I would suggest looking at this chapter of R4DS as it will help you take the data and make it tidy. https://r4ds.had.co.nz/tidy-data.html but this example might get you started
library(tidyverse)
library(lubridate)
#>
#> Attaching package: 'lubridate'
#> The following object is masked from 'package:base':
#>
#> date
exampdat <- tibble(
Dates=seq(ymd('2000-02-19'),ymd('2001-01-19'), by = 'months'),
NovoNordisk_Capital=c(rep(6116000, 11), 7858000),
NovoNordisk_Assets =c(rep(24593000, 11), 2890500),
NovoNordisk_DividendYield=c(9.73, .7, .94, .73, .74, .66, .58, .56, .58, .59, .62, .55),
OtherComp_Capital=c(rep(6116000, 11), 7858000),
OtherComp_Assets =c(rep(24593000, 11), 2890500),
OtherComp_DividendYield=c(9.73, .7, .94, .73, .74, .66, .58, .56, .58, .59, .62, .55)
)
exampdat %>%
pivot_longer(-Dates) %>%
separate(name, into=c("Company", "Type")) %>%
pivot_wider(names_from="Type", values_from="value")
#> # A tibble: 24 x 5
#> Dates Company Capital Assets DividendYield
#> <date> <chr> <dbl> <dbl> <dbl>
#> 1 2000-02-19 NovoNordisk 6116000 24593000 9.73
#> 2 2000-02-19 OtherComp 6116000 24593000 9.73
#> 3 2000-03-19 NovoNordisk 6116000 24593000 0.7
#> 4 2000-03-19 OtherComp 6116000 24593000 0.7
#> 5 2000-04-19 NovoNordisk 6116000 24593000 0.94
#> 6 2000-04-19 OtherComp 6116000 24593000 0.94
#> 7 2000-05-19 NovoNordisk 6116000 24593000 0.73
#> 8 2000-05-19 OtherComp 6116000 24593000 0.73
#> 9 2000-06-19 NovoNordisk 6116000 24593000 0.74
#> 10 2000-06-19 OtherComp 6116000 24593000 0.74
#> # ... with 14 more rows
Created on 2020-02-24 by the reprex package (v0.3.0)