I am working with a huge data set where all columns look something like this:
0
10
12
30
10
0
20
30
0
40
50
10
0
The idea is to make a simple plot in R where every time it reads a 0 the plot will begin in (0,0).
Do you have any idea of how I can do this?
Thanks in advance,
J
UPDATE:
I am a new user so I can't post any images!
Here's an example of the column I want to plot:
0
10
20
12
5
6
9
0
20
24
40
14
0
20
59
50
12
0
20
23
49
45
23
12
(...)
Image a line plot.
Instead of plotting a long line with all the values I want to plot several shorter lines with the first line plotting (0,10,20,12,5,6,9), the second line plotting (0,20,24,40,14) etc...
I would add an additional column specifying which subdataset your are:
Value Group
0 1
1 1
5 1
0 2
Etc.
You can then plot the subgroups using e.g. ggplot2:
ggplot(yourdata, aes(x = xcoor, y = Value, color = Group)) +
geom_line()
Which will draw the lines with different colors. Or using plot using something like:
split_dat = with(yourdata, split(Value, Group))
plot(split_dat[[1]])
for(i in 2:length(split_dat)) {
lines(split_dat[[i]])
}
Related
My general problem: I tend to struggle using ggplot, because it's very data-frame-centric but the objects I work with seem to fit matrices better than data frames. Here is an example (adapted a little).
I have a quantity x that can assume values 0:5, and a "context" that can have values 0 or 1. For each context I have 7 different frequency distributions over the quantity x. (More generally I could have more than two "contexts", more values of x, and more frequency distributions.)
I can represent these 7×2 frequency distributions as a list freqs of two matrices, say:
> freqs
$`context0`
x0 x1 x2 x3 x4 x5
sample1 20 10 10 21 37 2
sample2 34 40 6 10 1 8
sample3 52 4 1 2 17 25
sample4 16 32 25 11 5 10
sample5 28 2 10 4 21 35
sample6 22 13 35 12 13 5
sample7 9 5 43 29 4 10
$`context1`
x0 x1 x2 x3 x4 x5
sample1 15 21 14 15 14 21
sample2 27 8 6 5 29 25
sample3 13 7 5 26 48 0
sample4 33 3 18 11 13 22
sample5 12 23 40 11 2 11
sample6 5 51 2 28 5 9
sample7 3 1 21 10 63 2
or a 3D array.
Or I could use a data.table tablefreqs like this one:
> tablefreqs
context x0 x1 x2 x3 x4 x5
1: 0 20 10 10 21 37 2
2: 0 34 40 6 10 1 8
3: 0 52 4 1 2 17 25
4: 0 16 32 25 11 5 10
5: 0 28 2 10 4 21 35
6: 0 22 13 35 12 13 5
7: 0 9 5 43 29 4 10
8: 1 15 21 14 15 14 21
9: 1 27 8 6 5 29 25
10: 1 13 7 5 26 48 0
11: 1 33 3 18 11 13 22
12: 1 12 23 40 11 2 11
13: 1 5 51 2 28 5 9
14: 1 3 1 21 10 63 2
Now I'd like to draw the following line plot (there's a reason why I need line plots and not, say, histograms or bar plots):
The 7 frequency distributions for context 0, with x as x-axis and the frequency as y-axis, all in the same line plot (with some alpha).
The 7 frequency distributions for context 1, again with x as x-axis and the frequency as y-axis, all in the same line plot (with alpha), but displayed upside-down below the plot for context 0.
Ggplot would surely do this very nicely, but it seems to require some acrobatics with data tables:
– If I use the data table tablefreqs it's not clear to me how to plot all its rows having context==0 in the same plot: ggplot seems to only think column-wise, not row-wise. I could use the six values of x as table rows, but then the "context" values would also end up in a row, and I'm not sure I can subset a data table by values in a row, rather than in a column.
– If I use the matrix freqs, I could create a mini-data-table having x as one column and one frequency distribution as another column, input that into ggplot+geom_line, then go over all 7 frequency distributions in a for-loop maybe. Not clear to me how to tell ggplot to keep the previous plots in this case. Then another for-loop over the two "contexts".
I'd be grateful for suggestions on how to approach this problem in particular, and more generally on what objects to choose for storing this kind of data: matrices? data tables, maybe with a different structure than shown here? some other formats?
I would suggest to familiarize yourself with the concept of what is known as Tidy Data, which are principles for data handling and storage that are adopted by ggplot2 and a number of other packages.
You are free to use a matrix or list of matrices to store your data; however, you can certainly store the data as you describe it (and as I understand it) in a data frame or single table following the following convention of columns:
context | sample | x | freq
I'll show you how I would convert the tablefreqs dataset you shared with us into that format, then how I would go about creating a plot as you are describing it in your question. I'm assuming in this case you only have the two values for context, although you allude to there being more. I'm going to try to interpret correctly what you stated in your question.
