How to reshape data from long to wide format

Using reshape function:

reshape(dat1, idvar = "name", timevar = "numbers", direction = "wide")

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The new (in 2014) tidyr package also does this simply, with gather()/spread() being the terms for melt/cast.

Edit: Now, in 2019, tidyr v 1.0 has launched and set spread and gather on a deprecation path, preferring instead pivot_wider and pivot_longer, which you can find described in this answer. Read on if you want a brief glimpse into the brief life of spread/gather.

library(tidyr)
spread(dat1, key = numbers, value = value)

From github,

tidyr is a reframing of reshape2 designed to accompany the tidy data framework, and to work hand-in-hand with magrittr and dplyr to build a solid pipeline for data analysis. Just as reshape2 did less than reshape, tidyr does less than reshape2. It's designed specifically for tidying data, not the general reshaping that reshape2 does, or the general aggregation that reshape did. In particular, built-in methods only work for data frames, and tidyr provides no margins or aggregation.

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You can do this with the reshape() function, or with the melt() / cast() functions in the reshape package. For the second option, example code is

library(reshape)
cast(dat1, name ~ numbers)

Or using reshape2

library(reshape2)
dcast(dat1, name ~ numbers)

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Another option if performance is a concern is to use data.table's extension of reshape2's melt & dcast functions

(Reference: Efficient reshaping using data.tables)

library(data.table)

setDT(dat1)
dcast(dat1, name ~ numbers, value.var = "value")

#          name          1          2         3         4
# 1:  firstName  0.1836433 -0.8356286 1.5952808 0.3295078
# 2: secondName -0.8204684  0.4874291 0.7383247 0.5757814

And, as of data.table v1.9.6 we can cast on multiple columns

## add an extra column
dat1[, value2 := value * 2]

## cast multiple value columns
dcast(dat1, name ~ numbers, value.var = c("value", "value2"))

#          name    value_1    value_2   value_3   value_4   value2_1   value2_2 value2_3  value2_4
# 1:  firstName  0.1836433 -0.8356286 1.5952808 0.3295078  0.3672866 -1.6712572 3.190562 0.6590155
# 2: secondName -0.8204684  0.4874291 0.7383247 0.5757814 -1.6409368  0.9748581 1.476649 1.1515627

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With the devel version of tidyr ‘0.8.3.9000’, there is pivot_wider and pivot_longer which is generalized to do the reshaping (long -> wide, wide -> long, respectively) from 1 to multiple columns. Using the OP's data

-single column long -> wide

library(dplyr)
library(tidyr)
dat1 %>% 
    pivot_wider(names_from = numbers, values_from = value)
# A tibble: 2 x 5
#  name          `1`    `2`    `3`    `4`
#  <fct>       <dbl>  <dbl>  <dbl>  <dbl>
#1 firstName   0.341 -0.703 -0.380 -0.746
#2 secondName -0.898 -0.335 -0.501 -0.175

-> created another column for showing the functionality

dat1 %>% 
    mutate(value2 = value * 2) %>% 
    pivot_wider(names_from = numbers, values_from = c("value", "value2"))
# A tibble: 2 x 9
#  name       value_1 value_2 value_3 value_4 value2_1 value2_2 value2_3 value2_4
#  <fct>        <dbl>   <dbl>   <dbl>   <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
#1 firstName    0.341  -0.703  -0.380  -0.746    0.682   -1.41    -0.759   -1.49 
#2 secondName  -0.898  -0.335  -0.501  -0.175   -1.80    -0.670   -1.00    -0.349

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Using your example dataframe, we could:

xtabs(value ~ name + numbers, data = dat1)

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Other two options:

Base package:

df <- unstack(dat1, form = value ~ numbers)
rownames(df) <- unique(dat1$name)
df

sqldf package:

library(sqldf)
sqldf('SELECT name,
      MAX(CASE WHEN numbers = 1 THEN value ELSE NULL END) x1, 
      MAX(CASE WHEN numbers = 2 THEN value ELSE NULL END) x2,
      MAX(CASE WHEN numbers = 3 THEN value ELSE NULL END) x3,
      MAX(CASE WHEN numbers = 4 THEN value ELSE NULL END) x4
      FROM dat1
      GROUP BY name')

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Using base R aggregate function:

aggregate(value ~ name, dat1, I)

# name           value.1  value.2  value.3  value.4
#1 firstName      0.4145  -0.4747   0.0659   -0.5024
#2 secondName    -0.8259   0.1669  -0.8962    0.1681

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The base reshape function works perfectly fine:

df <- data.frame(
  year   = c(rep(2000, 12), rep(2001, 12)),
  month  = rep(1:12, 2),
  values = rnorm(24)
)
df_wide <- reshape(df, idvar="year", timevar="month", v.names="values", direction="wide", sep="_")
df_wide

Where

idvar is the column of classes that separates rows

timevar is the column of classes to cast wide

v.names is the column containing numeric values

direction specifies wide or long format

the optional sep argument is the separator used in between timevar class names and v.names in the output data.frame.

