Say I have this data frame:
treatment <- c(rep("A",6),rep("B",6),rep("C",6),rep("D",6),rep("E",6),rep("F",6))
year <- as.numeric(c(1999:2004,1999:2004,2005:2010,2005:2010,2005:2010,2005:2010))
variable <- c(runif(6,4,5),runif(6,5,6),runif(6,3,4),runif(6,4,5),runif(6,5,6),runif(6,6,7))
se <- c(runif(6,0.2,0.5),runif(6,0.2,0.5),runif(6,0.2,0.5),runif(6,0.2,0.5),runif(6,0.2,0.5),runif(6,0.2,0.5))
id <- 1:36
df1 <- as.data.table(cbind(id,treatment,year,variable,se))
df1$year <- as.numeric(df1$year)
df1$variable <- as.numeric(df1$variable)
df1$se <- as.numeric(df1$se)
As I mentioned in a previous question (draw two lines with the same origin using ggplot2 in R), I wanted to use ggplot2 to display my data in a specific way.
I managed to do so using the following script:
y1 <- df1[df1$treatment=='A'&df1$year==2004,]$variable
y2 <- df1[df1$treatment=='B'&df1$year==2004,]$variable
y3 <- df1[df1$treatment=='C'&df1$year==2005,]$variable
y4 <- df1[df1$treatment=='D'&df1$year==2005,]$variable
y5 <- df1[df1$treatment=='E'&df1$year==2005,]$variable
y5 <- df1[df1$treatment=='E'&df1$year==2005,]$variable
y6 <- df1[df1$treatment=='F'&df1$year==2005,]$variable
p <- ggplot(df1,aes(x=year,y=variable,group=treatment,color=treatment))+
geom_line(aes(y = variable, group = treatment, linetype = treatment, color = treatment),size=1.5,lineend = "round") +
scale_linetype_manual(values=c('solid','solid','solid','dashed','solid','dashed')) +
geom_point(aes(colour=factor(treatment)),size=4)+
geom_errorbar(aes(ymin=variable-se,ymax=variable+se),width=0.2,size=1.5)+
guides(colour = guide_legend(override.aes = list(shape=NA,linetype = c("solid", "solid",'solid','dashed','solid','dashed'))))
p+labs(title="Title", x="years", y = "Variable 1")+
theme_classic() +
scale_x_continuous(breaks=c(1998:2010), labels=c(1998:2010),limits=c(1998.5,2010.5))+
geom_segment(aes(x=2004, y=y1, xend=2005, yend=y3),colour='blue1',size=1.5,linetype='solid')+
geom_segment(aes(x=2004, y=y1, xend=2005, yend=y4),colour='blue1',size=1.5,linetype='dashed')+
geom_segment(aes(x=2004, y=y2, xend=2005, yend=y5),colour='red3',size=1.5,linetype='solid')+
geom_segment(aes(x=2004, y=y2, xend=2005, yend=y6),colour='red3',size=1.5,linetype='dashed')+
scale_color_manual(values=c('blue1','red3','blue1','blue1','red3','red3'))+
theme(text = element_text(size=12))
As you can see I used both geom_line and geom_segment to display the lines for my graph.
It's almost perfect but if you look closely, the segments that are drawn (between 2004 and 2005) do not display the same line size, even though I used the same arguments values in the script (i.e. size=1.5 and linetype='solid' or dashed).
Of course I could change manually the size of the segments to get similar lines, but when I do that, segments are not as smooth as the lines using geom_line.
Also, I get the same problem (different line shapes) by including the size or linetype arguments within the aes() argument.
Do you have any idea what causes this difference and how I can get the exact same shapes for both my segments and lines ?
It seems to be an anti-aliasing issue with geom_segment, but that seems like a somewhat cumbersome approach to begin with. I think I have resolved your issue by duplicating the A and B treatments in the original data frame.
