Commit 333591f5 authored by Paul McCarthy's avatar Paul McCarthy 🚵
Browse files

Merge branch 'rf/date-norm-in-cleaning' into 'master'

Rf/date norm in cleaning

See merge request !61
parents 8caed2de 37d137d3
......@@ -2,8 +2,8 @@ FUNPACK changelog
=================
2.3.0 (Under development)
-------------------------
2.3.0 (Tuesday 12th May 2020)
-----------------------------
Changed
......@@ -15,7 +15,11 @@ Changed
variables. This should give superior performance.
* Revisited the :meth:`.DataTable.merge` to optimise performance in all
scenarios.
* Improved performance of the :mod:`.fmrib` date/time normalisation routines.
* Improved performance of the :mod:`.fmrib` date/time normalisation routines,
and changed their usage so they are now applied as "cleaning" functions
after data import, rather than just before export. This means that date/
time columns can be subjected to the redundancy check (as they will have
a numeric type), and will improve data export performance.
2.2.1 (Monday 4th May 2020)
......
......@@ -6,7 +6,7 @@
#
__version__ = '2.3.0.dev0'
__version__ = '2.3.0'
"""The ``funpack`` versioning scheme roughly follows Semantic Versioning
conventions.
"""
......
......@@ -8,9 +8,20 @@
# Use local settings
config_file local
# Contains some FMRIB-specific plugin functions,
# including date/time normalisation.
plugin_file fmrib
# Drop non-numeric columns - the main output
# file only contains numeric data.
suppress_non_numerics
# Only import variables from FMRIB-curated categories,
# largely drawn from showcase categories
category_file fmrib/categories.tsv
#
# FUNPACK processing stages
# FUNPACK cleaning/processing stages
#
# - NA insertion
# - Categorical recoding
......@@ -38,33 +49,22 @@ config_file local
# - NA insertion
datacoding_file fmrib/datacodings_navalues.tsv
# - Categorical recoding
datacoding_file fmrib/datacodings_recoding.tsv
# - Cleaning
variable_file fmrib/variables_clean.tsv
# - Child value replacement
variable_file fmrib/variables_parentvalues.tsv
# - Processing
processing_file fmrib/processing.tsv
# FMRIB-curated categories, largely drawn from showcase categories
category_file fmrib/categories.tsv
#
# FMRIB processing of dates
#
# Date/timestamp normalisation (performed in the FUNPACK cleaning stage)
# Converts a date or date+time into a single value x, where floor(x) is the
# calendar year and the fraction day/time within the year *except* 'a day'
# is redefined as the time between 7am and 8pm (scanning only takes place
# within these hours.
#
plugin_file fmrib
date_format FMRIBImagingDate
time_format FMRIBImagingTime
type_file fmrib/datetime_formatting.tsv
# - Child value replacement
variable_file fmrib/variables_parentvalues.tsv
# Drop non-numeric columns - the main output file only contains numeric data.
suppress_non_numerics
# - Processing -
processing_file fmrib/processing.tsv
Type Clean
date normalisedDate
time normalisedAcquisitionTime
\ No newline at end of file
......@@ -89,12 +89,13 @@ def formatColumn(col,
# fall back to date/time formatting
# if relevant for this column
if formatter is None:
if vtype == util.CTYPES.date:
formatter = dateFormat
elif vtype == util.CTYPES.time or \
pdtypes.is_datetime64_any_dtype(series):
formatter = timeFormat
if formatter is None and pdtypes.is_datetime64_any_dtype(series):
# use dateFormat if we know the column
# is a date (and not datetime), otherwise
# use timeFormat if the column is a
# datetime, or unknown type.
if vtype == util.CTYPES.date: formatter = dateFormat
else: formatter = timeFormat
if formatter is not None:
log.debug('Formatting column %s%s with %s formatter',
......
......@@ -88,14 +88,17 @@ def load_FMRIBImaging(infile):
return df
@funpack.formatter('FMRIBImagingDate')
def normalisedDate(dtable, column, series):
@funpack.cleaner()
def normalisedDate(dtable, vid):
"""Converts date values into a numeric fractional year representation.
Converts a date into a single value x, where ``floor(x)`` is the calendar
year and the ``x mod 1`` is the fractional day within the year. The
conversion takes leap years into account.
"""
for col in dtable.columns(vid):
series = dtable[:, col.name]
datetimes = series.to_numpy()
years = datetimes.astype('datetime64[Y]')
days = datetimes.astype('datetime64[D]')
......@@ -106,12 +109,12 @@ def normalisedDate(dtable, column, series):
years = (years + 1970) .astype(np.float32)
leaps = pd.DatetimeIndex(datetimes).is_leap_year + 365
# calculate and return fraction of year
return pd.Series(years + (days / leaps), name=series.name)
# calculate fraction of year
dtable[:, col.name] = years + (days / leaps)
@funpack.formatter('FMRIBImagingTime')
def normalisedAcquisitionTime(dtable, column, series):
@funpack.cleaner()
def normalisedAcquisitionTime(dtable, vid):
"""Converts timestamps into a numeric fractional year representation.
Converts a date or date+time into a single value x, where `floor(x)` is the
......@@ -119,6 +122,8 @@ def normalisedAcquisitionTime(dtable, column, series):
redefined as the time between 7am and 8pm (UK BioBank scanning only takes
place within these hours).
