plot geom_lines() by different time periods - r

I have the following data:
structure(list(date_time = structure(c(1577836800, 1577840400,
1577844000, 1577847600, 1577851200, 1577854800, 1577858400, 1577862000,
1577865600, 1577869200, 1577872800, 1577876400, 1577880000, 1577883600,
1577887200, 1577890800, 1577894400, 1577898000, 1577901600, 1577905200,
1577908800, 1577912400, 1577916000, 1577919600, 1577923200, 1577926800,
1577930400, 1577934000, 1577937600, 1577941200, 1577944800, 1577948400,
1577952000, 1577955600, 1577959200, 1577962800, 1577966400, 1577970000,
1577973600, 1577977200, 1577980800, 1577984400, 1577988000, 1577991600,
1577995200, 1577998800, 1578002400, 1578006000, 1578009600, 1578013200,
1578016800, 1578020400, 1578024000, 1578027600, 1578031200, 1578034800,
1578038400, 1578042000, 1578045600, 1578049200, 1578052800, 1578056400,
1578060000, 1578063600, 1578067200, 1578070800, 1578074400, 1578078000,
1578081600, 1578085200, 1578088800, 1578092400, 1578096000, 1578099600,
1578103200, 1578106800, 1578110400, 1578114000, 1578117600, 1578121200,
1578124800, 1578128400, 1578132000, 1578135600, 1578139200, 1578142800,
1578146400, 1578150000, 1578153600, 1578157200, 1578160800, 1578164400,
1578168000, 1578171600, 1578175200, 1578178800, 1578182400, 1578186000,
1578189600, 1578193200), tzone = "UTC", class = c("POSIXct",
"POSIXt")), altura = c(80L, 67L, 69L, 86L, 110L, 137L, 161L,
178L, 182L, 174L, 153L, 126L, 102L, 86L, 84L, 94L, 113L, 137L,
161L, 180L, 189L, 183L, 164L, 136L, 107L, 86L, 77L, 81L, 95L,
115L, 137L, 157L, 171L, 175L, 167L, 149L, 127L, 107L, 94L, 91L,
99L, 114L, 134L, 156L, 173L, 181L, 177L, 160L, 136L, 112L, 94L,
85L, 87L, 98L, 114L, 133L, 152L, 166L, 172L, 167L, 152L, 133L,
113L, 99L, 93L, 97L, 109L, 128L, 150L, 168L, 178L, 175L, 161L,
140L, 117L, 99L, 88L, 87L, 94L, 109L, 129L, 151L, 168L, 176L,
173L, 159L, 138L, 116L, 98L, 88L, 89L, 102L, 123L, 147L, 168L,
180L, 180L, 166L, 145L, 120L)), row.names = c(NA, -100L), class = c("tbl_df",
"tbl", "data.frame"))
I produce this graph:
I wanna to produce the same graph with the lines in different colors considering the mean of different time periods. For example, 6 hours, 12 hours, or a day.
In another way. Plot lines with the mean of different time periods (6h, 12h, 24h)
Thank's all


You could do
library(tidyverse)
df %>%
mutate(hour = lubridate::hour(date_time),
date = as.Date(date_time)) %>%
ggplot(aes(date_time, altura)) +
geom_line() +
geom_line(data = . %>%
group_by(date, hour %/% 6) %>%
summarise_all(mean), aes(color = "6 hour")) +
geom_line(data = . %>%
group_by(date, hour %/% 12) %>%
summarise_all(mean), aes(color = "12 hour")) +
geom_line(data = . %>%
group_by(date) %>%
summarise_all(mean), aes(color = "day"))

Related

plot a list of tables vertically kable::extra and kable in R?

I need to plot a list of tables one below the other, displayed vertically. Any ideas?
The issue started here, then I've seen that knitr::kable and kableExtra may be helpful, but
I can't work my way around to put them vertically.
### empty list:
test_list <- list()
### make two tests to provide an example:
for (a in 1:2) {
myVar <- data[a]
myVarn <- names(myVar)
data$myVarUnlist <- unlist(myVar)
test_list[[a]] <- cor.test(data$myVar, data$VarC, data = data)
}
my_report <- list()
my_report_partial <- list()
library(report)
for (b in 1:2) {
my_report[[b]] <- report_table(test_list[[b]])
}
I've tried many things, such as:
my_report %>%
kableExtra::kbl(caption = "Recreating booktabs style table", longtable = TRUE) %>%
kableExtra::kable_classic(full_width = F, html_font = "Cambria")
what I get:
desired output: one under the other, vertically
I'd really appreaciate some help, thanks. My data is here:
structure(list(VarA = c(263L, 223L, NA, 257L, 285L, 211L, 210L,
NA, 147L, 311L, 342L, 97L, 216L, 241L, 296L, 296L, 211L, 60L,
339L, 318L, 358L, 167L, NA, 183L, 92L, 283L, 169L, NA, 298L,
NA, 162L, NA, 211L, 308L, 92L, 269L, NA, 197L, 280L, 259L, 313L,
252L, 98L, 258L, 201L, 341L, 456L, 308L, 252L, 64L, 259L, 158L,
161L, NA, NA, 129L, 264L, NA, 216L, 109L, 91L, 236L, 275L, 254L,
221L, NA, NA, NA, NA, NA, NA), VarB = c(145L, 120L, NA, 119L,
142L, 132L, 100L, NA, 64L, 144L, 164L, 56L, 102L, 136L, 139L,
135L, 91L, 32L, 123L, 164L, 145L, 93L, NA, 99L, 51L, 143L, 98L,
NA, 158L, NA, 79L, NA, 96L, 149L, 55L, 114L, NA, 94L, 137L, 130L,
135L, 113L, 61L, 113L, 117L, 154L, 199L, 152L, 142L, 42L, 111L,
74L, 92L, NA, NA, 85L, 116L, NA, 99L, 64L, 60L, 114L, 151L, 136L,
116L, NA, NA, NA, NA, NA, NA), VarC = c(145L, 121L, NA, 120L,
145L, 133L, 101L, NA, 64L, 146L, 166L, 58L, 103L, 136L, 142L,
135L, 91L, 34L, 123L, 167L, 148L, 93L, NA, 99L, 51L, 145L, 98L,
NA, 159L, NA, 81L, NA, 97L, 149L, 56L, 115L, NA, 96L, 137L, 132L,
135L, 113L, 62L, 113L, 118L, 154L, 199L, 154L, 145L, 43L, 112L,
74L, 92L, NA, NA, 86L, 116L, NA, 100L, 66L, 60L, 114L, 153L,
136L, 120L, NA, NA, NA, NA, NA, NA), myVarUnlist = c(145L, 120L,
NA, 119L, 142L, 132L, 100L, NA, 64L, 144L, 164L, 56L, 102L, 136L,
139L, 135L, 91L, 32L, 123L, 164L, 145L, 93L, NA, 99L, 51L, 143L,
98L, NA, 158L, NA, 79L, NA, 96L, 149L, 55L, 114L, NA, 94L, 137L,
130L, 135L, 113L, 61L, 113L, 117L, 154L, 199L, 152L, 142L, 42L,
111L, 74L, 92L, NA, NA, 85L, 116L, NA, 99L, 64L, 60L, 114L, 151L,
136L, 116L, NA, NA, NA, NA, NA, NA)), row.names = c(NA, -71L), class = "data.frame")
Obs:
I've seen many posts, such as.. but I couldn't apply to my case
this this this and this

Use dplyr::bind_rows() on the list of tables before passing to the kbl().
```{r}
### empty list:
test_list <- list()
### make two tests to provide an example:
for (a in 1:2) {
myVar <- data[[a]]
myVarn <- names(myVar)
data$myVarUnlist <- unlist(myVar)
test_list[[a]] <- cor.test(~ myVar + VarC, data = data)
}
my_report <- list()
my_report_partial <- list()
library(report)
library(kableExtra)
for (b in 1:2) {
my_report[[b]] <- report_table(test_list[[b]])
}
dplyr::bind_rows(my_report) %>%
kbl(caption = "Recreating booktabs style table") %>%
kableExtra::kable_classic(full_width = F, html_font = "Cambria")
```

