Read a data table in transpose form
Read a text (e.g., csv) file, find rows
with more than 3 sep characters.
Parse the initial contiguous block of
those into a matrix. Add
attributes headers,
footers, and a summary.
The initial application for this function is to read Table 6.16. Income and employment by industry in the National Income and Product Account tables published by the Bureau of Economic Analysis of the United States Department of Commerce.
read.transpose(file, header=TRUE, sep=',',
na.strings='---', ...)file |
the name of a file from which the data are to be read. |
header |
Logical: Is the second column of the identified data matrix to be interpreted as variable names? |
sep |
The field space separator character. |
na.strings |
character string(s) that translate into NA |
... |
optional arguments for |
1. txt <- readLines(file)
2. Split into fields.
3. Identify headers, Data, footers.
4. Recombine the second component of each Data row if necessary so all have the same number of fields.
5. Extract variable names
6. Numbers?
7. return the transpose
A matrix of the transpose of the rows with the
max number of fields with attributes
headers, footers,
other, and summary. If this
matrix can be coerced to numeric with no
NAs, it will be. Otherwise, it will be
left as character.
Spencer Graves
# Find demoFiles/*.csv
demoDir <- system.file('demoFiles', package='Ecdat')
(demoCsv <- dir(demoDir, pattern='csv$', full.names=TRUE))
# Use the fourth example
# to ensure the code will handle commas in a name
# and NAs
nipa6.16D <- read.transpose(demoCsv[4])
str(nipa6.16D)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.