I have a combined data set of 8826 Obs and 4 variables. My column names are tVec , yVec, tVec, yVec.
I need to plot the 2 yVec against the x axis as single tVec with legends. I tried the below but plots only one plot.
plotnew <- ggplot(data=combined, aes(x=tVec, y= yVec, colour='variable')) + geom_line()
Plot looks like this:
Any ides on this. Have tired many examples. Just not getting it right.
Thanks.
You need to format your input data.frame:
combined = data.frame(tVec=1:100,yVec=rnorm(100),tVec=101:200,yVec=rnorm(100))
df = rbind(data.frame(x=combined$tVec,y=combined$yVec,label="first"),
data.frame(x=combined$tVec.1,y=combined$yVec.1,label="second"))
library(ggplot2)
plotnew <- ggplot(data=df, aes(x, y, colour=label))+
geom_line()
Or
df = rbind(data.frame(x=combined$tVec,y=combined$yVec,label="first"),
data.frame(x=combined$tVec,y=combined$yVec.1,label="second"))
library(ggplot2)
plotnew <- ggplot(data=df, aes(x, y, colour=label))+
geom_line()
Hope that help
Related
I am trying to combine a line plot and horizontal barplot on the same plot. The difficult part is that the barplot is actually counts of the y values of the line plot.
Can someone show me how this can be done using the example below ?
library(ggplot2)
library(plyr)
x <- c(1:100)
dff <- data.frame(x = x,y1 = sample(-500:500,size=length(x),replace=T), y2 = sample(3:20,size=length(x),replace=T))
counts <- ddply(dff, ~ y1, summarize, y2 = sum(y2))
# line plot
ggplot(data=dff) + geom_line(aes(x=x,y=y1))
# bar plot
ggplot() + geom_bar(data=counts,aes(x=y1,y=y2),stat="identity")
I believe what I need is presented in the pseudocode below but I do not know how to write it out in R.
Apologies. I actually meant the secondary x axis representing the value of counts for the barplot, while primary y-axis is the y1.
ggplot(data=dff) + geom_line(aes(x=x,y=y1)) + geom_bar(data=counts , aes(primary y axis = y1,secondary x axis =y2),stat="identity")
I just want the barplots to be plotted horizontally, so I tried the code below which flip both the line chart and barplot, which is also not I wanted.
ggplot(data=dff) +
geom_line(aes(x=x,y=y1)) +
geom_bar(data=counts,aes(x=y2,y=y1),stat="identity") + coord_flip()
You can combine two plots in ggplot like you want by specifying different data = arguments in each geom_ layer (and none in the original ggplot() call).
ggplot() +
geom_line(data=dff, aes(x=x,y=y1)) +
geom_bar(data=counts,aes(x=y1,y=y2),stat="identity")
The following plot is the result. However, since x and y1 have different ranges, are you sure this is what you want?
Perhaps you want y1 on the vertical axis for both plots. Something like this works:
ggplot() +
geom_line(data=dff, aes(x=y1 ,y = x)) +
geom_bar(data=counts,aes(x=y1,y=y2),stat="identity", color = "red") +
coord_flip()
Maybe you are looking for this. Ans based on your last code you look for a double axis. So using dplyr you can store the counts in the same dataframe and then plot all variables. Here the code:
library(ggplot2)
library(dplyr)
#Data
x <- c(1:100)
dff <- data.frame(x = x,y1 = sample(-500:500,size=length(x),replace=T), y2 = sample(3:20,size=length(x),replace=T))
#Code
dff %>% group_by(y1) %>% mutate(Counts=sum(y2)) -> dff2
#Scale factor
sf <- max(dff2$y1)/max(dff2$Counts)
# Plot
ggplot(data=dff2)+
geom_line(aes(x=x,y=y1),color='blue',size=1)+
geom_bar(stat='identity',aes(x=x,y=Counts*sf),fill='tomato',color='black')+
scale_y_continuous(name="y1", sec.axis = sec_axis(~./sf, name="Counts"))
Output:
library(alr4)
par(mfrow = c(2,2))
ggplot(walleye, aes(x= age)) + geom_histogram() + facet_grid(~age)
I would like to create 4 histograms from the data set walleye. I would like the histograms to be for the length of the walleye. The for histograms should each have their own age for counting. I would like to restrict the ages from 1 to 4. How can I do that with ggplot?
