Let's say I'm creating the grouped barplot by something like this:
data <- data.frame(time = factor(1:3), type = LETTERS[1:4], values = runif(24)*10)
ggplot(data, aes(x = type, y = values, fill = time)) +
stat_summary(fun=mean, geom='bar', width=0.55, size = 1, position=position_dodge(0.75))
Inside each type I want to connect all bar tops (meaning to connect 3 bars for A, 3 bars for B, and so on) with the line.
I'd like to get something like that as a result:
Is there a way to do that ?
Thank you!
I changed the code to another logic that I prefer, that is to prepare the data before using ggplot().
Code
library(dplyr)
library(ggplot2)
data <- data.frame(time = factor(1:3), type = LETTERS[1:4], values = runif(24)*10)
pdata <- data %>% group_by(type,time) %>% summarise(values = mean(values,na.rm = TRUE)) %>% ungroup()
pdata %>%
ggplot(aes(x = type, y = values)) +
geom_col(
mapping = aes(fill = time, group = time),
width = 0.55,
size = 1,
position = position_dodge(0.75)
)+
geom_line(
mapping = aes(group = type),
size = 1,
position = position_dodge2(.75)
)
Output
I am new to R and trying to add count labels to my boxplots, so the sample size per boxplot shows in the graph.
This is my code:
bp_east_EC <-total %>% filter(year %in% c(1977, 2020, 2021, 1992),
sampletype == "groundwater",
East == 1,
#EB == 1,
#N59 == 1,
variable %in% c("EC_uS")) %>%
ggplot(.,aes(x = as.character(year), y = value, colour = as.factor(year))) +
theme_ipsum() +
ggtitle("Groundwater EC, eastern Curacao") +
theme(plot.title = element_text(hjust = 0.5, size=14)) +
theme(legend.position = "none") +
labs(x="", y="uS/cm") +
geom_jitter(color="grey", size=0.4, alpha=0.9) +
geom_boxplot() +
stat_summary(fun.y=mean, geom="point", shape=23, size=2) #shows mean
I have googled a lot and tried different things (with annotate, with return functions, mtext, etc), but it keeps giving different errors. I think I am such a beginner I cannot figure out how to integrate such suggestions into my own code.
Does anybody have an idea what the best way would be for me to approach this?
I would create a new variable that contained your sample sizes per group and plot that number with geom_label. I've generated an example of how to add count/sample sizes to a boxplot using the iris dataset since your example isn't fully reproducible.
library(tidyverse)
data(iris)
# boxplot with no label
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot()
# boxplot with label
iris %>%
group_by(Species) %>%
mutate(count = n()) %>%
mutate(mean = mean(Sepal.Length)) %>%
ggplot(aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
geom_label(aes(label= count , y = mean + 0.75), # <- change this to move label up and down
size = 4, position = position_dodge(width = 0.75)) +
geom_jitter(alpha = 0.35, aes(color = Species)) +
stat_summary(fun = mean, geom = "point", shape = 23, size = 6)
I have a faceted plot wherein I'd like to have the Y-axis labels and the associated values appear in descending order of values (and thereby changing the order of the labels) for each facet. What I have is this, but the order of the labels (and the corresponding values) is the same for each facet.
ggplot(rf,
aes(x = revenues,
y = reorder(AgencyName, revenues))) +
geom_point(stat = "identity",
aes(color = AgencyName),
show.legend = FALSE) +
xlab(NULL) +
ylab(NULL) +
scale_x_continuous(label = scales::comma) +
facet_wrap(~year, ncol = 3, scales = "free_y") +
theme_minimal()
Can someone point me to the solution?
The functions reorder_within and scale_*_reordered from the tidytext package might come in handy.
reorder_within recodes the values into a factor with strings in the form of "VARIABLE___WITHIN". This factor is ordered by the values in each group of WITHIN.
scale_*_reordered removes the "___WITHIN" suffix when plotting the axis labels.
Add scales = "free_y" in facet_wrap to make it work as expected.
Here is an example with generated data:
library(tidyverse)
# Generate data
df <- expand.grid(
year = 2019:2021,
group = paste("Group", toupper(letters[1:8]))
)
set.seed(123)
df$value <- rnorm(nrow(df), mean = 10, sd = 2)
df %>%
mutate(group = tidytext::reorder_within(group, value, within = year)) %>%
ggplot(aes(value, group)) +
geom_point() +
tidytext::scale_y_reordered() +
facet_wrap(vars(year), scales = "free_y")
I'm trying to create a depth profile graph with the variables depth, distance and temperature. The data collected is from 9 different points with known distances between them (distance 5m apart, 9 stations, 9 different sets of data). The temperature readings are according to these 9 stations where a sonde was dropped directly down, taking readings of temperature every 2 seconds. Max depth at each of the 9 stations were taken from the boat also.
So the data I have is:
Depth at each of the 9 stations (y axis)
Temperature readings at each of the 9 stations, at around .2m intervals vertical until the bottom was reached (fill area)
distance between the stations, (x axis)
Is it possible to create a depth profile similar to this? (obviously without the greater resolution in this graph)
I've already tried messing around with ggplot2 and raster but I just can't seem to figure out how to do this.
