Separate values from category in the same column-CodePudding

I have a column that looks like this

value 
Ford
2,300
Chevy
15
Toyota
245

And I want to transform it and I'm looking for something like this

Brand     Value
Ford      2,300
Chevy     15
Toyota    245

What's the best way to get here using a tidyvere solution?

CodePudding user response：

If these are alternate elements, easier option is to use a recycling logical vector to extract in base R

data.frame(Brand = df1$value[c(TRUE, FALSE)], Value = df1$value[c(FALSE, TRUE)])
   Brand Value
1   Ford 2,300
2  Chevy    15
3 Toyota   245

data

df1 <- structure(list(value = c("Ford", "2,300", "Chevy", "15", "Toyota", 
"245")), class = "data.frame", row.names = c(NA, -6L))

CodePudding user response：

You can also use dplyr::summarize() with @akrun's simple solution.

library(dplyr)

summarize(df1, Brand = value[c(T, F)], value = value[c(F, T)])
#>    Brand value
#> 1   Ford 2,300
#> 2  Chevy    15
#> 3 Toyota   245

CodePudding user response：

We could use lag() and filter out:

library(dplyr)

df1 %>% 
  mutate(Brand = lag(value)) %>% 
  filter(row_number() %% 2 == 0) %>% 
  select(Brand, Value=value)

   Brand Value
1   Ford 2,300
2  Chevy    15
3 Toyota   245

CodePudding user response：

Yet another solution:

library(tidyverse)

df <- data.frame(
  stringsAsFactors = FALSE,
  value = c("Ford", "2300", "Chevy", "15", "Toyota", "245")
)

filter(df, row_number() %% 2 == 1) %>% 
  {bind_cols(set_names(.,"brand"), filter(df, row_number() %% 2 == 0))}

#>    brand value
#> 1   Ford  2300
#> 2  Chevy    15
#> 3 Toyota   245