Subset, or reclassify, spatial data in R

Subset, or reclassify, spatial data in R - r

I have the following data that indicates how many points occur within each rectangle (spatial data generated with quadratcount() from the spatstat package):
structure(c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
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)))
My goal is to either create a subset of all the rectangles that have a Freq above 100, or add a separate column with a binary classification if the row has a Freq above 100 or not.
My approach was to create a data.frame first and then the idea would be to change it back to a spatial data format. This is my unsuccessful approach:
Qdf <- as.data.frame(Q)
Qdf <- subset(Qdf, Qdf$Freq>100)
From here on I am unable to further display the data on a map.
Your help is very appreciated!

Did you start with a planar point pattern (ppp) and then create the
quadratcount from there? In that case I recommend you use pixellate to get
the counts directly in a raster format (im class in spatstat):
library(spatstat)
X <- bei
plot(X, main = "")
nx <- 10
ny <- 5
Xqc <- quadratcount(bei, nx = nx, ny = ny)
plot(Xqc, main = "")
Xim <- pixellate(X, dimyx = c(ny, nx))
plot(Xim , main = "")
plot(Xqc, add = TRUE)
Xim2 <- Xim[Xim>100, drop=FALSE] # If drop=TRUE vector of values is returned
plot(Xim2, main = "")

I'm not familiar with spatstat package. But, since your data are basically in a spatial raster grid, you could convert them to raster format and uselibrary(raster) for spatial operations like subsetting, reclassifying, and displaying on maps:
xr = attributes(Q)$xbreaks[c(1, dim(Q)[1]+1L)]
yr = attributes(Q)$ybreaks[c(1, dim(Q)[2]+1L)]
r = raster(matrix(Q, nrow(Q)), xmn=xr[1], xmx=xr[2], ymn=yr[1], ymx=yr[2])
plot(r)
Now we can see where the count is greater than 100
plot(r>100)
Or, see the values, only where they are greater than 100.
r100 = reclassify(r, cbind(-Inf, 100, NA), right=FALSE)
plot(r100)

Related

Trouble with Error: Column `group` can't be modified because it's a grouping variable

I addressed this question in a previous post but because I did not get a satisfactory answer I've tried the following.
I've a dataset with 80-second intervals that I would like to transform into 240-second intervals. Here's a sample of it:
> head(dataraw)
GMT_DATE GMT_TIME ACTIVITY_X ACTIVITY_Y ACTIVITY_Z Vigilance Head-up Grazing Browsing Moving
1: 06/17/2018 09:36:00 78 38 87 0 35 0 35 1
2: 06/17/2018 09:37:20 18 17 25 0 46 0 0 26
3: 06/17/2018 09:38:40 7 4 8 0 69 0 0 0
4: 06/17/2018 09:40:00 4 0 4 0 70 0 0 0
5: 06/17/2018 09:41:20 11 8 14 0 29 0 0 11
6: 06/17/2018 09:42:40 27 20 34 0 0 58 0 0
Grooming Resting Fleeing Unknown End Total
1: 4 0 0 5 0 80
2: 8 0 0 0 0 80
3: 5 0 0 6 0 80
4: 10 0 0 0 0 80
5: 15 0 0 25 0 80
6: 10 0 0 12 0 80
However, note that some intervals are 160-seconds (rows 5 to 6), which I'm still not sure how to address that issue:
> head(dataraw[c(3626:3632),])
GMT_DATE GMT_TIME ACTIVITY_X ACTIVITY_Y ACTIVITY_Z Vigilance Head-up Grazing Browsing Moving
1: 06/20/2018 18:09:20 0 0 0 0 0 0 0 0
2: 06/20/2018 18:10:40 0 0 0 0 0 0 0 0
3: 06/20/2018 18:12:00 1 0 1 0 0 0 0 0
4: 06/20/2018 18:13:20 0 0 0 0 0 0 0 0
5: 06/20/2018 18:14:40 0 0 0 0 0 0 0 0
6: 06/20/2018 18:17:20 4 0 4 0 0 0 0 0
Grooming Resting Fleeing Unknown End Total
1: 0 0 0 0 80 80
2: 0 0 0 0 80 80
3: 0 0 0 0 80 80
4: 0 0 0 0 80 80
5: 0 0 0 0 80 80
6: 0 0 0 0 80 80
Anyways, I tried the script below for which I'm getting the error:
> library(dplyr)
> datarawnew<-dataraw %>%
+ tidyr::unite(datetime, GMT_DATE, GMT_TIME, sep = " ") %>%
+ mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S"),
+ row = 1) %>%
+ group_by(group = cut(datetime, breaks = "4 mins")) %>%
+ summarise_at(-1, sum) %>%
+ mutate_at(vars(starts_with("ACTIVITY")), ~. /row) %>%
+ ungroup() %>%
+ select(-row)
Error in summarise_impl(.data, dots) :
Column `group` can't be modified because it's a grouping variable
Could anybody please let me know what am I doing wrong? I can upload a dput() sample below:
> dput(dataraw[c(1:250),])
structure(list(GMT_DATE = c("06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
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"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018"), GMT_TIME = c("09:36:00", "09:37:20",
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"14:26:40", "14:28:00", "14:29:20", "14:30:40", "14:32:00", "14:33:20",
"14:34:40", "14:36:00", "14:37:20", "14:38:40", "14:40:00", "14:41:20",
"14:42:40", "14:44:00", "14:45:20", "14:46:40", "14:48:00", "14:49:20",
"14:50:40", "14:52:00", "14:53:20", "14:54:40", "14:56:00", "14:57:20",
"14:58:40", "15:00:00", "15:01:20", "15:02:40", "15:04:00", "15:05:20",
"15:06:40", "15:08:00"), ACTIVITY_X = c(78L, 18L, 7L, 4L, 11L,
27L, 19L, 23L, 21L, 19L, 24L, 25L, 13L, 15L, 31L, 52L, 71L, 141L,
103L, 59L, 43L, 85L, 129L, 81L, 106L, 86L, 129L, 82L, 67L, 145L,
120L, 95L, 97L, 139L, 160L, 147L, 83L, 102L, 84L, 90L, 92L, 84L,
95L, 121L, 84L, 58L, 72L, 72L, 52L, 65L, 83L, 57L, 61L, 72L,
82L, 88L, 116L, 125L, 126L, 79L, 49L, 51L, 77L, 84L, 99L, 96L,
90L, 72L, 74L, 61L, 86L, 71L, 52L, 24L, 52L, 55L, 53L, 37L, 49L,
57L, 58L, 59L, 45L, 53L, 72L, 49L, 60L, 77L, 79L, 93L, 110L,
76L, 108L, 63L, 78L, 78L, 83L, 66L, 40L, 30L, 75L, 29L, 30L,
37L, 39L, 38L, 41L, 48L, 16L, 58L, 75L, 81L, 85L, 64L, 51L, 31L,
33L, 76L, 65L, 76L, 63L, 75L, 59L, 60L, 44L, 54L, 51L, 68L, 75L,
93L, 82L, 83L, 86L, 79L, 67L, 59L, 94L, 75L, 47L, 28L, 66L, 58L,
53L, 34L, 31L, 40L, 35L, 45L, 33L, 47L, 42L, 24L, 25L, 26L, 21L,
26L, 30L, 47L, 34L, 28L, 31L, 48L, 33L, 45L, 33L, 41L, 40L, 44L,
53L, 25L, 38L, 27L, 44L, 96L, 42L, 55L, 49L, 44L, 46L, 45L, 51L,
58L, 36L, 27L, 35L, 53L, 44L, 44L, 60L, 29L, 36L, 38L, 39L, 36L,
37L, 32L, 23L, 35L, 46L, 58L, 63L, 67L, 166L, 123L, 44L, 53L,