Create the Tidy Data frame
Your data frame as shown contains columns x1 through x5 that have values for x spread across more than one column, when you really need these to be converted in the format shown above. This is called "gathering" your data, and you can do that with tidyr::gather().
First, I also need to replicate the naming of your samples according to the matrix dataset, so I'll do that and gather your data:
library(dplyr)
library(tidyr)
library(ggplot2)
# create the sample names
tablefreqs$sample <- rep(paste0('sample',1:7), 2)
# gather the columns together
df <- tablefreqs %>%
gather(key='x', value='freq', -c(context, sample))
Note that in the gather() function, we have to specify to leave alone the two columns df$context and df$sample, as they are not part of the gathering effort. But now we are left with df$x containing character vectors. We can't plot that, because we want the to be in the form of a number (at least... I'm assuming you do). For that, we'll convert using:
df$x <- as.numeric(gsub("[^[:digit:].]", "", df$x))
That extracts the number from each value in df$x and represents it as a number, not a character. We have the opposite issue with df$context, which is actually a discrete factor, and we should represent it as such in order to make plotting a bit easier:
df$context <- factor(df$context)
Create the Plot
Now we're ready to create the plot. From your description, I may not have this perfectly right, but it seems that you want a plot containing both context = 1 and context = 0, and when context = 1 the data should be "upside down". By that, I'm assuming you are talking about plotting df$freq when df$context == 0 and -df$freq when df$context == 1. We could do that using some fancy logic in the ggplot() call, but I find it's easier just to create a new column in your dataset to represent what we will be plotting on the y axis. We'll call this column df$freq_adj and use that for plotting:
df$freq_adj <- ifelse(df$context==1, -df$freq, df$freq)
Then we create the plot. I'll explain a bit below the result:
ggplot(df, aes(x=x, y=freq_adj)) +
geom_line(
aes(color=context, linetype=sample)
) +
geom_hline(yintercept=0, color='gray50') +
scale_x_continuous(expand=expansion(mult=0)) +
theme_bw()
Without some clearer description or picture of what you were looking to do, I took some liberties here. I used color to discriminate between the two values for context, and I'm using linetype to discriminate the different samples. I also added a line at 0, since it seemed appropriate to do so here, and the scale_x_continuous() command is removing the extra white space that is put in place at the extreme ends of the data.
An alternative that is maybe closer to your description would be to physically have a separation between the two plots, and represent context = 1 as a physically separate plot compared to context = 0, with one over top of the other.
Here's the code and plot:
ggplot(df, aes(x=x, y=freq_adj)) +
geom_line(aes(group=sample), alpha=0.3) +
facet_grid(context ~ ., scales='free_y') +
scale_x_continuous(expand=expansion(mult=0)) +
theme_bw()
There the use of aes(group=sample) is quite important, since I want all the lines for each sample to be the same (alpha setting and color), yet ggplot2 needs to know that the connections between the points should be based on "sample". This is done using the group= aesthetic. The scales='free_y' argument on facet_grid() allows the y axis scale to shrink and fit the data according to each facet.
I got a data in table form which look like this in R:
V1 V2
1 19 -1539
2 7 -1507
3 3 -1446
4 7 -1427
5 8 -1401
6 2 -422
7 22 4178
8 5 4277
9 10 4303
10 18 4431
....200 million more lines to go
I would like to plot a density plot for the value in the second column with respect to the label in the first column (i.e. each label has on density curve on a same graph). But I don't know how. Any suggestion?
If I understood the question correctly, this would end up somewhat like a density heatmap in the end. (Considering there are 200 million observations total and V1 has fairly considerable range of variation)
For that I would try ggplot and stat_binhex:
df <- read.table(text="V1 V2
1 19 -1539
2 7 -1507
3 3 -1446
4 7 -1427
5 8 -1401
6 2 -422
7 22 4178
8 5 4277
9 10 4303
10 18 4431")
library(ggplot2)
ggplot(data=df,aes(V1,V2)) +
stat_binhex() +
scale_fill_gradient(low="red", high="steelblue") +
scale_y_continuous() +
theme_bw()
stat_binhex should work well with large data and has several parameters that will help with presentation (like bins, binwidth. See ?stat_binhex)
OK I figure it out by myself
ggplot(data, aes(x=V2, color=V1)) + geom_density(aes(group=V1))
Should be able to do that.
However there is two thing I need to make sure first in order to let it run:
V1 is a factor
V2 is a numerical value
The data I got wasn't set directly by read.tables in the way I want, so I have to do the following before using ggplot:
data$V1 = as.factor(data$V1)
data$V2 = as.numeric(as.character(data$V2))
I have the following:
heads(dataframe):
ID Result Days
1 70 0
1 80 23
2 90 15
2 89 30
2 99 40
3 23 24
ect...
what I am trying to do is: Create a spaghetti plot with the above datast. What I use is this:
interaction.plot(dataframe$Days,dataframe$ID,dataframe$Result,xlab="Time",ylab="Results",legend=F) but none of the patient lines are continuous even when they were supposed to be a long line.
