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 would 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 at and already tried using the melt()
and the reshape()
functions as some people were suggesting in 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.
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:
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)
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
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 factor
s--it only works if is.vector
is TRUE
, and from the documentation for is.vector
, we find that:
is.vector
returnsTRUE
if x is a vector of the specified mode having no attributes other than names . It returnsFALSE
otherwise.
I'm using the sample data from @Jaap's answer , where the values in the year columns are factor
s.
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:
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.
you can also see many examples in R cookbook
olddata_wide <- read.table(header=TRUE, text='
subject sex control cond1 cond2
1 M 7.9 12.3 10.7
2 F 6.3 10.6 11.1
3 F 9.5 13.1 13.8
4 M 11.5 13.4 12.9
')
# Make sure the subject column is a factor
olddata_wide$subject <- factor(olddata_wide$subject)
olddata_long <- read.table(header=TRUE, text='
subject sex condition measurement
1 M control 7.9
1 M cond1 12.3
1 M cond2 10.7
2 F control 6.3
2 F cond1 10.6
2 F cond2 11.1
3 F control 9.5
3 F cond1 13.1
3 F cond2 13.8
4 M control 11.5
4 M cond1 13.4
4 M cond2 12.9
')
# Make sure the subject column is a factor
olddata_long$subject <- factor(olddata_long$subject)
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