I tried to use RichnessGrid to count species occurrence on the map. But I am constantly getting the error message
"Error in split.default(x = seq_len(nrow(x)), f = f, drop = drop, ...) :
group length is 0 but data length > 0".
By checking other posts, it seems that this is the error message for typos, which is not my case. Does anyone know how to trouble shot this problem?
My data look like this
I tried a few things: 1. change the resolution option or the type definition; 2. change header of my data; 3. look at the summary of my data and sample data. But nothing worked, and I still could not figure out where went wrong.
dput(head(clean))
#subset my df (clean) for RichnessGrid
dat<-clean %>% select(the.plant.list,longitude,latitude)
# tried to change header but still failed
dat <- dat %>% rename(species = the.plant.list)
head(dat)
RichnessGrid(dat, reso=60, type = "spnum")
#try sample data and code
data(lemurs)
e <- c(-125, -105, 30, 50)
RichnessGrid(lemurs, e, reso = 60, type = "spnum")
#compare sample data and my own
data(lemurs)
data(dat)
summary(lemurs)
summary(dat)
Related
I am using WarbleR in R to do some acoustic analyses. As freq_range couldn't detect all the bottom frequencies very well, I have created a data frame manually with all the right bottom frequencies, loaded this into R and turned it into a selection table. Traq_freq_contour and compare.methods and freq_DTW all work fine (although freq_DTW does give a warning message:
Warning message: In (0:(n - 1)) * f : NAs produced by integer overflow
However. If I try to do the function cross_correlation, I get the following error:
Error in if (ncol(spc1$amp) > ncol(spc2$amp)) { :
argument is of length zero
I do not get this error with a selection table with the bottom and top frequency added with the freq_range function in R instead of manually. What could be the issue here? The selection tables both look similar:
This is the selection table partly made by R through freq_range:
And this is the one with the bottom frequencies added manually (which has more sound files than the one before):
This is part of the code I use:
#Comparing methods for quantitative analysis of signal structure
compare.methods(X = stnew, flim = c(0.6,2.5), bp = c(0.6,2.5), methods = c("XCORR", "dfDTW"))
#Measure acoustic parameters with spectro_analysis
paramsnew <- spectro_analysis(stnew, bp = c(0.6,2), threshold = 20)
write.csv(paramsnew, "new_acoustic_parameters.csv", row.names = FALSE)
#Remove parameters derived from fundamental frequency
paramsnew <- paramsnew[, grep("fun|peakf", colnames(paramsnew), invert = TRUE)]
#Dynamic time warping
dm <- freq_DTW(stnew, length.out = 30, flim = c(0.6,2), bp = c(0.6,2), wl = 300, img = TRUE)
str(dm)
#Spectrographic cross-correlation
xcnew <- cross_correlation(stnew, wl = 300, na.rm = FALSE)
str(xc)
Any idea what I'm doing wrong?
I've got a list of around 20 shapefiles that I want to bind into one. These shapefiles have different number of fields - some have 1 and some have 2. Examples are shown below:
# 1 field
> dput(head(shp[[1]]))
structure(list(area = c(1.60254096388, 1.40740270051, 0.093933438653,
0.609245720277, 22.892748868, 0.0468096597394)), row.names = 0:5, class = "data.frame")
# 2 fields
> dput(head(shp[[3]]))
structure(list(per = c(61, 70, 79, 90, 57, 66), area = c(2218.8,
876.414, 2046.94, 1180.21, 1779.12, 122.668)), row.names = c(0:5), class = "data.frame")
I used the following code to bind them and it worked just as I wanted:
merged<- raster::bind(shp, keepnames= FALSE, variables = area)
writeOGR(merged, './shp', layer= 'area', driver="ESRI Shapefile")
However, I now need to subset one of the shapefiles in the list. I do it in this way:
shp[[3]]#data <- shp[[3]]#data %>% subset(Area >= 50)
names(shp[[3]]#data)[names(shp[[3]]#data) == "Area"] <- "area"
When I run the bind command, however, this now gives me an error:
merged<- raster::bind(shp, keepnames= FALSE, variables = area)
Error in `.rowNamesDF<-`(x, value = value) : invalid 'row.names' length
Calls: <Anonymous> ... row.names<- -> row.names<-.data.frame -> .rowNamesDF<-
Execution halted
I'm not sure why that is. The shapefile hasn't changed, they are just subsetted. I tried deleting the rownames in the way shown below and it still throws the same error.
rownames(shp[[3]]#data) <- NULL
What could it be?
