Why is auto.arima() giving me the Error: "'by' argument is much too small" - r

I am working on predicting intra-day sales for a retailer. We want to know if we can predict sales through the rest of the day, based off sales within that day. I'm working with roughly 3 years of data in a time series, which has given me roughly 26,000 rows of data.
I've never worked with a time series this large so my approach might be off. Or auto.arima() may not have been made to handle data this large.
I've tried limiting my data down to even 300 rows and had marginal success, but have not found anything that works with my larger data set. auto.arima() doesn't even have a by = argument from what I can find.
my_ts <- structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 3,
3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 7, 4, 1,
1, 0, 3, 1, 0, 8, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4,
1, 9, 1, 6, 5, 1, 0, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 3, 1, 0, 3, 5, 2, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 3, 1, 0, 0, 6, 0, 6, 0, 1, 2, 3, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 2, 4, 6, 5, 0, 1, 0, 2, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 2, 0, 2, 0, 0, 0, 1, 1,
3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 3, 0, 1, 0,
3, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 0,
8, 2, 7, 4, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2,
0, 0, 2, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 6, 2, 0, 1, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, -1, 2, 3, 1, 0, 0, 2, 5, 7, 0, -1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, -1, 6, 1, 2, 2, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 3, 0, 2, 0,
4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 4, 0, 0,
2, 2, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2,
2, 0, 2, 3, 6, 5, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 2, 2, 2, 1, 4, 3, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 3, 4, 0, 4, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 3, 2, 1, 4, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 3, 0, 3, 4, 3, 0, 0, 2, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 1, 1, 1, 0, 3,
0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 2,
0, 1, 1, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, -1,
1, 4, 1, 2, 9, 1, 4, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 4, 1, 0, 1, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 3, 1, 2, 1, 1, 4, 0, 3, 3, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 2, 2, 1, 6, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 2, 0, 1, 2, 1, 4, 0, 0, 5,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 5, 1, 1, 3, 3,
4, 4, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
1, 3, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2,
3, 0, 2, 8, 0, 2, 3, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 1, 2, 0, 1, 0, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 3, 1, 2, 2, 3, 1, 4, 4, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 4, 1, 2, 0, 0, 5, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 8, 0, 1, 1, 4, 0, 4, 3,
2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 6, 1, 0, 0, 0,
3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 8,
1, 2, 0, 4, 2, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3,
1, 6, 7, 0, 1, 0, 1, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 5, 0, 2, 1, 4, 1, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 2, 1, 4, 7, 5, 0, 7, 4, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 1, 6, 0, 4, 6, 2, 1, 3, 5, 3, 3, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 4, 3, 0, 1, 1, 3,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 6, 0, 0), index = structure(c(1435190400,
1435194000, 1435197600, 1435201200, 1435204800, 1435208400, 1435212000,
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1438369200, 1438372800, 1438376400, 1438380000, 1438383600, 1438387200,
1438390800, 1438394400, 1438398000, 1438401600, 1438405200, 1438408800,
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1438477200, 1438480800, 1438484400, 1438488000, 1438491600, 1438495200,
1438498800, 1438502400, 1438506000, 1438509600, 1438513200, 1438516800,
1438520400, 1438524000, 1438527600, 1438531200, 1438534800, 1438538400,
1438542000, 1438545600, 1438549200, 1438552800, 1438556400, 1438560000,
1438563600, 1438567200, 1438570800, 1438574400, 1438578000, 1438581600,
1438585200, 1438588800, 1438592400, 1438596000, 1438599600, 1438603200,
1438606800, 1438610400, 1438614000, 1438617600, 1438621200, 1438624800,
1438628400, 1438632000, 1438635600, 1438639200, 1438642800, 1438646400,
1438650000, 1438653600, 1438657200, 1438660800, 1438664400, 1438668000,
1438671600, 1438675200, 1438678800, 1438682400, 1438686000, 1438689600,
1438693200, 1438696800, 1438700400, 1438704000, 1438707600, 1438711200,
1438714800, 1438718400, 1438722000, 1438725600, 1438729200, 1438732800,
1438736400, 1438740000, 1438743600, 1438747200, 1438750800, 1438754400,
1438758000, 1438761600, 1438765200, 1438768800, 1438772400, 1438776000,
1438779600, 1438783200, 1438786800), class = c("POSIXct", "POSIXt"
), tzone = "UTC"), class = c("zooreg", "zoo"), frequency = 24)
fit1 <-auto.arima(my_ts,seasonal = TRUE)
I was hoping to get a model through arima, but I'm only getting the error:
"Error in seq.default(head(tt, 1), tail(tt, 1), deltat) :
'by' argument is much too small"

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Representing a correlation matrix without a "classical" heatmap

