Sorry I don't have example code for this question.
All I want to know is if it is possible to create multiple side-by-side boxplots in R representing different columns/variables within my data frame. Each boxplot would also only represent a single variable--I would like to set the y-scale to a range of (0,6).
If this isn't possible, how can I use something like the panel option in ggplot2 if I only want to create a boxplot using a single variable? Thanks!
Ideally, I want something like the image below but without factor grouping like in ggplot2. Again, each boxplot would represent completely separate and single columns.
ggplot2 requires that your data to be plotted on the y-axis are all in one column.
Here is an example:
set.seed(1)
df <- data.frame(
value = runif(810,0,6),
group = 1:9
)
df
library(ggplot2)
ggplot(df, aes(factor(group), value)) + geom_boxplot() + coord_cartesian(ylim = c(0,6)
The ylim(0,6) sets the y-axis to be between 0 and 6
If your data are in columns, you can get them into the longform using melt from reshape2 or gather from tidyr. (other methods also available).
You can do this if you reshape your data into long format
## Some sample data
dat <- data.frame(a=rnorm(100), b=rnorm(100), c=rnorm(100))
## Reshape data wide -> long
library(reshape2)
long <- melt(dat)
plot(value ~ variable, data=long)
Related
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))
I am trying to plot (on the same graph) two sets of data versus date from two different data frames. Both data frames have the same exact dates for each of the two measurements. I would like to plot these two sets of data on the same graph, with different colors. However, I can't get them on the same graph at all. R is already reading the date as date. I tried this:
qplot( date , NO3, data=qual.arn)
+ qplot( qual.arn$date , qual.arn$DIS.O2, "O2(aq)" , add=T)
and received this error.
Error in add_ggplot(e1, e2, e2name) :
argument "e2" is missing, with no default
I tried using the ggplot function instead of qplot, but I couldn't even plot one graph this way.
ggplot(date=qual.no3.s, aes(date,NO3))
Error: ggplot2 doesn't know how to deal with data of class uneval
PLEASE HELP. Thank you!
Since you didn't provide any data (please do so in future), here's a made up dataset for demonstrate a solution. There are (at least) two ways to do this: the right way and the wrong way. Both yield equivalent results in this very simple case.
# set up minimum reproducible example
set.seed(1) # for reproducible example
dates <- seq(as.Date("2015-01-01"),as.Date("2015-06-01"), by=1)
df1 <- data.frame(date=dates, NO3=rpois(length(dates),25))
df2 <- data.frame(date=dates, DIS.O2=rnorm(length(dates),50,10))
ggplot is designed to use data in "long" format. This means that all the y-values (the concentrations) are in a single column, and there is separate column which identifies the corresponding category ("NO3" or "DIS.O2" in your case). So first we merge the two data-sets based on date, then use melt(...) to convert from "wide" (categories in separate columns) to "long" format. Then we let ggplot worry about legends, colors, etc.
library(ggplot2)
library(reshape2) # for melt(...)
# The right way: combine the data-sets, then plot
df.mrg <- merge(df1,df2, by="date", all=TRUE)
gg.df <- melt(df.mrg, id="date", variable.name="Component", value.name="Concentration")
ggplot(gg.df, aes(x=date, y=Concentration, color=Component)) +
geom_point() + labs(x=NULL)
The "wrong" way to do this is by making separate calls to geom_point(...) for each layer. In your particular case this might be simpler, but in the long run it's better to use the other method.
# The wrong way: plot two sets of points
ggplot() +
geom_point(data=df1, aes(x=date, y=NO3, color="NO2")) +
geom_point(data=df2, aes(x=date, y=DIS.O2, color="DIS.O2")) +
scale_color_manual(name="Component",values=c("red", "blue")) +
labs(x=NULL, y="Concentration")
This question already has answers here:
Plot one numeric variable against n numeric variables in n plots
(4 answers)
Closed 5 years ago.
Is there a parsimonious way to create a pairs plot that only compares one variable to the many others? In other words, can I plot just one row or column of the standard pairs scatter plot matrix without using a loop?
