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

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.

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)

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

Using your example dataframe, we could:

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

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')

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

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

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.