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))
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'm creating several stacked barplots using ggplot. I'm grouping my results by year and I want to sort my data by a factor variable that has many levels (around 30). I want to display my cumulative summs but there are so many of them that they overlap.
My barplot looks OK for categories with big values, but I haven't managed to find a solution for categories that have small values.I tried setting different geom_text arguments. Now I would like to simply exclude the text for those categories from the barplot but dont know how.
ggplot(data=pivot, aes(x=YEAR, y=SUM, fill=GROUP))+
geom_bar(stat="identity")+
geom_text(aes(label=round(SUM)), vjust=1.6,
position = position_stack(), size=2.5)+
labs(x = "YEAR", y="Amount sold in EUR")
I think that my graphs look better with text over categories with bigger values so I want to include them in the final results but don't know how to select only a few for display.
My dataframe looks as follows:
> pivot
A tibble: 86 x 3
Groups: value [31]
value Year SUM
1 1 2011 771.
2 1 2012 999.
3 1 2013 1479.
4 1 2014 512.
5 1 2015 677.
6 3 2012 4.07
7 4 2012 7.92
8 4 2013 3.97
9 4 2014 41.2
10 5 2011 12.0
... with 76 more rows
I would like to display text on the barplot for values of SUM for category 1 as they are bigger but not for categories 3, 4 and 5. In the final result I would be content with displaying text only for categories 1, 24 and 26 but dont know how to select only them.
I want to plot a lot of boxplots in on particular style to compare them.
But when a group is empty the group "isn't plotted".
lets say I have a dataframe:
a b
1 1 5
2 1 4
3 1 6
4 1 4
5 2 9
6 2 8
7 2 9
8 3 NaN
9 3 NaN
10 3 NaN
11 4 2
12 4 8
and I use boxplot to plot it:
boxplot(b ~ a , df)
than I get the plot without group 3
(which I can't show because I did not have "10 reputation")
I found some solutions for removing empty groups via Google but my problem is the other way around.
And I found the solution via at=c(1,2,4) but as I generate an Rscript with python and different groups are empty I would prefer, that the groups aren't dropped at all.
Oh I don't think I have the time to grapple with additional packages.
Therefore I would be thankful for solutions without them.
You can get the group on the x-axis by
boxplot(b ~ a , df, na.action=na.pass)
Or
boxplot(b~factor(a), df)
I need help with a R plot, with a data format I have not worked with before. Please help if you know.
NUMBER FREQUENCY
10 1
11 1
12 3
10 45
11 2
12 3
i need a bar plot with numbers on X axis (continuous, not bins in histogram) and frequency on Y, but combined.
like
10 46
11 3
12 6
it seems simple enough, but i have 10,000 rows and large numbers in real data so I am looking for a good solution in R without doing it manually.
What about:
##tapply splits dd$FREQ by dd$NUM and "sums" them
barplot(tapply(dd$FREQUENCY, dd$NUMBER, sum))
to get:
Read in your data:
dd = read.table(textConnection("NUMBER FREQUENCY
10 1
11 1
12 3
10 45
11 2
12 3"), header=TRUE)
I want additional 'geoms' to only apply to a subset of the initial data. I would like this subset to be from each units created by facets=~.
My trials using subletting of either the data or of the plotted variables leads to subsetting of the whole data set, rather than the subletting of the units created by 'facets=~' and in two different ways (apparently dependant on the sorting of the data).
This difficulty is appears with any 'geom' while using 'facets'
library(ggplot2)
test.data<-data.frame(factor=rep(c("small", "big"), each=9),
x=c(c(1,2,3,3,3,2,1,1,1), 2*c(1,2,3,3,3,2,1,1,1)),
y=c(c(1,1,1,2,3,3,3,2,1), 2*c(1,1,1,2,3,3,3,2,1)))
factor x y
1 small 1 1
2 small 2 1
3 small 3 1
4 small 3 2
5 small 3 3
6 small 2 3
7 small 1 3
8 small 1 2
9 small 1 1
10 big 2 2
11 big 4 2
12 big 6 2
13 big 6 4
14 big 6 6
15 big 4 6
16 big 2 6
17 big 2 4
18 big 2 2
qplot(data=test.data,
x=x,
y=y,
geom="polygon",
facets=~factor)+
geom_polygon(data=test.data[c(2,3,4,5,6,2),],
aes(x=x,
y=y),
fill=I("red"))
qplot(data=test.data,
x=x,
y=y,
geom="polygon",
facets=~factor)+
geom_polygon(aes(x=x[c(2,3,4,5,6,2)],
y=y[c(2,3,4,5,6,2)]),
fill=I("red"))
The answer is to subset the data in a first step.
library(ggplot2)
library(plyr)
test.data<-data.frame(factor=rep(c("small", "big"), each=9),
x=c(c(1,2,3,3,3,2,1,1,1), 2*c(1,2,3,3,3,2,1,1,1)),
y=c(c(1,1,1,2,3,3,3,2,1), 2*c(1,1,1,2,3,3,3,2,1)))
subset.test<-ddply(.data=test.data,
.variables="factor",
function(data){
data[c(2,3,4,5,6,2),]})
qplot(data=test.data,
x=x,
y=y,
geom="polygon",
facets=~factor)+
geom_polygon(data=subset.test,
fill=I("red"))