I am trying to plot the following data
factor <- as.factor(c(1,2,3))
V1_mean <- c(100,200,300)
V2_mean <- c(350,150,60)
V1_stderr <- c(5,9,3)
V2_stderr <- c(12,9,10)
plot <- data.frame(factor,V1_mean,V2_mean,V1_stderr,V2_stderr)
I want to create a plot with factor on the x-axis, value on the y-axis and seperate lines for V1 and V2 (hence the points are the values of V1_mean on one line and V2_mean on the other). I would also like to add error bars for these means based on V1_stderr and V2_stderr
Many thanks
I'm not sure regarding your desired output, but here's a possible solution.
First of all, I wouldn't call your data plot as this is a stored function in R which is being commonly used
Second of all, when you want to plot two lines in ggplot you'll usually have to tide your data using functions such as melt (from reshape2 package) or gather (from tidyr package).
Here's an a possible approach
library(ggplot2)
library(reshape2)
dat <- data.frame(factor, V1_mean, V2_mean, V1_stderr, V2_stderr)
mdat <- cbind(melt(dat[1:3], "factor"), melt(dat[c(1, 4:5)], "factor"))
names(mdat) <- make.names(names(mdat), unique = TRUE)
ggplot(mdat, aes(factor, value, color = variable)) +
geom_point(aes(group = variable)) + # You can also add `geom_point(aes(group = variable)) + ` if you want to see the actual points
geom_errorbar(aes(ymin = value - value.1, ymax = value + value.1))
Related
I have a dataframe in R consisting of 104 columns, appearing as so:
id vcr1 vcr2 vcr3 sim_vcr1 sim_vcr2 sim_vcr3 sim_vcr4 sim_vcr5 sim_vcr6 sim_vcr7
1 2913 -4.782992840 1.7631999 0.003768704 1.376937 -2.096857 6.903021 7.018855 6.135139 3.188382 6.905323
2 1260 0.003768704 3.1577108 -0.758378208 1.376937 -2.096857 6.903021 7.018855 6.135139 3.188382 6.905323
3 2912 -4.782992840 1.7631999 0.003768704 1.376937 -2.096857 6.903021 7.018855 6.135139 3.188382 6.905323
4 2914 -1.311132669 0.8220594 2.372950077 -4.194246 -1.460474 -9.101704 -6.663676 -5.364724 -2.717272 -3.682574
5 2915 -1.311132669 0.8220594 2.372950077 -4.194246 -1.460474 -9.101704 -6.663676 -5.364724 -2.717272 -3.682574
6 1261 2.372950077 -0.7022792 -4.951318264 -4.194246 -1.460474 -9.101704 -6.663676 -5.364724 -2.717272 -3.682574
The "sim_vcr*" variables go all the way through sim_vcr100
I need two overlapping density density curves contained within one plot, looking something like this (except here you see 5 instead of 2):
I need one of the density curves to consist of all values contained in columns vcr1, vcr2, and vcr3, and I need another density curve containing all values in all of the sim_vcr* columns (so 100 columns, sim_vcr1-sim_vcr100)
Because the two curves overlap, they need to be transparent, like in the attached image. I know that there is a pretty straightforward way to do this using the ggplot command, but I am having trouble with the syntax, as well as getting my data frame oriented correctly so that each histogram pulls from the proper columns.
Any help is much appreciated.
