read.transpose {Ecdat} | R Documentation |
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 charactor. |
na.strings |
character string(s) that translate into NA |
... |
optional arguments for |
1. txt <- readLines(file)
2. Split into fields.
3. Identifiy 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)