H Roark wrote:
I have a data set covering a large number of cities with values for
characteristics such as land area, population, and employment. The problem I
have is that some cities lack observations for some of the characteristics and
I'd like a quick way to determine which cities have missing data. For example:
city<-c("A","A","A","B","B","C")
var<-c("sqmi","pop","emp","pop","emp","pop")
value<-c(10,100,40,30,10,20)
df<-data.frame(city,var,value)
In this data frame, city A has complete data for the three variables, while
city B is missing land area, and city C only has population data. In the full
data frame, my approach to finding the missing observations has been to create
a data frame with all combinations of 'city' and 'var', merge this onto the
original data frame, and then extract the observations with missing data for
'value':
city_unq<-c("A","B","C")
var_unq<-c("sqmi","pop","emp")
comb<-expand.grid(city=city_unq,var=var_unq)
mrg<-merge(comb,df,by=c("city","var"),all=T)
missing<-mrg[is.na(mrg$value),]
Perhaps the following, or a variation thereof?
subset(as.data.frame(table(city = df$city, var = df$var)), Freq == 0)
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