I have graph consisting of several ggplots combined using patchwork. I have made a ggplot only consisting of the y-axis. This works fine, but the facets are not aligned above each. Any ideas as how to make the facets have the same size and be above each other?
Here's the patchwork code:
y_axis + decreasers_p+late_bloomers_p + o2_lovers_p + solo_riders_p + plot_layout(widths = c(1, 10),
guides = "collect",
design =
"12
13
14
15")
And here's some of the ggplot code:
solo_riders_p <- ggplot(solo_riders, aes(x=days_incubated, y=emission))+
geom_point(aes(shape=compound, size=compound, fill=compound)) +
scale_shape_manual(values=c(21, 25))+
scale_size_manual(values=c(2.8, 2.5))+
scale_fill_manual(values=c("black", "grey"))+
labs(fill = "Compound", shape = "Compound", size = "Compound",
x = "Incubation time (days)",
y = "BVOC emission (nmol g-1 loi soil h-1)",
title = "solo_riders") +
theme_bw() +
facet_wrap(vars(jar), scales = "free_y")
y_axis <- ggplot(data.frame(l = decreasers_p$labels$y, x = 1, y = 1)) +
geom_text(aes(x, y, label = l), angle = 90) +
theme_void() +
coord_cartesian(clip = "off")
Here's an example of how I would like it to be (but without the titles above each row):
Related
I dont have data between 1 and 11 hrs (inyour text x axis) so I want to delete this portion from my graph.
How can I put zero values in each group instead of being clumped together?
I tried to plot the graph as follows:
plot <- ggplot(bgse_lmean, aes(time_h)) +
geom_point(aes(y = Conc, shape = Isolate, col = Isolate, group = Isolate), size = 2.5)+
geom_line(aes(y = Conc, group= Isolate, col = Isolate))+
scale_x_continuous(breaks=c(0,12, 16, 20, 24))+
xlab("Time (h)") + ylab("Conc mg/ml") +
scale_color_brewer(palette = "Set1") +
facet_wrap(parameters~.,
strip.position = "top",
nrow = 3)
plot
p2 <- plot + scale_x_break(c(1,11))
I'd like to map different variables for the x axis, y-axis, height of the ridges and gradient fill of the plot along the y-axis.
How can I reconfigure the below code such that the "fill" works with a different variable than that specified by x?
# Example for `geom_density_ridges_gradient()`
ggplot(lincoln_weather, aes(x = `Mean Temperature [F]`, y = `Month`, fill = stat(x))) +
geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_discrete(expand = c(0, 0)) +
scale_fill_viridis_c(name = "Temp. [F]", option = "C") +
coord_cartesian(clip = "off") +
labs(title = 'Temperatures in Lincoln NE in 2016') +
theme_ridges(font_size = 13, grid = TRUE) +
theme(axis.title.y = element_blank())
I am attempting to make a multi-panelled plot from three individual plots (see images).However, I am unable to rectify the bunched x-axis tick labels when the plots are in the multi-panel format. Following is the script for the individual plots and the multi-panel:
Individual Plot:
NewDat [[60]]
EstRes <- NewDat [[60]]
EstResPlt = ggplot(EstRes,aes(Distance3, `newBa`))+geom_line() + scale_x_continuous(n.breaks = 10, limits = c(0, 3500))+ scale_y_continuous(n.breaks = 10, limits = c(0,25))+ xlab("Distance from Core (μm)") + ylab("Ba:Ca concentration(μmol:mol)") + geom_hline(yintercept=2.25, linetype="dashed", color = "red")+ geom_vline(xintercept = 1193.9, linetype="dashed", color = "grey")+ geom_vline(xintercept = 1965.5, linetype="dashed", color = "grey") + geom_vline(xintercept = 2616.9, linetype="dashed", color = "grey") + geom_vline(xintercept = 3202.8, linetype="dashed", color = "grey")+ geom_vline(xintercept = 3698.9, linetype="dashed", color = "grey")
EstResPlt
Multi-panel plot:
MultiP <- grid.arrange(MigrPlt,OcResPlt,EstResPlt, nrow =1)
I have attempted to include:
MultiP <- grid.arrange(MigrPlt,OcResPlt,EstResPlt, nrow =1)+
theme(axis.text.x = element_text (angle = 45)) )
MultiP
but have only received errors. It's not necessary for all tick marks to be included. An initial, mid and end value is sufficient and therefore they would not need to all be included or angled. I'm just not sure how to do this. Assistance would be much appreciated.