Plot a list of tables in a single table using a for loop - R Studio

I have a list containing 10 tables from 10 correlation tests. I'm trying to pass it as an argument to report_table() from library(report), get it, and use it to plot them all at once with nice_table() from library(rempsyc) in a single report (one report under the other)
I've made a reproducible example with two tests:
### empty list:
test_list <- list()
### make two tests to provide an example:
for (a in 1:2) {
myVar <- data[a]
myVarn <- names(myVar)
data$myVarUnlist <- unlist(myVar)
test_list[[a]] <- cor.test(data$myVar, data$VarC, data = data)
}
### my list:
test_list
### I can plot one at once, but I'd like to plot both at the same time:
library(report)
library(rempsyc)
reports <- list()
for (b in 1:2) {
my_report <- report_table(test_list[[b]])
reports[[b]] <- nice_table(my_report)
}
### I have two reports, but how can I join them all?
### I wanted one under the other as a single table
reports
desired output:
data :
structure(list(VarA = c(263L, 223L, NA, 257L, 285L, 211L, 210L,
NA, 147L, 311L, 342L, 97L, 216L, 241L, 296L, 296L, 211L, 60L,
339L, 318L, 358L, 167L, NA, 183L, 92L, 283L, 169L, NA, 298L,
NA, 162L, NA, 211L, 308L, 92L, 269L, NA, 197L, 280L, 259L, 313L,
252L, 98L, 258L, 201L, 341L, 456L, 308L, 252L, 64L, 259L, 158L,
161L, NA, NA, 129L, 264L, NA, 216L, 109L, 91L, 236L, 275L, 254L,
221L, NA, NA, NA, NA, NA, NA), VarB = c(145L, 120L, NA, 119L,
142L, 132L, 100L, NA, 64L, 144L, 164L, 56L, 102L, 136L, 139L,
135L, 91L, 32L, 123L, 164L, 145L, 93L, NA, 99L, 51L, 143L, 98L,
NA, 158L, NA, 79L, NA, 96L, 149L, 55L, 114L, NA, 94L, 137L, 130L,
135L, 113L, 61L, 113L, 117L, 154L, 199L, 152L, 142L, 42L, 111L,
74L, 92L, NA, NA, 85L, 116L, NA, 99L, 64L, 60L, 114L, 151L, 136L,
116L, NA, NA, NA, NA, NA, NA), VarC = c(145L, 121L, NA, 120L,
145L, 133L, 101L, NA, 64L, 146L, 166L, 58L, 103L, 136L, 142L,
135L, 91L, 34L, 123L, 167L, 148L, 93L, NA, 99L, 51L, 145L, 98L,
NA, 159L, NA, 81L, NA, 97L, 149L, 56L, 115L, NA, 96L, 137L, 132L,
135L, 113L, 62L, 113L, 118L, 154L, 199L, 154L, 145L, 43L, 112L,
74L, 92L, NA, NA, 86L, 116L, NA, 100L, 66L, 60L, 114L, 153L,
136L, 120L, NA, NA, NA, NA, NA, NA), myVarUnlist = c(145L, 120L,
NA, 119L, 142L, 132L, 100L, NA, 64L, 144L, 164L, 56L, 102L, 136L,
139L, 135L, 91L, 32L, 123L, 164L, 145L, 93L, NA, 99L, 51L, 143L,
98L, NA, 158L, NA, 79L, NA, 96L, 149L, 55L, 114L, NA, 94L, 137L,
130L, 135L, 113L, 61L, 113L, 117L, 154L, 199L, 152L, 142L, 42L,
111L, 74L, 92L, NA, NA, 85L, 116L, NA, 99L, 64L, 60L, 114L, 151L,
136L, 116L, NA, NA, NA, NA, NA, NA)), row.names = c(NA, -71L), class = "data.frame")
Thanks in advance! :)
Additional question, I'm using the unlisted variable so that cor.test doesn't return me any errors, is there a way to keep the variable originals' name? (aka, VarA, VarB, etc) ? I've tried with the myVarn , but cor.test() won't run with that

Box-plot R calculating outliers

> summary(mydata)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 93 107 110 125 197
> range=1.5*(125-93)
> upper_whisker=125+range
> lower_whisker=93-range
> upper_whisker
[1] 173
> lower_whisker
[1] 45
> boxplot(mydata)$stats
[,1]
[1,] 56 #Lower whisker by boxplot
[2,] 93
[3,] 107
[4,] 125
[5,] 173
I tried looking up the formula for calculating after and before what values are the points to be considered outliers
It was
Above =>3rd Qu +(3rd Qu - 1st Qu)*1.5
Below =>1st Qu -(3rd Qu - 1st Qu)*1.5
For some reason they don't seem to match with the stats returned by boxplot function in R
I have a feeling it's something silly here
Are they calculated differently? Or am I reading the wrong answer from boxplot?
Edit:
I've used https://www.kaggle.com/uciml/pima-indians-diabetes-database
and ran
mydata=raw$Glucose[raw$Outcome==0]
EDIT2:
I suppose if
#max(min(x), Q1 - (IQR(x)*1.5)) #lower whisker
is returning min(x), there shouldn't be any outliers
and min(mydata) is 0
Edit 3:
Clearer view of Quantile
quantile(mydata)
0% 25% 50% 75% 100%
0 93 107 125 197
Edit 4:
Added vector as requested
c(85L, 89L, 116L, 115L, 110L, 139L, 103L, 126L, 99L, 97L, 145L,
117L, 109L, 88L, 92L, 122L, 103L, 138L, 180L, 133L, 106L, 159L,
146L, 71L, 105L, 103L, 101L, 88L, 150L, 73L, 100L, 146L, 105L,
84L, 44L, 141L, 99L, 109L, 95L, 146L, 139L, 129L, 79L, 0L, 62L,
95L, 112L, 113L, 74L, 83L, 101L, 110L, 106L, 100L, 107L, 80L,
123L, 81L, 142L, 144L, 92L, 71L, 93L, 151L, 125L, 81L, 85L, 126L,
96L, 144L, 83L, 89L, 76L, 78L, 97L, 99L, 111L, 107L, 132L, 120L,
118L, 84L, 96L, 125L, 100L, 93L, 129L, 105L, 128L, 106L, 108L,
154L, 102L, 57L, 106L, 147L, 90L, 136L, 114L, 153L, 99L, 109L,
88L, 151L, 102L, 114L, 100L, 148L, 120L, 110L, 111L, 87L, 79L,
75L, 85L, 143L, 87L, 119L, 0L, 73L, 141L, 111L, 123L, 85L, 105L,
113L, 138L, 108L, 99L, 103L, 111L, 96L, 81L, 147L, 179L, 125L,
119L, 142L, 100L, 87L, 101L, 197L, 117L, 79L, 122L, 74L, 104L,
91L, 91L, 146L, 122L, 165L, 124L, 111L, 106L, 129L, 90L, 86L,
111L, 114L, 193L, 191L, 95L, 142L, 96L, 128L, 102L, 108L, 122L,
71L, 106L, 100L, 104L, 114L, 108L, 129L, 133L, 136L, 155L, 96L,
108L, 78L, 161L, 151L, 126L, 112L, 77L, 150L, 120L, 137L, 80L,
106L, 113L, 112L, 99L, 115L, 129L, 112L, 157L, 179L, 105L, 118L,
87L, 106L, 95L, 165L, 117L, 130L, 95L, 0L, 122L, 95L, 126L, 139L,
116L, 99L, 92L, 137L, 61L, 90L, 90L, 88L, 158L, 103L, 147L, 99L,
101L, 81L, 118L, 84L, 105L, 122L, 98L, 87L, 93L, 107L, 105L,
109L, 90L, 125L, 119L, 100L, 100L, 131L, 116L, 127L, 96L, 82L,
137L, 72L, 123L, 101L, 102L, 112L, 143L, 143L, 97L, 83L, 119L,
94L, 102L, 115L, 94L, 135L, 99L, 89L, 80L, 139L, 90L, 140L, 147L,
97L, 107L, 83L, 117L, 100L, 95L, 120L, 82L, 91L, 119L, 100L,
135L, 86L, 134L, 120L, 71L, 74L, 88L, 115L, 124L, 74L, 97L, 154L,
144L, 137L, 119L, 136L, 114L, 137L, 114L, 126L, 132L, 123L, 85L,
84L, 139L, 173L, 99L, 194L, 83L, 89L, 99L, 80L, 166L, 110L, 81L,
154L, 117L, 84L, 94L, 96L, 75L, 130L, 84L, 120L, 139L, 91L, 91L,
99L, 125L, 76L, 129L, 68L, 124L, 114L, 125L, 87L, 97L, 116L,
117L, 111L, 122L, 107L, 86L, 91L, 77L, 105L, 57L, 127L, 84L,
88L, 131L, 164L, 189L, 116L, 84L, 114L, 88L, 84L, 124L, 97L,
110L, 103L, 85L, 87L, 99L, 91L, 95L, 99L, 92L, 154L, 78L, 130L,
111L, 98L, 143L, 119L, 108L, 133L, 109L, 121L, 100L, 93L, 103L,
73L, 112L, 82L, 123L, 67L, 89L, 109L, 108L, 96L, 124L, 124L,
92L, 152L, 111L, 106L, 105L, 106L, 117L, 68L, 112L, 92L, 183L,
94L, 108L, 90L, 125L, 132L, 128L, 94L, 102L, 111L, 128L, 92L,
104L, 94L, 100L, 102L, 128L, 90L, 103L, 157L, 107L, 91L, 117L,
123L, 120L, 106L, 101L, 120L, 127L, 162L, 112L, 98L, 154L, 165L,
99L, 68L, 123L, 91L, 93L, 101L, 56L, 95L, 136L, 129L, 130L, 107L,
140L, 107L, 121L, 90L, 99L, 127L, 118L, 122L, 129L, 110L, 80L,
127L, 158L, 126L, 134L, 102L, 94L, 108L, 83L, 114L, 117L, 111L,
112L, 116L, 141L, 175L, 92L, 106L, 105L, 95L, 126L, 65L, 99L,
102L, 109L, 153L, 100L, 81L, 121L, 108L, 137L, 106L, 88L, 89L,
101L, 122L, 121L, 93L)