I'm doing some analysis on a complex network. I have computed the degree correlation matrix, which looks like this:
data[1:5, 1:5]
1 2 3 4 5
1 6 19 11 16 5
2 19 10 16 12 6
3 11 16 7 11 10
4 16 12 11 5 9
5 5 6 10 9 8
And I'd like to plot it to obtain something akin to this:
I've tried to use ggplot but the results are not at all satisfying, this is my code:
library(reshape2)
library(ggplot2)
data = melt(data)
#The "melt" function was used to turn the matrix in a three column dataframe with columns named "Var1",
"Var2", and "value"
ggplot(data = data) +
theme_bw() +
geom_tile(aes(x = Var1, y = Var2, fill = value)) +
scale_fill_viridis(name = "") +
labs(x = "k2", y = "k1")
And this is what I get:
Is there any way to fix it?
P.S. Sorry if I couldn't post the images directly, but my reputation is not high enough
EDIT: I'm putting here the dput() output of my matrix, as asked in the comments:
structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0,
0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0,
0, 0, 2, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,
0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0,
0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 2, 1, 1, 1, 0, 0, 1,
2, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1,
0, 0, 2, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 1, 2, 0,
0, 1, 0, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0, 1, 1, 1, 0, 2, 0, 0, 0,
1, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 3, 2, 1, 0, 3, 3, 3, 1, 2, 2, 1,
1, 3, 3, 3, 2, 2, 1, 0, 1, 3, 2, 1, 1, 2, 0, 4, 1, 1, 1, 0, 0,
0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 2,
3, 0, 3, 6, 3, 2, 2, 3, 0, 2, 4, 2, 0, 6, 3, 1, 0, 3, 1, 2, 5,
6, 2, 3, 0, 2, 0, 0, 1, 0, 0, 0, 1, 3, 1, 1, 0, 2, 0, 0, 0, 0,
0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 3, 2, 7, 2, 1, 6, 3, 1,
3, 2, 2, 3, 2, 3, 9, 0, 1, 2, 4, 0, 6, 0, 3, 0, 4, 0, 2, 0, 2,
0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
0, 0, 0, 1, 6, 7, 3, 6, 0, 5, 0, 1, 0, 5, 1, 3, 4, 1, 5, 0, 0,
0, 4, 1, 5, 1, 2, 1, 1, 1, 5, 1, 4, 1, 0, 0, 1, 0, 0, 1, 1, 0,
2, 0, 0, 0, 2, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,
1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 3, 2, 6, 1, 5,
3, 0, 0, 2, 3, 0, 3, 2, 4, 5, 1, 1, 3, 2, 3, 4, 0, 2, 0, 2, 3,
0, 0, 1, 0, 0, 0, 1, 2, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1,
0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 2, 1, 1, 0, 0,
0, 1, 1, 1, 0, 0, 3, 2, 1, 0, 5, 3, 2, 6, 1, 3, 3, 1, 4, 2, 1,
6, 1, 2, 0, 4, 2, 4, 0, 1, 0, 3, 3, 4, 2, 3, 4, 1, 0, 3, 3, 0,
1, 1, 0, 4, 0, 2, 0, 1, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 1,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 2, 3, 2, 6,
5, 3, 2, 0, 2, 2, 4, 9, 3, 0, 4, 1, 5, 4, 7, 2, 1, 3, 5, 4, 1,
4, 3, 6, 3, 1, 0, 2, 0, 1, 3, 1, 1, 0, 1, 1, 2, 0, 0, 0, 0, 1,
2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 3, 3, 0, 0, 6, 2, 1, 0, 1, 5, 1,
0, 5, 1, 7, 1, 4, 2, 3, 2, 6, 1, 3, 1, 0, 0, 2, 1, 0, 2, 1, 0,
0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 1, 0, 1, 2, 0, 0, 1, 0, 0, 0, 4, 0, 1, 0, 0, 1, 1, 0,
3, 1, 1, 0, 2, 0, 3, 0, 1, 2, 0, 0, 0, 2, 0, 1, 1, 0, 6, 1, 0,
0, 0, 1, 1, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 2, 2, 2, 3, 0, 2, 3, 4, 1, 1,
0, 3, 0, 2, 3, 1, 2, 3, 3, 5, 6, 2, 6, 3, 4, 1, 4, 1, 3, 3, 3,
3, 1, 0, 2, 1, 0, 1, 6, 3, 6, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 2, 2, 4, 2, 5, 3, 3, 9, 5, 0, 3, 2, 1, 4, 7, 4, 2, 4, 6,
3, 4, 3, 12, 0, 3, 2, 3, 2, 2, 3, 1, 1, 0, 1, 5, 3, 2, 1, 2,
0, 6, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 2, 2, 1, 0,
1, 3, 1, 0, 0, 1, 0, 1, 4, 2, 1, 1, 0, 2, 1, 1, 1, 1, 2, 2, 3,
2, 0, 1, 2, 2, 3, 0, 1, 1, 0, 1, 4, 1, 3, 0, 1, 0, 3, 0, 1, 0,
1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 1, 0, 3, 3, 3, 4, 0, 0, 0, 2, 4, 1, 1, 3,
3, 1, 3, 2, 1, 3, 3, 2, 0, 1, 0, 2, 2, 0, 1, 2, 0, 0, 0, 2, 2,
1, 0, 3, 0, 5, 0, 0, 0, 1, 2, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 6,
2, 4, 2, 2, 4, 5, 4, 3, 7, 4, 3, 0, 3, 4, 1, 4, 4, 3, 6, 6, 1,
5, 3, 6, 2, 3, 1, 1, 2, 2, 2, 1, 2, 0, 2, 5, 2, 3, 0, 2, 0, 2,
0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 3, 3, 3, 1, 4, 1, 1, 1, 0, 1, 4,
2, 3, 3, 0, 1, 1, 4, 3, 3, 3, 5, 1, 3, 1, 7, 5, 3, 6, 2, 1, 1,
0, 6, 1, 1, 2, 3, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 3, 1, 9, 5, 5, 6, 5, 7, 1, 2, 2, 1, 1, 4, 1, 0, 2, 10, 5,
7, 5, 8, 3, 7, 3, 6, 5, 4, 3, 3, 7, 2, 0, 4, 3, 0, 1, 1, 0, 5,
0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 1, 1, 4,
1, 0, 3, 4, 1, 3, 1, 1, 2, 1, 6, 6, 4, 4, 3, 2, 5, 2, 4, 3, 2,
2, 4, 4, 2, 1, 2, 3, 1, 1, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 2, 1, 2, 3, 1, 0, 1, 2, 7, 4, 0, 3, 6, 0, 2, 4, 4, 10,
6, 3, 7, 5, 5, 10, 3, 11, 5, 6, 12, 11, 10, 6, 9, 2, 5, 12, 9,
2, 5, 1, 1, 4, 4, 1, 0, 0, 2, 1, 1, 3, 0, 1, 3, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1,
2, 0, 3, 0, 2, 2, 1, 5, 3, 2, 1, 4, 3, 5, 6, 7, 2, 5, 4, 12,
1, 3, 3, 3, 8, 3, 1, 6, 1, 1, 2, 3, 4, 0, 0, 1, 0, 3, 0, 0, 0,
0, 0, 2, 1, 2, 0, 1, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 2, 4, 4, 2, 4, 1, 3, 1, 6,
4, 1, 3, 3, 3, 7, 4, 5, 5, 1, 6, 7, 3, 3, 3, 5, 4, 4, 3, 4, 3,
1, 1, 3, 2, 1, 2, 1, 4, 4, 1, 1, 0, 0, 2, 2, 0, 1, 0, 3, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0,
1, 2, 1, 5, 0, 1, 3, 2, 3, 2, 0, 2, 3, 1, 3, 6, 3, 5, 4, 5, 4,
6, 3, 9, 6, 4, 3, 9, 4, 4, 6, 3, 1, 2, 2, 7, 5, 1, 1, 4, 3, 6,
1, 1, 0, 3, 4, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 6, 6, 5, 4, 4, 5,
6, 3, 6, 12, 1, 2, 6, 5, 8, 3, 10, 12, 7, 9, 6, 3, 11, 4, 13,
7, 7, 12, 2, 1, 5, 3, 9, 11, 0, 9, 11, 0, 17, 3, 3, 0, 4, 6,
3, 1, 0, 0, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 1, 0, 0, 4, 1, 1, 3, 0, 1,
0, 1, 1, 3, 2, 3, 1, 3, 6, 3, 0, 7, 4, 3, 3, 0, 1, 5, 2, 1, 1,
2, 5, 1, 2, 3, 1, 5, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 3, 3, 2, 2, 1, 1, 3, 1, 4, 3, 2, 1, 5, 3, 7, 5, 11, 3, 3,
