Melt function error: Error in match.names(clabs, names(xi)) : names do not match previous names

Question

I need to transform this dataset where there is a column/date and a row/variable/name:

Variable 1 (%), Variable 2($):

Name	Date 1	Date 2	Date 3
Name1	Value11%	Value12%	Value13%
Name2	Value21%	Value22%	Value23%
Name3	Value31%	Value32%	Value33%
Name1	Value11$	Value12$	Value13$
Name2	Value21$	Value22$	Value23$
Name3	Value31$	Value32$	Value33$

To become a dataset where there is a column/variable and a row/date/name:

Consolidated dataset:

Name	Date	Variable 1	Variable 2
Name1	Date 1	Value11$.	Value11%.
Name1	Date 2	Value12$.	Value12%.
Name1	Date 3	Value13$.	Value13%.
Name2	Date 1	Value21$.	Value21%.
Name2	Date 2	Value22$.	Value22%.
Name2	Date 3	Value23$.	Value23%.
Name3	Date 1	Value31$.	Value31%.
Name3	Date 2	Value32$.	Value32%.
Name3	Date 3	Value33$.	Value33%.

Right now, I have tried to use the first data set with the function melt with the name as id:

melt(data, id="Name)

Unfortunately, I have this error message: Error in match.names(clabs, names(xi)) : names do not match previous names

Which can be the mistake? Are there any other options to achieve my goal?

Alternatively, I have tried to import the data from the two variables in two different datasets and have used rbind to integrate them and checck through the indetical function if the column names were the sames and the result was TRUE. I do not know which can be the reason for this error.

Thank you so much for any help folks!

Data:

data <- data.frame(
  stringsAsFactors = FALSE,
              Name = c("Name1", "Name2", "Name3", "Name1", "Name2", "Name3"),
            Date.1 = c("Value11%","Value21%",
                       "Value31%","Value11$","Value21$","Value31$"),
            Date.2 = c("Value12%","Value22%",
                       "Value32%","Value12$","Value22$","Value32$"),
            Date.3 = c("Value13%","Value23%",
                       "Value33%","Value13$","Value23$","Value33$")
)

Answer 1

Instead of using melt() and cast() from the reshape2 package it is recommened to use pivot_wider() and pivot_longer() from the tidyr package for pivoting/reshaping between wide and long data formats.

Here is what that could look like for your data:

library(tidyverse)

data |>
  pivot_longer(-Name,
               names_to = "Date",
               values_to = "Variable1")
#> # A tibble: 18 × 3
#>    Name  Date   Variable1
#>    <chr> <chr>  <chr>    
#>  1 Name1 Date.1 Value11% 
#>  2 Name1 Date.2 Value12% 
#>  3 Name1 Date.3 Value13% 
#>  4 Name2 Date.1 Value21% 
#>  5 Name2 Date.2 Value22% 
#>  6 Name2 Date.3 Value23% 
#>  7 Name3 Date.1 Value31% 
#>  8 Name3 Date.2 Value32% 
#>  9 Name3 Date.3 Value33% 
#> 10 Name1 Date.1 Value11$ 
#> 11 Name1 Date.2 Value12$ 
#> 12 Name1 Date.3 Value13$ 
#> 13 Name2 Date.1 Value21$ 
#> 14 Name2 Date.2 Value22$ 
#> 15 Name2 Date.3 Value23$ 
#> 16 Name3 Date.1 Value31$ 
#> 17 Name3 Date.2 Value32$ 
#> 18 Name3 Date.3 Value33$