data manipulation

library(data.table)
library(pipeR)
resDT <- iris %>>% data.table %>>%
  transform(Species = as.character(Species), id = 1:nrow(.)) %>>%
  melt(id = 5:6, measure = c(1:4), variable.factor = FALSE) %>>%
  `[`( , `:=`(c("partName", "measure"), tstrsplit(variable, "\\."))) %>>%
  dcast.data.table(Species + partName + id~ measure, sum, value.var = "value") %>>%
  `[`( , id := NULL)
#        Species partName Length Width
#   1:    setosa    Petal    1.4   0.2
#   2:    setosa    Petal    1.4   0.2
#   3:    setosa    Petal    1.3   0.2
#   4:    setosa    Petal    1.5   0.2
#   5:    setosa    Petal    1.4   0.2
# ---
# 296: virginica    Sepal    6.7   3.0
# 297: virginica    Sepal    6.3   2.5
# 298: virginica    Sepal    6.5   3.0
# 299: virginica    Sepal    6.2   3.4
# 300: virginica    Sepal    5.9   3.0

library(dplyr)
library(tidyr)
resTbl <- iris %>>% tbl_df %>>% mutate(id = 1:nrow(.)) %>>%
  gather(variable, value, -Species, -id) %>>%
  separate(variable, c("partName", "measure")) %>>%
  spread(measure, value) %>>% select(-id)

# Source: local data frame [300 x 4]
#
#    Species partName Length Width
#     <fctr>    <chr>  <dbl> <dbl>
# 1   setosa    Petal    1.4   0.2
# 2   setosa    Sepal    5.1   3.5
# 3   setosa    Petal    1.4   0.2
# 4   setosa    Sepal    4.9   3.0
# 5   setosa    Petal    1.3   0.2
# 6   setosa    Sepal    4.7   3.2
# 7   setosa    Petal    1.5   0.2
# 8   setosa    Sepal    4.6   3.1
# 9   setosa    Petal    1.4   0.2
# 10  setosa    Sepal    5.0   3.6
# ..     ...      ...    ...   ...