I have this data frame
df <- data.frame(profile = rep(c(1,2), times = 1, each = 3), depth = c(100, 200, 300), value = 1:3)
This is my plot
ggplot() +
geom_bar(data = df, aes(x = profile, y = - depth, fill = value), stat = "identity")
My problem is the y labels which doesn't correspond to the depth values of the data frame
To help, my desired plot seems like this :
ggplot() +
geom_point(data = df, aes(x = profile, y = depth, colour = value), size = 20) +
xlim(c(0,3))
But with bar intead of points vertically aligned
nb : I don't want to correct it manually in changing ticks with scale_y_discrete(labels = (desired_labels))
Thanks for help
Considering you want a y-axis from 0 to -300, using facet_grid() seems to be a right option without summarising the data together.
ggplot() + geom_bar(data = df, aes(x = as.factor(profile), y = -depth, fill = value), stat = 'identity') + facet_grid(~ value)
I have it !
Thanks for your replies and to this post R, subtract value from previous row, group by
To resume; the data :
df <- data.frame(profile = rep(c(1,2), times = 1, each = 3), depth = c(100, 200, 300), value = 1:3)
Then we compute the depth step of each profile :
df$diff <- ave(df$depth, df$profile, FUN=function(z) c(z[1], diff(z)))
And finally the plot :
ggplot(df, aes(x = factor(profile), y = -diff, fill = value)) + geom_col()
Related
Here's my R code
ggplot(dat = Table, aes(x = Group, y = value, fill = Type)) +
geom_boxplot(alpha=0.08)+
geom_jitter()+
scale_fill_brewer(palette="Spectral")+
theme_minimal()
Like you can see the dots are in the middle of the boxplots. What can I add in geom_jitter to have each point in the righ boxplot and not in the middle like this ? I also tried geom_point, it gave the same result !
Thanks to the help now It works, but I wanted to add a line to connect the dots and I got this.. can someone tell how to really connect the dots with lines
I think if you group by interaction(Group, Type) and use position_jitterdodge() you should get what you're looking for.
ggplot(mtcars, aes(as.character(am), mpg, color = as.character(vs),
group = interaction(as.character(vs), as.character(am)))) +
geom_boxplot() +
geom_jitter(position = position_jitterdodge()) # same output with geom_point()
Edit - here's an example with manual jittering applied to data where the each subject appears once in each Group.
I looked for a built-in way to do this, and this answer comes close, but I couldn't get it to work in terms of using position_jitterdodge with position defined by the groups of Group/Type, but line grouping defined by id alone and not by Group/Type. Both aesthetics (position adjustment and series identification) rely on the same group parameter, but they each need a different value for it.
Table = data.frame(id = 1:4,
value = rnorm(8),
Group = rep(c("a","b"), each = 4),
Type = c("1", "2"))
library(dplyr)
Table %>%
mutate(x = as.numeric(as.factor(Group)) +
0.2 * scale(as.numeric(as.factor(Type))) +
rnorm(n(), sd = 0.06)) %>%
ggplot(aes(x = Group, y = value, fill = Type, group = interaction(Group, Type))) +
geom_boxplot(alpha=0.2)+
geom_point(aes(x = x)) +
geom_line(aes(x = x, group = id), alpha = 0.1) +
scale_fill_brewer(palette="Spectral")+
theme_minimal()
Best to use position_dodge instead if you want them to line up:
library(ggplot2)
Table <- tibble::tibble(
Group = rep(c("A", "B"), each = 20),
Type = factor(rep(c(1:2, 1:2), each = 10)),
value = rnorm(40, mean = 10)
)
ggplot(dat = Table, aes(x = Group, y = value, fill = Type)) +
geom_boxplot(alpha=0.08)+
geom_point(position = position_dodge(width = 0.75))+
scale_fill_brewer(palette="Spectral")+
theme_minimal()
To add a line, make sure group = ID goes in both the geom_point and geom_line calls:
library(ggplot2)
Table <- tibble::tibble(
Group = rep(c("A", "B"), each = 20),
Type = factor(rep(c(1:2, 1:2), each = 10)),
ID = factor(rep(1:20, times = 2)),
value = rnorm(40, mean = 10)
)
ggplot(dat = Table, aes(x = Group, y = value, fill = Type)) +
geom_boxplot(alpha = 0.08) +
geom_point(aes(group = ID), position = position_dodge(width = 0.75))+
geom_line(aes(group = ID), position = position_dodge(width = 0.75), colour = "grey")+
scale_fill_brewer(palette = "Spectral") +
theme_minimal()
I have a data frame ("Date", "A", "B"). I'm trying to use boxplot (by month) to analysis the data "A" for the row filtered by "B" and also for all the "A". I can only create two separate plots to do the boxplot for specific rows and for whole rows of data.
