I plotted data from different Heat results. However, the y-axis is always scaled from lowest to highest value found in the according value in the dataset.
I want to change the scale so that the y-axis indicates from 4.0 to 10.0
I put in ylim but this returns "Discrete value supplied to continuous scale"
ggplot(WDF, aes(x = Episode, y = Rating, color = Rating)) +
geom_point() +
ylim(4.0, 10.0)+
geom_jitter()+
facet_grid(. ~ Season)
Original Code without Error but also without correct Y axis scale
ggplot(WDF, aes(x = Version, y = Cells, color = Rating)) +
geom_jitter()+ facet_grid(. ~ Heat)
scale y-axis from 4.0 to 10.0
This is my result:
Your current data may look like this:
library(tidyverse)
WDF <- data.frame(
Rating = factor(round(runif(90, min = 5, max = 9.6), 1)),
Episode = runif(90, min = 0.1, max = 15.9),
Season = seq(1:9)
)
Rating is a factor variable. When you run:
ggplot(WDF, aes(x = Episode, y = Rating, color = Rating)) +
geom_point() + ylim(4.0, 10.0)+ geom_jitter()+ facet_grid(. ~ Season)
get error: Discrete value supplied to continuous scale.
Now, change Rating from factor to numeric:
WDF <- WDF %>% mutate(Rating_1 = as.numeric(as.character(Rating)))
Please note: as.character() here is important. Without it, you will get numeric but wrong numbers. You could try without it to see the difference.
Then, run your original code with new variable Rating_1:
ggplot(WDF, aes(x = Episode, y = Rating_1, color = Rating_1)) +
geom_point() + ylim(4.0, 10.0)+ geom_jitter()+ facet_grid(. ~ Season)
To produce the following:
Related
I am making a stratigraphic plot but somehow, my data points don't connect correctly.
The purpose of this plot is that the values on the x-axis are connected so you get an overview of the change in d18O throughout time (age, ma).
I've used the following script:
library(readxl)
R_pliocene_tot <- read_excel("Desktop/R_d18o.xlsx")
View(R_pliocene_tot)
install.packages("analogue")
install.packages("gridExtra")
library(tidyverse)
R_pliocene_Rtot <- R_pliocene_tot %>%
gather(key=param, value=value, -age_ma)
R_pliocene_Rtot
R_pliocene_Rtot %>%
ggplot(aes(x=value, y=age_ma)) +
geom_path() +
geom_point() +
facet_wrap(~param, scales = "free_x") +
scale_y_reverse() +
labs(x = NULL, y = "Age (ma)")
which leads to the following figure:
Something is wrong with the geom_path function, I guess, but I can't figure out what it is.
Though the comment seem solve the problem I don't think the question asked was answered. So here is some introduction about ggplot2 library regard geom_path
library(dplyr)
library(ggplot2)
# This dataset contain two group with random value for y and x run from 1->20
# The param is just to replicate the question param variable.
df <- tibble(x = rep(seq(1, 20, by = 1), 2),
y = runif(40, min = 1, max = 100),
group = c(rep("group 1", 20), rep("group 2", 20)),
param = rep("a param", 40))
df %>%
ggplot(aes(x = x, y = y)) +
# In geom_path there is group aesthetics which help the function to know
# which data point should is in which path.
# The one in the same group will be connected together.
# here I use the color to help distinct the path a bit more.
geom_path(aes(group = group, color = group)) +
geom_point() +
facet_wrap(~param, scales = "free_x") +
scale_y_reverse() +
labs(x = NULL, y = "Age (ma)")
In your data which work well with group = 1 I guessed all data points belong to one group and you just want to draw a line connect all those data point. So take my data example above and draw with aesthetics group = 1, you can see the result that have two line similar to the above example but now the end point of group 1 is now connected with the starting point of group 2.
So all data point is now on one path but the order of how they draw is depend on the order they appear in the data. (I keep the color just to help see it a bit clearer)
df %>%
ggplot(aes(x = x, y = y)) +
geom_path(aes(group = 1, color = group)) +
geom_point() +
facet_wrap(~param, scales = "free_x") +
scale_y_reverse() +
labs(x = NULL, y = "Age (ma)")
Hope this give you better understanding of ggplot2::geom_path
I'm aware there are similar posts but I could not get those answers to work in my case.
e.g. Here and here.
Example:
diamonds %>%
ggplot(aes(scale(price) %>% as.vector)) +
geom_density() +
xlim(-3, 3) +
facet_wrap(vars(cut))
Returns a plot:
Since I used scale, those numbers are the zscores or standard deviations away from the mean of each break.