With df being the data you mentioned in your post, you can try this:
Separate dataframes with next code, then plot:
library(tidyverse)
library(gdata)
#Index
i1 <- which(startsWith(names(df),pattern = 'vcr'))
i2 <- which(startsWith(names(df),pattern = 'sim'))
#Isolate
df1 <- df[,c(1,i1)]
df2 <- df[,c(1,i2)]
#Melt
M1 <- pivot_longer(df1,cols = names(df1)[-1])
M2 <- pivot_longer(df2,cols = names(df2)[-1])
#Plot 1
ggplot(M1) + geom_density(aes(x=value,fill=name), alpha=.5)
#Plot 2
ggplot(M2) + geom_density(aes(x=value,fill=name), alpha=.5)
Update
Use next code for one plot:
#Unique plot
#Melt
M <- pivot_longer(df,cols = names(df)[-1])
#Mutate
M$var <- ifelse(startsWith(M$name,'vcr',),'vcr','sim_vcr')
#Plot 3
ggplot(M) + geom_density(aes(x=value,fill=var), alpha=.5)
Using the dplyr package, first you can convert your data to long format using the function pivot_longer as follows:
df %<>% pivot_longer(cols = c(starts_with('vcr'), starts_with('sim_vcr')),
names_to = c('type'),
values_to = c('values'))
After using filter function you can create separate plots for each value type
For vcr columns:
df %>%
filter(str_detect(type, '^vcr')) %>%
ggplot(.) +
geom_density(aes(x = values, fill = type), alpha = 0.5)
The above produces the following plot:
for sim_vcr columns:
df %>%
filter(str_detect(type, '^sim_vcr')) %>%
ggplot(.) +
geom_density(aes(x = values, fill = type), alpha = 0.5)
The above code produces the following plot:
Another simple way to subset and prepare your data for ggplot is with gather() from tidyr which you can read more about. Heres how I do it. df being your data frame provided.
# Load tidyr to use gather()
library(tidyr)
#Split appart the data you dont want on their own, the first three columns, and gather them
df_vcr <- gather(data = df[,2:4])
#Gather the other columns in the dataframe
df_sim<- gather(data = df[,-c(1:4)])
#Plot the first
ggplot() +
geom_density(data = df_vcr,
mapping = aes(value, group = key, color = key, fill = key),
alpha = 0.5)
#Plot the second
ggplot() +
geom_density(data = df_sim,
mapping = aes(value, group = key, color = key, fill = key),
alpha = 0.5)
However I am a little unclear on what you mean by "all values in all of the sim_vcr* columns". Perhaps you want all of those values in one density curve? To do this, simply do not give ggplot any grouping info in the second case.
ggplot() + geom_density(data = df_sim,
mapping = aes(value),
fill = "grey50",
alpha = 0.5)
Notice here I can still specify the 'fill' for the curve outside of the aes() function and it will apply it too all curves instead of give each group specified in 'key' a different color.
I am trying to find the best way to create barplots in R with standard errors displayed. I have seen other articles but I cannot figure out the code to use with my own data (having not used ggplot before and this seeming to be the most used way and barplot not cooperating with dataframes). I need to use this in two cases for which I have created two example dataframes:
Plot df1 so that the x-axis has sites a-c, with the y-axis displaying the mean value for V1 and the standard errors highlighted, similar to this example with a grey colour. Here, plant biomass should the mean V1 value and treatments should be each of my sites.
Plot df2 in the same way, but so that before and after are located next to each other in a similar way to this, so pre-test and post-test equate to before and after in my example.
x <- factor(LETTERS[1:3])
site <- rep(x, each = 8)
values <- as.data.frame(matrix(sample(0:10, 3*8, replace=TRUE), ncol=1))
df1 <- cbind(site,values)
z <- factor(c("Before","After"))
when <- rep(z, each = 4)
df2 <- data.frame(when,df1)
Apologies for the simplicity for more experienced R users and particuarly those that use ggplot but I cannot apply snippets of code that I have found elsewhere to my data. I cannot even get enough code together to produce a start to a graph so I hope my descriptions are sufficient. Thank you in advance.
Something like this?
library(ggplot2)
get.se <- function(y) {
se <- sd(y)/sqrt(length(y))
mu <- mean(y)
c(ymin=mu-se, ymax=mu+se)
}
ggplot(df1, aes(x=site, y=V1)) +
stat_summary(fun.y=mean, geom="bar", fill="lightgreen", color="grey70")+
stat_summary(fun.data=get.se, geom="errorbar", width=0.1)
ggplot(df2, aes(x=site, y=V1, fill=when)) +
stat_summary(fun.y=mean, geom="bar", position="dodge", color="grey70")+
stat_summary(fun.data=get.se, geom="errorbar", width=0.1, position=position_dodge(width=0.9))
So this takes advantage of the stat_summary(...) function in ggplot to, first, summarize y for given x using mean(...) (for the bars), and then to summarize y for given x using the get.se(...) function for the error-bars. Another option would be to summarize your data prior to using ggplot, and then use geom_bar(...) and geom_errorbar(...).
