I'm plotting legs of a route to a ggmap. It works okay so far. I've been trying to add a label containing the order (n from the loop) of each leg.
I've tried +geom_text to the geom_leg() but I get the error :
Error in geom_leg(aes(x = startLon, y = startLat, xend = endLon, yend = endLat), :
non-numeric argument to binary operator
I'd appreciate any help adding a label to indicate the leg.
Data :
structure(c("53.193418", "53.1905138631287", "53.186744", "53.189836",
"53.1884117", "53.1902965", "53.1940384", "53.1934748", "53.1894004",
"53.1916771", "-2.881248", "-2.89043889005541", "-2.890165",
"-2.893896", "-2.88802", "-2.8919373", "-2.8972299", "-2.8814698",
"-2.8886692", "-2.8846099"), .Dim = c(10L, 2L))
Function :
create.map<-function(lst){
library("ggmap")
cncat<-c(paste(lst[,1],lst[,2],sep=","))
df2<-data.frame(cncat)
leg <-function(start, dest, order){
r<- route(from=start,to=dest,mode = c("walking"),structure = c("legs"))
c<- geom_leg(aes(x = startLon, y = startLat,xend = endLon, yend = endLat),
alpha = 2/4, size = 2, data = r,colour = 'blue')+
geom_text(aes(label = order), size = 3)
return (c)
}
a<-qmap('Chester, UK', zoom = 15, maptype = 'road')
for (n in 1:9){
l<-leg(as.character(df2[n,1]), as.character(df2[n+1,1]),n)
a<-a+l
}
a
}
Is this close? (Note: this calls your list of points way.points).
way.points <- as.data.frame(way.points,stringsAsFactors=FALSE)
library(ggmap)
rte.from <- apply(way.points[-nrow(way.points),],1,paste,collapse=",")
rte.to <- apply(way.points[-1,],1,paste,collapse=",")
rte <- do.call(rbind,
mapply(route, rte.from, rte.to, SIMPLIFY=FALSE,
MoreArgs=list(mode="walking",structure="legs")))
coords <- rbind(as.matrix(rte[,7:8]),as.matrix(rte[nrow(rte),9:10]))
coords <- as.data.frame(coords)
ggm <- qmap('Chester, UK', zoom = 15, maptype = 'road')
ggm +
geom_path(data=coords,aes(x=startLon,y=startLat),color="blue",size=2)+
geom_point(data=way.points,aes(x=as.numeric(V2),y=as.numeric(V1)),
size=10,color="yellow")+
geom_text(data=way.points,
aes(x=as.numeric(V2),y=as.numeric(V1), label=seq_along(V1)))
So this assembles a vector of from and to coordinates using apply(...), then uses mapply(...) to call route(...) with both vectors, returning the overall list of coordinates in a data frame rte. Because the coordinates are stored as, e.g. $startLat and $endLat, we form a coords data frame by adding the final $endLat and $endLong to rte to get the very last leg of the route. Then we use geom_path(...) to draw the path in one step. Finally we use geom_text(...) with x and y-values from the original way.points data frame, and we use geom_point(...) just to make them stand out a bit.
Here's a bare bones solution. I just added the labels to the finished ggmap object a. If you replace the line
a
with
lst2 <- data.frame(cbind(lst, leg = as.character(1:10) )
names(lst2) <- c("lat", "lon", "leg")
a <- a + geom_text(data=lst2,aes(x=lon,y=lat,label=leg),size=5, vjust = 0, hjust = -0.5)
return(a)
in your create.map function, you should get (roughly) the desired result. I might have reversed the lat and lon variables, and you probably want to tweak the size, location, etc. Hope this helps!
Related
I am generating a landscape pattern that evolves over time. The problem with the code is that I have clearly defined a window for the object bringing up the error but the window is not being recognised. I also do not see how any points are falling outside of the window, or how that would make a difference.