68L, 43L, 48L, 61L, 48L, 65L, 54L, 69L, 67L, 62L, 51L, 49L, 41L,
42L, 39L, 58L, 40L, 52L, 46L, 38L, 48L, 28L, 32L, 48L, 42L, 39L,
90L, 108L, 44L, 40L, 22L, 38L, 22L, 45L, 32L, 27L, 23L, 13L,
53L, 32L, 45L, 62L, 55L, 48L), ACTIVITY_Y = c(38L, 17L, 4L, 0L,
8L, 20L, 11L, 11L, 8L, 13L, 16L, 23L, 4L, 8L, 21L, 46L, 105L,
133L, 131L, 64L, 34L, 76L, 94L, 51L, 80L, 58L, 69L, 47L, 57L,
108L, 102L, 80L, 71L, 127L, 135L, 114L, 116L, 131L, 100L, 77L,
131L, 127L, 72L, 114L, 87L, 54L, 97L, 88L, 43L, 45L, 84L, 62L,
91L, 87L, 114L, 94L, 76L, 97L, 81L, 155L, 49L, 72L, 89L, 125L,
113L, 63L, 66L, 78L, 82L, 44L, 96L, 53L, 47L, 20L, 35L, 42L,
46L, 31L, 38L, 45L, 37L, 42L, 34L, 28L, 86L, 55L, 42L, 62L, 63L,
113L, 95L, 131L, 215L, 79L, 90L, 43L, 42L, 54L, 47L, 24L, 96L,
31L, 34L, 24L, 46L, 36L, 42L, 59L, 13L, 73L, 73L, 94L, 109L,
89L, 28L, 26L, 38L, 105L, 60L, 129L, 48L, 59L, 81L, 67L, 51L,
36L, 81L, 154L, 74L, 80L, 81L, 79L, 83L, 57L, 47L, 62L, 75L,
57L, 43L, 33L, 66L, 58L, 81L, 20L, 16L, 27L, 25L, 34L, 15L, 30L,
31L, 9L, 24L, 18L, 19L, 22L, 21L, 63L, 33L, 15L, 15L, 43L, 25L,
28L, 23L, 30L, 21L, 24L, 40L, 18L, 35L, 16L, 37L, 120L, 27L,
45L, 42L, 33L, 45L, 36L, 32L, 36L, 35L, 22L, 24L, 31L, 38L, 32L,
46L, 21L, 22L, 20L, 22L, 21L, 25L, 22L, 18L, 22L, 26L, 43L, 83L,
103L, 239L, 165L, 49L, 47L, 41L, 27L, 33L, 36L, 26L, 46L, 25L,
36L, 55L, 42L, 41L, 39L, 16L, 25L, 22L, 43L, 28L, 36L, 30L, 19L,
19L, 13L, 16L, 41L, 37L, 117L, 132L, 45L, 45L, 23L, 19L, 29L,
19L, 55L, 43L, 38L, 15L, 11L, 52L, 28L, 32L, 45L, 71L, 53L),
ACTIVITY_Z = c(87L, 25L, 8L, 4L, 14L, 34L, 22L, 25L, 22L,
23L, 29L, 34L, 14L, 17L, 37L, 69L, 127L, 194L, 167L, 87L,
55L, 114L, 160L, 96L, 133L, 104L, 146L, 95L, 88L, 181L, 157L,
124L, 120L, 188L, 209L, 186L, 143L, 166L, 131L, 118L, 160L,
152L, 119L, 166L, 121L, 79L, 121L, 114L, 67L, 79L, 118L,
84L, 110L, 113L, 140L, 129L, 139L, 158L, 150L, 174L, 69L,
88L, 118L, 151L, 150L, 115L, 112L, 106L, 110L, 75L, 129L,
89L, 70L, 31L, 63L, 69L, 70L, 48L, 62L, 73L, 69L, 72L, 56L,
60L, 112L, 74L, 73L, 99L, 101L, 146L, 145L, 151L, 241L, 101L,
119L, 89L, 93L, 85L, 62L, 38L, 122L, 42L, 45L, 44L, 60L,
52L, 59L, 76L, 21L, 93L, 105L, 124L, 138L, 110L, 58L, 40L,
50L, 130L, 88L, 150L, 79L, 95L, 100L, 90L, 67L, 65L, 96L,
168L, 105L, 123L, 115L, 115L, 120L, 97L, 82L, 86L, 120L,
94L, 64L, 43L, 93L, 82L, 97L, 39L, 35L, 48L, 43L, 56L, 36L,
56L, 52L, 26L, 35L, 32L, 28L, 34L, 37L, 79L, 47L, 32L, 34L,
64L, 41L, 53L, 40L, 51L, 45L, 50L, 66L, 31L, 52L, 31L, 57L,
154L, 50L, 71L, 65L, 55L, 64L, 58L, 60L, 68L, 50L, 35L, 42L,
61L, 58L, 54L, 76L, 36L, 42L, 43L, 45L, 42L, 45L, 39L, 29L,
41L, 53L, 72L, 104L, 123L, 291L, 206L, 66L, 71L, 79L, 51L,
58L, 71L, 55L, 80L, 60L, 78L, 87L, 75L, 65L, 63L, 44L, 49L,
45L, 72L, 49L, 63L, 55L, 42L, 52L, 31L, 36L, 63L, 56L, 123L,
160L, 117L, 63L, 46L, 29L, 48L, 29L, 71L, 54L, 47L, 27L,
17L, 74L, 43L, 55L, 77L, 90L, 72L), Vigilance = c(0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
7L, 18L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), `Head-up` = c(35L, 46L,
69L, 70L, 29L, 0L, 8L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 41L, 80L,
72L, 73L, 62L, 73L, 64L, 38L, 0L, 0L, 3L, 0L, 0L, 7L, 5L,
0L, 39L, 22L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
58L, 80L, 53L, 31L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 41L, 76L, 63L, 12L, 63L, 0L, 0L, 0L, 0L, 41L, 80L
), Grazing = c(0L, 0L, 0L, 0L, 0L, 58L, 66L, 72L, 67L, 38L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 63L, 0L,
9L, 75L, 80L, 68L, 69L, 7L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 5L, 0L, 18L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L), Browsing = c(35L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 21L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L), Moving = c(1L, 26L, 0L, 0L, 11L, 0L, 0L, 0L,
0L, 10L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
4L, 7L, 19L, 0L, 0L, 0L, 3L, 0L, 18L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 19L, 0L, 0L, 9L, 36L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 4L, 17L, 7L, 5L,
0L, 0L, 0L, 0L, 0L, 0L), Grooming = c(4L, 8L, 5L, 10L, 15L,
10L, 6L, 1L, 0L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 8L, 0L, 0L,
7L, 6L, 4L, 0L, 0L, 0L, 5L, 0L, 5L, 3L, 0L, 0L, 0L, 0L, 0L,
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, 8L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L), Resting = 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, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L), Fleeing = c(0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
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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, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L), Unknown = c(5L, 0L, 6L, 0L, 25L, 12L, 0L,
7L, 13L, 28L, 49L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 10L,
38L, 13L, 36L, 30L, 0L, 0L, 0L, 0L, 52L, 23L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 42L, 11L, 0L, 0L, 5L, 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, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
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0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 61L,
12L, 39L, 0L, 0L, 0L, 0L, 0L), End = c(0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 31L, 80L, 80L, 80L, 39L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 37L, 19L, 0L, 0L, 0L, 0L, 0L, 0L, 58L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 19L, 0L, 0L, 0L, 0L, 69L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 39L, 0L, 0L, 0L, 0L, 41L, 80L, 80L, 80L, 39L, 0L
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80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
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80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L)), row.names = c(NA, -250L), class = c("data.table",
"data.frame"), .internal.selfref = <pointer: 0x0000000002631ef0>)

