNTA NEGRA", "PUCUSANA", "LA PUNTA",
"PUNTA HERMOSA", "PACHACAMAC", "SAN BARTOLO", "SANTA MARIA"),
TOTALES = c(861L, 696L, 696L, 642L, 516L, 479L, 442L, 378L,
371L, 368L, 361L, 333L, 325L, 291L, 282L, 251L, 239L, 196L,
193L, 188L, 185L, 174L, 165L, 161L, 138L, 134L, 128L, 119L,
115L, 105L, 67L, 65L, 63L, 58L, 58L, 56L, 45L, 38L, 23L,
23L, 11L, 8L, 6L, 5L, 3L, 3L, 2L, 0L, 0L), HOMICIDIOS = c(1L,
7L, 0L, 1L, 2L, 0L, 0L, 1L, 7L, 4L, 4L, 4L, 0L, 0L, 0L, 2L,
0L, 0L, 7L, 0L, 0L, 0L, 0L, 4L, 0L, 0L, 2L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L), LESIONES = c(100L, 72L, 61L, 43L, 44L, 8L, 10L,
15L, 44L, 40L, 50L, 15L, 52L, 28L, 7L, 33L, 15L, 3L, 21L,
7L, 36L, 33L, 15L, 19L, 14L, 1L, 8L, 6L, 16L, 4L, 4L, 9L,
1L, 12L, 2L, 9L, 5L, 2L, 5L, 7L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L), VIO..DE.LA.LIBERTAD.PERSONAL = c(0L, 7L, 6L,
5L, 6L, 1L, 1L, 0L, 3L, 1L, 2L, 0L, 2L, 0L, 1L, 0L, 1L, 0L,
1L, 1L, 0L, 3L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L,
0L, 1L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L), VIO..DE.LA.LIBERTAD.SEXUAL = c(56L, 14L, 12L, 15L, 7L,
10L, 2L, 9L, 11L, 13L, 8L, 9L, 7L, 14L, 4L, 15L, 4L, 2L,
17L, 7L, 3L, 4L, 6L, 12L, 2L, 1L, 5L, 3L, 11L, 4L, 1L, 2L,
0L, 6L, 2L, 0L, 3L, 0L, 2L, 2L, 0L, 4L, 0L, 0L, 0L, 0L, 0L,
0L, 0L), HURTO.SIMPLE.Y.AGRAVADO = c(217L, 203L, 296L, 230L,
260L, 167L, 226L, 217L, 130L, 117L, 154L, 133L, 121L, 46L,
163L, 72L, 161L, 119L, 69L, 120L, 64L, 19L, 64L, 21L, 57L,
44L, 39L, 2L, 48L, 60L, 30L, 19L, 48L, 20L, 41L, 25L, 19L,
27L, 7L, 11L, 9L, 0L, 6L, 0L, 2L, 3L, 1L, 0L, 0L), ROBO.SIMPLE.Y.AGRAVADO = c(460L,
289L, 308L, 344L, 186L, 277L, 198L, 130L, 165L, 184L, 137L,
149L, 134L, 188L, 104L, 126L, 58L, 72L, 64L, 51L, 77L, 115L,
79L, 76L, 64L, 88L, 73L, 108L, 40L, 36L, 30L, 32L, 14L, 17L,
12L, 22L, 12L, 8L, 6L, 3L, 1L, 3L, 0L, 2L, 1L, 0L, 1L, 0L,
0L), MICRO.COM.DE.DROGAS = c(26L, 100L, 13L, 3L, 10L, 15L,
5L, 5L, 11L, 8L, 3L, 23L, 9L, 15L, 3L, 3L, 0L, 0L, 8L, 2L,
5L, 0L, 0L, 28L, 0L, 0L, 1L, 0L, 0L, 0L, 2L, 2L, 0L, 2L,
0L, 0L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L
), TENENCIA.ILEGAL.DE.ARMAS = c(1L, 4L, 0L, 1L, 1L, 1L, 0L,
1L, 0L, 1L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 6L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), LONGITUD = c(-77,