Also, plotting +/- 1 se is not a great practice (although it's used often enough). You'd be better served plotting legitimate confidence limits, which you could do, for instance, using the built-in mean_cl_normal function instead of the contrived get.se(...). mean_cl_normal returns the 95% confidence limits based on the assumption that the data is normally distributed (or you can set the CL to something else; read the documentation).
I used group_by and summarise_each function for this and std.error function from package plotrix
library(plotrix) # for std error function
library(dplyr) # for group_by and summarise_each function
library(ggplot2) # for creating ggplot
For df1 plot
# Group data by when and site
grouped_df1<-group_by(df1,site)
#summarise grouped data and calculate mean and standard error using function mean and std.error(from plotrix)
summarised_df1<-summarise_each(grouped_df1,funs(mean=mean,std_error=std.error))
# Define the top and bottom of the errorbars
limits <- aes(ymax = mean + std_error, ymin=mean-std_error)
#Begin your ggplot
#Here we are plotting site vs mean and filling by another factor variable when
g<-ggplot(summarised_df1,aes(site,mean))
#Creating bar to show the factor variable position_dodge
#ensures side by side creation of factor bars
g<-g+geom_bar(stat = "identity",position = position_dodge())
#creation of error bar
g<-g+geom_errorbar(limits,width=0.25,position = position_dodge(width = 0.9))
#print graph
g
For df2 plot
# Group data by when and site
grouped_df2<-group_by(df2,when,site)
#summarise grouped data and calculate mean and standard error using function mean and std.error
summarised_df2<-summarise_each(grouped_df2,funs(mean=mean,std_error=std.error))
# Define the top and bottom of the errorbars
limits <- aes(ymax = mean + std_error, ymin=mean-std_error)
#Begin your ggplot
#Here we are plotting site vs mean and filling by another factor variable when
g<-ggplot(summarised_df2,aes(site,mean,fill=when))
#Creating bar to show the factor variable position_dodge
#ensures side by side creation of factor bars
g<-g+geom_bar(stat = "identity",position = position_dodge())
#creation of error bar
g<-g+geom_errorbar(limits,width=0.25,position = position_dodge(width = 0.9))
#print graph
g
I am wondering how to dynamically set the x axis limits of a time series plot containing two time series with different dates. I have developed the following code to provide a reproducible example of my problem.
#Dummy Data
Data1 <- data.frame(Date = c("4/24/1995","6/23/1995","2/12/1996","4/14/1997","9/13/1998"), Area_2D = c(20,11,5,25,50))
Data2 <- data.frame(Date = c("6/23/1995","4/14/1996","11/3/1997","11/6/1997","4/15/1998"), Area_2D = c(13,15,18,25,19))
Data3 <- data.frame(Date = c("4/24/1995","6/23/1995","2/12/1996","4/14/1996","9/13/1998"), Area_2D = c(20,25,28,30,35))
Data4 <- data.frame(Date = c("6/23/1995","4/14/1996","11/3/1997","11/6/1997","4/15/1998"), Area_2D = c(13,15,18,25,19))
#Convert date column as date
Data1$Date <- as.Date(Data1$Date,"%m/%d/%Y")
Data2$Date <- as.Date(Data2$Date,"%m/%d/%Y")
Data3$Date <- as.Date(Data3$Date,"%m/%d/%Y")
Data4$Date <- as.Date(Data4$Date,"%m/%d/%Y")
#PLOT THE DATA
max_y1 <- max(Data1$Area_2D)
# Define colors to be used for cars, trucks, suvs
plot_colors <- c("blue","red")
plot(Data1$Date,Data1$Area_2D, col=plot_colors[1],
ylim=c(0,max_y1), xlim=c(min_x1,max_x1),pch=16, xlab="Date",ylab="Area", type="o")
par(new=T)
plot(Data2$Date,Data2$Area_2D, col=plot_colors[2],
ylim=c(0,max_y1), xlim=c(min_x1,max_x1),pch=16, xlab="Date",ylab="Area", type="o")
The main problem I see with the code above is there are two different x axis on the plot, one for Data1 and another for Data2. I want to have a single x axis spanning the date range determined by the dates in Data1 and Data2.