4, 11, 7, 7, 8, 5, 8, 7, 7, 9, 8, 2, 4, 8, 3, 0, 3, 10, 4, 7,
0, 3, 0, 3, 4, 2, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 4,
1, 0, 1, 2, 2, 0, 3, 1, 3, 2, 5, 3, 3, 3, 4, 4, 8, 1, 6, 10,
4, 8, 7, 8, 4, 6, 4, 5, 0, 3, 3, 1, 8, 2, 1, 0, 2, 1, 3, 1, 2,
0, 2, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 2, 2, 4, 1, 2, 3, 3, 0, 2, 4, 3, 3, 2, 6, 7,
6, 4, 6, 3, 5, 9, 13, 3, 5, 6, 4, 4, 9, 7, 11, 8, 3, 5, 15, 6,
1, 5, 3, 8, 14, 4, 3, 0, 3, 4, 1, 2, 5, 0, 3, 4, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 3, 3, 6, 0, 0, 1, 2, 2, 2, 2, 5, 5, 3, 12, 8, 4, 4,
7, 3, 8, 10, 4, 4, 4, 13, 9, 8, 4, 5, 13, 9, 1, 6, 7, 3, 7, 3,
0, 0, 1, 1, 1, 4, 3, 0, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 2, 5, 0, 4, 3, 2,
3, 3, 2, 0, 0, 3, 3, 4, 2, 11, 3, 4, 4, 7, 0, 7, 4, 9, 4, 2,
4, 8, 4, 0, 0, 6, 9, 0, 2, 3, 4, 9, 0, 4, 0, 7, 2, 1, 1, 2, 0,
3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 2, 1, 1, 0, 1, 0, 2, 1, 1, 0, 3, 3, 1, 1, 1, 6, 3,
2, 10, 1, 3, 6, 12, 1, 7, 8, 7, 13, 4, 2, 6, 6, 8, 2, 10, 9,
1, 4, 7, 5, 12, 2, 2, 0, 6, 5, 5, 3, 1, 3, 5, 7, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
0, 2, 4, 1, 3, 0, 0, 1, 3, 1, 2, 2, 1, 2, 3, 4, 6, 6, 4, 3, 2,
5, 9, 7, 11, 9, 8, 6, 2, 5, 6, 5, 9, 8, 1, 3, 5, 3, 10, 3, 3,
0, 4, 3, 4, 3, 4, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 4, 2, 2, 2,
3, 1, 2, 0, 2, 1, 7, 4, 9, 1, 3, 1, 1, 2, 8, 8, 8, 8, 4, 6, 5,
2, 4, 2, 6, 6, 0, 5, 4, 1, 8, 3, 1, 0, 3, 1, 2, 2, 0, 0, 4, 2,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 3, 0, 2, 1, 2, 2, 2,
1, 1, 2, 5, 1, 2, 4, 3, 4, 0, 8, 6, 4, 0, 4, 2, 6, 0, 3, 5, 3,
9, 3, 1, 0, 6, 5, 4, 3, 1, 1, 2, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 1, 0, 0, 2, 0, 0, 1, 5, 2, 1, 2, 3, 1, 4, 6, 5, 5,
0, 2, 5, 2, 4, 0, 3, 4, 0, 0, 5, 1, 2, 1, 0, 0, 1, 1, 0, 0, 0,
0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 0, 3, 1, 1, 1, 3, 3, 0, 0, 2, 5, 1, 2, 1, 6,
4, 2, 12, 3, 3, 7, 9, 2, 8, 4, 15, 13, 6, 10, 9, 6, 2, 3, 5,
8, 3, 8, 9, 7, 20, 6, 4, 0, 7, 6, 2, 2, 4, 1, 5, 2, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 0, 2, 3, 1, 0, 2, 1, 3, 1, 2, 2, 1, 3, 3, 9, 4, 2, 5,
11, 5, 3, 5, 6, 9, 9, 9, 8, 6, 6, 4, 8, 3, 2, 3, 6, 4, 14, 2,
2, 0, 2, 4, 3, 4, 2, 2, 2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1,
0, 0, 2, 0, 1, 0, 1, 0, 1, 2, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1,
1, 0, 0, 0, 3, 2, 0, 0, 4, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 2, 2, 1, 1,
5, 0, 2, 1, 9, 2, 3, 3, 5, 6, 2, 4, 3, 5, 3, 0, 8, 3, 0, 1, 5,
2, 6, 2, 2, 0, 1, 4, 4, 2, 1, 1, 2, 4, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 1, 2,
1, 1, 1, 1, 6, 2, 4, 3, 5, 3, 1, 2, 1, 1, 1, 4, 11, 3, 10, 3,
3, 7, 3, 7, 5, 4, 5, 5, 9, 6, 4, 5, 4, 6, 14, 2, 4, 0, 8, 2,
3, 2, 5, 3, 4, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 0, 1,
0, 2, 1, 0, 0, 1, 0, 4, 3, 0, 1, 4, 1, 8, 3, 4, 5, 3, 1, 3, 1,
7, 4, 1, 2, 6, 4, 6, 7, 5, 0, 6, 1, 2, 4, 1, 2, 2, 6, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 2, 2, 4, 2, 1, 6, 6, 6, 3, 5, 3, 1, 5, 0, 4, 3, 4, 6,
17, 5, 7, 8, 14, 7, 9, 12, 10, 8, 9, 2, 20, 14, 1, 6, 14, 6,
10, 5, 6, 0, 9, 9, 10, 7, 9, 4, 7, 10, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 1, 1, 3, 0, 0, 2, 4,
3, 0, 2, 3, 3, 3, 1, 6, 2, 0, 2, 2, 7, 5, 0, 1, 0, 2, 1, 2, 1,
1, 0, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 1, 0, 1, 0, 2,
1, 1, 0, 1, 0, 1, 1, 3, 0, 3, 1, 3, 0, 4, 2, 3, 1, 1, 0, 4, 2,
1, 2, 4, 5, 6, 1, 0, 0, 3, 1, 3, 4, 1, 2, 1, 6, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 1, 0, 2, 0, 0, 1,
3, 1, 2, 1, 1, 0, 0, 0, 0, 3, 4, 0, 3, 2, 3, 1, 7, 6, 4, 3, 6,
1, 7, 2, 0, 1, 8, 6, 9, 2, 3, 0, 0, 2, 2, 5, 2, 1, 2, 7, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 2, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 2, 0, 0, 0, 2, 2, 0, 2,
4, 6, 1, 4, 1, 4, 1, 2, 5, 3, 1, 5, 1, 6, 4, 1, 4, 2, 1, 9, 1,
1, 0, 2, 0, 1, 1, 1, 2, 1, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0,
1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 2, 2, 0, 3, 1, 2, 3, 1, 1, 1, 5,
4, 2, 4, 0, 2, 3, 0, 4, 3, 2, 10, 2, 3, 0, 2, 1, 0, 2, 2, 2,
2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 0, 1, 1, 0, 0, 1, 2, 4, 1, 3, 3, 2, 3, 0, 2, 4, 0, 2,
2, 4, 7, 1, 4, 0, 5, 1, 2, 0, 2, 0, 3, 10, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 2, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 3, 2, 1, 0, 0, 0, 2,
2, 5, 3, 2, 1, 4, 0, 1, 0, 4, 2, 1, 1, 5, 1, 9, 1, 1, 0, 2, 1,
2, 2, 0, 2, 1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 0,
1, 2, 0, 1, 3, 2, 4, 0, 2, 0, 1, 2, 2, 0, 2, 0, 0, 3, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 2, 0, 0, 2, 1, 1, 1, 0, 2, 1, 1, 0, 1, 1, 3, 2,
4, 0, 1, 2, 3, 4, 3, 5, 2, 4, 2, 1, 5, 2, 1, 2, 4, 2, 7, 3, 1,
0, 2, 1, 2, 3, 1, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 2, 0, 3, 2, 1, 0, 4, 0, 0, 7, 4, 3, 2, 7, 3,
2, 7, 1, 2, 5, 0, 4, 3, 6, 10, 2, 6, 0, 7, 5, 3, 10, 4, 3, 4,
6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), dim = c(75L, 75L), dimnames = list(
c("2", "3", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "18", "19", "20", "21", "22", "23", "24",
"25", "26", "27", "28", "29", "30", "31", "32", "33", "34",
"35", "36", "37", "38", "39", "40", "41", "42", "43", "44",
"45", "46", "47", "48", "49", "50", "51", "52", "53", "54",
"55", "56", "57", "58", "59", "60", "61", "62", "63", "64",
"65", "66", "67", "68", "69", "70", "71", "72", "73", "74",
"75", "77", "79", "81"), c("2", "3", "5", "6", "7", "8",
"9", "10", "11", "12", "13", "14", "15", "16", "18", "19",
"20", "21", "22", "23", "24", "25", "26", "27", "28", "29",
"30", "31", "32", "33", "34", "35", "36", "37", "38", "39",
"40", "41", "42", "43", "44", "45", "46", "47", "48", "49",
"50", "51", "52", "53", "54", "55", "56", "57", "58", "59",
"60", "61", "62", "63", "64", "65", "66", "67", "68", "69",
"70", "71", "72", "73", "74", "75", "77", "79", "81")))