-77.12, -77.08, -76.89, -77.04, -77.09, -76.99, -77.01, -77.05,
-77.05, -77, -77.02, -76.97, -76.94, -77.09, -76.99, -77.03,
-77, -77.13, -77.01, -77.05, -77.11, -77.08, -76.7, -77.02,
-76.92, -77, -76.96, -76.86, -77.06, -77.07, -77.12, -76.76,
-77.08, -77.03, -77.05, -77.11, -77.04, -77.09, -76.78, -77.16,
-76.81, -76.73, -76.77, -77.16, -76.76, -76.83, -76.73, -76.77
), LATITUD = c(-11.99, -12.04, -11.95, -12.04, -12.06, -12,
-12.16, -12.2, -11.93, -11.99, -12.04, -12.08, -12.16, -12.23,
-12.08, -11.79, -12.12, -12.1, -11.89, -12.11, -12.06, -11.69,
-11.88, -11.94, -12.15, -12.09, -12.08, -12.04, -11.98, -12.08,
-12.09, -12.07, -11.99, -11.88, -12.1, -12.08, -12.06, -12.09,
-12.04, -12.07, -11.81, -12.24, -12.32, -12.47, -12.07, -12.28,
-12.18, -12.38, -12.42)), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -49L), .Names = c("DISTRITO", "TOTALES",
"HOMICIDIOS", "LESIONES", "VIO..DE.LA.LIBERTAD.PERSONAL", "VIO..DE.LA.LIBERTAD.SEXUAL",
"HURTO.SIMPLE.Y.AGRAVADO", "ROBO.SIMPLE.Y.AGRAVADO", "MICRO.COM.DE.DROGAS",
"TENENCIA.ILEGAL.DE.ARMAS", "LONGITUD", "LATITUD"))

I've found a solution. Reading the documention for GGPLOT2 V. 0.9
It is the new function: guide_legend() that should be used inside guides().
This is a function that lets you have more control over legend labels.
This is the end code with the resulting output (See the last line):
ggmap(lima) + geom_point(data = limanov2, aes(x = LONGITUD , y = LATITUD, color = TOTALES,
size = TOTALES)) +
scale_size_continuous(name = "Cantidad\ndelitos",range = c(2,12)) +
scale_color_gradient(name = "Cantidad\ndelitos", low = "yellow", high = "red") +
theme(legend.text= element_text(size=14)) +
ggtitle("TOTAL DELITOS - LIMA NOV 2012") +
theme(plot.title = element_text(size = 12, vjust=2, family="Verdana", face="italic"),
legend.position = 'left') +
guides(colour = guide_legend())

R: ggmap: containing missing values (geom_point) when plottinng but no NAs values found in data.frame

I'm plotting some points over a map with ggmap package.
The problem is that i get the message: "Removed 12 rows containing missing values (geom_point)".
But i don't have any NAs. I've looked the data, and used:
sum(is.na(limanov2)) #Gives 0
to prove it.
