I am trying to make a pie chart in R that displays the percent of each section as a label. I was following this tutorial, (https://www.geeksforgeeks.org/r-pie-charts/). I modified it a little bit to make it similar to the data frame I am actually working with. When I try and run my code I get the error, "Error in edges * dx[i] : non-numeric argument to binary operator". It seems the error is coming from legend(), when I comment it out I get no error. Where am I going wrong?
Thanks in advance for any help.
df <- data.frame( geeks = c(23, 56, 20, 63),
labels = c("Mumbai", "Pune", "Chennai", "Bangalore"))
df <- mutate(df, percent = round(df$geeks/sum(df$geeks)*100, 1))
df_pie <- pie(df$geeks,
round(df$percent,1),
main = "City pie chart",
col = rainbow(length(df$geeks)),
legend("topright", c(df$labels),
cex = 0.5, fill = rainbow(length(df$geeks))))
legend is a stand-alone function, not an argument to pie
pie(df$geeks,
round(df$percent,1),
main = "City pie chart",
col = rainbow(length(df$geeks)))
legend("topright", c(df$labels), fill = rainbow(length(df$geeks)))
You might get a nicer look with ggplot though:
library(ggplot)
ggplot(df, aes(x = 1, y = percent, fill = labels)) +
geom_col() +
coord_polar(theta = "y") +
geom_text(aes(label = paste(percent, "%")),
position = position_stack(vjust = 0.5),
size = 8) +
theme_void(base_size = 20) +
scale_fill_brewer(name = NULL, palette = "Pastel2")
Related
I`ve got an issue with colouring a ggplot2 histogram.
R-Junk
ggplot(Hospital, aes(x=BodyTemperature)) +
geom_histogram(aes(fill = factor(BodyTemperature))) +
scale_x_continuous(breaks = seq(0, 100, by = 10)) +
ylab("prevalence") +
xlab("BodyTemperature") +
ggtitle("Temperature vs. prevalence")
So the histogram should plot the information (x-axis), that as higher the temperature gets, the worse it is. So for example „temperature“ at 36°C should be green, 38°C yellow, 40° red - going from left to right on the x-axis.
Y-Axis should provide how often these temperatures ocures in the Patientdata of the Hospital. The Data "BodyTemperature" is a list of 200+ Data like: "35.3" or "37.4" etc.
How can this chunk be fixed to provide the color changes? For a non-ggplot version ive already written this r-junk positiv:
```{r, fig.width=8}
color1 <- rep(brewer.pal(1, "Greens"))
color2 <- rep("#57c4fa", 0)
color3 <- brewer.pal(8, "Reds")
hist(Hospital$BodyTemperature[-357],
breaks = seq(from = 0, to = 100, by = 10),
main = "Temperature vs. prevalence",
ylab = "prevalence",
xlab = "Temperature",
col = c(color1, color2, color3))
```
The key is to make sure the bin intervals used for the fill scale match those used for the x axis. You can do this by setting the binwidth argument to geom_histogram(), and using ggplot2::cut_width() to break BodyTemperature into the same bins for the fill scale:
set.seed(13)
library(ggplot2)
# example data
Hospital <- data.frame(BodyTemperature = 36.5 + rchisq(100, 2))
ggplot(Hospital, aes(BodyTemperature)) +
geom_histogram(
aes(fill = cut_width(BodyTemperature, width = 1)),
binwidth = 1,
show.legend = FALSE
) +
scale_fill_brewer(palette = "RdYlGn", direction = -1) +
labs(
title = "Temperature vs. Prevalence",
x = "Body Temperature (°C)",
y = "Prevalence"
) +
theme_minimal()
Created on 2022-10-24 with reprex v2.0.2
I would like to set x axis on a heatmap ggtree.