You get the lines because you have missing values. The full range is not represented in your data. Here's one way to fill in the missing values using tidyr
library(dplyr)
library(tidyr)
full_range <- function(x) seq(min(x), max(x))
data %>%
as.data.frame() %>%
tibble::rownames_to_column("Var1") %>%
pivot_longer(-Var1, names_to="Var2") %>%
mutate(across(Var1:Var2, as.numeric)) %>% {
d <- .
expand_grid(Var1=full_range(d$Var1), Var2=full_range(d$Var2)) %>%
left_join(d) %>%
replace_na(list(value=0))
} %>%
ggplot() +
theme_bw() +
geom_tile(aes(x = Var1, y = Var2, fill = value)) +
scale_fill_viridis_c(name = "") +
labs(x = "k2", y = "k1")
that looks like this

tapply of two variables in a loop

My data :
TEST <- structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1,
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 3, 1, 0, 0,
0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 1, 1, 1,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2,
0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0,
1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 3, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,
0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0,
3, 0, 0, 2, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0,
1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0,
0, 0, 2, 0, 1, 1, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
3, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0,
0, 1, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 3, 0, 0, 1, 0, 0, 2, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0,
0, 0, 0, 1, 0, 0, 2, 0, 1, 2, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1,
0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 1, 1, 0, 0, 1,
0, 0, 0), .Dim = c(22L, 20L), .Dimnames = list(NULL, c("month",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "")))
and :
month <- c(1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2)
I want to sum by row, so I used this code :
su_test <- list()
for (i in 1:ncol(TEST)){
su_test[[i]] <- tapply(TEST[,i], month, sum)
}
su_test <- do.call(cbind, su_test)
and to check the quantile :
su_test_obs <- apply(su_test,1,quantile,c(0.1,0.9))
This is an observation simulation per month. However I also have the detail by area. :
TEST2 <- structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 3, 1, 0,
0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 1, 1,
1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0,
0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 3, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1,
0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0,
0, 3, 0, 0, 2, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3,
0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2,
0, 0, 0, 2, 0, 1, 1, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 3, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 3, 0, 0, 1, 0, 0, 2, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1,
0, 0, 0, 0, 1, 0, 0, 2, 0, 1, 2, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,
1, 0, 0, 0, 0, 2, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,
0, 1, 1), .Dim = c(22L, 20L), .Dimnames = list(NULL, c("month",
"area", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "", "", "")))
area <- c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
I would like to have the same result of su_test_obs
but with the details of the areas in addition like list(month, area) but I don't understand the logic.
Would you have a solution please? Maybe there is a solution simpler with dplyr?
Thanks

It would be much simpler if you convert your matrix to dataframe. We can then use aggregate which can be applied to multiple groups easily
df <- data.frame(TEST2)
apply(aggregate(.~month + area, df, sum)[-c(1, 2)], 1, quantile, c(0.1,0.9))
# [,1] [,2]
#10% 2 1.7
#90% 6 5.3

y-axis label of highest value not printed

I want to plot the hourly visits to a certain webpage of my website. The x-axis shows the hours (0 to 23), the y-axis shows the number of unique visits.
I'm supressing axes in plot() and adding them with axis(). I want only the lowest and highest y-values labelled:
axis(2,
at = seq(min(...), max(...), 1),
labels = c(min(...),
rep.int("", max(...) - min(...) - 1),
max(...)
)
)
(Ellipses in the code sample represend the column. I left this out for better visual clarity of the code structure.)
But in the plot, the label in the highest value does not appear:
Sometimes (depending on the range of values) I can get the highest value label to appear by changing rep.int("" ... to rep.int(" ", i.e., labelling the ticks with a space, but this doesn't work always.
Why does R not print the hightest label? And, more importantly:
How can I force R to print the highest label?
Complete code example:
sitzungen <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 8, 4, 0, 8, 3, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 2, 0, 0, 2, 0, 0, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 2, 2, 1, 0, 0, 0, 1, 0, 0, 1, 3, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 4, 1, 2, 1, 7, 7, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 3, 8, 4, 1, 2, 0, 1, 1, 0, 0, 5, 0, 3, 3, 2, 3, 1, 2, 0, 1, 2, 0, 0, 0, 1, 0, 1, 2, 3, 0, 0, 3, 1, 6, 3, 9, 1, 0, 2, 1, 4, 8, 2, 2, 2, 0, 0, 0, 2, 1, 3, 1, 1, 2, 1, 2, 3, 1, 4, 3, 0, 2, 3, 1, 3, 1, 5, 2, 0, 0, 1, 0, 1, 2, 1, 0, 3, 0, 1, 0, 3, 7, 2, 2, 1, 2, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 12, 15, 2, 2, 1, 0, 0, 0, 0, 0, 2, 3, 0, 3, 0, 2, 1, 1, 2, 2, 4, 2, 1, 4, 2, 1, 2, 2, 1, 0, 0, 0, 7, 0, 2, 4, 2, 0, 2, 3, 5, 2, 1, 4, 4, 2, 0, 2, 4, 0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 2, 1, 2, 1, 1, 2, 1, 4, 1, 1, 1, 0, 0, 3, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
stunde <- rep(0:23, 20)
stuendlich <- data.frame(cbind(stunde, sitzungen))
aggr <- aggregate(sitzungen ~ stunde, stuendlich, sum)
aggr <- rbind(aggr, c(24, aggr[which(aggr$stunde == "23"),]$sitzungen))
plot(aggr, type = "s", xlim = c(0, 24), axes = FALSE, xlab = "Stunde", ylab = "Sitzungen", main = "Sitzungen pro Stunde (kumuliert)")
axis(1, at = seq(0.5, 23.5, 1), labels = 0:23)
axis(2, at = seq(min(aggr$sitzungen), max(aggr$sitzungen), 1), labels = c(min(aggr$sitzungen), rep.int(" ", max(aggr$sitzungen) - min(aggr$sitzungen) - 1), max(aggr$sitzungen)))