library(spatstat)
library(dplyr)
# Define the window
win <- owin(c(0, 100), c(0, 100))
# Define the point cluster
cluster1 <- rMatClust(kappa = 0.0005, scale = 0.1, mu = 20,
win = win, center = c(5,5))
# define the spread of the points
spread_rate <- 1
new_nests_per_year<-5
years<-10
# Plot the initial cluster
plot(win, main = "Initial cluster")
points(cluster1, pch = 20, col = "red")
newpoints<-list()
# Loop for n years
for (i in 1:years) {
# Generate new points that spread from the cluster
newpoints[[1]] <-rnorm(new_nests_per_year, mean = centroid.owin(cluster1)$y, sd = spread_rate)
newpoints[[2]] <-rnorm(new_nests_per_year, mean = centroid.owin(cluster1)$x, sd = spread_rate)
# Convert the list to a data frame
newpoints_df <- data.frame(newpoints)
# Rename the columns of the data frame
colnames(newpoints_df) <- c("x", "y")
# Combine the new points with the existing points
cluster1_df <- data.frame(cluster1)
newtotaldf<-bind_rows(cluster1_df,newpoints_df)
cluster1<-as.ppp(newtotaldf, x = newtotaldf$x, y = newtotaldf$y,
window = win)
# Plot the updated cluster
plot(win, main = paste("Cluster after year", i))
points(cluster1, pch = 20, col = "red")
}
However, when I run line:
cluster1<-as.ppp(newtotaldf, x = newtotaldf$x, y = newtotaldf$y,
window = win)
I recieve the error:
Error: x,y coords given but no window specified
Why would this be the case?
In your code, if you use the command W = win it should solve the issue. I also believe you can simplify the command without specifying x and y:
## ...[previous code]...
cluster1 <- as.ppp(newtotaldf, W = win)
plot(win)
points(cluster1, pch = 20, col = "red")
I want to identify 3d cylinders in an rgl plot to obtain one attribute of the nearest / selected cylinder. I tried using labels to simply spell out the attribute, but I work on data with more than 10.000 cylinders. Therefore, it gets so crowded that the labels are unreadable and it takes ages to render.
I tried to understand the documentation of rgl and I guess the solution to my issue is selecting the cylinder in the plot manually. I believe the function selectpoints3d() is probably the way to go. I believe it returns all vertices within the drawn rectangle, but I don't know how to go back to the cylinder data? I could calculate which cylinder is closest to the mean of the selected vertices, but this seems like a "quick & dirty" way to do the job.
Is there a better way to go? I noticed the argument value=FALSE to get the indices only, but I don't know how to go back to the cylinders.
Here is some dummy data and my code:
# dummy data
cylinder <- data.frame(
start_X = rep(1:3, 2)*2,
start_Y = rep(1:2, each = 3)*2,
start_Z = 0,
end_X = rep(1:3, 2)*2 + round(runif(6, -1, 1), 2),
end_Y = rep(1:2, each = 3)*2 + round(runif(6, -1, 1), 2),
end_Z = 0.5,
radius = 0.25,
attribute = sample(letters[1:6], 6)
)
# calculate centers
cylinder$center_X <- rowMeans(cylinder[,c("start_X", "end_X")])
cylinder$center_Y <- rowMeans(cylinder[,c("start_Y", "end_Y")])
cylinder$center_Z <- rowMeans(cylinder[,c("start_Z", "end_Z")])
# create cylinders
cylinder_list <- list()
for (i in 1:nrow(cylinder)) {
cylinder_list[[i]] <- cylinder3d(
center = cbind(
c(cylinder$start_X[i], cylinder$end_X[i]),
c(cylinder$start_Y[i], cylinder$end_Y[i]),
c(cylinder$start_Z[i], cylinder$end_Z[i])),
radius = cylinder$radius[i],
closed = -2)
}
# plot cylinders
open3d()
par3d()
shade3d(shapelist3d(cylinder_list, plot = FALSE), col = "blue")
text3d(cylinder$center_X+0.5, cylinder$center_Y+0.5, cylinder$center_Z+0.5, cylinder$attribute, color="red")
# get attribute
nearby <- selectpoints3d(value=TRUE, button = "right")
nearby <- colMeans(nearby)
cylinder$dist <- sqrt(
(nearby["x"]-cylinder$center_X)**2 +