Melt your data then use ggplot with facet.
library("ggplot2")
library("reshape2")
#dummy data
df <- data.frame(x=1:10,
a=runif(10),
b=runif(10),
c=runif(10))
#melt your data
df_melt <- melt(df,"x")
#scatterplot per group
ggplot(df_melt,aes(x,value)) +
geom_point() +
facet_grid(.~variable)
I'll round it out with a base plotting option (using df from #zx8754):
layout(matrix(seq(ncol(df)-1),nrow=1))
Map(function(x,y) plot(df[c(x,y)]), names(df[1]), names(df[-1]))
Although arguably this is still a loop using Map.
For the fun, with lattice (with #zx8754 "df_melt"):
library(lattice)
xyplot(value ~ x | variable, data = df_melt, layout = c(3,1),
between = list(x=1))
My dataset looks similar to the one described here( i have more variables=columns and more observations):
dat=cbind(var1=c(100,20,33,400),var2=c(1,0,1,1),var3=c(0,1,0,0))
Now I want to create a bargraph with R where on the x axis one see the names of all the variable, and on the y axis the mean of the respective variable.
As a second task it would be great to show not only the mean, also the standard deviation within the same plot.
It would be nice, solving this with gglopt or qplot.
Thanks
Using base R:
dat <- cbind(var1=c(1,0.20,0.33,4),var2=c(1,0,1,1),var3=c(0,1,0,0))
dat <- as.data.frame(dat) # get this into a data frame as early as possible
barplot(sapply(dat,mean))
Using ggplot
library(ggplot2)
library(reshape2) # for melt(...)
df <- melt(dat)
ggplot(df, aes(x=variable,y=value)) +
stat_summary(fun.y=mean,geom="bar",color="grey20",fill="lightgreen")+
stat_summary(fun.data="mean_sdl",mult=1)
I have a dataframe with 3 columns, (Id, Lat, Long), you can construct a small section of this with the following data:
df <- data.frame(
Id=c(1,1,2,2,2,2,2,2,3,3,3,3,3,3),
Lat=c(58.12550, 58.17426, 58.46461, 58.45812, 58.45207, 58.44512, 58.43358, 58.42727, 57.77700, 57.76034, 57.73614, 57.72411, 57.70498, 57.68453),
Long=c(-5.098068, -5.314452, -4.914108, -4.899922, -4.887067, -4.873312, -4.852384, -4.840817, -5.666568, -5.648711, -5.617588, -5.594681, -5.557740, -5.509405))
The Id column is an index column. So all the rows with the same Id number have the coordinates for a single line. In my data frame this Id number varies from 1 through to 7696. So I have 7696 lines to plot.
Each Id number relates to an individual separate line of Lat and Long coordinates. What I want to do is overlay onto an existing plot all of these 7696 individual lines.
With the example data above this contains the Lat & Long coordinates for lines 1, 2, 3.
What is the best way to overlay all these lines onto an existing plot, I was thinking maybe some kind of loop?
Using ggplot2:
#dummy data
df <- data.frame(
Id=c(1,1,2,2,2,2,2,2,3,3,3,3,3,3),
Lat=c(58.12550, 58.17426, 58.46461, 58.45812, 58.45207, 58.44512, 58.43358, 58.42727, 57.77700, 57.76034, 57.73614, 57.72411, 57.70498, 57.68453),
Long=c(-5.098068, -5.314452, -4.914108, -4.899922, -4.887067, -4.873312, -4.852384, -4.840817, -5.666568, -5.648711, -5.617588, -5.594681, -5.557740, -5.509405))
library(ggplot2)
#plot
ggplot(data=df,aes(Lat,Long,colour=as.factor(Id))) +
geom_line()
Using base R:
#plot blank
with(df,plot(Lat,Long,type="n"))
#plot lines
for(i in unique(df$Id))
with(df[ df$Id==i,],lines(Lat,Long,col=i))
To be honest, I think that any approach to take is going to result in a very cluttered plot since you have so many Ids (unless their lines do not overlap much). Either way, I would probably use ggplot2 for this.
##
if( !("ggplot2" %in% installed.packages()[,1]) ){
install.packages("ggplot2",dependencies=TRUE)
}
library(ggplot2)
##
D <- data.frame(
Id=Id,
Lat=Lat,
Long=Long
)
##
ggplot(data=D,aes(x=Lat,y=Long,group=Id,color=Id))+
geom_point()+ ## you might want to omit geom_point() in your plot
geom_line()
##
The reason I used group=Id, color=Id in aes() rather than passing Id as a factor to aes() and just using color=Id is that you will end up with a legend containing 7000+ factor levels (the majority of which will not be visible in the plot area).