What is causing these upticks in this ggplot?

What is causing these upticks in this ggplot? - r

Example data:
df <- structure(
list(
group = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 7, 7, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11),
val = 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, 0.000141111115226522, 0, 0, 0, 0.00127000000793487, 0.00070555554702878, 0.000141111115226522, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00127000000793487, 0.000282222230453044, 0, 0, 0.000141111115226522, 0.000282222230453044, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.00070555554702878, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.000141111115226522, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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", "val"),
row.names = c(NA, -1000L),
class = c("data.table", "data.frame"),
.internal.selfref = <pointer: 0xe4d468>
)
I can plot this as a timeseries per group, like so:
ggplot(df, aes(x=unlist(by(val, group, seq_along)), y=val, group=group)) +
geom_line(alpha=0.5)
but I want to plot a rolling mean of the data, like so:
library(zoo)
ggplot(df, aes(x=unlist(by(val, group, seq_along)),
y=rollmean(val, 48, fill=NA), group=group)) +
geom_line(alpha=0.5)
But this adds upticks to the end of each line, that do not exist in the data:
The upticks at 130 and 670 do not exist in the data, nor do they exist in the rolling mean, as you can see with rollmean(df[group==5, val], 48, fill=NA). So what is causing them?

The first uptick does occur at exactly 132. In your rolling mean, you chose the default align, which sets it to center, meaning that each point is the mean of the previous k/2 and the future k/2 points. Since you set k=48, it means that point 132 will be the mean of (132-24):(132+24). You can verify that the first non-zero point is indeed 156.
# First non-zero value
min(which(df$val!=0))
# 156
You can also verify that the first non-zero value in the rolling mean is 132.
df$rollmean <- rollmean(df$val, 48, fill=NA)
min(which(df$rollmean!=0))
# 132
Additionally, it looks like you are applying your rolling mean across all groups, which you almost certainly don't want. Try splitting by group, like you did with by to create the time variable. Here is an example:
# Set a time variable before hand
df$time <- with(df, unlist(by(val, group, seq_along)))
df$group <- as.factor(df$group)
k=48
# Remove those groups wtihout enough values for rolling mean of k window
df.subset <- df[df$group %in% names(which(table(df$group) >= k)),]
# Calculate a rolling mean on each group
df.subset$rollmean <- unlist(by(df.subset$val, df.subset$group, FUN=rollmean, k=k, fill=NA))
# Plot
ggplot(df.subset, aes(x=time,
y=rollmean,
colour=group)) + geom_line()

Related

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

Missing value where TRUE/FALSE needed error in smcure model

I'm creating a cure model in R to predict Loan Default. I'm seeking someone to help me debug this error. I think it may have to do with my columns.
library(smcure)
smcure(Surv(DURATION, DEFAULT) ~ CHK_ACCT+HISTORY+NEW_CAR+USED_CAR+FURNITURE+`RADIO/TV`+EDUCATION+
RETRAINING+AMOUNT+SAV_ACCT+EMPLOYMENT+INSTALL_RATE+MALE_DIV+MALE_SINGLE+MALE_MAR_or_WID+
`CO-APPLICANT`+GUARANTOR+PRESENT_RESIDENT+REAL_ESTATE+PROP_UNKN_NONE+AGE+OTHER_INSTALL+RENT+
OWN_RES+NUM_CREDITS+JOB+NUM_DEPENDENTS+TELEPHONE+FOREIGN,
cureform=~CHK_ACCT+HISTORY+NEW_CAR+USED_CAR+FURNITURE+`RADIO/TV`+EDUCATION+RETRAINING+AMOUNT+SAV_ACCT+
EMPLOYMENT+INSTALL_RATE+MALE_DIV+MALE_SINGLE+MALE_MAR_or_WID+`CO-APPLICANT`+GUARANTOR+PRESENT_RESIDENT+
REAL_ESTATE+PROP_UNKN_NONE+AGE+OTHER_INSTALL+RENT+OWN_RES+NUM_CREDITS+JOB+NUM_DEPENDENTS+
TELEPHONE+FOREIGN,
model="ph", data = CD)
Error in while (convergence > eps & i < emmax) { :
missing value where TRUE/FALSE needed
Does anyone know what this error may mean?
Attached I have a subset of the data I used.
Data
structure(list(CHK_ACCT = c(0, 1, 3, 0, 0, 3, 3, 1, 3, 1, 1,
0, 1, 0, 0, 0, 3, 0, 1, 3, 3, 0, 0, 1, 3, 0, 3, 2, 1, 0, 1, 0,
1, 3, 2, 1, 3, 2, 2, 1, 3, 1, 1, 0, 0, 3, 3, 0, 3, 3, 1, 1, 3,
3, 1, 3, 1, 3, 2, 0, 1, 1, 1, 1, 3, 3, 3, 1, 3, 3, 3, 3, 0, 1,
0, 0, 0, 1, 3, 1, 3, 3, 3, 0, 0, 3, 1, 1, 0, 0, 3, 0, 3, 2, 1,
1, 3, 1, 1, 1, 3, 1, 3, 1, 3, 1, 3, 1, 0, 1, 1, 2, 1, 3, 0, 3,
0, 0, 0, 1, 0, 3, 3, 2, 1, 0, 0, 1, 1, 0, 1, 0, 3, 3, 3, 3, 3,
1, 1, 2, 2, 1, 0, 0, 3, 1, 0, 3, 0, 3, 3, 3, 2, 1, 1, 0, 0, 0,
1, 3, 3, 3, 3, 1, 3, 3, 0, 1, 3, 1, 0, 3, 1, 1, 0, 3, 0, 0, 3,
0, 3, 1, 0, 3, 1, 3, 1, 1, 0, 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 1
), DURATION = c(6, 48, 12, 42, 24, 36, 24, 36, 12, 30, 12, 48,
12, 24, 15, 24, 24, 30, 24, 24, 9, 6, 10, 12, 10, 6, 6, 12, 7,
60, 18, 24, 18, 12, 12, 45, 48, 18, 10, 9, 30, 12, 18, 30, 48,
11, 36, 6, 11, 12, 24, 27, 12, 18, 36, 6, 12, 36, 18, 36, 9,
15, 36, 48, 24, 27, 12, 12, 36, 36, 36, 7, 8, 42, 36, 12, 42,
11, 54, 30, 24, 15, 18, 24, 10, 12, 18, 36, 18, 12, 12, 12, 12,
24, 12, 54, 12, 18, 36, 20, 24, 36, 6, 9, 12, 24, 18, 12, 24,
14, 6, 15, 18, 36, 12, 48, 42, 10, 33, 12, 21, 24, 12, 10, 18,
12, 12, 12, 12, 12, 48, 36, 15, 18, 60, 12, 27, 12, 15, 12, 6,
