I have two lines that I wish to label with ggrepel.
I require a second, separate dataframe to have the label information.
The error I get is:
Error in `geom_label_repel()`:
! Problem while computing aesthetics.
ℹ Error occurred in the 2nd layer.
Caused by error in `FUN()`:
! object 'comp' not found
Run `rlang::last_error()` to see where the error occurred.
I thought when I assign new data to geom_label_repel(data = comp_label_df the old dataframe, df, would not be referenced. That is why I am puzzled by the error that refers to comp which is a column header in df.
How do I label the two lines at x = "2023-01-08"?
library("tidyverse")
library("ggrepel")
d1 <- as.Date("2023-01-07")
d2 <- as.Date("2023-01-08")
d3 <- as.Date("2023-01-09")
comp_label_df <- tibble(
x = c(d2, d2),
one = c(1.3, 2.5),
label = c("Line 1", "Line 2")
)
df <- tibble(
comp = c("A", "B", "A", "B", "A", "B"),
one = c(1, 1.1, 1.3, 2.5, 5, 8),
date = c(d1, d1, d2, d2, d3, d3)
)
# OK
ggplot(data = df, aes(x = date, y = one, group = comp)) +
geom_line()
# ERROR
ggplot(data = df, aes(x = date, y = one, group = comp)) +
geom_line() +
geom_label_repel(data = comp_label_df, aes(x = x, y = one, label = label))
aesthetics are passed on to lower layers if defined at the parent level. Since comp is not present in comp_label_df it returns an error. Specify aesthetics at geom level.
library(ggplot2)
library(ggrepel)
ggplot(data = df) +
geom_line(aes(x = date, y = one, group = comp)) +
geom_label_repel(data = comp_label_df, aes(x = x, y = one, label = label))
Related
The following works fine:
my_df <- data.frame(x_val = 1:10, y_val = sample(1:20,10),
labels = sample(c("a", "b"), 10, replace = T))
ggplot(data = my_df, aes(x = x_val, y = y_val)) + geom_line()
but if I chance x_val to factor, I am getting blank plot and message:
my_df <- data.frame(x_val = 1:10, y_val = sample(1:20,10),
labels = sample(c("a", "b"), 10, replace = T))
my_df$x_val <- as.factor(my_df$x_val)
ggplot(data = my_df, aes(x = x_val, y = y_val)) + geom_line()
message:
geom_path: Each group consists of only one observation. Do you
need to adjust the group aesthetic?
I can obviously drop factor conversion, but I need it in order to replace labels of x axis with scale_x_discrete(breaks = 1:10,labels= my_df$labels). Here is where I borrowed it link
Any thoughts?
Can you just leave x_val as numeric and use scale_x_continuous(breaks = 1:10,labels= my_df$labels) instead?
I know similar questions asked before but my question is different. Consider data points data1 that have colors with respect to x and y coordinates and I plot it with ggplot
x = 1:100
y = 1:100
d = expand.grid(x,y)
data1 <- data.frame(
xval = d$Var1,
yval = d$Var2,
col = d$Var1+d$Var2)
data2 <- data.frame(
xnew = c(1.5, 90.5),
ynew = c(95.5, 4))
ggplot(data1, aes(xval, yval, colour = col)) + geom_point()
But I want the last line don't plot anything and I want plot data2 points with respect to colors of data1. for example I paint what I want to plot for data2 :
I changed the last line to:
ggplot(data1, aes(xval, yval, colour = col)) +
geom_point(data = data2, aes(x = xnew, y = ynew))
Now I expect that ggplot draw just 2 points of data2, but I have an Error:
Don't know how to automatically pick scale for object of type function. Defaulting to continuous.
Error: Column colour must be a 1d atomic vector or a list
The problem is, that there is no mapping between col out of data1 and your data2.
Please try the following:
ggplot(data2, aes(x = xnew, y = ynew, colour = xnew)) + geom_point() +
scale_fill_gradientn(colours=c(2,1),
values = range(data1$xval),
rescaler = function(x,...) x,
oob = identity)
Code to reproduce:
myDat <- data.frame(Event = rep(c("Arrival", "Departure"), 3),
AtNode = c("StationA", "StationA", "Track", "Track", "StationB", "StationB"),
Lane = c("Lane1", "Lane1", "Lane2", "Lane2", "Lane1", "Lane1"),
atTime = c(10, 12, 18, 20, 34, 36),
Type = c("Station", "Station", "Track", "Track", "Station", "Station"),
Train = 1 )
ggplot(data =myDat, aes(x = atTime, y=factor(AtNode, levels = unique(paste(myDat[order(myDat$atTime),"AtNode"]))), group = Train, colour = Lane ))+
geom_point(data = myDat)+
geom_path(data = myDat[which(!grepl(pattern = "Track", myDat$Type)),])
Now i need to project the two green points (Y = "Track") on the orange line and color the line between the projected points the same color as the points.
Expected result: (without the points (Y ="Track")
Thanks in advance for every hint or trick!
Cheers
I don't think your output is the right way of showing what you want. You have factors on your y-axis, which means it ranges between 1 and 3.
Therefore, projecting a line there means nothing in terms of y-axis values.