The following code you posted:
> dataraw %>%
+ tidyr::unite(datetime, GMT_DATE, GMT_TIME, sep = " ") %>%
+ mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S"),
+ row = 1) %>%
+ group_by(group = cut(datetime, breaks = "4 mins")) %>%
+ summarise_at(-1, sum) %>%
+ mutate_at(vars(starts_with("ACTIVITY")), ~. /row) %>%
+ ungroup() %>%
+ select(-row)
never assigned this to a new data frame or over wrote the original data. I.e., you are missing:
newdataraw <- dataraw %>% ...
So, try running, e.g.,
newdataraw <- dataraw %>%
tidyr::unite(datetime, GMT_DATE, GMT_TIME, sep = " ") %>%
mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S"),
row = 1) %>%
group_by(group = cut(datetime, breaks = "4 mins")) %>%
summarise_at(-1, sum) %>%
mutate_at(vars(starts_with("ACTIVITY")), ~. /row) %>%
ungroup() %>%
select(-row)
If that solves it, then you just have the typo, as mentioned above..

Aggregating time stamped data into four minute intervals with exceptions

I need to transform time-stamped data with 80-second intervals into 4-minute (240-second) intervals.
The two main challenges I have is the large number of columns, and the fact that a few of the intervals are not 80-second, that's why I need help. Below is a head() sample of my dataset:
> head(dataraw)
GMT_DATE GMT_TIME ACTIVITY_X ACTIVITY_Y ACTIVITY_Z Vigilance Head-up Grazing Browsing Moving
1: 06/17/2018 09:36:00 78 38 87 0 35 0 35 1
2: 06/17/2018 09:37:20 18 17 25 0 46 0 0 26
3: 06/17/2018 09:38:40 7 4 8 0 69 0 0 0
4: 06/17/2018 09:40:00 4 0 4 0 70 0 0 0
5: 06/17/2018 09:41:20 11 8 14 0 29 0 0 11
6: 06/17/2018 09:42:40 27 20 34 0 0 58 0 0
Grooming Resting Fleeing Unknown End Total
1: 4 0 0 5 0 80
2: 8 0 0 0 0 80
3: 5 0 0 6 0 80
4: 10 0 0 0 0 80
5: 15 0 0 25 0 80
6: 10 0 0 12 0 80
As you can see, time-stamps have been taken every 80-seconds, although some of the time-stamps are 160-seconds as seen below on rows 5 and 6:
> head(dataraw[c(3626:3632),])
GMT_DATE GMT_TIME ACTIVITY_X ACTIVITY_Y ACTIVITY_Z Vigilance Head-up Grazing Browsing Moving
1: 06/20/2018 18:09:20 0 0 0 0 0 0 0 0
2: 06/20/2018 18:10:40 0 0 0 0 0 0 0 0
3: 06/20/2018 18:12:00 1 0 1 0 0 0 0 0
4: 06/20/2018 18:13:20 0 0 0 0 0 0 0 0
5: 06/20/2018 18:14:40 0 0 0 0 0 0 0 0
6: 06/20/2018 18:17:20 4 0 4 0 0 0 0 0
Grooming Resting Fleeing Unknown End Total
1: 0 0 0 0 80 80
2: 0 0 0 0 80 80
3: 0 0 0 0 80 80
4: 0 0 0 0 80 80
5: 0 0 0 0 80 80
6: 0 0 0 0 80 80
Hence, the best I can do is to aggregate by time-stamps having 00 in their seconds format. That is going from 09:36:00, to 09:40:00, to 09:44:00 etc.
How can I do this?
As for the values in columns ACTIVITY_X, ACTIVITY_Y and ACTIVITY_Z, they should be averaged when merged. For the rest of the columns, values can be summed when aggregated. Column Total will then have 240 for 4-minutes intervals (240 seconds).
I hope somebody can at least set me on the right track. Any input is truly appreciated!
> dput(dataraw[(1:280),])
structure(list(GMT_DATE = c("06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018", "06/17/2018",
"06/17/2018", "06/17/2018"), GMT_TIME = c("09:36:00", "09:37:20",
"09:38:40", "09:40:00", "09:41:20", "09:42:40", "09:44:00", "09:45:20",
"09:46:40", "09:48:00", "09:49:20", "09:50:40", "09:52:00", "09:53:20",
"09:54:40", "09:56:00", "09:57:20", "09:58:40", "10:00:00", "10:01:20",
"10:02:40", "10:04:00", "10:05:20", "10:06:40", "10:08:00", "10:09:20",
"10:10:40", "10:12:00", "10:13:20", "10:14:40", "10:16:00", "10:17:20",
"10:18:40", "10:20:00", "10:21:20", "10:22:40", "10:24:00", "10:25:20",
"10:26:40", "10:28:00", "10:29:20", "10:30:40", "10:32:00", "10:33:20",
"10:34:40", "10:36:00", "10:37:20", "10:38:40", "10:40:00", "10:41:20",
"10:42:40", "10:44:00", "10:45:20", "10:46:40", "10:48:00", "10:49:20",
"10:50:40", "10:52:00", "10:53:20", "10:54:40", "10:56:00", "10:57:20",
"10:58:40", "11:00:00", "11:01:20", "11:02:40", "11:04:00", "11:05:20",
"11:06:40", "11:08:00", "11:09:20", "11:10:40", "11:12:00", "11:13:20",
"11:14:40", "11:16:00", "11:17:20", "11:18:40", "11:20:00", "11:21:20",
"11:22:40", "11:24:00", "11:25:20", "11:26:40", "11:28:00", "11:29:20",
"11:30:40", "11:32:00", "11:33:20", "11:34:40", "11:36:00", "11:37:20",
"11:38:40", "11:40:00", "11:41:20", "11:42:40", "11:44:00", "11:45:20",
"11:46:40", "11:48:00", "11:49:20", "11:50:40", "11:52:00", "11:53:20",
"11:54:40", "11:56:00", "11:57:20", "11:58:40", "12:00:00", "12:01:20",
"12:02:40", "12:04:00", "12:05:20", "12:06:40", "12:08:00", "12:09:20",
"12:10:40", "12:12:00", "12:13:20", "12:14:40", "12:16:00", "12:17:20",
"12:18:40", "12:20:00", "12:21:20", "12:22:40", "12:24:00", "12:25:20",
"12:26:40", "12:28:00", "12:29:20", "12:30:40", "12:32:00", "12:33:20",
"12:34:40", "12:36:00", "12:37:20", "12:38:40", "12:40:00", "12:41:20",
"12:42:40", "12:44:00", "12:45:20", "12:46:40", "12:48:00", "12:49:20",
"12:50:40", "12:52:00", "12:53:20", "12:54:40", "12:56:00", "12:57:20",
"12:58:40", "13:00:00", "13:01:20", "13:02:40", "13:04:00", "13:05:20",
"13:06:40", "13:08:00", "13:09:20", "13:10:40", "13:12:00", "13:13:20",
"13:14:40", "13:16:00", "13:17:20", "13:18:40", "13:20:00", "13:21:20",
"13:22:40", "13:24:00", "13:25:20", "13:26:40", "13:28:00", "13:29:20",
"13:30:40", "13:32:00", "13:33:20", "13:34:40", "13:36:00", "13:37:20",
"13:38:40", "13:40:00", "13:41:20", "13:42:40", "13:44:00", "13:45:20",
"13:46:40", "13:48:00", "13:49:20", "13:50:40", "13:52:00", "13:53:20",
"13:54:40", "13:56:00", "13:57:20", "13:58:40", "14:00:00", "14:01:20",
"14:02:40", "14:04:00", "14:05:20", "14:06:40", "14:08:00", "14:09:20",
"14:10:40", "14:12:00", "14:13:20", "14:14:40", "14:16:00", "14:17:20",
"14:18:40", "14:20:00", "14:21:20", "14:22:40", "14:24:00", "14:25:20",
"14:26:40", "14:28:00", "14:29:20", "14:30:40", "14:32:00", "14:33:20",
"14:34:40", "14:36:00", "14:37:20", "14:38:40", "14:40:00", "14:41:20",
"14:42:40", "14:44:00", "14:45:20", "14:46:40", "14:48:00", "14:49:20",
"14:50:40", "14:52:00", "14:53:20", "14:54:40", "14:56:00", "14:57:20",
"14:58:40", "15:00:00", "15:01:20", "15:02:40", "15:04:00", "15:05:20",
"15:06:40", "15:08:00", "15:09:20", "15:10:40", "15:12:00", "15:13:20",
"15:14:40", "15:16:00", "15:17:20", "15:18:40", "15:20:00", "15:21:20",
"15:22:40", "15:24:00", "15:25:20", "15:26:40", "15:28:00", "15:29:20",
"15:30:40", "15:32:00", "15:33:20", "15:34:40", "15:36:00", "15:37:20",
"15:38:40", "15:40:00", "15:41:20", "15:42:40", "15:44:00", "15:45:20",
"15:46:40", "15:48:00"), ACTIVITY_X = c(78L, 18L, 7L, 4L, 11L,
27L, 19L, 23L, 21L, 19L, 24L, 25L, 13L, 15L, 31L, 52L, 71L, 141L,
103L, 59L, 43L, 85L, 129L, 81L, 106L, 86L, 129L, 82L, 67L, 145L,
120L, 95L, 97L, 139L, 160L, 147L, 83L, 102L, 84L, 90L, 92L, 84L,
95L, 121L, 84L, 58L, 72L, 72L, 52L, 65L, 83L, 57L, 61L, 72L,
82L, 88L, 116L, 125L, 126L, 79L, 49L, 51L, 77L, 84L, 99L, 96L,
90L, 72L, 74L, 61L, 86L, 71L, 52L, 24L, 52L, 55L, 53L, 37L, 49L,
57L, 58L, 59L, 45L, 53L, 72L, 49L, 60L, 77L, 79L, 93L, 110L,
76L, 108L, 63L, 78L, 78L, 83L, 66L, 40L, 30L, 75L, 29L, 30L,
