r - average of one variable in relation to the values of another variable in a data frame and withdraw values NA

The question is rather confusing. Question by averages of frequencia grouped by campanha and then only gives code examples where grouping is by local and especie .

I'll first group by campanha .

aggregate(frequencia ~ campanha, dados, mean, na.rm = TRUE)
#  campanha frequencia
#1        1   2.400000
#2        2  19.666667
#3        3   2.666667
#4        4  10.571429

Now, I'm going to group by local and espécie , both using the reshape2 package and the base function tapply . As you can see the results are identical, the only difference is that one assigns the value NaN when the mean can not be calculated and the other assigns NA . Also, to put 0 is exactly the same way.

library(reshape2)

dadomean1 <- dcast(dados, local  ~ especie, mean, value.var = "frequencia")
dadomean1[is.na(dadomean1)] <- 0
dadomean1
#  local   aa       bb  cc       dd
#1     A 21.5 2.666667   1 4.500000
#2     B 10.5 5.500000   0 5.333333


dadomean2 <- with(dados, tapply(frequencia, list(local, especie), mean))
dadomean2[is.na(dadomean2)] <- 0
dadomean2
#    aa       bb cc       dd
#A 21.5 2.666667  1 4.500000
#B 10.5 5.500000  0 5.333333

EDITION.

To calculate campaign averages grouped by especie and local but taking into account all campaigns and not just those in which the species is registered, it is best to define a mediaCamp function that does these calculations. Home Then,% w / w% is used again.

mediaCamp <- function(x){
  ncamp <- length(unique(dados$campanha))
  sum(x)/ncamp
}

dadomean3 <- aggregate(frequencia ~ especie + local, dados, mediaCamp)
dadomean3
#  especie local frequencia
#1      aa     A      21.50
#2      bb     A       2.00
#3      cc     A       0.50
#4      dd     A       2.25
#5      aa     B       5.25
#6      bb     B       2.75
#7      dd     B       4.00

DATA in aggregate format.

dados <-
structure(list(campanha = c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), local = structure(c(1L, 
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L), .Label = c("A", "B"), class = "factor"), especie = structure(c(1L, 
2L, 3L, 2L, 4L, 1L, 2L, 4L, 1L, 1L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 
4L), .Label = c("aa", "bb", "cc", "dd"), class = "factor"), frequencia = c(1L, 
2L, 1L, 1L, 7L, 50L, 1L, 8L, 2L, 3L, 3L, 33L, 5L, 1L, 1L, 18L, 
10L, 6L)), .Names = c("campanha", "local", "especie", "frequencia"
), class = "data.frame", row.names = c(NA, -18L))

I do not know if this is exactly what you want.

The average of each species in each location.

library(dplyr)
group_by(dados, especie, local)%>%summarise(Total=mean(frequencia))