Merge Multiple Data Frames - Error in Match.Names(Clabs, Names(Xi)):Names Do Not Match Previous Names

rbind error: names do not match previous names

The names (column names) of the first dataframe do not match the names of the second one. Just as the error message says.

> identical(names(xd.small[[1]]), names(xd.small[[2]]) )
[1] FALSE

If you do not care about the names of the 3rd or 4th columns of the second df, you can coerce them to be the same:

> names(xd.small[[1]]) <- names(xd.small[[2]]) 
> identical(names(xd.small[[1]]), names(xd.small[[2]]) )
[1] TRUE

Then things should proceed happily.

Error in match.names(clabs, names(xi)) : names do not match previous names

Its actually the most primal thing to do in R:

data:

df1 <- data.frame(boys_age = c(18,15,16,17,19), girls_age = c(16,14,18,17,15))

code:

library(data.table)
melt(setDT(df1), variable.name = "group", value.name = "age", measure.vars = c("boys_age", "girls_age"))[,2:1][,group:=sub("_.*$","",group)][]

result:

#    age group
# 1:  18  boys
# 2:  15  boys
# 3:  16  boys
# 4:  17  boys
# 5:  19  boys
# 6:  16 girls
# 7:  14 girls
# 8:  18 girls
# 9:  17 girls
#10:  15 girls

You seem to be keen on using ?rbind: (not practical though)

rbind(
    cbind.data.frame(age = df1$boys_age,  group = "boys"),
    cbind.data.frame(age = df1$girls_age,  group = "girls")
    )

#   age group
#1   18  boys
#2   15  boys
#3   16  boys
#4   17  boys
#5   19  boys
#6   16 girls
#7   14 girls
#8   18 girls
#9   17 girls
#10  15 girls

In the ?cbind section I'm making use of the recycling functionality R provides. Read about it.
Why am I using cbind.data.frame, otherwise cbind would create a matrix and therefore the age numerics would be converted to characters.

rbind produces Error in match.names(clabs, names(xi))

See names(df1)==names(df2). You'll find that the name of variable #47 differs between df1 and df2, which causes the error.

To fix this issue, the variable names must be the same. To resolve this issue, you can do:

names(df1)[47]=c("actions.mention") #change variable name
names(df1)==names(df2) #ensure names are the same
rbind(df1,df2) #rbind two data frames.

R cannot melt data.frame

It works for me. I did the following.

library(reshape2)
tableMS <- read.table(text='     X   Y        Z        T
1  375 855 455.7259 3777.856
2  395 969 347.8306   2506.7
3  449 811 309.9512 519.8513
4  451 774  278.291 717.8705
5  453 774  278.291 717.8705
6  455 774  278.291 717.8705
7  521 697  376.734 693.8541
8  529 855 455.7259 3777.856
9  531 855 455.7259 3777.856
10 609 774  278.291 717.8705',header=TRUE)

EDIT This still work even if you coerce Z and T to a list.

tableMS$Z <- as.list(tableMS$Z)
tableMS$T <- as.list(tableMS$T)

MeltTable <- melt(tableMS,id=c("X","Y"))
# MeltTable
# X   Y variable     value
# 1  375 855        Z  455.7259
# 2  395 969        Z  347.8306
# 3  449 811        Z  309.9512
# 4  451 774        Z  278.2910
# 5  453 774        Z  278.2910
# 6  455 774        Z  278.2910
# 7  521 697        Z  376.7340
# 8  529 855        Z  455.7259
# 9  531 855        Z  455.7259
# 10 609 774        Z  278.2910
# 11 375 855        T 3777.8560
# 12 395 969        T 2506.7000
# 13 449 811        T  519.8513
# 14 451 774        T  717.8705
# 15 453 774        T  717.8705
# 16 455 774        T  717.8705
# 17 521 697        T  693.8541
# 18 529 855        T 3777.8560
# 19 531 855        T 3777.8560
# 20 609 774        T  717.8705

edit don't work with reshape2 version 1.4.2

A workaround is to use data.table package. BTW this solution is faster.

library(data.table)
tableMS$Z <- as.vector(as.list(tableMS$Z))
tableMS$T <- as.vector(as.list(tableMS$T))
setDT(tableMS)
melt(tableMS,id=c("X","Y"))

Maxent: Error in match.names(clabs, names(xi)) : names do not match previous names

That is a bug that occurs when you make a maxent model with only one predictor. You can see it with the data from the example in ?maxent

library(dismo)
# example data
predictors <- stack(list.files(path=paste(system.file(package="dismo"), '/ex', sep=''), pattern='grd', full.names=TRUE ))
occ <- read.table(paste(system.file(package="dismo"), '/ex/bradypus.csv', sep=''), header=TRUE, sep=',')[,-1]
bg <- randomPoints(predictors, 1000)

# this works
me <- maxent(x=predictors[[1:2]], p =occ)

# fails
me <- maxent(x=predictors[[1]], p =occ)
#Error in match.names(clabs, names(xi)) : 
#  names do not match previous names

This is because with a single layer, the matrix is dropped (the cause of many R bugs...), illustrated here:

extract(predictors[[1:2]], occtrain[1:2,])
#     bio1 bio12
#[1,]  263  1639
#[2,]  263  1639

extract(predictors[[1]], occtrain[1:2,])
#[1] 263 263

I will fix that. But here is are two work-arounds.

= Either make a single layer RasterStack (as suggested by you); the simplest approach:

prd <- stack(predictors[[1]])
me <- maxent(x=prd, p =occ)

= Or make a data.frame with extracted raster values for presence and background points:

abs <- cbind(pa=0, bio1=extract(predictors[[1]], bg))
prs <- cbind(pa=1, bio1=extract(predictors[[1]], occ))

and use these data to build the maxent model

x <- data.frame(rbind(prs, abs))
m <- maxent(x[,2,drop=F], p=x[,1,drop=F] )

p <- predict(predictors, m)
plot(p)