(nearby["y"]-cylinder$center_Y)**2 +
(nearby["z"]-cylinder$center_Z)**2)
cylinder$attribute[which.min(cylinder$dist)]
If you call selectpoints3d(value = FALSE), you get two columns. The first column is the id of the object that was found. Your cylinders get two ids each. One way to mark the cylinders is to use "tags". For example, this modification of your code:
# dummy data
cylinder <- data.frame(
start_X = rep(1:3, 2)*2,
start_Y = rep(1:2, each = 3)*2,
start_Z = 0,
end_X = rep(1:3, 2)*2 + round(runif(6, -1, 1), 2),
end_Y = rep(1:2, each = 3)*2 + round(runif(6, -1, 1), 2),
end_Z = 0.5,
radius = 0.25,
attribute = sample(letters[1:6], 6)
)
# calculate centers
cylinder$center_X <- rowMeans(cylinder[,c("start_X", "end_X")])
cylinder$center_Y <- rowMeans(cylinder[,c("start_Y", "end_Y")])
cylinder$center_Z <- rowMeans(cylinder[,c("start_Z", "end_Z")])
# create cylinders
cylinder_list <- list()
for (i in 1:nrow(cylinder)) {
cylinder_list[[i]] <- cylinder3d(
center = cbind(
c(cylinder$start_X[i], cylinder$end_X[i]),
c(cylinder$start_Y[i], cylinder$end_Y[i]),
c(cylinder$start_Z[i], cylinder$end_Z[i])),
radius = cylinder$radius[i],
closed = -2)
# Add tag here:
cylinder_list[[i]]$material$tag <- cylinder$attribute[i]
}
# plot cylinders
open3d()
par3d()
shade3d(shapelist3d(cylinder_list, plot = FALSE), col = "blue")
text3d(cylinder$center_X+0.5, cylinder$center_Y+0.5, cylinder$center_Z+0.5, cylinder$attribute, color="red")
# Don't get values, get the ids
nearby <- selectpoints3d(value=FALSE, button = "right", closest = FALSE)
ids <- nearby[, "id"]
# Convert them to tags. If you select one of the labels, you'll get
# a blank in the list of tags, because we didn't tag the text.
unique(tagged3d(id = ids))
When I was trying this, I found that using closest = TRUE in selectpoints3d seemed to get too many ids; there may be a bug there.
I downloaded a monthly data from [NASA data][1] and saved in .txt and .asc format. I am trying to plot and extract the data from the ASCII file, but unfortunately I am unable to do so. I tried the following:
1.
infile <- "OMI/L3feb09.txt"
data <- as.matrix(read.table(infile, skip = 3, header = FALSE, sep = "\t"))
data[data == -9999] = NA
rr <- raster(data, crs = "+init=epsg:4326")
extent(rr) = c(179.375, 179.375+1.25*288, -59.5, -59.5+1*120)
Tried to extract for australia
adm <- getData("GADM", country="AUS", level=1)
rr = mask(rr, adm)
plot(rr)
library(rgdal)
r = raster("OMI/L3feb09.txt")
plot(r)
library(raster)
r = raster("OMI/L3feb09.txt")
plot(r)
4.Also tried,
df1 <- read.table("OMI/L3feb09.txt", skip = 11, header = FALSE, sep = "\t")
Tried the following from
Stackoverflow link 1
Stackoverflow link 2
The problem is there are strings in the file in between number, such as "lat = -55.5"
Appreciate any kind of help. Thank you
[2]: https://stackoverflow.com/questions/42064943/opening-an-ascii-file-using-r
So, I downloaded one file and played around with it! It is not the best solution, however, I hope it can give you an idea.
library(stringr)
# read data
data<-read.csv("L3_tropo_ozone_column_oct04",header = FALSE, skip = 3,sep = "")
# this "" will seperate lat = -59.5 to 3 rows, and will be easier to remove.
#Also each row in the data frame constrained by 2 rows of "lat", represents #data on the later "lat".
lat_index<-which(data[,1]=="lat")
#you need the last row that contains data not "lat string
lat_index<-lat_index-1
#define an empty array for results.
result<-array(NA, dim = c(120,288),dimnames = list(lat=seq(-59.5,59.5,1),
lon=seq(-179.375,179.375,1.25)))
I assumed data -on 3 three digits- on each latituide is dividable by 3 resulting in 288, which equals the lon grid number. Correct me if I'm wrong.