I think the problem is that that you subset #data (the attributes) but you should subset the entire object. Something like this
x <- shp[[3]] # for simplicity
x <- x[x$Area >= 50, ]
names(x)[names(x) == "Area"] <- "area"
shp[[3]] <- x
I have some code that loops over a list of study IDs (ids) and turns them into separate polygons/spatial points. On the first execution of the loop it produces the following error:
Error in (function (x) : attempt to apply non-function
This is from the raster::rasterToPoints function. I've looked at the examples in the help section for this function and passing fun=NULL seems to be an acceptable method (filters out all NA values). All the values are equal to 1 anyways so I tried passing a simple function like it suggests such as function(x){x==1}. When this didn't work, I also tried to just suppress the error message but without any luck using try() or tryCatch().
Main questions:
1. Why does this produce an error at all?
2. Why does it only display the error on the first run through the loop?
Reproducible example:
library(ggplot2)
library(raster)
library(sf)
library(dplyr)
pacific <- map_data("world2")
pac_mod <- pacific
coordinates(pac_mod) <- ~long+lat
proj4string(pac_mod) <- CRS("+init=epsg:4326")
pac_mod2 <- spTransform(pac_mod, CRS("+init=epsg:4326"))
pac_rast <- raster(pac_mod2, resolution=0.5)
values(pac_rast) <- 1
all_diet_density_samples <- data.frame(
lat_min = c(35, 35),
lat_max = c(65, 65),
lon_min = c(140, 180),
lon_max = c(180, 235),
sample_replicates = c(38, 278),
id= c(1,2)
)
ids <- all_diet_density_samples$id
for (idnum in ids){
poly1 = all_diet_density_samples[idnum,]
pol = st_sfc(st_polygon(list(cbind(c(poly1$lon_min, poly1$lon_min, poly1$lon_max, poly1$lon_max, poly1$lon_min), c(poly1$lat_min, poly1$lat_max, poly1$lat_max, poly1$lat_min, poly1$lat_min)))))
pol_sf = st_as_sf(pol)
x <- rasterize(pol_sf, pac_rast)
df1 <- raster::rasterToPoints(x, fun=NULL, spatial=FALSE) #ERROR HERE
df2 <- as.data.frame(df1)
density_poly <- all_diet_density_samples %>% filter(id == idnum) %>% pull(sample_replicates)
df2$density <- density_poly
write.csv(df2, paste0("pol_", idnum, ".csv"))
}
Any help would be greatly appreciated!
These are error messages, but not errors in the strict sense as the script continues to run, and the results are not affected. They are related to garbage collection (removal from memory of objects that are no longer in use) and this makes it tricky to pinpoint what causes it (below you can see a slightly modified example that suggests another culprit), and why it does not always happen at the same spot.
Edit (Oct 2022)
These annoying messages
Error in x$.self$finalize() : attempt to apply non-function
Error in (function (x) : attempt to apply non-function
Will disappear with the next release of Rcpp, which is planned for Jan 2023. You can also install the development version of Rcpp like this:
install.packages("Rcpp", repos="https://rcppcore.github.io/drat")
I am trying to use seqtime (https://github.com/hallucigenia-sparsa/seqtime) to analyze time-serie microbiome data, as follow:
meta = data.table::data.table(day=rep(c(15:27),each=3), condition =c("a","b","c"))
meta<- meta[order(meta$day, meta$condition),]
meta.ts<-as.data.frame(t(meta))
otu=matrix(1:390, ncol = 39)
oturar<-rarefyFilter(otu, min=0)
rarotu<-oturar$rar
time<-meta.ts[1,]
interp.otu<-interpolate(rarotu, time.vector = time,
method = "stineman", groups = meta$condition)
the interpolation returns the following error:
[1] "Processing group a"
[1] "Number of members 13"
intervals
0
12
[1] "Selected interval: 1"
[1] "Length of time series: 13"
[1] "Length of time series after interpolation: 1"
Error in stinepack::stinterp(time.vector, as.numeric(x[i, ]), xout = xout, :
The values of x must strictly increasing
I tried to change method to "hyman", but it returns the error below:
Error in interpolateSub(x = x, time.vector = time.vector, method = method) :
Time points must be provided in chronological order.