"""
for col in dtable.columns(vid):
series = dtable[:, col.name]
datetimes = series.to_numpy()
years = datetimes.astype('datetime64[Y]')
days = datetimes.astype('datetime64[D]')
......@@ -138,4 +143,4 @@ def normalisedAcquisitionTime(dtable, column, series):
leaps = pd.DatetimeIndex(datetimes).is_leap_year + 365
# calculate and return fraction of year
return pd.Series(years + (days + dayfracs) / leaps, name=series.name)
dtable[:, col.name] = years + (days + dayfracs) / leaps
......@@ -57,7 +57,7 @@
"\n",
"\n",
"**Important** The examples in this notebook assume that you have installed `funpack`\n",
"2.3.0.dev0 or newer."
"2.3.0 or newer."
]
},
{
......@@ -1664,7 +1664,7 @@
"\n",
"`funpack` has a large number of hand-crafted rules built in, which are\n",
"specific to variables found in the UK BioBank data set. These rules are part\n",
"of the ``fmrib`` configuration, which can be used by adding `-cfg fmrib` to\n",
"of the `fmrib` configuration, which can be used by adding `-cfg fmrib` to\n",
"the command-line options.\n",
"\n",
"\n",
......@@ -1679,7 +1679,7 @@
"metadata": {},
"outputs": [],
"source": [
"funpack -q -ow -cfg fmrib -d out.tsv ukb_dataset_1_only_column_names.tsv ukb_dataset_2_only_column_names.tsv"
"funpack -q -ow -cfg fmrib -d out.tsv ukbcols.csv"
]
},
{
......@@ -1888,5 +1888,5 @@
],
"metadata":{"kernelspec":{"display_name":"Bash","language":"bash","name":"bash"},"language_info":{"codemirror_mode":"shell","file_extension":".sh","mimetype":"text/x-sh","name":"bash"}},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 4
}
%% Cell type:markdown id: tags:
![win logo](win.png)
# `funpack` (https://git.fmrib.ox.ac.uk/fsl/funpack)
> Paul McCarthy <paul.mccarthy@ndcn.ox.ac.uk>
> ([WIN@FMRIB](https://www.win.ox.ac.uk/))
`funpack` is a command-line program which you can use to extract data from UK
BioBank (and other tabular) data.
You can give `funpack` one or more input files (e.g. `.csv`, `.tsv`), and it
will merge them together, perform some preprocessing, and produce a single
output file.
A large number of rules are built into `funpack` which are specific to the UK
BioBank data set. But you can control and customise everything that `funpack`
does to your data, including which rows and columns to extract, and which
cleaning/processing steps to perform on each column.
`funpack` comes installed with recent versions of
[FSL](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/). You can also install `funpack`
via `conda`:
> ```
> conda install -c conda-forge fmrib-unpack
> ```
Or using `pip`:
> ```
> pip install fmrib-unpack
> ```
Get command-line help by typing:
> ```
> funpack -h
> ```
**Important** The examples in this notebook assume that you have installed `funpack`
2.3.0.dev0 or newer.
2.3.0 or newer.
%% Cell type:code id: tags:
``` bash
funpack -V
```
%% Cell type:markdown id: tags:
### Contents
1. [Overview](#Overview)
1. [Import](#1.-Import)
2. [Cleaning](#2.-Cleaning)
3. [Processing](#3.-Processing)
4. [Export](#4.-Export)
2. [Examples](#Examples)
3. [Import examples](#Import-examples)
1. [Selecting variables (columns)](#Selecting-variables-(columns))
1. [Selecting individual variables](#Selecting-individual-variables)
2. [Selecting variable ranges](#Selecting-variable-ranges)
3. [Selecting variables with a file](#Selecting-variables-with-a-file)
4. [Selecting variables from pre-defined categories](#Selecting-variables-from-pre-defined-categories)
2. [Selecting subjects (rows)](#Selecting-subjects-(rows))
1. [Selecting individual subjects](#Selecting-individual-subjects)
2. [Selecting subject ranges](#Selecting-subject-ranges)
3. [Selecting subjects from a file](#Selecting-subjects-from-a-file)
4. [Selecting subjects by variable value](#Selecting-subjects-by-variable-value)
5. [Excluding subjects](#Excluding-subjects)
3. [Selecting visits](#Selecting-visits)
1. [Evaluating expressions across visits](#Evaluating-expressions-across-visits)
4. [Merging multiple input files](#Merging-multiple-input-files)
1. [Merging by subject](#Merging-by-subject)
2. [Merging by column](#Merging-by-column)
3. [Naive merging](#Merging-by-column)
4. [Cleaning examples](#Cleaning-examples)
1. [NA insertion](#NA-insertion)
2. [Variable-specific cleaning functions](#Variable-specific-cleaning-functions)
3. [Categorical recoding](#Categorical-recoding)
4. [Child value replacement](#Child-value-replacement)
5. [Processing examples](#Processing-examples)
1. [Sparsity check](#Sparsity-check)
2. [Redundancy check](#Redundancy-check)
3. [Categorical binarisation](#Categorical-binarisation)
6. [Custom cleaning, processing and loading - funpack plugins](#Custom-cleaning,-processing-and-loading---funpack-plugins)
1. [Custom cleaning functions](#Custom-cleaning-functions)
2. [Custom processing functions](#Custom-processing-functions)
3. [Custom file loaders](#Custom-file-loaders)
7. [Miscellaneous topics](#Miscellaneous-topics)
1. [Non-numeric data](#Non-numeric-data)
2. [Dry run](#Dry-run)
3. [Built-in rules](#Built-in-rules)
4. [Using a configuration file](#Using-a-configuration-file)
5. [Working with unknown/uncategorised variables](#Working-with-unknown/uncategorised-variables)
# Overview
`funpack` performs the following steps:
## 1. Import
All data files are loaded in, unwanted columns and subjects are dropped, and
the data files are merged into a single table (a.k.a. data frame). Multiple
files can be merged according to an index column (e.g. subject ID). Or, if the
input files contain the same columns/subjects, they can be naively
concatenated along rows or columns.