36, 27, 18, 21, 48, 6, 12, 36, 18, 6, 10, 36, 24, 24, 12, 9,
12, 24, 6, 24, 18, 15, 10, 36, 6, 18, 11, 24, 24, 15, 12, 24,
8, 21, 30, 12, 6, 12, 21, 36, 36, 21, 24, 18, 15, 9, 16, 12,
18, 24, 48, 27, 6, 45, 9, 6, 12, 24, 18), HISTORY = c(4, 2, 4,
2, 3, 2, 2, 2, 2, 4, 2, 2, 2, 4, 2, 2, 4, 0, 2, 2, 4, 2, 4, 4,
4, 2, 0, 1, 2, 3, 2, 2, 2, 4, 2, 4, 4, 2, 2, 2, 2, 2, 3, 4, 4,
4, 2, 2, 4, 2, 3, 3, 2, 2, 3, 1, 2, 4, 2, 4, 2, 4, 0, 0, 2, 2,
2, 2, 2, 2, 2, 4, 4, 4, 2, 4, 2, 3, 0, 2, 2, 2, 2, 2, 2, 4, 4,
2, 2, 0, 4, 4, 4, 4, 2, 0, 4, 2, 4, 3, 2, 2, 3, 4, 2, 4, 1, 2,
2, 2, 3, 2, 2, 4, 2, 4, 2, 4, 4, 4, 2, 4, 2, 4, 2, 4, 2, 2, 4,
4, 2, 3, 2, 2, 2, 4, 3, 2, 4, 2, 2, 2, 2, 2, 4, 1, 4, 4, 4, 4,
2, 2, 2, 4, 3, 2, 4, 1, 2, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 4, 0,
2, 3, 2, 3, 1, 2, 4, 2, 4, 3, 3, 1, 4, 4, 4, 1, 4, 2, 0, 2, 0,
2, 2, 2, 4, 4, 2, 2, 3), NEW_CAR = c(0, 0, 0, 0, 1, 0, 0, 0,
0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 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, 1, 0,
0, 0, 0, 0, 1, 1, 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, 1, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1,
1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), USED_CAR = c(0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 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, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,
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, 1, 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, 0, 0, 0, 0, 0, 1, 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, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0), FURNITURE = c(0,
0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 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, 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, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0,
0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 1, 0, 1), `RADIO/TV` = c(1, 1, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,
1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1,
0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,
0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 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, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1,
1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1,
1, 0, 1, 0, 0, 0), EDUCATION = c(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, 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, 1, 0, 0, 0, 0,
0, 1, 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, 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, 1, 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), RETRAINING = c(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, 1, 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, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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, 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, 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, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0), AMOUNT = c(1169,
5951, 2096, 7882, 4870, 9055, 2835, 6948, 3059, 5234, 1295, 4308,
1567, 1199, 1403, 1282, 2424, 8072, 12579, 3430, 2134, 2647,
2241, 1804, 2069, 1374, 426, 409, 2415, 6836, 1913, 4020, 5866,
1264, 1474, 4746, 6110, 2100, 1225, 458, 2333, 1158, 6204, 6187,
6143, 1393, 2299, 1352, 7228, 2073, 2333, 5965, 1262, 3378, 2225,
783, 6468, 9566, 1961, 6229, 1391, 1537, 1953, 14421, 3181, 5190,
2171, 1007, 1819, 2394, 8133, 730, 1164, 5954, 1977, 1526, 3965,
4771, 9436, 3832, 5943, 1213, 1568, 1755, 2315, 1412, 1295, 12612,
2249, 1108, 618, 1409, 797, 3617, 1318, 15945, 2012, 2622, 2337,
7057, 1469, 2323, 932, 1919, 2445, 11938, 6458, 6078, 7721, 1410,
1449, 392, 6260, 7855, 1680, 3578, 7174, 2132, 4281, 2366, 1835,
3868, 1768, 781, 1924, 2121, 701, 639, 1860, 3499, 8487, 6887,
2708, 1984, 10144, 1240, 8613, 766, 2728, 1881, 709, 4795, 3416,
2462, 2288, 3566, 860, 682, 5371, 1582, 1346, 1924, 5848, 7758,
6967, 1282, 1288, 339, 3512, 1898, 2872, 1055, 1262, 7308, 909,
2978, 1131, 1577, 3972, 1935, 950, 763, 2064, 1414, 3414, 7485,
2577, 338, 1963, 571, 9572, 4455, 1647, 3777, 884, 1360, 5129,
1175, 674, 3244, 4591, 3844, 3915, 2108, 3031, 1501, 1382, 951,
2760, 4297), SAV_ACCT = c(4, 0, 0, 0, 0, 4, 2, 0, 3, 0, 0, 0,
0, 0, 0, 1, 4, 4, 0, 2, 0, 2, 0, 1, 4, 0, 0, 3, 0, 0, 3, 0, 1,
4, 0, 0, 0, 0, 0, 0, 2, 2, 0, 1, 0, 0, 2, 2, 0, 1, 4, 0, 0, 4,
0, 4, 4, 0, 0, 0, 0, 4, 0, 0, 0, 4, 0, 3, 0, 4, 0, 4, 0, 0, 4,
0, 0, 0, 4, 0, 4, 2, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 4, 4, 3, 0,
4, 1, 0, 4, 1, 0, 0, 0, 4, 0, 0, 0, 4, 2, 1, 0, 0, 0, 2, 4, 4,
4, 2, 2, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 4, 0, 0, 0, 1, 4, 3, 2,
4, 0, 3, 0, 0, 0, 0, 1, 0, 1, 0, 3, 1, 0, 0, 3, 1, 0, 1, 0, 1,
4, 1, 0, 2, 0, 2, 2, 0, 3, 0, 0, 0, 0, 0, 0, 0, 4, 0, 2, 0, 0,
0, 0, 4, 3, 0, 0, 0, 0, 1, 0, 3, 1, 0, 0, 1, 0, 0, 1, 4, 0),
EMPLOYMENT = c(4, 2, 3, 3, 2, 2, 4, 2, 3, 0, 1, 1, 2, 4,
2, 2, 4, 1, 4, 4, 2, 2, 1, 1, 2, 2, 4, 2, 2, 4, 1, 2, 2,
4, 1, 1, 2, 2, 2, 2, 4, 2, 2, 3, 4, 1, 4, 0, 2, 2, 1, 4,
2, 2, 4, 2, 0, 2, 4, 1, 2, 4, 4, 2, 1, 4, 1, 2, 2, 2, 2,
4, 4, 3, 4, 4, 1, 3, 2, 1, 1, 4, 2, 4, 4, 2, 1, 2, 3, 3,