Your calculation was almost right, R uses this:
#max(min(x), Q1 - (IQR(x)*1.5)) #lower whisker
#min(max(x), Q3 + (IQR(x)*1.5)) #upper whisker
That's why, it picks the max/min between the min(x)/max(x), and the standard formula.
Here an example:
my_data <- mtcars$mpg
bp <- boxplot(my_data)
bp$stats
# [1,] 10.40 # lower whisker
# [2,] 15.35
# [3,] 19.20 # == median(my_data)
# [4,] 22.80
# [5,] 33.90 # upper whisker
max(min(my_data,na.rm=T), as.numeric(quantile(my_data, 0.25)) - (IQR(my_data)*1.5))
#[1] 10.4 #lower whisker
min(max(my_data,na.rm=T), as.numeric(quantile(my_data, 0.75)) + (IQR(my_data)*1.5))
#[1] 33.9 # upper whisker

I think there are few things to clarified. The first thing is that you should always provide a reproducible example for helping people to help you.
An outlier is defined as a data point that is located outside the whiskers of the boxplot (e.g: outside 1.5 times the interquartile range above the upper quartile and bellow the lower quartile).
The correct way to figure out how this work is simulating some Student's T data under a pre-specified a random number generator state.
set.seed(1)
mydata <- rt(100, df = 3)
boxplot(mydata)
summary(mydata)
Then we can calculate the interquartile range and the lower and upper bounds for outliers according to the rule in the text above
t <- as.vector(summary(mydata))
iqr.range <- t[5]-t[2]
upper_outliers <- t[5]+iqr.range*1.5
lower_outliers <- t[2]-iqr.range*1.5
Let's check the data which are defined as outliers, while the boxplot whiskers are the data points immediately before/after the lower/upper boundaries.
mydata[mydata<lower_outliers]
[1] -3.527006 -2.959327 -2.754192
mydata[mydata>upper_outliers]
[1] 3.080302 3.527205

Making a line graph in R with multiple treatments?

sorry for the multiple questions about R. I'm new and still learning! So I am currently trying to make a multiple-line line graph with my data. I have 3 treatment groups with 4 individuals each. I am planning on factoring those into 3 groups for R. First, I want to make sure my data is set up in such a way in excel that i could make this graph. Second, how would I go about doing this? Is ggplot the best tool or is there another package that could be utilized?
I would like to have my X-axis as the dates (these are 10.15.2015for eg.), my Y-axis as the weights, and my 3 treatment groups, Lean, AdLib, and HF, as the data lines. As I said above, I used datum$Group= factor(Datum$Group) to group the Pig individuals into their 3 treatment groups.
I have looked at other questions on here but it did not seem like they were what I wanted.
Here is my data:
dput(datum)
structure(list(X10.5.15 = c(56L, 54L, 61L, 39L, 52L, 66L, 48L,
49L, 59L, 55L, 37L, 59L), X10.26.15 = c(76L, 70L, 72L, 61L, 79L,
93L, 72L, 72L, 84L, 71L, 50L, 85L), X11.3.15 = c(82L, 76L, 88L,
67L, 90L, 102L, 83L, 83L, 100L, 96L, 56L, 100L), X11.10.15 = c(87L,
84L, 93L, 71L, 99L, 110L, 93L, 93L, 109L, 107L, 65L, 112L), X11.17.15 = c(93L,
90L, 100L, 77L, 106L, 116L, 101L, 100L, 121L, 122L, 71L, 119L
), X11.24.15 = c(102L, 99L, 109L, 86L, 113L, 124L, 107L, 108L,
128L, 128L, 80L, 122L), X12.3.15 = c(114L, 113L, 123L, 100L,
118L, 132L, 122L, 118L, 143L, 142L, 91L, 137L), X12.10.15 = c(117L,
117L, 125L, 106L, 134L, 141L, 130L, 126L, 152L, 151L, 98L, 148L
), X12.17.15 = c(125L, 122L, 134L, 112L, 150L, 154L, 135L, 134L,
162L, 162L, 106L, 160L), X12.22.15 = c(128L, 127L, 135L, 114L,
156L, 161L, 141L, 140L, 166L, 176L, 109L, 166L), X12.29.15 = c(135L,
130L, 142L, 119L, 155L, 164L, 149L, 149L, 174L, 195L, 121L, 176L
), X1.5.16 = c(138L, 135L, 150L, 129L, 167L, 172L, 163L, 154L,
185L, 205L, 128L, 182L), X1.12.16 = c(154L, 157L, 166L, 146L,
180L, 188L, 173L, 163L, 200L, 208L, 140L, 188L), X1.19.16 = c(148L,
151L, 165L, 141L, 180L, 182L, 171L, 176L, 211L, 219L, 149L, 197L
), X1.26.16 = c(154L, 151L, 171L, 148L, 192L, 196L, 181L, 179L,
212L, 230L, 156L, 205L), X2.2.16 = c(162L, 162L, 179L, 154L,
200L, 200L, 191L, 184L, 228L, 228L, 162L, 225L), X2.9.16 = c(172L,
169L, 187L, 164L, 203L, 202L, 188L, 194L, 237L, 253L, 168L, 234L
), X2.16.16 = c(173L, 167L, 192L, 162L, 211L, 215L, 199L, 202L,
233L, 258L, 173L, 238L), X2.23.16 = c(185L, 174L, 202L, 172L,
220L, 218L, 208L, 204L, 253L, 254L, 185L, 239L), X2.29.16 = c(183L,
169L, 202L, 166L, 216L, 220L, 204L, 206L, 256L, 269L, 187L, 252L
), Pig = c(102L, 105L, 108L, 204L, 101L, 104L, 106L, 602L, 103L,
107L, 205L, 603L), Group = structure(c(3L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L), .Label = c("AdLib", "HF", "Lean"), class = "factor")), .Names = c("X10.5.15",
"X10.26.15", "X11.3.15", "X11.10.15", "X11.17.15", "X11.24.15",
"X12.3.15", "X12.10.15", "X12.17.15", "X12.22.15", "X12.29.15",
"X1.5.16", "X1.12.16", "X1.19.16", "X1.26.16", "X2.2.16", "X2.9.16",
"X2.16.16", "X2.23.16", "X2.29.16", "Pig", "Group"), row.names = c(NA,
-12L), class = "data.frame")
Thanks for your help in advance!

