I am creating scatterplots in R using ggplot2 to visualize populations over time. My data set looks like the following:
sampling_period cage total_1 total_2 total_3
4 y 34 95 12
4 n 89 12 13
5 n 23 10 2
I have been making individual scatterplots for the variables total_1, total_2, and total_3 with the following code:
qplot(data=BLPcaged, x=sampling_period, y=total_1, color=cage_or_control)
qplot(data=BLPcaged, x=sampling_period, y=total_2, color=cage_or_control)
qplot(data=BLPcaged, x=sampling_period, y=total_3, color=cage_or_control)
I want to create a scatterplot that contains the information about all three populations over time. I want the final product to be composed of three scatterplots one on top of each other and have the same scale for the axes. This way I could compare all three populations in one visualization.
I know that I can use facet to make different plots for the levels of a factor, but can it also be used to create different plots for different variables?
You can use melt() to reshape your data with total as a factor that you can facet on:
BLPcaged = data.frame(sampling_period=c(4,4,5),
cage=c('y','n','n'),
total_1=c(34,89,23),
total_2=c(95,12,10),
total_3=c(12,13,2))
library(reshape2)
BLPcaged.melted = melt(BLPcaged,
id.vars=c('sampling_period','cage'),
variable.name='total')
So now BLPcaged.melted looks like this:
sampling_period cage total value
1 4 y total_1 34
2 4 n total_1 89
3 5 n total_1 23
4 4 y total_2 95
5 4 n total_2 12
6 5 n total_2 10
7 4 y total_3 12
8 4 n total_3 13
9 5 n total_3 2
You can then facet this by total:
ggplot(BLPcaged.melted, aes(sampling_period, value, color=cage)) +
geom_point() +
facet_grid(total~.)
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.
Following are first 15 rows of my data:
> head(df,15)
frame.group class lane veh.count mean.speed
1 [22,319] 2 5 9 23.40345
2 [22,319] 2 4 9 24.10870
3 [22,319] 2 1 11 14.70857
4 [22,319] 2 3 8 20.88783
5 [22,319] 2 2 6 16.75327
6 (319,616] 2 5 15 22.21671
7 (319,616] 2 2 16 23.55468
8 (319,616] 2 3 12 22.84703
9 (319,616] 2 4 14 17.55428
10 (319,616] 2 1 13 16.45327
11 (319,616] 1 1 1 42.80160
12 (319,616] 1 2 1 42.34750
13 (616,913] 2 5 18 30.86468
14 (319,616] 3 3 2 26.78177
15 (616,913] 2 4 14 32.34548
'frame.group' contains time intervals, 'class' is the vehicle class i.e. 1=motorcycles, 2=cars, 3=trucks and 'lane' contains lane numbers. I want to create 3 scatter plots with frame.group as x-axis and mean.speed as y-axis, 1 for each class. In a scatterplot for one vehicle class e.g. cars, I want 5 plots i.e. one for each lane. I tried following:
cars <- subset(df, class==2)
by(cars, lane, FUN = plot(frame.group, mean.speed))
There are two problems:
1) R does not plot as expected i.e. 5 plots for 5 different lanes.
2) Only one is plotted and that too is box-plot probably because I used intervals instead of numbers as x-axis.
How can I fix the above issues? Please help.
Each time a new plot command is issued, R replaces the existing plot with the new plot. You can create a grid of plots by doing par(mfrow=c(1,5)), which will be 1 row with 5 plots (other numbers will have other numbers of rows and columns). If you want a scatterplot instead of a boxplot you can use plot.default
It is easier to do all this with the ggplot2 library instead of the base graphics, and the resulting plot will look much nicer:
library(ggplot2)
ggplot(cars,aes(x=frame.group,y=mean.speed))+geom_point()+facet_wrap(~lane)
See the ggplot2 documentation for more details: http://docs.ggplot2.org/current/
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'm trying to create a stacked bar graph using ggplot 2. My data in its wide form, looks like this. The numbers in each cell are the frequency of responses.
activity yes no dontknow
Social events 27 3 3
Academic skills workshops 23 5 8
Summer research 22 7 7
Research fellowship 20 6 9
Travel grants 18 8 7
Resume preparation 17 4 12
RAs 14 11 8
Faculty preparation 13 8 11
Job interview skills 11 9 12
Preparation of manuscripts 10 8 14
Courses in other campuses 5 11 15
Teaching fellowships 4 14 16
TAs 3 15 15
Access to labs in other campuses 3 11 18
Interdisciplinary research 2 11 18
Interdepartamental projects 1 12 19
I melted this table using reshape2 and
melted.data(wide.data,id.vars=c("activity"),measure.vars=c("yes","no","dontknow"),variable.name="haveused",value.name="responses")
That's as far as I can get. I want to create a stacked bar chart with activities on the x axis, frequency of responses in the y axis, and each bar showing the distribution of the yes, nos and dontknows
I've tried
ggplot(melted.data,aes(x=activity,y=responses))+geom_bar(aes(fill=haveused))
but I'm afraid that's not the right solution
Any help is much appreciated.
You haven't said what it is that's not right about your solution. But some issues that could be construed as problems, and one possible solution for each, are:
The x axis tick mark labels run into each other. SOLUTION - rotate the tick mark labels;
The order in which the labels (and their corresponding bars) appear are not the same as the order in the original dataframe. SOLUTION - reorder the levels of the factor 'activity';
To position text inside the bars set the vjust parameter in position_stack to 0.5
The following might be a start.
# Load required packages
library(ggplot2)
library(reshape2)
# Read in data
df = read.table(text = "
activity yes no dontknow
Social.events 27 3 3
Academic.skills.workshops 23 5 8
Summer.research 22 7 7
Research.fellowship 20 6 9
Travel.grants 18 8 7
Resume.preparation 17 4 12
RAs 14 11 8
Faculty.preparation 13 8 11
Job.interview.skills 11 9 12
Preparation.of.manuscripts 10 8 14
Courses.in.other.campuses 5 11 15
Teaching.fellowships 4 14 16
TAs 3 15 15
Access.to.labs.in.other.campuses 3 11 18
Interdisciplinay.research 2 11 18
Interdepartamental.projects 1 12 19", header = TRUE, sep = "")
# Melt the data frame
dfm = melt(df, id.vars=c("activity"), measure.vars=c("yes","no","dontknow"),
variable.name="haveused", value.name="responses")
# Reorder the levels of activity
dfm$activity = factor(dfm$activity, levels = df$activity)
# Draw the plot
ggplot(dfm, aes(x = activity, y = responses, group = haveused)) +
geom_col(aes(fill=haveused)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.25)) +
geom_text(aes(label = responses), position = position_stack(vjust = .5), size = 3) # labels inside the bar segments
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"))