4, 4, 4, 4, 4, 1, 3, 2, 4, 3, 4, 3, 2, 3, 1, 2, 4, 3, 1,
4, 4, 1, 3, 2, 4, 4, 3, 1, 2, 3, 2, 4, 2, 4, 1, 2, 2, 2,
0, 2, 3, 2, 1, 2, 3, 4, 2, 2, 3, 2, 1, 1, 2, 2, 1, 3, 4,
3, 2, 4, 4, 2, 2, 4, 3, 2, 4, 4, 3, 2, 4, 1, 3, 0, 4, 2,
0, 1, 3, 4, 4, 2, 0, 2, 1, 0, 2, 4, 3, 4, 1, 2, 2, 2, 4,
2, 4, 0, 3, 2, 2, 3, 2, 3, 2, 4, 2, 1, 4, 4), INSTALL_RATE = c(4,
2, 2, 2, 3, 2, 3, 2, 2, 4, 3, 3, 1, 4, 2, 4, 4, 2, 4, 3,
4, 2, 1, 3, 2, 1, 4, 3, 3, 3, 3, 2, 2, 4, 4, 4, 1, 4, 2,
4, 4, 3, 2, 1, 4, 4, 4, 1, 1, 4, 4, 1, 3, 2, 4, 1, 2, 2,
3, 4, 2, 4, 4, 2, 4, 4, 2, 4, 4, 4, 1, 4, 3, 2, 4, 4, 4,
2, 2, 2, 1, 4, 3, 4, 3, 4, 4, 1, 4, 4, 4, 4, 4, 4, 4, 3,
4, 4, 4, 3, 4, 4, 3, 4, 2, 2, 2, 2, 1, 1, 1, 4, 3, 4, 3,
4, 4, 2, 1, 3, 3, 4, 3, 4, 4, 4, 4, 4, 4, 3, 1, 4, 2, 4,
2, 4, 2, 4, 4, 2, 2, 4, 3, 2, 4, 4, 1, 4, 3, 4, 2, 1, 4,
2, 4, 2, 3, 4, 2, 1, 3, 4, 4, 2, 4, 1, 4, 4, 2, 4, 4, 4,
3, 4, 2, 4, 2, 4, 4, 4, 1, 2, 4, 4, 4, 4, 2, 2, 4, 1, 2,
4, 4, 2, 4, 2, 1, 4, 4, 4), MALE_DIV = c(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, 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, 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, 1, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1), MALE_SINGLE = c(1, 0, 1, 1, 1, 1, 1, 1, 0, 0,
0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1,
1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0,
1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,
0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0,
0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0,
1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1,
0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1,
1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0,
1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0,
0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0),
MALE_MAR_or_WID = c(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, 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,
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, 0, 0, 1, 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, 1, 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, 0, 0, 0, 0, 0, 1, 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, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0), `CO-APPLICANT` = 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, 1, 0,
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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), GUARANTOR = c(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, 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, 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, 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, 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, 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), PRESENT_RESIDENT = c(4, 2, 3, 4, 4, 4, 4,
2, 4, 2, 1, 4, 1, 4, 4, 2, 4, 3, 2, 2, 4, 3, 3, 4, 1, 2,
4, 3, 2, 4, 3, 2, 2, 4, 1, 2, 3, 2, 2, 3, 2, 1, 4, 4, 4,
4, 4, 2, 4, 2, 2, 2, 2, 1, 4, 2, 1, 2, 2, 4, 1, 4, 4, 2,
4, 4, 2, 1, 4, 4, 2, 2, 4, 1, 4, 4, 3, 4, 2, 1, 1, 3, 4,
4, 4, 2, 1, 4, 3, 3, 4, 3, 3, 4, 4, 4, 2, 4, 4, 4, 4, 4,
2, 3, 4, 3, 4, 2, 2, 2, 2, 4, 3, 2, 1, 1, 3, 3, 4, 3, 2,
2, 2, 4, 3, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 2, 2, 3, 2, 2,
2, 1, 2, 2, 4, 2, 4, 3, 2, 4, 4, 4, 1, 4, 4, 4, 4, 1, 3,
2, 4, 1, 3, 4, 4, 2, 2, 1, 4, 4, 3, 1, 2, 2, 1, 1, 1, 4,
2, 4, 1, 2, 2, 4, 4, 2, 4, 3, 1, 4, 3, 4, 2, 2, 4, 3, 1,
4, 4, 3), REAL_ESTATE = c(1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0,
1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0,
0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0,
0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1,
0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0,
0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0), PROP_UNKN_NONE = c(0,
0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 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, 1, 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, 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, 1, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 1, 1), AGE = c(67, 22, 49, 45, 53, 35,
53, 35, 61, 28, 25, 24, 22, 60, 28, 32, 53, 25, 44, 31, 48,
44, 48, 44, 26, 36, 39, 42, 34, 63, 36, 27, 30, 57, 33, 25,
31, 37, 37, 24, 30, 26, 44, 24, 58, 35, 39, 23, 39, 28, 29,
30, 25, 31, 57, 26, 52, 31, 23, 23, 27, 50, 61, 25, 26, 48,
29, 22, 37, 25, 30, 46, 51, 41, 40, 66, 34, 51, 39, 22, 44,
47, 24, 58, 52, 29, 27, 47, 30, 28, 56, 54, 33, 20, 54, 58,
61, 34, 36, 36, 41, 24, 24, 35, 26, 39, 39, 32, 30, 35, 31,
23, 28, 25, 35, 47, 30, 27, 23, 36, 25, 41, 24, 63, 27, 30,
40, 30, 34, 29, 24, 29, 27, 47, 21, 38, 27, 66, 35, 44, 27,
30, 27, 22, 23, 30, 39, 51, 28, 46, 42, 38, 24, 29, 36, 20,
48, 45, 38, 34, 36, 30, 36, 70, 36, 32, 33, 20, 25, 31, 33,
26, 34, 33, 26, 53, 42, 52, 31, 65, 28, 30, 40, 50, 36, 31,
74, 68, 20, 33, 54, 34, 36, 29, 21, 34, 28, 27, 36, 40),
OTHER_INSTALL = c(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, 1,
0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,
0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0,
1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 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, 0,