library(ggplot2)
library(reshape2)
#Remove the 'X' from the dates
names(datum) <- sub("^X", "", names(datum))
We should reshape the data to long format. The idea is to have one column for each type of data.
datum_mlt <- melt(datum, id=c("Group", "Pig"), variable.name="dates")
head(datum_mlt)
# Group Pig dates value
# 1 Lean 102 10.5.15 56
# 2 Lean 105 10.5.15 54
# 3 Lean 108 10.5.15 61
# 4 Lean 204 10.5.15 39
# 5 AdLib 101 10.5.15 52
# 6 AdLib 104 10.5.15 66
As you can see there is a column for values, dates, ids, and treatment groups. This makes it easier to organize the information for plotting.
There are ten thousand ways to do this depending on how you want the data to look. You did not specify, so here is one example. We can clean up the axes and make everything look better if the format is correct:
p <- ggplot(datum_mlt, aes(x=dates, y=value, colour=Group, group=Pig))
p + geom_line()
Edit
Before grouping individuals, I would first remove the 'Pig' column, it looks like it helps, but it doesn't.
datum2 <- datum[names(datum) != "Pig"]
library(dplyr)
datum2 %<>% group_by(Group) %>% summarise_all(mean)
d_melt <- melt(datum2, id="Group")
We plot the data. And try to make it look a little nicer.
p <- ggplot(d_melt, aes(x=variable, y=value, colour=Group, group=Group))
p <- p + geom_line()
p <- p + scale_x_discrete(name="Date", breaks=unique(d_melt$variable)[c(T,F,F)])
p + ggtitle("Grouped Weights Over Time") + theme_minimal()

