Hallo Everyone
I have the kind of problem that one should never have because one must
always plan well and communicate with your team. But now I haven't so here
is my problem.
I have data coming in on a daily basis from surveys in 10 towns. The
questionnaire has 62 variables but some of the regions have used older
versions of the questionnaire that have a few variables less. I want to
combine everything a single dataframe on a daily basis. The problem is now
that i cannot rbind() the data because the number of variables do not
correspond. I have found that i can first subset all datasets to keep just
the variables that they all have in common but that is very unsatisfactory.
What I want to do is to use a complete list of variable names and look at
each data frame and create variable names where they are missing and fill it
with NAs. At least then I can merge the data and use the data that I have
short example
# Make a data frame with 4 variables
var1=c(1,2,3,4,5,6)
var2=c("a","b","a","b","a","a")
var3=c(1,NA,NA,2,3,NA)
var4=c(100,200,300,100,200,300)
df1=data.frame(cbind(var1,var2,var3,var4))
# Data frame 2 and three has two of the 4 variables and 4 has eveything
df2=df1[,c(1,2,4)]
df3=df1[,c(2,3,4)]
df4=df1
# I wanted to do this but it produces an error because the number of
variable differ
df=data.frame(rbind(df1,df2,df3,df4))
#I have figured out how to print the names of variable that do match the
'master' list (in this case df1):
# example with df3
names(df3[,na.omit(match(names(df1),names(df3)))])
#What I need is the name of the variable that each specific data frame does
NOT contain
# Something like this, but this gives an error
names(df1[-names(df3[,na.omit(match(names(df1),names(df3)))])])
thanks in advance
Christiaan
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