Don't use " " or ""; use NA:
axis(2, at = seq(min(aggr$sitzungen), max(aggr$sitzungen), 1),
labels = c(min(aggr$sitzungen),
rep.int(NA, max(aggr$sitzungen) - min(aggr$sitzungen) - 1),
max(aggr$sitzungen)))

Unable to plot weekly data with ggplot2

I can plot Daily but Week yields
Error: geom_path: Each group consist of only one observation.
Do you need to adjust the group aesthetic? Yes.
With this type of data:
DailyDF2 <-
structure(list(Group.date = structure(c(15023, 15024, 15027,
15029, 15031, 15035, 15036, 15037, 15039, 15040, 15041, 15043,
15046, 15048, 15050, 15054, 15056, 15057, 15059, 15061, 15062,
15063, 15064, 15068, 15070, 15071, 15073, 15078, 15079, 15080,
15085, 15089, 15090, 15092, 15095, 15099, 15100, 15103, 15104,
15105, 15106, 15107, 15109, 15110, 15111, 15112, 15113, 15120,
15121, 15122, 15124, 15127, 15128, 15132, 15133, 15134, 15141,
15142, 15146, 15148, 15153, 15155, 15156, 15161, 15162, 15169,
15173, 15174, 15177, 15180, 15181, 15182, 15183, 15186, 15187,
15188, 15190, 15195, 15196, 15197, 15198, 15199, 15201, 15202,
15203, 15204, 15205, 15206, 15207, 15208, 15209, 15211, 15212,
15213, 15214, 15215, 15216, 15218, 15219, 15220, 15223, 15224,
15225, 15226, 15227, 15228, 15229, 15230, 15231, 15232, 15233,
15235, 15236, 15237, 15239, 15241, 15243, 15244, 15245, 15246,
15247, 15248, 15249, 15250, 15251, 15252, 15253, 15254, 15255,
15257, 15258, 15259, 15260, 15261, 15262, 15263, 15264, 15265,
15266, 15267, 15268, 15269, 15271, 15274, 15275, 15276, 15278,
15279, 15280, 15281, 15282, 15283, 15284, 15285, 15286, 15287,
15288, 15289, 15290, 15291, 15292, 15293, 15294, 15295, 15296,
15297, 15298, 15299, 15300, 15301, 15302, 15303, 15304, 15305,
15306, 15307, 15308, 15309, 15310, 15311, 15313, 15314, 15315,
15316, 15317, 15318, 15320, 15321, 15322, 15323, 15325, 15327,
15328, 15329, 15330, 15331, 15332, 15333, 15334, 15335, 15336,
15337, 15338, 15342, 15343, 15344, 15345, 15346, 15347, 15348,
15350, 15351, 15352, 15353, 15354, 15356, 15357, 15358, 15359,
15361, 15362, 15363, 15364, 15365, 15367, 15368, 15369, 15370,
15372, 15373, 15374, 15375, 15376, 15377, 15378, 15379, 15380,
15381, 15382, 15383, 15384, 15385, 15386, 15387, 15389, 15390,
15391, 15392, 15393, 15394, 15398, 15399, 15400, 15401, 15403,
15404, 15405, 15406, 15407, 15408, 15409, 15410, 15411, 15412,
15413, 15414, 15415, 15416, 15417, 15418, 15419, 15420, 15421,
15422, 15423, 15424, 15425, 15428, 15429, 15430, 15433, 15434,
15435, 15437, 15438, 15439, 15440, 15441, 15442, 15443, 15444,
15446, 15447, 15448, 15449, 15450, 15451, 15454, 15455, 15456,
15457, 15459, 15460, 15462, 15463, 15464, 15465, 15466, 15467,
15468, 15469, 15470, 15471, 15474, 15475, 15476, 15477, 15478,
15481, 15482, 15483, 15484, 15485, 15488, 15489, 15490, 15491,
15492, 15495, 15496, 15497, 15498, 15500, 15501, 15502, 15503,
15504, 15505, 15506, 15507, 15508, 15509, 15510, 15511, 15512,
15514, 15515, 15516, 15518, 15519, 15520, 15522, 15525, 15526,
15527, 15528, 15529, 15530, 15531, 15532, 15533, 15534, 15536,
15537, 15539, 15540, 15541, 15542, 15544, 15545, 15546, 15547,
15548, 15549, 15550, 15551, 15552, 15553, 15554, 15555, 15558,
15559, 15560, 15561, 15562, 15563, 15565, 15566, 15568, 15569,
15572, 15573, 15574, 15575, 15576, 15578, 15579, 15580, 15581,
15582, 15583, 15584, 15587, 15588, 15589, 15590, 15591, 15593,
15594, 15595, 15596, 15597, 15600, 15602, 15603, 15604, 15605,
15606, 15607, 15609, 15610, 15611, 15612, 15614, 15615, 15616,
15617, 15618, 15621, 15622, 15623, 15624, 15625, 15626, 15628,
15629, 15630, 15631, 15632, 15633, 15634, 15636, 15637, 15638,
15639, 15641, 15642, 15643, 15644, 15645, 15646, 15647, 15649,
15650, 15651, 15652, 15654, 15655, 15656, 15657, 15658, 15659,
15660, 15661, 15662, 15663, 15664, 15665, 15666, 15667, 15670,
15672, 15673, 15674, 15675, 15676, 15677, 15678, 15679, 15680,
15681, 15682, 15684, 15685, 15686, 15687, 15688, 15689, 15690,
15693, 15694, 15695, 15696, 15699, 15700, 15701, 15702, 15703,
15708, 15709, 15712, 15713, 15715, 15716, 15717, 15719, 15720,
15722, 15723, 15724, 15726, 15727, 15728, 15730, 15731, 15733,
15734, 15735, 15736, 15737, 15738, 15739, 15740, 15741, 15742,
15743, 15744, 15745, 15746, 15747, 15748, 15749, 15750, 15751,
15752, 15753, 15754, 15755, 15756, 15757, 15758, 15759, 15760,
15761, 15762), class = "Date"), X.hpm = c(4, 5, 3, 1, 3, 1, 2,
1, 2, 3, 1, 4, 1, 1, 14, 1, 1, 5, 1, 1, 1, 1, 5, 2, 2, 9, 0,
5, 1, 1, 1, 3, 1, 8, 1, 6, 5, 1, 2, 2, 3, 4, 1, 2, 2, 4, 4, 3,
1, 1, 1, 11, 2, 1, 5, 4, 5, 1, 1, 3, 1, 2, 1, 1, 4, 6, 1, 0,
0, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 3, 2, 0, 0, 0, 1, 4, 1, 0, 1,
0, 1, 2, 1, 1, 0, 0, 0, 27, 5, 2, 1, 0, 13, 1, 0, 0, 1, 0, 2,
3, 0, 0, 0, 0, 1, 0, 1, 0, 0, 2, 0, 1, 1, 3, 0, 0, 1, 3, 0, 0,
1, 0, 15, 1, 0, 0, 1, 0, 4, 16, 0, 0, 4, 3, 3, 0, 0, 1, 1, 2,
2, 0, 2, 1, 2, 0, 1, 4, 0, 4, 0, 3, 3, 14, 7, 2, 2, 2, 0, 6,
5, 0, 0, 0, 1, 3, 1, 2, 0, 1, 0, 1, 1, 5, 1, 1, 0, 1, 0, 0, 0,
0, 0, 1, 4, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 5,
2, 1, 0, 3, 1, 6, 3, 0, 1, 0, 2, 1, 0, 3, 0, 0, 0, 1, 0, 0, 1,
0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 1, 0, 1, 0, 1, 1,
1, 0, 2, 3, 3, 0, 15, 0, 1, 3, 1, 1, 3, 5, 4, 0, 4, 4, 5, 4,
1, 0, 0, 3, 2, 0, 0, 0, 2, 0, 1, 2, 6, 0, 0, 5, 0, 0, 0, 0, 2,
0, 1, 0, 1, 3, 0, 3, 0, 4, 0, 1, 0, 1, 2, 3, 3, 4, 0, 5, 3, 3,
1, 3, 1, 0, 1, 36, 2, 0, 1, 1, 10, 1, 2, 1, 3, 0, 0, 0, 1, 0,
2, 9, 1, 0, 0, 2, 0, 1, 34, 0, 1, 0, 2, 1, 0, 0, 0, 0, 0, 2,