This is my code
ggtree(working_tree,open.angle=15, size=0.1) %<+% avian %<+% color +
aes(color = I(colour)) +
geom_tippoint(size = 2,) +
geom_tiplab(size = 3, colour = "black") +
theme_tree2()
# I want to rotate the x axis and get the positive number
p1 <- revts(p) + scale_x_continuous(labels = abs)
h1 <- gheatmap(p1, landuse,
offset = 15, width = 0.05, font.size = 3, colnames_position = "top", colnames_angle = 0,
colnames_offset_y = 0, hjust = 0) +
scale_fill_manual(breaks = c("Forest", "Jungle rubber", "Rubber", "Oil palm"),
values = c("#458B00", "#76EE00", "#1874CD", "#00BFFF"), name = "Land use system",
na.value = "white")
, and I got this picture
The problem is that when I showed the heatmap, the x axis automatically changes the range itself from 0 to 60. However, the range I want is from 0 to 80.
Does anyone know how to do this or have any experiences for this?
Updated
I already solved the case by using the function scale_x_continous like this
scale_x_continuous(breaks = seq(-80,0,20), labels = abs(seq(-80,0,20)))
For anyone interested in geological timescale in R, I suggest to use the package deeptime
I want to divide the y axis for the attached figure to take part with a score <25 occupies the majority of the figure while the remaining represent a minor upper part.
I browsed that and I am aware that I should use scale_y_discrete(limits .I used this p<- p+scale_y_continuous(breaks = 1:20, labels = c(1:20,"//",40:100)) but it doesn't work yet.
I used the attached data and this is my code
Code
p<-ggscatter(data, x = "Year" , y = "Score" ,
color = "grey", shape = 21, size = 3, # Points color, shape and size
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
add = "loess", #reg.line
conf.int = T,
cor.coef = F, cor.method = "pearson",
xlab = "Year" , ylab= "Score")
p<-p+ coord_cartesian(xlim = c(1980, 2020));p
Here is as close as I could get getting a fake axis break and resizing the upper area of the plot. I still think it's a bad idea and if this were my plot I'd much prefer a more straightforward axis transform.
First, we'd need a function that generates a transform that squeezes all values above some threshold:
library(ggplot2)
library(scales)
# Define new transform
my_transform <- function(threshold = 25, squeeze_factor = 10) {
force(threshold)
force(squeeze_factor)
my_transform <- trans_new(
name = "trans_squeeze",
transform = function(x) {
ifelse(x > threshold,
((x - threshold) * (1 / squeeze_factor)) + threshold,
x)
},
inverse = function(x) {
ifelse(x > threshold,
((x - threshold) * squeeze_factor) + threshold,
x)
}
)
return(my_transform)
}
Next we apply that transformation to the y-axis and add a fake axis break. I've used vanilla ggplot2 code as I find the ggscatter() approach confusing.
ggplot(data, aes(Year, Score)) +
geom_point(color = "grey", shape = 21, size = 3) +
geom_smooth(method = "loess", fill = "lightgray") +
# Add fake axis lines
annotate("segment", x = -Inf, xend = -Inf,
y = c(-Inf, Inf), yend = c(24.5, 25.5)) +
# Apply transform to y-axis
scale_y_continuous(trans = my_transform(25, 10),
breaks = seq(0, 80, by = 10)) +
scale_x_continuous(limits = c(1980, 2020), oob = oob_keep) +
theme_classic() +
# Turn real y-axis line off
theme(axis.line.y = element_blank())
You might find it informative to read Hadley Wickham's view on discontinuous axes. People sometimes mock weird y-axes.
I am fairly new to vegan and ggplot, I have drawn a species diversity plot in vegan. Ggplot has better graph so I was wondering if these codes could be modified to ggplot code.