0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 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, 0, 0, 0, 1, 0, 0,
1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0), RENT = c(0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0,
0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 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, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,
0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,
0, 0, 1, 0, 0, 0, 1, 1, 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,
1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0,
0, 0, 1, 1, 0, 0, 1, 0, 0), OWN_RES = c(1, 1, 1, 0, 0, 0,
1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,
1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0,
0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,
1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0,
0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1,
1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1,
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1,
1, 0, 0, 1), NUM_CREDITS = c(2, 1, 1, 1, 2, 1, 1, 1, 1, 2,
1, 1, 1, 2, 1, 1, 2, 3, 1, 1, 3, 1, 2, 1, 2, 1, 1, 2, 1,
2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1,
2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 4, 1,
1, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2,
2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1,
2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 2,
1, 2, 1, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 3, 1, 1, 1, 1,
1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 2,
2, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 2, 2, 2,
2, 2, 1, 1, 2, 1, 3, 1, 2, 3, 1, 1, 1, 1, 2, 2, 4, 1, 1),
JOB = c(2, 2, 1, 2, 2, 1, 2, 3, 1, 3, 2, 2, 2, 1, 2, 1, 2,
2, 3, 2, 2, 2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 1, 3, 1,
2, 2, 2, 2, 3, 2, 1, 2, 1, 3, 2, 0, 1, 2, 1, 3, 2, 2, 2,
1, 3, 2, 3, 1, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 1, 3, 1,
3, 3, 2, 2, 1, 2, 2, 2, 1, 1, 1, 3, 2, 2, 3, 2, 2, 2, 1,
2, 2, 2, 2, 2, 2, 3, 1, 2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 2,
1, 2, 2, 2, 3, 2, 2, 3, 2, 3, 1, 2, 2, 2, 1, 2, 3, 2, 2,
2, 1, 2, 2, 2, 2, 1, 2, 1, 0, 3, 3, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 3, 2, 2, 1, 2, 1, 2, 2, 2, 3, 2, 2, 2, 2, 2,
2, 2, 2, 3, 2, 2, 3, 2, 2, 3, 2, 2, 3, 1, 2, 2, 2, 3, 0,
2, 2, 3, 1, 2, 2, 2, 3, 2, 2, 2, 3), NUM_DEPENDENTS = c(1,
1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2,
1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,
1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1,
1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1,
1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,
1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1,
1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1,
2, 2, 1, 1, 1, 1, 1, 1, 1), TELEPHONE = c(1, 0, 0, 0, 0,
1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1,
0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1,
1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1,
1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,
0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0,
1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0,
1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,
0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1,
0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0,
1, 1, 0, 1, 1), FOREIGN = c(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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 1, 0, 0, 0, 0, 1, 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),
DEFAULT = c(0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,
1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0,
1, 0, 1, 0, 0, 0, 0, 0, 1, 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, 0, 0, 1,
0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,
1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1)), row.names = c(NA,
-200L), class = c("tbl_df", "tbl", "data.frame"))

Using R to create forest plots of coefficients for regressions on subsamples

I have a dataset of chess positions made up of 100 groups, with each group taking one of 50 positions ("Position_number") and one of two colours ("stm_white"). I want to run a linear regression for each Position_number subsample, where stm_white is the explanatory variable and stm_perform is the outcome variable. Then, I want to display the coefficient of stm_white and the associated confidence interval for each regression in a forest plot. The idea is to be able to easily see which Position_number subsample gives significant coefficients for stm_white, and to compare coefficients across positions. For example, the plot would have 50 y-axis categories labelled with each position number, the x-axis would represent the coefficient range, and the plot would display a horizontal confidence bar for each position number.
Where I'm stuck:
Getting the confidence interval bounds for each regression
Plotting each of the 50 coefficients (with confidence intervals) on one plot. (I think this is called a forest plot?)
