Turn list of company names into tickers

I have a list of company names that I would like to turn into tickers. Here is the reproducible code to create the list of names that I have:

companynames=structure(list(V1 = structure(1:41, .Label = c("AETNA INC", "ANTHEM INC", 
"APPLE INC", "ASPEN INSURANCE HOLDINGS LTD", "BARRICK GOLD CORP", 
"BEST BUY CO INC", "CAREFUSION CORP", "CBS CORP-CLASS B NON VOTING", 
"CIGNA CORP", "COMPUTER SCIENCES CORP", "COMPUWARE CORP", "COVENTRY HEALTH CARE INC", 
"DELPHI AUTOMOTIVE PLC", "DST SYSTEMS INC", "EINSTEIN NOAH RESTAURANT GRO", 
"ENSCO PLC-CL A", "EXPEDIA INC", "FIFTH STREET FINANCE CORP", 
"GENERAL MOTORS CO", "GENWORTH FINANCIAL INC-CL A", "GREEN BRICK PARTNERS INC", 
"HESS CORP", "HUMANA INC", "HUNTINGTON INGALLS INDUSTRIE", "LEGG MASON INC", 
"MARKET VECTORS GOLD MINERS", "MARVELL TECHNOLOGY GROUP LTD", 
"MICROSOFT CORP", "NCR CORPORATION", "NVR INC", "OAKTREE CAPITAL GROUP LLC", 
"REPUBLIC AIRWAYS HOLDINGS IN", "SEAGATE TECHNOLOGY", "SPRINT COMMUNICATIONS INC", 
"STARZ - A", "STATE BANK FINANCIAL CORP", "SYMMETRICOM INC", 
"TESSERA TECHNOLOGIES INC", "UNITEDHEALTH GROUP INC", "VIRGIN MEDIA INC/OLD", 
"XEROX CORP"), class = "factor")), .Names = "V1", class = "data.frame", row.names = c(NA, 
-41L))

This gives me something along the lines of:

head(companynames)
                            V1
1                    AETNA INC
2                   ANTHEM INC
3                    APPLE INC
4 ASPEN INSURANCE HOLDINGS LTD
5            BARRICK GOLD CORP
6              BEST BUY CO INC

I would like another column that outputed the tickers of each of these companies. So for the first row I should get AET, second row would be ATHN, and third row would be AAPL, etc. My example is in R, but any solution in python or R would be very helpful. I am not sure if there is already a function that does it or how the best approach would be to create a function if it does not exist.

You can use @Joshual Ulrich's TTR package to get a mapping of company names to tickers and perform lookups against your companynames object. Ideally, your list of names would be accurate / properly formatted, but since it's not you will have to do a bit of extra leg work to get some of the symbols. For example,

stock.symbols <- TTR::stockSymbols()
stock.symbols$adj_name <- gsub("[\\.\\,]", "", toupper(stock.symbols$Name)) # quick adjustments
##
companynames$Symbol <- sapply(companynames[,1], function(x) {
  stock.symbols[grep(x, stock.symbols$adj_name)[1], 1]
})
##
R> na.omit(companynames)
#                      V1        Symbol
#1                     AETNA INC    AET
#2                    ANTHEM INC   ANTM
#3                     APPLE INC   AAPL
#5             BARRICK GOLD CORP    ABX
#6               BEST BUY CO INC    BBY
#9                    CIGNA CORP     CI
#10       COMPUTER SCIENCES CORP    CSC
#13        DELPHI AUTOMOTIVE PLC   DLPH
#14              DST SYSTEMS INC    DST
#17                  EXPEDIA INC   EXPE
#18    FIFTH STREET FINANCE CORP    FSC
#19            GENERAL MOTORS CO     GM
#21     GREEN BRICK PARTNERS INC   GRBK
#22                    HESS CORP    HES
#23                   HUMANA INC    HUM
#24 HUNTINGTON INGALLS INDUSTRIE    HII
#25               LEGG MASON INC     LM
#27 MARVELL TECHNOLOGY GROUP LTD   MRVL
#28               MICROSOFT CORP   MSFT
#29              NCR CORPORATION    NCR
#30                      NVR INC    NVR
#31    OAKTREE CAPITAL GROUP LLC    OAK
#32 REPUBLIC AIRWAYS HOLDINGS IN   RJET
#33           SEAGATE TECHNOLOGY    STX
#36    STATE BANK FINANCIAL CORP   STBZ
#38     TESSERA TECHNOLOGIES INC   TSRA
#39       UNITEDHEALTH GROUP INC    UNH
#41                   XEROX CORP    XRX

So just using a few basic transformations (setting the Names column to uppercase and removing .s and ,s), you can match 28 out of 41 of the inputs. Most of the remaining non-matching cases could probably be solved by simple substitutions of either your input names or the adj_names column in stock.symbols, e.g. CORP vs CORPORATION, etc... And as pointed out in the comments above, if you have company names that aren't traded on any of the NASDAQ, AMEX, or NYSE exchanges, you will have to pull in some more external data.