My questions is:
How do i dynamically create an x axis for both series? (i.e select the minimum and maximum date from the data frames 'Data1' and 'Data2')
The solution is to combine the data into one data.frame, and base the x-axis on that. This approach works very well with the ggplot2 plotting package. First we merge the data and add an ID column, which specifies to which dataset it belongs. I use letters here:
Data1$ID = 'A'
Data2$ID = 'B'
merged_data = rbind(Data1, Data2)
And then create the plot using ggplot2, where the color denotes which dataset it belongs to (can easily be changed to different colors):
library(ggplot2)
ggplot(merged_data, aes(x = Date, y = Area_2D, color = ID)) +
geom_point() + geom_line()
Note that you get one uniform x-axis here. In this case this is fine, but if the timeseries do not overlap, this might be problematic. In that case we can use multiple sub-plots, known as facets in ggplot2:
ggplot(merged_data, aes(x = Date, y = Area_2D)) +
geom_point() + geom_line() + facet_wrap(~ ID, scales = 'free_x')
Now each facet has it's own x-axis, i.e. one for each sub-dataset. What approach is most valid depends on the specific situation.
What is the best way to construct a barplot to compare two sets of data?
e.g. dataset:
Number <- c(1,2,3,4)
Yresult <- c(1233,223,2223,4455)
Xresult <- c(1223,334,4421,0)
nyx <- data.frame(Number, Yresult, Xresult)
What I want is Number across X and bars beside each other representing the individual X and Y values
It is better to reshape your data into long format. You can do that with for example the melt function of the reshape2 package (alternatives are reshape from base R, melt from data.table (which is an extended implementation of the melt function of reshape2) and gather from tidyr).
Using your dataset:
# load needed libraries
library(reshape2)
library(ggplot2)
# reshape your data into long format
nyxlong <- melt(nyx, id=c("Number"))
# make the plot
ggplot(nyxlong) +
geom_bar(aes(x = Number, y = value, fill = variable),
stat="identity", position = "dodge", width = 0.7) +
scale_fill_manual("Result\n", values = c("red","blue"),
labels = c(" Yresult", " Xresult")) +
labs(x="\nNumber",y="Result\n") +
theme_bw(base_size = 14)
which gives the following barchart:
I am trying to do the comparison of my observed and modeled data sets for two stations. One station is called station "red" and another is called "blue". I was able to create the facets but when I tried to add two series in one facet, only one facet got updated while other didn't.
This means for blue only one series is plotted and for red two series are plotted.
The code I used is as follows:
# install.packages("RCurl", dependencies = TRUE)
require(RCurl)
out <- postForm("https://dl.dropbox.com/s/ainioj2nn47sis4/watersurf1.csv?dl=1", format="csv")
watersurf <- read.csv(textConnection(out))
watersurf[1:100,]
watersurf$coupleid <- factor(rep(unlist(by(watersurf$id,watersurf$group1,
function(x) {ave(as.numeric(unique(x)),FUN=seq_along)}
)),each=6239))
p <- ggplot(data=watersurf,aes(x=time,y=data,group=id))+geom_line(aes(linetype=group1),size=1)+facet_wrap(~coupleid)
p
Is it also possible to add a third series in the graph but of unequal length (i.e not same interval)?
The output is
I followed the example on this page to create the graphs.
http://www.ats.ucla.edu/stat/r/faq/growth.htm
Is this what you are looking for,
ggplot(data = watersurf, aes( x = time, y = data))
+ geom_line(aes(linetype = group1, colour = group1), size = 0.2)
+ facet_wrap(~ id)