# function to split a string into a vector in which each string has three letter/numbers
split_n_parts<-function(input_string,n){
# dislove it to many elements or by number
input_string_1<-unlist(str_extract_all(input_string,boundary("character")))
output_string<-vector(length = length(input_string_1)/n)
for ( x in 1:length(output_string)){
output_string[x]<-paste0(input_string_1[c(x*3-2)],
input_string_1[c(x*3-1)],
input_string_1[c(x*3)])
}
return(as.numeric(output_string))
}
Here, the code loops, collects, write each lat data in the result array
# loop over rows constrainted by 2 lats, process it and assign to an array
for (i in 1:length(lat_index)){
if(i ==1){
for(j in 1:lat_index[i]){
if(j==1){
row_j<-paste0(data[j,])
}else{
row_j<-paste0(row_j,data[j,])
}
}
}else{
ii<-i-1
lower_limit<-lat_index[ii]+4
upper_limit<-lat_index[i]
for(j in lower_limit:upper_limit){
if(j==lower_limit){
row_j<-paste0(data[j,])
}else{
row_j<-paste0(row_j,data[j,])
}
}
}
result[i,]<-split_n_parts(row_j,3)
}
Here, is the final array and image
#plot as image
image(result)
EDIT: To continue the solution and put the end-result:
# because data is IN DOBSON UNITS X 10
result<-result/10
#melt to datafrome
library(plyr)
result_df<-adply(result, c(1,2))
result_df$lat<-as.numeric(as.character(result_df$lat))
result_df$lon<-as.numeric(as.character(result_df$lon))
# plotting
library(maps)
library(ggplot2)
library(tidyverse)
world_map <- map_data("world")
#colors
jet.colors <-colorRampPalette(c("white", "cyan", "#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000"))
ggplot() +
geom_raster(data=result_df,aes(fill=V1,x=lon,y=lat))+
geom_polygon(data = world_map, aes(x = long, y = lat, group = group),
fill=NA, colour = "black")+
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))+
scale_fill_gradientn(colors = jet.colors(7))
I have two matrices of bearing and speed data for ocean currents
bearings <- matrix(data = c(170.0833, 175.6863, 182.3538, 180.3335, 170.8965,
178.3276, 182.3693, 187.2196, 182.3533, 168.3498,
189.1664, 187.6813, 187.0393, 180.2259, 166.8412,
193.4223, 188.5367, 182.4128, 175.2626, 167.3058,
192.2930, 185.5073, 175.0302, 168.6284, 167.8392),
ncol = 5, nrow = 5, byrow = F)
speed <- matrix(data = c(0.1389173, 0.1585099, 0.1796583, 0.2021887, 0.2117295,
0.1196745, 0.1463118, 0.1637266, 0.1730471, 0.1804999,
0.1309982, 0.1546123, 0.1593298, 0.1517513, 0.1550037,
0.1621728, 0.1694083, 0.1606560, 0.1459710, 0.1457233,
0.1659898, 0.1535861, 0.1396885, 0.1294339, 0.1337756),
ncol = 5, nrow = 5, byrow = F)
I wanted to graph the direction of the current bearings with arrows, while the magnitude/speed of the current is represented by the length of the arrow, a bit something like these maps:
Wind stress figure taken from Shankar et al. 2002
I know the package oce may be able to do something like that, but it specifically works with different types of oceanographic data rather than matrices/data frames that I'm using.
Anyone happen to know how to do that? I have gotten as far as making them into raster objects using the raster() function from the raster library:
library(raster)
bearing.rst <- raster(bearings,
xmn = 66,
xmx = 67.3333,
ymn = 10.6667,
ymx = 12)
speed.rst <- raster(speed,
xmn = 66,
xmx = 67.3333,
ymn = 10.6667,
ymx = 12)
Ideally I'd do this with base R graphics, or with a package that plays nice with base R graphics (e.g. not ggplot2 or lattice).
Graph from:
Shankar, D., Vinayachandra, P.N., & Unnikrishnan, A.S. (2002). The monsoon currents in the north Indian Ocean. Progress in Oceanography, 52: 62-120. doi: 10.1016/S0079-6611(02)00024-1
with base R:
plot(bearing.rst) # your base map, I use this because I didn't have it
Get your starting coordinates:
arr.coor <- rasterToPoints(bearing.rst)
arr.coor <- cbind(arr.coor[,-3], bearing=c(t(bearings)), speed=c(t(speed)))
Calculate your finishing coordinates with trigonometric functions:
x1 <- arr.coor[,1] + arr.coor[,4] * cos(arr.coor[,3]*pi/180)
y1 <- arr.coor[,2] + arr.coor[,4] * sin(arr.coor[,3]*pi/180)
arr.coor <- cbind(arr.coor, x1, y1)
Plot your arrows:
arrows(arr.coor[,1],arr.coor[,2],arr.coor[,5],arr.coor[,6])
I guess the same principal could work with ggplot2. The idea is to get a table with all your arrows origin and end.