There are several options to resolve the crowded axes. Let's consider the following example which parallels your case. The default labelling strategy wouldn't overcrowd the x-axis.
library(ggplot2)
library(patchwork)
library(scales)
df <- data.frame(
x = seq(0, 3200, by = 20),
y = cumsum(rnorm(161))
)
p <- ggplot(df, aes(x, y)) +
geom_line()
(p + p + p) / p &
scale_x_continuous(
name = "Distance (um)"
)
However, because you've given n.breaks = 10 to the scale, it becomes crowded. So a simple solution would just be to remove that.
(p + p + p) / p &
scale_x_continuous(
n.breaks = 10,
name = "Distance (um)"
)
Alternatively, you could convert the micrometers to millimeters, which makes the labels less wide.
(p + p + p) / p &
scale_x_continuous(
n.breaks = 10,
labels = label_number(scale = 1e-3, accuracy = 0.1),
name = "Distance (mm)"
)
Yet another alternative is to put breaks only every n units, in the case below, a 1000. This happens to coincide with omitting n.breaks = 10 by chance.
(p + p + p) / p &
scale_x_continuous(
breaks = breaks_width(1000),
name = "Distance (um)"
)
Created on 2021-11-02 by the reprex package (v2.0.1)
I thought it would be better to show with an example.
What I mean was, you made MigrPlt, OcResPlt, EstResPlt each with ggplot() +...... For plot that you want to rotate x axis, add + theme(axis.text.x = element_text (angle = 45)).
For example, in iris data, only rotate x axis text for a like
a <- ggplot(iris, aes(Sepal.Width, Sepal.Length)) +
geom_point() +
theme(axis.text.x = element_text (angle = 45))
b <- ggplot(iris, aes(Petal.Width, Petal.Length)) +
geom_point()
gridExtra::grid.arrange(a,b, nrow = 1)
I'm having a hard time dealing with this plot.
The height of values in ANI>96 making it hard to read the red and blue percentage text.
I failed to break the y-axis by looking at answers from other posts in StackOverflow.
Any suggestions?
Thanks.
library(data.table)
library(ggplot2)
dt <- data.table("ANI"= sort(c(seq(79,99),seq(79,99))), "n_pairs" = c(5, 55, 13, 4366, 6692, 59568, 382873, 397996, 1104955, 282915,
759579, 261170, 312989, 48423, 120574, 187685, 353819, 79468, 218039, 66314, 41826, 57668, 112960, 81652, 28613,
64656, 21939, 113656, 170578, 238967, 610234, 231853, 1412303, 5567, 4607268, 5, 14631942, 0, 17054678, 0, 3503846, 0),
"same/diff" = rep(c("yes","no"), 21))
for (i in 1:nrow(dt)) {
if (i%%2==0) {
next
}
total <- dt$n_pairs[i] + dt$n_pairs[i+1]
dt$total[i] <- total
dt$percent[i] <- paste0(round(dt$n_pairs[i]/total *100,2), "%")
dt$total[i+1] <- total
dt$percent[i+1] <- paste0(round(dt$n_pairs[i+1]/total *100,2), "%")
}
ggplot(data=dt, aes(x=ANI, y=n_pairs, fill=`same/diff`)) +
geom_text(aes(label=percent), position=position_dodge(width=0.9), hjust=0.75, vjust=-0.25) +
geom_bar(stat="identity") + scale_x_continuous(breaks = dt$ANI) +
labs(x ="ANI", y = "Number of pairs", fill = "Share one common species taxonomy?") +
theme_classic() + theme(legend.position="bottom")
Here is the list of major changes I made:
I reduced the y axis by zooming into the chart with coord_cartesian (which is called by coord_flip).
coord_flip shouuld also improve the readability of the chart by switching x and y. I don't know if the switch is a desirable output for you.
Also now position_dodge, works as expected: two bars next to each other with the labels on top (on the left in this case).
I set geom_bar before geom_text so that the text is always in front of the bars in the chart.
