Tag Archivio per: Operation


In this R tutorial, we will see how to perform the aggregation operations by grouping the data and returning the median in the grouped rows.

This operation has to be performed on a dataframe. Let’s create the dataframe with seven rows and five columns.

#create a dataframe-market that has 7 rows and 5 columns.
market=data.frame(market_id=c(1,2,1,4,3,4,5),market_name=c(‘M1’,‘M2’,‘M3’,
‘M4’,‘M3’,‘M4’,‘M3’),market_place=c(‘India’,‘USA’,‘India’,‘Australia’,‘USA’,
‘India’,‘Australia’),market_type=c(‘grocery’,‘bar’,‘grocery’,‘restaurent’,
‘grocery’,‘bar’,‘grocery’),market_squarefeet=c(120,342,220,110,342,220,110))

#display the market dataframe
print(market)

Result

Now, we will return the median in a column by grouping the similar values in another column.

Method 1: Aggregate()

Here, we use the aggregate() function that takes three parameters.

Syntax

aggregate(dataframe_object$grouped, list(dataframe_object$grouping), FUN=median)

Parameters

  1. The first parameter takes the variable column (grouped) which returns the median  per group.
  2. The second parameter takes a single or multiple column (grouping) in a list such that the values are grouped in these columns.
  3. The third parameter takes FUN, which takes the median function to return the median in the grouped values.

Example 1
In this example, we group the values in the market_place column and get the median  in the market_squarefeet column grouped by the market_place column.

#create a dataframe-market that has 7 rows and 5 columns.
market=data.frame(market_id=c(1,2,1,4,3,4,5),market_name=c(‘M1’,‘M2’,‘M3’,
‘M4’,‘M3’,‘M4’,‘M3’),market_place=c(‘India’,‘USA’,‘India’,‘Australia’,‘USA’,
‘India’,‘Australia’),market_type=c(‘grocery’,‘bar’,‘grocery’,‘restaurent’,
‘grocery’,‘bar’,‘grocery’),market_squarefeet=c(120,342,220,110,342,220,110))
 
#get the median of square feet in group by grouping market_place
print(aggregate(market$market_squarefeet, list(market$market_place), FUN=median))

Result

We can see that the similar values (Australia, India and USA) in the market_place column are grouped and returned the median of the grouped values in the market_square feet column.

Example 2
In this example, we group the values in the market_type column and get the median in the market_squarefeet column grouped by the market_type column.

#create a dataframe-market that has 7 rows and 5 columns.
market=data.frame(market_id=c(1,2,1,4,3,4,5),market_name=c(‘M1’,‘M2’,‘M3’,
‘M4’,‘M3’,‘M4’,‘M3’),market_place=c(‘India’,‘USA’,‘India’,‘Australia’,‘USA’,
‘India’,‘Australia’),market_type=c(‘grocery’,‘bar’,‘grocery’,‘restaurent’,
‘grocery’,‘bar’,‘grocery’),market_squarefeet=c(120,342,220,110,342,220,110))
 
#get the median of square feet in group by grouping market_type
print(aggregate(market$market_squarefeet, list(market$market_type), FUN=median))

Result

We can see that the similar values (bar, grocery, and restaurent) in the market_type column are grouped and returned the median of the grouped values in the market_square feet column.

Example 3
In this example, we group the values in the market_type and market_place columns and get the median in the market_squarefeet column grouped by the market_type and market_place columns.

#create a dataframe-market that has 7 rows and 5 columns.
market=data.frame(market_id=c(1,2,1,4,3,4,5),market_name=c(‘M1’,‘M2’,‘M3’,‘M4’,‘M3’,
‘M4’,‘M3’),market_place=c(‘India’,‘USA’,‘India’,‘Australia’,‘USA’,‘India’,‘Australia’),
market_type=c(‘grocery’,‘bar’,‘grocery’,‘restaurent’,‘grocery’,‘bar’,‘grocery’),
market_squarefeet=c(120,342,220,110,342,220,110))
 
#get the median of square feet in group by grouping market_place and market_type
print(aggregate(market$market_squarefeet, list(market$market_place,market$market_type), FUN=median))

Result

We can see that the similar values from the two columns were grouped and returned the median in each  grouped value in the market_square feet column.

Method 2: Dplyr

Here, we use the group_by() function with summarise_at() function which are available in the dplyr library to perform the group_by() funtion with the median operation.

Syntax

dataframe_object%>% group_by(grouping) %>% summarise_at(vars(grouped), list(name = median))

Where:

  1. group_by() takes one parameter, i.e. grouping column
  2. summarise_at() takes two parameters:
  1. The first parameter takes the variable column (grouped) which returns the median per group.
  2. The second parameter takes the median function through the list.

Finally, we first summarize with the median and load it into the group. Then, we load the grouped column into the dataframe object.

It returns a tibble. 

