I have a data frame with: Fail [3,3,3,1] and Pass [50,40,50,10]
I just want to make a barplot of Fail and Pass
b_f <- barplot(dat_record$Fail[1], horiz = TRUE, ylab = "FAIL", las = 2, col = "red", xlim = c(0,200))
b_p <- barplot(dat_record$Pass[2], horiz = TRUE, ylab = "PASS", las = 2, col = "green", xlim = c(0,200))
How can i put this two barplots on top of eachother in one graphic/diagram, like this:
And second question:
How can i do this properly with ggplot2? I tried it out, but i always failed with:
ggplot(dat_failpass, aes = (x = fail, fill = "red")+
geom_bar(position = "dodge")+
coord_flip()
Can someone answer me this two question or can you give me any tipps? I'm new into this.
Thank you.
Since you want just the first value of the vectors "Fail" and "Pass" value, this code chunk must plot what you want:
library(ggplot2)
fail = c(3, 3, 3, 1)
pass = c(50, 40, 50, 10)
df = data.frame(value = c(fail[1], pass[1]), label = c('Fail', 'Pass'))
ggplot(df, aes(x = label, y = value)) +
geom_bar(stat = 'identity', position = 'stack') +
coord_flip() +
labs(y = 'Count') +
theme(axis.title.y = element_blank())
Here is the output:
Let us know if this solution solved your problem.
Using your data in this format, here the code for plot:
library(tidyverse)
#Data
df <- structure(list(Fail = c(3, 3, 3, 1), Pass = c(50, 40, 50, 10)), class = "data.frame", row.names = c(NA,
-4L))
Code:
#Reshape and plot
df %>% pivot_longer(cols = everything()) %>%
#Plot
ggplot(aes(x=name,y=value))+
geom_bar(stat = 'identity',fill='gray')+
coord_flip()+
theme_bw()
Output:
Related
I'm trying to combine two heatmaps. I want var_a and var_x on the y axis with for example: var_a first and then var_x. I don't know if I should do this by changing the dataframe or combining them, or if I can do this in ggplot.
Below I have some example code and a drawing of what I want (since I don't know if I explained it right).
I hope someone has ideas how I can do this either in the dataframe or in ggplot!
Example code:
df_one <- data.frame(
vars = c("var_a", "var_b", "var_c"),
corresponding_vars = c("var_x", "var_y", "var_z"),
expression_organ_1_vars = c(5, 10, 20),
expression_organ_2_vars = c(50, 2, 10),
expression_organ_3_vars = c(5, 10, 3)
)
df_one_long <- pivot_longer(df_one,
cols=3:5,
names_to = "tissueType",
values_to = "Expression")
expression.df_one <- ggplot(df_one_long,
mapping = aes(y=tissueType, x=vars, fill = Expression)) +
geom_tile()
expression.df_one
df_two <- data.frame(
corresponding_vars = c("var_x", "var_y", "var_z"),
expression_organ_1_corresponding_vars = c(100, 320, 120),
expression_organ_2_corresponding_vars = c(23, 30, 150),
expression_organ_3_corresponding_vars = c(89, 7, 200)
)
df_two_long <- pivot_longer(df_one,
cols=3:5,
names_to = "tissueType",
values_to = "Expression")
expression.df_two <- ggplot(df_two_long,
mapping = aes(y=tissueType, x=vars, fill = Expression)) +
geom_tile()
expression.df_two
Drawing:
You can bind your data frames together and pivot into a longer format so that vars and corresponding vars are in the same column, but retain a grouping variable to facet by:
df_two %>%
mutate(cor = corresponding_vars) %>%
rename_with(~sub('corresponding_', '', .x)) %>%
bind_rows(df_one %>% rename(cor = corresponding_vars)) %>%
pivot_longer(contains('expression'), names_to = 'organ') %>%
mutate(organ = gsub('expression_|_vars', '', organ)) %>%
group_by(cor) %>%
summarize(vars = vars, organ = organ, value = value,
cor = paste(sort(unique(vars)), collapse = ' cor ')) %>%
ggplot(aes(vars, organ, fill = value)) +
geom_tile(color = 'white', linewidth = 1) +
facet_grid(.~cor, scales = 'free_x', switch = 'x') +
scale_fill_viridis_c() +
coord_cartesian(clip = 'off') +
scale_x_discrete(expand = c(0, 0)) +
theme_minimal(base_size = 16) +
theme(strip.placement = 'outside',
axis.text.x = element_blank(),
axis.ticks.x.bottom = element_line(),
panel.spacing.x = unit(3, 'mm'))
Okay, so I solved the issue for my own project, which is to convert it to a scatter plot. I combined both datasets and then used a simple scatterplot.