Calculate overlap ellipses regions in R

I have two datasets with ellipses centroids coordinates (X & Y), Areas, Major and Minor axes (and other variables). My aim is to calculate the overlap between the groups of ellipses and plot them.
The main question is regarding calculations of overlapping regions.
For plotting I would use draw.ellipse function but I'm open to other alternatives.
The two datasets are very similar, here's a subset of one of the two datasets.
dput(slide0[,2:5])
structure(list(Area = c(15453600L, 89L, 151L, 80L, 72L, 126L,
228L, 140L, 192L, 180L, 226L, 96L, 128L, 214L, 176L, 102L, 180L,
229L, 326L, 148L, 86L, 151L, 148L, 255L, 102L, 226L, 224L, 207L,
136L, 115L, 133L, 199L, 124L, 268L, 172L, 207L, 128L, 216L, 136L,
292L, 232L, 79L, 107L, 190L, 104L, 190L, 226L, 136L, 102L, 148L,
130L, 138L, 180L, 200L, 130L, 177L, 183L, 164L, 140L, 292L, 180L,
161L, 79L, 161L, 80L, 207L, 140L, 151L, 190L, 112L, 208L, 207L,
293L, 146L, 116L, 364L, 238L, 154L, 400L, 190L, 208L, 240L, 158L,
98L, 107L, 133L, 217L, 86L, 112L, 86L, 96L, 138L, 224L, 220L,
102L, 154L, 200L, 158L, 190L, 375L, 281L, 604L, 304L, 346L, 186L,
320L, 454L, 200L, 194L, 281L, 247L, 176L, 148L, 252L, 88L, 208L,
236L, 268L, 238L, 262L, 130L, 130L, 160L, 164L, 186L, 186L, 424L,
364L, 192L, 136L, 247L, 148L, 214L, 236L, 560L, 376L, 104L, 180L,
300L, 70L, 343L, 420L, 389L, 84L, 248L, 314L, 302L, 144L, 60L,
88L, 107L, 95L, 116L, 89L, 62L, 124L, 115L, 72L, 140L, 330L,
762L, 931L, 390L, 298L, 256L, 288L, 52L, 48L, 122L, 247L, 88L,
299L, 324L, 96L, 121L, 52L, 70L, 97L, 758L, 112L, 234L, 147L,
75L, 80L, 96L, 84L, 97L, 36L, 44L, 79L, 40L, 121L, 48L, 79L,
54L, 52L, 61L, 52L, 89L, 92L, 200L, 60L, 156L, 64L, 72L, 200L,
48L, 44L, 136L, 136L, 112L, 84L, 98L, 84L, 58L, 42L, 48L, 70L,
54L, 104L, 75L, 80L, 88L, 37L, 95L, 160L, 79L, 137L, 138L, 172L,
80L, 32L, 148L, 314L, 180L, 236L, 234L, 72L, 124L, 121L, 164L,
255L, 207L, 122L, 414L, 254L, 85L, 133L, 80L, 151L, 102L, 61L,
147L, 220L, 192L, 340L, 200L, 133L, 164L, 136L, 136L, 140L, 281L,
208L, 400L, 112L, 96L, 240L, 234L, 136L, 80L, 177L, 130L, 207L,
121L, 248L, 104L, 88L, 192L, 200L, 359L, 276L, 84L, 96L, 177L,
214L, 104L, 92L, 102L, 147L, 136L, 208L, 281L, 112L, 151L, 146L,
161L, 96L, 21L, 44L, 82L, 124L, 92L, 64L, 84L, 97L, 98L, 16L,
60L, 115L, 126L, 60L, 75L, 80L, 86L, 84L, 89L, 96L, 82L, 72L,
200L, 80L, 70L, 79L, 80L, 177L, 44L, 79L, 122L, 75L, 104L, 84L,
158L, 139L, 146L, 69L, 281L, 448L, 68L, 170L, 115L, 236L, 244L,
97L, 104L, 84L, 70L, 70L, 96L, 68L, 96L, 70L, 89L, 380L, 44L,
122L, 320L, 556L, 140L, 208L, 288L, 225L, 302L, 304L, 180L, 210L,
160L, 194L, 151L, 172L, 320L, 256L, 208L, 341L, 346L, 107L, 98L,
51L, 79L, 292L, 229L, 192L, 484L, 176L, 146L, 115L, 89L, 69L,
151L, 72L, 651L, 512L, 384L, 124L, 128L, 82L, 128L, 124L, 121L,
72L, 398L, 190L, 354L, 182L, 228L, 531L, 354L, 236L, 298L, 238L,
638L, 400L, 247L, 166L, 210L, 156L, 224L, 264L, 89L, 228L, 254L,
181L, 247L, 200L, 310L, 238L, 177L, 144L, 133L, 340L, 311L, 146L,
322L, 220L, 190L, 398L, 166L, 228L, 161L, 137L, 128L, 86L, 104L,
42L, 116L, 60L, 192L, 398L, 264L, 255L, 447L, 244L, 324L, 182L,
247L, 383L, 292L, 484L, 760L, 550L, 740L, 884L, 414L, 400L, 344L,
416L, 380L, 476L, 298L, 154L, 82L, 199L, 767L, 228L, 420L, 238L,
146L, 176L, 326L, 273L, 126L, 104L, 166L, 266L, 226L, 76L, 133L,
256L, 136L, 61L, 314L, 144L, 260L, 200L, 147L, 356L, 540L, 239L,
200L, 186L, 217L, 69L, 208L, 346L, 382L, 207L, 300L, 140L, 161L,
398L, 176L, 228L, 112L, 160L, 266L, 190L, 254L, 84L, 732L, 224L,
532L, 244L, 384L, 255L, 369L, 432L, 216L, 288L, 236L, 207L, 302L,
232L, 151L, 86L, 124L, 44L, 128L, 124L, 292L, 102L, 144L, 92L,
225L, 293L, 266L, 549L, 284L, 144L, 254L, 136L, 186L, 180L, 164L,
166L, 146L, 200L, 236L, 186L, 148L, 154L, 226L, 158L, 140L, 160L,
136L, 102L, 341L, 253L, 121L, 121L, 340L, 224L, 148L, 216L, 220L,
366L, 188L, 154L, 228L, 236L, 208L, 240L, 324L, 172L, 302L, 164L,
420L, 254L, 88L, 224L, 210L, 124L, 264L, 315L, 332L, 190L, 128L,
130L, 220L, 166L, 107L, 112L, 158L, 116L, 98L, 147L, 192L, 186L,
112L, 210L, 84L, 256L, 274L, 200L, 192L, 192L, 170L, 107L, 302L,
244L, 299L, 170L, 115L, 186L, 208L, 156L, 216L, 220L, 332L, 451L,
340L, 210L, 128L, 176L, 456L, 128L, 495L, 194L, 316L, 236L, 199L,
870L, 248L, 172L, 192L, 240L, 200L, 192L, 208L, 126L, 344L, 384L,
420L, 176L, 167L, 128L, 86L, 151L, 96L, 104L, 200L, 208L, 72L,
176L, 116L, 192L, 79L, 96L, 328L, 568L, 302L, 229L, 130L, 354L,
89L, 84L, 112L, 160L, 92L, 217L, 341L, 240L, 369L, 268L, 136L,
324L, 192L, 326L, 89L, 146L, 158L, 194L, 214L, 161L, 96L, 172L,
136L, 172L, 240L, 180L, 108L, 524L, 102L, 138L, 166L, 146L, 177L,
137L, 140L, 140L, 274L, 576L, 264L, 86L, 225L, 396L, 239L, 434L,
782L, 322L, 112L, 80L, 200L, 136L, 144L, 238L, 96L, 130L, 86L,
102L, 92L, 356L, 255L, 264L, 288L, 140L, 299L, 264L, 299L, 186L,
146L, 383L, 214L, 375L, 186L, 273L, 284L, 122L, 284L, 72L, 79L,
292L, 192L, 396L, 276L, 228L, 260L, 172L, 172L, 107L, 80L, 104L,
98L, 186L, 226L, 104L, 180L, 85L, 144L, 120L, 248L, 315L, 151L,
166L, 236L, 136L, 540L, 341L, 200L, 226L, 208L, 112L, 160L, 96L,
260L, 262L, 130L, 177L, 116L, 98L, 112L, 208L, 239L, 176L, 151L,
186L, 130L, 177L, 172L, 122L, 183L, 133L, 248L, 146L, 217L, 746L,
199L, 224L, 225L, 140L, 172L, 244L, 274L, 158L, 136L, 344L, 172L,
82L, 200L, 70L, 95L, 239L, 176L, 180L, 147L, 116L, 89L, 79L,
89L, 112L, 240L, 200L, 326L, 84L, 264L, 266L, 266L, 262L, 247L,
164L, 217L, 210L, 122L, 214L, 316L, 217L, 364L, 192L, 660L, 764L,
584L, 228L, 498L, 314L, 188L, 130L, 166L, 255L, 477L, 200L, 816L,
176L, 247L, 252L, 126L, 100L), Mean = c(175.038, 100.18, 99.781,
116.9, 108.375, 94.373, 105.987, 102.993, 90.74, 114.856, 99.412,
123.5, 99.008, 146.168, 88.818, 99.814, 87.244, 68.223, 86.868,