If I understand what you are trying to do correctly, this should help:
library(alr4)
library(ggplot2)
ggplot(subset(walleye, age<5), aes(x=length)) + geom_histogram() + facet_grid(~age)
This way you are only plotting the subset of the data where age is 1-4, and you are actually plotting histograms of length.
You could try this too (adding another line of code on top of your code):
library(alr4)
library(ggplot2)
p <- ggplot(walleye, aes(x= age)) + geom_histogram() + facet_grid(~age)
p %+% subset(walleye, age %in% 1:4)
I want to make a line chart in plotly so that it does not have the same color on its whole length. The color is given continuous scale. It is easy in ggplot2 but when I translate it to plotly using ggplotly function the variable determining color behaves like categorical variable.
require(dplyr)
require(ggplot2)
require(plotly)
df <- data_frame(
x = 1:15,
group = rep(c(1,2,1), each = 5),
y = 1:15 + group
)
gg <- ggplot(df) +
aes(x, y, col = group) +
geom_line()
gg # ggplot2
ggplotly(gg) # plotly
ggplot2 (desired):
plotly:
I found one work-around that, on the other hand, behaves oddly in ggplot2.
df2 <- df %>%
tidyr::crossing(col = unique(.$group)) %>%
mutate(y = ifelse(group == col, y, NA)) %>%
arrange(col)
gg2 <- ggplot(df2) +
aes(x, y, col = col) +
geom_line()
gg2
ggplotly(gg2)
I also did not find a way how to do this in plotly directly. Maybe there is no solution at all. Any ideas?
It looks like ggplotly is treating group as a factor, even though it's numeric. You could use geom_segment as a workaround to ensure that segments are drawn between each pair of points:
gg2 = ggplot(df, aes(x,y,colour=group)) +
geom_segment(aes(x=x, xend=lead(x), y=y, yend=lead(y)))
gg2
ggplotly(gg2)
Regarding #rawr's (now deleted) comment, I think it would make sense to have group be continuous if you want to map line color to a continuous variable. Below is an extension of the OP's example to a group column that's continuous, rather than having just two discrete categories.
set.seed(49)
df3 <- data_frame(
x = 1:50,
group = cumsum(rnorm(50)),
y = 1:50 + group
)
Plot gg3 below uses geom_line, but I've also included geom_point. You can see that ggplotly is plotting the points. However, there are no lines, because no two points have the same value of group. If we hadn't included geom_point, the graph would be blank.
gg3 <- ggplot(df3, aes(x, y, colour = group)) +
geom_point() + geom_line() +
scale_colour_gradient2(low="red",mid="yellow",high="blue")
gg3
ggplotly(gg3)
Switching to geom_segment gives us the lines we want with ggplotly. Note, however, that line color will be based on the value of group at the first point in the segment (whether using geom_line or geom_segment), so there might be cases where you want to interpolate the value of group between each (x,y) pair in order to get smoother color gradations:
gg4 <- ggplot(df3, aes(x, y, colour = group)) +
geom_segment(aes(x=x, xend=lead(x), y=y, yend=lead(y))) +
scale_colour_gradient2(low="red",mid="yellow",high="blue")
ggplotly(gg4)
I am working in the updated ggplot2 (2.2.0). A great new feature is the ability to plot along each x-axis for an uneven number of plots, as per this page.
I wish to plot with the same x-axis in each facet. However, this does not work when missing data is introduced. As an example, I shall draw upon the example data in this question.
