I want to create a plot overlaying 1000 simulations of a MA(2) process (1 plot, 1000 lines). I cant seem to get ggplot to plot more than one of these 1000 series. There are many posts about this problem on here, and it seems that the problem for most is solved by using the melt function in the reshape2 library, however, this does not solve my problem. I am unsure what my problem is other than that other datasets/examples from here seem to work in plotting multiple lines, so I am wondering if it is the data my function is generating. Sorry if this is a simple fix, I just cant seem to find an answer.
#create function to simulate MA(2) process
sim<-function(n,sigma,mu,theta) {
epsilon<-rnorm(n,0,sigma)
z<-matrix(nrow=n,ncol=1001)
z<-replicate(1000,
z[,1]<-as.numeric(mu+epsilon[1:n]+theta*epsilon[2:n-1]) )
}
#run simulation, add time vector
z <-sim(23,0.5,0.61,0.95)
time<-matrix(seq(1,23))
z<-data.frame(time,z)
#collapse data
df <- data.frame(melt(zz, id.vars = 'time', variable.name = 'series'))
df[["series"]] <- gsub("X", "series", df[["series"]])
#attempt to plot using ggplot2's 'group' and 'color'
ggplot(data=df, aes(x=time,y=value, group=series)) +
geom_line(aes(color = series)) +
theme(legend.position='none')
It's not clear to me how you want to show 1000 lines in a single plot. This sounds like a bad idea. Furthermore, the code you're using to simulate random data from an MA(2) process doesn't work; why not use arima.sim, which is there for exactly that purpose.
Here is an example plotting 10 random time series with data generated from an MA(2) process.
set.seed(2017);
replicate(10, arima.sim(model = list(ma = c(-.7, .1)), n = 100)) %>%
as_tibble() %>%
mutate(time = 1:n()) %>%
gather(key, val, -time) %>%
ggplot(aes(time, val, group = key)) +
geom_line(alpha = 0.4)
Related
I can see many posts on this topic, but none addresses this question. Apologies if I missed a relevant answer. I have a large protein expression dataset, with samples like so as the columns:
rep1_0hr, rep1_16hr, rep1_24hr, rep1_48hr, rep1_72hr .....
and 2000+ proteins in the rows. In other words each sample is a different developmental timepoint.
If it is of any interest, the original dataset is 'mulvey2015' from the pRolocdata package in R, which I converted to a SummarizedExperiment object in RStudio.
I first ran k-means clustering on the data (an assay() of a SummarizedExperiment dataset, to get 12 clusters:
k_mul <- kmeans(scale(assay(mul)), centers = 12, nstart = 10)
Then:
summary(k_mul)
produced the expected output.
I would like the visualisation to look like this, with samples on the x-axis and expression on the y-axis. The plots look like they have been generated using facet_wrap() in ggplot:
For ggplot the data need to be provided as a dataframe with a column for the cluster identity of an individual protein. Also the data need to be in long format. I tried pivoting (pivot_longer) the original dataset, but of course there are a very large number of data points. Moreover, the image I posted shows that for any one plot, the number of coloured lines is smaller than the total number of proteins, suggesting that there might have been dimension reduction on the dataset first, but I am unsure. Up till now I have been running the kmeans algorithm without dimension reduction. Can I get guidance please for how to produce this plot?
Here is my attempt at reverse engeneering the plot:
library(pRolocdata)
library(dplyr)
library(tidyverse)
library(magrittr)
library(ggplot2)
mulvey2015 %>%
Biobase::assayData() %>%
magrittr::extract2("exprs") %>%
data.frame(check.names = FALSE) %>%
tibble::rownames_to_column("prot_id") %>%
mutate(.,
cl = kmeans(select(., -prot_id),
centers = 12,
nstart = 10) %>%
magrittr::extract2("cluster") %>%
as.factor()) %>%
pivot_longer(cols = !c(prot_id, cl),
names_to = "Timepoint",
values_to = "Expression") %>%
ggplot(aes(x = Timepoint, y = Expression, color = cl)) +
geom_line(aes(group = prot_id)) +
facet_wrap(~ cl, ncol = 4)
As for you questions, pivot_longer is usually quite performant unless it fails to find unique combinations in keys or problems related with data type conversion. The plot can be improved by:
tweaking the alpha parameter of geom_lines (e.g. alpha = 0.5), in order to provide an idea of density of lines
finding a good abbreviation and order for Timepoint
changing axis.text.x orientation
Here is my own, very similar solution to the above.