37L, 39L, 38L, 41L, 48L, 16L, 58L, 75L, 81L, 85L, 64L, 51L, 31L,
33L, 76L, 65L, 76L, 63L, 75L, 59L, 60L, 44L, 54L, 51L, 68L, 75L,
93L, 82L, 83L, 86L, 79L, 67L, 59L, 94L, 75L, 47L, 28L, 66L, 58L,
53L, 34L, 31L, 40L, 35L, 45L, 33L, 47L, 42L, 24L, 25L, 26L, 21L,
26L, 30L, 47L, 34L, 28L, 31L, 48L, 33L, 45L, 33L, 41L, 40L, 44L,
53L, 25L, 38L, 27L, 44L, 96L, 42L, 55L, 49L, 44L, 46L, 45L, 51L,
58L, 36L, 27L, 35L, 53L, 44L, 44L, 60L, 29L, 36L, 38L, 39L, 36L,
37L, 32L, 23L, 35L, 46L, 58L, 63L, 67L, 166L, 123L, 44L, 53L,
68L, 43L, 48L, 61L, 48L, 65L, 54L, 69L, 67L, 62L, 51L, 49L, 41L,
42L, 39L, 58L, 40L, 52L, 46L, 38L, 48L, 28L, 32L, 48L, 42L, 39L,
90L, 108L, 44L, 40L, 22L, 38L, 22L, 45L, 32L, 27L, 23L, 13L,
53L, 32L, 45L, 62L, 55L, 48L, 10L, 2L, 11L, 29L, 52L, 18L, 17L,
17L, 10L, 1L, 33L, 19L, 22L, 10L, 23L, 46L, 81L, 115L, 97L, 111L,
75L, 44L, 75L, 86L, 35L, 32L, 24L, 18L, 20L, 29L), ACTIVITY_Y = c(38L,
17L, 4L, 0L, 8L, 20L, 11L, 11L, 8L, 13L, 16L, 23L, 4L, 8L, 21L,
46L, 105L, 133L, 131L, 64L, 34L, 76L, 94L, 51L, 80L, 58L, 69L,
47L, 57L, 108L, 102L, 80L, 71L, 127L, 135L, 114L, 116L, 131L,
100L, 77L, 131L, 127L, 72L, 114L, 87L, 54L, 97L, 88L, 43L, 45L,
84L, 62L, 91L, 87L, 114L, 94L, 76L, 97L, 81L, 155L, 49L, 72L,
89L, 125L, 113L, 63L, 66L, 78L, 82L, 44L, 96L, 53L, 47L, 20L,
35L, 42L, 46L, 31L, 38L, 45L, 37L, 42L, 34L, 28L, 86L, 55L, 42L,
62L, 63L, 113L, 95L, 131L, 215L, 79L, 90L, 43L, 42L, 54L, 47L,
24L, 96L, 31L, 34L, 24L, 46L, 36L, 42L, 59L, 13L, 73L, 73L, 94L,
109L, 89L, 28L, 26L, 38L, 105L, 60L, 129L, 48L, 59L, 81L, 67L,
51L, 36L, 81L, 154L, 74L, 80L, 81L, 79L, 83L, 57L, 47L, 62L,
75L, 57L, 43L, 33L, 66L, 58L, 81L, 20L, 16L, 27L, 25L, 34L, 15L,
30L, 31L, 9L, 24L, 18L, 19L, 22L, 21L, 63L, 33L, 15L, 15L, 43L,
25L, 28L, 23L, 30L, 21L, 24L, 40L, 18L, 35L, 16L, 37L, 120L,
27L, 45L, 42L, 33L, 45L, 36L, 32L, 36L, 35L, 22L, 24L, 31L, 38L,
32L, 46L, 21L, 22L, 20L, 22L, 21L, 25L, 22L, 18L, 22L, 26L, 43L,
83L, 103L, 239L, 165L, 49L, 47L, 41L, 27L, 33L, 36L, 26L, 46L,
25L, 36L, 55L, 42L, 41L, 39L, 16L, 25L, 22L, 43L, 28L, 36L, 30L,
19L, 19L, 13L, 16L, 41L, 37L, 117L, 132L, 45L, 45L, 23L, 19L,
29L, 19L, 55L, 43L, 38L, 15L, 11L, 52L, 28L, 32L, 45L, 71L, 53L,
4L, 1L, 8L, 17L, 42L, 12L, 9L, 6L, 5L, 0L, 30L, 16L, 16L, 19L,
51L, 68L, 111L, 108L, 105L, 97L, 69L, 22L, 54L, 80L, 22L, 19L,
20L, 29L, 15L, 22L), ACTIVITY_Z = c(87L, 25L, 8L, 4L, 14L, 34L,
22L, 25L, 22L, 23L, 29L, 34L, 14L, 17L, 37L, 69L, 127L, 194L,
167L, 87L, 55L, 114L, 160L, 96L, 133L, 104L, 146L, 95L, 88L,
181L, 157L, 124L, 120L, 188L, 209L, 186L, 143L, 166L, 131L, 118L,
160L, 152L, 119L, 166L, 121L, 79L, 121L, 114L, 67L, 79L, 118L,
84L, 110L, 113L, 140L, 129L, 139L, 158L, 150L, 174L, 69L, 88L,
118L, 151L, 150L, 115L, 112L, 106L, 110L, 75L, 129L, 89L, 70L,
31L, 63L, 69L, 70L, 48L, 62L, 73L, 69L, 72L, 56L, 60L, 112L,
74L, 73L, 99L, 101L, 146L, 145L, 151L, 241L, 101L, 119L, 89L,
93L, 85L, 62L, 38L, 122L, 42L, 45L, 44L, 60L, 52L, 59L, 76L,
21L, 93L, 105L, 124L, 138L, 110L, 58L, 40L, 50L, 130L, 88L, 150L,
79L, 95L, 100L, 90L, 67L, 65L, 96L, 168L, 105L, 123L, 115L, 115L,
120L, 97L, 82L, 86L, 120L, 94L, 64L, 43L, 93L, 82L, 97L, 39L,
35L, 48L, 43L, 56L, 36L, 56L, 52L, 26L, 35L, 32L, 28L, 34L, 37L,
79L, 47L, 32L, 34L, 64L, 41L, 53L, 40L, 51L, 45L, 50L, 66L, 31L,
52L, 31L, 57L, 154L, 50L, 71L, 65L, 55L, 64L, 58L, 60L, 68L,
50L, 35L, 42L, 61L, 58L, 54L, 76L, 36L, 42L, 43L, 45L, 42L, 45L,
39L, 29L, 41L, 53L, 72L, 104L, 123L, 291L, 206L, 66L, 71L, 79L,
51L, 58L, 71L, 55L, 80L, 60L, 78L, 87L, 75L, 65L, 63L, 44L, 49L,
45L, 72L, 49L, 63L, 55L, 42L, 52L, 31L, 36L, 63L, 56L, 123L,
160L, 117L, 63L, 46L, 29L, 48L, 29L, 71L, 54L, 47L, 27L, 17L,
74L, 43L, 55L, 77L, 90L, 72L, 11L, 2L, 14L, 34L, 67L, 22L, 19L,
18L, 11L, 1L, 45L, 25L, 27L, 21L, 56L, 82L, 137L, 158L, 143L,
147L, 102L, 49L, 92L, 117L, 41L, 37L, 31L, 34L, 25L, 36L), Vigilance = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
7L, 18L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 13L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), `Head-up` = c(35L, 46L, 69L,
70L, 29L, 0L, 8L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 41L, 80L, 72L,
73L, 62L, 73L, 64L, 38L, 0L, 0L, 3L, 0L, 0L, 7L, 5L, 0L, 39L,
22L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 58L, 80L, 53L,
31L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 41L, 76L, 63L, 12L, 63L, 0L, 0L, 0L, 0L, 41L, 80L,
80L, 30L, 0L, 0L, 2L, 14L, 11L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 41L, 80L),
Grazing = c(0L, 0L, 0L, 0L, 0L, 58L, 66L, 72L, 67L, 38L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 63L, 0L,
9L, 75L, 80L, 68L, 69L, 7L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 5L, 0L, 18L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 18L, 0L, 0L, 28L, 26L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L), Browsing = c(35L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 21L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L), Moving = c(1L, 26L, 0L, 0L, 11L, 0L, 0L,
0L, 0L, 10L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 4L, 7L, 19L, 0L, 0L, 0L, 3L, 0L, 18L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 19L, 0L, 0L, 9L, 36L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 4L, 17L, 7L,
5L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 24L, 0L, 0L, 11L, 7L, 10L,
30L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Grooming = c(4L, 8L,
5L, 10L, 15L, 10L, 6L, 1L, 0L, 4L, 0L, 0L, 0L, 0L, 0L, 0L,
8L, 0L, 0L, 7L, 6L, 4L, 0L, 0L, 0L, 5L, 0L, 5L, 3L, 0L, 0L,
0L, 0L, 0L, 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,
8L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Resting = 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, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Fleeing = 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, 3L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L), Unknown = c(5L, 0L, 6L, 0L,
25L, 12L, 0L, 7L, 13L, 28L, 49L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 10L, 38L, 13L, 36L, 30L, 0L, 0L, 0L, 0L, 52L,
23L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 42L,
11L, 0L, 0L, 5L, 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, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 61L, 12L, 39L, 0L, 0L, 0L, 0L, 0L, 0L,
8L, 1L, 0L, 0L, 6L, 0L, 12L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), End = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 31L, 80L,
80L, 80L, 39L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 37L, 19L,
0L, 0L, 0L, 0L, 0L, 0L, 58L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 19L, 0L, 0L, 0L, 0L, 69L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 39L, 0L, 0L, 0L,
0L, 41L, 80L, 80L, 80L, 39L, 0L, 0L, 0L, 79L, 80L, 39L, 14L,
59L, 34L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 39L, 0L
), Total = c(80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L,
80L, 80L, 80L, 80L, 80L, 80L, 80L)), row.names = c(NA, -280L
), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x0000000006b01ef0>)