If no idvar exists, create one before using the reshape() function:

df$id   <- c(rep("year1", 12), rep("year2", 12))
df_wide <- reshape(df, idvar="id", timevar="month", v.names="values", direction="wide", sep="_")
df_wide

Just remember that idvar is required! The timevar and v.names part is easy. The output of this function is more predictable than some of the others, as everything is explicitly defined.

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There's very powerful new package from genius data scientists at Win-Vector (folks that made vtreat, seplyr and replyr) called cdata. It implements "coordinated data" principles described in this document and also in this blog post. The idea is that regardless how you organize your data, it should be possible to identify individual data points using a system of "data coordinates". Here's a excerpt from the recent blog post by John Mount:

The whole system is based on two primitives or operators cdata::moveValuesToRowsD() and cdata::moveValuesToColumnsD(). These operators have pivot, un-pivot, one-hot encode, transpose, moving multiple rows and columns, and many other transforms as simple special cases. It is easy to write many different operations in terms of the cdata primitives. These operators can work-in memory or at big data scale (with databases and Apache Spark; for big data use the cdata::moveValuesToRowsN() and cdata::moveValuesToColumnsN() variants). The transforms are controlled by a control table that itself is a diagram of (or picture of) the transform.

We will first build the control table (see blog post for details) and then perform the move of data from rows to columns.

library(cdata)
# first build the control table
pivotControlTable <- buildPivotControlTableD(table = dat1, # reference to dataset
                        columnToTakeKeysFrom = 'numbers', # this will become column headers
                        columnToTakeValuesFrom = 'value', # this contains data
                        sep="_")                          # optional for making column names

# perform the move of data to columns
dat_wide <- moveValuesToColumnsD(tallTable =  dat1, # reference to dataset
                    keyColumns = c('name'),         # this(these) column(s) should stay untouched 
                    controlTable = pivotControlTable# control table above
                    ) 
dat_wide

#>         name  numbers_1  numbers_2  numbers_3  numbers_4
#> 1  firstName  0.3407997 -0.7033403 -0.3795377 -0.7460474
#> 2 secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357

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much easier way!

devtools::install_github("yikeshu0611/onetree") #install onetree package

library(onetree)
widedata=reshape_toWide(data = dat1,id = "name",j = "numbers",value.var.prefix = "value")
widedata

        name     value1     value2     value3     value4
   firstName  0.3407997 -0.7033403 -0.3795377 -0.7460474
  secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357

if you want to go back from wide to long, only change Wide to Long, and no changes in objects.

reshape_toLong(data = widedata,id = "name",j = "numbers",value.var.prefix = "value")

        name numbers      value
   firstName       1  0.3407997
  secondName       1 -0.8981073
   firstName       2 -0.7033403
  secondName       2 -0.3347941
   firstName       3 -0.3795377
  secondName       3 -0.5013782
   firstName       4 -0.7460474
  secondName       4 -0.1745357

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Using only dplyr and map.

library(dplyr)
library(purrr)
set.seed(45)
dat1 <- data.frame(
  name = rep(c("firstName", "secondName"), each=4),
  numbers = rep(1:4, 2), value = rnorm(8)
)
longer_to_wider <- function(data, name_from, value_from){
  group <- colnames(data)[!(colnames(data) %in% c(name_from,value_from))]
  data %>% group_by(.data[[group]]) %>%
    summarise( name = list(.data[[name_from]]), 
               value = list(.data[[value_from]])) %>%
    {
      d <- data.frame(
        name = .[[name_from]] %>% unlist() %>% unique()
      )
      e <- map_dfc(.[[group]],function(x){
          y <- data_frame(
            x = data %>% filter(.data[[group]] == x) %>% pull(value_from)
          )
          colnames(y) <- x
          y
      })
      cbind(d,e)
    }
}
longer_to_wider(dat1, "name", "value")
#    name          1          2          3          4
# 1  firstName  0.3407997 -0.7033403 -0.3795377 -0.7460474
# 2 secondName -0.8981073 -0.3347941 -0.5013782 -0.1745357

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