Letter count in sequence

BDk

Hi, I'm quite new with the software. I would like to count the number of letter in a random sentence (e.g.:GGGAATCGTCA), e.g. how many 'A' occurred in it and put it into a new column. Is there some operator that could be used for it?  Thank you in advance!

Marco_Barradas

Hi @BDk

You can use a Process Documents and split the tokens and specify count occurrences 

Spoiler

<?xml version="1.0" encoding="UTF-8"?><process version="9.10.011">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="9.10.011" expanded="true" name="Process">
    <parameter key="logverbosity" value="init"/>
    <parameter key="random_seed" value="-1"/>
    <parameter key="send_mail" value="never"/>
    <parameter key="notification_email" value=""/>
    <parameter key="process_duration_for_mail" value="30"/>
    <parameter key="encoding" value="UTF-8"/>
    <process expanded="true">
      <operator activated="true" class="text:create_document" compatibility="9.4.000" expanded="true" height="68" name="Create Document" width="90" x="112" y="34">
        <parameter key="text" value="GGGAATCGTCA"/>
        <parameter key="add label" value="false"/>
        <parameter key="label_type" value="nominal"/>
      </operator>
      <operator activated="true" class="text:process_documents" compatibility="9.4.000" expanded="true" height="103" name="Process Documents" width="90" x="246" y="34">
        <parameter key="create_word_vector" value="true"/>
        <parameter key="vector_creation" value="Term Occurrences"/>
        <parameter key="add_meta_information" value="true"/>
        <parameter key="keep_text" value="false"/>
        <parameter key="prune_method" value="none"/>
        <parameter key="prune_below_percent" value="3.0"/>
        <parameter key="prune_above_percent" value="30.0"/>
        <parameter key="prune_below_rank" value="0.05"/>
        <parameter key="prune_above_rank" value="0.95"/>
        <parameter key="datamanagement" value="double_sparse_array"/>
        <parameter key="data_management" value="auto"/>
        <process expanded="true">
          <operator activated="true" class="text:tokenize" compatibility="9.4.000" expanded="true" height="68" name="Tokenize" width="90" x="112" y="34">
            <parameter key="mode" value="regular expression"/>
            <parameter key="characters" value=".:"/>
            <parameter key="expression" value="|"/>
            <parameter key="language" value="English"/>
            <parameter key="max_token_length" value="3"/>
          </operator>
          <connect from_port="document" to_op="Tokenize" to_port="document"/>
          <connect from_op="Tokenize" from_port="document" to_port="document 1"/>
          <portSpacing port="source_document" spacing="0"/>
          <portSpacing port="sink_document 1" spacing="0"/>
          <portSpacing port="sink_document 2" spacing="0"/>
        </process>
      </operator>
      <connect from_op="Create Document" from_port="output" to_op="Process Documents" to_port="documents 1"/>
      <connect from_op="Process Documents" from_port="example set" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
    </process>
  </operator>
</process>

BDk

Do I need some extension for this 'process documents' operator? I've an education version of the software and I could not find this operator.

BDk

OK, found the extension, sorry. It works for 1 row fine, thanks Marco. Could it be multiplied?
I've a table that has 1000+ rows and all contains a letter sequence like the one that posted above. I would like to count the letters in each one by one. But with the posted solution it only works for 1 row or if I enter all the 1000+ via 'create document' it only counts the letters together in all rows...

Marco_Barradas

HI @BDk

You'll need to use a Process Documents or Process Documents from Data or Files it depends on how your data was originally collected.