This is my code:
library(maps)
library(ggmap)
lima <- get_map(location = "lima", zoom = 11)
ggmap(lima) + geom_point(data = limanov2, aes(x = LONGITUD , y = LATITUD, color = TOTALES,
size = TOTALES)) +
scale_color_gradient(low = "yellow", high = "red")
My data:
structure(list(DISTRITO = c("SAN JUAN DE LURIGANCHO", "CALLAO",
"LOS OLIVOS", "ATE VITARTE", "LIMA CERCADO", "SAN MARTÍN", "SANTIAGO DE SURCO",
"CHORILLOS", "COMAS", "INDEPENDENCIA", "EL AGUSTINO", "LA VICTORIA",
"SAN JUAN DE MIRAFLORES", "VILLA EL SALVADOR", "S. MIGUEL", "CARABAYLLO",
"MIRAFLORES", "PTE. PIEDRA", "SAN BORJA", "VENTANILLA", "SURQUILLO",
"BREÑA", "ANCÓN", "EL RIMAC", "BARRANCO", "LA MOLINA", "SAN LUIS",
"STA. ANITA", "LURIGANCHO", "P. LIBRE", "MAGDALENA", "LA PERLA",
"CHACLACAYO", "SAN ISIDRO", "J. MARÍA", "BELLAVISTA", "LINCE",
"C. DE LA LEGUA", "CIENEGUILLA", "STA.ROSA", "LURÍN", "PTA.NEGRA",
"PUCUSANA", "LA PUNTA", "PTA. HERMOSA", "PACHACAMAC", "SAN BARTOLO",
"SANTA MARÍA"), TOTALES = c(861L, 696L, 696L, 642L, 516L, 479L,
442L, 378L, 371L, 368L, 361L, 333L, 325L, 291L, 282L, 251L, 239L,
223L, 196L, 193L, 188L, 185L, 174L, 161L, 138L, 134L, 128L, 119L,
115L, 105L, 67L, 65L, 63L, 58L, 56L, 45L, 38L, 23L, 23L, 11L,
8L, 6L, 5L, 3L, 3L, 2L, 0L, 0L), HOMICIDIOS = c(1L, 7L, 0L, 1L,
2L, 0L, 0L, 1L, 7L, 4L, 4L, 4L, 0L, 0L, 0L, 2L, 0L, 1L, 0L, 7L,
0L, 0L, 0L, 4L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), LESIONES = c(100L,
72L, 61L, 43L, 44L, 8L, 10L, 15L, 44L, 40L, 50L, 15L, 52L, 28L,
7L, 33L, 15L, 27L, 3L, 21L, 7L, 36L, 33L, 19L, 14L, 1L, 8L, 6L,
16L, 4L, 4L, 9L, 1L, 2L, 9L, 5L, 2L, 5L, 7L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L), VIO..DE.LA.LIBERTAD.PERSONAL = c(0L, 7L,
6L, 5L, 6L, 1L, 1L, 0L, 3L, 1L, 2L, 0L, 2L, 0L, 1L, 0L, 1L, 1L,
0L, 1L, 1L, 0L, 3L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L,
0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), VIO..DE.LA.LIBERTAD.SEXUAL = c(56L,
14L, 12L, 15L, 7L, 10L, 2L, 9L, 11L, 13L, 8L, 9L, 7L, 14L, 4L,
15L, 4L, 12L, 2L, 17L, 7L, 3L, 4L, 12L, 2L, 1L, 5L, 3L, 11L,
4L, 1L, 2L, 0L, 2L, 0L, 3L, 0L, 2L, 2L, 0L, 4L, 0L, 0L, 0L, 0L,
0L, 0L, 0L), HURTO.SIMPLE.Y.AGRAVADO = c(217L, 203L, 296L, 230L,
260L, 167L, 226L, 217L, 130L, 117L, 154L, 133L, 121L, 46L, 163L,
72L, 161L, 84L, 119L, 69L, 120L, 64L, 19L, 21L, 57L, 44L, 39L,
2L, 48L, 60L, 30L, 19L, 48L, 41L, 25L, 19L, 27L, 7L, 11L, 9L,
0L, 6L, 0L, 2L, 3L, 1L, 0L, 0L), ROBO.SIMPLE.Y.AGRAVADO = c(460L,
289L, 308L, 344L, 186L, 277L, 198L, 130L, 165L, 184L, 137L, 149L,
134L, 188L, 104L, 126L, 58L, 96L, 72L, 64L, 51L, 77L, 115L, 76L,
64L, 88L, 73L, 108L, 40L, 36L, 30L, 32L, 14L, 12L, 22L, 12L,
8L, 6L, 3L, 1L, 3L, 0L, 2L, 1L, 0L, 1L, 0L, 0L), MICRO.COM.DE.DROGAS = c(26L,
100L, 13L, 3L, 10L, 15L, 5L, 5L, 11L, 8L, 3L, 23L, 9L, 15L, 3L,
3L, 0L, 2L, 0L, 8L, 2L, 5L, 0L, 28L, 0L, 0L, 1L, 0L, 0L, 0L,
2L, 2L, 0L, 0L, 0L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L,
0L, 0L), TENENCIA.ILEGAL.DE.ARMAS = c(1L, 4L, 0L, 1L, 1L, 1L,
0L, 1L, 0L, 1L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 6L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), LONGITUD = c(-77, -77.12,
-77.08, -76.89, -77.04, -77.09, -76.99, -77.01, -77.05, -77.05,
-77, -77.02, -76.97, -76.94, -77.09, -76.99, -77.03, -77.08,
-77, -77.13, -77.01, -77.05, -77.11, -76.7, -77.02, -76.92, -77,
-76.96, -76.86, -77.06, -77.07, -77.12, -76.76, -77.03, -77.05,
-77.11, -77.04, -77.09, -76.78, -77.16, -76.81, -76.73, -76.77,
-77.16, -76.76, -76.83, -76.73, -76.77), LATITUD = c(-11.99,
-12.04, -11.97, -12.04, -12.06, -12, -12.16, -12.2, -11.93, -11.99,
