I have a dataset set with 34 columns and 600+ rows.
I successfully managed to reshape it for my data to be predicted for 5 columns (5 years) using reshape2
Dataset_name <- melt(data=XYZ, id.vars=c("A", "B", "C",.... {so on minus 5 columns}))
Now I have the reshaped data and plotted the graph and since it has 600+ points in each column, I cant make sense of it.
Is it possible for me to plot the top Row 1 to Row 50 in one graph and in another Row 51 to Row 100 and so on?
Also, I want to connect the dots to see whether they varied over the years.
Thanks.
Dataset
You can assign rows numbers (first 50 designated as 1, second 50 as 2...) and use that variable in facet_wrap. Each facet would thus hold 50 data points. Here's an example on the iris dataset which comes shipped with R.
library(ggplot2)
nrow(iris) # 150, let's do 50 obs. per facet
iris <- iris[sample(1:nrow(iris)), ] # shuffle the dataset
iris$desig <- rep(c("set1", "set2", "set3"), each = 50)
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
theme_bw() +
geom_point() +
facet_wrap(~ desig)
Related
So I have a dataframe containing the 10 most upregulated genes in cancer-samples compared to control-samples and the 10 most downregulated genes.
It looks like this:
I want to create a neat boxplot to compare the spread of each gene between patient-samples and control-samples (there are 4 samples of each type).
The problems I have is that I don't get all boxes along-side each other in a row/in the same graph, but like this:
I would also like it to show the gene's boxes sorted by the "log2FC-value", and not be in alphabetical order. Does anyone know how to fix this??
This is my code I used:
#Im using the dataframe called "Dataframe"
#Make a column for the genenames
Dataframe$genenames <- rownames(Dataframe)
#Get data into a long-format
long_Dataframe <- gather(Dataframe, key="samples",
value="values", -c(log2FC, gennames,))
#Creating a new column called "group", stating if each row belongs to patient/control
long_Dataframe$group <- rep(c("Control", "Patient"), each=40)
#Order rows by log2FC - from lowest to highest
long_Dataframe <- long_Dataframe[order(long_Dataframe$log2FC), ,
drop=FALSE]
#Use long data for boxplot of top 20 up/downregulated genes
Boxplot_top20 <- ggplot(long_Dataframe, aes(x=genenames, y=values, fill=group)) +
geom_boxplot() +
scale_fill_manual(values=c("green", "red")) +
theme_light() +
facet_wrap(~genenames, scales="free")
You may use geom_boxplot(position=position_dodge()) instead of facet_wrap() to place your boxplots by pair within group.
I have a dataframe with the following columns; Sample, Read_length, Length, Rep, Year, Sex. Each unique sample has 6 Length values (2 Read_length conditions x 3 Reps). I would like to plot Length vs Year in such a way that each group of 3 repeats is visually linked on the plot, so I can see the variation. I am using colour and point shape to distinguish between the 2 read-lengths and between Male & Female.
ggplot(data1, aes(x = Year, y = Length, shape = Sex, colour = Read_length)) + geom_point(size = 3) + scale_shape_manual(values = c(1, 4))
Is there a way to group first by read_length, and then by sample name, to generate the groups of three (and how to then plot that)?
Take your input data and use group_by() from dplyr. This will allow ggplot, and many other tidyverse functions to process each sample separately.
data %>% group_by(Sample)
I have two data frames z (1 million observations) and b (500k observations).
z= Tracer time treatment
15 0 S
20 0 S
25 0 X
04 0 X
55 15 S
16 15 S
15 15 X
20 15 X
b= Tracer time treatment
2 0 S
35 0 S
10 0 X
04 0 X
20 15 S
11 15 S
12 15 X
25 15 X
I'd like to create grouped boxplots using time as a factor and treatment as colour. Essentially I need to bind them together and then differentiate between them but not sure how. One way I tried was using:
zz<-factor(rep("Z", nrow(z))
bb<-factor(rep("B",nrow(b))
dumB<-merge(z,zz) #this won't work because it says it's too big
dumB<-merge(b,zz)
total<-rbind(dumB,dumZ)
But z and zz merge won't work because it says it's 10G in size (which can't be right)
The end plot might be similar to this example: Boxplot with two levels and multiple data.frames
Any thoughts?
Cheers,
EDIT: Added boxplot
I would approach it as follows:
# create a list of your data.frames
l <- list(z,b)
# assign names to the dataframes in the list
names(l) <- c("z","b")
# bind the dataframes together with rbindlist from data.table
# the id parameter will create a variable with the names of the dataframes
# you could also use 'bind_rows(l, .id="id")' from 'dplyr' for this
library(data.table)
zb <- rbindlist(l, id="id")
# create the plot
ggplot(zb, aes(x=factor(time), y=Tracer, color=treatment)) +
geom_boxplot() +
facet_wrap(~id) +
theme_bw()
which gives:
Other alternatives for creating your plot:
# facet by 'time'
ggplot(zb, aes(x=id, y=Tracer, color=treatment)) +
geom_boxplot() +
facet_wrap(~time) +
theme_bw()
# facet by 'time' & color by 'id' instead of 'treatment'
ggplot(zb, aes(x=treatment, y=Tracer, color=id)) +
geom_boxplot() +
facet_wrap(~time) +
theme_bw()
In respons to your last comment: to get everything in one plot, you use interaction to distinguish between the different groupings as follows:
ggplot(zb, aes(x=treatment, y=Tracer, color=interaction(id, time))) +
geom_boxplot(width = 0.7, position = position_dodge(width = 0.7)) +
theme_bw()
which gives:
The key is you do not need to perform a merge, which is computationally expensive on large tables. Instead assign a new variable and value (source c(b,z) in my code below) to each dataframe and then rbind. Then it becomes straight forward, my solution is very similar to #Jaap's just with different faceting.