Also I want to convert the above dataframe to something like this:
ID Result Days
1 70 0
1 80 23
2 90 0
2 89 15
2 99 25
3 23 0
ect... ( I am trying to take the first (or minimum) of each id and have their dating starting from zero and up). Also in the spaghetti plot i want all patients to have the same color IF a condition in met, and another color if the condition is not met.
Thank you for your time and patience.
How about this, using ggplot2 and data.table
# libs
library(ggplot2)
library(data.table)
# your data
df <- data.table(ID=c(1,1,2,2,2,3),
Result=c(70,80,90,89,99,23),
Days=c(0,23,15,30,40,24))
# adjust each ID to start at day 0, sort
df <- merge(df, df[, list(min_day=min(Days)), by=ID], by='ID')
df[, adj_day:=Days-min_day]
df <- df[order(ID, Days)]
# plot
ggplot(df, aes(x=adj_day, y=Result, color=factor(ID))) +
geom_line() + geom_point() +
theme_bw()
Contents of updated data.frame (actually a data.table):
ID Result Days min_day adj_day
1 70 0 0 0
1 80 23 0 23
2 90 15 15 0
2 89 30 15 15
2 99 40 15 25
3 23 24 24 0
You can handle the color coding easily using scale_color_manual()
I have an imputed dataset that I'm analysing, and I'm trying to draw boxplots, but I can't wrap my head around the proper procedure.
my data (a sample, original has 20 observations per imputation and 13 vars per group, all values range from 0 to 25):
.imp .id FTE_RM FTE_PD OMZ_RM OMZ_PD
1 1 25 25 24 24
1 2 4 0 2 6
1 3 11 5 3 2
1 4 12 3 3 3
2 1 20 15 15 15
2 2 4 1 2 3
2 3 0 0 0 6
2 4 20 0 0 0
.imp signifies the imputation round, .id the identifer for each observartion.
I want to draw all the FTE_* variables in a single plot (and the `OMZ_* in another), but wonder what to do with all the imputations, can I just include all values? The imputated data now has 500 observations. With for instance an ANOVA I'd need to average the ANOVA results by 5 to get back to 20 observations. But is this needed for a boxplot as well, since I only deal with medians, means, max. and min.?
Such as:
data_melt <- melt(df[grep("^FTE_", colnames(df))])
ggplot(data_melt, aes(x=variable, y=value))+geom_boxplot()
I've played a couple of times with ggplot, but consider myself a complete newbie.
I assume you want to keep the identifier for .imp and .id after melting so rather put:
data_melt <- melt(df,c(".imp",".id"))
For completeness of the dataframe it probably helps to introduce a column that identifies the type - FTE vs. OMZ:
data_melt$type <- ifelse(grepl("FTE",data_melt$variable),"FTE","OMZ")
Having this data.frame you can, for example, facet on the type (alternatively you can just use a simple filter statement on data_melt to restrict to one type):
ggplot(data_melt, aes(x=variable, y=value))+geom_boxplot()+facet_wrap(~type,scales="free_x")
This would look like this.
EDIT: fixed the data mess-up
I am trying to plot two graphs using different columns from the same data file. As the range of one graph is far greater than the other, I am setting the y-axis to a logarithmic scale. As the domain of values are also very small for both graphs, I am also setting the x-axis to a logarithmic scale.
I have no problem plotting the graphs except that gnuplot does not plot the first points in the data file (where x = 0).
The code that I am using to plot the graphs is thus:
set xrange [1:2500]
set yrange [1:2000]
set log x
set log y
plot "datafile.txt" using 1:2 with lines, "datafile.txt" using 1:3 with lines
Note that, because I am using a logarithmic scale for both axes, I cannot include the value of zero in either range.
An excerpt of the data file that I am using is thus:
Table of Results: Range: {-50...50}
Dim #AvgP #AvgNP
0 0 1743
1 0 564
2 0 914
3 0 1040
4 0 1072
5 0 1005
6 0 815
7 1 689
8 3 525
9 4 433
10 3 350
11 0 255
12 1 216
13 2 140
14 2 84
15 1 57
16 0 38
17 0 16
18 0 15
19 1 7
20 0 2
21 0 1
22 0 1
23 0 0
24 0 0
25 0 0
. . .
. . .
. . .
The file that is plotted is thus:
Notice how the first value of the second graph is not plotted.
As you note, the x=0 point isn't defined on a logarithmic axis, so it being omitted is what you should expect to happen. If you want to force that point to be included, shift the x values by adding 1 to each value, and give an appropriate axis label to make clear what it being plotted.
plot "datafile.txt" using ($1+1):2 with lines, "datafile.txt" using ($1+1):3 with lines
With this, you should see the missing point on the green line. The red line will be unchanged, as the value y=0 can't be plotted either. You could shift the y values as well, if desired.
Here's how it looks: