Programmatic Data Import#

	`loadd()`	`csvReadM()` `csvReadSA()`	`xlsReadM()` `xlsReadSA()`	`load()`
	`loadd()`	`csvReadM()` `csvReadSA()`	`xlsReadM()` `xlsReadSA()`	`load()`
File types
GAUSS (GDAT, DAT, FMT)	X			X (Except GDAT)
SAS, SPSS, Stata	X
CSV and delimited text	X	X		(Deprecated)
Excel (XLS, XLSX)	X		X

In most cases, you should use loadd() to load data from:

Excel (XLS, XLSX)
CSV or other delimited text files.
Stata (DTA), SAS (SAS7BDAT), SPSS or GAUSS Datasets (GDAT or DAT).
GAUSS Matrix files (FMT), or HDF5 datasets.

Load all variables from a dataset#

To load all variables from a dataset using loadd(), only the file name is passed in.

// Create file name with full path
dataset = getGAUSSHome("examples/housing.csv");

// Load all variables from the file
housing = loadd(dataset);

// Preview the loaded data
head(housing);

The code above prints the first five rows of the data to the screen.

    taxes             beds            baths              new            price             size
0000        4.0000000        2.0000000        0.0000000        279.90000        2048.0000
0000        2.0000000        1.0000000        0.0000000        146.50000        912.00000
0000        4.0000000        2.0000000        0.0000000        237.70000        1654.0000
0000        3.0000000        2.0000000        0.0000000        200.00000        2068.0000
0000        3.0000000        3.0000000        0.0000000        159.90000        1477.0000

Note

By default, loadd() will try and determine whether the first row of CSV and Excel files contains variable names. If it is not guessin correctly, this can be overridden using the loadFileControl structure.

GAUSS formula string basics#

GAUSS formula strings allow you to represent a model or collection of variables in a compact and intuitive manner, using the variable names in the dataset.

Formula strings can be used to:

Specify which variables to load.
Create interaction terms.
Perform transformations on variables before loading.
Specify variable types including dates, categorical variables, and strings.
Include or exclude intercepts from models.

A formula string can be used to tell GAUSS whether to load specific variables, to exclude specific variables, to load an intercept.

Operator	Purpose
.	Represents all variables.
+	Adds a variable.
-	Removes a variable.
1	Represents an intercept term.
*	Adds an interaction term and includes both original variables.
:	Adds an interaction between two variables, but does not include either of the original variables.

Formula strings also allow data transformations during loading.

Keyword	Purpose
cat	Load a variable as categorical column in a dataframe.
date	Load a variable as date column in a dataframe.
str	Load a variable as string column in a dataframe.
$	Indicates that a variable is stored in the file as a string and that the variable should be passed to the keyword or procedure as a string column.

Load a subset of variables using a formula string#

In this example, we use a formula string to load two variables, unemployment and hourly_earn, from the detroit.dta file.

// Create file name with full path
dataset = getGAUSSHome("examples/detroit.dta");

// Load two specific variables from the file
detroit = loadd(dataset, "unemployment + hourly_earn");

// Preview the data
head(detroit);

The first five rows of the detroit dataframe look like:

unemployment      hourly_earn
000000        2.9800000
0000000        3.0900000
2000000        3.2300000
3000000        3.3300000
5000000        3.4600000

Load all variables except one using a formula string#

// Create file name with full path
dataset = getGAUSSHome("examples/xle_daily.xlsx");

// Load all variables except for date
xle = loadd(dataset, ". -date");

// Preview data
head(xle);

The first five rows of the xle dataframe look like:

Adj Close           Volume
158432        15807900.
978832        30280200.
495384        19258900.
545837        24621000.
136948        15678400.

Load categorical variables using a formula string#

Some datasets such as, GDAT, SAS, Stata (.dta), and SPSS store variable type information. GAUSS will automatically identify categorical variables from these files.

// Create file name with full path
dataset = getGAUSSHome("examples/auto2.dta");

// GAUSS will load price as numeric
// and rep78 as categorical, because this
// information is contained in the dataset
auto = loadd(dataset, "price + rep78");

// Check types
("Variable" $| getColNames(auto)) $~ getColTypes(auto);

The automatically loaded variable types are:

    type
  number
category

Excel, CSV, and other text files do not store variable type descriptions and can only pass string or numeric data to GAUSS. In these cases, GAUSS uses intelligent type detection to auto-detect variable types, including categorical data.

If a categorical variable is not automatically detected by GAUSS, use the cat keyword with loadd() to specify that a string variable should represent categorical data.

// Create file name with full path
dataset = getGAUSSHome( "examples/yarn.xlsx");

// Load amplitude as a categorical variable and cycles as numeric
yarn = loadd(dataset, "cat(amplitude) + cycles");

// Check types
getColTypes(yarn);

    type
category
  number

Load and transform variables using a formula string#

Data transformations can be implemented during loading by including the appropriate GAUSS procedure in the formula string.

// Create file name with full path
dataset = getGAUSSHome("examples/housing.csv");

// Load price variable and perform natural log transform
ln_price = loadd(dataset, "ln(price)");

You can also use your own procedures in formula strings as shown below:

// Create file name with full path
dataset = getGAUSSHome("examples/housing.csv");

// Load price variable and perform first difference of natural log
ln_price_d = loadd(dataset, "lndiff(price)");

proc (1) = lndiff(x);
    local ln_x;

    ln_x = ln(x);

    retp(ln_x - lagn(ln_x,1));
endp;

Note

Procedures used in formula strings must take a single column vector as input and return a column vector of the same length.

If your procedure needs the variable loaded as a string, you can prepend the variable name with a dollar sign $ to tell GAUSS to load the variable as a string array and pass it to your procedure.

// Create file name with full path
dataset = getGAUSSHome("examples/nba_ht_wt.xls");

// Load school variable as a string and pass to is_nc procedure
nba = loadd(dataset, "is_nc($school) + height + weight");

proc (1) = is_nc(name);
    retp(name .$== "North Carolina");
endp;

Load dates using a formula string#

GAUSS will automatically detect date variables if they are in one of the recognizable, pre-existing formats.

// Create file name with full path
dataset = getGAUSSHome("examples/eurusd_tick.csv");

// Load variables and specify that the variable named
// date, should be loaded as a date vector
eur_usd = loadd(dataset);

The first five rows of our eur_usd dataframe looks like:

            date              bid              ask
20081031 1251450        1.2739000        1.2736000
20081031 1251470        1.2740000        1.2737000
20081031 1251550        1.2741000        1.2738000
20081031 1251580        1.2738000        1.2735000
20081031 1251590        1.2739000        1.2736000

GAUSS will automatically detect many standard date formats:

Format	Example
%Y%m%d	20181031
%d-%m-%Y	31-10-2018
%m-%d-%Y	10-31-2018
%Y-%m-%d	2018-10-31
%m/%d/%Y	10/31/2018
%Y/%m/%d	2018/10/31
%d %B %Y	31 October 2018
%Y%m%d%H%M	201810311830
%Y%m%d %H%M	20181031 1830
%d-%m-%Y %R	31-10-2018 18:30
%Y-%m-%d %R	2018-10-31 18:30
%m/%d/%Y %R	10/31/2018 18:30
%Y/%m/%d %R	2018/10/31 18:30
%d %B %Y %R	31 October 2018 18:30
%Y%m%d%H%M%S	20181031183000
%Y%m%d %H%M%S	20181031 183000
%T	18:30:00
%d-%m-%Y %T	31-10-2018 18:30:00
%Y-%m-%d %T	2018-10-31 18:30:00
%m/%d/%Y %T	10/31/2018 18:30:00
%Y/%m/%d %T	2018/10/31 18:30:00
%m/%d/%Y %T %p	10/31/2018 18:30:00 PM
%Y/%m/%d %T.%j	2018/10/31 18:30:00.000
%m/%d/%Y %T.%j	10/31/2018 18:30:00.000
%d %B %Y %R	31 October 2018 18:30:00
%Y-%m-%dT%T	2018-10-31T18:30
%Y-%m-%dT%TT	2018-10-31T18:30T
%Y-%m-%dT%T	2018-10-31T18:30:00
%Y-%m-%dT%TT	2018-10-31T18:30:00T
%Y-%m-%dT%T.%j	2018-10-31T18:30:00.000
%Y-%m-%dT%T.%jT	2018-10-31T18:30:00.000T

Loading non-standard date formats#

If a date variable is not in a recognizable format, the date keyword should be used in a formula string to indicate that loadd() should load a variable as a date. In this case, GAUSS allows you to specify any arbitrary date format using BSD strftime specifiers to denote the date elements.

Note

The full list of strftime format specifiers can be found in the documentation for strctoposix().

The strftime specifier tells GAUSS how to interpret the date elements of the text. For example consider a file containing the contents below:

"date","price"
"January, 1982",12.83
"February, 2004",19.21

The table below shows how we use the first date observation, "January, 2004" to create the format string "%B, %Y".

Original Contents	Description	Type	Format string contents
January	The full name of the month.	Date	`%B`
,	A comma.	Literal	,
(space)	A space.	Literal	(space)
1982	A four digit year.	Date	`%Y`

Now pass the format string as the second input to the date keyword. Assuming our file is called date_test.csv, the code would look like this:

// Load 'date' with custom date format, using a strftime specifier
data = loadd("date_test.csv", "date(date, '%B, %Y') + price");

Note that the format specifier is enclosed in single ticks.

Loading variables as a string using a formula string#

In most cases, GAUSS will auto-detect when a variable is a string variable. However, in the case a string variable is not correctly identified by GAUSS, the str keyword should be used, within a GAUSS formula string. This will specify that a variable should be loaded as a string variable in a dataframe.

Consider the nba_ht_wt.xls dataset.

// Create file name with full path
dataset = getGAUSSHome("examples/nba_ht_wt.xls");

// Load all variables based on GAUSS's smart
// type detection. 'pos' will be loaded as a categorical variable,
// 'player' as a string variable. Load
// 'height' and 'weight' as numeric.
nba = loadd(dataset);

Without any formula strings, two of the variables, player and School will be loaded as strings:

>> asdf(getcolnames(nba), "Variable")~getcoltypes(nba)

      Variable             type
        Player           string
           Pos         category
        Height           number
        Weight           number
           Age           number
        School           string
         BDate             date

Now, let’s load the variables player, Pos, and age. This time we will specify that we want Pos to be loaded as a string rather than a category:

// Create file name with full path
dataset = getGAUSSHome("examples/nba_ht_wt.xls");

// Load Player, Pos, and Age
// Specify Pos as string variable
nba = loadd(dataset, "player + str(Pos) + age");

The player variable will automatically load as a string variable, the age variable will automatically load as a numeric, and Pos loads as a categorical variable:

>> asdf(getcolnames(nba_subset), "Variable")~getcoltypes(nba_subset)

      Variable             type
        player           string
           Pos           string
           age           number

Note

This loads a variable as a string type in a dataframe. If you want to load a variable into a GAUSS string array, use loaddsa().

Loading an interaction term using a formula string#

Use the : operator in a formula string to load a pure interaction term between the variables on the left and right of the colon.

// Create file name with full path
dataset = getGAUSSHome("examples/housing.csv");

// Load and create a variable that is the interaction (element-by-element product)
// 'new' and 'baths'. Do not load either 'new' or 'baths'.
housing = loadd(dataset, "new:baths");

Use the * operator in a formula string to load a each variable on the left and right of the *, as well as an interaction term between the two.

// Create file name with full path
dataset = getGAUSSHome("examples/housing.csv");

// Load and create a variable that is the interaction (element-by-element product)
// 'new' and 'baths'. Also load the variables 'new' and 'baths'.
housing = loadd(dataset, "new*baths");

Advanced data loading options#

loadd() allows you to control various data import options such as:

The header row.
The row range.
Missing values handling.
Loading intercepts.
Delimiters and quotations for CSV files.
Specifying the sheet of an XLS or XLSX file.

by passing in the loadFileControl structure.

Basic usage of the `loadFileControl` structure#

As with all GAUSS control structures, there are four steps to using the loadFileControl structure.

Declare an instance of the structure.
Fill the structure with default values.
Modify the settings that you want to change.
Pass the structure to loadd().

Modify the row range loaded by `loadd()`#

The ld_ctl.row_range.first and ld_ctl.row_range.last members of the loadFileControl structure specify the row range for importing.

// Create file name with full path
dataset = getGAUSSHome("examples/housing.csv");

// 1. Declare ld_ctl to be an instance of a 'loadFileControl' structure
struct loadFileControl ld_ctl;

// 2. Fill 'ld_ctl' with default settings
ld_ctl = loadFileControlCreate();

// 3. Change the row range to load rows 9-21
ld_ctl.row_range.first = 9;
ld_ctl.row_range.last = 21;

// Pass the loadFileControl structure as the final input
// Note the use of the '.' operator to note that all variables should be loaded
housing = loadd(dataset, ".", ld_ctl);

Specify the row containing the variable names in a text or Excel file#

By default, loadd() assumes that if variable names are included in an Excel or delimited text file, they will be in the first row. The header_row member of the loadFileControl structure allows you to control which row is interpreted as variable names.

For example consider a file containing:

// 'headroom' was reported in inches
"mpg","headroom"
21,144
35,90
12,160

Assuming this file is named auto_headers.csv and is in our current working directory, we can load this file, correctly specifying that the variable names are in the second row using a loadFileControl structure:

// Declare structure and fill with default settings
struct loadFileControl ld_ctl;
ld_ctl = loadFileControlCreate();

// Specify the row containing the variable names
ld_ctl.header_row = 2;

// Load the data, using the settings in 'ld_ctl'
auto = loadd("auto_headers.csv", ".", ld_ctl);

Specify string values to import as missing values#

Prior to calling the loadd() procedure, use the ld_ctl.missing_vals_str member of the loadFileControl structure to specify values that should be treated as missing upon import.

GAUSS identifies both “.” and “” as missing values by default.

For example, if we have the following data file:

id,price,transaction
11032,12.34,"purchase"
11210,99.21,"exchange"
11087,34.21,"NA"
11249,129.20,"purchase"
10277,19.43,"unknown"

and we want to specify both "NA" and "unknown" as missing values, we would use the following code:

// Declare structure and fill with default settings
struct loadFileControl ld_ctl;
ld_ctl = loadFileControlCreate();

// Specify that "NA" and "unknown" should be imported as missing values
ld_ctl.missing_vals_str = { "NA" "unknown" };

// Load variables, specifying that 'transaction' should be a categorical
// variable and any string observations matching either "NA" or
// "unknown" should be interpreted as missing values.
transactions = loadd("missing_value.csv", "id + price + cat(transaction)", ld_ctl);

Specify a CSV file delimiter programmatically#

By default, loadd() expects files with a .csv file extension to use a comma as the delimiter. To change the file delimiter use the delimiter member of the loadFileControl structure.

Common Data File Delimiters
Name	Symbol
Comma	“,”
Space	“ “
Tab	“\t”
Pipe	“\|”
Semi-colon	“;”

For example, a space delimited file like this:

length width
31
22
44

named space_separated.csv can be loaded like this:

// Declare structure and fill with default settings
struct loadFileControl ld_ctl;
ld_ctl = loadFileControlCreate();

// Specify space as the file delimiter
ld_ctl.csv.delimiter = " ";

// Load all variables from a space separated text file
x = loadd("space_separated.csv", ".", ld_ctl);

Specify the CSV file quotation character#

The quote character tells GAUSS which text should be treated as a single element. For example, if we have a space separated file with spaces in the variable names like this:

'length cm' 'width cm'
31
22
44

without the single tick marks, it would look like we have four variable names, but only two variables. The tick marks tell GAUSS that the space before cm is part of the variable name.

However, by default, GAUSS assumes that double quotes, ", are the quote mark. We can use the .csv.quotechar member of the loadFileControl structure to set the quote mark to a single tick as shown below:

// Declare structure and fill with default settings
struct loadFileControl ld_ctl;
ld_ctl = loadFileControlCreate();

// Specify space as the file delimiter
ld_ctl.csv.delimiter = " ";

// Specify the quote character to be a single tick
ld_ctl.csv.quotechar = "'";

// Load all variables from a space separated text file
x = loadd("space_separated.csv", ".", ld_ctl);

The Excel Data Tools#

Load a specific range from an Excel file#

You can load a specified range of an Excel file into a GAUSS numeric matrix or string array with xlsReadM() and xlsReadSA(), respectively.

fname = getGAUSSHome("examples/xle_daily.xlsx");

// Load data from a specific range of an Excel file into a numeric matrix
x = xlsReadM(fname, "B2:C19");

fname = getGAUSSHome("examples/yarn.xlsx");

// Load data from a specific range of an Excel file into a string array
x_sa = xlsReadSA(fname, "A2:B9");

More details can be found in the Command Reference pages for xlsReadM() and xlsReadSA().

Check the number of sheets in an Excel spreadsheet#

Use the xlsGetSheetCount() procedure to count the number of sheets contained in the filename.

// File name with full path
fname = getGAUSShome("examples/yarn.xlsx");

// Count sheets
nsheets = xlsGetSheetCount(fname);

Full details and more examples can be found in the Command Reference page for xlsGetSheetCount().

Check the size of an Excel spreadsheet#

Use the xlsGetSheetSize() procedure to count the size of a specific sheet, sheetname, in filename. The sheet name is an optional argument and the first sheet will be checked by default.

// File name with full path
fname = getGAUSShome("examples/yarn.xlsx");

// Leave out optional sheet number
{ r, c } = xlsGetSheetSize(fname);

Full details and more examples can be found in the Command Reference page for xlsGetSheetSize().

Check the type of Excel cells#

Use the xlsGetCellTypes() procedure to check the cell format types of a specific row in an Excel spreadsheet.

// File name with full path
fname = getGAUSShome("examples/xle_daily.xlsx");

// Specify sheet number
sheet = 1;

// Specify row
row = 1;

// Get cell types
cell_types = xlsGetCellTypes(fname, sheet, row);

Full details and more examples can be found in the Command Reference page for xlsGetCellTypes().

Importing data from the internet#

Data can be directly imported from onlines sources to GAUSS:

Using loadd() to load directly from a URL.
Using the FRED database integration.
Using DBNOMICS database integration.

Loading data from a URL#

The loadd() procedure supports data loading from a URL.

// Specify the URL
url = "https://github.com/aptech/tspdlib/raw/master/examples/pd_gdef.gdat";

// Load data
pd_gdef = loadd(url);

// Preview data
head(pd_gdef[., "Year" "AUT" "DEU"]);

      Year              AUT              DEU
1995-01-01       -1.0248736       -2.5337111
1996-01-01      -0.64834945       -1.0192022
1997-01-01     -0.079297470       -2.1558221
1998-01-01       0.23284856       -2.5249485
1999-01-01     -0.043695305       -2.9874769

Loading data from FRED#

Getting started#

Importing data from FRED with the GAUSS FRED integration requires a FRED API key, which can be directly requested from the FRED API Request page. Once an API key is obtained it can be set in GAUSS by:

1. Setting the API key directly at the top of your program.

FRED_API_KEY = "your_api_key"

Setting the environment variable FRED_API_KEY to your API key.

3. Editing the gauss.cfg file and modifying the fred_api_key.

fred_api_key = your_api_key

Searching for FRED series#

FRED series can be located using the fred_search() procedure. The fred_search() procedure takes a string search input and returns related series’ IDs.

/*
** This example requires that you first set
** the FRED API key in GAUSS
*/

// Search FRED for dataset related to
// 'produce price index'
fred_search("producer price index");

frequency  frequency_short group_popularity              id       last_updated  observation_end observation_star       popularity     realtime_end   realtime_start seasonal_adjustm seasonal_adjustm            title           units      units_short
  Monthly                 M        80.000000           PPIACO 2022-11-15 07:52       2022-10-01       1913-01-01        80.000000       2022-11-23       2022-11-23 Not Seasonally A              NSA Producer Price I   Index 1982=100   Index 1982=100
  Monthly                 M        79.000000          WPU0911 2022-11-15 07:52       2022-10-01       1926-01-01        79.000000       2022-11-23       2022-11-23 Not Seasonally A              NSA Producer Price I   Index 1982=100   Index 1982=100
  Monthly                 M        79.000000            PCEPI 2022-10-28 08:40       2022-09-01       1959-01-01        78.000000       2022-11-23       2022-11-23 Seasonally Adjus               SA Personal Consump   Index 2012=100   Index 2012=100
  Monthly                 M        78.000000  PCU325211325211 2022-11-15 07:55       2022-10-01       1976-06-01        78.000000       2022-11-23       2022-11-23 Not Seasonally A              NSA Producer Price I Index Dec 1980=1 Index Dec 1980=1

Importing data series from FRED#

FRED data series are imported using the fred_load() procedure and the FRED series ID.

// Download all observations of 'PPIACO'
// into a GAUSS dataframe
PPI = fred_load("PPIACO");

// Preview first five rows of 'PPI'
head(PPI);

      date           PPIACO
1913-01-01        12.100000
1913-02-01        12.000000
1913-03-01        12.000000
1913-04-01        12.000000
1913-05-01        11.900000

Advanced FRED importing tools#

GAUSS FRED functions use a parameter list for passing advanced settings. This list is constructed using the fred_set() function.

The fred_set() function creates a running list of parameters you want to pass to the FRED functions. It is specified by first listing a parameter name, then the associated parameter value.

// Create a FRED parameter list with
// 'frequency' set to 'q' (quarterly)
params_GDP = fred_set("frequency", "q");

Additional parameters values can be added to an existing parameter list:

// Set 'aggregation_method' to end-of-period using
// the previously created parameter list 'params_GDP'
params_GDP = fred_set("aggregation_method", "eop", params_GDP);

The parameter list is then passed to the fred_load() function.

Example: Aggregating FRED data from monthly to quarterly

The frequency parameter can be used to specify the frequency of data imported from FRED. The specified frequency can only be the same or lower than the frequency of the original series.

Specifier	Description
`"d"`	Daily
`"w"`	Weekly
`"bw"`	Biweekly
`"m"`	Quarterly
`"sa"`	Semiannual
`"a"`	Annual

The default aggregation method is to use averaging. However, the aggregation_method parameter can be used to specify an aggregation method. Aggregation options include:

Specifier	Description
`"avg"`	Average
`"sum"`	Sum
`"eop"`	End of period

// Set parameter list
params_cpi = fred_set("frequency", "q", "aggregation_method", "eop");

// Load quarterly CPI
cpi_q_eop  = fred_load("CPIAUCSL", params_cpi);

// Preview data
head(cpi_q_eop);

      date         CPIAUCSL
1947-01-01        22.000000
1947-04-01        22.080000
1947-07-01        22.840000
1947-10-01        23.410000
1948-01-01        23.500000

Loading data from DBNOMICS#

Searching for DBNOMICS series#

The dbnomics_search() procedure takes a string search input and returns series details including:

Series code.
Series description.
Direct hash.
The time indexed at.
Series name.
Number of matching series.
Series number.
Provider code.
Provider name.

