ascii_plots
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Published
20 hours ago •
dzerbino
dzerbino
Convenience function for quick and dirty data analysis
ascii_plots
These are just some silly scripts which I like to have on my command line when I'm doing quick and dirty data analysis and can't be bothered to start R. They all receive the data by piping, typically downstream of awk, cut...
They all handle non-numeric data as NA.
author
Use
For a quick demo, run:
> sh demo.sh
cor - correlation:
Takes in stdin a file with two columns, print out Pearson correlation.
> cut -f1,2 test.tsv | ./cor
0.987425
summary:
Takes in stdin a tab delimited data file with or without headers (anything numeric is assumed to be data, anything else NA) and prints out basic stats on each column (position, header (or first value), min, mean, max, sum)
> cat test.tsv | ./summary
COL | 1 | 2 | 3 | 4 |
NAM | A | B | C | D |
MIN | 7 | 5 | 9 | 0 |
AVG | 3 | 1.75 | 3.75 | 0 |
MAX | 7 | 5 | 9 | 0 |
SUM | 12 | 7 | 15 | 0 |
hist - histogram:
Either:
- Takes in a single column of numbers, displays histogram
- Takes in a double column of numbers, and displays a weighted histogram of the data, assuming the first column are values and the second column weights.
The size of the bins is 1 by default, but can be specified as an option
> awk 'func r(){return sqrt(-2*log(rand()))*cos(6.2831853*rand())}BEGIN{for(i=0;i<10000;i++)s=s"\n"0.5*r();print s}' | ./hist 0.1
-1.8 | 0.0001 | 1 |
-1.6 | 0.0004 | 4 |
-1.5 | 0.0005 | 5 |
-1.4 | 0.0007 | 7 |
-1.3 | 0.0018 | 18 |
-1.2 | 0.004 | 40 | **
-1.1 | 0.0058 | 58 | **
-1 | 0.0085 | 85 | ****
-0.9 | 0.0126 | 126 | ******
-0.8 | 0.0197 | 197 | **********
-0.7 | 0.0285 | 285 | **************
-0.6 | 0.0349 | 349 | *****************
-0.5 | 0.0422 | 422 | *********************
-0.4 | 0.0532 | 532 | ***************************
-0.3 | 0.0634 | 634 | ********************************
-0.2 | 0.0681 | 681 | **********************************
-0.1 | 0.0756 | 756 | **************************************
0 | 0.1557 | 1557 | ********************************************************************************
0.1 | 0.0743 | 743 | **************************************
0.2 | 0.0698 | 698 | ***********************************
0.3 | 0.0628 | 628 | ********************************
0.4 | 0.0546 | 546 | ****************************
0.5 | 0.042 | 420 | *********************
0.6 | 0.0351 | 351 | ******************
0.7 | 0.0252 | 252 | ************
0.8 | 0.0208 | 208 | **********
0.9 | 0.014 | 140 | *******
1 | 0.0104 | 104 | *****
1.1 | 0.0065 | 65 | ***
1.2 | 0.0035 | 35 | *
1.3 | 0.002 | 20 | *
1.4 | 0.0014 | 14 |
1.5 | 0.0009 | 9 |
1.6 | 0.0005 | 5 |
1.7 | 0.0001 | 1 |
1.8 | 0.0001 | 1 |
1.9 | 0.0002 | 2 |
2 | 0.0001 | 1 |
TOTAL | 1 | 10000 |
bars:
Like histogram, but for categorical data:
> cut -f1 test.tsv | ./bars
1.0 | 0.25 | 1 | ********************************************************************************
4.0 | 0.25 | 1 | ********************************************************************************
7.0 | 0.25 | 1 | ********************************************************************************
A | 0.25 | 1 | ********************************************************************************
TOTAL | 1 | 4 |
scatter:
Takes in a double column of numbers, and displays a sketchy ascii density plot.
