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"))
Related
I've seen some other examples (especially using geom_col() and stat_bin()) to add frequency or count numbers on top of bars. I'm trying to get this to work with geom_histogram() where I have a discrete (string), not continuous, x variable.
library(tidyverse)
d <- cars |>
mutate( discrete_var = factor(speed))
ggplot(d, aes(x = discrete_var)) +
geom_histogram(stat = "count") +
stat_bin(binwidth=1, geom='text', color='white', aes(label=..count..),
position=position_stack(vjust = 0.5)) +
Gives me an error because StatBin requires a continuous x variable. Any quick fix ideas?
The error message gives you the answer: ! StatBin requires a continuous x variable: the x variable is discrete.Perhaps you want stat="count"?
So instead of stat_bin() use stat_count()
And for further reference here is a reproducible example:
library(tidyverse)
d <- cars |>
mutate( discrete_var = factor(speed))
ggplot(data = d,
aes(x = discrete_var)) +
geom_histogram(stat = "count") +
stat_count(binwidth = 1,
geom = 'text',
color = 'white',
aes(label = ..count..),
position = position_stack(vjust = 0.5))
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 2 datasets that span full genomes, separated by chromosomes (scaffolds), for 2 group comparisons and i want to overlap them in a single graph.
the way i was doing was as follow:
ggplot(NULL, aes(color = as_factor(scaffold))) +
geom_smooth(data = windowStats_SBvsOC, aes(x = mid2, y = Fst_group1_group5), se=F) +
geom_smooth(data = windowStats_SCLvsSCU, aes(x = mid2, y = Fst_group3_group4), se=F) +
scale_y_continuous(expand = c(0,0), limits = c(0, 1)) +
scale_x_continuous(labels = chrom$chrID, breaks = axis_set$center) +
scale_color_manual(values = rep(c("#276FBF", "#183059"), unique(length(chrom$chrID)))) +
scale_size_continuous(range = c(0.5,3)) +
labs(x = NULL,
y = "Fst (smoothed means)") +
theme_minimal() +
theme(
legend.position = "none",
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
axis.title.y = element_text(),
axis.text.x = element_text(angle = 60, size = 8, vjust = 0.5))
this way, i get each chromosome with alternating colors, and the smoothing is per chromosome. but i wanted the colors to be different between the 2 groups so i can distinguish when they are overlapped like this. is there a way to do it? i can only do it once i remove the color by scaffold, but then the smoothing gets done across the whole genome and i don't want that!
my dataset is big, so i'm attaching it here!
i'm running this in rstudio 2022.02.3, R v.3.6.2 and package ggplot2
EDIT: i've figured out! i just needed to change color = as_factor(scaffold) to group = as_factor(scaffold); and then add the aes(color) to each geom_smooth() function.
I want to show where network people live and how they are connected. First, I drew a map of the 15 municipalities (based on SpatialPolygonsDataFrame, geom_polygon of ggplot2). Second, I placed the network people around the centroids of the polygons. After the third variant in "Three ways of visualizing a graph on a map" by Markus Konrad, I have so far created two layers https://datascience.blog.wzb.eu/2018/05/ 31 / three-ways-of-visualizing-a-graph-on-a-map /). As mapcoords I used coord_fixed (ratio = 1/1). To achieve a good result, I had to make manual adjustments in annotation_custom.
My questions:
First, is there a way to adapt the layers to each other without manual intervention?
Second, are there simpler solutions to geographically locate network people and their connections?my result so far
maptheme <- theme(panel.grid = element_blank()) +
theme(axis.text = element_blank()) +
theme(axis.ticks = element_blank()) +
theme(axis.title = element_blank()) +
theme(legend.position = "bottom") +
theme(panel.grid = element_blank()) +
theme(panel.background = element_rect(fill = "#596673")) +
theme(plot.margin = unit(c(0, 0, 0.5, 0), 'cm'))
mapcoords <- coord_fixed(ratio=1/1)
theme_transp_overlay <- theme(
panel.background = element_rect(fill = "transparent", color = NA),
plot.background = element_rect(fill = "transparent", color = NA))
ArlMap <- ggplot(ARLmap.data, aes(long, lat)) +
geom_polygon(aes(group=group), colour='white', fill='grey')+
theme(axis.text=element_blank())+
theme(axis.ticks=element_blank())+
theme(axis.title=element_blank())+
mapcoords + maptheme
nodes <- ggplot(nwdata) +
geom_point(aes(x = xkor, y = ykor, size = Btw),
shape = 21, fill = "white", color = "black", # draw nodes
stroke = 0.5) +
scale_size_continuous(guide = FALSE, range = c(1, 6)) +
mapcoords + maptheme + theme_transp_overlay
ArlMap +
annotation_custom(ggplotGrob(nodes), xmin = min(ARLmap.data$long)+900, xmax = max(ARLmap.data$long)-1200, ymin = min(ARLmap.data$lat)+1500, ymax = max(ARLmap.data$lat))
...
I'm at the goal. I came to the solution by consistently starting from a geographical approach: 1. The nodes of the network receive lon / lat coordinates. These are determined as rotation coordinates around the centroids of the geographical unit. 2. The connections between the nodes are provided with new start and end points on the basis of the lon / lat coordinates. 3. The plot is limited to the basic functions plot, lines and points.enter image description here
I've found that with Plotly with R, when I'm faceting plots, they often don't translate properly from R to Plotly.
For example, my graph plotted in R looks like so:
When I send it to plotly, it looks like so:
(Some data has been hidden from both plots for confidentiality reasons)
My code looks like so:
plot <- ggplot(sytoxG_data_no_NC) +
geom_ribbon(data = confidence_intervals_SG, mapping = aes(x = time_elapsed, ymin = phenotype_value.NC.lower, ymax = phenotype_value.NC.upper,
fill = "red", colour = NULL), alpha = 0.6) +
scale_fill_manual(name = "Legend",
values = c('red'),
labels = c('Negative Control')) +
xlab("Time Elapsed") +
ylab("Sytox Green") +
ggtitle("Sytox Green - Facets: Pathway") +
facet_wrap(~Pathway, ncol=6, scales = "fixed") +
theme(panel.grid = element_blank(),
axis.ticks.length = unit(0, "cm"),
panel.background = element_rect(fill = "white"),
strip.text.x = element_text(size=4),
axis.text = element_blank())
response <- py$ggplotly(plot, kwargs=list(world_readable=FALSE, filename="SG_sparklines_by_pathway", fileopt="overwrite"))
The issue might very well be with geom_ribbon rather than facets... Can you please upgrade your "plotly" package and give it another try?
I wound up using facet_grid instead of facet_wrap. Something like this:
+ facet_grid(~Pathway, scales = "free", space="free")