Myujth

I have some trouble to convert my data.frame from a wide table to a long table.
At the moment it looks like this:

Code Country 1950 1951 1952 1953 1954
AFG Afghanistan 20,249 21,352 22,532 23,557 24,555
ALB Albania 8,097 8,986 10,058 11,123 12,246

Now I like to transform this data.frame into a long data.frame.
Something like this:

Code Country Year Value
AFG Afghanistan 1950 20,249
AFG Afghanistan 1951 21,352
AFG Afghanistan 1952 22,532
AFG Afghanistan 1953 23,557
AFG Afghanistan 1954 24,555
ALB Albania 1950 8,097
ALB Albania 1951 8,986
ALB Albania 1952 10,058
ALB Albania 1953 11,123
ALB Albania 1954 12,246

I have looked and tried it already with the melt() and the reshape() functions
as some people were suggesting similar questions.
However, so far I only get messy results.

If it is possible I would like to do it with the reshape() function since
it looks a little bit nicer to handle.

reshape() takes a while to get used to, just as melt/cast. Here is a solution with reshape, assuming your data frame is called d:

reshape(d, direction = "long", varying = list(names(d)[3:7]), v.names = "Value", 
 idvar = c("Code","Country"), timevar = "Year", times = 1950:1954)

Three alternative solutions:

1: With reshape2

library(reshape2)
long <- melt(wide, id.vars = c("Code", "Country"))

giving:

 Code Country variable value
1 AFG Afghanistan 1950 20,249
2 ALB Albania 1950 8,097
3 AFG Afghanistan 1951 21,352
4 ALB Albania 1951 8,986
5 AFG Afghanistan 1952 22,532
6 ALB Albania 1952 10,058
7 AFG Afghanistan 1953 23,557
8 ALB Albania 1953 11,123
9 AFG Afghanistan 1954 24,555
10 ALB Albania 1954 12,246

Some alternative notations that give the same result:

# you can also define the id-variables by column number
melt(wide, id.vars = 1:2)

# as an alternative you can also specify the measure-variables
# all other variables will then be used as id-variables
melt(wide, measure.vars = 3:7)
melt(wide, measure.vars = as.character(1950:1954))

2: With data.table

You can use the same melt function as in the reshape2 package (which is an extended & improved implementation). melt from data.table has also more parameters that the melt-function from reshape2. You can for example also specify the name of the variable-column:

library(data.table)
long <- melt(setDT(wide), id.vars = c("Code","Country"), variable.name = "year")

Some alternative notations:

melt(setDT(wide), id.vars = 1:2, variable.name = "year")
melt(setDT(wide), measure.vars = 3:7, variable.name = "year")
melt(setDT(wide), measure.vars = as.character(1950:1954), variable.name = "year")

3: With tidyr

library(tidyr)
long <- wide %>% gather(year, value, -c(Code, Country))

Some alternative notations:

wide %>% gather(year, value, -Code, -Country)
wide %>% gather(year, value, -1:-2)
wide %>% gather(year, value, -(1:2))
wide %>% gather(year, value, -1, -2)
wide %>% gather(year, value, 3:7)
wide %>% gather(year, value, `1950`:`1954`)

If you want to exclude NA values, you can add na.rm = TRUE to the melt as well as the gather functions.

Another problem with the data is that the values will be read by R as character-values (as a result of the , in the numbers). You can repair that with gsub and as.numeric:

long$value <- as.numeric(gsub(",", "", long$value))

Or directly with data.table or dplyr:

# data.table
long <- melt(setDT(wide),
 id.vars = c("Code","Country"),
 variable.name = "year")[, value := as.numeric(gsub(",", "", value))]

# tidyr and dplyr
long <- wide %>% gather(year, value, -c(Code,Country)) %>% 
 mutate(value = as.numeric(gsub(",", "", value)))

Data:

wide <- read.table(text="Code Country 1950 1951 1952 1953 1954
AFG Afghanistan 20,249 21,352 22,532 23,557 24,555
ALB Albania 8,097 8,986 10,058 11,123 12,246", header=TRUE, check.names=FALSE)

Using reshape package:

#data
x <- read.table(textConnection(
"Code Country 1950 1951 1952 1953 1954
AFG Afghanistan 20,249 21,352 22,532 23,557 24,555
ALB Albania 8,097 8,986 10,058 11,123 12,246"), header=TRUE)

library(reshape)

x2 <- melt(x, id = c("Code", "Country"), variable_name = "Year")
x2[,"Year"] <- as.numeric(gsub("X", "" , x2[,"Year"]))

Since this answer is tagged with r-faq, I felt it would be useful to share another alternative from base R: stack.

Note, however, that stack does not work with factors--it only works if is.vector is TRUE, and from the documentation for is.vector, we find that:

is.vector returns TRUE if x is a vector of the specified mode having no attributes other than names. It returns FALSE otherwise.

I'm using the sample data from @Jaap's answer, where the values in the year columns are factors.

Here's the stack approach:

cbind(wide[1:2], stack(lapply(wide[-c(1, 2)], as.character)))
## Code Country values ind
## 1 AFG Afghanistan 20,249 1950
## 2 ALB Albania 8,097 1950
## 3 AFG Afghanistan 21,352 1951
## 4 ALB Albania 8,986 1951
## 5 AFG Afghanistan 22,532 1952
## 6 ALB Albania 10,058 1952
## 7 AFG Afghanistan 23,557 1953
## 8 ALB Albania 11,123 1953
## 9 AFG Afghanistan 24,555 1954
## 10 ALB Albania 12,246 1954

Here is another example showing the use of gather from tidyr. You can select the columns to gather either by removing them individually (as I do here), or by including the years you want explicitly.

Note that, to handle the commas (and X's added if check.names = FALSE is not set), I am also using dplyr's mutate with parse_number from readr to convert the text values back to numbers. These are all part of the tidyverse and so can be loaded together with library(tidyverse)

wide %>%
 gather(Year, Value, -Code, -Country) %>%
 mutate(Year = parse_number(Year)
 , Value = parse_number(Value))

Returns:

 Code Country Year Value
1 AFG Afghanistan 1950 20249
2 ALB Albania 1950 8097
3 AFG Afghanistan 1951 21352
4 ALB Albania 1951 8986
5 AFG Afghanistan 1952 22532
6 ALB Albania 1952 10058
7 AFG Afghanistan 1953 23557
8 ALB Albania 1953 11123
9 AFG Afghanistan 1954 24555
10 ALB Albania 1954 12246