## 2. Cleaning
The following cleaning steps are applied to each column:
1. **NA value replacement:** Specific values for some columns are replaced
with NA, for example, variables where a value of `-1` indicates *Do not
know*.
2. **Variable-specific cleaning functions:** Certain columns are
re-formatted; for example, the [ICD10](https://en.wikipedia.org/wiki/ICD-10)
disease codes can be converted to integer representations.
3. **Categorical recoding:** Certain categorical columns are re-coded.
4. **Child value replacement:** NA values within some columns which are
dependent upon other columns may have values inserted based on the values
of their parent columns.
## 3. Processing
During the processing stage, columns may be removed, merged, or expanded into
additional columns. For example, a categorical column may be expanded into a set
of binary columns, one for each category.
A column may also be removed on the basis of being too sparse, or being
redundant with respect to another column.
## 4. Export
The processed data can be saved as a `.csv`, `.tsv`, or `.hdf5` file.
# Examples
Throughout these examples, we are going to use a few command line
options, which you will probably **not** normally want to use:
- `-ow` (short for `--overwrite`): This tells `funpack` not to complain if
the output file already exists.
- `-q` (short for `--quiet`): This tells `funpack` to be quiet. Without the
`-q` option, `funpack` can be quite verbose, which can be annoying, but is
very useful when things go wrong. A good strategy is to tell `funpack` to
produce verbose output using the `--noisy` (`-n` for short) option, and to
send all of its output to a log file with the `--log_file` (or `-lf`)
option. For example:
> ```
> funpack -n -n -n -lf log.txt out.tsv in.tsv
> ```
Here's the first example input data set, with UK BioBank-style column names:
%% Cell type:code id: tags:
``` bash
cat data_01.tsv
```
%% Cell type:markdown id: tags:
The numbers in each column name typically represent:
1. The variable ID
2. The visit, for variables which were collected at multiple points in time.
3. The "instance", for multi-valued variables.
Note that one **variable** is typically associated with several **columns**,
although we're keeping things simple for this first example - there is only
one visit for each variable, and there are no mulit-valued variables.
> _Most but not all_ variables in the UK BioBank contain data collected at
> different visits, the times that the participants visited a UK BioBank
> assessment centre. However there are some variables (e.g. [ICD10 diagnosis
> codes](https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=41202)) for which
> this is not the case.
# Import examples
## Selecting variables (columns)
You can specify which variables you want to load in the following ways, using
the `--variable` (`-v` for short), `--category` (`-c` for short) and
`--column` (`-co` for short) command line options:
* By variable ID
* By variable ranges
* By a text file which contains the IDs you want to keep.
* By pre-defined variable categories
* By column name
### Selecting individual variables
Simply provide the IDs of the variables you want to extract:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -v 1 -v 5 out.tsv data_01.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
### Selecting variable ranges
The `--variable`/`-v` option accepts MATLAB-style ranges of the form
`start:step:stop` (where the `stop` is inclusive):
%% Cell type:code id: tags:
``` bash
funpack -q -ow -v 1:3:10 out.tsv data_01.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
### Selecting variables with a file
If your variables of interest are listed in a plain-text file, you can simply
pass that file:
%% Cell type:code id: tags:
``` bash
echo -e "1\n6\n9" > vars.txt
funpack -q -ow -v vars.txt out.tsv data_01.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
### Selecting variables from pre-defined categories
Some UK BioBank-specific categories are [built into
`funpack`](#Built-in-rules), but you can also define your own categories - you
just need to create a `.tsv` file, and pass it to `funpack` via the
`--category_file` (`-cf` for short):
%% Cell type:code id: tags:
``` bash
echo -e "ID\tCategory\tVariables" > custom_categories.tsv
echo -e "1\tCool variables\t1:5,7" >> custom_categories.tsv
echo -e "2\tUncool variables\t6,8:10" >> custom_categories.tsv
cat custom_categories.tsv
```
%% Cell type:markdown id: tags:
Use the `--category` (`-c` for short) to select categories to output. You can
refer to categories by their ID:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -cf custom_categories.tsv -c 1 out.tsv data_01.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
Or by name:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -cf custom_categories.tsv -c uncool out.tsv data_01.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
### Selecting column names
If you are working with data that has non-UK BioBank style column names, you
can use the `--column` (`-co` for short) to select individual columns by their
name, rather than the variable with which they are associated. The `--column`
option accepts full column names, and also shell-style wildcard patterns:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -co 4-0.0 -co "??-0.0" out.tsv data_01.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