-12.04, -12.08, -12.16, -12.23, -12.08, -11.79, -12.12, -11.88,
-12.1, -11.89, -12.11, -12.06, -11.69, -11.94, -12.15, -12.09,
-12.08, -12.04, -11.98, -12.08, -12.09, -12.07, -11.99, -12.1,
-12.08, -12.06, -12.09, -12.04, -12.07, -11.81, -12.24, -12.32,
-12.47, -12.07, -12.28, -12.18, -12.38, -12.42)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -48L), .Names = c("DISTRITO",
"TOTALES", "HOMICIDIOS", "LESIONES", "VIO..DE.LA.LIBERTAD.PERSONAL",
"VIO..DE.LA.LIBERTAD.SEXUAL", "HURTO.SIMPLE.Y.AGRAVADO", "ROBO.SIMPLE.Y.AGRAVADO",
"MICRO.COM.DE.DROGAS", "TENENCIA.ILEGAL.DE.ARMAS", "LONGITUD",
"LATITUD"))

You have values outside of the base map zoom range... try changing your zoom parameter.
library(maps)
library(ggmap)
lima <- get_map(location = "lima", zoom = 10)
ggmap(lima) +
geom_point(data = limanov2,
aes(x = LONGITUD , y = LATITUD,
color = TOTALES, size = TOTALES)) +
scale_color_gradient(low = "yellow", high = "red")

Merge multiple datasets based on date and match only common dates in R

I have 4 data frames and I want to combines these data based on date. I want to create a new dataframe and merge data only when all of the data frames have the same common date. The data for 4 data frames are as follows:
coffee <- structure(list(date = structure(list(sec = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), min = c(0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), 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), mday = 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L), mon = c(0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L), year = c(110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L), wday = c(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, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 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), yday = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 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), isdst = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L)), .Names = c("sec", "min",
"hour", "mday", "mon", "year", "wday", "yday", "isdst"), class = c("POSIXlt",
"POSIXt")), discharge = c(46200, 46300, 46400, 46500, 46500,
46600, 46500, 46600, 46600, 46500, 46500, 46500, 46400, 46400,
46300, 46300, 40700, 40700, 40600, 40500, 40500, 40400, 40400,
40400, 40300, 40300, 40200, 33800, 34300, 34600, 35000, 35200,
35300, 35500, 35600, 38300, 38000, 37900, 37800, 37700, 37600,
37400, 37400, 37200, 37100, 37000, 36900, 33000, 33300, 33400,
33500, 33600, 33600, 33600, 33600, 33500, 33500, 33500, 33500,
33400, 34000, 31600, 31600, 31600, 31700, 31700, 31600, 31600,
31500, 31400, 31400, 31300, 31200, 31100, 31000, 32100, 32500,
32700, 32800, 32800, 32900, 32900, 32900, 32900, 32800, 32900,
32900, 32900, 32800, 32800, 32700, 32700, 32700, 32600, 32700,
32600, 32600, 32600, 32600, 32600)), .Names = c("date", "discharge"
), row.names = 3:102, class = "data.frame")
borne <- structure(list(date = structure(list(sec = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), min = c(0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), 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), mday = 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L), mon = c(0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L), year = c(110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L), wday = c(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, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 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), yday = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 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), isdst = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L)), .Names = c("sec", "min",
