I want to visualize many time series at once. I am new at R, and have spent about 6 hours searching the web and reading about how to tackle this relatively simple problem. My dataset has five time points arranged as rows, and 100 columns. I can easily plot any column against the time points with qplot(time, var2, geom="line"). But I want to learn how to do this for a flexible number of columns, and how to print 6 to 12 of the individual graphs on one page.
Here I learned about the multiplot function, got that to work in terms of layout.
What I am stuck on is how for get the list of variables into a FOR statement so I can have one statement to plot all the variables against the same five time points.
this is what I am playing with. It makes 9 plots, 3 columns wide, but I do not know how to get all my variables into the array for yvars?
for (i in 1:9) {
p1 = qplot(symbol,yvar, geom ="smooth", main = i))
plots[[i]] <- p1 # add each plot into plot list
}
multiplot(plotlist = plots, cols = 3)
Stupidly on my part right now it makes 9 identical plots. So how do I create the list so the above will cycle through all my columns and make those plots?
first melt all your data using the reshape2 package
datm <- melt(your.original.data.frame, id = "time")
Now plot it using facets:
qplot(time, value, data = datm, facets= variable ~ ., geom="point")
Let me know if this works. If you could, please upload your data, it would help tremendously.
Related
I'm trying to make a stack of histograms (or a ridgeplot) so I can compare distributions at certain timepoints in my observations.
I used this source for the histogram, and this for the ridge plots.
However, I cannot figure out how to set up my code to make either a stacked histogram of each length (L) by week, so that I can see L distributions at different weeks. I have tried the fill option in ggplot (which in the example seems to produce automatic color differences for the weeks because it is in the aes()?) and other "stacks" using the y= argument, but haven't had much success, I think due to the way my data is set up. If anyone can help me figure out how to make multiple histograms by week, that would be useful!
Thanks!
#fake data
L = rnorm(100, mean=10, sd=2)
t = c((rep.int(7,10)), (rep.int(14,20)), rep.int(21,30), rep.int(28,20), (rep.int(31, 20)), (rep.int(36,10)))
fake = data.frame(cbind(L,t))
#subset data into weeks for convenience
dayofweek = seq(7,120,7)
fake2 = as.data.frame(subset(fake, t %in% dayofweek))
fake2$week <- floor(fake2$t/7)
#Plots, basic code
ggplot(fake2, aes(x=L, fill=week)) +
geom_histogram()
I tried facet_grid before, but for some reason facet_wrap actually at least separated the graphs correctly, AND magically made the color fill work:
ggplot(fake2, aes(x=L, fill = week)) +
geom_histogram()+
facet_wrap(.~week)
I have several datasets and my end goal is to do a graph out of them, with each line representing the yearly variation for the given information. I finally joined and combined my data (as it was in a per month structure) into a table that just contains the yearly means for each item I want to graph (column depicting year and subsequent rows depicting yearly variation for 4 different elements)
I have one factor that is the year and 4 different variables that read yearly variations, thus I would like to graph them on the same space. I had the idea to joint the 4 columns into one by factor (collapse into one observation per row and the year or factor in the subsequent row) but seem unable to do that. My thought is that this would give a structure to my y axis. Would like some advise, and to know if my approach to the problem is effective. I am trying ggplot2 but does not seem to work without a defined (or a pre defined range) y axis. Thanks
I would suggest next approach. You have to reshape your data from wide to long as next example. In that way is possible to see all variables. As no data is provided, this solution is sketched using dummy data. Also, you can change lines to other geom you want like points:
library(tidyverse)
set.seed(123)
#Data
df <- data.frame(year=1990:2000,
v1=rnorm(11,2,1),
v2=rnorm(11,3,2),
v3=rnorm(11,4,1),
v4=rnorm(11,5,2))
#Plot
df %>% pivot_longer(-year) %>%
ggplot(aes(x=factor(year),y=value,group=name,color=name))+
geom_line()+
theme_bw()
Output:
We could use melt from reshape2 without loading multiple other packages
library(reshape2)
library(ggplot2)
ggplot(melt(df, id.var = 'year'), aes(x = factor(year), y = value,
group = variable, color = variable)) +
geom_line()
-output plot
Or with matplot from base R
matplot(as.matrix(df[-1]), type = 'l', xaxt = 'n')
data
set.seed(123)
df <- data.frame(year=1990:2000,
v1=rnorm(11,2,1),
v2=rnorm(11,3,2),
v3=rnorm(11,4,1),
v4=rnorm(11,5,2))
This is my first post, so go easy. Up until now (the past ~5 years?) I've been able to either tweak my R code the right way or find an answer on this or various other sites. Trust me when I say that I've looked for an answer!
I have a working script to create the attached boxplot in basic R.
http://i.stack.imgur.com/NaATo.jpg
This is fine, but I really just want to "jazz" it up in ggplot, for vain reasons.
I've looked at the following questions and they are close, but not complete:
Why does a boxplot in ggplot requires axis x and y?
How do you draw a boxplot without specifying x axis?
My data is basically like "mtcars" if all the numerical variables were on the same scale.
All I want to do is plot each variable on the same boxplot, like the basic R boxplot I made above. My y axis is the same continuous scale (0 to 1) for each box and the x axis simply labels each month plus a yearly average (think all the mtcars values the same on the y axis and the x axis is each vehicle model). Each box of my data represents 75 observations (kind of like if mtcars had 75 different vehicle models), again all the boxes are on the same scale.