## Selecting subjects (rows)
`funpack` assumes that the first column in every input file is a subject
ID. You can specify which subjects you want to load via the `--subject` (`-s`
for short) option. You can specify subjects in the same way that you specified
variables above, and also:
* By specifying a conditional expression on variable values - only subjects
for which the expression evaluates to true will be imported
* By specifying subjects to exclude
### Selecting individual subjects
%% Cell type:code id: tags:
``` bash
funpack -q -ow -s 1 -s 3 -s 5 out.tsv data_01.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
### Selecting subject ranges
%% Cell type:code id: tags:
``` bash
funpack -q -ow -s 2:2:10 out.tsv data_01.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
### Selecting subjects from a file
%% Cell type:code id: tags:
``` bash
echo -e "5\n6\n7\n8\n9\n10" > subjects.txt
funpack -q -ow -s subjects.txt out.tsv data_01.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
### Selecting subjects by variable value
The `--subject` option accepts *variable expressions* - you can write an
expression performing numerical comparisons on variables (denoted with a
leading `v`) and combine these expressions using boolean algebra. Only
subjects for which the expression evaluates to true will be imported. For
example, to only import subjects where variable 1 is greater than 10, and
variable 2 is less than 70, you can type:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -sp -s "v1 > 10 && v2 < 70" out.tsv data_01.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
The following symbols can be used in variable expressions:
| Symbol | Meaning |
|---------------------------|---------------------------------|
| `==` | equal to |
| `!=` | not equal to |
| `>` | greater than |
| `>=` | greater than or equal to |
| `<` | less than |
| `<=` | less than or equal to |
| `na` | N/A |
| `&&` | logical and |
| <code>&#x7c;&#x7c;</code> | logical or |
| `~` | logical not |
| `contains` | Contains sub-string |
| `all` | all columns must meet condition |
| `any` | any column must meet condition |
| `()` | to denote precedence |
Non-numeric (i.e. string) variables can be used in these expressions in
conjunction with the `==`, `!=`, and `contains` operators. An example of such
an expression is given in the section on [non-numeric
data](#Non-numeric-data), below.
The `all` and `any` symbols allow you to control how an expression is
evaluated across multiple columns which are associated with one variable
(e.g. separate columns for each visit). We will give an example of this in the
section on [selecting visits](#Selecting-visits), below.
### Excluding subjects
The `--exclude` (`-ex` for short) option allows you to exclude subjects - it
accepts individual IDs, an ID range, or a file containing IDs. The
`--exclude`/`-ex` option takes precedence over the `--subject`/`-s` option:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -s 1:8 -ex 5:10 out.tsv data_01.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
## Selecting visits
Many variables in the UK BioBank data contain observations at multiple points in
time, or visits. `funpack` allows you to specify which visits you are interested
in. Here is an example data set with variables that have data for multiple
visits (remember that the second number in the column names denotes the visit):
%% Cell type:code id: tags:
``` bash
cat data_02.tsv
```
%% Cell type:markdown id: tags:
We can use the `--visit` (`-vi` for short) option to get just the last visit for
each variable:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -vi last out.tsv data_02.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
You can also specify which visit you want by its number:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -vi 1 out.tsv data_02.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
> Variables which are not associated with specific visits (e.g. [ICD10
> diagnosis codes](https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=41202))
> will not be affected by the `-vi` option.
### Evaluating expressions across visits
The variable expressions described above in the section on [selecting
subjects](#Selecting-subjects-by-variable-value) will be applied to all of
the columns associated with a variable. By default, an expression will
evaluate to true where the values in _any_ column asssociated with the
variable evaluate to true. For example, we can extract the data for subjects
where the values of any column of variable 2 were less than 50:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -v 2 -s 'v2 < 50' out.tsv data_02.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
We can use the `any` and `all` operators to control how an expression is
evaluated across the columns of a variable. For example, we may only be
interested in subjects for whom all columns of variable 2 were greater than
50:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -v 2 -s 'all(v2 < 50)' out.tsv data_02.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
We can use `any` and `all` in expressions involving multiple variables:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -v 2,3 -s 'any(v2 < 50) && all(v3 >= 40)' out.tsv data_02.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
## Merging multiple input files
If your data is split across multiple files, you can specify how `funpack`
should merge them together.
