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I just encountered such graph attached where two colors of geom_point are used (I believe it is made by ggplot2). Similarly, I would like to have dots of one color to range from size 1 to 5, and have another color for a series of dots for the range 10 to 50. I have however no clue on how to add two different ranges of point in one graph.
At the basic step I have:
a <- c(1,2,3,4,5)
b <- c(10,20,30,40,50)
Species <- factor(c("Species1","Species2","Species3","Species4","Species5"))
bubba <- data.frame(Sample1=a,Sample2=b,Species=Species)
bubba$Species=factor(bubba$Species, levels=bubba$Species)
xm=melt(bubba,id.vars = "Species", variable.name="Samples", value.name = "Size")
str(xm)
ggplot(xm,aes(x= Samples,y= fct_rev(Species)))+geom_point(aes(size=Size))+scale_size(range = range(xm$Size))+theme_bw()
Any would have clues where I should look into ? Thanks!
I've got an approach that gets 90% of the way there, but I'm not sure how to finish the deed. To get a single legend for size, I used a transformation to convert input size to display size. That makes the legend appearance conform to the display. What I don't have figured out yet is how to apply a similar transformation to the fill so that both can be integrated into the same legend.
Here's the transformation, which in this case shrinks everything 10 or more:
library(scales)
shrink_10s_trans = trans_new("shrink_10s",
transform = function(y){
yt = if_else(y >= 10, y*0.1, y)
return(yt)
},
inverse = function(yt){
return(yt) # Not 1-to-1 function, picking one possibility
}
)
Then we can use this transformation on the size to selectively shink only the dots that are 10 or larger. This works out nicely for the legend, aside from integrating the fill encoding with the size encoding.
ggplot(xm,aes(x= Samples,y= fct_rev(Species), fill = Size < 10))+
geom_point(aes(size=Size), shape = 21)+
scale_size_area(trans = shrink_10s_trans, max_size = 10,
breaks = c(1,2,3,10,20,30,40),
labels = c(1,2,3,10,20,30,40)) +
scale_fill_manual(values = c(rgb(136,93,100, maxColorValue = 255),
rgb(236,160,172, maxColorValue = 255))) +
theme_bw()
a <- c(1, 2, 3, 4, 5)
b <- c(10, 20, 30, 40, 50)
Species <- factor(c("Species1", "Species2", "Species3", "Species4", "Species5"))
bubba <- data.frame(Sample1 = a, Sample2 = b, Species = Species)
bubba$Species <- factor(bubba$Species, levels = bubba$Species)
xm <- reshape2::melt(bubba, id.vars = "Species", variable.name = "Samples", value.name = "Size")
ggplot(xm, aes(x = Samples, y = fct_rev(Species))) +
geom_point(aes(size = Size, color = Size)) +
scale_color_continuous(breaks = c(1,2,3,10,20,30), guide = guide_legend()) +
scale_size(range = range(xm$Size), breaks = c(1,2,3,10,20,30)) +
theme_bw()
Here's a cludge. I haven't got time to figure out the legend at the moment. Note that 1 and 10 are the same size, but a different colour, as are 3 and 40.
# Create data frame
a <- c(1, 2, 3, 4, 5)
b <- c(10, 20, 30, 40, 50)
Species <- factor(c("Species1", "Species2", "Species3", "Species4", "Species5"))
bubba <- data.frame(Sample1 = a, Sample2 = b, Species = Species)
# Restructure data
xm <- reshape2::melt(bubba, id.vars = "Species", variable.name = "Samples", value.name = "Size")
# Calculate bubble size
bubble_size <- function(val){
ifelse(val > 3, (1/15) * val + (1/3), val)
}
# Calculate bubble colour
bubble_colour <- function(val){
ifelse(val > 3, "A", "B")
}
# Calculate bubble size and colour
xm %<>%
mutate(bub_size = bubble_size(Size),
bub_col = bubble_colour(Size))
# Plot data
ggplot(xm, aes(x = Samples, y = fct_rev(Species))) +
geom_point(aes(size = bub_size, fill = bub_col), shape = 21, colour = "black") +
theme(panel.grid.major = element_line(colour = alpha("gray", 0.5), linetype = "dashed"),
text = element_text(family = "serif"),
legend.position = "none") +
scale_size(range = c(1, 20)) +
scale_fill_manual(values = c("brown", "pink")) +
ylab("Species")
I think you are looking for bubble plots in R
https://www.r-graph-gallery.com/bubble-chart/
That said, you probably want to build the right and left the side of the graphic separately and then combine.