# First we are going to duplicate and rename the 'shared' treatments
library(dplyr)
library(ggplot2)
df1 %>%
filter(treatment %in% c("A", "B")) %>%
mutate(treatment = ifelse(treatment == "A",
"AA", "BB")) %>%
bind_rows(df1) %>% # This rejoins with the original data
# Now we create `treatment_group` and `line_type` variables
mutate(treatment_group = ifelse(treatment %in% c("A", "C", "D", "AA"),
"treatment1",
"treatment2"), # This variable will denote color
line_type = ifelse(treatment %in% c("AA", "BB", "D", "F"),
"type1",
"type2")) %>% # And this variable denotes the line type
# Now pipe into ggplot
ggplot(aes(x = year, y = variable,
group = interaction(treatment_group, line_type), # grouping by both linetype and color
color = treatment_group)) +
geom_line(aes(x = year, y = variable, linetype = line_type),
size = 1.5, lineend = "round") +
geom_point(size=4) +
# The rest here is more or less the same as what you had
geom_errorbar(aes(ymin = variable-se, ymax = variable+se),
width = 0.2, size = 1.5) +
scale_color_manual(values=c('blue1','red3')) +
scale_linetype_manual(values = c('dashed', 'solid')) +
labs(title = "Title", x = "Years", y = "Variable 1") +
scale_x_continuous(breaks = c(1998:2010),
limits = c(1998.5, 2010.5))+
theme_classic() +
theme(text = element_text(size=12))
Which will give you the following
My numbers are different since they were randomly generated.
You can then modify the legend to your liking, but my recommendation is using something like geom_label and then be sure to set check_overlap = TRUE.
Hope this helps!
Related
I have some data in which each observation contains 2 factors, classes (a letter between A and E), and flag (0 or 1). After applying a group_by(classes,flag) and a summarize(frequency=n()), I get a data frame similar to this one:
classes <-as.factor(c("A", "A", "B", "B", "C", "C", "D", "D", "E", "E"))
flag <- as.factor(rep(c(0,1),10))
quantity <- c(856, 569, 463, 125, 795, 313, 1000, 457, 669, 201)
df <- data.frame(classes, flag, quantity)
I managed to get the chart that I want (ordered bars, one for each level of classes, each bar filled with the proportion of flag) with this code:
ggplot(df, aes(x = reorder(classes, -quantity), y = quantity)) +
geom_bar(aes(fill = as.factor(flag)), stat="identity") +
theme(axis.text.x=element_text(angle = 90, hjust = 1)) +
labs(x = NULL, y = "Quantity", fill = "flag") +
scale_fill_manual(values=c("firebrick","dodgerblue4"),
labels=c("1"="Yes","0"="No"))+
theme(axis.ticks = element_blank())
However, I am not sure how to use the geom_text() to include both the total count on top of each bar, and the proportion of the fill value inside the bars.
Thanks for helping!
I don't know a way to automate this, probably it's easiest to calculate proportions and sums outside the plot.
It's easier to reorder the classes outside the plot, so that your text can take over the factor-levels.
df$x <- reorder(df$classes, -df$quantity)
Next you can calculate the statistics you want. Below I assumed that if we split df by classes, it is always the order flag = 0, flag = 1, so we can take x[2]/x[1] as proportion.
text_df <- data.frame(
class = sapply(split(df$classes, df$classes), unique),
sum = sapply(split(df$quantity, df$classes), sum),
prop = sapply(split(df$quantity, df$classes), function(x){x[2]/(x[1]+x[2])})
)
Then we let text_df$class take on the same ordering as df$x.
text_df$class <- factor(text_df$class, levels = levels(df$x))
Then we make the plot similar to your example, remember we reordered the x-variable earlier:
ggplot(df, aes(x = x, y = quantity)) +
geom_bar(aes(fill = as.factor(flag)), stat="identity") +
theme(axis.text.x=element_text(angle = 90, hjust = 1)) +
labs(x = NULL, y = "Quantity", fill = "flag") +
scale_fill_manual(values=c("firebrick","dodgerblue4"),
labels=c("1"="Yes","0"="No"))+
theme(axis.ticks = element_blank())
And add two geoms for text, one for the proportion, one for the sum; both with a y-offset.
+geom_text(data = text_df,
aes(x = class,
y = sum + 100, # some offset
label = sum)) +
geom_text(data = text_df,
aes(x = class,
y = sum - 100, # opposite offset
label = scales::percent(prop)))
And I think that did the trick. Good luck!
I want to plot a line graph, with multiple lines, coloured depending on a grouping variable. Now I want to set the legend labels via scale-command:
scale_color_manual(values = colors_values, labels = ...)
The legend labels are as following: "x^2", "x^3", "x^4" etc., where the range is dynamically created. I would now like to dynamically create the expression as label text, i.e.
"x^2" should become x2
"x^3" should become x3
etc.
The amount of legend labels varies, so I thought about something like as.expression(sprintf("x^%i", number)), which does of course not work as label parameter for the scale function.
I have searched google and stack overflow, however, I haven't found a working solution yet, so I hope someone can help me here.