### Merging by subject
For example, let's say we have these two input files (shown side-by- side):
%% Cell type:code id: tags:
``` bash
echo " " | paste data_03.tsv - data_04.tsv
```
%% Cell type:markdown id: tags:
Note that each file contains different variables, and different, but
overlapping, subjects. By default, when you pass these files to `funpack`, it
will output the intersection of the two files (more formally known as an
*inner join*), i.e. subjects which are present in both files:
%% Cell type:code id: tags:
``` bash
funpack -q -ow out.tsv data_03.tsv data_04.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
If you want to keep all subjects, you can instruct `funpack` to output the union
(a.k.a. *outer join*) via the `--merge_strategy` (`-ms` for short) option:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -ms outer out.tsv data_03.tsv data_04.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
### Merging by column
Your data may be organised in a different way. For example, these next two
files contain different groups of subjects, but overlapping columns:
%% Cell type:code id: tags:
``` bash
echo " " | paste data_05.tsv - data_06.tsv
```
%% Cell type:markdown id: tags:
In this case, we need to tell `funpack` to merge along the row axis, rather than
along the column axis. We can do this with the `--merge_axis` (`-ma` for short)
option:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -ma rows out.tsv data_05.tsv data_06.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
Again, if we want to retain all columns, we can tell `funpack` to perform an
outer join with the `-ms` option:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -ma rows -ms outer out.tsv data_05.tsv data_06.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
### Naive merging
Finally, your data may be organised such that you simply want to "paste", or
concatenate them together, along either rows or columns. For example, your
data files might look like this:
%% Cell type:code id: tags:
``` bash
echo " " | paste data_07.tsv - data_08.tsv
```
%% Cell type:markdown id: tags:
Here, we have columns for different variables on the same set of subjects, and
we just need to concatenate them together horizontally. We do this by using
`--merge_strategy naive` (`-ms naive` for short):
%% Cell type:code id: tags:
``` bash
funpack -q -ow -ms naive out.tsv data_07.tsv data_08.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
For files which need to be concatenated vertically, such as these:
%% Cell type:code id: tags:
``` bash
echo " " | paste data_09.tsv - data_10.tsv
```
%% Cell type:markdown id: tags:
We need to tell `funpack` which axis to concatenate along, again using the `-ma`
option:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -ms naive -ma rows out.tsv data_09.tsv data_10.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
# Cleaning examples
Once the data has been imported, a sequence of cleaning steps are applied to
each column.
## NA insertion
For some variables it may make sense to discard or ignore certain values. For
example, if an individual selects *Do not know* to a question such as *How
much milk did you drink yesterday?*, that answer will be coded with a specific
value (e.g. `-1`). It does not make any sense to include these values in most
analyses, so `funpack` can be used to mark such values as *Not Available
(NA)*.
A large number of NA insertion rules, specific to UK BioBank variables, are
coded into `funpack`, and are applied when you use the `-cfg fmrib` option
(see the section below on [built-in rules](#Built-in-rules)). You can also
specify your own rules via the `--na_values` (`-nv` for short) option.
Let's say we have this data set:
%% Cell type:code id: tags:
``` bash
cat data_11.tsv
```
%% Cell type:markdown id: tags:
For variable 1, we want to ignore values of -1, for variable 2 we want to
ignore -1 and 0, and for variable 3 we want to ignore 1 and 2:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -nv 1 " -1" -nv 2 " -1,0" -nv 3 "1,2" out.tsv data_11.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
The `--na_values` option expects two arguments:
* The variable ID
* A comma-separated list of values to replace with NA
## Variable-specific cleaning functions
A small number of cleaning/preprocessing functions are built into `funpack`,
which can be applied to specific variables. For example, some variables in the
UK BioBank contain ICD10 disease codes, which may be more useful if converted
to a numeric format (e.g. to make them easy to load into MATLAB). Imagine
that we have some data with ICD10 codes:
%% Cell type:code id: tags:
``` bash
cat data_12.tsv
```
%% Cell type:markdown id: tags:
We can use the `--clean` (`-cl` for short) option with the built-in
`codeToNumeric` cleaning function to convert the codes to a numeric
representation<sup>*</sup>:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -cl 1 "codeToNumeric('icd10')" out.tsv data_12.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
> <sup>*</sup>The `codeToNumeric` function will replace each ICD10 code with
> the corresponding *Node* number, as defined in the UK [BioBank ICD10 data
> coding](http://biobank.ctsu.ox.ac.uk/crystal/coding.cgi?id=19).
The `--clean` option expects two arguments:
* The variable ID
* The cleaning function to apply. Some cleaning functions accept
arguments - refer to the command-line help for a summary of available
functions.
You can define your own cleaning functions by passing them in as a
`--plugin_file` (see the [section on custom plugins
below](#Custom-cleaning,-processing-and-loading---funpack-plugins)).
### Example: flattening hierarchical data
Several variables in the UK Biobank (including the ICD10 disease
categorisations) are organised in a hierarchical manner - each value is a
child of a more general parent category. The `flattenHierarchical` cleaninng
function can be used to replace each value in a data set with the value that
corresponds to a parent category. Let's apply this to our example ICD10 data
set.
%% Cell type:code id: tags:
``` bash
funpack -q -ow -cl 1 "flattenHierarchical(name='icd10')" out.tsv data_12.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
### Aside: ICD10 mapping file
`funpack` has a feature specific to these ICD10 disease categorisations - you
can use the `--icd10_map_file` (`-imf` for short) option to tell `funpack` to
save a file which contains a list of all ICD10 codes that were present in the
input data, and the corresponding numerical codes that `funpack` generated:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -cl 1 "codeToNumeric('icd10')" -imf icd10_codes.tsv out.tsv data_12.tsv
cat icd10_codes.tsv
```
%% Cell type:markdown id: tags:
## Categorical recoding
You may have some categorical data which is coded in an awkward manner, such as
in this example, which encodes the amount of some item that an individual has
consumed:
![data coding example](coding.png)
You can use the `--recoding` (`-re` for short) option to recode data like this
into something more useful. For example, given this data:
%% Cell type:code id: tags:
``` bash
cat data_13.tsv
```
%% Cell type:markdown id: tags:
Let's recode it to be more monotonic:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -re 1 "300,444,555" "3,0.25,0.5" out.tsv data_13.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
The `--recoding` option expects three arguments:
* The variable ID
* A comma-separated list of the values to be replaced
* A comma-separated list of the values to replace them with
## Child value replacement
Imagine that we have these two questions:
* **1**: *Do you currently smoke cigarettes?*
* **2**: *How many cigarettes did you smoke yesterday?*
Now, question 2 was only asked if the answer to question 1 was *Yes*. So for
all individuals who answered *No* to question 1, we will have a missing value
for question 2. But for some analyses, it would make more sense to have a
value of 0, rather than NA, for these subjects.