dfsa_mul <- data.frame(scale(assay(mul)))
dfsa_mul2 <- rownames_to_column(dfsa_mul, "protID")
add the kmeans $cluster column to the dfsa_mul2 dataframe. Only change clus to a factor after executing pivot_longer
dfsa_mul2$clus <- ksa_mul$cluster
dfsa_mul2 %>%
pivot_longer(cols = -c("protID", "clus"),
names_to = "samples",
values_to = "expression") %>%
ggplot(aes(x = samples, y = expression, colour = factor(clus))) +
geom_line(aes(group = protID)) +
facet_wrap(~ factor(clus))
This generates a series of plots identical to the graphs posted by #sbarbit.
I am trying to plot 400 ecdf graphs in one image using ggplot.
As far as I know ggplot does not support the par(new=T) option.
So the first solution I thought was use the grid.arrange function in gridExtra package.
However, the ecdfs I am generating are in a for loop format.
Below is my code, but you could ignore the steps for data processing.
i=1
for(i in 1:400)
{
test<-subset(df,code==temp[i,])
test<-test[c(order(test$Distance)),]
test$AI_ij<-normalize(test$AI_ij)
AI = test$AI_ij
ggplot(test, aes(AI)) +
stat_ecdf(geom = "step") +
scale_y_continuous(labels = scales::percent) +
theme_bw() +
new_theme +
xlab("Calculated Accessibility Value") +
ylab("Percent")
}
So I have values stored in "AI" in the for loop.
In this case how should I plot 400 graphs in the same chart?
This is not the way to put multiple lines on a ggplot. To do this, it is far easier to pass all of your data together and map code to the "group" aesthetic to give you one ecdf line for each code.
By far the hardest part of answering this question was attempting to reverse-engineer your data set. The following data set should be close enough in structure and naming to allow the code to be run on your own data.
library(dplyr)
library(BBmisc)
library(ggplot2)
set.seed(1)
all_codes <- apply(expand.grid(1:16, LETTERS), 1, paste0, collapse = "")
temp <- data.frame(sample(all_codes, 400), stringsAsFactors = FALSE)
df <- data.frame(code = rep(all_codes, 100),
Distance = sqrt(rnorm(41600)^2 + rnorm(41600)^2),
AI_ij = rnorm(41600),
stringsAsFactors = FALSE)
Since you only want the first 400 codes from temp that appear in df to be shown on the plot, you can use dplyr::filter to filter out code %in% test[[1]] rather than iterating through the whole thing one element at a time.
You can then group_by code, and arrange by Distance within each group before normalizing AI_ij, so there is no need to split your data frame into a new subset for every line: the data is processed all at once and the data frame is kept together.
