table restructure split R [closed] - r

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I have a table where I have a variable Technology, which includes "AllRenewables", "Biomass","Solar","Offshore wind", "Onshore wind" and "Wind".
I would like that the "All Renewables" is split into "Biomass","Solar","Offshore wind", "Onshore wind" and that "Wind" technology should be split into ""Offshore wind", "Onshore wind".
The table looks approximately as follows:
Table
Year Country Technology Changes
2000 A Solar 1
2000 A Wind 2
2000 A Onshore wind 2
2000 A All Renewables 3
It should look as follows after the re-structuring:
Table
Year Country Technology Changes
2000 A Solar 1
2000 A Onshore wind 2
2000 A Offshore wind 2
2000 A Onshore wind 3
2000 A Biomass 3
2000 A Solar 3
2000 A Onshore wind 3
2000 A Offshore wind 3
If anybody could help, I would be really really thankful.
Sarah

Answer 1

You could rename factor levels and use tidyr::separate_rows
lvls <- c(
"Biomass, Solar, Offshore wind, Onshore wind",
"Onshore wind",
"Solar",
"Offshore wind, Onshore wind")
levels(df$Technology) <- lvls;
library(tidyverse)
df %>% separate_rows(Technology, sep = ", ") %>%
group_by_all() %>%
slice(1) %>%
ungroup() %>%
arrange(Changes)
## A tibble: 7 x 4
# Year Country Technology Changes
# <int> <fct> <chr> <int>
#1 2000 A Solar 1
#2 2000 A Offshore wind 2
#3 2000 A Onshore wind 2
#4 2000 A Biomass 3
#5 2000 A Offshore wind 3
#6 2000 A Onshore wind 3
#7 2000 A Solar 3
Explanation: We redefine factor levels such that "All Renewables" becomes "Biomass, Solar, Offshore wind, Onshore wind" and "Wind" becomes "Offshore wind, Onshore wind". Then we use tidyr::separate_rows to split entries with a comma into separate rows. All that remains are removal of duplicates and re-ordering of rows.
Sample data
df <- read.table(text =
"Year Country Technology Changes
2000 A 'Solar' 1
2000 A 'Wind' 2
2000 A 'Onshore wind' 2
2000 A 'All Renewables' 3", header = T)

Answer 2

Just a question of merging (with tidyverse) :
# Your data:
df <- read.csv(textConnection("Y, A, B, C
2000,A,Solar,1
2000,A,Wind,2
2000,A,Onshore wind,2
2000,A,All Renewables,3"),stringsAsFactors=FALSE)
# Your synonyms:
c <- read.csv(textConnection("B, D
All Renewables,Biomass
All Renewables,Solar
All Renewables,Offshore wind
All Renewables,Onshore wind
Wind,Offshore wind
Wind,Onshore wind"),stringsAsFactors=FALSE)
df %>% left_join(c,by="B") %>% mutate(B=coalesce(D,B)) %>% select(-D)
# Y A B C
#1 2000 A Solar 1
#2 2000 A Offshore wind 2
#3 2000 A Onshore wind 2
#4 2000 A Onshore wind 2
#5 2000 A Biomass 3
#6 2000 A Solar 3
#7 2000 A Offshore wind 3
#8 2000 A Onshore wind 3