"hour", "mday", "mon", "year", "wday", "yday", "isdst"), class = c("POSIXlt",
"POSIXt")), discharge = c(78500, 74100, 77600, 75600, 79000,
75500, 76600, 72300, 75700, 75400, 75700, 78700, 76900, 76500,
72800, 75100, 74700, 80200, 75200, 74900, 74700, 73600, 69900,
73600, 70600, 74100, 75800, 73100, 71400, 72300, 71300, 72400,
72700, 72200, 69400, 72600, 68900, 67700, 66000, 64800, 66700,
68400, 65500, 66600, 63600, 106000, 106000, 109000, 110000, 110000,
110000, 110000, 114000, 112000, 112000, 111000, 110000, 109000,
108000, 108000, 106000, 105000, 110000, 113000, 113000, 112000,
111000, 110000, 93500, 62600, 62700, 63300, 63300, 63300, 63300,
63000, 63200, 62900, 62600, 62900, 62500, 62400, 62900, 62800,
62200, 62500, 62200, 62100, 62200, 62100, 59300, 60000, 60000,
60100, 60500, 60700, 60800, 60700, 60900, 61100)), .Names = c("date",
"discharge"), row.names = 3:102, class = "data.frame")
buk <-structure(list(date = structure(list(sec = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), min = c(0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), 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), mday = 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L), mon = c(0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L), year = c(110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L), wday = c(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, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 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), yday = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 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), isdst = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L)), .Names = c("sec", "min",
"hour", "mday", "mon", "year", "wday", "yday", "isdst"), class = c("POSIXlt",
"POSIXt")), discharge = c(78500, 74100, 77600, 75600, 79000,
75500, 76600, 72300, 75700, 75400, 75700, 78700, 76900, 76500,
72800, 75100, 74700, 80200, 75200, 74900, 74700, 73600, 69900,
73600, 70600, 74100, 75800, 73100, 71400, 72300, 71300, 72400,
72700, 72200, 69400, 72600, 68900, 67700, 66000, 64800, 66700,
68400, 65500, 66600, 63600, 106000, 106000, 109000, 110000, 110000,
110000, 110000, 114000, 112000, 112000, 111000, 110000, 109000,
108000, 108000, 106000, 105000, 110000, 113000, 113000, 112000,
111000, 110000, 93500, 62600, 62700, 63300, 63300, 63300, 63300,
63000, 63200, 62900, 62600, 62900, 62500, 62400, 62900, 62800,
62200, 62500, 62200, 62100, 62200, 62100, 59300, 60000, 60000,
60100, 60500, 60700, 60800, 60700, 60900, 61100)), .Names = c("date",
"discharge"), row.names = 3:102, class = "data.frame")
ten <- structure(list(date = structure(list(sec = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), min = c(30L, 0L, 30L, 0L, 30L,
0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L,
0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L,
0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L,
0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L,
0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L,
0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L,
0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L, 30L, 0L), hour = c(0L,
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, 18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L,
22L, 22L, 23L, 23L, 0L, 0L, 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, 18L, 18L,