`funpack` can handle these sorts of dependencies by way of *child value
replacement*. For question 2, we can define a conditional variable expression
such that when both question 2 is NA and question 1 is *No*, we can insert a
value of 0 into question 2.
This scenario is demonstrated in this example data set (where, for
question 1 values of `1` and `0` represent *Yes* and *No* respectively):
%% Cell type:code id: tags:
``` bash
cat data_14.tsv
```
%% Cell type:markdown id: tags:
We can fill in the values for variable 2 by using the `--child_values` (`-cv`
for short) option:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -cv 2 "v1 == 0" "0" out.tsv data_14.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
The `--child_values` option expects three arguments:
* The variable ID
* An expression evaluating some condition on the parent variable(s)
* A value to replace NA with where the expression evaluates to true.
# Processing examples
After every column has been cleaned, the entire data set undergoes a series of
processing steps. The processing stage may result in columns being removed or
manipulated, or new columns being added.
The processing stage can be controlled with these options:
* `--prepend_process` (`-ppr` for short): Apply a processing function before
the built-in processing
* `--append_process` (`-apr` for short): Apply a processing function after the
built-in processing
A default set of processing steps are applied when you apply the `fmrib`
configuration profile by using `-cfg fmrib` - see the section on [built-in
rules](#Built-in-rules).
The `--prepend_process` and `--append_process` options require two arguments:
* The variable ID(s) to apply the function to, or `all` to denote all
variables.
* The processing function to apply. The available processing functions are
listed in the command line help, or you can write your own and pass it in
as a plugin file
([see below](#Custom-cleaning,-processing-and-loading---funpack-plugins)).
## Sparsity check
The `removeIfSparse` process will remove columns that are deemed to have too
many missing values. If we take this data set:
%% Cell type:code id: tags:
``` bash
cat data_15.tsv
```
%% Cell type:markdown id: tags:
Imagine that our analysis requires at least 8 values per variable to work. We
can use the `minpres` option to`funpack` to drop any columns which do not meet
this threshold:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -apr all "removeIfSparse(minpres=8)" out.tsv data_15.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
You can also specify `minpres` as a proportion, rather than an absolute number.
e.g.:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -apr all "removeIfSparse(minpres=0.65, abspres=False)" out.tsv data_15.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
## Redundancy check
You may wish to remove columns which contain redundant information. The
`removeIfRedundant` process calculates the pairwise correlation between all
columns, and removes columns with a correlation above a threshold that you
provide. Imagine that we have this data set:
%% Cell type:code id: tags:
``` bash
cat data_16.tsv
```
%% Cell type:markdown id: tags:
The data in column `2-0.0` is effectively equivalent to the data in column
`1-0.0`, so is not of any use to us. We can tell `funpack` to remove it like
so:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -apr all "removeIfRedundant(0.9)" out.tsv data_16.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
The `removeIfRedundant` process can also calculate the correlation of the
patterns of missing values between variables - Consider this example:
%% Cell type:code id: tags:
``` bash
cat data_17.tsv
```
%% Cell type:markdown id: tags:
All three columns are highly correlated, but the pattern of missing values in
column `3-0.0` is different to that of the other columns.
If we use the `nathres` option, `funpack` will only remove columns where the
correlation of both present and missing values meet the thresholds. Note that
the column which contains more missing values will be the one that gets
removed:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -apr all "removeIfRedundant(0.9, nathres=0.6)" out.tsv data_17.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
## Categorical binarisation
The `binariseCategorical` process takes a column containing categorical
labels, and replaces it with a set of new binary columns, one for each
category. Imagine that we have this data:
%% Cell type:code id: tags:
``` bash
cat data_18.tsv
```
%% Cell type:markdown id: tags:
We can use the `binariseCategorical` process to split column `1-0.0` into a
separate column for each category:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -apr 1 "binariseCategorical" out.tsv data_18.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
There are a few options to `binariseCategorical`, including controlling whether
the original column is removed, and also the naming of the newly created
columns:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -apr 1 "binariseCategorical(replace=False, nameFormat='{vid}:{value}')" out.tsv data_18.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
# Custom cleaning, processing and loading - `funpack` plugins
If you want to apply some specific cleaning or processing function to a
variable, you can code your functions up in python, and then tell `funpack` to
apply them.