// Search DBNOMICS for series
// related to GDP and France
dbnomics_search("GDP France");

            code         dir_hash       indexed_at             name nb_matching_seri        nb_series    provider_code    provider_name       updated_at
     gov_10a_exp fab720436a1f72a3 2022-04-22T11:17 General governme        12800.000           129902         Eurostat         Eurostat 2022-04-22T00:00
CHELEM-TRADE-IND 7cfd7c7a78b84ede 2022-09-02T09:35 CHELEM - Interna        4937.0000           797304            CEPII Centre d'�tudes                .
    nasa_10_f_cp aa9a1cb003e7d567 2022-10-15T10:03 Financial accoun        4330.0000           163206         Eurostat         Eurostat 2022-10-15T00:00
    nasa_10_f_bs 5fe95bd760e282a4 2022-10-27T11:06 Financial balanc        3670.0000           344205         Eurostat         Eurostat 2022-10-27T00:00
    nasa_10_f_tr b69e7b63f3d59d44 2022-10-27T11:06 Financial transa        3655.0000           329303         Eurostat         Eurostat 2022-10-27T00:00

Loading series from DBNOMICS#

The dbnomics_series() procedure loads data series from the DBNOMICS database using a list of series IDs. Each series ID should be formatted as provider_code/dataset_code/series_code. Series can belong to any provider and dataset.

For example, consider the CPI series from the IMF.

Provider: International Monetary Fund (IMF)
Dataset: Consumer Price Index (CPI) (CPI)
Series: Annual – Austria – Transport (A.AT.PCPIT_IX)

The series ID is "IMF/CPI/A.AT.PCPIT_IX". It can be used with the dbnomics_series() procedure to load the series into a GAUSS dataframe:

// Load IMF CPI data series
cpi_imf = dbnomics_series("IMF/CPI/A.AT.PCPIT_IX");

// Preview data
head(cpi_imf);
tail(cpi_imf);

Provider: International Monetary Fund (IMF)
Dataset: Consumer Price Index (CPI) (CPI)
Series: Annual-Austria-Transport (A.AT.PCPIT_IX)
Data last indexed on 2024-02-08T02:52:10.970Z

period_start_day    A.AT.PCPIT_IX
      1958-01-01                .
      1959-01-01                .
      1960-01-01                .
      1961-01-01                .
      1962-01-01                .

period_start_day    A.AT.PCPIT_IX
      2019-01-01        101.72145
      2020-01-01        99.991667
      2021-01-01        106.62500
      2022-01-01        123.93333
      2023-01-01        126.04167

Combining dataframes#

Merging dataframes#

In GAUSS merging:

Is done using the outerJoin() or innerJoin() procedures.
Is done completely with data in memory.
The innerJoin() function only keeps matching observations from both the left and right table.
The outerJoin() function keeps observations either from both data sources or the left-hand data source.
Allows for one-to-one, one-to-many, many-to-one, and many-to-many joining operations.