0, 5, 2, 4, 22, 1, 0, 1, 0, 2, 0, 1, 0, 0, 0, 3, 4, 0, 1, 1,
2, 1, 6, 1, 0, 0, 0, 0, 5, 1, 0, 8, 1, 2, 0, 2, 1, 56, 1, 2,
0, 3, 6, 10, 0, 2, 0, 0, 4, 6, 4, 0, 1, 8, 2, 2, 1, 0, 7, 3,
1, 0, 2, 1, 2, 1, 1, 2, 1, 5, 1, 3, 1, 2, 1, 5, 2, 0, 1, 2, 1,
32, 0, 0, 2, 0, 1, 17, 3, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1,
0, 1, 0, 2, 3, 4, 0, 2, 1, 4, 3, 0, 0, 0, 2, 5, 0, 0, 1, 2, 1,
2, 1, 1, 0, 1, 1, 0, 6, 0, 2, 1, 0, 0, 1, 0, 0, 3, 2, 0, 0, 6,
1, 0, 1, 13, 0, 0, 0, 1, 24, 4, 1, 0, 4, 3, 1, 1, 1, 0, 2, 3,
0, 3, 0, 2, 0, 1, 4, 0, 1, 0, 6, 1, 5, 9, 4, 0, 0, 0, 0, 1, 2,
0, 0, 0, 0, 0), X.hospice = c(2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,
2, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3,
1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 2, 3, 1, 0, 0, 0, 0, 0, 0,
1, 2, 0, 0, 1, 0, 0, 0, 2, 1, 1, 0, 0, 0, 2, 2, 3, 2, 2, 2, 0,
2, 2, 3, 2, 7, 3, 3, 2, 2, 3, 6, 2, 3, 1, 1, 2, 1, 0, 0, 1, 1,
2, 0, 10, 0, 0, 3, 3, 12, 2, 0, 1, 1, 3, 0, 0, 1, 1, 0, 1, 0,
1, 2, 6, 3, 3, 2, 0, 0, 5, 3, 0, 3, 1, 1, 0, 0, 0, 0, 0, 0, 1,
2, 2, 4, 0, 0, 1, 2, 1, 2, 1, 2, 0, 5, 5, 0, 0, 1, 2, 0, 0, 0,
6, 1, 0, 2, 0, 0, 3, 4, 1, 0, 1, 2, 0, 2, 1, 2, 1, 0, 5, 1, 0,
1, 0, 2, 3, 1, 1, 1, 0, 3, 3, 2, 4, 1, 2, 1, 1, 2, 3, 2, 1, 2,
1, 1, 0, 2, 0, 6, 3, 1, 2, 2, 0, 1, 1, 2, 0, 1, 2, 0, 1, 1, 1,
0, 1, 3, 6, 0, 0, 1, 2, 3, 0, 1, 1, 2, 6, 1, 2, 1, 0, 2, 4, 1,
1, 5, 1, 0, 2, 1, 1, 1, 1, 0, 2, 2, 0, 0, 4, 4, 1, 1, 3, 1, 0,
0, 1, 0, 3, 5, 0, 2, 3, 3, 10, 2, 4, 0, 1, 3, 0, 0, 0, 2, 4,
3, 0, 0, 0, 0, 1, 0, 3, 2, 1, 2, 0, 0, 1, 0, 0, 1, 1, 1, 0, 3,
1, 4, 0, 1, 2, 0, 4, 0, 1, 1, 9, 3, 3, 2, 2, 0, 1, 1, 0, 3, 1,
5, 1, 1, 0, 2, 2, 1, 3, 2, 3, 3, 1, 1, 3, 2, 1, 1, 0, 1, 0, 0,
1, 0, 0, 0, 1, 1, 1, 1, 2, 0, 1, 2, 3, 1, 0, 0, 0, 1, 3, 1, 0,
1, 1, 2, 0, 2, 0, 0, 1, 0, 0, 1, 1, 4, 0, 2, 1, 3, 1, 2, 2, 0,
6, 2, 1, 1, 2, 4, 2, 1, 0, 2, 1, 2, 1, 0, 0, 2, 4, 0, 2, 0, 2,
3, 2, 2, 0, 1, 2, 10, 5, 0, 0, 2, 1, 2, 2, 0, 2, 2, 1, 0, 1,
1, 1, 4, 5, 3, 0, 0, 1, 1, 2, 2, 0, 0, 0, 1, 1, 2, 2, 1, 1, 1,
1, 1, 0, 1, 3, 1, 1, 0, 1, 0, 2, 1, 2, 5, 0, 0, 3, 6, 7, 1, 1,
4, 4, 2, 2, 0, 1, 4, 1, 4, 0, 0, 1, 0, 1, 1, 2, 1, 1, 0, 1, 0,
1, 1, 1, 0, 0, 1, 0, 0, 2, 1, 2, 2, 2, 1, 2, 2, 5, 1, 0, 1, 1,
0, 3, 0, 1, 4, 3, 0, 2, 0, 2, 4, 6, 1, 2, 1, 1, 1, 2, 3, 1, 2,
6, 3, 0, 0, 7, 6, 2, 1, 2, 1, 1, 19), X.palliative = c(1, 0,
3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 2, 1, 2,
1, 0, 1, 1, 0, 0, 0, 0, 2, 1, 3, 4, 0, 1, 1, 1, 1, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 6, 0, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 3, 0, 0, 0, 6, 0, 1, 0, 0,
0, 2, 1, 0, 0, 1, 1, 0, 1, 0, 3, 2, 1, 1, 0, 0, 0, 2, 1, 2, 7,
0, 1, 1, 2, 0, 0, 2, 1, 3, 1, 0, 0, 2, 0, 7, 0, 4, 0, 1, 0, 0,
1, 1, 1, 0, 0, 1, 3, 1, 6, 0, 4, 0, 2, 2, 8, 3, 1, 1, 1, 0, 3,
0, 0, 0, 0, 0, 1, 2, 0, 5, 0, 0, 1, 1, 0, 1, 1, 0, 0, 2, 4, 0,
0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 4, 1, 0, 0, 0, 0, 0, 0,
1, 1, 0, 0, 0, 0, 0, 0, 2, 0, 2, 0, 0, 3, 1, 0, 0, 0, 0, 0, 0,
0, 2, 0, 0, 1, 0, 0, 0, 0, 2, 0, 1, 0, 1, 0, 1, 1, 3, 0, 0, 2,
0, 0, 2, 2, 0, 1, 3, 1, 1, 1, 0, 0, 3, 4, 4, 3, 4, 1, 6, 1, 0,
0, 0, 2, 2, 2, 0, 0, 0, 1, 1, 1, 0, 0, 4, 3, 1, 2, 0, 0, 3, 0,
2, 2, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 2, 1, 0, 1, 0, 0,
0, 0, 0, 2, 4, 0, 3, 1, 0, 0, 0, 1, 1, 0, 2, 0, 0, 0, 1, 0, 0,
1, 0, 0, 0, 2, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 5, 0, 4, 2,
2, 1, 1, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 1, 0, 0, 1, 0, 0, 8, 0, 0, 2, 4, 2, 0, 0, 3, 0, 7, 9, 12,
0, 2, 0, 0, 0, 0, 0, 1, 0, 7, 7, 1, 2, 6, 2, 2, 0, 2, 1, 1, 0,
0, 0, 0, 0, 3, 1, 2, 0, 2, 2, 3, 2, 1, 0, 0, 1, 1, 0, 4, 0, 1,
0, 0, 0, 2, 0, 1, 4, 1, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1,
0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 3, 0, 0, 0, 2, 0, 0, 1,
1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 1, 0, 1,
0, 1, 3, 3, 0, 0, 4, 2, 1, 0, 1, 2, 4, 2, 1, 0, 0, 3, 1, 1, 0,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 3, 1, 0, 4, 0), X.pedpc = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 4, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 1, 0, 0, 0, 8, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 7, 1, 1, 1, 0,
4, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 2, 2, 2, 0, 1, 1, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 1, 2, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0,
1, 1, 1, 0, 3, 1, 1, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 1, 0, 0, 6, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 1, 2, 2, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
2, 2, 2, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 2,
0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0,
1, 0, 0, 1, 1, 0, 1, 1, 2, 0, 2, 2, 0, 1, 0, 1, 2, 0, 0, 2, 0,
0, 0, 0, 0, 0, 0, 4, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 2, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0), X.pediatric = c(1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
3, 0, 1, 1, 2, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0,
0, 1, 1, 0, 0, 1, 0, 0, 1, 2, 0, 0, 0, 4, 2, 3, 2, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 4, 0, 0, 0, 1, 0, 1, 1, 0, 0, 2, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 2, 2, 0, 0, 5, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 0, 3,
0, 2, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 2, 2, 2,
