I have a time series with forecast and confidence interval data, I wanted to plot them simultaneously with a legend using ggplot2. I'm doing it by the code below:
set.seed(321)
library(ggplot2)
#create some dummy data similar to mine
sample <- rnorm(350)
forecast <- rnorm(24)
upper <- forecast+2*sd(forecast)
lower <- forecast-2*sd(forecast)
## wrap data into a data.frame
df1 = data.frame(time = seq(325,350,length=26), M = sample[325:350], isin = "observations")
df2 = data.frame(time = seq(351,374,length=24), M = forecast , isin = "my_forecast")
df3 = data.frame(time = seq(351,374,length=24), M = upper ,isin = "upper_bound")
df4 = data.frame(time = seq(351,374,length=24), M = lower, isin = "lower_bound")
df = rbind(df1, df2, df3, df4)
In a previous question #Matthew Plourde suggested me a nice answer:
ggplot(df1, aes(x = time, y = M)) + geom_line(colour='blue') +
geom_smooth(aes(x=time, y=M, ymax=upper, ymin=lower),
colour='red', data=df2, stat='identity')
Now, I wanted to include a legend with "observations" and "my_forecast". I tryed with
ggplot(df1, aes(x = time, y = M)) + geom_line(colour='blue') +
geom_smooth(aes(x=time, y=M, ymax=upper, ymin=lower),
colour='red', data=df2, stat='identity')+ scale_colour_manual(values=c(observations='blue', my_forecast='red'))
but it doesn't display a legend.
You need to move the colour parameters into aes to create a legend.
ggplot(df1, aes(x = time, y = M)) + geom_line(aes(colour = 'blue')) +
geom_smooth(aes(x = time, y = M, ymax = upper, ymin = lower, colour = 'red'),
data = df2, stat = 'identity') +
scale_colour_manual("", values = c("blue", "red"),
labels = c("observations", "my forecast"))
Related
For each treatment tmt, I want to plot the means using stat_summary in ggplot2 with different colour size. I find that the there are mulitple means being plotted over the current points. Not sure how to rectify it.
df <- data.frame(x = rnorm(12, 4,1), y = rnorm(12, 6,4), tmt = rep(c("A","B","C"), each = 4))
ggplot(aes(x = x, y = y, fill = tmt), data = df) +
geom_point(shape=21, size=5, alpha = 0.6) +
scale_fill_manual(values=c("pink","blue", "purple")) +
stat_summary(aes(fill = tmt), fun = 'mean', geom = 'point', size = 5) +
scale_fill_manual(values=c("pink","blue", "purple"))
Plot without the last two lines of code
Plot with the entire code
Using stat_summary you compute the mean of y for each pair of x and tmt. If you want the mean of x and the mean of y per tmt I would suggest to manually compute the means outside of ggplot and use a second geom_point to plot the means. In my code below I increased the size and used rectangles for the means:
df <- data.frame(x = rnorm(12, 4,1), y = rnorm(12, 6,4), tmt = rep(c("A","B","C"), each = 4))
library(ggplot2)
library(dplyr)
df_mean <- df |>
group_by(tmt) |>
summarise(across(c(x, y), mean))
ggplot(aes(x = x, y = y, fill = tmt), data = df) +
geom_point(shape=21, size=5, alpha = 0.6) +
geom_point(data = df_mean, shape=22, size=8, alpha = 0.6) +
scale_fill_manual(values=c("pink","blue", "purple"))
I have the following graph and code:
Graph
ggplot(long2, aes(x = DATA, y = value, fill = variable)) + geom_area(position="fill", alpha=0.75) +
scale_y_continuous(labels = scales::comma,n.breaks = 5,breaks = waiver()) +
scale_fill_viridis_d() +
scale_x_date(date_labels = "%b/%Y",date_breaks = "6 months") +
ggtitle("Proporcions de les visites, només 9T i 9C") +
xlab("Data") + ylab("% visites") +
theme_minimal() + theme(legend.position="bottom") + guides(fill=guide_legend(title=NULL)) +
annotate("rect", fill = "white", alpha = 0.3,
xmin = as.Date.character("2020-03-16"), xmax = as.Date.character("2020-06-22"),
ymin = 0, ymax = 1)
But it has some sawtooth, how am I supposed to smooth it out?
I believe your situation is roughly analogous to the following, wherein we have missing x-positions for one group, but not the other at the same position. This causes spikes if you set position = "fill".
library(ggplot2)
x <- seq_len(100)
df <- data.frame(
x = c(x[-c(25, 75)], x[-50]),
y = c(cos(x[-c(25, 75)]), sin(x[-50])) + 5,
group = rep(c("A", "B"), c(98, 99))
)
ggplot(df, aes(x, y, fill = group)) +
geom_area(position = "fill")
To smooth out these spikes, it has been suggested to linearly interpolate the data at the missing positions.
# Find all used x-positions
ux <- unique(df$x)
# Split data by group, interpolate data groupwise
df <- lapply(split(df, df$group), function(xy) {
approxed <- approx(xy$x, xy$y, xout = ux)
data.frame(x = ux, y = approxed$y, group = xy$group[1])
})
# Recombine data
df <- do.call(rbind, df)
# Now without spikes :)
ggplot(df, aes(x, y, fill = group)) +
geom_area(position = "fill")
Created on 2022-06-17 by the reprex package (v2.0.1)
P.S. I would also have expected a red spike at x=50, but for some reason this didn't happen.