I set scale_y_continuous to change the labels of the y axis (in the chart the x axis because of the switch) to improve the readability of the zeros.
ggplot(data=dt, aes(x = ANI, y = n_pairs, fill = `same/diff`)) +
geom_bar(stat = "identity", position = position_dodge2(width = 1), width = 0.8) +
geom_text(aes(label = percent), position = position_dodge2(width = 1), hjust = 0, size = 3) +
scale_x_continuous(breaks = dt$ANI) +
scale_y_continuous(labels = scales::comma) +
labs(x ="ANI", y = "Number of pairs", fill = "Share one common species taxonomy?") +
theme_classic() +
theme(legend.position = "bottom") +
coord_flip(ylim = c(0, 2e6))
EDIT
Like this columns and labels are stacked but labels never overlap.
ggplot(data=dt, aes(x = ANI, y = n_pairs, fill = `same/diff`)) +
geom_bar(stat = "identity", width = 0.8) +
geom_text(aes(label = percent,
hjust = ifelse(`same/diff` == "yes", 1, 0)),
position = "stack", size = 3) +
scale_x_continuous(breaks = dt$ANI) +
scale_y_continuous(labels = scales::comma) +
labs(x ="ANI", y = "Number of pairs", fill = "Share one common species taxonomy?") +
theme_classic() +
theme(legend.position = "bottom") +
coord_flip(ylim = c(0, 2e6))
Alternatively, you can avoid labels overlapping with check_overlap = TRUE, but sometimes one of the labels will not be shown.
ggplot(data=dt, aes(x = ANI, y = n_pairs, fill = `same/diff`)) +
geom_bar(stat = "identity", width = 0.8) +
geom_text(aes(label = percent), hjust = 1, position = "stack", size = 3, check_overlap = TRUE) +
scale_x_continuous(breaks = dt$ANI) +
scale_y_continuous(labels = scales::comma) +
labs(x ="ANI", y = "Number of pairs", fill = "Share one common species taxonomy?") +
theme_classic() +
theme(legend.position = "bottom") +
coord_flip(ylim = c(0, 2e6))
I have two scatter plots obtained from two sets of data that I would like to overlay, when using the ggplo2 for creating single plot i am using log scale and than ordering the numbers sothe scatter plot falls into kind if horizontal S shape. Byt when i want to overlay, the information about reordering gets lost, and the plot loses its shape.
this is how the df looks like (one has 1076 entries and the other 1448)
protein Light_Dark log10
AT1G01080 1.1744852 0.06984755
AT1G01090 1.0710359 0.02980403
AT1G01100 0.4716955 -0.32633823
AT1G01320 156.6594802 2.19495668
AT1G02500 0.6406005 -0.19341276
AT1G02560 1.3381804 0.12651467
AT1G03130 0.6361147 -0.19646458
AT1G03475 0.7529015 -0.12326181
AT1G03630 0.7646064 -0.11656207
AT1G03680 0.8340107 -0.07882836
this is for single plot:
p1 <- ggplot(ratio_log_ENR4, aes(x=reorder(protein, -log10), y=log10)) +
geom_point(size = 1) +
#coord_cartesian(xlim = c(0, 1000)) +
geom_hline(yintercept=0.1, col = "red") + #check gene
geom_hline(yintercept=-0.12, col = "red") +#check gene
labs(x = "Protein")+
theme_classic()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
labs(y = "ratio Light_Dark log10")+
labs(x="Protein")
image=p1
ggsave(file="p1_ratio_data_ENR4_cys.svg", plot=image, width=10, height=8)
and for over lay:
p1_14a <- ggplot(ratio_log_ENR1, aes(x=reorder(protein, -log10), y=log10)) +
geom_point(size = 1) +
#coord_cartesian(xlim = c(0, 1000)) +
geom_hline(yintercept=0.1, col = "red") + #check gene
geom_hline(yintercept=-0.12, col = "red") +#check gene
labs(x = "Protein")+
theme_classic()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
labs(y = "ratio Light_Dark log10")+
labs(x="Protein")+
geom_point()+
geom_point(data=ratio_log_ENR4, color="red")
p=ggplot(ratio_log_ENR1, aes(x=reorder(protein, -log10), y=log10)) +
geom_point(size = 1) +
#coord_cartesian(xlim = c(0, 1000)) +
geom_hline(yintercept=0.1, col = "red") + #check gene
geom_hline(yintercept=-0.12, col = "red") +#check gene
labs(x = "Protein")+
theme_classic()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
labs(y = "ratio Light_Dark log10")+
labs(x="Protein")
p = p + geom_point(data=ratio_log_ENR4, aes(x=reorder(protein, -log10), y=log10), color ="red" )
p
I tried to change classes... but it cant be the problem since for single plot its working like it is
The easiest solution I see for you is just binding together your two dataframes before plotting.
a$color <- 'red'
b$color <- 'blue'
ab <- a %>%
rbind(b)
ggplot(ab, aes(x = fct_reorder(protein, -log10), y = log10, color = color)) +
geom_point() +
scale_color_identity()
You can find a nice cheat-sheet for working with factors here: https://stat545.com/block029_factors.html