For me, the correct way of showing your data would be like this
ggplot(data =myDat,
aes(x = atTime, y=factor(AtNode, levels = unique(paste(myDat[order(myDat$atTime),"AtNode"]))),
group = AtNode, colour = Lane ))+
geom_point()+
geom_line() +
labs(y = 'AtNode')
However, to do it how you asked, you can do some simple trigonometry to project your line segment
x1 = 1 + tan(asin(2/sqrt(484)))*6 #y projection given x = 18
x2 = 1 + tan(asin(2/sqrt(484)))*8 #y projection given x = 20
foo = data.frame(x = c(18,20), y = c(x1, x2), Lane = "Lane2")
ggplot(data = myDat, aes(x = atTime, y=factor(AtNode, levels = unique(paste(myDat[order(myDat$atTime),"AtNode"]))), group = 1, colour = Lane ))+
geom_path(data = myDat[which(!grepl(pattern = "Track", myDat$Type)),]) +
geom_line(data = foo, aes(x = x, y = y, color = Lane), size = 1) +
scale_y_discrete(drop = FALSE)
I don't think there is a quick solution to this, but you could do something like this:
myDat$AtNode <- factor(myDat$AtNode, levels = unique(paste(myDat[order(myDat$atTime),"AtNode"]))) #Generate factor here so we can use in imputation calculation
impute_rows <- which(myDat$Type == "Track") #Select rows to impute
slope_df <- myDat[impute_rows + c(-1,1), ] #Select rows before and after imputation to calculate slope
line <- lm(as.numeric(AtNode) ~ atTime, data = slope_df) #Get slope of line so we can do the calculations
df <- data.frame(x = myDat[impute_rows, "atTime"], y = myDat[impute_rows, "atTime"]*line$coefficients[["atTime"]] + line$coefficients[["(Intercept)"]], Lane = myDat[impute_rows,"Lane"], Train = myDat[impute_rows,"Train"])
ggplot(data =myDat, aes(x = atTime, y=AtNode, group = Train, colour = Lane ))+
geom_path(data = myDat[which(!grepl(pattern = "Track", myDat$Type)),]) +
geom_path(data = df, aes(x = x, y = y), size = 2) +
scale_y_discrete(drop = FALSE)
The idea is as follows:
Identify the rows you want to impute: which()
Identify the rows before and after the ones to impute slope_df
Using the rows before and after the desired values to impute generate equation of line you want to impute along (using the slope_df)
Generate data based on the line df <- data.frame(...)
Note that you also need the scale_y_discrete(drop = FALSE) so that the Track level isn't removed from the plot.
I have a matrix (pred_matrix, dim = 1e6, 250), the rows are "pixelstacks" of 250 NDVI values of a Landsat scene, from which i did a "fuzzy cmeans" classification witch 6 centers (classes), stored in the list results. I want now to plot a random subset of each class of the 1e6 rows. This is my quick and dirty code so far:
random_index <- floor(runif(10000, 1, 1e6+1))
random_cluster <- results[[6]]$cluster[random_index]
random_pred_matrix <- pred_matrix[random_index, ]
dates_subse_after_pred <- rdn_num[rm_na_pred_df]
random_res <- cbind(random_pred_matrix, random_cluster)
random_res <- t(random_res)
random_res <- cbind(c(dates_subse_after_pred, 1), random_res)
df_1 <- data.frame(random_res[1:250,c(TRUE, random_cluster==1)])
df_2 <- data.frame(random_res[1:250,c(TRUE, random_cluster==2)])
df_3 <- data.frame(random_res[1:250,c(TRUE, random_cluster==3)])
df_4 <- data.frame(random_res[1:250,c(TRUE, random_cluster==4)])
df_5 <- data.frame(random_res[1:250,c(TRUE, random_cluster==5)])
df_6 <- data.frame(random_res[1:250,c(TRUE, random_cluster==6)])
df_1.long <- melt(df_1, id.vars = 1)
df_1.long$X1 <- as.Date(df_1.long$X1)
df_2.long <- melt(df_2, id.vars = 1)
df_2.long$X1 <- as.Date(df_2.long$X1)
df_3.long <- melt(df_3, id.vars = 1)
df_3.long$X1 <- as.Date(df_3.long$X1)
df_4.long <- melt(df_4, id.vars = 1)
df_4.long$X1 <- as.Date(df_4.long$X1)
df_5.long <- melt(df_5, id.vars = 1)
df_5.long$X1 <- as.Date(df_5.long$X1)
df_6.long <- melt(df_6, id.vars = 1)
df_6.long$X1 <- as.Date(df_6.long$X1)
ggplot(df_1.long) +
geom_line( aes(x = X1, y= value, group = variable), color = "lightblue")
ggplot(df_2.long) +
geom_line( aes(x = X1, y= value, group = variable), color = "blue")
ggplot(df_3.long) +
geom_line( aes(x = X1, y= value, group = variable), color = "lightgreen")
ggplot(df_4.long) +
geom_line( aes(x = X1, y= value, group = variable), color = "green")
ggplot(df_5.long) +
geom_line( aes(x = X1, y= value, group = variable), color = "pink")
ggplot(df_6.long) +
geom_line( aes(x = X1, y= value, group = variable), color = "red")
After this i have just hit 6 times the export button in rstudio and inserted it all in a word document...
Is there a way to do this in a loop? Or even produce a final pdf containing the 6 plots?
Separate file
I think what you are after is having the following six times in your code.
ggsave("filename.png", # or pdf if you like
plot = last_plot(), # or give ggplot object name as in myPlot,
width = 5, height = 5,
units = "in", # other options c("in", "cm", "mm"),
dpi = 300)
For example,
library(ggplot2)
p1 <- ggplot(df_1.long) +
geom_line( aes(x = X1, y= value, group = variable),
color = "lightblue")
ggsave("df1.png", plot = p1, dpi = 300)
All in one
If you want all the six files in one pdf, then first do
pdf("file_name.pdf")
# do your ggplots here
p1
p2
p6
dev.off()
If you are using Rstudio I would recommend writing your code in a Rmarkdown file and then exporting to pdf directly.
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!