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))

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.

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)

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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)

---

With tidyr_1.0.0, another option is pivot_longer

library(tidyr)
pivot_longer(df1, -c(Code, Country), values_to = "Value", names_to = "Year")
# A tibble: 10 x 4
#   Code  Country     Year  Value 
#   <fct> <fct>       <chr> <fct> 
# 1 AFG   Afghanistan 1950  20,249
# 2 AFG   Afghanistan 1951  21,352
# 3 AFG   Afghanistan 1952  22,532
# 4 AFG   Afghanistan 1953  23,557
# 5 AFG   Afghanistan 1954  24,555
# 6 ALB   Albania     1950  8,097 
# 7 ALB   Albania     1951  8,986 
# 8 ALB   Albania     1952  10,058
# 9 ALB   Albania     1953  11,123
#10 ALB   Albania     1954  12,246

data

df1 <- structure(list(Code = structure(1:2, .Label = c("AFG", "ALB"), class = "factor"), 
    Country = structure(1:2, .Label = c("Afghanistan", "Albania"
    ), class = "factor"), `1950` = structure(1:2, .Label = c("20,249", 
    "8,097"), class = "factor"), `1951` = structure(1:2, .Label = c("21,352", 
    "8,986"), class = "factor"), `1952` = structure(2:1, .Label = c("10,058", 
    "22,532"), class = "factor"), `1953` = structure(2:1, .Label = c("11,123", 
    "23,557"), class = "factor"), `1954` = structure(2:1, .Label = c("12,246", 
    "24,555"), class = "factor")), class = "data.frame", row.names = c(NA, 
-2L))

---

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

---

Here's a sqldf solution:

sqldf("Select Code, Country, '1950' As Year, `1950` As Value From wide
        Union All
       Select Code, Country, '1951' As Year, `1951` As Value From wide
        Union All
       Select Code, Country, '1952' As Year, `1952` As Value From wide
        Union All
       Select Code, Country, '1953' As Year, `1953` As Value From wide
        Union All
       Select Code, Country, '1954' As Year, `1954` As Value From wide;")

To make the query without typing in everything, you can use the following:

Thanks to G. Grothendieck for implementing it.

ValCol <- tail(names(wide), -2)

s <- sprintf("Select Code, Country, '%s' As Year, `%s` As Value from wide", ValCol, ValCol)
mquery <- paste(s, collapse = "\n Union All\n")

cat(mquery) #just to show the query
 #> Select Code, Country, '1950' As Year, `1950` As Value from wide
 #>  Union All
 #> Select Code, Country, '1951' As Year, `1951` As Value from wide
 #>  Union All
 #> Select Code, Country, '1952' As Year, `1952` As Value from wide
 #>  Union All
 #> Select Code, Country, '1953' As Year, `1953` As Value from wide
 #>  Union All
 #> Select Code, Country, '1954' As Year, `1954` As Value from wide

sqldf(mquery)

 #>    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

Unfortunately, I don't think that PIVOT and UNPIVOT would work for R SQLite. If you want to write up your query in a more sophisticated manner, you can also take a look at these posts:

Using sprintf writing up sql queries

Pass variables to sqldf

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You can also use the cdata package, which uses the concept of (transformation) control table:

# 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)

library(cdata)
# build control table
drec <- data.frame(
    Year=as.character(1950:1954),
    Value=as.character(1950:1954),
    stringsAsFactors=FALSE
)
drec <- cdata::rowrecs_to_blocks_spec(drec, recordKeys=c("Code", "Country"))

# apply control table
cdata::layout_by(drec, wide)

I am currently exploring that package and find it quite accessible. It is designed for much more complicated transformations and includes the backtransformation. There is a tutorial available.

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