I have the following two tibbles:
pc.tbl
Date status_is `Consumption France`
<date> <chr> <dbl>
1 2018-03-21 Forecast -1364
2 2018-03-22 Forecast -1368
3 2018-03-23 Forecast -1294
4 2018-03-24 Forecast -1050
5 2018-03-25 Forecast -1036
6 2018-03-26 Forecast -1223
nt
Date `Seasonal Normal Temperature` geography_is status_is `Consumption Residential France` `TIGF Consumption` `Consumption Industrial France`
<date> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
1 2018-04-09 9.93 France Normal 1070 96.0 427
2 2018-04-10 10.0 France Normal 1060 95.0 426
3 2018-04-11 10.1 France Normal 1050 94.0 425
4 2018-04-12 10.2 France Normal 1040 94.0 424
5 2018-04-13 10.4 France Normal 1030 93.0 423
6 2018-04-14 10.5 France Normal 910 82.0 393
When I use full join:
p = full_join(pc.tbl, nt), by = "Date")
I get:
Date status_is.x `Consumption France` `Seasonal Normal Temperature` geography_is status_is.y `Consumption Residential France` `TIGF Consumption` `Consumption Industr~
<date> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
1 2018-03-21 Forecast -1364 NA NA NA NA NA NA
2 2018-03-22 Forecast -1368 NA NA NA NA NA NA
3 2018-03-23 Forecast -1294 NA NA NA NA NA NA
4 2018-03-24 Forecast -1050 NA NA NA NA NA NA
5 2018-03-25 Forecast -1036 NA NA NA NA NA NA
6 2018-03-26 Forecast -1223 NA NA NA NA NA NA
7 2018-03-27 Forecast -1142 NA NA NA NA NA NA
8 2018-04-09 NA NA 9.93 France Normal 1070 96.0 427
9 2018-04-10 NA NA 10.0 France Normal 1060 95.0 426
10 2018-04-11 NA NA 10.1 France Normal 1050 94.0 425
Obviously I do not want the column status_is to be split in 2. If you look at the types of the columns, they are both characters. I also checked to see if the names are equal and they are:
names(nt)[4] == names(pc.tbl)[2]
[1] TRUE
I am really stumped here