# Example dataset
dataf <- data.frame(x=c(1:30), A=rnorm(30,20,5), B=rnorm(30,15,0.5))
datam <- melt(dataf, id="x")
# Plot in ggplot2
ggplot(datam, aes(factor(x), value)) +
geom_bar(stat="identity") +
facet_wrap(~variable,nrow = 2,scales = "free")
When I remove particular rows and introduce missing data (as per my code below), I am no longer to have the same x-axis labels for both "A" and "B".
# Remove certain rows to introduce missing data
datam <- datam[-c(2, 4, 58), ]
I can plot missing values and align to the bottom x-axis using scales = fixed although I no longer have x-axis tick mark labels for group "A" in the facet.
ggplot(datam, aes(factor(x), value)) +
geom_bar(stat="identity") +
facet_wrap(~variable,nrow = 2,scales = "fixed")
How do have the exact same x-axis labels showing for every facet, even when there is missing data?
Thank you.
You could do something like:
library(reshape2)
library(ggplot2)
dataf <- data.frame(x=c(1:30), A=rnorm(30,20,5), B=rnorm(30,15,0.5))
datam <- melt(dataf, id="x")
datam2 <- datam[-c(2, 4, 58), ] #mising rows
graph<-ggplot(datam2, aes(factor(x), value)) +
geom_bar(stat="identity") +
facet_wrap(~variable,nrow = 2,scales = "fixed")
library(gtable)
library(grid)
g <- ggplotGrob(graph) #retrieve grob from graph
panels <- grep("panel", g$layout$name)
top <- unique(g$layout$t[panels])
all <- gtable:::rbind_gtable(gtable:::rbind_gtable(g[seq.int(min(top)), ],
g[max(top)+1,], "first"),
g[seq(min(top)+1, nrow(g)),], "first")
grid.newpage()
grid.draw(all)
Pretty sure that a solution I found on SO at some point. But can't remember the original post.
Here is a solution using only ggplot2 functionality. There is a drop parameter in scale_x_discreet' and scale_y_discreet' when set to FALSE, it shows all factors regardless if no data exist for that factor.
See ? scale_x_discreet. The first step is to factor datam$x.
library(ggplot2)
dataf <- data.frame(x=c(1:30), A=rnorm(30,20,5), B=rnorm(30,15,0.5))
datam <- melt(dataf, id="x")
# missing data
datam <- datam[-c(2, 4, 58), ]
# solution
ggplot(datam, aes(factor(x), value)) +
geom_bar(stat="identity") +
facet_wrap(~variable,nrow = 2, scales = "free") +
scale_x_discrete(drop=FALSE)
Am having trouble making my faceted plot only display data, as opposed to displaying facets with no data.
The following code:
p<- ggplot(spad.data, aes(x=Day, y=Mean.Spad, color=Inoc))+
geom_point()
p + facet_grid(N ~ X.CO2.)
Gives the following graphic:
I have played around with it for a while but can't seem to figure out a solution.
Dataframe viewable here: https://docs.google.com/spreadsheets/d/11ZiDVRAp6qDcOsCkHM9zdKCsiaztApttJIg1TOyIypo/edit?usp=sharing
Reproducible Example viewable here: https://docs.google.com/document/d/1eTp0HCgZ4KX0Qavgd2mTGETeQAForETFWdIzechTphY/edit?usp=sharing
Your issue lies in the missing observations for your x- and y variables. Those don't influence the creation of facets, that is only influenced by the levels of faceting variables present in the data. Here is an illustration using sample data:
#generate some data
nobs=100
set.seed(123)
dat <- data.frame(G1=sample(LETTERS[1:3],nobs, T),
G2 = sample(LETTERS[1:3], nobs, T),
x=rnorm(nobs),
y=rnorm(nobs))
#introduce some missings in one group
dat$x[dat$G1=="C"] <- NA
#attempt to plot
p1 <- ggplot(dat, aes(x=x,y=y)) + facet_grid(G1~G2) + geom_point()
p1 #facets are generated according to the present levels of the grouping factors
#possible solution: remove the missing data before plotting
p2 <- ggplot(dat[complete.cases(dat),], aes(x=x, y=y)) + facet_grid(G1 ~G2) + geom_point()
p2