I'm trying to create a line graph depicting different trajectories over time for two groups/conditions. I have two groups for which the data 'eat' was collected at five time points (1,2,3,4,5).
I'd like the lines to connect the mean point for each group at each of five time points, so I'd have two points at Time 1, two points at Time 2, and so on.
Here's a reproducible example:
#Example data
library(tidyverse)
library(ggplot2)
eat <- sample(1:7, size = 30, replace = TRUE)
df <- data.frame(id = rep(c(1, 2, 3, 4, 5, 6), each = 5),
Condition = rep(c(0, 1), each = 15),
time = c(1, 2, 3, 4, 5),
eat = eat
)
df$time <- as.factor(df$time)
df$Condition <- as.factor(df$Condition)
#Create the plot.
library(ggplot2)
ggplot(df, aes(x = time, y = eat, fill = Condition)) + geom_line() +
geom_point(size = 4, shape = 21) +
stat_summary(fun.y = mean, colour = "red", geom = "line")
The problem is, I need my lines to go horizontally (ie to show two different colored lines moving across the x-axis). But this code just connects the dots vertically:
If I don't convert Time to a factor, but only convert Condition to a factor, I get a mess of lines. The same thing happens in my actual data, as well.
I'd like it to look like this aesthetically, with the transparent error envelopes wrapping each line. However, I don't want it to be curvy, I want the lines to be straight, connecting the means at each point.
Here's the lines running in straight segments through the means of each time, with the range set to be the standard deviation of the points at the time. One stat.summary makes the mean line with the colour aesthetic, the other makes the area using the inherited fill aesthetic. ggplot2::mean_se is a convenient function that takes a vector and returns a data frame with the mean and +/- some number of standard errors. This is the right format for thefun.data argument to stat_summary, which passes these values to the geom specified. Here, geom_ribbon accepts ymin and ymax values to plot a ribbon across the graph.
library(tidyverse)
set.seed(12345)
eat <- sample(1:7, size = 30, replace = T)
df <- data.frame(
Condition = rep(c(0, 1), each = 15),
time = c(1, 2, 3, 4, 5),
eat = eat
)
df$Condition <- as.factor(df$Condition)
ggplot(df, aes(x = time, y = eat, fill = Condition)) +
geom_point(size = 4, shape = 21, colour = "black") +
stat_summary(geom = "ribbon", fun.data = mean_se, alpha = 0.2) +
stat_summary(
mapping = aes(colour = Condition),
geom = "line",
fun.y = mean,
show.legend = FALSE
)
Created on 2018-07-09 by the reprex package (v0.2.0).
Here's my best guess at what you want:
# keep time as numeric
df$time = as.numeric(as.character(df$time))
ggplot(df, aes(x = time, y = eat, group = Condition)) +
geom_smooth(
aes(fill = Condition, linetype = Condition),
method = "lm",
level = 0.65,
color = "black",
size = 0.3
) +
geom_point(aes(color = Condition))
Setting the level = 0.65 is about +/- 1 standard deviation on the linear model fit.
I think this code will get you most of the way there
library(tidyverse)
eat <- sample(1:7, size = 30, replace = TRUE)
tibble(id = rep(c(1, 2, 3, 4, 5, 6), each = 5),
Condition = factor(rep(c(0, 1), each = 15)),
time = factor(rep(c(1, 2, 3, 4, 5), 6)),
eat = eat) %>%
ggplot(aes(x = time, y = eat, fill = Condition, group = Condition)) +
geom_point(size = 4, shape = 21) +
geom_smooth()
geom_smooth is what you were looking for, I think. This creates a linear model out of the points, and as long as your x value is a factor, it should use the mean and connect the points that way.
I wonder if there is the possibility to change the fill main colour according to a categorical variable
Here is a reproducible example
df = data.frame(x = c(rnorm(10, mean = 0),
rnorm(10, mean = 3)),
y = c(rnorm(10, mean = 0),
rnorm(10, mean = 3)),
grp = c(rep('a', times = 10),
rep('b', times = 10)),
val = rep(1:10, times = 2))
ggplot(data = df,
aes(x = x,
y = y)) +
geom_point(pch = 21,
aes(color = grp,
fill = val,
size = val))
Of course it is easy to change the circle colour/shape, according to the variable grp, but I'd like to have the a group in shades of red and the b group in shades of blue.
I also thought about using facets, but don't know if the fill gradient can be changed for the two panels.
Anyone knows if that can be done, without gridExtra?
Thanks!