This is how I current get a list of the coefficients for each regression:
fits <- by(df, df[,"Position_number"],
function(x) lm(stm_perform ~ stm_white, data = x))
# Combine coefficients from each model
do.call("rbind", lapply(fits, coef))
And here is a sample of 10 positions (apologies if there's a better way to show reproducible data):
>dput(droplevels(dfMWE[,c("Position_number","stm_white","stm_perform")]))
structure(list(Position_number = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10), stm_white = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1), stm_perform = c(0.224847134350316, -0.252000458803946,
0.263005239459311, -0.337712202569111, 0.525880930891169, -0.5,
0.514387184165999, 0.520136722035817, -0.471249436107731, -0.557311633762293,
-0.382774969095054, -0.256365477992672, -0.592466230584332, 0.420100239642119,
0.35728693116738, -0.239203909010858, 0.492804918290949, -0.377349804212738,
0.498560888290847, 0.650604627933873, 0.244481117928803, 0.225852022298169,
0.448376452689039, 0.305090287270497, 0.275461757157464, 0.0232950364735793,
-0.117225030904946, 0.103523492101814, 0.098301745397805, 0.435599509759579,
-0.323024628921732, -0.790798102797238, 0.326223812111678, -0.331305043692668,
0.300230596737942, -0.340292005855252, 0.196181480575316, -0.0606495585093978,
0.789844179758131, -0.0862623926308338, -0.560150145231903, 0.697345078589853,
-0.425719796345476, 0.65321716721887, -0.878090073942596, 0.393712176214572,
0.636076899687882, 0.530184680003902, -0.567228844342952, 0.767024918145021,
-0.207303615824231, -0.332581578126777, -0.511510891217792, 0.227871326531416,
-0.0140876421179904, -0.891010911045765, -0.617225030904946,
-0.335142021445235, -0.517262524432376, 0.676301669492737, 0.375998241382333,
-0.0882899718631629, -0.154706189382, -0.108431333126633, 0.204584592662721,
0.475554538879339, 0.0840205872617279, -0.403370826694226, -0.74253555894307,
0.182570385474772, -0.484175014735265, -0.332581578126777, -0.427127748605496,
0.474119069108831, -0.0668284645696687, -0.0262098994728823,
-0.255269593134965, -0.313699742316688, -0.485612815834001, 0.302654921410147,
-0.425719796345476, 0.65321716721887, 0.393712176214572, 0.60766106412682,
0.530184680003902, 0.384135895746244, 0.564400490240421, 0.767024918145021,
0.702182602090521, 0.518699777929559, -0.281243170101218, -0.283576305897061,
0.349395372066127, -0.596629173305774, 0.0849108889395813, -0.264122555898524,
0.593855385236178, -0.418698521631085, 0.269754586702576, -0.719919005947152,
0.510072446927438, -0.0728722513945044, -0.0849108889395813,
0.0650557537775339, 0.063669188530584, -0.527315973006493, -0.716423694102939,
-0.518699777929559, 0.349395372066127, -0.518699777929559, 0.420100239642119,
-0.361262250888275, 0.431358608116332, 0.104596852632671, 0.198558626418023,
0.753386077785615, 0.418698521631085, -0.492804918290949, -0.636076899687882,
-0.294218640287997, 0.617225030904946, -0.333860575416878, -0.544494573083008,
-0.738109032540419, -0.192575818328721, -0.442688366237707, 0.455505426916992,
0.13344335621046, 0.116471711943561, 0.836830966002895, -0.125024693001636,
0.400603203290743, -0.363923100312118, -0.157741327529574, -0.281243170101218,
-0.326223812111678, -0.548774335859742, 0.104058949158278, -0.618584122089031,
-0.148779202375097, -0.543066492022212, -0.790798102797238, -0.541637702714763,
0.166337530816562, -0.431358608116332, -0.471249436107731, -0.531618297828107,
-0.135452994588696, 0.444109038883147, -0.309993792719686, 0.472684026993507,
-0.672509643334985, -0.455505426916992, -0.0304828450187082,
-0.668694956307332, 0.213036720610531, -0.370611452782498, -0.100361684849949,
-0.167940159469667, -0.256580594295053, 0.41031649686005, 0.544494573083008,
-0.675040201040299, 0.683816314193659, 0.397841906825283, 0.384135895746244,
0.634743335052317, 0.518699777929559, -0.598013765769344, -0.524445461120661,
-0.613136820153143, 0.12949974225673, -0.337712202569111, -0.189904841395243,
0.588289971863163, 0.434184796930767, -0.703385003471829, 0.505756208411145,
0.445530625978324, -0.167137309739621, 0.437015271896404, -0.550199353253537,
-0.489927553072562, -0.791748837508184, 0.434184796930767, 0.264122555898524,
-0.282408276808469, -0.574280203654524, 0.167940159469667, -0.439849854768097,
-0.604912902007957, 0.420100239642119, 0.35728693116738, 0.239220254140668,
-0.276612130560829, -0.25746444105693, 0.593855385236178, -0.632070012100074,
0.314483587504712, 0.650604627933873, -0.226860086923233, -0.702182602090521,
0.25746444105693, -0.174474012638818, 0.0166045907672774, 0.535915926945102,
0.141635395826102, 0.420100239642119, 0.557311633762293, 0.593855385236178,
0.6961287704296, 0.0444945730830079, -0.234005329233511, 0.448376452689039,
-0.86655664378954, 0.22107824319756, 0.148051654147426, 0.543066492022212,
-0.448376452689039, 0.373300918333268)), row.names = c(NA, -220L
), groups = structure(list(Position_number = c(0, 1, 2, 3, 4,
5, 6, 7, 8, 9, 10), .rows = structure(list(1:20, 21:40, 41:60,
61:80, 81:100, 101:120, 121:140, 141:160, 161:180, 181:200,
201:220), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), row.names = c(NA, 11L), class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"))

confint() can get you the confidence interval of a model.
forestplot() from the forestplot R package can make you a forest plot.
library(dplyr)
library(forestplot)
results <- lapply(unique(df$Position_number), function(pos) {
fit = filter(df, Position_number == pos) %>%
lm(data = ., stm_perform ~ stm_white)
stm_white_lm_index = 2 # the second term in lm() output is "stm_white"
coefficient = coef(fit)[stm_white_lm_index]
lb = confint(fit)[stm_white_lm_index,1] # lower bound confidence
ub = confint(fit)[stm_white_lm_index,2] # upper bound confidence
output = data.frame(Position_number = pos, coefficient, lb, ub)
return(output)
}) %>% bind_rows() # bind_rows() combines output from each model in the list
with(results, forestplot(Position_number, coefficient, lb, ub))
The forest plot shows the "Position_number" labels on the left and the regression coefficients of "stm_white" with the 95% confidence intervals plotted. You can further customize the plot. See forestplot::forestplot() or this introduction by Max Gordon for details.

Error message when reorganizing duplicated columns in a dataframe with specifics names

Ive been struggling to solve this.