96.189, 133.523, 119.159, 137.311, 109.8, 102.98, 95.407, 112.612,
106.957, 147.382, 76.904, 101.774, 67.02, 118.984, 85.851, 77.355,
91.894, 91.773, 67.833, 103.397, 110.363, 124.513, 112.595, 123.794,
87.847, 88.058, 102.268, 105.354, 89.184, 141.618, 151.291, 135.746,
86.283, 87.389, 102.32, 106.592, 90.153, 100.055, 84.701, 93.264,
97.586, 99.317, 88.05, 94.038, 99.665, 89.763, 100.072, 82.714,
116.325, 102.879, 102.911, 90.317, 92.217, 99.594, 72.425, 91.336,
90.514, 96.197, 103.89, 106.513, 103.647, 98.216, 98.338, 122.513,
104.49, 114.467, 121.278, 109.327, 97.616, 93.223, 98.767, 132.302,
91.964, 86.58, 85.559, 98.5, 90.643, 87.21, 79.715, 90.995, 73.584,
96.153, 118.086, 112.441, 118.208, 115.199, 143.731, 132.555,
128.76, 79.83, 106.466, 83.142, 88.091, 94.243, 110.254, 118.489,
97.24, 74.771, 117, 89.706, 96.08, 93.846, 105.515, 102.225,
120.75, 97.828, 109.059, 102.245, 106.357, 97.891, 114.566, 92.344,
100.858, 130.467, 114.619, 133.125, 147.027, 95.673, 108.178,
104.57, 108.214, 123.924, 110.233, 115.861, 124.905, 106.996,
81.987, 93.914, 111.611, 118.7, 138.432, 150.972, 156.842, 163.371,
102.764, 141.629, 136.694, 163.287, 133.194, 104.5, 113.645,
149.705, 127.354, 91.405, 152.614, 148.102, 148.295, 151.462,
175.646, 148.139, 153.636, 159.909, 173.027, 131.5, 167.656,
176.066, 147.385, 108.643, 139.732, 105.799, 130.384, 131.346,
115.776, 110.573, 124.5, 126.167, 157.393, 107.66, 104.5, 113.75,
114.354, 97.825, 115.091, 104.521, 91.316, 103.056, 97.788, 118.361,
126.5, 114.629, 130.413, 120.07, 119.083, 139.359, 117.75, 99.583,
92.68, 128.667, 112.727, 105.206, 115.838, 112.946, 81.69, 139.439,
120.131, 152.517, 140.024, 165.854, 147.943, 137.944, 149.625,
154.4, 139.625, 135.58, 169.405, 97.253, 104.8, 109.215, 97.299,
117.406, 110.953, 107.138, 125.938, 72.791, 142.236, 121.117,
127.542, 118.175, 128.278, 132.556, 127.116, 132.098, 120.69,
140.57, 114.943, 142.087, 136.551, 153.835, 157.098, 159.688,
91.841, 119, 122.82, 135.707, 125.764, 115.781, 112.762, 139.22,
126.759, 125.378, 116.353, 122.706, 123.086, 90.249, 101.139,
128.018, 71.812, 83.365, 101.421, 108.568, 113.36, 120.787, 136.712,
136.908, 118.826, 96.091, 65.258, 80.337, 81.307, 99.708, 79.43,
110.613, 133.293, 119.548, 120.531, 145.672, 152.551, 112.212,
99.511, 174.431, 124.639, 96.213, 96.519, 73.313, 95.089, 107.768,
83.267, 61.609, 75.427, 71.286, 71.432, 46.537, 45.476, 43.022,
62.453, 57.119, 70.371, 51.357, 61.375, 83.333, 82.426, 79.96,
116.067, 105.707, 87.325, 112.895, 109.095, 117.281, 111.854,
148.683, 133.125, 104.845, 127.963, 137.671, 138.911, 140.825,
81.808, 96.727, 105.228, 97.221, 111.427, 132.558, 128.643, 81.949,
80.36, 92.705, 118.899, 144.306, 74.203, 143.779, 101.706, 97.2,
112.246, 128.369, 138.031, 120.625, 89.94, 106.357, 128.086,
123.375, 131.397, 150.083, 132.829, 145.618, 91.547, 116.659,
106.016, 112.1, 130.752, 116.714, 109.788, 123.153, 88.089, 87.811,
76.714, 105.028, 116.762, 110.763, 86.912, 124.126, 113.262,
100.559, 82.238, 100.933, 84.739, 88.28, 103.925, 123.255, 131.902,
101.19, 91.973, 133.035, 145.464, 138.7, 114.074, 141.062, 149.383,
153.36, 154.899, 157.742, 150.806, 136.886, 148.42, 161.966,
128.024, 154.977, 156.037, 183.664, 134.484, 143.471, 174.431,
112.766, 84.021, 109.633, 102.055, 122.228, 119.169, 126.669,
92.72, 84.557, 109.622, 115.52, 98.305, 100.016, 86.705, 88.524,
93.91, 121.433, 115.186, 138.775, 112.171, 139.945, 111.459,
137.648, 148.56, 151.7, 147.475, 130.412, 150.611, 144.699, 124.879,
127.666, 133.582, 128.419, 113.841, 143.732, 129.874, 147.084,
125.908, 98.857, 128.482, 135.695, 98.767, 94.26, 96.024, 68.207,
96.417, 122.438, 122.296, 105.178, 113.808, 123.345, 120.631,
77.151, 102.319, 118.283, 111.146, 103.658, 117.19, 119.658,
131.902, 141.754, 136.67, 130.594, 123.112, 99.02, 128.873, 143.095,
92.819, 113.151, 127.487, 113.512, 121.085, 147.4, 142.526, 151.355,
134.403, 146.5, 138.858, 155.043, 132.473, 126.667, 68.433, 70.066,
147.763, 146.615, 128.263, 130.767, 121.531, 125.728, 159.721,
132.185, 106.326, 162.808, 150.12, 133, 153.806, 138.598, 138.126,
120.59, 121.269, 139.493, 152.304, 71.971, 82.636, 98.034, 82.251,
102.697, 122.014, 97.708, 75.286, 52.676, 98.066, 94.866, 95.919,
82.12, 109.942, 91.606, 86.238, 125.225, 105.902, 119.013, 126.057,
124.857, 109.714, 120.282, 149.981, 81.801, 70.83, 142.661, 149.28,
139.454, 137.897, 119.04, 140.558, 148.685, 140.614, 164.547,
149.968, 136.695, 153.167, 171.521, 148.609, 151.662, 140.758,
139.857, 138.801, 116.912, 97.34, 61.571, 80.522, 88.731, 98.472,
74.622, 69.777, 82.164, 103.745, 99.347, 93.855, 102.203, 94.188,
98.527, 68.627, 63.579, 70.838, 87.632, 102.745, 92.405, 88.791,
70.331, 92.744, 106.656, 79.491, 61.176, 78.644, 98.818, 87.208,
98.686, 91.403, 75.044, 94.814, 87.394, 102.771, 90.769, 80.698,
58.295, 94.543, 81.545, 82.067, 95.034, 85.513, 89.924, 98.911,
68.598, 95.787, 71.672, 76.026, 79.773, 87.8, 71.2, 95.482, 87.57,
119.214, 77.196, 75.767, 62.02, 76.571, 89.089, 79.957, 75.821,
80.648, 85.476, 93.469, 78.106, 82.495, 83.729, 95.271, 85.641,
89.673, 86.904, 74.656, 105.274, 91.341, 106.678, 118.634, 100.529,
62.436, 72.042, 110.141, 78.265, 66.938, 87.006, 73.267, 94.289,
87.381, 97.522, 83.391, 147.455, 76.407, 94.759, 112.042, 104.347,
122.056, 104.762, 129.169, 125.432, 120.088, 84.685, 94.495,
92.923, 128.421, 120.352, 118.516, 106.805, 92.767, 98.958, 109.758,
121.721, 103.106, 123.208, 134.923, 69.045, 78.149, 60.458, 161.341,
174.319, 140.344, 157.43, 145.615, 66.009, 105.366, 112.884,
81.873, 109.462, 124.463, 106.618, 92.798, 111.902, 109.031,
110.728, 122.747, 146.079, 95.329, 71.645, 71.396, 105.551, 125.577,
108.667, 96.525, 129.236, 112.384, 108.867, 124.959, 115.832,