I would like to add as a row underneath the equivalent non scaled raw number that corresponds to each.
Tried:
diamonds %>%
ggplot(aes(scale(price) %>% as.vector)) +
geom_density() +
xlim(-3, 3) +
facet_wrap(vars(cut)) +
geom_text(aes(label = price))
Gives:
Error: geom_text requires the following missing aesthetics: y
My primary question is how can I add the raw values underneath -3:3 of each break? I don't want to change those breaks, I still want 6 breaks between -3:3.
Secondary question, how can I get -3 and 3 to actually show up in the chart? They have been trimmed.
[edit]
I've been trying to make it work with geom_text but keep hitting errors:
diamonds %>%
ggplot(aes(x = scale(price) %>% as.vector)) +
geom_density() +
xlim(-3, 3) +
facet_wrap(vars(cut)) +
geom_text(label = price)
Error in layer(data = data, mapping = mapping, stat = stat, geom = GeomText, :
object 'price' not found
I then tried changing my call to geom_text()
geom_text(data = diamonds, aes(price), label = price)
This results in the same error message.
You can make a custom labeling function for your axis. This takes each label on the axis and performs a custom transform for you. In your case you could paste the z score, a line break, and the z-score times the standard deviation plus the mean. Because of the distribution of prices in the diamonds data set, this means that z scores below about -1 represent negative prices. This may not be a problem in your own data. For clarity I have drawn in a vertical line representing $0
labeller <- function(x) {
paste0(x,"\n", scales::dollar(sd(diamonds$price) * x + mean(diamonds$price)))
}
diamonds %>%
ggplot(aes(scale(price) %>% as.vector)) +
geom_density() +
geom_vline(aes(xintercept = -0.98580251364833), linetype = 2) +
facet_wrap(vars(cut)) +
scale_x_continuous(label = labeller, limits = c(-3, 3)) +
xlab("price")
We can use the sec_axis functionality in scale_x_continuous. To use this functionality we need to manually scale your data. This will add a secondary axis at the top of the plot, not underneath. So it's not quite exactly what you're looking for.
library(tidyverse)
# manually scale the data
mean_price <- mean(diamonds$price)
sd_price <- sd(diamonds$price)
diamonds$price_scaled <- (diamonds$price - mean_price) / sd_price
# make the plot
ggplot(diamonds, aes(price_scaled))+
geom_density()+
facet_wrap(~cut)+
scale_x_continuous(sec.axis = sec_axis(~ mean_price + (sd_price * .)),
limits = c(-3, 4), breaks = -3:3)
You could cheat a bit by passing some dummy data to geom_text:
geom_text(data = tibble(label = round(((-3:3) * sd_price) + mean_price),
y = -0.25,
x = -3:3),
aes(x, y, label = label))
I have a long format dataset with 3 variables. Im plotting two of the variables and faceting by the other one, using ggplot2. I'd like to plot the standard error bars of the observations from each facet too, but I've got no idea how. Anyone knows?
Here´s a picture of what i've got. I'd like to have the standard error bars on each facet. Thanks!!
Edit: here's some example data and the plot.
data <- data.frame(rep(c("1","2","3","4","5","6","7","8","9","10",
"11","12","13","14","15","16","17","18","19","20",
"21","22","23","24","25","26","27","28","29","30",
"31","32"), 2),
rep(c("a","b","c","d","e","f","g","h","i","j","k","l"), 32),
rnorm(n = 384))
colnames(data) <- c("estado","sector","VA")
ggplot(data, aes(x = estado, y = VA, col = sector)) +
facet_grid(.~sector) +
geom_point()
If all you want is the mean & standard error bar associated with each "estado"-"sector" combination, you can leave ggplot to do all the work, by replacing the geom_point() line with stat_summary():
ggplot(data,
aes(x = estado, y = VA, col = sector)) +
facet_grid(. ~ sector) +
stat_summary(fun.data = mean_se)
See ?mean_se from the ggplot2 package for more details on the function. The default parameter option gives you the mean as well as the range for 1 standard error above & below the mean.
If you want to show the original points, just add back the geom_point() line. (Though I think the plot would be rather cluttered for the reader, in that case...)
