Reshaping data.frame from wide to long format

Three alternative solutions:

1) 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")

which gives:

> long
    Code     Country year  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:

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")

2) 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`)

3) With reshape2:

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

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))

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NOTES:

• reshape2 is retired. Only changes necessary to keep it on CRAN will be made. (source)
• If you want to exclude NA values, you can add na.rm = TRUE to the melt as well as the gather functions.

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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)