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I did a rfm analysis using package "rfm". The results are in tibble and I can't seem to figure out how to export it to .csv. I tried argument below but it exported a blank file.
> dim(bmdata4RFM)
[1] 1182580 3
> str(bmdata4RFM)
'data.frame': 1182580 obs. of 3 variables:
$ customer_ID: num 0 0 0 0 0 0 0 0 0 0 ...
$ sales_date : Factor w/ 366 levels "1/1/2018 0:00:00",..: 267 275 286 297 300 301 302 303 304 305 ...
$ sales : num 101541 110543 60932 75472 43588 ...
> head(bmdata4RFM,5)
customer_ID sales_date sales
1 0 6/30/2017 0:00:00 101540.70
2 0 7/1/2017 0:00:00 110543.35
3 0 7/2/2017 0:00:00 60932.20
4 0 7/3/2017 0:00:00 75471.93
5 0 7/4/2017 0:00:00 43587.70
> library(rfm)
> # convert date from factor to date format
> bmdata4RFM[,2] <- as.Date(as.character(bmdata4RFM[,2]), format = "%m/%d/%Y")
> rfm_result_v2
# A tibble: 535,868 x 9
customer_id date_most_recent recency_days transaction_count amount recency_score frequency_score monetary_score rfm_score
<dbl> <date> <dbl> <dbl> <dbl> <int> <int> <int> <dbl>
1 0 2018-06-30 12 366 42462470. 5 5 5 555
2 1 2018-06-30 12 20 2264. 5 5 5 555
3 2 2018-01-12 181 24 1689 3 5 5 355
4 3 2018-05-04 69 27 1984. 4 5 5 455
5 6 2017-12-07 217 12 922. 2 5 5 255
6 7 2018-01-15 178 19 1680. 3 5 5 355
7 9 2018-01-05 188 19 2106 2 5 5 255
8 20 2018-04-11 92 4 414. 4 5 5 455
9 26 2018-02-10 152 1 72 3 1 2 312
10 48 2017-12-20 204 1 90 2 1 3 213
11 68 2017-09-30 285 1 37 1 1 1 111
12 70 2017-12-17 207 1 18 2 1 1 211
13 104 2017-08-11 335 1 90 1 1 3 113
14 120 2017-07-27 350 1 19 1 1 1 111
15 134 2018-01-13 180 1 275 3 1 4 314
16 153 2018-06-24 18 10 1677 5 5 5 555
17 155 2018-05-28 45 1 315 5 1 4 514
18 171 2018-06-11 31 6 3485. 5 5 5 555
19 172 2018-05-24 49 1 93 5 1 3 513
20 174 2018-06-06 36 3 347. 5 4 5 545
# ... with 535,858 more rows
> write.csv(rfm_result_v2,"bmdataRFMFunction_output071218v2.csv")
465
The problem seems to be that the result of the rfm_table_order is not only a tibble: looking at this question already solved, and using its data, you can know this: Show activity on this post. OP asks for a CSV output.
Set the destination path. Path + filename + extension i.e. "/Users/USERNAME/Downloads/mydata.csv" or the filename + extension if the folder is the same as the working directory Note: You can use the function write.csv in R as write.csv2 () to separate the rows with a semicolon for R export to csv data.
It looks like you have a matrix instead of a tibble. In any case, you can use the write.xlsx function of the openxlsx package. Also, you can round the values of the matrix with the round () function.
Just as the windows users, you can save data with the function write.xlsx () Exporting data to different software is as simple as importing them. The library “haven” provides a convenient way to export data to First of all, import the library.
OP asks for a CSV output.
Being very picky, write.table(rfm_result$rfm , file = "your_path\\df.csv") creates a TSV.
If you want a CSV add the sep="," parameter and also you'l likely want to not write out the row names so also use row.names=FALSE
write.table(rfm_result$rfm , file = "your_path\\df.csv", sep=",", row.names=FALSE)
The problem seems to be that the result of the rfm_table_order is not only a tibble: looking at this question already solved, and using its data, you can know this:
> class(rfm_result)
[1] "rfm_table_order" "tibble" "data.frame"
So if for example choose this:
> rfm_result$rfm
# A tibble: 325 x 9
customer_id date_most_recent recency_days transaction_count amount recency_score frequency_score monetary_score rfm_score
<int> <date> <dbl> <dbl> <int> <int> <int> <int> <dbl>
1 1 2017-08-06 353 1 145 4 1 2 412
2 2 2016-10-15 648 1 268 2 1 3 213
3 5 2016-12-14 588 1 119 3 1 1 311
4 7 2017-04-27 454 1 290 3 1 3 313
5 8 2016-12-07 595 3 835 2 5 5 255
6 10 2017-07-31 359 1 192 4 1 2 412
7 11 2017-08-16 343 1 278 4 1 3 413
8 12 2017-10-14 284 2 294 5 4 3 543
9 15 2016-07-12 743 1 206 2 1 2 212
10 17 2017-05-22 429 2 405 4 4 4 444
# ... with 315 more rows
You can export it with this command:
write.table(rfm_result$rfm , file = "your_path\\df.csv")
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