As an example, let's say we have some data like this:
%% Cell type:code id: tags:
``` bash
cat data_19.tsv
```
%% Cell type:markdown id: tags:
## Custom cleaning functions
But for our analysis, we are only interested in the even values for columns 1
and 2. Let's write a cleaning function which replaces all odd values with NA:
%% Cell type:code id: tags:
``` bash
cat plugin_1.py | pygmentize
```
%% Cell type:markdown id: tags:
To use our custom cleaner function, we simply pass our plugin file to `funpack`
using the `--plugin_file` (`-p` for short) option:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -p plugin_1.py -cl 1 drop_odd_values -cl 2 drop_odd_values out.tsv data_19.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
## Custom processing functions
Recall that **cleaning** functions are applied independently to each column,
whereas **processing** functions may be applied to multiple columns
simultaneously, and may add and/or remove columns. Let's say we want to derive
a new column from columns `1-0.0` and `2-0.0` in our example data set. Our
plugin file might look like this:
%% Cell type:code id: tags:
``` bash
cat plugin_2.py | pygmentize
```
%% Cell type:markdown id: tags:
Again, to use our plugin, we pass it to `funpack` via the `--plugin`/`-p`
option:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -p plugin_2.py -apr "1,2" "sum_squares" out.tsv data_19.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
## Custom file loaders
You might want to load some auxillary data which is in an awkward format that
cannot be automatically parsed by `funpack`. For example, you may have a file
which has acquisition date information separated into *year*, *month* and
*day* columns, e.g.:
%% Cell type:code id: tags:
``` bash
cat data_20.tsv
```
%% Cell type:markdown id: tags:
These three columns would be better loaded as a single column. So we can write a
plugin to load this file for us. We need to write two functions:
* A "sniffer" function, which returns information about the columns contained
in the file.
* A "loader" function which loads the file, returning it as a
`pandas.DataFrame`.
%% Cell type:code id: tags:
``` bash
cat plugin_3.py | pygmentize
```
%% Cell type:markdown id: tags:
And to see it in action:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -p plugin_3.py -l data_20.tsv my_datefile_loader out.tsv data_20.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
# Miscellaneous topics
## Non-numeric data
Many UK Biobank variables contain non-numeric data, such as alpha-numeric
codes and unstructured text. If you want to select subjects on the basis of
such columns, [variable expressions](#Selecting-subjects-by-variable-value)
contain some simple mechanisms for doing so. Here is an example of a file
containing both numeric and non-numeric data:
%% Cell type:code id: tags:
``` bash
cat data_21.tsv | column -t -s $'\t'
```
%% Cell type:markdown id: tags:
Let's say we are only interested in subjects where variable 1 contains a value
in the `A` category:
> Note the use of the `-nb` (`--no_builtins`) option here - this tells
> `funpack` to ignore its built-in variable table, which contains information
> about the type of each UK BioBank variable, and would otherwise interfere
> with this example.
%% Cell type:code id: tags:
``` bash
funpack -nb -q -ow -s "v1 contains 'A'" out.tsv data_21.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
By default, `funpack` will save columns containing non-numeric data to the
main output file, just like any other column. However, there are a couple of
options you can use to control what `funpack` does with non-numeric data.
If you only care about numeric columns, you can use the
`--suppress_non_numerics` option (`-esn` for short) - this tells `funpack` to
discard all columns that are not numeric:
%% Cell type:code id: tags:
``` bash
funpack -nb -q -ow -esn out.tsv data_21.tsv
cat out.tsv
```
%% Cell type:markdown id: tags:
You can also tell `funpack` to save all non-numeric columns to a separate file,
using the `--write_non_numerics` option (`-wnn` for short):
%% Cell type:code id: tags:
``` bash
funpack -nb -q -ow -esn -wnn out.tsv data_21.tsv
cat out.tsv
cat out_non_numerics.tsv
```
%% Cell type:markdown id: tags:
## Dry run
The `--dry_run` (`-d` for short) option allows you to see what `funpack` is
going to do - it is useful to perform a dry run before running a large
processing job, which could take a long time. For example, if we have a
complicated configuration such as the following, we can use the `--dry_run`
option to check that `funpack` is going to do what we expect:
%% Cell type:code id: tags:
``` bash
funpack \
-nb -q -ow -d \
-nv 1 "7,8,9" \
-re 2 "1,2,3" "100,200,300" \
-cv 3 "v4 != 20" "25" \
-cl 4 "makeNa('< 50')" \
-apr all "removeIfSparse(minpres=0.5)" \
out.tsv data_01.tsv
```
%% Cell type:markdown id: tags:
## Built-in rules
`funpack` has a large number of hand-crafted rules built in, which are
specific to variables found in the UK BioBank data set. These rules are part
of the ``fmrib`` configuration, which can be used by adding `-cfg fmrib` to
of the `fmrib` configuration, which can be used by adding `-cfg fmrib` to
the command-line options.
We can use the `--dry_run` (`-d` for short) option, along with some dummy data
files which just contain the UK BioBank column names, to get a summary of
these rules:
%% Cell type:code id: tags:
``` bash
funpack -q -ow -cfg fmrib -d out.tsv ukb_dataset_1_only_column_names.tsv ukb_dataset_2_only_column_names.tsv
funpack -q -ow -cfg fmrib -d out.tsv ukbcols.csv
```
%% Cell type:markdown id: tags:
All of these rules are coded in a set of `tsv` files which are installed along
the `funpack` source code:
%% Cell type:code id: tags:
``` bash
ukbdir=`python -c "import os.path as op; import funpack; print(op.dirname(funpack.__file__))"`
ls -l $ukbdir/configs/fmrib/
```
%% Cell type:markdown id: tags:
The key files are:
* **`variables_*.tsv`**: Child value replacement, and cleaning rules for each
variable.
* **`datacodings_*.tsv`**: NA insertion and recoding rules for data codings -
these are used when rules are not explicitly specified in the
`variables_*.tsv` files, for a variable which uses a given data coding.
