I am trying to reproduce something similar to this map using ggplot2:
This is what I've done so far:
load("mapdata.Rdata")
> ls() #2 datasets: "depth" for basemap (geom_contour) and "data" is use to construct geom_bin2d
[1] "data" "depth"
> head(data)
latitude longitude GRcounts
740 67.20000 -57.83333 0
741 67.11667 -57.80000 0
742 67.10000 -57.93333 1
743 67.06667 -57.80000 0
751 67.15000 -58.15000 0
762 67.18333 -58.15000 0
ggplot(data=data,aes(x =longitude, y =latitude))
+theme_bw()
+ stat_bin2d(binwidth = c(0.5, 0.5))
+geom_contour(data=depth,aes(lon, lat, z=dn),colour = "black", bins=5)
+ xlim(c(-67,-56)) + ylim(c(65,71))
Which gives me this map:
The last step is to display over my geom_bin2d circles with size proportional to the sum of the counts (Grcounts) within each bin.
Any tips on how to do so in ggplot (preferably) would be much appreciated.
follow-up question: alignment mismatch between stat_bin2d and stat_summary2d when using facet_wrap
When I run the following code on the diamonds data set, there are no apparent problem: However if I do run the same code on my data, I do get misalignment problems. Any thoughts on what may cause this problem?
p<-ggplot(diamonds,aes(x =carat, y =price,colour=cut))+
stat_summary2d(fun=sum,aes(z=depth,group=cut),bins=10)
p+facet_wrap(~cut)
df <- ggplot_build(p)$data[[1]]
summary(df)##now 5 groups, 1 panel
df$x<-with(df,(xmin+xmax)/2)
df$y<-with(df,(ymin+ymax)/2)
plot1<-ggplot(diamonds,aes(carat, price))+ stat_bin2d(bins=10)
plot1+geom_point(data=df,aes(x,y,size=value,group=group),color="red",shape=1)+facet_wrap(~group)
This is my Rcode and plot:
p<-ggplot(dat,aes(x =longitude, y =latitude,colour=SizeClass))+
stat_summary2d(fun=sum,aes(z=GRcounts,group=SizeClass),bins=10)
p+facet_wrap(~SizeClass)
df <- ggplot_build(p)$data[[1]]
summary(df)##now 4 groups, 1 panel
df$x<-with(df,(xmin+xmax)/2)
df$y<-with(df,(ymin+ymax)/2)
plot1<-ggplot(dat,aes(longitude, latitude))+ stat_bin2d(bins=10)
plot1+geom_point(data=df,aes(x,y,size=value,group=group),color="red",shape=1)+facet_wrap(~group)
> head(dat[c(7,8,14,21)])###mydata
latitude longitude GRcounts SizeClass
742 67.10000 -57.93333 1 (100,150)
784 67.21667 -57.95000 1 (100,150)
756 67.11667 -57.80000 1 (<100)
1233 68.80000 -59.55000 2 (100,150)
1266 68.68333 -59.60000 2 (100,150)
1288 68.66667 -59.65000 1 (100,150)
My data set can be downloaded here: data
As your dataset doesn't work on my computer will use diamonds dataset as example.
Make new plot of your data with stat_summary2d() and set z= as argument you want to sum (in your case GRcounts) and provide fun=sum to sum those values. Store it as some object.
p<-ggplot(diamonds,aes(carat,price))+stat_summary2d(fun=sum,aes(z=depth))
Use function ggplot_build() to get data used for plot. Coordinates of rectangles are in columns xmin, xmax, ymin and ymax and sum are in column value.
df <- ggplot_build(p)$data[[1]]
head(df)
fill xbin ybin value ymax ymin yint xmax xmin xint PANEL group
1 #55B1F7 [0.2,0.36] [326,943] 641318.2 942.5667 326.0000 1 0.3603333 0.2000000 1 1 1
2 #1A3955 [0.2,0.36] (943,1.56e+03] 75585.5 1559.1333 942.5667 2 0.3603333 0.2000000 1 1 1
3 #132B43 [0.2,0.36] (1.56e+03,2.18e+03] 415.8 2175.7000 1559.1333 3 0.3603333 0.2000000 1 1 1
4 #132B43 [0.2,0.36] (2.18e+03,2.79e+03] 304.4 2792.2667 2175.7000 4 0.3603333 0.2000000 1 1 1
5 #244D71 (0.36,0.521] [326,943] 179486.8 942.5667 326.0000 1 0.5206667 0.3603333 2 1 1
6 #2D5F8A (0.36,0.521] (943,1.56e+03] 271688.9 1559.1333 942.5667 2 0.5206667 0.3603333 2 1 1
For the points calculate x and y positions as mean of xmin,xmax and ymin,ymax.
df$x<-with(df,(xmin+xmax)/2)
df$y<-with(df,(ymin+ymax)/2)
Use this new data frame to add points to your original plot with stat_bin2d().
ggplot(diamonds,aes(carat,price))+stat_bin2d()+
geom_point(data=df,aes(x=x,y=y,size=value),color="red",shape=1)
UPDATE - solution with facetting
To use facet_wrap() and combine stat_bin2d() and points you should use some workaround as there seems to be some problem.