This is my dataframe:
df<-structure(list(CESP6.MRVE3 = c(3.39731078, 3.351139056, 3.541515582,
3.329084161, 3.117085569, 2.630336741, 3.263587913, 3.2854739,
2.115273321, 1.255217018, 0.558756742, -0.478780166, -0.667471284,
-0.814286812, -1.489226751, -1.957340764, -2.397829895, -2.106376737,
-1.799451962, -0.699075436, -0.686257093, -0.397822857, -0.079821449,
-0.532685698, -0.502453448, 0.124038498, -0.183209859, -0.207727845,
-0.421813003, -1.550648396, -0.940915675, -0.282337163, -0.282104914,
0.051723363, -0.095957822, -0.356446953, -0.586070428, -0.003895889,
-0.706760138, -0.499624387, -0.265363282, 0.33515752, -0.056408244,
-1.048118284, -1.251337786, -1.19362493, -1.519219549, -1.663016155,
-1.036524208, -1.006369535, -1.066137285, -0.991731905, -1.463586221,
-1.419036564, -1.121612261, -1.571668563, -1.494677088, -1.21322393,
-0.758818549, -0.418730576, -0.080007787, 0.240368738, 0.050667688,
-0.157657302, -0.327569328, -0.305106237, -0.070845132, 0.339675669,
0.435446235, 0.323159091, 0.305477906, 0.500815644, 0.697451866,
0.504088089, 0.185252695, 0.149946628, 0.098092312, -0.119733149,
-0.03216457, -0.430422859, 0.056069088, 0.079253559, 0.651283822,
0.302448429, 0.282680678, 0.137663163, 0.037606861, 0.175752544,
0.619292269, 0.252188188, 0.629112963, 0.504017872, 0.236836216,
-0.235883757, -0.3413341, -0.004198349, 0.018409018, 0.710295005,
0.514988938, 0.4701157, 0.528261383, 0.569070737, -0.062206474,
-0.597656818, -0.783107161, -0.670433093, -0.638114278, -0.705795463,
-0.793188095, -0.502023489, -0.202656896, -0.335732122, -0.624201387,
-0.282459676, -0.598342848, -0.705957332, -0.547667373, -0.703550544,
-0.958645635, -0.74899049, -0.367393055, 0.188666063, 0.852927168,
1.13423605, 1.400150892, 1.028440852, 0.96449997, 1.732501378,
2.084243089, 1.743609682, 1.587293682, 1.309112969, 1.048557137,
1.193750599, 1.337290323, 0.911695704, 0.793437415, 0.976832863,
0.682248176, 0.479172951, 0.59335648, 0.825741995, 0.201656837,
0.087715955, -0.200253782, -0.281464293, -0.423751438, -0.849923161,
-0.548758555, -0.775574083, -0.812678164, -0.918994164, -0.712146965,
-0.987741584, -0.999307348, -0.264546715, -0.110574162, 0.445984485,
0.453985892, 0.342420128, 0.344738943, 0.105759274, 0.000519907,
-0.4401135, -0.669592699, -0.851879843, -0.5891282, -0.32810787,
-0.253702489, -0.260806569, -0.02863203, 0.183398233, 0.067082233,
0.157891587, -0.261587611, -0.320423005, -0.704508163, -0.89442019,
-0.835053597, -0.499715859, -0.48286866, -0.675799609, -0.894779279,
-1.288575885, -1.754459056, -1.278111386, -1.262629372, -1.163618032,
-0.725183796, -0.505528651, -0.866084482, -1.142400482, -0.924687626,
-1.223090192, -0.949406191, -0.314712258, -0.191172534, 0.25963682,
0.058215332, -0.397812115, -0.393406735, -0.201598324, -0.067770047,
0.584115939, 0.347944216, 0.773070977, 0.653014674, 1.011737463,
0.958085133, 0.961624857, 1.128838184, 1.255330131, 1.110456892,
1.18112197, 1.675882603, 2.111364617, 1.567423735, 1.148732617,
1.60076288, 1.300850854, 1.642159736, 0.943546194, 0.645076929,
0.905164902, 1.041656849, 0.744552768, 1.02046761, 0.953718782,
0.627979887, 0.420442978, 0.16096378, -0.26340993, -0.277350812,
-0.239205128, -0.364511195, -0.034423222, -0.376277538, -0.197843302,
0.077061607, -0.304504157, -0.2299545, -1.078212789, -0.954884041,
-0.682276674, -0.435351899, -0.449226081, -0.174387872, -0.15653074,
-1.569106437, -1.558230383), ABEV3.JBSS3 = c(-0.125505311, -0.381380131,
-0.294037578, -0.298621426, -0.277723674, 0.387057047, 0.237627028,
-0.176649161, -0.102841696, -0.204853657, -0.010728477, -0.088211983,
0.291087137, -0.008015111, -0.500672558, -0.327838349, -0.381776712,
-0.444424104, -1.109018008, -1.502966426, -1.311740904, -1.240197666,
-1.028982199, -0.656137935, 1.150241417, 1.038229456, 1.044291093,
0.66550656, 0.792223738, 1.085375662, 0.698835245, 0.35779654,
0.052567206, 0.612173986, 0.083389453, 0.319778861, -0.009651158,
-0.512944035, -0.334300455, -0.687285673, -0.249287579, -0.843563768,
-0.350401788, -0.258858551, -0.224415656, -0.168036304, 0.283834704,
0.714414741, 0.161747239, -0.066064038, -0.374848571, -0.193950819,
-0.130798896, -0.071837601, -0.208030136, 0.039322473, -0.03913429,
0.248536034, -0.052184956, -0.222250405, -0.172315854, 0.492137096,
0.095606735, -0.194468769, -0.44615296, -0.284927438, -0.294029686,
-0.388295819, -0.078033498, -0.263580547, -0.260100853, -0.300811787,
0.256223106, 0.450348286, 0.327373124, 0.170197277, 0.040449544,
0.541357351, 0.129345389, 0.848962225, 0.541458608, 0.374610532,
0.276471485, 0.500596665, 0.462785388, 0.219492511, 0.589427062,
0.190642529, 0.064439938, 0.586956432, 0.136880927, 0.06906965,
-0.110678083, 0.292483896, 0.230154219, -0.0457206, -0.18159542,
-0.597798011, -0.384963802, -0.235357022, -0.228649899, -0.143879233,
-0.104917938, 0.0021069, -0.023450205, -0.023515654, -0.189718245,
0.384734706, 0.199177601, 0.213302781, 0.097427962, -0.441038856,