Here's a reproducible example:
poly.term <- runif(100, 1, 60)
resp <- rnorm(100, 40, 5)
poly.degree <- 2:4
geom.colors <- scales::brewer_pal(palette = "Set1")(length(poly.degree))
plot.df <- data.frame()
for (i in poly.degree) {
mydat <- na.omit(data.frame(x = poly.term, y = resp))
fit <- lm(mydat$y ~ poly(mydat$x, i, raw = TRUE))
plot.df <- rbind(plot.df, cbind(mydat, predict(fit), sprintf("x^%i", i)))
}
colnames(plot.df) <- c("x","y", "pred", "grp")
ggplot(plot.df, aes(x, y, colour = grp)) +
stat_smooth(method = "loess", se = F) +
geom_line(aes(y = pred))
scale_color_manual(values = geom.colors
# here I want to change legend labels
# lables = expresion???
)
I would like to have the legend labels to be x2, x3 and x4.
ggplot(plot.df, aes(x, y, colour = grp)) +
stat_smooth(method = "loess", se = F) +
geom_line(aes(y = pred)) +
scale_color_manual(values = setNames(geom.colors,
paste0("x^",poly.degree)),
labels = setNames(lapply(poly.degree, function(i) bquote(x^.(i))),
paste0("x^",poly.degree)))
It's important to ensure correct mapping if you change values or labels in the scale. Thus, you should always use named vectors.
this is my first stack overflow post and I am a relatively new R user, so please go gently!
I have a data frame with three columns, a participant identifier, a condition (factor with 2 levels either Placebo or Experimental), and an outcome score.
set.seed(1)
dat <- data.frame(Condition = c(rep("Placebo",10),rep("Experimental",10)),
Outcome = rnorm(20,15,2),
ID = factor(rep(1:10,2)))
I would like to construct a bar plot with two bars with the mean outcome score for each condition and the standard deviation as an error bar. I would like to then overlay lines connecting points for each participant's score in each condition. So the plot displays the individual response as well as the group mean.If it is also possible I would like to include an axis break.
I don't seem to be able to find any advice in other threads, apologies if I am repeating a question.
Many Thanks.
p.s. I realise that presenting data in this way will not be to everyones tastes. It is for a specific requirement!
This ought to work:
library(ggplot2)
library(dplyr)
dat.summ <- dat %>% group_by(Condition) %>%
summarize(mean.outcome = mean(Outcome),
sd.outcome = sd(Outcome))
ggplot(dat.summ, aes(x = Condition, y = mean.outcome)) +
geom_bar(stat = "identity") +
geom_errorbar(aes(ymin = mean.outcome - sd.outcome,
ymax = mean.outcome + sd.outcome),
color = "dodgerblue", width = 0.3) +
geom_point(data = dat, aes(x = Condition, y = Outcome),
color = "firebrick", size = 1.2) +
geom_line(data = dat, aes(x = Condition, y = Outcome, group = ID),
color = "firebrick", size = 1.2, alpha = 0.5) +
scale_y_continuous(limits = c(0, max(dat$Outcome)))
Some people are better with ggplot's stat functions and arguments than I am and might do it differently. I prefer to just transform my data first.
set.seed(1)
dat <- data.frame(Condition = c(rep("Placebo",10),rep("Experimental",10)),
Outcome = rnorm(20,15,2),
ID = factor(rep(1:10,2)))
dat.w <- reshape(dat, direction = 'wide', idvar = 'ID', timevar = 'Condition')
means <- colMeans(dat.w[, 2:3])
sds <- apply(dat.w[, 2:3], 2, sd)
ci.l <- means - sds
ci.u <- means + sds
ci.width <- .25
bp <- barplot(means, ylim = c(0,20))
segments(bp, ci.l, bp, ci.u)
segments(bp - ci.width, ci.u, bp + ci.width, ci.u)
segments(bp - ci.width, ci.l, bp + ci.width, ci.l)
segments(x0 = bp[1], x1 = bp[2], y0 = dat.w[, 2], y1 = dat.w[, 3], col = 1:10)
points(c(rep(bp[1], 10), rep(bp[2], 10)), dat$Outcome, col = 1:10, pch = 19)
Here is a method using the transfomations inside ggplot2
ggplot(dat) +
stat_summary(aes(x=Condition, y=Outcome, group=Condition), fun.y="mean", geom="bar") +
stat_summary(aes(x=Condition, y=Outcome, group=Condition), fun.data="mean_se", geom="errorbar", col="green", width=.8, size=2) +
geom_line(aes(x=Condition, y=Outcome, group=ID), col="red")
I need to add a legend of the two lines (best fit line and 45 degree line) on TOP of my two plots. Sorry I don't know how to add plots! Please please please help me, I really appreciate it!!!!