First, create two plots - one for sums with stat_summary2d() and one for counts with stat_bin2d(). Both plots should be faceted.
plot1 <- ggplot(dat,aes(x =longitude, y =latitude))+
stat_summary2d(fun=sum,aes(z=GRcounts),bins=10)+facet_wrap(~SizeClass)
plot2 <- ggplot(dat,aes(longitude, latitude))+ stat_bin2d(bins=10)+
facet_wrap(~SizeClass)
Now extract data from both plots using ggplot_build() and store them as objects. For the sums data frame (df1) calculated x and y coordinates as in example above.
df1 <- ggplot_build(plot1)$data[[1]]
df1$x<-with(df,(xmin+xmax)/2)
df1$y<-with(df,(ymin+ymax)/2)
df2<-ggplot_build(plot2)$data[[1]]
Now plot your data using those new data frames - df1 for points and df2 for rectangles. With geom_rect() you will get rectangles which fill= depend on count. For faceting use column PANEL.
ggplot()+geom_rect(data=df2,aes(xmin=xmin,xmax=xmax,
ymin=ymin,ymax=ymax,fill=count))+
geom_point(data=df1,aes(x=x,y=y,size=value),shape=1,color="red")+
facet_wrap(~PANEL)
Related
I need some help. Here is my data which i want to plot. I want to keep $path.ID on y axis and numerics of all other columns added stepwise. this is a subset of very large dataset so i want to pathID labels attached to each line. and also the values of the other columns with each point if possible.
head(table)
Path.ID sc st rc rt
<chr> <dbl> <dbl> <dbl> <dbl>
1 map00230 1 12 5 52
2 map00940 1 20 10 43
3 map01130 NA 15 8 34
4 map00983 NA 14 5 28
5 map00730 NA 5 3 26
6 map00982 NA 16 2 24
somewhat like this
Thank you
Here is the pseudo code.
library(tidyr)
library(dplyr)
library(ggplot2)
# convert your table into a long format - sorry I am more used to this type of data
table_long <- table %>% gather(x_axis, value, sc:rt)
# Plot with ggplot2
ggplot() +
# draw line
geom_line(data=table_long, aes(x=x_axis, y=value, group=Path.ID, color=Path.ID)) +
# draw label at the last x_axis in this case is **rt**
geom_label(data=table_long %>% filter(x_axis=="rt"),
aes(x=x_axis, y=value, label=Path.ID, fill=Path.ID),
color="#FFFFFF")
Note that with this code if a Path.ID doesn't have the rt value then it will not have any label
p<-ggplot() +
# draw line
geom_line(data=table_long, aes(x=x_axis, y=value, group=Path.ID, color=Path.ID)) +
geom_text(data=table_long %>% filter(x_axis=="rt"),
aes(x=x_axis, y=value, label=Path.ID),
color= "#050505", size = 3, check_overlap = TRUE)
p +labs(title= "title",x = "x-lable", y="y-label")
I had to use geom_text as i had large dataset and it gave me somewhat more clear graph
thank you #sinh it it helped a lot.
I am using the ..count.. transformation in geom_bar and get the warning
position_stack requires non-overlapping x intervals when some of my categories have few counts.
This is best explained using some mock data (my data involves direction and windspeed and I retain names relating to that)
#make data
set.seed(12345)
FF=rweibull(100,1.7,1)*20 #mock speeds
FF[FF>60]=59
dir=sample.int(10,size=100,replace=TRUE) # mock directions
#group into speed classes
FFcut=cut(FF,breaks=seq(0,60,by=20),ordered_result=TRUE,right=FALSE,drop=FALSE)
# stuff into data frame & plot
df=data.frame(dir=dir,grp=FFcut)
ggplot(data=df,aes(x=dir,y=(..count..)/sum(..count..),fill=grp)) + geom_bar()
This works fine, and the resulting plot shows the frequency of directions grouped according to speed. It is of relevance that the velocity class with the fewest counts (here "[40,60)") will have 5 counts.
However more velocity classes leads to a warning. For instance, with
FFcut=cut(FF,breaks=seq(0,60,by=15),ordered_result=TRUE,right=FALSE,drop=FALSE)
the velocity class with the fewest counts (now "[45,60)") will have only 3 counts and ggplot2 will warn that
position_stack requires non-overlapping x intervals
and the plot will show data in this category spread out along the x axis.
