I have an example data, which does not have x- and y-axis information. I would like to make a bubble plot using R package ggplot2, and arrange the bubbles in a circled manner.
data <- data.frame(group = paste("Group", letters[1:11]),
value = sample(seq(1,100),11))
Thanks a lot.
You can just put a dummy value for y and make group your x values in aes.
ggplot(data, aes(x = group, y = 0, size = value)) +
coord_polar() +
geom_point()
Related
I want to plot a segmented bar plot in ggplot2. Here is part of my dataframe, I want to plot the proportion of output(0 and 1) for each x1(0 and 1). But when I use the following code, what I plot is just black bars without any segmentation. What's the problem in here?
fig = ggplot(data=df, mapping=aes(x=x1, fill=output)) + geom_bar(stat="count", width=0.5, position='fill')
The output plot is here
You need factor variables for your task:
library(ggplot2)
df <- data.frame(x1=sample(0:1,100,replace = T),output=sample(0:1,100,replace = T))
ggplot(data = df, aes(x = as.factor(x1), fill = as.factor(output))) +
geom_histogram(stat = "count")+
labs(x="x11")
which give me:
I'm trying to plot bar graphs in ggplot2 and running into an issue.
Starting with the variables as this
PalList <- c(9, 9009, 906609, 99000099)
PalList1 <- as_tibble(PalList)
Index <- c(1,2,3,4)
PalPlotList <- cbind(Index, PalList)
PPL <- as_tibble(PalPlotList)
and loading the tidyverse library(tidyverse), I tried plotting like this:
PPL %>%
ggplot(aes(x=PalList)) +
geom_bar()
It doesn't matter whether I'm accessing PPL or PalList, I'm still ending up with this (axes and labels may change, but not the chart area):
Even this still gave a blank plot, only now in classic styling:
ggplot(PalList1, aes(value)) +
geom_bar() +
theme_classic()
If I try barplot(PalList), I get an expected result. But I want the control of ggplot. Any suggestions on how to fix this?
An option is to specify the x, y in aes, create the geom_bar with stat as 'identity', and change the x-axis tick labels
library(ggplot2)
ggplot(PPL, aes(x = Index, y = PalList)) +
geom_bar(stat = 'identity') +
scale_x_continuous(breaks = Index, labels = PalList)
I am trying to create a heat map, where each tile is a month. If I use the dates as a data date type, I get lines over my image that are not acceptable.
So my solution has been to to treat the date as a factor
The problem is that the axis is not presentable. Is there a way to merge the two plots with the x axis from the first plot, but the tiles from the second plot?
ggplot(df, aes(x = factor(Var2),
y = factor(desc(Var1)),
fill = value)) +
As a reproducible example:
df <- read.table("","Var1","Var2","value","Col"
"1",2001-01-31,2001-01-31,-0.0118511587908436,"blue"
"2",2001-02-28,2001-01-31,0,"white"
"3",2001-03-30,2001-01-31,0,"white"
"4",2001-04-30,2001-01-31,0,"white"
"5",2001-05-31,2001-01-31,0,"white"
"6",2001-06-29,2001-01-31,0,"white")
ggplot(dff, aes(x = factor(Var2),
y = factor(desc(Var1)),
fill = Col)) +
geom_tile() +
scale_x_discrete(breaks=pretty(p$Var2))
Output from example
Use dates but manually remove the grid. You can do it by adding + theme(panel.grid = element_blank()) to the plot.
I want to make a line chart in plotly so that it does not have the same color on its whole length. The color is given continuous scale. It is easy in ggplot2 but when I translate it to plotly using ggplotly function the variable determining color behaves like categorical variable.
require(dplyr)
require(ggplot2)
require(plotly)
df <- data_frame(
x = 1:15,
group = rep(c(1,2,1), each = 5),
y = 1:15 + group
)
gg <- ggplot(df) +
aes(x, y, col = group) +
geom_line()
gg # ggplot2
ggplotly(gg) # plotly
ggplot2 (desired):
plotly:
I found one work-around that, on the other hand, behaves oddly in ggplot2.
df2 <- df %>%
tidyr::crossing(col = unique(.$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 trying to create a graph where because there are so many points on the graph, at the edges of the green it starts to fade to black while the center stays green. The code I am currently using to create this graph is:
plot(snb$px,snb$pz,col=snb$event_type,xlim=c(-2,2),ylim=c(1,6))
I looked into contour plotting but that did not work for this. The coloring variable is a factor variable.
Thanks!
This is a great problem for ggplot2.
First, read the data in:
snb <- read.csv('MLB.csv')
With your data frame you could try plotting points that are partly transparent, and setting them to be colored according to the factor event_type:
require(ggplot2)
p1 <- ggplot(data = snb, aes(x = px, y = py, color = event_type)) +
geom_point(alpha = 0.5)
print(p1)
and then you get this:
Or, you might want to think about plotting this as a heatmap using geom_bin2d(), and plotting facets (subplots) for each different event_type, like this:
p2 <- ggplot(data = snb, aes(x = px, y = py)) +
geom_bin2d(binwidth = c(0.25, 0.25)) +
facet_wrap(~ event_type)
print(p2)
which makes a plot for each level of the factor, where the color will be the number of data points in each bins that are 0.25 on each side. But, if you have more than about 5 or 6 levels, this might look pretty bad. From the small data sample you supplied, I got this
If the levels of the factors don't matter, there are some nice examples here of plots with too many points. You could also try looking at some of the examples on the ggplot website or the R cookbook.
Transparency could help, which is easily achieved, as #BenBolker points out, with adjustcolor:
colvect = adjustcolor(c("black", "green"), alpha = 0.2)
plot(snb$px, snb$pz,
col = colvec[snb$event_type],
xlim = c(-2,2),
ylim = c(1,6))
It's built in to ggplot:
require(ggplot2)
p <- ggplot(data = snb, aes(x = px, y = pz, color = event_type)) +
geom_point(alpha = 0.2)
print(p)