With ggplot
bearings <- c(170.0833, 175.6863, 182.3538, 180.3335, 170.8965,
178.3276, 182.3693, 187.2196, 182.3533, 168.3498,
189.1664, 187.6813, 187.0393, 180.2259, 166.8412,
193.4223, 188.5367, 182.4128, 175.2626, 167.3058,
192.2930, 185.5073, 175.0302, 168.6284, 167.8392)
speed <- c(0.1389173, 0.1585099, 0.1796583, 0.2021887, 0.2117295,
0.1196745, 0.1463118, 0.1637266, 0.1730471, 0.1804999,
0.1309982, 0.1546123, 0.1593298, 0.1517513, 0.1550037,
0.1621728, 0.1694083, 0.1606560, 0.1459710, 0.1457233,
0.1659898, 0.1535861, 0.1396885, 0.1294339, 0.1337756)
df <- data.frame(x = rep(1:5,5),
y = rep(1:5, each = 5),
bearings = bearings,
speed = speed)
df$dx <- sin((df$bearings)/360*pi*2)*df$speed
df$dy <- cos((df$bearings)/360*pi*2)*df$speed
ggplot(df, aes(x, y)) +
geom_segment(aes(xend = x + dx, yend = y + dy),
arrow = arrow(length = unit(0.1,"cm"))) +
theme_bw()
So I use the following functions for plotting most of the data I have to plot. I created it thanks to different chunks of code that I have found online. So far I have never encountered any issue with it.
Here is the plotting function first.
library(ggplot2)
library(reshape2)
#' Plot a given mean with error bars
#' #param resultTable The table with all the result to plot
#' #param techniques The name of the techniques in the form of a list/vector
#' #param nbTechs The number of given techniques
#' #param ymin The minimum value for y
#' #param ymax The maximum value for y
#' #param xAxisLabel The label for the x (vertical) axis
#' #param yAxisLable The label for the y (horizontal) axis
#' #return
#'
barChartTime <- function(resultTable, techniques, nbTechs = -1, ymin, ymax, xAxisLabel = "I am the X axis", yAxisLabel = "I am the Y Label"){
#tr <- t(resultTable)
if(nbTechs <= 0){
stop('Please give a positive number of Techniques, nbTechs');
}
tr <- as.data.frame(resultTable)
nbTechs <- nbTechs - 1 ; # seq will generate nb+1
#now need to calculate one number for the width of the interval
tr$CI2 <- tr$upperBound_CI - tr$mean_time
tr$CI1 <- tr$mean_time - tr$lowerBound_CI
#add a technique column
tr$technique <- factor(seq.int(0, nbTechs, 1));
breaks <- c(as.character(tr$technique));
print(tr)
g <- ggplot(tr, aes(x=technique, y=mean_time)) +
geom_bar(stat="identity",fill = I("#CCCCCC")) +
geom_errorbar(aes(ymin=mean_time-CI1, ymax=mean_time+CI2),
width=0, # Width of the error bars
size = 1.1
) +
#labs(title="Overall time per technique") +
labs(x = xAxisLabel, y = yAxisLabel) +
scale_y_continuous(limits = c(ymin,ymax)) +
scale_x_discrete(name="",breaks,techniques)+
coord_flip() +
theme(panel.background = element_rect(fill = 'white', colour = 'white'),axis.title=element_text(size = rel(1.2), colour = "black"),axis.text=element_text(size = rel(1.2), colour = "black"),panel.grid.major = element_line(colour = "#DDDDDD"),panel.grid.major.y = element_blank(), panel.grid.minor.y = element_blank())+
geom_point(size=4, colour="black") # dots
print(g)
}
Now, here is (a simplified version of the data) data that I am using (and that reproduces the error):
EucliP,AngularP,EucliR,AngularR,EucliSp,AngularSp,EucliSl,AngularSl
31.6536,30.9863,64.394,92.7838,223.478,117.555,44.7374,25.4852
12.3592,40.7639,70.2508,176.55,10.3927,145.909,143.025,126.667
14.572,8.98445,113.599,150.551,47.1545,54.3019,10.7038,47.7004
41.7957,20.9542,55.1732,67.1647,52.364,41.3655,62.7036,75.65