I would generate table with all possible times (without seconds, for example), where second column is group index, then do left_join from dplyr to the original table, at the end aggregate by this artificial index.
Btw, it's very convenient to do so, if you plan to make a plot in ggplot, you just write aes(x=.., y=.., col=index)

First, make standard POSIXct format from your columns GMT_DATE, GMT_TIME
then
time_seq_by_seconds = seq(as.POSIXct("2017-06-17 09:36:00"), as.POSIXct("2017-06-24 10:04:00"), 1)
number_of_groups = round(length(time_seq_by_seconds) / 80) +1
groups = do.call(c, lapply(1:number_of_groups, function(x){ rep(x,80)} ))
groups = groups[1:length(time_seq_by_seconds)]
indexed = as.data.frame(cbind(as.character(time_seq_by_seconds), groups))
colnames(indexed) = c("datetime","group")
library(dplyr)
joined = left_join(dataraw, indexed, by = c("GMT_DATETIME" = "datetime"))

Instead of using any hacky way of dealing with date-time treat them as POSIXct objects. We can combine GMT_DATE and GMT_TIME into one datetime column and convert them to actual date time objects. We can now create groups of 4 minute interval each using cut and then sum them all together. I created an extra column row with value 1 which can be later used to calculate average of "ACTIVITY" columns.
library(dplyr)
dataraw %>%
tidyr::unite(datetime, GMT_DATE, GMT_TIME, sep = " ") %>%
mutate(datetime = as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S"),
row = 1) %>%
group_by(group = cut(datetime, breaks = "4 mins")) %>%
summarise_at(-1, sum) %>%
mutate_at(vars(starts_with("ACTIVITY")), ~. /row) %>%
ungroup() %>%
select(-row)
# A tibble: 94 x 15
# group ACTIVITY_X ACTIVITY_Y ACTIVITY_Z Vigilance `Head-up` Grazing Browsing..
# <fct> <dbl> <dbl> <dbl> <int> <int> <int> <int>
# 1 2018… 34.3 19.7 40 0 150 0 35...
# 2 2018… 14 9.33 17.3 0 99 58 0...
# 3 2018… 21 10 23 0 8 205 0...
# 4 2018… 22.7 17.3 28.7 0 0 38 0...
# 5 2018… 19.7 11 22.7 0 41 0 0...
# 6 2018… 88 94.7 130 7 225 0 0...
# 7 2018… 68.3 76.3 103 18 199 0 0...
# 8 2018… 98.3 73.7 123. 0 38 63 0...
# 9 2018… 107 69 128. 0 3 164 0...
#10 2018… 98 70.7 121. 0 12 144 21...
# … with 84 more rows, and 3 more variables: Unknown <int>, End <int>, Total <int>

Plot visreg over an boxplot (GLM with binominal predictor)

I fitted some GLMs with a binominal predictor and would like to plot them with visreg. I usually plot the raw data with par(new=T) as well for better clarity. I don't really like the normal outcome here (x-axis 0-1 in 0.2 steps, a lot of data points just at 0 and 1) and was thinking about plotting the visreg over boxplot since they look much better with binominal data. However, I can't get the two plots to align since there are always two different "starts" and "ends" in the plot. How can I make it so that the visreg line starts at the "No" and ends at the "Yes" of the boxplot?
fit <- glm (Cov.herb ~ Fire, family=gaussian, data=data)
boxplot(data$Cov.herb ~ data$Fire, ylim=c(0,100), axes=F, ylab="Herb cover [%]", xlab="Fire")
axis(1, xaxp=c(1,2,1), xaxt="n")
mtext(text=c("No","Yes"),side=1,line=0.5,at=c(1,2))
axis(2, las=1)
box()
par(new=T)
visreg(fit, scale = "response", type="conditional",line=list(col="red", lwd=1), ylim=c(0,100), xlim=c(0,1), rug=F, axes=F, ann=F)
example plot
Cheers,
Alex
data:
structure(list(Cov.herb = c(40L, 80L, 30L, 2L, 40L, 8L, 5L, 5L,
20L, 45L, 55L, 55L, 35L, 40L, 65L, 70L, 2L, 15L, 1L, 1L, 1L,
25L, 10L, 1L, 10L, 5L, 5L, 15L, 10L, 5L, 15L, 5L, 5L, 35L, 1L,
1L, 35L, 1L, 10L, 5L, 5L, 10L, 5L, 10L, 10L, 20L, 10L, 0L, 3L,
1L, 2L, 4L, 1L, 10L, 30L, 10L, 1L, 2L, 0L, 15L, 25L, 50L, 15L,
35L, 30L, 5L, 5L, 1L, 1L, 1L, 10L, 0L, 0L, 5L, 2L, 1L, 10L, 0L,
2L, 1L, 1L, 5L, 1L, 15L, 1L, 1L, 1L, 0L, 5L, 25L, 3L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 3L, 1L, 1L, 0L, 5L, 1L, 1L, 1L, 1L, 7L, 1L,
1L, 1L, 1L, 5L, 0L, 2L, 3L, 5L, 3L, 1L, 1L, 2L, 0L, 2L, 0L, 10L,
1L, 20L, 3L, 5L, 20L, 3L, 20L, 5L, 10L, 15L, 30L, 0L, 20L, 45L,
1L, 1L, 2L, 1L, 3L, 0L, 5L, 0L, 35L, 1L, 5L, 25L, 0L, 0L, 40L,
3L, 15L, 10L, 3L, 50L, 30L, 10L, 1L, 0L, 5L, 10L, 10L, 2L, 2L,
5L, 1L, 2L, 1L, 1L, 0L, 0L, 1L, 2L, 5L, 15L, 0L, 1L, 1L, 1L,
1L, 0L, 1L, 5L, 1L, 5L, 35L, 1L, 0L, 1L, 0L, 5L, 1L, 1L, 3L,
15L, 1L, 3L, 1L, 0L, 0L, 0L, 15L, 0L, 1L, 1L, 3L, 35L, 80L, 10L,
2L, 10L, 3L, 3L, 2L, 10L, 50L, 20L, 40L, 2L, 40L, 45L, 25L, 5L,
25L, 50L, 35L, 15L, 45L, 10L, 5L, 15L, 2L, 30L, 2L, 3L, 15L,
5L, 45L, 35L, 20L, 70L, 20L, 10L, 30L, 25L, 8L, 4L, 45L, 60L,
35L, 5L, 40L, 30L, 0L, 30L, 3L, 4L, 25L, 15L, 10L, 15L, 25L,
20L, 7L, 25L, 25L, 40L, 35L, 30L, 40L, 25L, 50L, 30L, 25L, 60L,
15L, 25L, 25L, 50L, 30L, 20L, 2L, 3L, 20L, 25L, 35L, 30L, 10L,
15L, 65L, 10L, 20L, 20L, 2L, 7L, 20L, 25L, 30L, 30L, 9L, 20L,
40L, 7L, 20L, 15L, 15L, 30L, 20L, 35L, 8L, 40L, 20L, 3L, 55L,
35L, 10L, 10L, 65L, 20L, 35L, 60L, 45L, 20L, 10L, 35L, 15L, 20L,
15L, 40L, 10L, 10L, 60L, 60L, 40L, 10L, 10L, 25L, 8L, 20L, 40L,
15L, 25L, 5L, 20L, 20L, 20L, 25L, 30L, 35L, 20L, 110L, 50L, 20L,
20L, 10L, 45L, 25L, 20L, 55L, 10L, 5L, 15L, 15L, 1L, 10L, 15L,
15L, 10L, 30L, 20L, 40L, 55L, 55L, 20L, 30L, 10L, 50L, 40L, 5L,
15L, 10L, 30L, 15L, 20L, 5L, 45L, 50L, 25L, 45L, 30L, 7L, 25L,
30L, 5L, 7L, 50L, 60L, 50L, 10L, 30L, 50L, 15L, 15L, 30L, 15L,
25L, 40L, 10L, 2L, 60L, 20L, 65L, 5L, 15L, 3L, 15L, 40L, 50L,