library(ggplot2)
#Create some mock data
t<-seq(1,55,by=2)
z<-data.frame(tracer=sample(t,size = 10,replace = T), time=c(0,15), treatment=c("S","X"))
b<-data.frame(tracer=sample(t,size = 10,replace = T), time=c(0,15), treatment=c("S","X"))
#Add a variable to each table to id itself
b$source<-"b"
z$source<-"z"
#concatenate the tables together
all<-rbind(b,z)
ggplot(all, aes(source, tracer, group=interaction(treatment,source), fill=treatment)) +
geom_boxplot() + facet_grid(~time)
I am trying to visualize large chunks of consecutive missing data side-by-side on ranges of 3, 5 and 10 years sampled daily. Hopefully using ggplot2 since I already have some aesthetics functions done.
I imagined this would come from a barplot or maybe some heatmap variation, but I am not too sure how to use them with time-series data.
I chose a black/white list of bars because I think it is easier to observe where (1) lies large chunks of missing data and (2) if they are occurring on different moments in time (which would be important to choose which stations to use, etc), while being (3) relatively easy to observe many bars which would not be true to the more conventional line plots for time-series.
This is a draft of what I had in mind.
Here is some example data for 5 stations (in practice this could be up to over 80):
#Data from 5 different stations sampled daily.
df <- cbind(seq(as.Date(("2010/01/01")),by="day",length.out=365*5),data.frame(matrix(rnorm(365*5*5),365*5,5)))
colnames(df) <- c("timestamp","st1","st2","st3","st4","st5")
#Add varying ranges of missing consecutive amount of days to observe result on visualization.
df[1:50,"st1"] <- NA # 50
df[51:200,"st2"] <- NA # 150
df[1:400,"st3"] <- NA # 400
df[501:1300,"st5"] <- NA # 800
Here's a rough stab at it...Alter the scales and theme elements to your liking...
library(ggplot2)
library(scales)
library(reshape2)
melt(df, id.vars = "timestamp") -> k
k$value <- ifelse(is.na(k$value), "NA", "Not NA")
ggplot(data = k) +
geom_point(aes(x = timestamp, y = variable, fill = value, colour = value), shape =22) +
scale_x_date() +
theme_bw()
I'm a bit out of my depth with this one here. I have the following code that generates two equally sized matrices:
MAX<-100
m<-5
n<-40
success<-matrix(runif(m*n,0,1),m,n)
samples<-floor(MAX*matrix(runif(m*n),m))+1
the success matrix is the probability of success and the samples matrix is the corresponding number of samples that was observed in each case. I'd like to make a bar graph that groups each column together with the height being determined by the success matrix. The color of each bar needs to be a color (scaled from 1 to MAX) that corresponds to the number of observations (i.e., small samples would be more red, for instance, whereas high samples would be green perhaps).
Any ideas?
Here is an example with ggplot. First, get data into long format with melt:
library(reshape2)
data.long <- cbind(melt(success), melt(samples)[3])
names(data.long) <- c("group", "x", "success", "count")
head(data.long)
# group x success count
# 1 1 1 0.48513473 8
# 2 2 1 0.56583802 58
# 3 3 1 0.34541582 40
# 4 4 1 0.55829073 64
# 5 5 1 0.06455401 37
# 6 1 2 0.88928606 78
Note melt will iterate through the row/column combinations of both matrices the same way, so we can just cbind the resulting molten data frames. The [3] after the second melt is so we don't end up with repeated group and x values (we only need the counts from the second melt). Now let ggplot do its thing:
library(ggplot2)
ggplot(data.long, aes(x=x, y=success, group=group, fill=count)) +
geom_bar(position="stack", stat="identity") +
scale_fill_gradient2(
low="red", mid="yellow", high="green",
midpoint=mean(data.long$count)
)
Using #BrodieG's data.long, this plot might be a little easier to interpret.
library(ggplot2)
library(RColorBrewer) # for brewer.pal(...)
ggplot(data.long) +
geom_bar(aes(x=x, y=success, fill=count),colour="grey70",stat="identity")+
scale_fill_gradientn(colours=brewer.pal(9,"RdYlGn")) +
facet_grid(group~.)
Note that actual values are probably different because you use random numbers in your sample. In future, consider using set.seed(n) to generate reproducible random samples.
Edit [Response to OP's comment]
You get numbers for x-axis and facet labels because you start with matrices instead of data.frames. So convert success and samples to data.frames, set the column names to whatever your test names are, and prepend a group column with the "list of factors". Converting to long format is a little different now because the first column has the group names.
library(reshape2)
set.seed(1)
success <- data.frame(matrix(runif(m*n,0,1),m,n))
success <- cbind(group=rep(paste("Factor",1:nrow(success),sep=".")),success)
samples <- data.frame(floor(MAX*matrix(runif(m*n),m))+1)
samples <- cbind(group=success$group,samples)
data.long <- cbind(melt(success,id=1), melt(samples, id=1)[3])
names(data.long) <- c("group", "x", "success", "count")
One way to set a threshold color is to add a column to data.long and use that for fill:
threshold <- 25
data.long$fill <- with(data.long,ifelse(count>threshold,max(count),count))
Putting it all together:
library(ggplot2)
library(RColorBrewer)
ggplot(data.long) +
geom_bar(aes(x=x, y=success, fill=fill),colour="grey70",stat="identity")+
scale_fill_gradientn(colours=brewer.pal(9,"RdYlGn")) +
facet_grid(group~.)+
theme(axis.text.x=element_text(angle=-90,hjust=0,vjust=0.4))
Finally, when you have names for the x-axis labels they tend to get jammed together, so I rotated the names -90°.