Minor edits to readme file. (#148)

README.md

@@ -1,6 +1,8 @@
 # Command line utilities for tabular data files
 
-This is a set of command line utilities for working with tab-separated value files. They were originally developed in Perl and used for day-to-day work in a large scale data mining environment. One of the tools was re-written in D as an exercise exploring the language. Significant performance gains and agreeable programmer characteristics soon led to writing additional utilities in D. Information on the D programming language is available at [dlang.org](https://dlang.org/).
+This is a set of command line utilities for manipulating large tabular data files. Files of numeric and text data commonly found in machine learning, data mining, and similar environments. Filtering, sampling, statistical calculations and other operations are supported.
+
+The tools were originally developed in Perl and used for day-to-day work in a large scale data mining environment. One of the tools was re-written in D as an exercise exploring the language. Significant performance gains and agreeable programmer characteristics soon led to writing additional utilities in D. Information on the D programming language is available at [dlang.org](https://dlang.org/).
 
 File an [issue](https://github.com/eBay/tsv-utils-dlang/issues) if you have problems, questions or suggestions.
 
@@ -30,7 +32,7 @@ File an [issue](https://github.com/eBay/tsv-utils-dlang/issues) if you have prob
 
 ## Tools overview
 
-These tools perform data manipulation and statistical calculations on delimited data. They are intended for large files. Larger than ideal for loading entirely in memory in an application like R, but not so big as to necessitate moving to Hadoop or similar distributed compute environments. The features supported are useful both for standalone analysis and for preparing data for use in R, Pandas, and similar toolkits.
+These tools perform data manipulation and statistical calculations on tab delimited data. They are intended for large files. Larger than ideal for loading entirely in memory in an application like R, but not so big as to necessitate moving to Hadoop or similar distributed compute environments. The features supported are useful both for standalone analysis and for preparing data for use in R, Pandas, and similar toolkits.
 
 The tools work like traditional Unix command line utilities such as `cut`, `sort`,  and `grep`, and are intended to complement these tools. Each tool is a standalone executable. They follow common Unix conventions for pipeline programs. Data is read from files or standard input, results are written to standard output. The field separator defaults to TAB, but any character can be used. Input and output is UTF-8, and all operations are Unicode ready, including regular expression match (`tsv-filter`). Documentation is available for each tool by invoking it with the `--help` option. TSV format is similar to CSV, see [Comparing TSV and CSV formats](docs/TipsAndTricks.md#comparing-tsv-and-csv-formats) for the differences.