df.combined <- dplyr::full_join(df_two_long, df_one_long,
by = c("vars", "corresponding_vars", "tissueType"))
ggplot(df.combined,
aes(x=vars, y=tissueType, colour=Expression.x, size = Expression.y)) +
geom_point()
It's not a solution with heatmaps, but I don't know how to do that at the moment.
I am making a line plot of several groups and want to make a visualization where one of the groups lines are highlighted
ggplot(df) + geom_line(aes(x=timepoint ,y=var, group = participant_id, color=color)) +
scale_color_identity(labels = c(red = "g1",gray90 = "Other"),guide = "legend")
However, the group lines are partially obscured by the other groups lines
How can I make these lines always on top of other groups lines?
The simplest way to do this is to plot the gray and red groups on different layers.
First, let's try to replicate your problem with a dummy data set:
set.seed(1)
df <- data.frame(
participant_id = rep(1:50, each = 25),
timepoint = factor(rep(0:24, 50)),
var = c(replicate(50, runif(1, 50, 200) + runif(25, 0.3, 1.5) *
sin(0:24/(0.6*pi))^2/seq(0.002, 0.005, length = 25))),
color = rep(sample(c("red", "gray90"), 50, TRUE, prob = c(1, 9)), each = 100)
)
Now we apply your plotting code:
library(ggplot2)
ggplot(df) +
geom_line(aes(x=timepoint ,y=var, group = participant_id, color = color)) +
scale_color_identity(labels = c(red = "g1", gray90 = "Other"),
guide = "legend") +
theme_classic()
This looks broadly similar to your plot. If instead we plot in different layers, we get:
ggplot(df, aes(timepoint, var, group = participant_id)) +
geom_line(data = df[df$color == "gray90",], aes(color = "Other")) +
geom_line(data = df[df$color == "red",], aes(color = "gl")) +
scale_color_manual(values = c("red", "gray90")) +
theme_classic()
Created on 2022-06-20 by the reprex package (v2.0.1)
You can use factor releveling to bring the line (-s) of interest to front.
First, let's plot the data as is, with the red line partly hidden by others.
library(ggplot2)
library(dplyr)
set.seed(13)
df <-
data.frame(timepoint = rep(c(1:100), 20),
participant_id = paste0("p_", sort(rep(c(1:20), 100))),
var = abs(rnorm(2000, 200, 50) - 200),
color = c(rep("red", 100), rep("gray90", 1900)))
ggplot(df) +
geom_line(aes(x = timepoint ,
y = var,
group = participant_id, color = color)) +
scale_color_identity(labels = c(red = "g1", gray90 = "Other"),
guide = "legend")
Now let's bring p_1 to front by making it the last factor level.
df %>%
mutate(participant_id = factor(participant_id)) %>%
mutate(participant_id = relevel(participant_id, ref = "p_1")) %>%
mutate(participant_id = factor(participant_id, levels = rev(levels(participant_id)))) %>%
ggplot() +
geom_line(aes(x=timepoint,
y=var,
group = participant_id,
color = color)) +
scale_color_identity(labels = c(red = "g1", gray90 = "Other"),
guide = "legend")
I am trying to add labels to a beeswarm plot I am making using ggplot2. However, it seems as if the labels are pointing to the center line, and not the individual dots. Here is my code:
library(ggbeeswarm)
library(tidyverse)
DataTest <- tibble(Category = c(LETTERS),
Year = runif(26, 2016, 2016),
Size = runif(26, min = 5, max = 10),
SalesGrowth = runif(26, -1, 1))
ggplot() +
coord_flip() +
geom_quasirandom(DataTest,
mapping = aes(factor(Year),
SalesGrowth,
size = Size)) +
geom_label_repel(DataTest %>% filter(Category %in% c('A', 'B', 'C')),
mapping = aes(factor(Year),
SalesGrowth,
label = Category),
box.padding = 2) +
scale_size_binned() +
theme(legend.position = "none")
And here is what the output is looking like visually. I want my labels to point to the respective dots.