I think there are two ways to do this. The first is using the alpha aesthetic for your val column. This is a quick and easy way to accomplish your goal but may not be exactly what you want:
ggplot(data = df,
aes(x = x,
y = y)) +
geom_point(pch = 21,
aes(alpha=val,
fill = grp,
size = val)) + theme_minimal()
The second way would be to do something similar to this post: Vary the color gradient on a scatter plot created with ggplot2. I edited the code slightly so its not a range from white to your color of interest but from a lighter color to a darker color. This requires a little bit of work and using the scale_fill_identity function which basically takes a variable that has the colors you want and maps them directly to each point (so it doesn't do any scaling).
This code is:
#Rescale val to [0,1]
df$scaled_val <- rescale(df$val)
low_cols <- c("firebrick1","deepskyblue")
high_cols <- c("darkred","deepskyblue4")
df$col <- ddply(df, .(grp), function(x)
data.frame(col=apply(colorRamp(c(low_cols[as.numeric(x$grp)[1]], high_cols[as.numeric(x$grp)[1]]))(x$scaled_val),
1,function(x)rgb(x[1],x[2],x[3], max=255)))
)$col
df
ggplot(data = df,
aes(x = x,
y = y)) +
geom_point(pch = 21,
aes(
fill = col,
size = val)) + theme_minimal() +scale_fill_identity()
Thanks to this other post I found a way to visualize the fill bar in the legend, even though that wasn't what I meant to do.
Here's the ouptup
And the code
df = data.frame(x = c(rnorm(10, mean = 0),
rnorm(10, mean = 3)),
y = c(rnorm(10, mean = 0),
rnorm(10, mean = 3)),
grp = factor(c(rep('a', times = 10),
rep('b', times = 10)),
levels = c('a', 'b')),
val = rep(1:10, times = 2)) %>%
group_by(grp) %>%
mutate(scaledVal = rescale(val)) %>%
ungroup %>%
mutate(scaledValOffSet = scaledVal + 100*(as.integer(grp) - 1))
scalerange <- range(df$scaledVal)
gradientends <- scalerange + rep(c(0,100,200), each=2)
ggplot(data = df,
aes(x = x,
y = y)) +
geom_point(pch = 21,
aes(fill = scaledValOffSet,
size = val)) +
scale_fill_gradientn(colours = c('white',
'darkred',
'white',
'deepskyblue4'),
values = rescale(gradientends))
Basically one should rescale fill values (e.g. between 0 and 1) and separate them using another order of magnitude, provided by the categorical variable grp.
This is not what I wanted though: the snippet can be improved, of course, to make the whole thing less manual, but still lacks the simple usual discrete fill legend.
My data looks like this:
var1, var2, mean, std
1 , 2 , 3 , 4
etc..
I want to plot these into a heat map that looks like this one but I want to add text labels inside each cell, in this style: mean±std (i.e. mean plus minus error). in above case, the value in the cell would be 3±4 for var1 column = 1 and var2 row = 2, and similarly different values for other cells.
It's not important that it is a heatmap, it could be the label to a point or to a bar, i just want to generate the labels so that I get the strings "mean±std" for each label: 3±4. In my case, I will be making a heatmap where the colors are based on the value of mean, such as in here: https://stackoverflow.com/a/14290705/1504411
Thank you!
You can use plotmath in geom_text by setting parse = TRUE. Based on #beetroot's answer:
ggplot(dat) +
geom_text(aes(x = 1, y = 2.5,
label = paste(mean, std, sep = "%+-%")),
parse = TRUE)
You can create labels with geom_text and paste the mean and sd values with the plus-minus-sign as the seperator (\u00B1 is the respective unicode):
dat <- data.frame(var1 = 1, var2 = 2, mean = 3, std = 4)
ggplot(dat) +
geom_text(aes(x = 1, y = 2.5, label = paste(mean, std, sep = "\u00B1")))
Thanks to beetroot's and Roland's answer this was my final code that worked (plus some bells and whistles):
p1 <- ggplot(r_output, aes(var1, var2)) +
geom_tile(aes(fill = mean))+
geom_text(aes(fill = mean, label = paste(round(mean, 2), round(std, 2), sep = "\u00B1")), size = 2)+
scale_fill_gradient(low = "red", high = "blue") +
say I have the means of two datasets that I want to plot as barplots with error bars next to each other in ggplot2, or base
Each dataset consists of a matrix of numbers
10 20 12
10 20 12
10 20 12
which is then transformed into a mean vector of for example 3 elements
10 20 12
What I want to do is to take both mean vectors and plot them as a bar plot where the first element of one is besides the first element of the other
Dataset1Element1Bar-Dataset2Element1Bar Dataset1Element2Bar-Dataset2Element2Bar etc
Give each bar an error bar, say of standard deviation. I know I can calculate it through sd but I'm not sure how to stick it into the graph in the proper form
And lastly color them by their element number (ie Element 1)
I have the code to do one dataset but I'm not sure where to go from there.
