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Groupby - Statistical Aggregations

Groupby - Statistical Aggregations
GroupBy: Statistical Aggregations Measures of Central Tendency: ModeThis workflow shows which cities have hosted the most Olympic Games (Winter vs.Summer) aggregating by mode in combination with unique concatenate with count.This is a useful trick to double-check aggregation results. Measures of Dispersion: SDThis workflow shows data aggregation by computing the SD of the variable "Age" in theSummer and Winter Olympic Games. Measures of Central Tendency: Mean vs. MedianThis workflow shows aggregation methods based on measures of central tendency of thevariable "Age" in the Summer and Winter Olympic Games. Covariance vs. CorrelationThis workflow shows the difference between covariance vs. correlation by aggregating on athlete demographic variables ("Age", "Height", and "Weight")separated for the 2014 Winter and 2016 Summer Olympics. Group by season,aggregate by age(mean vs. median)Filter by ID, nameand gamesConvert ageto integerPlot MCTRound up valuesFilter by gamesGroup by season,aggregate by city(mode and unique concatenate with count)Group by season,aggregate by age(SD)Round up valuesGroup by season,aggregate by age,weight and height(correlation) Convert age,height,weight to doubleFiler by ID, nameand sportFilter atheletesin 2014 and 2016Display citiesAssign coloursRead Olympics datasetConcatenate meanand SDFill missing valueof height and weightwith meanPlot SDerror barsSplit row by seasonZ-scorenormalization Summer ->aggregate by age,weight and height (covariance)Z-scorenormalizationWinter ->aggregate by age,weight and height (covariance)Split row by season GroupBy DuplicateRow Filter String To Number Bar Chart Column Expressions DuplicateRow Filter GroupBy GroupBy Column Expressions GroupBy String To Number DuplicateRow Filter Row Filter Table View Color Manager Excel Reader Concatenate Missing Value R View (Table) Row Splitter Normalizer GroupBy Normalizer GroupBy Display covariance Row Splitter Display correlation GroupBy: Statistical Aggregations Measures of Central Tendency: ModeThis workflow shows which cities have hosted the most Olympic Games (Winter vs.Summer) aggregating by mode in combination with unique concatenate with count.This is a useful trick to double-check aggregation results. Measures of Dispersion: SDThis workflow shows data aggregation by computing the SD of the variable "Age" in theSummer and Winter Olympic Games. Measures of Central Tendency: Mean vs. MedianThis workflow shows aggregation methods based on measures of central tendency of thevariable "Age" in the Summer and Winter Olympic Games. Covariance vs. CorrelationThis workflow shows the difference between covariance vs. correlation by aggregating on athlete demographic variables ("Age", "Height", and "Weight")separated for the 2014 Winter and 2016 Summer Olympics. Group by season,aggregate by age(mean vs. median)Filter by ID, nameand gamesConvert ageto integerPlot MCTRound up valuesFilter by gamesGroup by season,aggregate by city(mode and unique concatenate with count)Group by season,aggregate by age(SD)Round up valuesGroup by season,aggregate by age,weight and height(correlation) Convert age,height,weight to doubleFiler by ID, nameand sportFilter atheletesin 2014 and 2016Display citiesAssign coloursRead Olympics datasetConcatenate meanand SDFill missing valueof height and weightwith meanPlot SDerror barsSplit row by seasonZ-scorenormalization Summer ->aggregate by age,weight and height (covariance)Z-scorenormalizationWinter ->aggregate by age,weight and height (covariance)Split row by season GroupBy DuplicateRow Filter String To Number Bar Chart Column Expressions DuplicateRow Filter GroupBy GroupBy Column Expressions GroupBy String To Number DuplicateRow Filter Row Filter Table View Color Manager Excel Reader Concatenate Missing Value R View (Table) Row Splitter Normalizer GroupBy Normalizer GroupBy Display covariance Row Splitter Display correlation

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