I tried two have 2 geom_boxplot under one ggplot(), but two boxplot just overlap with each other. Here is the code I used. Does anyone know how I can combime those two boxplot into one, so two boxplots will share same x axis, and each month in x axis will have two boxes.
ggplot() +
geom_boxplot(data = df %>% filter(B == 1),
aes(x = Month, y = A, group=Month, fill = "Chamber_no fire"), outlier.shape = T) +
geom_boxplot(data = df, aes(x = Month, y = A, group=Month, fill="Chamber"), outlier.shape = T) +
theme_bw() +
theme(panel.grid.major = element_blank()) +
scale_x_continuous(breaks=seq(2,12,1), minor_breaks = F) +
geom_hline(yintercept = 0, linetype="dotted")
ggsave("sate_meas_O3_NOx_5km_nofire.png", width = 6, height = 4, units = "in")
One approach to achieve your desired result is to
Bind the filtered dataset and the total dataset by row and add an identifier id for each dataset which could easily be done via dplyr::bind_rows.
Make a boxplot where you map id on the fill aesthetic and group by both id and Month using interaction
Set the legend labels via scale_fill_discrete
As you provided no data I make use of a random example data set:
set.seed(42)
df <- data.frame(
Month = sample(2:12, 100, rep = TRUE),
A = rnorm(100),
B = sample(1:2, 100, rep = TRUE)
)
library(ggplot2)
library(dplyr)
d <- bind_rows(list(b1 = df %>% filter(B == 1),
all = df), .id = "id")
ggplot(data = d, mapping = aes(x = Month, y = A, group=interaction(Month, id), fill = id)) +
geom_boxplot(outlier.shape = T, position = "dodge") +
scale_fill_discrete(labels = c(b1 = "Chamber_no fire", all = "Chamber")) +
theme_bw() +
theme(panel.grid.major = element_blank()) +
scale_x_continuous(breaks=seq(2,12,1), minor_breaks = F) +
geom_hline(yintercept = 0, linetype="dotted")
When using ggplot2 to make faceted plots, I'm having trouble getting individual labels in each facet when I also specify a grouping parameter. Without specifying group = ..., things work fine, but I'm trying to make plots of paired data that emphasize the before vs. after treatment changes.
Here is an example:
library(tidyr)
library(ggplot2)
set.seed(253)
data <- data.frame(Subject = LETTERS[1:10],
Day1.CompoundA = rnorm(10, 4, 2),
Day2.CompoundA = rnorm(10, 7, 2),
Day1.CompoundB = rnorm(10, 5, 2),
Day2.CompoundB = rnorm(10, 5.5, 2))
# Compare concentration of compounds by day
A <- t.test(data$Day1.CompoundA, data$Day2.CompoundA, paired = TRUE)
B <- t.test(data$Day1.CompoundB, data$Day2.CompoundB, paired = TRUE)
data.long <- gather(data, key = DayCompound, value = Concentration, -Subject) %>%
separate(DayCompound, c("Day", "Compound"))
# text to annotate graphs
graphLabels <- data.frame(Compound = c("CompoundA", "CompoundB"),
Pval = paste("p =", c(signif(A$p.value, 2),
signif(B$p.value, 2))))
Ok, now that the data are set up, I can make a boxplot just fine:
ggplot(data.long, aes(x = Day, y = Concentration)) +
geom_boxplot() +
facet_wrap(~ Compound) +
geom_text(data = graphLabels, aes(x = 1.5, y = 10, label = Pval))
But if I want to show line plots that emphasize the paired nature of the data by showing each subject in a different color, the facet labels don't work.
ggplot(data.long, aes(x = Day, y = Concentration, color = Subject, group = Subject)) +
geom_point() + geom_line() +
facet_wrap(~ Compound) +
geom_text(data = graphLabels, aes(x = 1.5, y = 10, label = Pval))
# Error in eval(expr, envir, enclos) : object 'Subject' not found
Any suggestions?
When you map aesthetics (i.e. aes(...,color = Subject)) in the top level ggplot() call, those mappings are passed on to each layer, which means that each layer expects data to have variables by those names.