result<-barplot(bardata, main="Mean Coverage", names.arg=namePosTargetGroup, ylab="mean Magnitude", cex.names=.4,col=c("red","blue","green"))
legend(10,legend=c("Group1","Group2","Group3"),fill = c("red","blue","green"))
A lot of what I look up gives the answer for one thing or another but its difficult to figure out how to combine them together.
I would generally not recommend plotting just a bar chart with error bars. There are many other ways to plot your data, which reveal the data and its structure a lot better.
Especially if you just have very few cases, plotting means with bars is not good. A good explanation can be found here: Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm
I find it difficult to give you a good solution, since I don't know your research-question. Knowing what you actually want to show or emphasis would make things easier.
I will give you two suggestions, one for a small dataset, one for a bigger one. All of them are created with ggplot2. I'm not coloring them by their "element number" but by their origin ("dataset 1/2"), since I find it easier to accomplish a proper graphic this way.
Small Dataset
Use geom_jitter to display all your cases, avoiding overplotting.
# import hadleyverse
library(magrittr)
library(dplyr)
library(tidyr)
library(ggplot2)
# generate small amount of data
set.seed(1234)
df1 <- data.frame(v1 = rnorm(5, 4, 1),
v2 = rnorm(5, 5, 1),
v3 = rnorm(5, 6, 1),
origin = rep(factor("df1", levels = c("df1", "df2")), 5))
df2 <- data.frame(v1 = rnorm(5, 4.5, 1),
v2 = rnorm(5, 5.5, 1),
v3 = rnorm(5, 6.5, 1),
origin = rep(factor("df2", levels = c("df1", "df2")), 5))
# merge dataframes and gather in long format
pdata <- bind_rows(df1, df2) %>%
gather(id, variable, -origin)
# plot data
ggplot(pdata, aes(x = id, y = variable, fill = origin, colour = origin)) +
stat_summary(fun.y = mean, geom = "point", position = position_dodge(width = .5),
size = 30, shape = "-", show_guide = F, alpha = .7) + # plot mean as "-"
geom_jitter(position = position_jitterdodge(jitter.width = .3, jitter.height = .1,
dodge.width = .5),
size = 4, alpha = .85) +
labs(x = "Variable", y = NULL) + # adjust legend
theme_light() # nicer theme
"Big" Dataset
If you have more datapoints, you can use geom_violin to summarise them.
set.seed(12345)
df1 <- data.frame(v1 = rnorm(50, 4, 1),
v2 = rnorm(50, 5, 1),
v3 = rnorm(50, 6, 1),
origin = rep(factor("df1", levels = c("df1", "df2")), 50))
df2 <- data.frame(v1 = rnorm(50, 4.5, 1),
v2 = rnorm(50, 5.5, 1),
v3 = rnorm(50, 6.5, 1),
origin = rep(factor("df2", levels = c("df1", "df2")), 50))
# merge dataframes
pdata <- bind_rows(df1, df2) %>%
gather(id, variable, -origin)
# plot with violin plot
ggplot(pdata, aes(x = id, y = variable, fill = origin)) +
geom_violin(adjust = .6) +
stat_summary(fun.y = mean, geom = "point", position = position_dodge(width = .9),
size = 6, shape = 4, show_guide = F) +
guides(fill = guide_legend(override.aes = list(colour = NULL))) +
labs(x = "Variable", y = NULL) +
theme_light()
Version with mean and sd
If you insist on plotting the mean with standard deviation, here is how it could be done.
# merge dataframes and compute limits for sd
pdata <- bind_rows(df1, df2) %>%
gather(id, variable, -origin) %>%
group_by(origin, id) %>% # group data for limit calculation
mutate(upper = mean(variable) + sd(variable), # upper limit for error bar
lower = mean(variable) - sd(variable)) # lower limit for error bar
# plot
ggplot(pdata, aes(x = id, y = variable, fill = origin)) +
stat_summary(fun.y = mean, geom = "bar", position = position_dodge(width = .9),
size = 3) +
geom_errorbar(aes(ymin = lower, ymax = upper),
width = .2, # Width of the error bars
position = position_dodge(.9))