Here is a tidyverse option that might work depending on the data, and some caveats on its usage:
library(tidyverse)
starting_df %>%
rownames_to_column() %>%
gather(variable, value, -rowname) %>%
spread(rowname, value)
rownames_to_column() is necessary if the original dataframe has meaningful row names, otherwise the new column names in the new transposed dataframe will be integers corresponding to the orignal row number. If there are no meaningful row names you can skip rownames_to_column() and replace rowname with the name of the first column in the dataframe, assuming those values are unique and meaningful. Using the tidyr::smiths sample data would be:
smiths %>%
gather(variable, value, -subject) %>%
spread(subject, value)
Using the example starting_df with the tidyverse approach will throw a warning message about dropping attributes. This is related to converting columns with different attribute types into a single character column. The smiths data will not give that warning because all columns except for subject are doubles.
The earlier answer using as.data.frame(t()) will convert everything to a factor
if there are mixed column types unless stringsAsFactors = FALSE is added,
whereas the tidyverse option converts everything to a character by default if
there are mixed column types.