135.868,83.7135,14.0262,69.7183,44.987,35.9599,19.5183,66.0365
33.5359,17.2129,6.95909,47.518,224.561,91.4999,67.1279,31.4079
25.7285,33.6705,17.4725,58.45,43.1709,113.847,28.9496,20.0574
48.4742,127.588,75.0804,89.1176,31.4494,27.9548,38.4563,126.248
31.9831,80.0161,19.9592,145.891,55.2789,142.738,94.5126,136.099
17.4044,52.3866,49.9976,150.891,104.936,77.2849,232.23,35.6963
153.359,151.897,41.8876,46.3893,79.5218,75.2011,68.9786,91.8972
And here is the code that I am using:
data = read.table("*Path_to_file*.csv", header=T, sep=",")
data$EucliPLog = (data$EucliP) #Before here I used to use a log transform that I tried to remove for some testing
data$EucliRLog = (data$EucliR) #Same thing
data$EucliSpLog = (data$EucliSp) #Same thing
data$EucliSlLog = (data$EucliSl) #Same thing
a1 = t.test(data$EucliPLog)$conf.int[1]
a2 = t.test(data$EucliPLog)$conf.int[2]
b1 = t.test(data$EucliRLog)$conf.int[1]
b2 = t.test(data$EucliRLog)$conf.int[2]
c1 = t.test(data$EucliSpLog)$conf.int[1]
c2 = t.test(data$EucliSpLog)$conf.int[2]
d1 = t.test(data$EucliSlLog)$conf.int[1]
d2 = t.test(data$EucliSlLog)$conf.int[2]
analysisData = c()
analysisData$ratio = c("Sl","Sp","R","P")
analysisData$pointEstimate = c(exp(mean(data$EucliSlLog)),exp(mean(data$EucliSpLog)),exp(mean(data$EucliRLog)),exp(mean(data$EucliPLog)))
analysisData$ci.max = c(exp(d2), exp(c2),exp(b2), exp(a2))
analysisData$ci.min = c(exp(d1), exp(c1),exp(b1), exp(a1))
datatoprint <- data.frame(factor(analysisData$ratio),analysisData$pointEstimate, analysisData$ci.max, analysisData$ci.min)
colnames(datatoprint) <- c("technique", "mean_time", "lowerBound_CI", "upperBound_CI ")
barChartTime(datatoprint,analysisData$ratio ,nbTechs = 4, ymin = 0, ymax = 90, "", "Title")
So If I do use the log() that I mention in the comments of the last piece of code, everything works fine and I get my plots displayed. However, I tried removing the log and I get the famous
Error in matrix(value, n, p) :
'data' must be of a vector type, was 'NULL'
I have tried looking for null values in my data but there are none and I do not know where to look at next. Would love to get some help with that.
Thanks in advance
Edit: Here is the result of dput on datatoprint:
structure(list(technique = structure(c(3L, 4L, 2L, 1L), .Label = c("P",
"R", "Sl", "Sp"), class = "factor"), mean_time = c(1.04016257618464e+32,
1.64430609815788e+36, 7.5457775364611e+20, 3.85267453902928e+21
), lowerBound_CI = c(6.64977706609883e+50, 5.00358136618364e+57,
2.03872433045407e+30, 4.93863589006376e+35), `upperBound_CI ` = c(16270292584857.9,
540361462434140, 279286207454.44, 30055062.6409769)), .Names = c("technique",
"mean_time", "lowerBound_CI", "upperBound_CI "), row.names = c(NA,
-4L), class = "data.frame")
And the dput on analysisData:
structure(list(ratio = c("Sl", "Sp", "R", "P"), pointEstimate = c(1.04016257618464e+32,
1.64430609815788e+36, 7.5457775364611e+20, 3.85267453902928e+21
), ci.max = c(6.64977706609883e+50, 5.00358136618364e+57, 2.03872433045407e+30,
4.93863589006376e+35), ci.min = c(16270292584857.9, 540361462434140,
279286207454.44, 30055062.6409769)), .Names = c("ratio", "pointEstimate",
"ci.max", "ci.min"))
Without the log I don't have anything on display because the value are above 10^40++ whereas with the log it's below the upper limit (90).
I don' get the error you get though.