* **`processing.tsv`**: List of all processing functions that are applied, in
order.
* **`categories.tsv`**: Variable categories, for use with the `--category`/`-c`
option.
If you are not happy with some of the rules defined in these files, you have the
following options:
1. Override them on the command-line, or in a configuration file (see below)
2. Modify them in place.
3. Create your own versions of the files, and pass them via the following
command-line options:
* `--variable_file` (`-vf` for short)
* `--datacoding_file` (`-df` for short)
* `--type_file` (`-tf` for short)
* `--processing_file` (`-pf` for short)
* `--category_file` (`-cf` for short)
Rules which you provide on the command-line or via the `-vf` and `-df` options
will override built-in rules for the same variables/datacodings.
The variable and datacoding rules can be stored across multiple files - for
example, you may want to write all of the NA insertion rules in one file, and
all of the child value replacement rules in another. ``funpack`` will merge
all of the built-in files and any additionally provided files together, so you
do not need to maintain large and unwieldy variable/datacoding tables.
## Using a configuration file
`funpack` has an extensive command-line interface, but you don't need to pass
all of the settings via the command-line. Instead, you can put them into a
file, and give that file to `funpack` with the `--config` (`-cfg` for short)
option. You need to use the long-form of each command-line option, without the
leading `--`.
Let's take our example from the [dry run section](#Dry-run), and put all of
the options into a configuration file:
%% Cell type:code id: tags:
``` bash
cat <<EOF > config.txt
no_builtins
quiet
overwrite
na_values 1 "7,8,9"
recoding 2 "1,2,3" "100,200,300"
child_values 3 "v4 != 20" "25"
clean 4 "makeNa('< 50')"
append_process all "removeIfSparse(minpres=0.5)"
EOF
cat config.txt
```
%% Cell type:markdown id: tags:
Now we can pass this file to `funpack` instead of having to pass all of the
command line options:
%% Cell type:code id: tags:
``` bash
funpack -d -cfg config.txt out.tsv data_01.tsv
```
%% Cell type:markdown id: tags:
## Working with unknown/uncategorised variables
Future UK BioBank data releases may contain new variables that are not present
in the built-in variable table used by `funpack`, and thus are not recognised
as UKB variables.
Furthermore, if you are working with the hand-crafted variable categories from
the [built-in `fmrib` configuration](#Built-in-rules), or your own categories,
a new data release may contain variables which are not included in any
category.
To help you identify these new variables, `funpack` has the ability to inform
you about variables that it does not know about, or that are not categorised.
To demonstrate, let's create a dummy variable table which lists all of the
variables that we do know about - variables 1 to 5. We'll also create a custom
category file, which adds variables 1 to 3 to a category:
%% Cell type:code id: tags:
``` bash
echo -e "ID\n1\n2\n3\n4\n5\n" > custom_variables.tsv
echo -e "ID\tCategory\tVariables" > custom_categories.tsv
echo -e "1\tmy category\t1:3" >> custom_categories.tsv
cat custom_variables.tsv
cat custom_categories.tsv
```
%% Cell type:markdown id: tags:
Now we can use the `--write_unknown_vars` option (`-wu` for short) to generate
a summary of the variables which `funpack` did not recognise, or which were
not in any category:
> Again we are using the `-nb` (`--no_builtins`) option, which tells `funpack`
> not to load its built-in table of UK BioBank variables.
%% Cell type:code id: tags:
``` bash
funpack -nb -q -ow -vf custom_variables.tsv -cf custom_categories.tsv -wu out.tsv data_01.tsv
cat out_unknown_vars.txt
```
%% Cell type:markdown id: tags:
This file contains a summary of the columns that were uncategorised or not
recognised, and whether they were exported to the output file. If an
unknown/uncategorised variable was not exported (e.g. it was removed during
[processing](#Processing-examples) by a sparsity or redundancy check), the
`exported` column will contain a `0` for that variable.
If you are specifically interested in querying or working with these
unknown/uncategorised variables, you can select them with the
automatically-generated `unknown`/`uncategorised` categories:
%% Cell type:code id: tags:
``` bash
funpack -nb -q -ow -vf custom_variables.tsv -cf custom_categories.tsv -c unknown unknowns.tsv data_01.tsv
funpack -nb -q -ow -vf custom_variables.tsv -cf custom_categories.tsv -c uncategorised uncategorised.tsv data_01.tsv
echo "Unknown variables:"
cat unknowns.tsv
echo "Uncategorised variables:"
cat uncategorised.tsv
```
......
......@@ -51,7 +51,7 @@ Get command-line help by typing:
**Important** The examples in this notebook assume that you have installed `funpack`
2.3.0.dev0 or newer.
2.3.0 or newer.
```
......@@ -1153,7 +1153,7 @@ funpack \
`funpack` has a large number of hand-crafted rules built in, which are
specific to variables found in the UK BioBank data set. These rules are part
of the ``fmrib`` configuration, which can be used by adding `-cfg fmrib` to
of the `fmrib` configuration, which can be used by adding `-cfg fmrib` to
the command-line options.
......@@ -1163,7 +1163,7 @@ these rules:
```
funpack -q -ow -cfg fmrib -d out.tsv ukb_dataset_1_only_column_names.tsv ukb_dataset_2_only_column_names.tsv
funpack -q -ow -cfg fmrib -d out.tsv ukbcols.csv
```
......
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