84.398, 109.375, 86.628, 106.676, 118.657, 113.338, 120.794,
127.88, 133.464, 98.284, 114.703, 92.94, 102.952, 107.424, 99.102,
81.107, 110.479, 137.737, 163.38, 151.75, 152.721, 133.849, 139.288,
117.071, 150.302, 142.243, 131.826, 127.75, 114.162, 118.68,
118.971, 102.493, 130.689, 159.573, 169.638, 178.302, 166.951,
156.924, 101.371, 108.549, 91.515, 93.934, 110.864, 111.93, 131.61,
109.916, 105.618, 116.5, 101.277, 114.187, 119.904, 105.36, 127.161,
154.102, 60.025, 49.68, 136.403, 138.291, 61.87, 94.677, 95.404,
98.725, 98.675, 93.288, 88.75, 78.517, 67.692, 90.513, 97.99,
88.561, 90.403, 80.934, 81.885, 97.8, 86.376, 85.306, 75.233,
111.649, 118.692, 103.755, 91.964, 98.381, 117.338, 164.294,
86.323, 58.865, 66.819, 116.067, 121.83, 127.481, 134.677, 112.538,
96.477, 98.662, 96.006, 81.853, 97.816, 106.143, 89.558, 114.297,
139.307, 134.272, 130.72, 130.2, 88.085, 108.413, 142.852, 133.355,
121.06, 89.391, 95.952, 96.659, 133.213, 125, 100.683, 92.884,
106.593, 107.779, 99.766, 103.653, 62.968, 116.287, 126.256,
135.006, 128.476, 132.255, 124.1, 149.011, 127.481, 136.932,
144.322, 113.102, 129.422, 122.393, 115.367, 111.584, 144.991,
116.725, 147.935, 135.571, 137.036, 171.311, 129.237, 128.368,
144.16, 115.514, 123.665, 129.138, 145.752, 118.607, 129.668,
113.674, 117.143, 108.503, 96.714, 137.52, 107.203, 111.435,
90.057, 119.367, 110.799, 101.931, 113.069, 120.825, 119.478,
106.346, 118.9, 161.491, 102.136, 93.389, 102.111, 96.429, 120.19
), Min = c(25L, 85L, 88L, 103L, 99L, 77L, 70L, 70L, 68L, 95L,
75L, 109L, 77L, 117L, 69L, 87L, 65L, 47L, 47L, 72L, 112L, 95L,
117L, 77L, 85L, 68L, 94L, 83L, 130L, 58L, 83L, 49L, 92L, 53L,
57L, 72L, 72L, 49L, 83L, 86L, 102L, 93L, 112L, 76L, 69L, 89L,
77L, 69L, 128L, 131L, 117L, 65L, 67L, 72L, 86L, 67L, 78L, 64L,
73L, 78L, 82L, 56L, 77L, 79L, 71L, 81L, 57L, 92L, 71L, 87L, 70L,
73L, 72L, 58L, 71L, 57L, 68L, 90L, 72L, 79L, 77L, 64L, 96L, 90L,
100L, 101L, 88L, 77L, 70L, 85L, 106L, 72L, 63L, 66L, 76L, 72L,
71L, 57L, 67L, 46L, 56L, 78L, 82L, 80L, 87L, 111L, 100L, 107L,
54L, 84L, 51L, 62L, 74L, 85L, 88L, 68L, 50L, 80L, 58L, 66L, 74L,
84L, 83L, 105L, 80L, 86L, 62L, 57L, 68L, 89L, 56L, 78L, 90L,
85L, 103L, 116L, 80L, 85L, 85L, 94L, 84L, 88L, 78L, 102L, 85L,
64L, 58L, 90L, 100L, 112L, 126L, 127L, 134L, 83L, 120L, 117L,
131L, 119L, 80L, 78L, 109L, 84L, 73L, 116L, 116L, 122L, 130L,
149L, 110L, 136L, 130L, 126L, 104L, 134L, 151L, 128L, 95L, 114L,
84L, 108L, 92L, 90L, 90L, 107L, 98L, 124L, 84L, 88L, 103L, 89L,
87L, 87L, 90L, 74L, 85L, 85L, 99L, 109L, 99L, 107L, 95L, 102L,
108L, 98L, 83L, 61L, 110L, 98L, 79L, 93L, 94L, 62L, 111L, 96L,
126L, 119L, 154L, 123L, 123L, 122L, 129L, 107L, 113L, 138L, 76L,
84L, 90L, 80L, 93L, 93L, 90L, 109L, 55L, 105L, 94L, 99L, 95L,
107L, 103L, 107L, 107L, 75L, 99L, 95L, 113L, 112L, 130L, 122L,
135L, 65L, 79L, 104L, 87L, 94L, 71L, 85L, 113L, 106L, 103L, 80L,
85L, 97L, 58L, 76L, 98L, 50L, 65L, 72L, 81L, 88L, 106L, 107L,
99L, 85L, 77L, 46L, 60L, 61L, 80L, 59L, 78L, 83L, 96L, 100L,
109L, 119L, 85L, 77L, 131L, 77L, 67L, 65L, 50L, 72L, 74L, 56L,
45L, 62L, 64L, 65L, 39L, 39L, 38L, 53L, 50L, 57L, 43L, 56L, 66L,
62L, 64L, 91L, 90L, 73L, 93L, 82L, 89L, 81L, 121L, 104L, 69L,
107L, 118L, 111L, 120L, 65L, 78L, 87L, 77L, 96L, 98L, 108L, 64L,
69L, 75L, 107L, 98L, 42L, 132L, 75L, 62L, 62L, 101L, 110L, 106L,
76L, 96L, 97L, 92L, 94L, 123L, 105L, 122L, 62L, 87L, 87L, 82L,
94L, 101L, 90L, 97L, 68L, 44L, 56L, 82L, 74L, 86L, 66L, 105L,
87L, 67L, 59L, 70L, 58L, 54L, 77L, 104L, 98L, 85L, 65L, 96L,
89L, 87L, 95L, 101L, 123L, 117L, 136L, 106L, 128L, 90L, 117L,
104L, 91L, 132L, 132L, 140L, 100L, 108L, 129L, 61L, 64L, 67L,
80L, 91L, 80L, 75L, 63L, 61L, 85L, 67L, 67L, 73L, 69L, 59L, 70L,
105L, 88L, 114L, 72L, 112L, 79L, 104L, 129L, 112L, 120L, 95L,
119L, 122L, 92L, 104L, 113L, 103L, 82L, 106L, 98L, 125L, 83L,
63L, 100L, 107L, 83L, 63L, 73L, 53L, 73L, 98L, 91L, 84L, 88L,
81L, 86L, 54L, 80L, 84L, 82L, 53L, 81L, 68L, 85L, 88L, 84L, 87L,
80L, 80L, 92L, 99L, 58L, 80L, 89L, 88L, 89L, 94L, 100L, 113L,
110L, 103L, 113L, 122L, 114L, 95L, 59L, 56L, 114L, 117L, 110L,
112L, 92L, 103L, 131L, 100L, 85L, 118L, 116L, 109L, 129L, 102L,
121L, 98L, 92L, 108L, 132L, 50L, 48L, 64L, 52L, 58L, 86L, 71L,
49L, 42L, 67L, 80L, 68L, 55L, 69L, 70L, 62L, 96L, 81L, 75L, 79L,
86L, 77L, 77L, 90L, 59L, 47L, 90L, 116L, 102L, 97L, 101L, 112L,
114L, 131L, 132L, 115L, 99L, 102L, 136L, 114L, 126L, 106L, 108L,
93L, 88L, 72L, 46L, 66L, 56L, 75L, 54L, 49L, 63L, 73L, 77L, 79L,
84L, 74L, 68L, 51L, 51L, 52L, 57L, 88L, 71L, 61L, 55L, 75L, 75L,
57L, 47L, 59L, 74L, 70L, 68L, 69L, 52L, 69L, 65L, 77L, 52L, 65L,
37L, 66L, 60L, 57L, 79L, 66L, 62L, 72L, 42L, 66L, 44L, 52L, 63L,
72L, 44L, 78L, 72L, 91L, 59L, 59L, 53L, 51L, 58L, 59L, 59L, 59L,
70L, 64L, 54L, 71L, 58L, 71L, 67L, 76L, 54L, 53L, 81L, 61L, 90L,
85L, 63L, 40L, 50L, 78L, 48L, 43L, 51L, 50L, 67L, 68L, 57L, 57L,
57L, 50L, 71L, 81L, 82L, 88L, 81L, 96L, 100L, 78L, 57L, 71L,
72L, 104L, 89L, 88L, 52L, 59L, 76L, 86L, 111L, 78L, 102L, 105L,
51L, 59L, 53L, 125L, 140L, 106L, 129L, 116L, 40L, 54L, 73L, 52L,
82L, 84L, 75L, 73L, 95L, 89L, 100L, 104L, 117L, 79L, 48L, 43L,
89L, 78L, 69L, 62L, 108L, 83L, 85L, 97L, 88L, 59L, 88L, 66L,
88L, 97L, 95L, 103L, 110L, 101L, 85L, 94L, 73L, 71L, 79L, 76L,
52L, 88L, 90L, 101L, 117L, 122L, 117L, 104L, 89L, 108L, 94L,
102L, 113L, 78L, 88L, 94L, 85L, 105L, 136L, 131L, 143L, 131L,
127L, 52L, 76L, 58L, 68L, 88L, 79L, 89L, 82L, 76L, 99L, 58L,
89L, 89L, 68L, 84L, 95L, 45L, 43L, 107L, 122L, 40L, 75L, 74L,
73L, 82L, 69L, 67L, 65L, 51L, 75L, 73L, 73L, 76L, 56L, 68L, 81L,
65L, 70L, 55L, 84L, 90L, 89L, 65L, 76L, 82L, 96L, 58L, 38L, 47L,
94L, 100L, 106L, 114L, 87L, 70L, 70L, 83L, 61L, 84L, 89L, 72L,