19L, 19L, 20L, 20L, 21L, 21L, 22L, 22L, 23L, 23L, 0L, 0L, 1L,
1L, 2L), mday = c(31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L,
31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L,
31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L,
31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 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), mon = c(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,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L), year = c(109L, 109L, 109L, 109L, 109L, 109L,
109L, 109L, 109L, 109L, 109L, 109L, 109L, 109L, 109L, 109L, 109L,
109L, 109L, 109L, 109L, 109L, 109L, 109L, 109L, 109L, 109L, 109L,
109L, 109L, 109L, 109L, 109L, 109L, 109L, 109L, 109L, 109L, 109L,
109L, 109L, 109L, 109L, 109L, 109L, 109L, 109L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 110L, 110L, 110L), wday = c(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, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L
), yday = c(364L, 364L, 364L, 364L, 364L, 364L, 364L, 364L, 364L,
364L, 364L, 364L, 364L, 364L, 364L, 364L, 364L, 364L, 364L, 364L,
364L, 364L, 364L, 364L, 364L, 364L, 364L, 364L, 364L, 364L, 364L,
364L, 364L, 364L, 364L, 364L, 364L, 364L, 364L, 364L, 364L, 364L,
364L, 364L, 364L, 364L, 364L, 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), isdst = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L)), .Names = c("sec", "min", "hour", "mday", "mon",
"year", "wday", "yday", "isdst"), class = c("POSIXlt", "POSIXt"
)), discharge = c(51000, 50300, 50700, 49800, 50800, 50700, 50500,
50500, 50800, 50700, 50300, 50800, 50600, 50500, 50100, 50500,
50100, 50600, 49600, 50600, 49900, 49900, 50600, 50300, 50800,
49700, 50200, 50700, 49000, 51100, 50600, 50900, 50900, 51000,
50700, 50800, 51700, 52000, 51000, 51100, 52000, 51300, 51600,
51800, 52100, 51400, 51500, 54800, 54600, 54100, 54100, 54900,
53900, 54000, 54500, 54700, 53800, 54100, 53900, 53700, 53900,
54500, 53100, 54000, 54000, 53300, 52800, 53300, 53000, 53700,
54200, 53200, 53700, 53500, 54000, 53300, 53600, 55000, 53500,
52800, 54000, 53600, 55300, 54300, 53600, 54400, 54400, 54000,
54200, 53800, 53600, 53400, 54300, 53200, 53500, 53500, 53700,
52900, 53600, 53300)), .Names = c("date", "discharge"), row.names = 16094:16193, class = "data.frame")
Now, I want to merge the above mentioned data frames based on the common date. All the data frames should have data/discharge on the same data. For example all the data frames have data on 2010-01-01 00:00, then I want to take all the data and if one data frame has half hourly data I would want to check if the data interval and exact date match with other data frames. Finally, I need a solution where all the data are listed for the common dates.
I cannot directly use rbind here because some of the data is missing.

You can use merge but you should coerce your POSIXlt to POSIXct or date, since the first is of type list. Here I am using Reduce to process all the list once.
Reduce(function(x,y){
x$date <- as.POSIXct(x$date)
y$date <- as.POSIXct(y$date)
merge(x,y,by='date')},
list(coffee,borne,buk,ten))
# date discharge.x discharge.y discharge.x discharge.y
# 1 2010-01-03 00:00:00 33300 110000 110000 54800
# 2 2010-01-03 01:00:00 33400 110000 110000 54100
# 3 2010-01-03 02:00:00 33500 110000 110000 54900
# 4 2010-01-03 03:00:00 33600 110000 110000 54000
# 5 2010-01-03 04:00:00 33600 114000 114000 54700
# 6 2010-01-03 05:00:00 33600 112000 112000 54100
# 7 2010-01-03 06:00:00 33600 112000 112000 53700

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