This could be achieved like so:
Make use of position_quasirandom in geom_label_repel
As a general rule when using ggrepel, pass the whole data to geom_label_repel and set undesired labels equal to "" instead of filtering the data.
library(ggplot2)
library(ggbeeswarm)
library(ggrepel)
DataTest <- data.frame(Category = c(LETTERS),
Year = runif(26, 2016, 2016),
Size = runif(26, min = 5, max = 10),
SalesGrowth = runif(26, -1, 1))
set.seed(42)
ggplot() +
coord_flip() +
geom_quasirandom(DataTest,
mapping = aes(factor(Year),
SalesGrowth,
size = Size)) +
geom_label_repel(data = DataTest, mapping = aes(factor(Year),
SalesGrowth,
label = ifelse(Category %in% c('A', 'B', 'C'), Category, "")),
position=position_quasirandom(),
box.padding = 2, seed = 42) +
scale_size_binned() +
theme(legend.position = "none")
I recently asked this question. However, I am asking a separate question now as the scope of my new question falls outside the range of the last question.
I am trying to create a heatmap in ggplot... however, outside of the axis I am trying to plot geom_tile. The issue is I cannot find a consistent way to get it to work. For example, the code I am using to plot is:
library(colorspace)
library(ggplot2)
library(ggnewscale)
library(tidyverse)
asd <- expand_grid(paste0("a", 1:9), paste0("b", 1:9))
df <- data.frame(
a = asd$`paste0("a", 1:9)`,
b = asd$`paste0("b", 1:9)`,
c = sample(20, 81, replace = T)
)
# From discrete to continuous
df$a <- match(df$a, sort(unique(df$a)))
df$b <- match(df$b, sort(unique(df$b)))
z <- sample(10, 18, T)
# set color palettes
pal <- rev(diverging_hcl(palette = "Blue-Red", n = 11))
palEdge <- rev(sequential_hcl(palette = "Plasma", n = 11))
# plot
ggplot(df, aes(a, b)) +
geom_tile(aes(fill = c)) +
scale_fill_gradientn(
colors = pal,
guide = guide_colorbar(
frame.colour = "black",
ticks.colour = "black"
),
name = "C"
) +
theme_classic() +
labs(x = "A axis", y = "B axis") +
new_scale_fill() +
geom_tile(data = tibble(a = 1:9,
z = z[1:9]),
aes(x = a, y = 0, fill = z, height = 0.3)) +
geom_tile(data = tibble(b = 1:9,
z = z[10:18]),
aes(x = 0, y = b, fill = z, width = 0.3)) +
scale_fill_gradientn(
colors = palEdge,
guide = guide_colorbar(
frame.colour = "black",
ticks.colour = "black"
),
name = "Z"
)+
coord_cartesian(clip = "off", xlim = c(0.5, NA), ylim = c(0.5, NA)) +
theme(aspect.ratio = 1,
plot.margin = margin(10, 15.5, 25, 25, "pt")
)
This produces something like this:
However, I am trying to find a consistent way to plot something more like this (which I quickly made in photoshop):
The main issue im having is being able to manipulate the coordinates of the new scale 'outside' of the plotting area. Is there a way to move the tiles that are outside so I can position them in an area that makes sense?
There are always the two classic options when plotting outside the plot area:
annotate/ plot with coord_...(clip = "off")
make different plots and combine them.