87L, 111L, 113L, 98L, 102L, 67L, 93L, 128L, 102L, 89L, 57L, 75L,
67L, 89L, 93L, 77L, 59L, 91L, 89L, 76L, 70L, 50L, 85L, 100L,
102L, 100L, 95L, 99L, 114L, 114L, 116L, 109L, 95L, 105L, 104L,
100L, 91L, 116L, 92L, 114L, 114L, 114L, 134L, 100L, 106L, 112L,
87L, 99L, 104L, 112L, 97L, 105L, 85L, 92L, 85L, 75L, 98L, 59L,
80L, 66L, 83L, 79L, 80L, 86L, 97L, 91L, 75L, 95L, 109L, 72L,
71L, 78L, 79L, 105L), Max = c(255L, 124L, 117L, 140L, 123L, 124L,
167L, 157L, 149L, 152L, 145L, 137L, 129L, 180L, 157L, 117L, 129L,
110L, 248L, 157L, 162L, 161L, 173L, 158L, 129L, 140L, 157L, 144L,
189L, 122L, 133L, 98L, 178L, 159L, 131L, 127L, 140L, 110L, 139L,
159L, 174L, 153L, 148L, 110L, 114L, 141L, 157L, 142L, 162L, 187L,
179L, 122L, 133L, 161L, 160L, 136L, 133L, 114L, 120L, 137L, 126L,
142L, 114L, 128L, 116L, 137L, 123L, 147L, 150L, 131L, 142L, 126L,
144L, 103L, 125L, 185L, 173L, 133L, 157L, 152L, 141L, 178L, 153L,
130L, 142L, 154L, 146L, 143L, 154L, 118L, 202L, 131L, 131L, 125L,
134L, 127L, 115L, 119L, 120L, 193L, 203L, 209L, 173L, 250L, 184L,
183L, 204L, 174L, 217L, 180L, 187L, 149L, 144L, 216L, 192L, 161L,
132L, 229L, 152L, 163L, 125L, 155L, 127L, 153L, 129L, 152L, 172L,
223L, 163L, 166L, 202L, 148L, 212L, 162L, 175L, 197L, 124L, 156L,
148L, 126L, 230L, 146L, 192L, 167L, 152L, 129L, 163L, 189L, 148L,
194L, 201L, 195L, 195L, 152L, 188L, 186L, 242L, 162L, 224L, 186L,
255L, 195L, 161L, 227L, 210L, 210L, 198L, 214L, 248L, 180L, 238L,
245L, 200L, 205L, 252L, 179L, 139L, 189L, 197L, 177L, 252L, 202L,
150L, 163L, 201L, 204L, 151L, 133L, 133L, 154L, 125L, 178L, 129L,
124L, 152L, 117L, 144L, 166L, 149L, 154L, 173L, 152L, 201L, 161L,
134L, 235L, 175L, 138L, 183L, 152L, 141L, 111L, 172L, 171L, 190L,
173L, 182L, 202L, 159L, 199L, 234L, 169L, 166L, 218L, 154L, 146L,
142L, 153L, 180L, 156L, 137L, 149L, 108L, 217L, 159L, 171L, 169L,
181L, 186L, 154L, 193L, 222L, 209L, 144L, 198L, 185L, 226L, 243L,
197L, 138L, 189L, 153L, 255L, 162L, 221L, 170L, 184L, 178L, 180L,
197L, 185L, 194L, 182L, 151L, 197L, 158L, 115L, 143L, 167L, 176L,
164L, 193L, 196L, 198L, 141L, 117L, 140L, 124L, 138L, 144L, 217L,
225L, 159L, 153L, 200L, 241L, 166L, 136L, 254L, 186L, 148L, 190L,
173L, 145L, 183L, 155L, 119L, 112L, 81L, 86L, 59L, 68L, 50L,
80L, 74L, 95L, 71L, 67L, 108L, 126L, 130L, 141L, 145L, 123L,
177L, 168L, 195L, 193L, 204L, 179L, 209L, 190L, 169L, 201L, 171L,
143L, 130L, 133L, 142L, 139L, 210L, 161L, 118L, 104L, 137L, 134L,
253L, 185L, 165L, 161L, 180L, 212L, 194L, 186L, 156L, 122L, 126L,
218L, 204L, 255L, 204L, 169L, 207L, 161L, 157L, 143L, 170L, 196L,
154L, 140L, 174L, 158L, 192L, 138L, 142L, 210L, 171L, 133L, 166L,
148L, 158L, 127L, 196L, 160L, 178L, 140L, 153L, 186L, 122L, 150L,
188L, 242L, 252L, 156L, 183L, 190L, 222L, 194L, 248L, 183L, 237L,
193L, 255L, 171L, 234L, 205L, 255L, 192L, 196L, 245L, 217L, 131L,
157L, 192L, 181L, 201L, 228L, 146L, 155L, 217L, 217L, 156L, 169L,
151L, 138L, 155L, 158L, 174L, 199L, 193L, 185L, 181L, 195L, 179L,
218L, 208L, 177L, 240L, 210L, 210L, 167L, 171L, 192L, 196L, 205L,
203L, 183L, 191L, 224L, 177L, 209L, 128L, 195L, 130L, 87L, 142L,
175L, 202L, 144L, 221L, 243L, 190L, 144L, 134L, 167L, 173L, 254L,
189L, 225L, 232L, 245L, 234L, 209L, 187L, 139L, 214L, 237L, 162L,
163L, 218L, 171L, 184L, 241L, 220L, 242L, 175L, 231L, 190L, 194L,
169L, 172L, 93L, 135L, 225L, 203L, 167L, 169L, 167L, 163L, 219L,
184L, 183L, 242L, 193L, 160L, 201L, 245L, 157L, 179L, 186L, 203L,
193L, 135L, 178L, 181L, 142L, 255L, 168L, 137L, 128L, 94L, 143L,
117L, 184L, 138L, 175L, 150L, 128L, 186L, 177L, 226L, 229L, 206L,
171L, 225L, 240L, 157L, 130L, 224L, 203L, 209L, 240L, 152L, 247L,
205L, 161L, 235L, 188L, 183L, 218L, 249L, 206L, 185L, 235L, 204L,
227L, 203L, 200L, 90L, 115L, 174L, 159L, 159L, 123L, 141L, 154L,
137L, 129L, 135L, 122L, 207L, 121L, 112L, 152L, 159L, 135L, 134L,
181L, 117L, 149L, 166L, 172L, 87L, 111L, 128L, 137L, 152L, 117L,
125L, 145L, 145L, 152L, 190L, 106L, 97L, 154L, 150L, 123L, 133L,
121L, 178L, 156L, 127L, 159L, 125L, 112L, 118L, 129L, 146L, 121L,
109L, 168L, 108L, 110L, 82L, 147L, 158L, 111L, 111L, 124L, 124L,
155L, 154L, 113L, 143L, 153L, 124L, 115L, 135L, 124L, 134L, 139L,
141L, 169L, 193L, 115L, 127L, 167L, 173L, 134L, 176L, 136L, 141L,
145L, 193L, 140L, 255L, 141L, 132L, 155L, 150L, 195L, 145L, 175L,
174L, 244L, 156L, 136L, 140L, 179L, 193L, 176L, 255L, 171L, 141L,
160L, 142L, 154L, 166L, 174L, 110L, 123L, 78L, 223L, 233L, 223L,
216L, 189L, 148L, 198L, 185L, 148L, 186L, 174L, 205L, 137L, 141L,
186L, 129L, 156L, 186L, 133L, 137L, 146L, 167L, 231L, 197L, 191L,
162L, 184L, 143L, 190L, 153L, 161L, 158L, 134L, 148L, 151L, 145L,
166L, 170L, 164L, 123L, 154L, 133L, 157L, 138L, 159L, 172L, 166L,
236L, 255L, 193L, 215L, 180L, 191L, 165L, 209L, 241L, 188L, 151L,
167L, 174L, 162L, 116L, 185L, 213L, 228L, 250L, 236L, 184L, 222L,
164L, 177L, 127L, 190L, 209L, 255L, 163L, 173L, 151L, 221L, 167L,
190L, 190L, 207L, 214L, 114L, 74L, 199L, 185L, 162L, 125L, 135L,
161L, 132L, 122L, 140L, 123L, 125L, 133L, 127L, 116L, 119L, 164L,
119L, 123L, 123L, 110L, 119L, 188L, 173L, 132L, 136L, 142L, 202L,
250L, 133L, 117L, 97L, 146L, 159L, 172L, 184L, 153L, 134L, 255L,
127L, 130L, 134L, 133L, 143L, 189L, 171L, 181L, 184L, 183L, 132L,
147L, 163L, 199L, 188L, 164L, 130L, 155L, 192L, 182L, 131L, 168L,
133L, 146L, 147L, 192L, 97L, 159L, 160L, 189L, 148L, 167L, 140L,
204L, 144L, 150L, 194L, 142L, 171L, 148L, 151L, 167L, 207L, 176L,
203L, 167L, 193L, 254L, 181L, 188L, 213L, 178L, 194L, 198L, 173L,
145L, 162L, 151L, 153L, 179L, 140L, 220L, 176L, 230L, 124L, 211L,
189L, 136L, 166L, 182L, 165L, 151L, 176L, 223L, 150L, 154L, 129L,
145L, 164L)), .Names = c("Area", "Mean", "Min", "Max"), class = "data.frame", row.names = c(NA,
-866L))

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