45L, 30L, 5L, 45L, 15L, 25L, 65L, 15L, 50L, 55L, 30L, 10L, 35L,
15L, 20L, 20L, 10L, 20L, 15L, 45L, 40L, 10L, 7L, 25L, 20L, 60L,
4L, 7L, 40L, 60L, 50L, 50L, 10L, 50L, 5L, 10L, 50L, 20L, 40L,
20L, 25L, 25L, 35L, 10L, 2L, 15L, 60L, 25L, 30L, 20L, 25L, 10L,
10L, 20L, 40L, 40L, 45L, 10L, 35L, 60L, 50L, 10L, 40L, 50L, 25L,
20L, 25L, 25L, 45L, 20L, 30L, 65L, 30L, 35L, 40L, 25L, 15L, 10L,
50L, 25L, 45L, 40L, 20L, 5L, 65L, 5L, 10L, 15L, 7L, 20L, 45L,
15L, 5L, 20L, 20L, 20L, 50L, 15L, 20L, 30L, 25L, 45L, 45L, 35L,
40L, 45L, 4L, 10L, 20L, 20L, 30L, 15L, 30L, 50L, 35L, 45L, 25L,
25L, 10L, 5L, 30L, 30L, 10L, 70L, 25L, 25L, 7L, 20L, 5L, 20L,
8L, 15L, 10L, 20L, 10L, 7L, 15L, 15L, 40L, 50L, 15L, 20L, 8L,
45L, 40L, 15L, 25L, 40L, 20L, 35L, 40L, 70L, 20L, 20L, 40L, 5L,
20L, 7L, 40L, 10L, 5L, 45L, 20L, 10L, 20L, 20L, 45L, 15L, 7L,
30L, 30L, 35L, 10L, 20L, 5L, 15L, 35L, 40L, 40L, 10L, 5L, 15L,
70L, 20L, 85L, 15L, 7L, 55L, 55L, 5L, 20L, 25L, 5L, 30L, 20L,
8L, 30L, 40L, 25L, 10L, 5L, 30L, 10L, 5L, 10L, 35L, 2L, 10L,
10L, 10L, 90L, 45L, 60L, 7L, 1L, 15L), Fire = c(0L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 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, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 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, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 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, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 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, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L,
0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 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, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L,
1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L,
1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L,
0L, 0L, 1L, 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("Cov.herb",
"Fire"), class = "data.frame", row.names = c(2L, 3L, 4L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 29L, 30L, 31L, 32L, 33L,
34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 43L, 44L, 45L, 46L, 47L,
48L, 49L, 50L, 51L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 74L, 75L, 76L,
77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 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, 153L, 154L, 155L, 161L, 162L, 163L, 164L, 165L, 166L,
167L, 169L, 170L, 171L, 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, 209L, 211L, 213L, 214L, 215L,
216L, 217L, 218L, 219L, 220L, 221L, 222L, 223L, 224L, 225L, 226L,
227L, 228L, 229L, 230L, 231L, 232L, 233L, 234L, 235L, 236L, 237L,
238L, 239L, 240L, 241L, 242L, 243L, 244L, 245L, 246L, 247L, 248L,
249L, 250L, 251L, 252L, 253L, 254L, 255L, 256L, 257L, 258L, 259L,
260L, 261L, 262L, 263L, 269L, 270L, 274L, 275L, 276L, 277L, 279L,
280L, 281L, 282L, 283L, 284L, 285L, 286L, 287L, 288L, 289L, 290L,
291L, 292L, 293L, 294L, 295L, 296L, 297L, 298L, 299L, 300L, 301L,
302L, 303L, 304L, 305L, 306L, 307L, 308L, 309L, 310L, 311L, 312L,
313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L, 321L, 322L, 323L,
324L, 325L, 326L, 327L, 328L, 329L, 330L, 331L, 332L, 333L, 334L,
335L, 336L, 337L, 338L, 339L, 340L, 341L, 342L, 343L, 344L, 345L,
346L, 347L, 349L, 350L, 351L, 352L, 353L, 354L, 355L, 356L, 357L,
358L, 359L, 360L, 361L, 362L, 363L, 364L, 365L, 366L, 367L, 368L,
369L, 370L, 371L, 372L, 373L, 374L, 375L, 376L, 377L, 378L, 380L,
381L, 382L, 383L, 384L, 385L, 386L, 387L, 388L, 389L, 390L, 391L,
392L, 393L, 394L, 395L, 396L, 397L, 398L, 399L, 400L, 401L, 402L,
403L, 404L, 405L, 406L, 407L, 408L, 409L, 410L, 411L, 412L, 413L,
414L, 415L, 416L, 417L, 418L, 419L, 420L, 421L, 422L, 423L, 424L,
425L, 426L, 427L, 428L, 429L, 430L, 431L, 432L, 433L, 434L, 435L,
436L, 437L, 438L, 439L, 440L, 441L, 443L, 444L, 445L, 446L, 447L,
448L, 449L, 450L, 451L, 453L, 454L, 455L, 457L, 458L, 459L, 460L,
461L, 463L, 464L, 465L, 466L, 467L, 468L, 469L, 470L, 471L, 472L,
473L, 474L, 475L, 476L, 477L, 478L, 479L, 480L, 481L, 482L, 483L,
484L, 485L, 486L, 487L, 488L, 489L, 490L, 491L, 492L, 493L, 494L,
495L, 496L, 497L, 498L, 499L, 500L, 501L, 502L, 503L, 504L, 505L,
506L, 507L, 508L, 509L, 510L, 511L, 512L, 513L, 514L, 515L, 516L,
517L, 518L, 519L, 520L, 521L, 522L, 523L, 524L, 525L, 526L, 527L,
528L, 529L, 530L, 531L, 532L, 533L, 534L, 535L, 536L, 537L, 538L,
539L, 540L, 541L, 542L, 543L, 544L, 545L, 546L, 547L, 548L, 549L,
551L, 552L, 553L, 554L, 555L, 556L, 557L, 558L, 559L, 560L, 561L,
562L, 563L, 564L, 565L, 566L, 567L, 568L, 569L, 570L, 571L, 572L,
573L, 574L, 575L, 576L, 577L, 578L, 579L, 580L, 581L, 582L, 583L,
584L, 585L, 587L, 588L, 589L, 590L, 591L, 592L, 593L, 594L, 595L,
596L, 597L, 598L, 599L, 600L, 601L, 602L, 603L, 604L, 605L, 606L,
607L, 608L, 609L, 610L, 611L, 612L, 613L, 614L, 615L, 616L, 617L,
618L, 619L, 620L, 621L, 622L, 623L, 624L, 625L, 626L, 628L, 629L,
631L, 632L, 633L, 634L, 635L, 636L, 637L, 638L, 639L, 640L, 641L,
642L, 643L, 644L, 645L, 646L, 648L, 649L, 650L, 651L, 652L, 653L,
654L, 655L, 656L, 657L, 658L, 659L, 660L, 661L, 662L, 663L, 664L,
665L, 666L, 667L, 668L, 669L, 670L, 671L, 672L, 673L, 674L, 675L,
676L, 677L, 678L, 679L, 680L, 682L, 683L, 684L, 685L, 686L, 687L,
689L, 690L, 691L, 692L, 693L, 694L, 697L, 698L, 699L, 700L, 701L,
702L, 704L, 705L, 706L, 707L))

So, my point was that doing it this way would give you more flexibility with your plotting. For example,
# Fit model
fit <- glm (Cov.herb ~ Fire, family=gaussian, data=data)
# Get model data for plotting
vis.out <- visreg(fit, scale = "response", plot = FALSE)
# Load library
library(ggplot2)
# Create plot
p <- ggplot(data = data)
p <- p + geom_boxplot(aes(x = as.factor(Fire), y = Cov.herb, fill = as.factor(Fire)), alpha = 0.3, outlier.alpha = 1)
p <- p + xlab("Fire") + ylab("Herb cover [%]")
p <- p + geom_ribbon(data = vis.out$fit, aes(x = Fire + 1, ymin = visregLwr, ymax = visregUpr), fill = "lightgrey")
p <- p + geom_line(data = vis.out$fit, aes(x = Fire + 1, y = visregFit), colour = "salmon", size = 1.25)
p <- p + scale_x_discrete(labels = c("No", "Yes"))
p <- p + theme(legend.position = "none")
print(p)
gives,
Is that the sort of thing you're looking for? (You could also add all the data points using geom_point to plot on top of the boxes. I think that usually looks pretty cool.)

How can I visualize 1D numeric data with R / tikz?

I have a text file with 166898 lines where each line has a single non-negative number. I would like to visualize it in the following way:
The x-axis should range from minimum_in_file to maximum_in_file.
The y-axis should range from 1 to 166898
The graph / bar plot should be increasing and visualize how many numbers are equal to the x-value or lower.
Create data
#!/usr/bin/env python
import random
minimum_in_file = 0
maximum_in_file = 378864471
numbers = []
for i in range(166898):
numbers.append(random.randint(minimum_in_file, maximum_in_file))
numbers = sorted(numbers)
with open("times-sorted.txt", 'a') as f:
for number in numbers:
f.write(str(number) + "\n")
Real data
When I execute dput(head(mydata,20)) I get:
structure(list(X0 = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L)), .Names = "X0", row.names = c(NA,
20L), class = "data.frame")
and
> dput(head(mydata,1000))
structure(list(X0 = 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, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 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, 2L, 2L, 3L, 3L, 5L, 6L, 7L, 8L, 8L,
8L, 8L, 11L, 12L, 13L, 13L, 13L, 16L, 18L, 19L, 20L, 20L, 23L,
25L, 26L, 27L, 35L, 37L, 37L, 39L, 41L, 41L, 45L, 46L, 47L, 48L,
48L, 48L, 50L, 52L, 53L, 55L, 56L, 62L, 65L, 66L, 67L, 67L, 70L,
79L, 79L, 80L, 83L, 85L, 86L, 88L, 88L, 89L, 91L, 96L, 97L, 99L,
100L, 101L, 101L, 101L, 102L, 103L, 104L, 104L, 107L, 109L, 109L,
109L, 109L, 111L, 111L, 111L, 111L, 112L, 112L, 112L, 112L, 113L,
113L, 114L, 114L, 114L, 115L, 115L, 115L, 116L, 117L, 118L, 119L,
120L, 120L, 124L, 124L, 124L, 124L, 125L, 125L, 127L, 127L, 128L,
128L, 128L, 129L, 129L, 129L, 130L, 130L, 131L, 132L, 132L, 132L,
133L, 133L, 134L, 134L, 134L, 134L, 136L, 136L, 137L, 137L, 138L,
139L, 140L, 141L, 141L, 142L, 143L, 143L, 143L, 144L, 144L, 144L,
145L, 145L, 146L, 147L, 147L, 149L, 149L, 150L, 150L, 150L, 150L,
150L, 151L, 151L, 151L, 151L, 151L, 152L, 152L, 153L, 154L, 154L,
154L, 154L, 155L, 156L, 157L, 157L, 158L, 158L, 158L, 158L, 159L,
160L, 160L, 160L, 160L, 161L, 161L, 163L, 163L, 163L, 164L, 164L,
164L, 164L, 164L, 165L, 165L, 166L, 166L, 167L, 167L, 167L, 167L,
168L, 168L, 168L, 169L, 169L, 170L, 170L, 171L, 171L, 172L, 172L,
172L, 172L, 173L, 173L, 173L, 174L, 174L, 175L, 175L, 175L, 176L,
176L, 176L, 176L, 177L, 177L, 177L, 177L, 177L, 179L, 179L, 179L,
180L, 180L, 180L, 180L, 181L, 181L, 182L, 182L, 182L, 182L, 183L,
183L, 184L, 184L, 184L, 184L, 185L, 185L, 185L, 186L, 187L, 187L,
187L, 187L, 188L, 188L, 188L, 188L, 189L, 189L, 189L, 189L, 190L,
190L, 190L, 190L, 191L, 191L, 191L, 191L, 191L, 191L, 191L, 191L,
191L, 192L, 192L, 193L, 193L, 194L, 194L, 195L, 195L, 195L, 197L,
197L, 197L, 197L, 197L, 198L, 198L, 198L, 198L, 198L, 198L, 199L,
199L, 199L, 199L, 199L, 199L, 199L, 200L, 200L, 200L, 200L, 200L,
200L, 200L, 200L, 200L, 201L, 201L, 203L, 203L, 203L, 204L, 204L,
204L, 205L, 205L, 206L, 206L, 206L, 206L, 206L, 206L, 206L, 207L,
207L, 207L, 207L, 207L, 207L, 208L, 208L, 209L, 209L, 209L, 209L,
209L, 209L, 210L, 210L, 210L, 210L, 210L, 211L, 211L, 212L, 213L,
213L, 213L, 213L, 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L,
215L)), .Names = "X0", row.names = c(NA, 1000L), class = "data.frame")
What I've tried
I sorted the data with the unix tool sort and tried the following with R version 3.0.1 (2013-05-16):
> mydata = read.csv("times-sorted.txt")
> accumulated_sum<-cumsum(mydata)
Warning message:
In lapply(X = x, FUN = .Generic, ...) :
integer overflow in 'cumsum'; use 'cumsum(as.numeric(.))'
> plot(accumulated_sum)
But I don't understand how to fix the warning and the result is crappy:
How can I visualize it in a nice way?

See this source code here (https://svn.r-project.org/R/trunk/src/main/cum.c), and the statement
if(sum > INT_MAX || sum < 1 + INT_MIN) where INT_MAX is .Machine$integer.max ,possibly this limit is being exceeded since you are applying cumsum to the entire dataset and not the variable of interest.
Since you have not posted your dataset structure, I think the row indices are being passed to cumsum and hence the warning,
N=166898
vec=1:N
#produces warning "Warning message: integer overflow in 'cumsum'; use 'cumsum(as.numeric(.))'"
cumsum(vec)
You need a cumulative frequency plot. Following is sample example adapted from
(http://www.r-tutor.com/elementary-statistics/quantitative-data/cumulative-frequency-graph)
Sample Example
#For reproducibility
set.seed(100)
N=166898
vec=1:N
#Assuming min_val, max_val
min_val = 0
max_val = 378864471
min_break = 1e8
max_break = 4e8
seq_by = 1e8
#Create random values dataset
random_values = sample(min_val:max_val,N,replace = T)
DF=data.frame(vec,random_values)
#Compute Cumulative Frequency
#You can control the buckets by appropriate inputs to breaks
breaks = seq(min_break, max_break, by=seq_by)
#Creates buckets [x,y), [y,z) etc.
DF.cut = cut( DF$random_values, breaks, right=FALSE)
#Computes count of observations in various buckets and cumulative frequency
DF.freq=table(DF.cut)
DF.cumfreq = c(0, cumsum(DF.freq))
#Plot Data
plot(breaks, DF.cumfreq,main="Cumulative Frequency of XYZ",xlab="Range of Values",ylab="# of Observations < X")
lines(breaks, DF.cumfreq)
Your data
I have plotted the below plot using the data sample you provided, but the following should work for your file(s) now.
#Replace the appropriate filename here
mydata = read.table("times-sorted.txt")
min_val_new = min(mydata)
max_val_new = max(mydata)
breaks_new = seq(from=min_val_new,to=max_val_new,length.out=5)
#Creates buckets [x,y), [y,z) etc.
DF.cut_new = cut(mydata[,1], breaks_new, right=FALSE)
#Computes count of observations in various buckets and cumulative frequency
DF.freq_new=table(DF.cut_new)
DF.cumfreq_new = c(0, cumsum(DF.freq_new))
#Plot Data
plot(breaks_new, DF.cumfreq_new,main="Cumulative Frequency of ABC",xlab="Range of Values",ylab="# of Observations < X")
lines(breaks_new, DF.cumfreq_new)
Exponential Plot
Define your breakpoints cutoff1=10000 and cutoff2=60000 and include them in 'breaks' calculation and plotting using ggplot2 with log axis
set.seed(100)
require(ggplot2)
N=166898
vec=1:N
#Assuming min_val, max_val
min_val = 0
max_val = 378864471
min_break=0
max_break=4e8
#Create random values dataset
random_values = sample(min_val:max_val,N,replace = T)
DF=data.frame(vec,random_values)
#Define your data breakpoints
cutoff1=10000
cutoff2=60000
#Compute Cumulative Frequency
#You can control the buckets by appropriate inputs to breaks
breaks = c(min_break,cutoff1,seq(cutoff2, max_break, length.out=30))
#Creates buckets [x,y), [y,z) etc.
DF.cut = cut( DF$random_values, breaks, right=FALSE)
#Computes count of observations in various buckets and cumulative frequency
DF.freq=table(DF.cut)
DF.cumfreq = c(0, cumsum(DF.freq))
#Plot Data
#plot(breaks, DF.cumfreq,main="Cumulative Frequency of XYZ",xlab="Range of Values",ylab="# of Observations < X")
#lines(breaks, DF.cumfreq)
gg.df=data.frame(breaks,DF.cumfreq)
ggplot(gg.df, aes(x = breaks,y=DF.cumfreq)) + geom_line() + scale_x_log10() +
xlab("Range of Values: Log Axis") +
ylab("# of Observations < X") +
ggtitle("Cumulative Frequency of Variable")

Logit regression with WinBUGS/R2WinBUGS not converging to likelihood estimates

I am a Win-7 user with R 2.15.2
Can someone help me why is the following model not converging well close to simple logit model estimates?
Edited
Mydata <- structure(list(gg = c(13.659955, 6.621436486, 3.017166776, 2.516795069,
3.928538296, 4.211960532, 3.235445955, 5.152860411, 18.96466673,
5.904678823, 4.987622293, 1.170687541, 3.088224149, 4.738065529,
3.263022593, 6.050017999, 5.650762257, 2.058924721, 3.138591919,
7.169083435, 11.30381738, 3.036991188, 4.559013218, 3.978760664,
3.617455798, 2.430111184, 4.440319959, 2.200267742, 6.003166495,
3.114161526, 3.812363457, 11.12816724, 15.6564348, 13.50562576,
5.154056904, 6.26451889, 1.849669635, 1.816757851, 3.861868285,
2.884542233, 2.993444924, 2.724235493, 2.694159089, 1.973597356,
4.371300647, 3.559035718, 3.59124243, 6.587196681, 10.03402072,
4.805158339, 4.491460392, 1.627936721, 1.278291553, 0.978710462,
3.08635052, 2.58594947, 2.354973563, 1.657519171, 2.946994656,
2.110549733, 6.095182338, 6.000660354, 6.691960157, 1.796172588,
2.531234555, 2.992017156, 2.882403206, 6.066420081, 5.930524609,
5.972280022, 0.915755208, 2.398369176, 2.614677323, 3.904309459,
2.120045842, 2.643895472, 4.756728133, 4.420426549, 5.787620043,
10.14764507, 10.17993063, 4.529582451, 2.132173713, 3.573017249,
2.749427487, 5.79057317, 3.30235352, 2.248377801, 3.37704153,
3.270636543, 8.070781663, 10.63482235, 11.91206431, 2.030994094,
3.843890691, 11.08130402, 10.56164432, 2.476733196, 2.672953595,
4.73313584, 2.577761507, 4.397950314, 16.02648073, 1.979318271,
4.124346958, 18.95385675, 5.364182526, 3.964093091, 4.985103684,
5.887163026, 6.302977032, 3.594056904, 2.1592159, 2.506654073,
2.739740977, 5.840023906, 14.66431143, 1.453209431, 1.405915159,
6.133806412, 14.46294647, 15.7475232, 13.91882816, 3.667160495,
2.472826568, 2.208138746, 2.313856848, 6.677046217, 5.960024468,
2.647365332, 1.960115215, 2.392076123, 2.796502016, 2.147861348,
3.825593606, 2.427949377, 3.111598763, 5.248771445, 3.333653698,
3.937363645, 1.98551364, 4.090444736, 1.897938127, 3.772878129,
1.708975251, 2.149179807, 1.985344614, 3.445809412, 1.537484672,
3.210425705, 3.635236243, 3.340757476, 2.426647042, 16.65453923,
2.207695228, 17.05735446, 9.148235024, 3.289077716, 2.534469392,
1.827870535, 2.952377144, 2.218054092, 3.787767414, 1.862731602,
6.699397675, 2.572247774, 2.42783988, 3.429506609, 2.152120465,
6.651671979, 3.389825443, 1.787049217, 2.368181588, 2.358737508,
3.470515609, 1.693830224, 4.124049967, 2.894483735, 1.90612459,
3.447950783, 11.73691853, 12.78330833, 3.860448673, 2.571402925,
2.66582863, 3.206179619, 2.546483922, 3.047299616, 2.052597638,
4.894592388, 2.869309084, 2.851843536, 1.729619574, 2.276794788,
12.68354926, 3.318892896, 10.71333438, 3.318892896, 10.71333438,
1.908315292, 3.111350542, 1.847059969, 1.835544656, 2.618919198,
2.341098688, 2.920079188, 1.830521448, 4.95152276, 10.18359208,
11.37645094, 3.44132725, 11.51865833, 14.45180625, 12.61570573,
10.80784104, 7.814012406, 14.48656688, 1.019333021, 2.816970615,
7.101350458, 1.764183823, 4.821036885, 2.251823823, 1.569206635,
1.791906542, 3.114256927, 2.496611198, 2.776086885, 2.477500021,
4.801364906, 3.642995521, 4.279315979, 4.956759854, 3.710843927,
15.75846719, 3.032181385, 11.60129177, 3.443892792, 2.642908469,
5.329881656, 2.303688396, 2.681312552, 2.12796449, 2.773085833,
4.189449094, 3.521328188, 1.761418094, 2.455309406, 2.526749219,
10.27017188, 11.70942021, 6.537176688, 3.665906281, 4.212212875,
7.625210531, 10.74009115, 10.74009115, 10.58339073, 11.77831083,
2.604995813, 4.744413865, 7.29975851, 1.740470719, 1.112195677,
2.604728, 2.58798274, 1.059064521, 4.349711208, 2.392650167,
2.699035417, 2.153652521, 3.151369396, 3.414949031, 2.581368219,
2.234642333, 2.392818135, 6.51472726, 2.648815969, 15.47915854,
3.258030083, 1.990451802, 2.6811855, 2.440177469, 2.925240396,
3.668138604, 2.917453344, 1.9656045, 2.452494323, 4.299348479,
4.765304604, 16.62252406, 12.88866406, 2.488524583, 1.916951229,
1.714486281, 2.816999552, 1.784181771, 2.585827844, 2.720170823,
2.186287625, 3.07640024, 4.327105385, 4.086370688, 4.592656531,
2.571516719, 6.396392573, 18.74246573, 14.24556208), ss = c(-7.707850005,
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-43.04649469, -40.17308802, -42.42595479, -39.31338521, -40.29414865,
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-17.20437385, -50.21773375, -50.43229958, -51.10613073, -44.76030594,
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119.6048186, 174.3630449, 240.233224, 97.64322396, 93.5913059,
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138.205053, 118.2084279, 226.6079498, 123.4014062, 197.9286116,
126.6151589, 128.7066092, 220.1513453, 171.8320615, 399.8383894,
379.5150089, 446.4220118, 66.78233993, 120.2137058, 165.0139027,
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0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L,
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1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
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0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L,
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1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L)), .Names = c("gg", "ss", "dd",
"OutCome"), class = "data.frame", row.names = c(1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
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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,
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174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 184L,
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260L, 261L, 262L, 263L, 264L, 265L, 266L, 267L, 268L, 269L, 270L,
271L, 272L, 273L, 274L, 275L, 276L, 277L, 278L, 279L, 280L, 281L,
282L, 283L, 284L, 285L, 286L, 287L, 288L, 289L, 290L, 291L, 292L,
293L, 294L, 295L, 296L, 297L, 298L, 299L, 300L, 301L, 302L, 303L,
304L, 305L, 306L, 307L, 308L, 309L, 310L, 311L, 312L, 313L, 314L,
315L, 316L, 317L, 318L, 319L, 320L, 321L, 322L, 323L, 324L, 325L,
326L, 349L, 350L))
Model code for likelihood estimates:
Simplelogit <- glm(OutCome ~ gg+ss+dd, data = Mydata, family = "binomial")
Model code using R2WinBUGS: (EDITED)
model1 ="
model
{
# likelihood
for(i in 1:N)
{
Y[i] ~ dbin(p[i],N)
logit(p[i])<- beta1[1]+beta1[2]*X[1]+beta1[3]*X[2]+beta1[4]*X[3]
}
#prior
beta1[1]~dnorm(1,1.0E-02)
beta1[2]~dnorm(1,1.0E-02)
beta1[3]~dnorm(1,1.0E-02)
beta1[4]~dnorm(1,1.0E-02)
}
"
writeLines(model1,con='Model.txt')
x1 <- unlist(Mydata$gg)
x2 <- unlist(Mydata$ss)
x3 <- unlist(Mydata$dd)
N=c(nrow(Mydata))
datalist <- list(N=N,Y=c(Mydata$OutCome),X=c(x1,x2,x3))
inits <- function() list(beta1=c((Simplelogit$coefficients[,1])))
MyPara <- c("beta1")
require(R2WinBUGS)
BayesianModel <- bugs(datalist,inits,MyPara,model.file='Model.txt',n.chains=1,n.iter=54000,n.burnin=4000,n.sim=50000,program=c('WinBUGS'),debug=FALSE,codaPkg=FALSE,save.history=TRUE,bugs.directory='C:/Program Files/WinBUGS14/',working.directory = getwd()) #,over.relax=TRUE
as.numeric(BayesianModel$summary[c(1:4)),1])
#results:
-48.63550 3.47384 -0.69866 0.09043
And then with Traditional method / without using bayesian method
Simplelogit <- glm(OutCome ~ gg+ss+dd, data = Mydata, family = "binomial")
c(as.matrix(Simplelogit$coefficients[c(1:4),1]))
# result is:
-20.71281 3.47408 -0.31233 -0.03906
Please suggest if I need to use different model of change the prior or the syntax...

I have not run the code, but I can spot two errors:
There is no Mydata$yy, so the vector is too short (only 616, should be 3*308). Should be x3<-unlist(Mydata$dd).
And you did not notice the error, because the indexing in the logit line is wrong. Should be something like
logit(p[i])<- beta1[1]+beta1[2]*X[i]+beta1[3]*X[i+2*N]+beta1[4]*X[i+3*N]

The jags version (I hate installing RWinBugs)
# Assuming your data have been saved in mydata.rdata
load("mydata.rdata")
library("rjags")
model1 ="
model
{
# likelihood
for(i in 1:N)
{
logit(p[i])<- beta0+betagg*gg[i]+betass*ss[i]+betadd*dd[i];
Y[i] ~ dbin(p[i],N); # Should be dbern probably
}
#prior
beta0~dnorm(1,1.0E-02);
betagg~dnorm(1,1.0E-02);
betass~dnorm(1,1.0E-02);
betadd~dnorm(1,1.0E-02);
}
"
writeLines(model1,con='Model.txt')
datalist <- with(Mydata, list(N=nrow(Mydata),Y=as.numeric(OutCome),gg=gg,ss=ss,dd=dd))
# A bit of cheating: initial values adapted after first run
inits <- list(beta0=-8,betagg=0.2,betass=0.05,betadd=0.002)
m <- jags.model("Model.txt",datalist,init=inits)
update(m, 1000)
x <- coda.samples(m, c("beta0","betagg","betass","betadd"), n.iter=10000)
plot(x) # Well, not prettty, but acceptable

Another solution using stan
load("mydata.rdata")
library(rstan)
library(ggmcmc)
library(coda)
model1 ="
data {
int<lower=0> N;
int<lower=0,upper=1> Y[N];
real gg[N];
real ss[N];
real dd[N];
}
parameters{
real beta0;
real betagg;
real betass;
real betadd;
}
model
{
#prior
beta0 ~ normal(-2,30);
betagg ~ normal(20,30);
betass ~ normal(-3,30);
betadd ~ normal(-10,40);
# likelihood
for(i in 1:N)
{
Y[i] ~ bernoulli(inv_logit(beta0+betagg*gg[i]+betass*ss[i]+betadd*dd[i]));
}
}
"
MyPar = scale(Mydata[,-4])
datalist <- list(N=nrow(Mydata),
Y=as.numeric(Mydata$OutCome),
gg=MyPar[,"gg"],ss=MyPar[,"ss"],dd=MyPar[,"dd"])
m <- stan(model_code=model1,iter=20000,data= datalist,n.chains=4)
ggmcmc(ggs(m))
print(m)

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