One of the problems I've come across is how to make ggplot2 distinguish between say 5m depth temperature reading at station 1 and 5m temperature reading at station 5 since they have the same depth value.
Even if you can guide me towards another program that would allow me to create a graph like this, that would be great
[ REVISION ]
(Please comment me if you know more suitable interpolation methods, especially not needing to cut under bottoms data.)
ggplot() needs long data form.
library(ggplot2)
# example data
max.depths <- c(1.1, 4, 4.7, 7.7, 8.2, 7.8, 10.7, 12.1, 14.3)
depth.list <- sapply(max.depths, function(x) seq(0, x, 0.2))
temp.list <- list()
set.seed(1); for(i in 1:9) temp.list[[i]] <- sapply(depth.list[[i]], function(x) rnorm(1, 20 - x*0.5, 0.2))
set.seed(1); dist <- c(0, sapply(seq(5, 40, 5), function(x) rnorm(1, x, 1)))
dist.list <- sapply(1:9, function(x) rep(dist[x], length(depth.list[[x]])))
main.df <- data.frame(dist = unlist(dist.list), depth = unlist(depth.list) * -1, temp = unlist(temp.list))
# a raw graph
ggplot(main.df, aes(x = dist, y = depth, z = temp)) +
geom_point(aes(colour = temp), size = 1) +
scale_colour_gradientn(colours = topo.colors(10))
# a relatively raw graph (don't run with this example data)
ggplot(main.df, aes(x = dist, y = depth, z = temp)) +
geom_raster(aes(fill = temp)) + # geom_contour() +
scale_fill_gradientn(colours = topo.colors(10))
If you want a graph such like you showed, you have to do interpolation. Some packages give you spatial interpolation methods. In this example, I used akima package but you should think seriously that which interpolation methods to use.
I used nx = 300 and ny = 300 in below code but I think it would be better to decide those values carefully. Large nx and ny gives a high resolution graph, but don't foreget real nx and ny (in this example, real nx is only 9 and ny is 101).
library(akima); library(dplyr)
interp.data <- interp(main.df$dist, main.df$depth, main.df$temp, nx = 300, ny = 300)
interp.df <- interp.data %>% interp2xyz() %>% as.data.frame()
names(interp.df) <- c("dist", "depth", "temp")
# draw interp.df
ggplot(interp.df, aes(x = dist, y = depth, z = temp)) +
geom_raster(aes(fill = temp)) + # geom_contour() +
scale_fill_gradientn(colours = topo.colors(10))
# to think appropriateness of interpolation (raw and interpolation data)
ggplot(interp.df, aes(x = dist, y = depth, z = temp)) +
geom_raster(aes(fill = temp), alpha = 0.3) + # interpolation
scale_fill_gradientn(colours = topo.colors(10)) +
geom_point(data = main.df, aes(colour = temp), size = 1) + # raw
scale_colour_gradientn(colours = topo.colors(10))
Bottoms don't match !!I found ?interp says "interpolation only within convex hull!", oops... I'm worrid about the interpolation around the problem-area, is it OK ? If no problem, you need only cut the data under the bottoms. If not, ... I can't answer immediately (below is an example code to cut).
bottoms <- max.depths * -1
# calculate bottom values using linear interpolation
approx.bottoms <- approx(dist, bottoms, n = 300) # n must be the same value as interp()'s nx
# change temp values under bottom into NA
library(dplyr)
interp.cut.df <- interp.df %>% cbind(bottoms = approx.bottoms$y) %>%
mutate(temp = ifelse(depth >= bottoms, temp, NA)) %>% select(-bottoms)
ggplot(interp.cut.df, aes(x = dist, y = depth, z = temp)) +
geom_raster(aes(fill = temp)) +
scale_fill_gradientn(colours = topo.colors(10)) +
geom_point(data = main.df, size = 1)
If you want to use stat_contour
It is harder to use stat_contour than geom_raster because it needs a regular grid form. As far as I see your graph, your data (depth and distance) don't form a regular grid, it means it is much difficult to use stat_contour with your raw data. So I used interp.cut.df to draw a contour plot. And stat_contour have a endemic problem (see How to fill in the contour fully using stat_contour), so you need to expand your data.