The latter option usually gives much more flexibility and way less headaches, in my humble opinion.
library(colorspace)
library(tidyverse)
library(patchwork)
asd <- expand_grid(paste0("a", 1:9), paste0("b", 1:9))
df <- data.frame(
a = asd$`paste0("a", 1:9)`,
b = asd$`paste0("b", 1:9)`,
c = sample(20, 81, replace = T)
)
# From discrete to continuous
df$a <- match(df$a, sort(unique(df$a)))
df$b <- match(df$b, sort(unique(df$b)))
z <- sample(10, 18, T)
# set color palettes
pal <- rev(diverging_hcl(palette = "Blue-Red", n = 11))
palEdge <- rev(sequential_hcl(palette = "Plasma", n = 11))
# plot
p_main <- ggplot(df, aes(a, b)) +
geom_tile(aes(fill = c)) +
scale_fill_gradientn("C",colors = pal,
guide = guide_colorbar(frame.colour = "black",
ticks.colour = "black")) +
theme_classic() +
labs(x = "A axis", y = "B axis")
p_bottom <- ggplot() +
geom_tile(data = tibble(a = 1:9, z = z[1:9]),
aes(x = a, y = 0, fill = z, height = 0.3)) +
theme_void() +
scale_fill_gradientn("Z",limits = c(0,10),
colors = palEdge,
guide = guide_colorbar(
frame.colour = "black", ticks.colour = "black"))
p_left <- ggplot() +
theme_void()+
geom_tile(data = tibble(b = 1:9, z = z[10:18]),
aes(x = 0, y = b, fill = z, width = 0.3)) +
scale_fill_gradientn("Z",limits = c(0,10),
colors = palEdge,
guide = guide_colorbar( frame.colour = "black", ticks.colour = "black"))
p_left + p_main +plot_spacer()+ p_bottom +
plot_layout(guides = "collect",
heights = c(1, .1),
widths = c(.1, 1))
Created on 2021-02-21 by the reprex package (v1.0.0)
I would like to produce a plot like the one obtained with the code below. However, I would like to dodge by "replicate", but without actually mapping an aesthetic (because I would like to assign fill and colors to other aesthetics).
dataset <- data_frame(sample = rep(c("Sample1","Sample2","Sample3", "Sample4"), each = 25),
replicate = sample(x = c("A", "B"), size = 100, replace = TRUE),
value = rnorm(n = 100, mean = 0, sd = 10))
ggplot(data = dataset, aes(x = sample, y = value, fill = replicate)) +
geom_point(position = position_jitterdodge(jitter.width = 0.15, dodge.width = 0.75),
show.legend = F)
I had hope using group = replicate instead of fill = replicate but this doesn't work. I can imagine a workaround using for example alpha = replicate as an aesthetic and setting scale_alpha_manual(values = c(1, 1)) in case of duplicates, but I don't find this solution ideal and would like to keep all aesthetics available (other than x and y available for further use)
ggplot(data = dataset, aes(x = sample, y = value, alpha = replicate)) +
geom_point(position = position_jitterdodge(jitter.width = 0.15, dodge.width = 0.75),
show.legend = F) +
scale_alpha_manual(values = c(1, 1))
The plot that I expect to get is:
I hope my question makes sense, any hint ?
Best,
Yvan
You could unite the sample and replicate columns and use that as the x-axis, injecting a 'Placeholder' value for spacing between samples.
library(tidyverse)
set.seed(20181101)
dataset <- data_frame(sample = rep(c("Sample1","Sample2","Sample3", "Sample4"), each = 25),
replicate = sample(x = c("A", "B"), size = 100, replace = TRUE),
value = rnorm(n = 100, mean = 0, sd = 10))
dataset %>%
bind_rows({
#create a dummy placeholder to allow for spacing between samples
data.frame(sample = unique(dataset$sample),
replicate = rep("Placeholder", length(unique(dataset$sample))),
stringsAsFactors = FALSE)
}) %>%
#unite the sample & replicate columns, and use it as the new x-axis
unite(sample_replicate, sample, replicate, remove = FALSE) %>%
ggplot(aes(x = sample_replicate, y = value, color = replicate)) +
geom_jitter() +
#only have x-axis labels for each sample
scale_x_discrete(breaks = paste0("Sample", 1:length(unique(dataset$sample)), "_B"),
labels = paste0("Sample ", 1:length(unique(dataset$sample)))) +
labs(x = "Sample") +
#don't show the Placeholder value in the legend
scale_color_discrete(breaks = c("A", "B"))