Maybe you could try something like below?
set.seed(1)
library(dplyr)
dat = data.frame(estado = factor(rep(1:32, 2)),
sector = rep(letters[1:12], 32),
VA = rnorm(384))
se = function(x) {
sd(x)/sqrt(length(x))
}
dat_sum = dat %>% group_by(estado, sector) %>%
summarise(mu = mean(VA), se = se(VA))
dat_plot = full_join(dat, dat_sum)
ggplot(dat_plot, aes(estado, y = VA, color = sector)) +
geom_jitter() +
geom_errorbar(aes(estado, y = mu, color = sector,
ymin = mu - se, ymax = mu + se)) +
facet_grid(.~sector)
In R with ggplot, I want to create a spaghetti plot (2 quantitative variables) grouped by a third variable to specify line color. Secondly, I want to aggregate that grouping variable with the line type or width.
Here's an example using the airquality dataset. I want the line's color to represent the month, and the summer months to have a different line width from non-summer months.
First, I created an indicator variable for the aggregated groups:
airquality$Summer <- with(airquality, ifelse(Month >= 6 & Month < 9, 1, 0))
I would like something like this, but with differing line widths:
However, this fails:
library(ggplot2)
ggplot(data = airquality, aes(x=Wind, y = Temp, color = as.factor(Month), group = Summer)) +
geom_point() +
geom_line(linetype = as.factor(Summer))
This also fails (specifying airquality$Summer):
ggplot(data = airquality, aes(x=Wind, y = Temp,
color = as.factor(Month), group = airquality$Summer)) +
geom_point() +
geom_line(linetype = as.factor(airquality$Summer))
I attempted this solution, but get another error:
lty <- setNames(c(0, 1), levels(airquality$Summer))
ggplot(data = airquality, aes(x=Wind, y = Temp,
color = as.factor(Month), group = airquality$Summer)) +
geom_point() +
geom_line(linetype = as.factor(airquality$Summer)) +
scale_linetype_manual(values = lty)
Any ideas?
EDIT:
My actual data show very clear trends, and I want to differentiate the top line from all the others below. My goal is to convince people they should make more than just the minimum payment on their student loans:
You just need to change the group to Month and putlinetype in aes:
ggplot(data = airquality, aes(x=Wind, y = Temp, color = as.factor(Month), group = Month)) +
geom_point() +
geom_line(aes(linetype = factor(Summer)))
If you want to specify the linetype you can use a few methods. Here is one way:
lineT <- c("solid", "dotdash")
names(lineT) <- c("1","0")
ggplot(data = airquality, aes(x=Wind, y = Temp, color = as.factor(Month))) +
geom_point() +
geom_line(aes(linetype = factor(Summer))) +
scale_linetype_manual(values = lineT)
I have this data set and I want to fill the area under each line. However I get an error saying:
Error: stat_bin() must not be used with a y aesthetic.
Additionally, I need to use alpha value for transparency. Any suggestions?
library(reshape2)
library(ggplot2)
dat <- data.frame(
a = rnorm(12, mean = 2, sd = 1),
b = rnorm(12, mean = 4, sd = 2),
month = c("JAN","FEB","MAR",'APR',"MAY","JUN","JUL","AUG","SEP","OCT","NOV","DEC"))
dat$month <- factor(dat$month,
levels = c("JAN","FEB","MAR",'APR',"MAY","JUN","JUL","AUG","SEP","OCT","NOV","DEC"),
ordered = TRUE)
dat <- melt(dat, id="month")
ggplot(data = dat, aes(x = month, y = value, colour = variable)) +
geom_line() +
geom_area(stat ="bin")
I want to fill the area under each line
This means we will need to specify the fill aesthetic.
I get an error saying "Error: stat_bin() must not be used with a y aesthetic."
This means we will need to delete your stat ="bin" code.
Additionally, I need to use alpha value for transparency.
This means we need to put alpha = <some value> in the geom_area layer.
Two other things: (1) since you have a factor on the x-axis, we need to specify a grouping so ggplot knows which points to connect. In this case we can use variable as the grouper. (2) The default "position" of geom_area is to stack the areas rather than overlap them. Because you ask about transparency I assume you want them overlapping, so we need to specify position = 'identity'.
ggplot(data = dat, aes(x = month, y = value, colour = variable)) +
geom_line() +
geom_area(aes(fill = variable, group = variable),
alpha = 0.5, position = 'identity')
To get lines across categorical variables, use the group aesthetic:
ggplot(data = dat, aes(x = month, y = value, colour = variable, group = variable)) +
#geom_line(position = 'stack') + # redundant, but this is where lines are drawn
geom_area(alpha = 0.5)
To change the color inside, use the fill aesthetic.