I am using R version 3.6.1 and I am a bit new to R.
Please can anyone tell me what I am doing wrong/ how to go around these errors?
Many thanks!
I used quite some time stumbling around trying to figure this out. It all comes down to the data structure of meta and the resulting time variable used as input for the time.vector parameter.
When meta.ts is being converted to a data frame, all strings are automatically converted to factors - this includes day.
To adjust, you can edit your code to the following:
library(seqtime)
meta <- data.table::data.table(day=rep(c(15:27),each=3), condition =c("a","b","c"))
meta <- meta[order(meta$day, meta$condition),]
meta.ts <- as.data.frame(t(meta), stringsAsFactors = FALSE) # Set stringsAsFactors = FALSE
otu <- matrix(1:390, ncol = 39)
oturar <- rarefyFilter(otu, min=0)
rarotu <- oturar$rar
time <- as.integer(meta.ts[1,]) # Now 'day' is character, so convert to integer
interp.otu <- interpolate(rarotu, time.vector = time,
method = "stineman", groups = meta$condition)
As a bonus, read this blogpost for information on the stringsAsFactors parameter. Strings automatically being converted to Factors is a common bewilderment.
I send you a message because I would like realise an PCA in R with the package ade4.
I have the data "PAYSAGE" :
All the variables are numeric, PAYSAGE is a data frame, there are no NAS or blank.
But when I do :
require(ade4)
ACP<-dudi.pca(PAYSAGE)
2
I have the message error :
**You can reproduce this result non-interactively with:
dudi.pca(df = PAYSAGE, scannf = FALSE, nf = NA)
Error in if (nf <= 0) nf <- 2 : missing value where TRUE/FALSE needed
In addition: Warning message:
In as.dudi(df, col.w, row.w, scannf = scannf, nf = nf, call = match.call(), :
NAs introduced by coercion**
I don't understand what does that mean. Have you any idea??
Thank you so much
I'd suggest sharing a data set/example others could access, if possible. This seems data-specific and with NAs introduced by coercion you may want to check the type of your input - typeof(PAYSAGE) - the manual for dudi.pca states it takes a data frame of numeric values as input.
Yes, for example :
ag_div <- c(75362,68795,78384,79087,79120,73155,58558,58444,68795,76223,50696,0,17161,0,0)
canne <- c(rep(0,10),5214,6030,0,0,0)
prairie_el<- c(60, rep(0,13),76985)
sol_nu <- c(18820,25948,13150,9903,12097,21032,35032,35504,25948,20438,12153,33096,15748,33260,44786)
urb_peu_d <- c(448,459,5575,5902,5562,458,6271,6136,459,1850,40,13871,40,13920,28669)
urb_den <- c(rep(0,12),14579,0,0)
veg_arbo <- c(2366,3327,3110,3006,3049,2632,7546,7620,3327,37100,3710,0,181,0,181)
veg_arbu <- c(18704,18526,15768,15527,15675,18886,12971,12790,18526,15975,22216,24257,30962,24001,14523)
eau <- c(rep(0,10),34747,31621,36966,32165,28054)
PAYSAGE<-data.frame(ag_div,canne,prairie_el,sol_nu,urb_peu_d,urb_den,veg_arbo,veg_arbu,eau)
require(ade4)
ACP<-dudi.pca(PAYSAGE)