I am trying to create a chart for each ID (column 1) plotting foo and bar by dayt on each chart, and bar needs to be on an inverted axis...
my data has form
ID <- rep(6:10, times=5)
foo <-rnorm(n=25, mean=0, sd=1)
bar <-rnorm(n=25, mean=10, sd=1)
dayt <-rnorm(n=25, mean= 1, sd=1)
df <-data.frame(ID,dat,x,y)
I have no idea where to go from here except that I know ggplot2 allows multiple objects to be added to a chart easily...
I am trying something like this
require(ggplot2)
require(plyr)
require(gridExtra)
pl <- dlply(df, .(ID), function(dat) {
ggplot(data = dat, aes(x = dayt, y = foo)) + geom_line() +
geom_point() + xlab("x-label") + ylab("y-label") +
geom_smooth(method = "lm")
})
ml <- do.call(marrangeGrob, c(pl, list(nrow = 5, ncol = 1)))
ggsave("my_plots.pdf", ml, height = 8, width = 11, units = "in")
but cant figure out how to add the second data to each plot as well as invert the axis...
any help would be great!
thanks
zr
It sounds like you want to create a simple scatter plot, with multiple charts for each ID and a reversed Y axis.
If you want to create one plot with multiple charts for each ID, you can use ggplot's faceting functions (facet_grid or facet_wrap). You can reverse the Y axis with the scale_y_reverse() function.
Here's one way to go about it:
library(ggplot2) # Load the library
p <- ggplot(df, aes(x=x, y=y)) + # Tell ggplot what you're plotting
geom_point() + # Tell ggplot it's a scatter plot
facet_wrap(~ ID) + # Plot one chart for each ID
scale_y_reverse() # Reverse the axis
p # Display the chart
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:
I'm trying to plot a line graph (data points between 0 and 2.5, with interval of 0.5). I want to plot some bars in the same chart on the right-hand axis (between 0 and 60 with interval of 10). I am making some mistake in my code such that the bars get plotted in the left hand axis.
Here's some sample data and code:
Month <- c("J","F","M","A")
Line <- c(2.5,2,0.5,3.4)
Bar <- c(30,33,21,40)
df <- data.frame(Month,Line,Bar)
ggplot(df, aes(x=Month)) +
geom_line(aes(y = Line,group = 1)) +
geom_col(aes(y=Bar))+
scale_y_continuous("Line",
sec.axis = sec_axis(trans= ~. /50, name = "Bar"))
Here's the output
Thanks in advance.
Try this approach with scaling factor. It is better if you work with a scaling factor between your variables and then you use it for the second y-axis. I have made slight changes to your code:
library(tidyverse)
#Data
Month <- c("J","F","M","A")
Line <- c(2.5,2,0.5,3.4)
Bar <- c(30,33,21,40)
df <- data.frame(Month,Line,Bar)
#Scale factor
sfactor <- max(df$Line)/max(df$Bar)
#Plot
ggplot(df, aes(x=Month)) +
geom_line(aes(y = Line,group = 1)) +
geom_col(aes(y=Bar*sfactor))+
scale_y_continuous("Line",
sec.axis = sec_axis(trans= ~. /sfactor, name = "Bar"))
Output:
I would like to show in the same plot interpolated data and a histogram of the raw data of each predictor. I have seen in other threads like this one, people explain how to do marginal histograms of the same data shown in a scatter plot, in this case, the histogram is however based on other data (the raw data).
Suppose we see how price is related to carat and table in the diamonds dataset:
library(ggplot2)
p = ggplot(diamonds, aes(x = carat, y = table, color = price)) + geom_point()
We can add a marginal frequency plot e.g. with ggMarginal
library(ggExtra)
ggMarginal(p)
How do we add something similar to a tile plot of predicted diamond prices?
library(mgcv)
model = gam(price ~ s(table, carat), data = diamonds)
newdat = expand.grid(seq(55,75, 5), c(1:4))
names(newdat) = c("table", "carat")
newdat$predicted_price = predict(model, newdat)
ggplot(newdat,aes(x = carat, y = table, fill = predicted_price)) +
geom_tile()
Ideally, the histograms go even beyond the margins of the tileplot, as these data points also influence the predictions. I would, however, be already very happy to know how to plot a histogram for the range that is shown in the tileplot. (Maybe the values that are outside the range could just be added to the extreme values in different color.)
PS. I managed to more or less align histograms to the margins of the sides of a tile plot, using the method of the accepted answer in the linked thread, but only if I removed all kind of labels. It would be particularly good to keep the color legend, if possible.
EDIT:
eipi10 provided an excellent solution. I tried to modify it slightly to add the sample size in numbers and to graphically show values outside the plotted range since they also affect the interpolated values.