I've plotted a confusion matrix (predicting 5 outcomes) in R using ggplot and scales for geom_text labeling.
The way geom_text(aes(label = percent(Freq/sum(Freq))) is written in code, it's showing Frequency of each box divided by sum of all observations, but what I want to do is get Frequency of each box divided by sum Frequency for each Reference.
In other words, instead of A,A = 15.8%,
it should be A,A = 15.8%/(0.0%+0.0%+0.0%+0.0%+15.8%%) = 100.0%
library(ggplot2)
library(scales)
valid_actual <- as.factor(c("A","B","B","C","C","C","E","E","D","D","A","A","A","E","E","D","D","C","B"))
valid_pred <- as.factor(c("A","B","C","C","E","C","E","E","D","B","A","B","A","E","D","E","D","C","B"))
cfm <- confusionMatrix(valid_actual, valid_pred)
ggplotConfusionMatrix <- function(m){
mytitle <- paste("Accuracy", percent_format()(m$overall[1]),
"Kappa", percent_format()(m$overall[2]))
p <-
ggplot(data = as.data.frame(m$table) ,
aes(x = Reference, y = Prediction)) +
geom_tile(aes(fill = log(Freq)), colour = "white") +
scale_fill_gradient(low = "white", high = "green") +
geom_text(aes(x = Reference, y = Prediction, label = percent(Freq/sum(Freq)))) +
theme(legend.position = "none") +
ggtitle(mytitle)
return(p)
}
ggplotConfusionMatrix(cfm)
The problem is that, as far as I know, ggplot is not able to do group calculation. See this recent post for similar question.
To solve your problem you should take advantage of the dplyrpackage.
This should work
library(ggplot2)
library(scales)
library(caret)
library(dplyr)
valid_actual <- as.factor(c("A","B","B","C","C","C","E","E","D","D","A","A","A","E","E","D","D","C","B"))
valid_pred <- as.factor(c("A","B","C","C","E","C","E","E","D","B","A","B","A","E","D","E","D","C","B"))
cfm <- confusionMatrix(valid_actual, valid_pred)
ggplotConfusionMatrix <- function(m){
mytitle <- paste("Accuracy", percent_format()(m$overall[1]),
"Kappa", percent_format()(m$overall[2]))
data_c <- mutate(group_by(as.data.frame(m$table), Reference ), percentage =
percent(Freq/sum(Freq)))
p <-
ggplot(data = data_c,
aes(x = Reference, y = Prediction)) +
geom_tile(aes(fill = log(Freq)), colour = "white") +
scale_fill_gradient(low = "white", high = "green") +
geom_text(aes(x = Reference, y = Prediction, label = percentage)) +
theme(legend.position = "none") +
ggtitle(mytitle)
return(p)
}
ggplotConfusionMatrix(cfm)
And the result:
I would like to plot a barplot but I have dates on the x axis and I want those dates to be correctly spaced (as it is NON categorical)
set.seed(1)
m = matrix(abs(rnorm(6)),3,2)
rownames(m) = as.Date(c('2011-01-01','2011-01-03','2011-01-10'))
barplot(t(m),beside=T,col=c('red','blue'),las=2)
On this example I would like 14984 to be offset on the right.
I'd rather a graphics solution but ggplot2 is fine too
Would you mind to use ´ggplot´ instead?
library(ggplot2)
set.seed(1)
df <- data.frame(y=abs(rnorm(6)),
x=rep(as.Date(c('2011-01-01','2011-01-03','2011-01-10')),
times = 2),
g = factor(rep(c(1,2), each = 3)))
ggplot(aes(x=x, y=y, group = g, fill = g), data = df) +
geom_bar(stat = 'identity', position = 'dodge')
You can improve axis formatting with `scale_x_date´
library(scales)
ggplot(aes(x=x, y=y, group = g, fill = g), data = df) +
geom_bar(stat = 'identity', position = 'dodge') +
scale_x_date(breaks = '1 day') +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5))
And customize it to your purpose
ggplot(aes(x=x, y=y, group = g, fill = g), data = df) +
geom_bar(stat = 'identity', position = 'dodge') +
scale_x_date(breaks = '1 day') +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) +
scale_fill_manual('My\nclasses', values = c('1'='red', '2' = 'blue')) +
labs(list(title = 'Barplot\n', x = ('Date'), y = 'Values'))
With graphics, you probably have to prepare the data appropriately (with missing values for dates you don't consider) in order to do this. Then you can use barplot.
# matrix definition
set.seed(1)
m = matrix(abs(rnorm(6)),3,2)
rownames(m) = as.Date(c('2011-01-01','2011-01-03','2011-01-10'))
# get all dates in between
dts <- do.call(":", as.list(range(rownames(m))))
dts <- dts[!dts%in%rownames(m)]
mat <- matrix(NA, nrow=length(dts), ncol=2, dimnames=list(dts, NULL))
# combine with original matrix
m <- rbind(m, mat)
m <- m[order(rownames(m)), ]
which(!is.na(m[,1]))
# plot
barplot(t(m), beside=T, col=c('red','blue'),las=2, axes=FALSE, axisnames=FALSE)
axis(2)
axis(1, at=3*which(!is.na(m[,1]))-1, labels=rownames(m[!is.na(m[,1]),]))
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