-0.135622705, -0.243433982, -0.495118173, -0.271310708, 0.030242585,
0.139531651, 0.436238773, 0.493263611, 0.217706506, 0.29989523,
0.237565553, 0.472981704, 0.566133628, 0.630258808, 0.63567496,
0.614964025, 0.536179492, 0.643532101, 0.744112137, 0.422100176,
-0.059911786, 0.03937728, 0.356412173, -0.134954303, 0.076588935,
-0.64833274, -0.674525275, -0.693299752, -0.453365201, -0.547303564,
-0.419950956, 0.089010339, 0.096362947, -0.129839643, -0.425386692,
-0.175452141, -0.193908904, -0.04075698, -0.194705398, -0.169934733,
-0.194200866, -0.047831569, 0.282748467, 0.23397399, 0.277135969,
0.233187551, 0.242158901, 0.164019854, 0.303626634, 0.454852156,
0.668987392, 0.807303201, 0.883682553, 1.13426259, 0.662896114,
0.843813977, 0.119865559, -0.090855431, -0.138666708, -0.033896042,
0.243456566, -0.103719281, -0.4641125, -0.323532464, -0.151026025,
0.359881782, 0.246578849, 0.140386314, 0.550648636, 0.674446045,
0.215661512, 0.294305092, -0.039315556, 0.12093671, -0.180429765,
0.400150271, 0.187492824, -0.08289034, -0.053918989, -0.33560318,
-0.30663183, -0.385088593, -0.155154042, -0.466520517, -0.092395337,
-0.321507641, -0.363191832, -0.394220481, -0.877205699, -0.64888989,
-0.393145968, -0.713231528, -1.132026116, -1.415646764, -0.897320899,
-0.568359605, -0.672625738, -0.239146044, 0.204333651, -0.085741854,
0.14225624, 0.26089982, -0.044657285, 0.363986295, 0.579402447,
0.411908885, 0.466679551, 0.512413417, 0.83848511, 0.994556802,
0.753835812, 0.724415849, 0.053694859, 0.08878324, 0.150008762,
0.395107198, 0.110195579, 0.093347503, -1.15091863, -0.282910481,
-0.267494329, -0.215633377, 0.022036947, -0.42029273, -0.194231093,
-0.195269798, -0.321790104, -0.20927361, -0.2899946, -0.183287477,
-0.255627209, -0.132475285, -0.411259818, -0.449716581, -0.291400772,
-0.083074907, -0.093468127, 0.092911226), ABEV3.GOAU4 = c(-0.096399151,
-0.430291395, -0.181638865, -0.199604415, -0.159431687, 0.126334543,
0.089393571, 0.136005281, -0.03229346, 0.172789718, 0.262458491,
-0.002791521, -0.021944077, -0.341429824, -0.525159639, -0.464133097,
-0.517862913, -0.67650318, -1.187509124, -1.416482581, -1.415968328,
-1.382063821, -1.091037279, -1.096116528, 0.720332754, 0.756782036,
0.796100948, 0.446452774, 0.171031998, 0.690521674, 0.704946805,
0.315973347, -0.031650677, 0.570725299, 0.068873875, 0.097688834,
-0.240430809, -0.740941134, -0.567215724, -0.573140453, -0.243983969,
-0.621932849, -0.263288653, -0.200571151, 0.036715276, 0.173668511,
0.347069065, 0.654867781, 0.597926809, 0.429790496, 0.196410541,
0.31726632, 0.459138342, 0.569311069, 0.279142271, 0.348981807,
0.16238236, 0.575433053, 0.428321318, 0.104258311, -0.019633933,
0.422912806, 0.266988077, 0.066998376, -0.116219151, -0.141298401,
-0.19722313, -0.1914569, -0.236365387, -0.491794497, -0.421288579,
-0.556880117, -0.229081401, -0.068908674, -0.212288629, -0.153465335,
-0.244479614, -0.023461407, -0.245150402, 0.308404245, 0.475357481,
0.380619757, 0.41113401, 0.592493743, 0.530633985, 0.305546401,
0.523686642, 0.160648213, 0.09591049, 0.421847483, 0.072874025,
0.106599435, 0.027276116, 0.232026103, 0.16728838, -0.080847933,
-0.246268709, -0.57456745, -0.356768734, -0.237099961, -0.399463673,
-0.2755675, 0.048149575, 0.304419759, 0.318665792, 0.226293745,
0.119352773, 0.42783454, 0.279877325, 0.32614751, 0.271059925,
-0.023507036, -0.013488402, -0.09907994, -0.29331371, -0.232295503,
-0.111114867, -0.16721036, 0.081604599, 0.206354586, 0.100592285,
0.286520943, 0.223986468, 0.446020918, 0.63331568, 0.568748719,
0.534344186, 0.447394879, 0.567396844, 0.563333837, 0.57638453,
0.429272794, 0.111144816, 0.089114295, 0.064547334, -0.286800135,
-0.064424159, -0.383909906, -0.66322489, -0.7757677, -0.517977319,
-0.71085332, -0.822704743, -0.207450802, -0.395237254, -0.352869613,
-0.887948863, -0.750150147, -0.751164425, -0.497259918, -0.400306682,
-0.512670394, -0.313522244, 0.239024494, 0.349880274, 0.379190851,
0.461891683, 0.624779948, 0.335123438, 0.374621449, 0.570216916,
0.579202638, 0.462424094, 0.78597874, 0.892257259, 0.960568264,
0.65125328, 0.363279395, -0.041466663, -0.155862861, -0.201283637,
-0.209411615, 0.040248824, 0.006015054, -0.183820553, -0.135176358,
0.13330541, 0.37533986, 0.636691258, 0.385335454, 0.52855691,
0.674160712, 0.478056884, 0.437213369, 0.120272397, 0.191469702,
0.122658672, 0.639945099, 0.628256104, 0.16096527, -0.108691239,
-0.141242384, -0.424118385, -0.467506675, -0.550203578, -0.750193279,
-0.372565326, -0.353742033, -0.329162808, -0.623567341, -0.724240093,
-0.257807481, -0.2342445, -0.1342342, -0.123378421, -0.385912896,
-0.151162909, 0.094611656, 0.096987632, 0.557835076, 0.889877861,
0.727343386, 0.861922611, 0.872095338, 0.610747869, 0.767709439,
0.921272421, 0.966526362, 1.036186801, 1.18415628, 1.129751747,
0.801794532, 0.745528276, 0.483164564, -0.102247876, -0.020375854,
-0.249007786, -0.317981244, -0.465776031, -0.340009801, -1.570170265,
-1.063900081, -1.037629896, -0.921009852, -0.63286961, -1.026932617,