It seems that 5 is the minimum size for a group to have for this to work correctly.
I would appreciate knowing if this is a feature or a bug in stat_bin (which geom_bar is using) or if I am simply abusing geom_bar.
Also, any suggestions how to get around this would be appreciated.
Sincerely
This occurs because df$dir is numeric, so the ggplot object assumes a continuous x-axis, and aesthetic parameter group is based on the only known discrete variable (fill = grp).
As a result, when there simply aren't that many dir values in grp = [45,60), ggplot gets confused over how wide each bar should be. This becomes more visually obvious if we split the plot into different facets:
ggplot(data=df,
aes(x=dir,y=(..count..)/sum(..count..),
fill = grp)) +
geom_bar() +
facet_wrap(~ grp)
> for(l in levels(df$grp)) print(sort(unique(df$dir[df$grp == l])))
[1] 1 2 3 4 6 7 8 9 10
[1] 1 2 3 4 5 6 7 8 9 10
[1] 2 3 4 5 7 9 10
[1] 2 4 7
We can also check manually that the minimum difference between sorted df$dir values is 1 for the first three grp values, but 2 for the last one. The default bar width is thus wider.
The following solutions should all achieve the same result:
1. Explicitly specify the same bar width for all groups in geom_bar():
ggplot(data=df,
aes(x=dir,y=(..count..)/sum(..count..),
fill = grp)) +
geom_bar(width = 0.9)
2. Convert dir to a categorical variable before passing it to aes(x = ...):
ggplot(data=df,
aes(x=factor(dir), y=(..count..)/sum(..count..),
fill = grp)) +
geom_bar()
3. Specify that the group parameter should be based on both df$dir & df$grp:
ggplot(data=df,
aes(x=dir,
y=(..count..)/sum(..count..),
group = interaction(dir, grp),
fill = grp)) +
geom_bar()
This doesn't directly solve the issue, because I also don't get what's going on with the overlapping values, but it's a dplyr-powered workaround, and might turn out to be more flexible anyway.
Instead of relying on geom_bar to take the cut factor and give you shares via ..count../sum(..count..), you can easily enough just calculate those shares yourself up front, and then plot your bars. I personally like having this type of control over my data and exactly what I'm plotting.
First, I put dir and FF into a data frame/tbl_df, and cut FF. Then count lets me group the data by dir and grp and count up the number of observations for each combination of those two variables, then calculate the share of each n over the sum of n. I'm using geom_col, which is like geom_bar but when you have a y value in your aes.
library(tidyverse)
set.seed(12345)
FF <- rweibull(100,1.7,1) * 20 #mock speeds
FF[FF > 60] <- 59
dir <- sample.int(10, size = 100, replace = TRUE) # mock directions
shares <- tibble(dir = dir, FF = FF) %>%
mutate(grp = cut(FF, breaks = seq(0, 60, by = 15), ordered_result = T, right = F, drop = F)) %>%
count(dir, grp) %>%
mutate(share = n / sum(n))
shares
#> # A tibble: 29 x 4
#> dir grp n share
#> <int> <ord> <int> <dbl>
#> 1 1 [0,15) 3 0.03
#> 2 1 [15,30) 2 0.02
#> 3 2 [0,15) 4 0.04
#> 4 2 [15,30) 3 0.03
#> 5 2 [30,45) 1 0.01
#> 6 2 [45,60) 1 0.01
#> 7 3 [0,15) 6 0.06
#> 8 3 [15,30) 1 0.01
#> 9 3 [30,45) 2 0.02
#> 10 4 [0,15) 6 0.06
#> # ... with 19 more rows
ggplot(shares, aes(x = dir, y = share, fill = grp)) +
geom_col()
I am working with longitudinal data and assess the utilization of a policy over 13 months. In oder to get some barplots with the different months on my x-axis, I converted my data from wide Format to Long Format.
So now, my dataset looks like this
id month hours
1 1 13
1 2 16
1 3 20
2 1 0
2 2 0
2 3 10
I thought, after reshaping I could easily use my newly created "month" variable as a factor and plot some graphs. However, it does not work out and tells me it's a list or an atomic vector. Transforming it into a factor did not work out - I would desperately Need it as a factor.
Does anybody know how to turn it into a factor?
Thank you very much for your help!
EDIT.