I intended to include them in a different color in the histograms at the side. I hereby attempted to count them towards the lower and upper end of the plotted range. I also attempted to plot the sample size in numbers somewhere on the plot. However, I failed with both.
This was my attempt to graphically illustrate the sample size beyond the plotted area:
plot_data = diamonds
plot_data <- transform(plot_data, carat_range = ifelse(carat < 1 | carat > 4, "outside", "within"))
plot_data <- within(plot_data, carat[carat < 1] <- 1)
plot_data <- within(plot_data, carat[carat > 4] <- 4)
plot_data$carat_range = as.factor(plot_data$carat_range)
p2 = ggplot(plot_data, aes(carat, fill = carat_range)) +
geom_histogram() +
thm +
coord_cartesian(xlim=xrng)
I tried to add the sample size in numbers with geom_text. I tried fitting it in the far right panel but it was difficult (/impossible for me) to adjust. I tried to put it on the main graph (which would anyway probably not be the best solution), but it didn’t work either (it removed the histogram and legend, on the right side and it did not plot all geom_texts). I also tried to add a third row of plots and writing it there. My attempt:
n_table_above = nrow(subset(diamonds, table > 75))
n_table_below = nrow(subset(diamonds, table < 55))
n_table_within = nrow(subset(diamonds, table >= 55 & table <= 75))
text_p = ggplot()+
geom_text(aes(x = 0.9, y = 2, label = paste0("N(>75) = ", n_table_above)))+
geom_text(aes(x = 1, y = 2, label = paste0("N = ", n_table_within)))+
geom_text(aes(x = 1.1, y = 2, label = paste0("N(<55) = ", n_table_below)))+
thm
library(egg)
pobj = ggarrange(p2, ggplot(), p1, p3,
ncol=2, widths=c(4,1), heights=c(1,4))
grid.arrange(pobj, leg, text_p, ggplot(), widths=c(6,1), heights =c(6,1))
I would be very happy to receive help on either or both tasks (adding sample size as text & adding values outside plotted range in a different color).
Based on your comment, maybe the best approach is to roll your own layout. Below is an example. We create the marginal plots as separate ggplot objects and lay them out with the main plot. We also extract the legend and put it outside the marginal plots.
Set-up
library(ggplot2)
library(cowplot)
# Function to extract legend
#https://github.com/hadley/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs
g_legend<-function(a.gplot){
tmp <- ggplot_gtable(ggplot_build(a.gplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend) }
thm = list(theme_void(),
guides(fill=FALSE),
theme(plot.margin=unit(rep(0,4), "lines")))
xrng = c(0.6,4.4)
yrng = c(53,77)
Plots
p1 = ggplot(newdat, aes(x = carat, y = table, fill = predicted_price)) +
geom_tile() +
theme_classic() +
coord_cartesian(xlim=xrng, ylim=yrng)
leg = g_legend(p1)
p1 = p1 + thm[-1]
p2 = ggplot(diamonds, aes(carat)) +
geom_line(stat="density") +
thm +
coord_cartesian(xlim=xrng)
p3 = ggplot(diamonds, aes(table)) +
geom_line(stat="density") +
thm +
coord_flip(xlim=yrng)
plot_grid(
plot_grid(plotlist=list(p2, ggplot(), p1, p3), ncol=2,
rel_widths=c(4,1), rel_heights=c(1,4), align="hv", scale=1.1),
leg, rel_widths=c(5,1))
UPDATE: Regarding your comment about the space between the plots: This is an Achilles heel of plot_grid and I don't know if there's a way to fix it. Another option is ggarrange from the experimental egg package, which doesn't add so much space between plots. Also, you need to save the output of ggarrange first and then lay out the saved object with the legend. If you run ggarrange inside grid.arrange you get two overlapping copies of the plot:
# devtools::install_github('baptiste/egg')
library(egg)
pobj = ggarrange(p2, ggplot(), p1, p3,
ncol=2, widths=c(4,1), heights=c(1,4))
grid.arrange(pobj, leg, widths=c(6,1))
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'm trying to plot a histogram using ggplot which has some space between the bars.
This is no problem with discrete data:
b= data.frame(x=sample(LETTERS[1:3],size=50, replace=T))
ggplot(b, aes(x=x)) + geom_bar(width=.3)
However, using continuous data, width seems to have no effect.
a= data.frame(x=rnorm(100))
ggplot(a, aes(x=x, width=.5)) +
geom_bar(width=.3, binwidth=1)
How can a histogram with spaced bars be archived for continuous data?
I think doing this is a really bad idea (and ggplot2 doesn't support it).
Here is one possibility:
breaks <- pretty(range(a$x), n = 6, min.n = 1)
mids <- 0.5 * (breaks[-1L] + breaks[-length(breaks)])
ggplot(a, aes(x = cut(x, breaks = breaks, labels = mids))) +
geom_bar(width=.3)