Extracting parts of data.frame - r

Question

I have an issue while extracting and creating a new data.frame on the basis of previous one.
So we have:
> head(data.raw)
date id contacted contacted_again region
1 2015-11-29 234 CHAT EMAIL APAC
2 2015-11-29 234 EMAIL EMAIL APAC
3 2015-11-27 257 PHONE PHONE EMEA
4 2015-11-27 278 PHONE EMAIL APAC
5 2015-11-27 293 CHAT EMAIL EMEA
6 2015-11-27 243 EMAIL EMAIL EMEA
market
1 AU/NZ
2 SE Asia (English)
3 Spain
4 China Mainland
5 DACH
6 DACH
However, one I write
data.ru <- data.raw[data.raw$market=="Russia",]
I receive the following mess:
date id contacted contacted_again region market
67 2015-11-25 334 CHAT EMAIL EMEA Russia
NA <NA> <NA> <NA> <NA> <NA> <NA>
NA.1 <NA> <NA> <NA> <NA> <NA> <NA>
NA.2 <NA> <NA> <NA> <NA> <NA> <NA>
NA.3 <NA> <NA> <NA> <NA> <NA> <NA>
NA.4 <NA> <NA> <NA> <NA> <NA> <NA>
How should I write a command to receive just a normal data.frame with all rows that $market=="Russia" without any NAs?

Answer 1

I would just use the subset function.
test <- data.frame(x = c("USA", "USA", "USA", "Russia", "Russia", NA), y = c("Orlando", "Boston", "Memphis", NA, "St. Petersburg", "Mexico City"))
print(test)
x y
1 USA Orlando
2 USA Boston
3 USA Memphis
4 Russia <NA>
5 Russia St. Petersburg
6 <NA> Mexico City
subset(test, x == "Russia")
x y
4 Russia <NA>
5 Russia St. Petersburg

Answer 2

You may want to try: data.ru <- data.raw[data.raw$market %in% "Russia",]
Explanation: I am assuming you have empty lines in your dataset, which are read as NAs (missing value). Since R cannot know if a given NA is equal to "Russia" or not, the generated data frame includes them.
Illustration in code:
# create sample dataset
example.df <- data.frame(market=c(NA, "Russia", NA), outcome = c(1,2,3))
# match market using ==
example.df$market == "Russia"
example.df[example.df$market == "Russia",]
# match market using %in%
example.df$market %in% "Russia"
example.df[example.df$market %in% "Russia",]