GAUSS 23

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// Load an Excel file from the aptech website
file_url = “http://www.aptech.com/wp-content/uploads/2019/03/skincancer2.xlsx”;
skin_cancer = loadd(file_url);

// Print the first 5 rows of the dataframe
head(skin_cancer);

Smart data type detection in GAUSS 23 figures out the variable type so you do not have to specify it manually. Automatically detects nearly 40 popular date formats.

Replace old code like this:

load X[127,4] = mydata.txt;

with

X = loadd(“mydata.txt”);

No new code to learn, use the .gdat file extension with loadd and saved to load and store your dataframes.

hitters = loadd(“islr_hitters.xlsx”);
tau = 0.90;
call quantileFit(hitters, “ln(salary) ~ AtBat + Hits + HmRun”, tau);

// Load data
fname = getGAUSShome(“examples/auto2.dta”);
auto = loadd(fname);

// Declare control structure
struct olsmtControl ctl;
ctl = olsmtControlCreate();

// Turn on residuals
ctl.res = 1;

// Turn on HAC errors
ctl.cov = “hac”;
call olsmt(auto, “mpg ~ weight + foreign”, ctl);

// Return a 1 if the observation is between the listed dates
match = between(unemp[.,”DATE”], “2020-03”, “2020-08”);

// Select the matching observations
unemp = selif(unemp, match);

// Daily hotel room price
hotel_price = { 238, 405, 405, 329, 238 };

// Daily temperature forecast
temperature = { 89, 94, 110, 103, 97 };

// Decrease the price by 10% if the
// temperature will be more than 100 degrees
new_price = where(temperature .> 100,
hotel_price .* 0.9,
hotel_price);

new_price = 238 405 364.50 296.10 238

For a complete list of all GAUSS 23 offers please see the complete changelog.

GAUSS highlight’s Important Parts of Your Data

Easily Generate New Variables

Aggregate by String, Category or Date

• Options for how to handle missing values.

• Load and combine data from CSV, Excel, SAS, Stata, SPSS.

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