Any help would be greatly appreciated. I am using bray in vegan.
library(vegan)
library(mass)
data <- read.table("data.txt", header = T)
attach(data)
rownames(data) <- c("TCI1", "TCI2", "TCI3", "TCII1", "TCII2", "TCII3", "TCIII1", "TCIII2", "TCIII3", "TCIV1", "TCIV2", "TCIV3",
"NCI1", "NCI2", "NCI3", "NCII1", "NCII2", "NCII3", "NCIII1", "NCIII2", "NCIII3", "NCIV1", "NCIV2", "NCIV3","TFI1", "TFI2", "TFI3", "TFII1", "TFII2", "TFII3", "TFIII1", "TFIII2", "TFIII3", "TFIV1", "TFIV2", "TFIV3",
"NFI1", "NFI2", "NFI3", "NFII1", "NFII2", "NFII3", "NFIII1", "NFIII2", "NFIII3", "NFIV1", "NFIV2", "NFIV3")
bcdist <- vegdist(data, "bray")
bcmds <- isoMDS(bcdist, k = 2)
plot(bcmds$points, type = "n", xlab = "", ylab = "")
text(bcmds$points, dimnames(data)[[1]])
You can indeed create a plot that looks like the imgur image. First I created some made-up data for your weeds. Then I called ggplot2 and put the weed names at the points, but made the points transparent.
x <- seq(from = -1, to = 1, .025)
df <- data.frame(valuesX = sample(x, size = 48, replace = TRUE),
valuesY = sample(x, size = 48, replace = TRUE),
seeds = c("TCI1", "TCI2", "TCI3", "TCII1", "TCII2", "TCII3", "TCIII1", "TCIII2", "TCIII3", "TCIV1", "TCIV2", "TCIV3",
"NCI1", "NCI2", "NCI3", "NCII1", "NCII2", "NCII3", "NCIII1", "NCIII2", "NCIII3", "NCIV1", "NCIV2", "NCIV3","TFI1", "TFI2", "TFI3", "TFII1", "TFII2", "TFII3", "TFIII1", "TFIII2", "TFIII3", "TFIV1", "TFIV2", "TFIV3",
"NFI1", "NFI2", "NFI3", "NFII1", "NFII2", "NFII3", "NFIII1", "NFIII2", "NFIII3", "NFIV1", "NFIV2", "NFIV3")
)
ggplot(df, aes(x = valuesX, y = valuesY)) +
geom_point(colour = "transparent") +
geom_text(data = df, aes(label = seeds), hjust = 1.5) +
theme_bw() +
labs(x = "Your axis label", y = "", title = "Weed Distribution") +
theme(axis.ticks= element_blank()) +
theme(plot.title = element_text(face = "bold", size = 12))
You can adjust all the elements of the plot as you see fit.
I'm having a difficult time understanding how factor labels interact with legends. How do I set the order of levels so that the ggplot2 system creates a legend that I envision?
In the below code, the plot associate the Reds with the negative side of the interval and the blues with the positive.
require(ggplot2)
require(RColorBrewer)
set.seed(1492) #discovery!
sample = data.frame(x = c(1:20), y = 1, obs = runif(20, -150, 150))
the.breaks = seq(-100, 100, by = 20)
sample$interval = factor(findInterval(sample$obs, vec = the.breaks, all.inside = TRUE),
labels = the.breaks, levels = c(1:length(the.breaks)))
pal = rev(brewer.pal(11, "RdBu"))
p = ggplot(sample, aes(x, y, colour = interval))
p = p + geom_point(size = 10)
p = p + scale_colour_manual(values = pal, limits = the.breaks, labels = the.breaks)
p = p + guides(colour = guide_legend(override.aes = list(size = 3, shape = 19)))
p
This works fine, but I really don't like the
pal = rev(brewer.pal(11, "RdBu"))
statement - seems inelegant. I'd like to be able to replace the use of scale_colour_manual with something like
p = p + scale_colour_brewer(palette="RdBu", type="div", limits = the.breaks, labels = the.breaks)
but when I do, the Reds get associated with the positive end.
You can order your factor levels in any way you like, this should help you get them the right colours. Try this explanation or any others out there on ordering factor levels. Note that the opts command in the blog post is out-dated.
Also check out this question.
And this one as well.