-0.820491669, -0.776928688, -0.946585198, -0.885233799, -0.704386355,
-0.566254448, -0.368276634, -0.18623385, -0.294874117, -0.373172858,
-0.119951402, -0.30977034, -0.171638433, 0.042436838), CESP6.PETR4 = c(2.698640815,
2.652045837, 2.937365971, 2.693377102, 2.525824567, 2.41656399,
2.862150684, 2.900076928, 1.863639826, 1.008959961, 0.514595849,
-0.256362475, -0.403757133, -0.396946322, -0.584390173, -1.167579363,
-1.610877236, -1.383908548, -1.022309956, 0.109713073, -0.055233353,
0.190886461, 0.309187948, -0.156291597, -0.30059622, 0.286481151,
0.296480384, 0.163715631, -0.082612787, -1.340165322, -0.513087952,
0.20085019, 0.239042993, 0.382556698, 0.304313167, 0.1043124,
0.028408418, 0.758674211, 0.05223711, -0.033558189, 0.366707604,
0.694160217, 0.395916686, -0.577539063, -0.72881314, -0.754184002,
-0.987906311, -1.25343505, -0.688963788, -0.811146228, -0.74705021,
-0.734494061, -1.208907367, -1.25060588, -0.915127869, -1.405019953,
-1.62368792, -1.329858462, -1.009641863, -0.634321728, -0.350541464,
0.141639442, -0.545804409, -0.553248261, -0.633673464, -0.507395774,
-0.27893641, 0.310638386, 0.208673313, 0.225799877, 0.295641233,
0.113251723, 0.585166069, 0.603783309, 0.151709553, 0.028362486,
0.032725064, -0.136476023, -0.315568427, -0.659823856, -0.117800827,
0.221773969, 0.007795396, -0.302471931, -0.410873338, -0.39885031,
-1.085810235, -1.054211642, -0.806443275, -1.079632465, -0.657076316,
-0.557718886, -0.835380104, -1.277295984, -1.509685494, -1.144474042,
-1.155481559, -0.669095185, -0.644999167, -0.439679033, -0.185158578,
-0.231111753, -0.492336636, -0.797549622, -0.951696368, -0.476968843,
-0.262181828, -0.301116356, -0.358826767, -0.386270618, 0.215001924,
0.150164182, 0.118623546, 0.378731462, 0.158306258, -0.203076502,
-0.003926142, -0.095999899, -0.682752753, -0.681154161, -0.402003801,
0.228528551, 0.841775696, 1.033265605, 1.425179951, 1.053639314,
0.964704787, 1.641515597, 1.956835732, 1.330349437, 1.193487899,
1.195777487, 1.355085724, 1.504127401, 1.592053644, 1.398489212,
1.356839892, 1.237747488, 1.19881296, 1.2674302, 1.194556764,
1.195780113, 0.869500888, 0.80290591, 0.45232283, 0.187376404,
-0.056079345, -0.631874644, -0.631233608, -0.944047554, -0.955055071,
-0.992183169, -0.604474292, -0.697772165, -0.932501224, -0.428138645,
-0.298988286, 0.360635704, 0.634623039, 0.544039959, 0.367129227,
0.123298235, -0.054096423, -0.63590362, -0.921432358, -1.232015439,
-0.869034853, -0.513398966, -0.428503268, -0.346213679, -0.196530199,
0.07495971, 0.100171161, 0.352885186, 0.094375095, 0.056022881,
-0.44168753, -0.591846174, -0.519931829, -0.223229701, -0.391739792,
-0.527268531, -0.678759974, -0.923015403, -1.438119705, -1.252582204,
-1.313698404, -0.771359622, -0.587737234, -0.445023209, -0.593691176,
-0.827946605, -0.598371809, -0.935924344, -0.972835075, -0.444267029,
-0.493892553, -0.156973059, -0.352768357, -0.797448223, -1.040262169,
-0.991062616, -1.074676242, -0.601162537, -0.851321181, -0.469880465,
-0.580039109, -0.362162059, -0.43987247, -0.419606677, 0.092574228,
0.224863817, 0.37539617, 0.537419199, 0.865078882, 1.16970802,
0.496785391, -0.043373253, 0.531413762, 0.343594667, 0.626733129,
0.227532042, 0.32162806, 0.350196106, 0.460837142, 0.329987502,
0.442543651, 0.346373109, 0.236372342, 0.015098265, -0.060805717,
-0.328466934, 0.134454161, 0.246901626, 0.160148771, 0.629457008,
0.497541128, 0.895891808, 1.232485252, 1.229078697, 1.190252852,
0.495464304, 0.580735244, 0.968928048, 1.225314422, 0.944622658,
1.717385877, 1.537335917, 0.04526216, -0.29505436), CESP6.MRVE3.new = c(2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2), ABEV3.JBSS3.new = 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), ABEV3.GOAU4.new = 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), CESP6.PETR4.new = c(2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)), class = "data.frame", row.names = c(NA,
-250L))
As you can seeI have columns named with begin names plus ".new"
I want do put them together, It is possible to do this with dplyr package. So the output will be:
CESP6.MRVE3|CESP6.MRVE3.new|ABEV3.JBSS3|ABEV3.JBSS3.new|ABEV3.GOAU4|ABEV3.GOAU4.new|CESP6.PETR4|CESP6.PETR4.new
This is what I am doing:
df %>%
select(flatten_chr(map(colnames(df), ~c(.x , paste0(.x , ".new")))))
This is the wrong messge I am receveing:
Error: Unknown columns `CESP6.MRVE3.new.new`, `ABEV3.JBSS3.new.new`, `ABEV3.GOAU4.new.new` and `CESP6.PETR4.new.new`
In addition: Warning message:
'glue::collapse' is deprecated.
Use 'glue_collapse' instead.
See help("Deprecated") and help("glue-deprecated").
What am I doing wrong??? Any help?

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,
0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 5, 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, 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, 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?

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