Subsetting a data set and plotting means - r

Question

I have a data set including Year, Site, and Species Count. I am trying to write a code that reflects in some years, the counts were done twice. For those years I have to find the mean count at each site for each species (there are two different species), and plot those means. This is the code I have generated:
DataSet1 <- subset(channel_islands,
channel_islands$SpeciesName=="Hypsypops ubicundus, adult" |
channel_islands$SpeciesName=="Paralabrax clathratus,adult")
years<-unique(DataSet1$Year)
Hypsypops_mean <- NULL
Paralabrax_mean <- NULL
Mean <- NULL
years <- unique(DataSet1$Year)
for(i in 1:length(years)){
data_year <- DataSet1[which(DataSet1$Year == years[i]), ]
Hypsypops<-data_year[which(data_year$SpeciesName=="Hypsypops rubicundus,adult"), ]
Paralabrax<-data_year[which(data_year$SpeciesName=="Paralabrax clathratus,adult"), ]
UNIQUESITE<-unique(unique(data_year$Site))
for(m in 1:(length(UNIQUESITE))){
zz<-Hypsypops[Hypsypops$Site==m,]
if(length(zz$Site)>=2){
Meanp <- mean(Hypsypops$Count[Hypsypops$Site==UNIQUESITE[m]])
Hypsypops_mean <- rbind(Hypsypops_mean,
c(UNIQUESITE[m], years[i], round(Meanp,2),
'Hypsypops rubicundus,adult'))
}
kk <- Paralabrax[Paralabrax$Site==m, ]
if(length(kk$Site)>=2){
Meane <- mean(Paralabrax$Count[Paralabrax$Site==UNIQUESITE[m]])
Paralabrax_mean <- rbind(Paralabrax_mean,
c(UNIQUESITE[m], years[i], round(Meane, 2),
'Paralabrax clathratus,adult'))
}
}
if(i==1){
Mean<-rbind(Hypsypops_mean, Paralabrax_mean)
}
if(i>1){
Mean<-rbind(DataMean, Hypsypops_mean, Paralabrax_mean)
}
Hypsypops_mean<-NULL
Paralabrax_mean<-NULL
}
Mean <- as.data.frame(Mean,stringsAsFactors=F)
names(Mean) <- c('Site','Year','mean_count','SpeciesName')
Mean$Site <- as.integer(Mean$Site)
Mean$Year <- as.integer(Mean$Year)
Mean$mean_count <- as.numeric(Mean$mean_count)
par(mfrow=c(5,5), oma=c(4,2,4,2), mar=c(5.5,4,3,0))
for(i in 1:length(years)){
if(any(Mean$Year==years[i])) {
year1<-Mean[which(Mean$Year==years[i]),]
Species<-unique(as.character(year1$SpeciesName))
Colors<-c("pink","purple")[Species]
Data_Hr<-year1[year1$SpeciesName=="Hypsypops rubicundus,adult",]
Data_Pc<-year1[year1$SpeciesName=="Paralabrax clathratus,adult",]
plot(Data_Hr$mean_count~Data_Pc$mean_count,
xlab=c("Hypsypops rubicundus"),
ylab=c("Paralabrax clathratus"),main=years[i],pch=16)
}
}
It's a lot I'm sorry, I'm not sure of a way to streamline the process. But I keep getting an error:
Error in names(Mean) <- c("Site", "Year", "mean_count", "SpeciesName")
: 'names' attribute [4] must be the same length as the vector [0]
Not sure how I can debug this.

Answer 1

Not sure why you want to do this with an elaborate loop code. It sounds like you are trying to summarise your data.
This can be done in different ways. Here is a solution using dplyr:
DataSet1 %>%
group_by(Year, SpeciesName, Site) %>%
summarise(nrecords = n(),
Count = mean(Count))
To get a better answer, it might be helpful to post a subset of the data and the intended result you are after.