Merge two data frames by a max number condition in r - r

Question

Cheers, I have a data frame df1 with the Major City with max visitors in 2011.
df1:
Country City Visitors_2011
UK London 100000
USA Washington D.C 200000
USA New York 100000
France Paris 100000
The other data frame df2 consists of top visited cities in the country for 2012:
df2:
Country City Visitors_2012
USA Washington D.C 200000
USA New York 100000
USA Las Angeles 100000
UK London 100000
UK Manchester 100000
France Paris 100000
France Nice 100000
The Output I would need is:
Logic: To obtain df3, merge df1 and df2 by Country and City and if you can't find city in df1 then add that volume to biggest city in df1.
Example: Los Angeles visitor count here is added to Washington D.C because Los Angeles is not present in df1 and Washington D.C has more visitors(2012) than New York.
df3:
Country City Visitors_2011 Visitors_2012
UK London 100000 200000
USA Washington D.C 200000 300000
USA New York 100000 100000
France Paris 100000 200000
Can anyone point me to the right direction?

Answer 1

Assume df1.txt and df2.txt contain your space-delimited dataframes.
Here is a solution in base R:
df1 <- read.table("df1.txt", header = T, stringsAsFactors = F);
df2 <- read.table("df2.txt", header = T, stringsAsFactors = F);
# Merge with all = TRUE, see ?merge
df <- merge(df1, df2, all = TRUE);
# Deal with missing values
tmp <- lapply(split(df, df$Country), function(x) {
# Make sure NA's are at the bottom
x <- x[order(x$Visitors_2011), ];
# Select first max Visitors_2012 entry
idx <- which.max(x$Visitors_2012);
# Add any NA's to max entry
x$Visitors_2012[idx] <- x$Visitors_2012[idx] + sum(x$Visitors_2012[is.na(x$Visitors_2011)]);
# Return dataframe
return(x[!is.na(x$Visitors_2011), ])});
# Bind list entries into dataframe
df <- do.call(rbind, tmp);
print(df);
Country City Visitors_2011 Visitors_2012
France France Paris 100000 200000
UK UK London 100000 200000
USA.6 USA New_York 100000 100000
USA.7 USA Washington_D.C 200000 300000

Answer 2

A dplyr approach:
library(dplyr)
max.cities <- df1 %>% group_by(Country) %>% summarise(City = City[which.max(Visitors_2011)])
result <- df2 %>% mutate(City=ifelse(City %in% df1$City, City,
max.cities$City[match(Country, max.cities$Country)])) %>%
group_by(Country,City) %>%
summarise(Visitors_2012=sum(Visitors_2012)) %>%
left_join(df1,., by=c("Country", "City"))
Notes:
First, compute the City that has the max visitors group_by Country in df1 and set that to a separate data frame max.cities.
mutate the City column in df2 so that if the City is in df1, then the name is unchanged; otherwise, the City from max.cites that matches the Country is used.
Once the City has been suitably modified, group_by both Country and City and sum up the Visitors_2012.
Finally, left_join with df1 by c("Country", "City") to get the final result.
The result using your posted data is as expected:
print(result)
## Country City Visitors_2011 Visitors_2012
##1 UK London 100000 200000
##2 USA Washington D.C 200000 300000
##3 USA New York 100000 100000
##4 France Paris 100000 200000