Matching and Merging

city<-data.frame(
  city=I(c("Chicago","Seattle","New York","San Francisco", "Los Angeles")),
  state=I(c("IL","WA","NY","CA","CA")),
  population=c(2715000,634535,8337000,825863,3858000),
  coastal=c(F,F,T,T,T))
weather<-data.frame(
  city=I(c("Chicago","Washington","Seattle","New York","Kansas City","Atlanta","San Francisco", "Los Angeles")),
  average_high=c(57,62,58,57,59,63,64,72),
  average_low=c(50,55,53,49,50,59,57,68),
  inch_snow=c(T,T,F,T,T,F,F,F)
  )

Check Data

city

weather

“I” function will stop R from turning string to factors (ex. city names). Similar to stringsAsFactors=F

MATCH

“match” function to match same name

city$city

[1] "Chicago"       "Seattle"       "New York"      "San Francisco" "Los Angeles"  

weather$city

[1] "Chicago"       "Washington"    "Seattle"       "New York"      "Kansas City"  
[6] "Atlanta"       "San Francisco" "Los Angeles"  

Match tables

match(city$city, weather$city)

[1] 1 3 4 7 8

Subset

create subset to extract values

weather$city[match(city$city, weather$city)]

[1] "Chicago"       "Seattle"       "New York"      "San Francisco" "Los Angeles"  

“in” can be used too

city$city %in% weather$city

[1] TRUE TRUE TRUE TRUE TRUE

use “merge” and by.x and by.y to specify columns in X table and Y table to merge. This is useful if spellings are different (ex. one variable uses capital “C” while other does not)

merge(city, weather, by.x="city", by.y="city")