What am I missing?
Though I don't think mtcars makes a great example for this, here it is:
First, we make the data (hopefully) more similar to yours by using a column instead of rownames.
mt = mtcars
mt$car = row.names(mtcars)
Then we reshape to long format:
mt_long = reshape2::melt(mt, id.vars = "car")
Then the plot is easy:
library(ggplot2)
ggplot(mt_long, aes(x = variable, y = value)) +
geom_boxplot()
Using ggplot all but requires data in "long" format rather than "wide" format. If you want something to be mapped to a graphical dimension (x-axis, y-axis, color, shape, etc.), then it should be a column in your data. Luckily, it's usually quite easy to get data in the right format with reshape2::melt or tidyr::gather. I'd recommend reading the Tidy Data paper for more on this topic.
I am trying to build from a question similar to mine (and from which I borrowed the self-contained example and title inspiration). I am trying to apply transparency individually to each line of a ggparcoord or somehow add two layers of ggparcoord on top of the other. The detailed description of the problem and format of data I have for the solution to work is provided below.
I have a dataset with thousand of lines, lets call it x.
library(GGally)
x = data.frame(a=runif(100,0,1),b=runif(100,0,1),c=runif(100,0,1),d=runif(100,0,1))
After clustering this data I also get a set of 5 lines, let's call this dataset y.
y = data.frame(a=runif(5,0,1),b=runif(5,0,1),c=runif(5,0,1),d=runif(5,0,1))
In order to see the centroids y overlaying x I use the following code. First I add y to x such that the 5 rows are on the bottom of the final dataframe. This ensures ggparcoord will put them last and therefore stay on top of all the data:
df <- rbind(x,y)
Next I create a new column for df, following the question advice I referred such that I can color differently the centroids and therefore can tell it apart from the data:
df$cluster = "data"
df$cluster[(nrow(df)-4):(nrow(df))] <- "centroids"
Finally I plot it:
p <- ggparcoord(df, columns=1:4, groupColumn=5, scale="globalminmax", alphaLines = 0.99) + xlab("Sample") + ylab("log(Count)")
p + scale_colour_manual(values = c("data" = "grey","centroids" = "#94003C"))
The problem I am stuck with is from this stage and onwards. On my original data, plotting solely x doesn't lead to much insight since it is a heavy load of lines (on this data this is equivalent to using ggparcoord above on x instead of df:
By reducing alphaLines considerably (0.05), I can naturally see some clusters due to the overlapping of the lines (this is again running ggparcoord on x reducing alphaLines):
It makes more sense to observe the centroids added to df on top of the second plot, not the first.
However, since everything it is on a single dataframe, applying such a high value for alphaLine makes the centroid lines disappear. My only option is then to use ggparcoord (as provided above) on df without decreasing the alphaValue:
My goal is to have the red lines (centroid lines) on top of the second figure with very low alpha. There are two ways I thought so far but couldn't get it working:
(1) Is there any way to create a column on the dataframe, similar to what is done for the color, such that I can specify the alpha value for each line?
(2) I originally attempted to create two different ggparcoords and "sum them up" hoping to overlay but an error was raised.
The question may contain too much detail, but I thought this could motivate better the applicability of the answer to serve the interest of other readers.
The answer I am looking for would use the provided data variables on the current format and generate the plot I am looking for. Better ways to reconstruct the data is also welcomed, but using the current structure is preferred.
In this case I think it easier to just use ggplot, and build the graph yourself. We make slight adjustments to how the data is represented (we put it in long format), and then we make the parallel coordinates plot. We can now map any attribute to cluster that you like.
library(dplyr)
library(tidyr)
# I start the same as you
x <- data.frame(a=runif(100,0,1),b=runif(100,0,1),c=runif(100,0,1),d=runif(100,0,1))
y <- data.frame(a=runif(5,0,1),b=runif(5,0,1),c=runif(5,0,1),d=runif(5,0,1))
# I find this an easier way to combine the two data.frames, and have an id column
df <- bind_rows(data = x, centroids = y, .id = 'cluster')
# We need to add id's, so we know which points to connect with a line
df$id <- 1:nrow(df)
# Put the data into long format
df2 <- gather(df, 'column', 'value', a:d)
# And plot:
ggplot(df2, aes(column, value, alpha = cluster, color = cluster, group = id)) +
geom_line() +
scale_colour_manual(values = c("data" = "grey", "centroids" = "#94003C")) +
scale_alpha_manual(values = c("data" = 0.2, "centroids" = 1)) +
theme_minimal()
I am making heat maps from correlations. I have two columns that represent ID's and a third column that gives the correlation between those two datapoints. I am struggling to get qplot to keep the order of my data in the file. Link to data:
https://www.dropbox.com/s/3l9p1od5vjt0p4d/SNPS.txt?n=7399684
Here is the code I am using to make the plot:
test <- qplot(x=x, y=y, data=PCIT, fill = col1, geom = "tile")
I have tried several order options but they don't seem to do the trick? Ideas?
Thanks and Happy Holidays
You need to set the levels of the factors x and y to be in the order you want them (as they come in from the file). Try
PCIT$x <- factor(PCIT$x, levels=unique(as.character(PCIT$x)))
and similarly with y.