Transform Data in R -

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i have uploaded data set r. dataset has 2 columns

user_id merchant_id 514729  14852,16695 1240327 23590 7457    211 359027  2483 463149  5802 514730  5460,1896 41953   7183,147105 927805  304,3909,4151,32,3,39171

as can see user ids associated multiple merchants. looking transform data in such way have following schema

user id merchantid1 merchantid2 merchantid3 merchantid 4 123445  0           1           0           1 123453  1           0           0           0

basically want create matrix of userid , merchant ids 1 or 0 based on if user_id has merchant_id or not.

any suggestions/help on how can accomplish this?

i looking use build recommendation system. great.

my interpretation after following:

library(splitstackshape) csplit_e(mydf, "merchant_id", ",", type = "character", fill = 0) ##   user_id merchant_id merchant_id_147105 merchant_id_14852 merchant_id_16695 ## 1  514729 14852,16695                  0                 1                 1 ## 2 1240327       23590                  0                 0                 0 ## 3    7457         211                  0                 0                 0 ## 4  359027        2483                  0                 0                 0 ## 5  463149        5802                  0                 0                 0 ## 6  514730   5460,1896                  0                 0                 0 ## 7   41953 7183,147105                  1                 0                 0 ##   merchant_id_1896 merchant_id_211 merchant_id_23590 merchant_id_2483 ## 1                0               0                 0                0 ## 2                0               0                 1                0 ## 3                0               1                 0                0 ## 4                0               0                 0                1 ## 5                0               0                 0                0 ## 6                1               0                 0                0 ## 7                0               0                 0                0 ##   merchant_id_5460 merchant_id_5802 merchant_id_7183 ## 1                0                0                0 ## 2                0                0                0 ## 3                0                0                0 ## 4                0                0                0 ## 5                0                1                0 ## 6                1                0                0 ## 7                0                0                1

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