You either need to specify the data and mapping separately in each layer, or unmap them explicitly:
ggplot(data.long, aes(x = Day, y = Concentration, color = Subject, group = Subject)) +
geom_point() + geom_line() +
facet_wrap(~ Compound) +
geom_text(data = graphLabels, aes(x = 1.5, y = 10, label = Pval,color = NULL,group= NULL))
There is also an inherit.aes argument that you can set to FALSE in any layer you don't want pulling in those other mappings, e.g.
ggplot(data.long, aes(x = Day, y = Concentration, color = Subject, group = Subject)) +
geom_point() + geom_line() +
facet_wrap(~ Compound) +
geom_text(data = graphLabels, aes(x = 1.5, y = 10, label = Pval),inherit.aes = FALSE)
Here is the code for the plot
library(ggplot2)
df <- data.frame(gp = factor(rep(letters[1:3], each = 10)), y = rnorm(30))
library(plyr)
ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y))
ggplot(df, aes(x = gp, y = y)) +
geom_point() +
geom_point(data = ds, aes(y = mean), colour = 'red', size = 3)
I want to have a legend for this plot that will identify the data values and mean values some thing like this
Black point = Data
Red point = Mean.
How can I achieve this?
Use a manual scale, i.e. in your case scale_colour_manual. Then map the colours to values in the scale using the aes() function of each geom:
ggplot(df, aes(x = gp, y = y)) +
geom_point(aes(colour="data")) +
geom_point(data = ds, aes(y = mean, colour = "mean"), size = 3) +
scale_colour_manual("Legend", values=c("mean"="red", "data"="black"))
You can combine the mean variable and data in the same data.frame and colour /size by column which is a factor, either data or mean
library(reshape2)
# in long format
dsl <- melt(ds, value.name = 'y')
# add variable column to df data.frame
df[['variable']] <- 'data'
# combine
all_data <- rbind(df,dsl)
# drop sd rows
data_w_mean <- subset(all_data,variable != 'sd',drop = T)
# create vectors for use with scale_..._manual
colour_scales <- setNames(c('black','red'),c('data','mean'))
size_scales <- setNames(c(1,3),c('data','mean') )
ggplot(data_w_mean, aes(x = gp, y = y)) +
geom_point(aes(colour = variable, size = variable)) +
scale_colour_manual(name = 'Type', values = colour_scales) +
scale_size_manual(name = 'Type', values = size_scales)
Or you could not combine, but include the column in both data sets
dsl_mean <- subset(dsl,variable != 'sd',drop = T)
ggplot(df, aes(x = gp, y = y, colour = variable, size = variable)) +
geom_point() +
geom_point(data = dsl_mean) +
scale_colour_manual(name = 'Type', values = colour_scales) +
scale_size_manual(name = 'Type', values = size_scales)
Which gives the same results
I'm trying to annotate a bar chart with the percentage of observations falling into that bucket, within a facet. This question is very closely related to this question:
Show % instead of counts in charts of categorical variables but the introduction of faceting introduces a wrinkle. The answer to the related question is to use stat_bin w/ the text geom and then have the label be constructed as so:
stat_bin(geom="text", aes(x = bins,
y = ..count..,
label = paste(round(100*(..count../sum(..count..)),1), "%", sep="")
)
This works fine for an un-faceted plot. However, with facets, this sum(..count..) is summing over the entire collection of observations without regard for the facets. The plot below illustrates the issue---note that the percentages do not sum to 100% within a panel.
Here the actually code for the figure above:
g.invite.distro <- ggplot(data = df.exp) +
geom_bar(aes(x = invite_bins)) +
facet_wrap(~cat1, ncol=3) +
stat_bin(geom="text", aes(x = invite_bins,
y = ..count..,
label = paste(round(100*(..count../sum(..count..)),1), "%", sep="")
),
vjust = -1, size = 3) +
theme_bw() +
scale_y_continuous(limits = c(0, 3000))
UPDATE: As per request, here's a small example re-producing the issue:
df <- data.frame(x = c('a', 'a', 'b','b'), f = c('c', 'd','d','d'))
ggplot(data = df) + geom_bar(aes(x = x)) +
stat_bin(geom = "text", aes(
x = x,
y = ..count.., label = ..count../sum(..count..)), vjust = -1) +
facet_wrap(~f)
Update geom_bar requires stat = identity.
Sometimes it's easier to obtain summaries outside the call to ggplot.
df <- data.frame(x = c('a', 'a', 'b','b'), f = c('c', 'd','d','d'))
# Load packages
library(ggplot2)
library(plyr)
# Obtain summary. 'Freq' is the count, 'pct' is the percent within each 'f'
m = ddply(data.frame(table(df)), .(f), mutate, pct = round(Freq/sum(Freq) * 100, 1))
# Plot the data using the summary data frame
ggplot(data = m, aes(x = x, y = Freq)) +
geom_bar(stat = "identity", width = .7) +
geom_text(aes(label = paste(m$pct, "%", sep = "")), vjust = -1, size = 3) +
facet_wrap(~ f, ncol = 2) + theme_bw() +
scale_y_continuous(limits = c(0, 1.2*max(m$Freq)))