Finally, you plot this using the group aesthetic. Note that because you have 400 lines on one plot, you need to make each line faint in order to see the overall pattern more clearly. We do this by setting the alpha value to 0.05 inside stat_ecdf
Note also that there are multiple packages with a function called normalize and I don't know which one you are using. I have guessed you are using BBmisc
So you can get rid of the loop and do:
df %>%
filter(code %in% temp[[1]]) %>%
group_by(code) %>%
arrange(Distance, by_group = TRUE) %>%
mutate(AI = normalize(AI_ij)) %>%
ggplot(aes(AI, group = code)) +
stat_ecdf(geom = "step", alpha = 0.05) +
scale_y_continuous(labels = scales::percent) +
theme_bw() +
xlab("Calculated Accessibility Value") +
ylab("Percent")
I have a time series data frame similar to data created below. Measurements of 5 variables are taken on each individual. Individuals have unique ID numbers. Note that in this data set each individual is of the same length (each has 1000 observations), but in my real data set each individual is of has different lengths (teach individual has a different number of observations). For each individual, I want to plot all 5 variables on top of one another (i.e. all on the y axis) and plot them against time (x axis). I want to print each of these plots to an external document of some kind (pdf, or whatever is recommended for this application) with one plot per page, meaning each individual will have its own page with a single plot. I want these time series plots to be "interactive", in that I can move my mouse over a point, and it will tell me what time individual data points are at. My goal in doing this is exploring the association between peaks, valleys, and other regions between the 5 variables. I am not sure if ggplot2 is still the best application for this, but I would still like for the plots to be aesthetically appealing so that it will be easier to see patterns in the data. Also, is pasting these plots to a pdf the most sensible route? Or would I be better off using R notebook or some other application?
ID <- rep(c("A","B","C"), each=1000)
time <- rep(c(1:1000), times = 3)
one <- rnorm(1000)
two <- rnorm(1000)
three <- rnorm(1000)
four <- rnorm(1000)
five<-rnorm(1000)
data<- data.frame(cbind(ID,time,one,two,three,four,five))
Try using the plotly package. And since you want it to be interactive, you'll want to export as something like html rather than pdf.
To produce a single faceted plot (note I added stringAsFactors = FALSE to your sample data):
library(tidyverse)
library(plotly)
ID <- rep(c("A","B","C"), each=1000)
time <- rep(c(1:1000), times = 3)
one <- rnorm(1000)
two <- rnorm(1000)
three <- rnorm(1000)
four <- rnorm(1000)
five<-rnorm(1000)
data<- data.frame(cbind(ID,time,one,two,three,four,five),
stringsAsFactors = FALSE)
data_long <- data %>%
gather(variable,
value,
one:five) %>%
mutate(time = as.numeric(time),
value = as.numeric(value))
plot <- data_long %>%
ggplot(aes(x = time,
y = value,
color = variable)) +
geom_point() +
facet_wrap(~ID)
interactive_plot <- ggplotly(plot)
htmlwidgets::saveWidget(interactive_plot, "example.html")
If you want to produce and export an interactive plot for every ID programmatically:
walk(unique(data_long$ID),
~ htmlwidgets::saveWidget(ggplotly(data_long %>%
filter(ID == .x) %>%
ggplot(aes(x = time,
y = value,
color = variable)) +
geom_point() +
labs(title = paste(.x))),
paste("plot_for_ID_", .x, ".html", sep = "")))
Edit: I changed map() to walk() so that the plots are produced without console output (previously just a list with 3 empty elements).
I want to plot a chart in R where it will show me vertical lines for each type in facet.
df is the dataframe with person X takes time in minutes to reach from A to B and so on.
I have tried below code but not able to get the result.
df<-data.frame(type =c("X","Y","Z"), "A_to_B"= c(20,56,57), "B_to_C"= c(10,35,50), "C_to_D"= c(53,20,58))
ggplot(df, aes(x = 1,y = df$type)) + geom_line() + facet_grid(type~.)
I have attached image from excel which is desired output but I need only vertical lines where there are joins instead of entire horizontal bar.
I would not use facets in your case, because there are only 3 variables.
So, to get a similar plot in R using ggplot2, you first need to reformat the dataframe using gather() from the tidyverse package. Then it's in long or tidy format.
To my knowledge, there is no geom that does what you want in standard ggplot2, so some fiddling is necessary.