> awk 'func r(){return sqrt(-2*log(rand()))*cos(6.2831853*rand())}BEGIN{for(i=0;i<10000;i++)s=s"\n"0.5*r()"\t"0.5*r();print s}' | ./scatter
---------------------------------------------------------------------------------------------------------------------- 2.00418
| ' ' |
| ' |
| ' ' |
| ' ' ' ' |
| ' ' ' ` '' `' ' ' |
| ' ' ' ' ' ' ' ` ' ' ' ' |
| ' ' ''' '' ' ' ' '' ' ' ' ' |
| ' ' '' ' ' ,`' ,'`' ` ''' ` `' ' ' '' ' |
| ' '' ` `' ' '`'' '' ' `' ''``''`''`'';' `, '' '' `' ' |
| , ' ''''' ````'' '`,`!' ,,;` `;`'> ``'```'``,'` '`' `' ' ` |
| ' '' '''`,'`; ' '``',`````;!!,,;'`;,;''! ,;,!,;'';'`, '` `'' ` ' ' |
| ' ' ,'`'`,,`,;',``,;;,`!~!;{,,!'!!!,!>!;`!~,,;'`,';`''`'' '''` ' ` ' ' |
| ' `' ` `' ',;, `' ',`!~`!; !']~{-!{~~]>;!!-!{!;';!!~;;;' !'`;`','''``' ` ' |
| `'' '''';'',;,;'>>)>,)~;-{]|~-j~]t~)]{t~)~!->]-!>!;,!`>`,`, ,;;,'`','' |
| ' ''' ''',' `, `!``;;;,~]];!!>]!)-{]vt|vj]n-~-{|j,)>-~n!]~{~!!>'>!;`!`,`` , ` '` ` ' |
| '' ' `' '`'',`;;;~!-{`~~|>{~{]v>{|)XX|v-~{otnjCtC)v;{tX)]->;)>>,`!],`;; `;` ; '` ' ' |
| ' ' '`'' , '`',`!!,!)>!-~{{j|,j|-0njC||0U0CoXn|]o-tUjC{|U)|>-]->{~{!!,;' !` ,`' '' ' ' ' ' ' |
| ' ` ` ` !;- ;>,-;|>{~>!{]|-U]j]XUU00kjXkj00)|jtXjjttnv]n-`]{;{!~!~!-!';,''`' '' ' ` ' |
| ' ' ' ' ;,``'`'`,!>!]])-|t{t|{-U{)|ntCtCnkvkvqCXZUC&Z~0||)-!|{{|;>),]!~; ,`,`';' ''' '' ' |
| ` ' ' ,`'`''``;'`', ,;{)]{{tn)]]{UvdjCZv#-jtC0tZtC|UZ0jUUUC{{0]|!>]{>~~~~{!;`,; ';;;' ' ' ' '|
| ' ' ' ,''``;`~,!];;>!;!|-)]CZvt{UXZqCC$ZnZ|$nokXCUUkjXt]--X|t])>`]!->``;;`>;`'' '' ' |
| '' '`''''' '', `,'>!)!!->)]|))|UtX)|tnvt{|nZvqU0nqdC0#{v)Uqnt|{--t{)])!;,>`-,,;,';`''`` ' ` |
| ' ,' ,'``,;'';!,>>]~~~>~]>]q)|0vnvjjCZvqvnqtX0n)qttvv{X)]t~|]j!),]' !;`,``'``'' '''' ' |
| ' ' '` ````!,``'>>'{;>~;!;~{|j!)]nZtXnj|U0Udtd0njXvjj){nn>]]){{`~;>>- ,;!, `,'`,' ' ' |
| ' ' '''`'` ;`>,>;,;-,~~`-)>]t~t|{-n)t{{tnU]jXUv]n-~],;-t;~;!{!~>,`;,`,`;`'` '` `'' '' |
|' ' `' '' '',,`,`;~;,,,;!-|~-tj-])v!>|]t--j)>Uv]>-~]~!;;!,~-,>'!',,'``''' ',`' ` ' ' ' ' |
| ' ` `'> ',;```!;~;|!~;~->>-,]]~>-;~]))]!>!-`-)-,]-{~,;,`;`',',`'; ,`,'`'' ' ' |
| ' ' `''' ''`'',,,;;!!-,{`-];>~-,>-~~>;{!)];`;--,>`;!,;`;;`'`; ` '''' ' ` |
| ' ' ' '''` `` `';`';`,;>!,!~!~,~-;>~;!!``!,>!`',!`,`'`,, '' ,', '''' |
| ' ''' ' `'`; ''`;`;``> ';>;,!>'''>!>'`;;;;` `'' `' '`'' ' ' |
| ' ` ``' `'` ' '''`!`;`!'`,`'` ``;;'!` `! ,'`;',` '' ' ' |
| ' ' ' '' ' ` ,' ` `', ,'`''';'`'``''' ''''```' `, '' `'' ' |
| ' '' ' '' ' `' ''' ` ' `', '''' ' ` ' ''' ' |
| '' ' ` ' ` `' ' ' `' ' ' ' |
| ' ' ' , ' ' ' ' ' ' |
| ' '' ' ' ` ' |
| ' ' ' |
| ' |
---------------------------------------------------------------------------------------------------------------------- -1.7106
-1.826500 1.910550
curve:
Draws a curve from a single column of numbers [NOTE: requires scatter to be in the same directory]
> awk 'BEGIN{for(i=0;i<100;i++)s=s"\n"sin(i/10);print s}' | ./curve
---------------------------------------------------------------------------------------------------------------------- 0.999574
| $$$$$$ $ $$$$$ $ |
| $ $ $ $ $$ |
| $$ $ $ $ |
| $ $ $ |
| $ $ $ |
| $ $ $ |
| $ $ $ |
| $ $ $ |
| $ $ |
| $ $ $ |
| $ $ |
| $ $ |
| $ $ $ |
| $ |
| $ $ $ $ |
| $ |
| $ $ $ |
| $ |
|$ $ $ |
| $ $ |
| $ |
| $ $ $ |
| |
| $ $ $ |
| $ |
| $ $ |
| $ $|
| $ |
| $ $ |
| $ |
| $ |
| $ $ |
| $ $ |
| $ $ |
| $ $ |
| $ $ |
| $$$ $$ $ |
| $ |
---------------------------------------------------------------------------------------------------------------------- -0.999923
2.000000 101.000000
column_descriptions:
Extracts the header and a number of sample values from each column:
> cat test.tsv | ./column_descriptions
1 A 3 sampled numerical values: 4.000000 ± 2.449490 (total = 12.000000)
2 B 5, NA, 2
3 C NA, 6, 9
4 D NA
dzerbino