Transpose a dataframe into R

Question

I have a dataframe in R, columns have dates and values and rows have countries.

 LOCATION jan 1999 fev 1999  mar 1999
 EU28     86.70000 85.50000  85.90000
 JPN      99.76086 99.76086  101.47350
 USA      90.87153 91.62469  91.56094

I tried to transpose using the function t, however, it transforms the dataframe into an array, and would like it to continue as a dataframe, however, as follows:

    Data      EU28  JPN        USA
    jan 1999  86.7  99.76086   90.87153
    fev 1999  85.5  99.76086   91.62469
    mar 1999  85.9  101.47350  91.56094

Function I used: df <- t(df)

Answer 1

Just do:

as.data.frame(t(df))

EDIT: As I said in the comment, maybe it’s better to use '_' instead of space in the name of variables. In this case, we have:

> df
    LOCATION jan_1999 fev_1999  mar_1999
1  EU28      86.70000 85.50000  85.90000
2  JPN       99.76086 99.76086 101.47350
3  USA       90.87153 91.62469  91.56094

After the application of the function:

> as.data.frame(t(df))
                 V1         V2         V3
LOCATION  EU28       JPN        USA      
jan_1999   86.70000   99.76086   90.87153
fev_1999   85.50000   99.76086   91.62469
mar_1999   85.90000  101.47350   91.56094

Note that you will need to delete the first row and rename the columns to reach the output desired

Answer 2

You can do what the question asks with the xtabs after reformatting the data from wide format to long format.
In this solution row.names(df2) gives the dates.

df2 <- reshape2::melt(df, id.vars = 'LOCATION')
df2 <- xtabs(value ~ variable + LOCATION, df2)
df2 <- as.data.frame.matrix(df2)
#         EU28       JPN      USA
#jan.1999 86.7  99.76086 90.87153
#fev.1999 85.5  99.76086 91.62469
#mar.1999 85.9 101.47350 91.56094

To have a column with the dates may be

df2 <- cbind.data.frame(Data = row.names(df2), df2)
row.names(df2) <- NULL
df2
#      Data EU28       JPN      USA
#1 jan.1999 86.7  99.76086 90.87153
#2 fev.1999 85.5  99.76086 91.62469
#3 mar.1999 85.9 101.47350 91.56094

Another way is with the package dplyr. In the solution below the first column is the column of dates.

library(tidyverse)

df %>%
  gather(variable, value, -LOCATION) %>%
  xtabs(formula = value ~ variable + LOCATION, data = .) %>%
  as.data.frame.matrix() %>% 
  rownames_to_column(var = "Data")
#      Data EU28       JPN      USA
#1 fev.1999 85.5  99.76086 91.62469
#2 jan.1999 86.7  99.76086 90.87153
#3 mar.1999 85.9 101.47350 91.56094

Dice.

df <- read.table(text = "
LOCATION 'jan 1999' 'fev 1999'  'mar 1999'
 EU28     86.70000 85.50000  85.90000
 JPN      99.76086 99.76086  101.47350
 USA      90.87153 91.62469  91.56094
", header = TRUE)