Here is an example
type=factor(rep(c("A","B","C"),5))
xvariable=seq(1,15)
yvariable=2*xvariable+rnorm(15,0,2)
newdata=data.frame(type,xvariable,yvariable)
p = ggplot(newdata,aes(x=xvariable,y=yvariable))
p+geom_point(size=3)+ facet_wrap(~ type) +
geom_abline(intercept =0, slope =1,color="red",size=1)+
stat_smooth(method="lm", se=FALSE,size=1)
Here is another approach which uses aesthetic mapping to string constants to identify different groups and create a legend.
First an alternate way to create your test data (and naming it DF instead of newdata)
DF <- data.frame(type = factor(rep(c("A", "B", "C"), 5)),
xvariable = 1:15,
yvariable = 2 * (1:15) + rnorm(15, 0, 2))
Now the ggplot code. Note that for both geom_abline and stat_smooth, the colour is set inside and aes call which means each of the two values used will be mapped to a different color and a guide (legend) will be created for that mapping.
ggplot(DF, aes(x = xvariable, y = yvariable)) +
geom_point(size = 3) +
geom_abline(aes(colour="one-to-one"), intercept =0, slope = 1, size = 1) +
stat_smooth(aes(colour="best fit"), method = "lm", se = FALSE, size = 1) +
facet_wrap(~ type) +
scale_colour_discrete("")
Try this:
# original data
type <- factor(rep(c("A", "B", "C"), 5))
x <- 1:15
y <- 2 * x + rnorm(15, 0, 2)
df <- data.frame(type, x, y)
# create a copy of original data, but set y = x
# this data will be used for the one-to-one line
df2 <- data.frame(type, x, y = x)
# bind original and 'one-to-one data' together
df3 <- rbind.data.frame(df, df2)
# create a grouping variable to separate stat_smoothers based on original and one-to-one data
df3$grp <- as.factor(rep(1:2, each = nrow(df)))
# plot
# use original data for points
# use 'double data' for abline and one-to-one line, set colours by group
ggplot(df, aes(x = x, y = y)) +
geom_point(size = 3) +
facet_wrap(~ type) +
stat_smooth(data = df3, aes(colour = grp), method = "lm", se = FALSE, size = 1) +
scale_colour_manual(values = c("red","blue"),
labels = c("abline", "one-to-one"),
name = "") +
theme(legend.position = "top")
# If you rather want to stack the two keys in the legend you can add:
# guide = guide_legend(direction = "vertical")
#...as argument in scale_colour_manual
Please note that this solution does not extrapolate the one-to-one line outside the range of your data, which seemed to be the case for the original geom_abline.
I have a dataframe a with three columns :
GeneName, Index1, Index2
I draw a scatterplot like this
ggplot(a, aes(log10(Index1+1), Index2)) +geom_point(alpha=1/5)
Then I want to color a point whose GeneName is "G1" and add a text box near that point, what might be the easiest way to do it?
You could create a subset containing just that point and then add it to the plot:
# create the subset
g1 <- subset(a, GeneName == "G1")
# plot the data
ggplot(a, aes(log10(Index1+1), Index2)) + geom_point(alpha=1/5) + # this is the base plot
geom_point(data=g1, colour="red") + # this adds a red point
geom_text(data=g1, label="G1", vjust=1) # this adds a label for the red point
NOTE: Since everyone keeps up-voting this question, I thought I would make it easier to read.
Something like this should work. You may need to mess around with the x and y arguments to geom_text().
library(ggplot2)
highlight.gene <- "G1"
set.seed(23456)
a <- data.frame(GeneName = paste("G", 1:10, sep = ""),
Index1 = runif(10, 100, 200),
Index2 = runif(10, 100, 150))
a$highlight <- ifelse(a$GeneName == highlight.gene, "highlight", "normal")
textdf <- a[a$GeneName == highlight.gene, ]
mycolours <- c("highlight" = "red", "normal" = "grey50")
a
textdf
ggplot(data = a, aes(x = Index1, y = Index2)) +
geom_point(size = 3, aes(colour = highlight)) +
scale_color_manual("Status", values = mycolours) +
geom_text(data = textdf, aes(x = Index1 * 1.05, y = Index2, label = "my label")) +
theme(legend.position = "none") +
theme()