The OP's graph code was posted in a comment. Here it is.
library(ggplot2)
ggplot(data, aes(x = hours, y = month)) + geom_density() + labs(title = 'Distribution of hours')
# Loading ggplot2
library(ggplot2)
# Placing example in dataframe
data <- read.table(text = "
id month hours
1 1 13
1 2 16
1 3 20
2 1 0
2 2 0
2 3 10
", header = TRUE)
# Converting month to factor
data$month <- factor(data$month, levels = 1:12, labels = 1:12)
# Plotting grouping by id
ggplot(data, aes(x = month, y = hours, group = id, color = factor(id))) + geom_line()
# Plotting hour density by month
ggplot(data, aes(hours, color = month)) + geom_density()
The problem seems to be in the aes. geom_density only needs a x value, if you think about it a little, y doesn't make sense. You want the density of the x values, so on the vertical axis the values will be the values of that density, not some other values present in the dataset.
First, read in the data.
Indirekte_long <- read.table(text = "
id month hours
1 1 13
1 2 16
1 3 20
2 1 0
2 2 0
2 3 10
", header = TRUE)
Now graph it.
library(ggplot2)
g <- ggplot(Indirekte_long, aes(hours))
g + geom_density() + labs(title = 'Distribution of hours')
Here is my simplified data :
company <-c(rep(c(rep("company1",4),rep("company2",4),rep("company3",4)),3))
product<-c(rep(c(rep(c("product1","product2","product3","product4"),3)),3))
week<-c( c(rep("w1",12),rep("w2",12),rep("w3",12)))
mydata<-data.frame(company=company,product=product,week=week)
mydata$rank<-c(rep(c(1,3,2,3,2,1,3,2,3,2,1,1),3))
mydata=mydata[mydata$company=="company1",]
And, R code I used :
ggplot(mydata,aes(x = week,fill = as.factor(rank))) +
geom_bar(position = "fill")+
scale_y_continuous(labels = percent_format())
In the bar plot, I want to label the percentage by week, by rank.
The problem is the fact that the data doesn't have percentage of rank. And the structure of this data is not suitable to having one.
(of course, the original data has much more observations than the example)
Is there anyone who can teach me How I can label the percentage in this graph ?
I'm not sure I understand why geom_text is not suitable. Here is an answer using it, but if you specify why is it not suitable, perhaps someone might come up with an answer you are looking for.
library(ggplot2)
library(plyr)
mydata = mydata[,c(3,4)] #drop unnecessary variables
data.m = melt(table(mydata)) #get counts and melt it
#calculate percentage:
m1 = ddply(data.m, .(week), summarize, ratio=value/sum(value))
#order data frame (needed to comply with percentage column):
m2 = data.m[order(data.m$week),]
#combine them:
mydf = data.frame(m2,ratio=m1$ratio)
Which gives us the following data structure. The ratio column contains the relative frequency of given rank within specified week (so one can see that rank == 3 is twice as abundant as the other two).
> mydf
week rank value ratio
1 w1 1 1 0.25
4 w1 2 1 0.25
7 w1 3 2 0.50
2 w2 1 1 0.25
5 w2 2 1 0.25
8 w2 3 2 0.50
3 w3 1 1 0.25
6 w3 2 1 0.25
9 w3 3 2 0.50
Next, we have to calculate the position of the percentage labels and plot it.
#get positions of percentage labels:
mydf = ddply(mydf, .(week), transform, position = cumsum(value) - 0.5*value)
#make plot
p =
ggplot(mydf,aes(x = week, y = value, fill = as.factor(rank))) +
geom_bar(stat = "identity")
#add percentage labels using positions defined previously
p + geom_text(aes(label = sprintf("%1.2f%%", 100*ratio), y = position))
Is this what you wanted?
I'm working with ggplot2 to generate some geom_line plots which i've already generated from another data.frame which is not important to mention here. but it also contains the same id value as the following dataframe.
I have this data frame called df:
id X Y total
1 3214 6786 10000
2 4530 5470 10000
3 2567 7433 10000
4 1267 8733 10000
5 2456 7544 10000
6 6532 6532 10000
7 5642 4358 10000
What i want to do is create custom legend which present for a specific id the percentage of X and Y on each of the geom_line for when the id variable is the same. So basically for each geom_line of e.g(id=1, draw the percentage for that id in the geom_line plot)
I've tried to use geom_text, but the problem is that it's printing everything in one line which i cannot see anything of it.
how this can be done ??
EDIT
olddf dataframe is something like that:
id pos X Y Z
1
1.....
1
2
3
4
3 ......
.
.
that's the code that i've tried
for(i in df$id)
{
test = subset(olddf, id==i)
mdata <- melt(test, id=c("pos","id"))
pl = ggplot() + geom_line(data=mdata, aes(x=pos, y=value, color=variable)) + geom_text(data=df, aes(x=6000, y=0.1, label=(X*total)/100), size=5)
}
The answer (as discussed in chat) is quite straightforward:
Change geom_text(data = df, ...) to geom_text(data = df[df$id == i, ], ...)