library(dplyr)
# 1st: change NA into a temp's out range value (I used 0)
interp.contour.df <- interp.cut.df
interp.contour.df[is.na(interp.contour.df)] <- 0
# 2nd: expand the df (It's a little complex, so please use this function)
contour.support.func <- function(df) {
colname <- names(df)
names(df) <- c("x", "y", "z")
Range <- as.data.frame(sapply(df, range))
Dim <- as.data.frame(t(sapply(df, function(x) length(unique(x)))))
arb_z = Range$z[1] - diff(Range$z)/20
df2 <- rbind(df,
expand.grid(x = c(Range$x[1] - diff(Range$x)/20, Range$x[2] + diff(Range$x)/20),
y = seq(Range$y[1], Range$y[2], length = Dim$y), z = arb_z),
expand.grid(x = seq(Range$x[1], Range$x[2], length = Dim$x),
y = c(Range$y[1] - diff(Range$y)/20, Range$y[2] + diff(Range$y)/20), z = arb_z))
names(df2) <- colname
return(df2)
}
interp.contour.df2 <- contour.support.func(interp.contour.df)
# 3rd: check the temp range (these values are used to define contour's border (breaks))
range(interp.cut.df$temp, na.rm=T) # 12.51622 20.18904
# 4th: draw ... the bottom border is dirty !!
ggplot(interp.contour.df2, aes(x = dist, y = depth, z = temp)) +
stat_contour(geom="polygon", breaks = seq(12.51622, 20.18904, length = 11), aes(fill = ..level..)) +
coord_cartesian(xlim = range(dist), ylim = range(bottoms), expand = F) + # cut expanded area
scale_fill_gradientn(colours = topo.colors(10)) # breaks's length is 11, so 10 colors are needed
# [Note]
# You can define the contour's border values (breaks) and colors.
contour.breaks <- c(12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5)
# = seq(12.5, 20.5, 1) or seq(12.5, 20.5, length = 9)
contour.colors <- c("darkblue", "cyan3", "cyan1", "green3", "green", "yellow2","pink", "darkred")
# breaks's length is 9, so 8 colors are needed.
# 5th: vanish the bottom border by bottom line
approx.df <- data.frame(dist = approx.bottoms$x, depth = approx.bottoms$y, temp = 0) # 0 is dummy value
ggplot(interp.contour.df2, aes(x = dist, y = depth, z = temp)) +
stat_contour(geom="polygon", breaks = contour.breaks, aes(fill = ..level..)) +
coord_cartesian(xlim=range(dist), ylim=range(bottoms), expand = F) +
scale_fill_gradientn(colours = contour.colors) +
geom_line(data = approx.df, lwd=1.5, color="gray50")
bonus: legend technic
library(dplyr)
interp.contour.df3 <- interp.contour.df2 %>% mutate(temp2 = cut(temp, breaks = contour.breaks))
interp.contour.df3$temp2 <- factor(interp.contour.df3$temp2, levels = rev(levels(interp.contour.df3$temp2)))
ggplot(interp.contour.df3, aes(x = dist, y = depth, z = temp)) +
stat_contour(geom="polygon", breaks = contour.breaks, aes(fill = ..level..)) +
coord_cartesian(xlim=range(dist), ylim=range(bottoms), expand = F) +
scale_fill_gradientn(colours = contour.colors, guide = F) + # add guide = F
geom_line(data = approx.df, lwd=1.5, color="gray50") +
geom_point(aes(colour = temp2), pch = 15, alpha = 0) + # add
guides(colour = guide_legend(override.aes = list(colour = rev(contour.colors), alpha = 1, cex = 5))) + # add
labs(colour = "temp") # add
You want to treat this as a 3-D surface with temperature as the z dimension. The given plot is a contour plot and it looks like ggplot2 can do that with stat_contour.
I'm not sure how the contour lines are computed (often it's linear interpolation along a Delaunay triangulation). If you want more control over how to interpolate between your x/y grid points, you can calculate a surface model first and feed those z coordinates into ggplot2.
I checked a few examples online and I am not sure that it can be done because every plot with 2 different variables (continuous and discrete) has one of 2 options:
legend regarding the continuous variable
legend regarding the discrete variable
Just for visualization, I put here an example. Imagine that I want to have a legend for the blue line. Is it possible to do that??
The easiest approach would be to map it to a different aesthetic than you already use:
library(ggplot2)
ggplot(mtcars, aes(x = mpg, y = hp)) +
geom_point(aes(colour = as.factor(gear), size = cyl)) +
geom_smooth(method = "loess", aes(linetype = "fit"))
There area also specialised packages for adding additional colour legends:
library(ggplot2)
library(ggnewscale)
ggplot(mtcars, aes(x = mpg, y = hp)) +
geom_point(aes(colour = as.factor(gear), size = cyl)) +
new_scale_colour() +
geom_smooth(method = "loess", aes(colour = "fit"))
Beware that if you want to tweak colours via a colourscale, you must first add these before calling the new_scale_colour(), i.e.:
ggplot(mtcars, aes(x = mpg, y = hp)) +
geom_point(aes(colour = as.factor(gear), size = cyl)) +
scale_colour_manual(values = c("red", "green", "blue")) +
new_scale_colour() +
geom_smooth(method = "loess", aes(colour = "fit")) +
scale_colour_manual(values = "purple")
EDIT: To adress comment: yes it is possible with a line that is data independent, I was just re-using the data for brevity of example. See below for arbitrary line (also should work with the ggnewscale approach):
ggplot(mtcars, aes(x = mpg, y = hp)) +
geom_point(aes(colour = as.factor(gear), size = cyl)) +
geom_line(data = data.frame(x = 1:30, y = rnorm(10, 200, 10)),
aes(x, y, linetype = "arbitrary line"))