However, it's possible to produce the plot using geom_segment() and cumsum():
library(tidyverse)
# First reformat and calculate cummulative sums by type.
# This works because factor names begins with A,B,C
# and are thus ordered correctly.
df <- df %>%
gather(-type, key = "route", value = "time") %>%
group_by(type) %>%
mutate(cummulative_time = cumsum(time))
segment_length <- 0.2
df %>%
mutate(route = fct_rev(route)) %>%
ggplot(aes(color = route)) +
geom_segment(aes(x = as.numeric(type) + segment_length, xend = as.numeric(type) - segment_length, y = cummulative_time, yend = cummulative_time)) +
scale_x_discrete(limits=c("1","2","3"), labels=c("Z", "Y","X"))+
coord_flip() +
ylim(0,max(df$cummulative_time)) +
labs(x = "type")
EDIT
This solutions works because it assigns values to X,Y,Z in scale_x_discrete. Be careful to assign the correct labels! Also compare this answer.
I have a dataset in which I have one numeric variable and many categorical variables. I would like to make a grid of density plots, each showing the distribution of the numeric variable for different categorical variables, with the fill corresponding to subgroups of each categorical variable. For example:
library(tidyverse)
library(nycflights13)
dat <- flights %>%
select(carrier, origin, distance) %>%
mutate(origin = origin %>% as.factor,
carrier = carrier %>% as.factor)
plot_1 <- dat %>%
ggplot(aes(x = distance, fill = carrier)) +
geom_density()
plot_1
plot_2 <- dat %>%
ggplot(aes(x = distance, fill = origin)) +
geom_density()
plot_2
I would like to find a way to quickly make these two plots. Right now, the only way I know how to do this is to create each plot individually, and then use grid_arrange to put them together. However, my real dataset has something like 15 categorical variables, so this would be very time intensive!
Is there a quicker and easier way to do this? I believe that the hardest part about this is that each plot has its own legend, so I'm not sure how to get around that stumbling block.
This solutions gives all the plots in a list. Here we make a single function that accepts a variable that you want to plot, and then use lapply with a vector of all the variables you want to plot.
fill_variables <- vars(carrier, origin)
func_plot <- function(fill_variable) {
dat %>%
ggplot(aes(x = distance, fill = !!fill_variable)) +
geom_density()
}
plotlist <- lapply(fill_variables, func_plot)
If you have no idea of what those !! mean, I recommend watching this 5 minute video that introduces the key concepts of tidy evaluation. This is what you want to use when you want to create this sorts of wrapper functions to do stuff programmatically. I hope this helps!
Edit: If you want to feed an array of strings instead of a quosure, you can change !!fill_variable for !!sym(fill_variable) as follows:
fill_variables <- c('carrier', 'origin')
func_plot <- function(fill_variable) {
dat %>%
ggplot(aes(x = distance, fill = !!sym(fill_variable))) +
geom_density()
}
plotlist <- lapply(fill_variables, func_plot)
Alternative solution
As #djc wrote in the comments, I'm having trouble passing the column names into 'fill_variables'. Right now I am extracting column names using the following code...
You can separate the categorical and numerical variables like; cat_vars <- flights[, sapply(flights, is.character)] for categorical variables and cat_vars <- flights[, sapply(flights, !is.character)] for continuous variables and then pass these vectors into the wrapper function given by mgiormenti
Full code is given below;
library(tidyverse)
library(nycflights13)
cat_vars <- flights[, sapply(flights, is.character)]
cont_vars<- flights[, !sapply(flights, is.character)]
dat <- flights %>%
select(carrier, origin, distance) %>%
mutate(origin = origin %>% as.factor,
carrier = carrier %>% as.factor)
func_plot_cat <- function(cat_vars) {
dat %>%
ggplot(aes(x = distance, fill = !!cat_vars)) +
geom_density()
}
func_plot_cont <- function(cont_vars) {
dat %>%
ggplot(aes(x = distance, fill = !!cont_vars)) +
geom_point()
}
plotlist_cat_vars <- lapply(cat_vars, func_plot_cat)
plotlist_cont_vars<- lapply(cont_vars, func_plot_cont)
print(plotlist_cat_vars)
print(plotlist_cont_vars)