Consider two dataframes. The first contains ID and Age:

    ID      Age
  John       22
  Mary       18
 Susan       34
Connie       45

The second contains ID and Occupation:

   ID      Occupation
 John         Teacher
 Mary         Surgeon
Susan       Developer
Tyler           Nurse

First, create the dataframes:

// Create ID strings
string ID1 = { "John", "Mary", "Susan", "Connie" };
string ID2 = { "John", "Mary", "Susan", "Tyler" };

// Create age vector
age = { 22, 18, 34, 45 };

// Create occupation string
string Occupation = { "Teacher", "Surgeon", "Developer", "Nurse" };

// Create first df
df1 = asDF(ID1, "ID") ~ asDF(age, "Age");

// Create second df
df2 = asDF(ID2, "ID") ~ asDF(Occupation, "Occupation");

Next, merge df2 with df1 using ID as a key:

// Merge dataframes
df3 = outerJoin(df2, "ID", df1, "ID", "full");

The df3 dataframe contains:

    ID       Occupation              Age
  John          Teacher        22.000000
  Mary          Surgeon        18.000000
 Susan        Developer        34.000000
 Tyler            Nurse                .
Connie                .        45.000000

The df3 dataframe contains all observations from both the df1 and df2 dataframes, even if they aren’t matched, because of the "full" option.

To keep the matches to the keys from the df2 dataframe, exclude the "full" option:

// Merge dataframes
df3 = outerJoin(df2, "ID", df1, "ID");

Now df3 includes:

   ID       Occupation              Age
 John          Teacher        22.000000
 Mary          Surgeon        18.000000
Susan        Developer        34.000000
Tyler            Nurse                .

Appending dataframes#

When appending dataframes that contain categorical variables, the dfappend() procedure should be used to ensure that the category labels and keys are matched in the resulting dataframe.

Consider the example below, which loads data from a STATA dataset and a CSV file.

// Create file name with full path and load data
fname = getGAUSSHome("examples/tips2.dta");
tips_dta = loadd(fname, "tip + day");

// Create file name with full path and load data
fname = getGAUSSHome("examples/tips2.csv");
tips_csv = loadd(fname, "tip + day");

// Take a small sample of rows
tips_dta = tips_dta[1:3,.];
tips_csv = tips_csv[220:223,.];

tips_dta =        tip     day
            1.0100000     Sun
            1.6600000     Sun
            3.5000000     Sun

tips_csv =       tip      day
           1.4400000      Sat
           3.0900000      Sat
           2.2000000      Fri
           3.4800000      Fri

These two dataframes contain the same variables, tip and day. Note that:

The tips_csv dataframe day variable has two categories, Sat and Fri.
The tips_dta dataframe day variable has one category, Sun.

The dfappend() procedure should be used to vertically concatenate these dataframes and ensure that all categories are appropriately dealt with.

// Create a new dataframe with both
tips_full = dfappend(tips_dta, tips_csv);

tips_stacked =        tip     day
                1.0100000     Sun
                1.6600000     Sun
                3.5000000     Sun
                1.4400000     Sat
                3.0900000     Sat
                2.2000000     Fri
                3.4800000     Fri

Programmatic Data Import#

Load all variables from a dataset#

GAUSS formula string basics#

Load a subset of variables using a formula string#

Load all variables except one using a formula string#

Load categorical variables using a formula string#

Load and transform variables using a formula string#

Load dates using a formula string#

Loading non-standard date formats#

Loading variables as a string using a formula string#

Loading an interaction term using a formula string#

Advanced data loading options#

Basic usage of the loadFileControl structure#

Modify the row range loaded by loadd()#

Specify the row containing the variable names in a text or Excel file#

Specify string values to import as missing values#

Specify a CSV file delimiter programmatically#

Specify the CSV file quotation character#

The Excel Data Tools#

Load a specific range from an Excel file#

Check the number of sheets in an Excel spreadsheet#

Check the size of an Excel spreadsheet#

Check the type of Excel cells#

Importing data from the internet#

Loading data from a URL#

Loading data from FRED#

Getting started#

Searching for FRED series#

Importing data series from FRED#

Advanced FRED importing tools#

Loading data from DBNOMICS#

Searching for DBNOMICS series#

Loading series from DBNOMICS#

Combining dataframes#

Merging dataframes#

Appending dataframes#

Basic usage of the `loadFileControl` structure#

Modify the row range loaded by `loadd()`#