0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 2, 1, 3, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 2, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 2, 0, 0, 0, 0), HashTag = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), .Names = c("Group.date",
"X.hpm", "X.hospice", "X.palliative", "X.pedpc", "X.pediatric",
"HashTag"), row.names = c(NA, -545L), class = "data.frame")
And by plotting with this code:
ggplot(DailyDF2, aes(Group.date)) +
geom_line(aes(y = HashTag, colour = "HashTag")) +
geom_line(aes(y = X.hpm, colour = "#hpm")) +
geom_line(aes(y = X.hospice, colour = "#hospice")) +
geom_line(aes(y = X.palliative, colour="#palliative")) +
geom_line(aes(y = X.pedpc, colour = "#pedpc")) +
geom_line(aes(y = X.pediatric, colour="#pediatric")) +
ylab(label="Top 5 Hash Tags Frequency") +
xlab("Day")+
theme(axis.text.x=element_text(angle=-45, hjust=0.001))
I get this:
When I use my weekly data:
WeeklyDF2 <-
structure(list(Group.date = c("2011-07", "2011-08", "2011-09",
"2011-10", "2011-11", "2011-12", "2011-13", "2011-14", "2011-15",
"2011-16", "2011-17", "2011-18", "2011-19", "2011-20", "2011-21",
"2011-22", "2011-23", "2011-24", "2011-25", "2011-26", "2011-27",
"2011-28", "2011-29", "2011-30", "2011-31", "2011-32", "2011-33",
"2011-34", "2011-35", "2011-36", "2011-37", "2011-38", "2011-39",
"2011-40", "2011-41", "2011-42", "2011-43", "2011-44", "2011-45",
"2011-46", "2011-47", "2011-48", "2011-49", "2011-50", "2011-51",
"2011-52", "2012-01", "2012-02", "2012-03", "2012-04", "2012-05",
"2012-06", "2012-07", "2012-08", "2012-09", "2012-10", "2012-11",
"2012-12", "2012-13", "2012-14", "2012-15", "2012-16", "2012-17",
"2012-18", "2012-19", "2012-20", "2012-21", "2012-22", "2012-23",
"2012-24", "2012-25", "2012-26", "2012-27", "2012-28", "2012-29",
"2012-30", "2012-31", "2012-32", "2012-33", "2012-34", "2012-35",
"2012-36", "2012-37", "2012-38", "2012-39", "2012-40", "2012-41",
"2012-42", "2012-43", "2012-44", "2012-45", "2012-46", "2012-47",
"2012-48", "2012-49", "2012-50", "2012-51", "2012-52", "2013-00",
"2013-01", "2013-02", "2013-03", "2013-04", "2013-05", "2013-06",
"2013-07", "2013-08"), X.hpm = c(9, 7, 4, 10, 16, 8, 8, 13, 7,
1, 12, 12, 12, 13, 5, 14, 10, 6, 4, 4, 5, 6, 1, 2, 3, 5, 6, 6,
34, 15, 6, 1, 4, 8, 17, 21, 10, 6, 10, 33, 15, 8, 9, 1, 5, 4,
1, 9, 13, 4, 4, 3, 0, 5, 3, 24, 14, 22, 5, 2, 14, 3, 4, 8, 13,
15, 40, 13, 6, 13, 37, 4, 2, 34, 4, 7, 12, 6, 11, 60, 23, 14,
13, 12, 7, 12, 11, 36, 23, 5, 2, 10, 10, 7, 8, 10, 2, 5, 7, 14,
30, 9, 9, 8, 25, 3, 0), X.hospice = c(2, 0, 0, 4, 2, 1, 0, 0,
0, 0, 4, 0, 3, 7, 0, 0, 3, 1, 0, 3, 1, 0, 4, 9, 7, 17, 17, 5,
13, 20, 6, 3, 16, 13, 0, 10, 6, 12, 10, 10, 8, 8, 8, 13, 11,
7, 12, 6, 5, 11, 7, 13, 14, 6, 4, 14, 9, 24, 4, 9, 6, 4, 3, 9,
8, 19, 5, 8, 10, 14, 3, 2, 5, 6, 7, 5, 6, 11, 9, 10, 6, 8, 9,
18, 7, 6, 14, 6, 4, 7, 6, 11, 18, 12, 11, 6, 3, 2, 2, 8, 10,
5, 10, 17, 18, 18, 21), X.palliative = c(1, 3, 0, 0, 1, 0, 0,
1, 0, 0, 0, 0, 5, 5, 1, 2, 8, 1, 2, 2, 1, 0, 0, 0, 1, 7, 2, 0,
7, 7, 3, 3, 7, 12, 6, 7, 11, 4, 11, 20, 5, 8, 4, 6, 1, 2, 6,
2, 0, 4, 4, 3, 2, 4, 5, 8, 10, 19, 6, 1, 10, 7, 2, 2, 6, 1, 6,
4, 4, 2, 3, 1, 6, 10, 3, 1, 1, 2, 8, 8, 33, 0, 16, 12, 4, 6,
10, 6, 1, 9, 2, 2, 2, 3, 5, 2, 2, 3, 0, 2, 7, 7, 10, 7, 0, 11,
4), X.pedpc = c(0, 0, 0, 0, 0, 0, 0, 0, 5, 1, 0, 2, 1, 0, 0,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 9, 0, 0, 0, 0, 1, 1,
0, 0, 0, 11, 6, 4, 8, 0, 1, 5, 2, 3, 8, 1, 0, 4, 0, 0, 1, 7,
1, 2, 0, 0, 1, 0, 0, 0, 3, 0, 5, 2, 0, 1, 0, 0, 0, 7, 1, 1, 3,
0, 2, 0, 6, 2, 0, 2, 2, 3, 7, 4, 2, 6, 0, 1, 3, 1, 4, 0, 1, 0,
0, 0, 0, 2, 1, 0, 1, 1, 0), X.pediatric = c(1, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 6, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 2, 1, 3, 4, 3, 1, 3, 4, 11, 0, 5, 3, 2, 0, 2, 0,
0, 0, 0, 0, 1, 0, 1, 0, 1, 4, 9, 0, 1, 2, 0, 1, 1, 0, 0, 0, 1,
0, 0, 0, 0, 5, 5, 3, 0, 0, 1, 1, 1, 7, 0, 3, 1, 0, 4, 3, 0, 1,
2, 0, 1, 6, 1, 4, 0, 0, 4, 0, 1, 1, 2, 0, 1, 1, 8, 0), HashTag = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0)), .Names = c("Group.date", "X.hpm", "X.hospice", "X.palliative",
"X.pedpc", "X.pediatric", "HashTag"), row.names = c(NA, -107L
), class = "data.frame")
And when I plot with a similar code:
ggplot(WeeklyDF2, aes(Group.date))+
geom_line(aes(y = HashTag, colour = "HashTag")) +
geom_line(aes(y = X.hpm, colour = "#hpm")) +
geom_line(aes(y = X.hospice, colour = "#hospice")) +
geom_line(aes(y = X.palliative, colour="#palliative")) +
geom_line(aes(y = X.pedpc, colour = "#pedpc")) +
geom_line(aes(y = X.pediatric, colour="#pediatric")) +
ylab(label="Top 5 Hash Tags Frequency") +
xlab("Week")+
theme(axis.text.x=element_text(angle=-45, hjust=0.001))
I get the following warnings:
geom_path: Each group consist of only one observation. Do you need to adjust the group aesthetic?
geom_path: Each group consist of only one observation. Do you need to adjust the group aesthetic?
geom_path: Each group consist of only one observation. Do you need to adjust the group aesthetic?
geom_path: Each group consist of only one observation. Do you need to adjust the group aesthetic?
geom_path: Each group consist of only one observation. Do you need to adjust the group aesthetic?
geom_path: Each group consist of only one observation. Do you need to adjust the group aesthetic?
And my plot looks like this:
Any ideas?
UPDATE My WeeklyDF2$Group.date is a character vector. My DailyDF2$Group.date is a "double". Should WeeklyDF2$Group.date <- as.double.POSIXlt(WeeklyDF2$Group.date) or WeeklyDF2$Group.date <- as.double(WeeklyDF2$Group.date) fix the issue?

Ista was correct: WeeklyDF2$Group.date <- as.numeric(as.factor(WeeklyDF$Group.date))
Was what I need to do to correct the issue so plotting with :
ggplot(WeeklyDF2, aes(Group.date))+
geom_line(aes(y = HashTag, colour = "HashTag")) +
geom_line(aes(y = X.hpm, colour = "#hpm")) +
geom_line(aes(y = X.hospice, colour = "#hospice")) +
geom_line(aes(y = X.palliative, colour="#palliative")) +
geom_line(aes(y = X.pedpc, colour = "#pedpc")) +
geom_line(aes(y = X.pediatric, colour="#pediatric")) +
ylab(label="Top 5 Hash Tags Frequency") +
xlab("Week")+
theme(axis.text.x=element_text(angle=-45, hjust=0.001))
The solution yielded a sequence (1,2,3,4,5,6,7.......) but better than nothing.
It would be nice to know how to plot the variables by the proper week date format in R. Would any one know how to do so? I mean, would anyone know how to convert a excel date (MM/DD/YY) as well as the odd date format pulled from an api (18FEB2011:16:24:00.00) to a week date format for R?

Stacked barplot with partitioned main and sub x label

I'm new to R. I want to create a stacked barplot based on the following data. I want to plot these data into a stacked barplot that describes percentage of disease score for each combination of genotype and race in x axis, e.g. 76R-race 1, rmc-race 1. As well how to simplify the x axis, by splitting each race to have 76R and rmc combination instead of labelling each combination, i.e. instead of labelling each bar with 76R-race 1, rmc-race 1, etc, how to label race 1 as main axis and have 76R and rmc as sub-axis, and so on.
disease.nov <- data.frame(disease.score = c(0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 2, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7, 5, 5, 4, 4, 6, 5, 8, 5, 5, 5, 6, 4, 7, 5, 8, 6, 1, 2, 2, 4, 5, 8, 5, 6, 7, 4, 4, 4, 2, 3, 5, 6, 7, 7, 5, 2, 6, 6, 6, 4, 5, 8, 7, 5, 2, 5, 6, 3, 7, 4, 7, 7, 8, 6, 8, 8, 7, 9, 9, 7, 4, 9, 9, 5, 3, 8, 8, 6, 5, 7, 7, 8, 6, 6, 5, 7, 7, 8, 8, 8, 8, 7, 6, 6, 8, 4, 7, 7, 8, 6, 7, 7, 8, 6, 5, 6, 7, 7, 4, 6, 8, 7, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
genotype=gl(2,52,624, labels=c("76R","rmc")),
race=gl(6,104,624, labels=c("race 1","race 2","race 3","WAC 7673","WAC 7591","control")))

I'm not sure if this is what you're looking for:
library(ggplot2)
library(scales)
ggplot(disease.nov, aes(x=genotype, fill=factor(disease.score))) +
geom_bar(position="fill") +
facet_wrap(~ race) +
scale_y_continuous(labels = percent_format())
Which produces:

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