Example 1
In this example, we group the values in the market_place column and get the median in the market_squarefeet column grouped by the market_place column.

library(«dplyr»)
 
#get the median  of square feet in group by grouping market_place
print(market %>% group_by(market_place) %>% summarise_at(vars(market_squarefeet), list(name = median)))

Result

We can see that the similar values (Australia, India and USA) in the market_place column are grouped and returned the median from each grouped value in the market_square feet column.

Example 2
In this example, we group the values in the market_type column and get the median  in the market_squarefeet column grouped by the market_type  column.

library(«dplyr»)
 
#get the median  of square feet in group by grouping market_type
print(market %>% group_by(market_type) %>% summarise_at(vars(market_squarefeet), list(name = median)))

Result

We can see that the similar values (bar, grocery, and restaurant) in the market_type column are grouped and returned the median in each  grouped value in the market_square feet column.

Conclusion

It is possible to group the single or multiple columns with the other numeric columns to return the median from the numeric column using the aggregate() function. Similarly, we can use the groupby() function with the summarise_at() function to group the similar values in a column and return the median from the grouped values with respect to another column.



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In this R tutorial, we will see how to perform the aggregation operations by grouping the data and returning the total sum for the grouped rows.

This operation has to be performed on a dataframe. Let’s create the dataframe with seven rows and five columns.

#create a dataframe-market that has 7 rows and 5 columns.
market=data.frame(market_id=c(1,2,1,4,3,4,5),market_name=c(‘M1’,‘M2’,‘M3’,
‘M4’,‘M3’,‘M4’,‘M3’),market_place=c(‘India’,‘USA’,‘India’,‘Australia’,‘USA’,
‘India’,‘Australia’),market_type=c(‘grocery’,‘bar’,‘grocery’,‘restaurent’,
‘grocery’,‘bar’,‘grocery’),market_squarefeet=c(120,342,220,110,342,220,110))

#display the market dataframe
print(market)

Result

Now, we will return the total sum of a column by grouping the similar values in another column.

Method 1: Aggregate()

Here, we use the aggregate() function that takes three parameters.

Syntax

aggregate(dataframe_object$grouped, list(dataframe_object$grouping), FUN=sum)

Parameters

  1. The first parameter takes the variable column (grouped) which returns the sum of values per group.
  2. The second parameter takes a single or multiple column (grouping) in a list such that the values are grouped in these columns.
  3. The third parameter takes FUN, which takes the sum function to return the total sum on the grouped values.

Example 1
In this example, we group the values in the market_place column and get the sum of the values in the market_squarefeet column grouped by the market_place column.

#create a dataframe-market that has 7 rows and 5 columns.
market=data.frame(market_id=c(1,2,1,4,3,4,5),market_name=c(‘M1’,‘M2’,‘M3’,
‘M4’,‘M3’,‘M4’,‘M3’),market_place=c(‘India’,‘USA’,‘India’,‘Australia’,‘USA’,
‘India’,‘Australia’),market_type=c(‘grocery’,‘bar’,‘grocery’,‘restaurent’,
‘grocery’,‘bar’,‘grocery’),market_squarefeet=c(120,342,220,110,342,220,110))
 
#get the sum of square feet in group by grouping market_place
print(aggregate(market$market_squarefeet, list(market$market_place), FUN=sum))

Result

We can see that the similar values (Australia, India and USA) in the market_place column are grouped and returned the sum of the grouped values in the market_square feet column.

Example 2
In this example, we group the values in the market_type column and get the sum in the market_squarefeet column grouped by the market_type  column.

#create a dataframe-market that has 7 rows and 5 columns.
market=data.frame(market_id=c(1,2,1,4,3,4,5),market_name=c(‘M1’,‘M2’,‘M3’,
‘M4’,‘M3’,‘M4’,‘M3’),market_place=c(‘India’,‘USA’,‘India’,‘Australia’,‘USA’,
‘India’,‘Australia’),market_type=c(‘grocery’,‘bar’,‘grocery’,‘restaurent’,
‘grocery’,‘bar’,‘grocery’),market_squarefeet=c(120,342,220,110,342,220,110))
 
#get the sum of square feet in group by grouping market_type
print(aggregate(market$market_squarefeet, list(market$market_type), FUN=sum))

Result

We can see that the similar values (bar, grocery, and restaurent) in the market_type column are grouped and returned the sum of the grouped values in the market_square feet column.

Example 3
In this example, we group the values in the market_type and market_place columns and get the sum of the values in the market_squarefeet column grouped by the market_type and market_place columns.

#create a dataframe-market that has 7 rows and 5 columns.
market=data.frame(market_id=c(1,2,1,4,3,4,5),market_name=c(‘M1’,‘M2’,‘M3’,
‘M4’,‘M3’,‘M4’,‘M3’),market_place=c(‘India’,‘USA’,‘India’,‘Australia’,‘USA’,
‘India’,‘Australia’),market_type=c(‘grocery’,‘bar’,‘grocery’,‘restaurent’,
‘grocery’,‘bar’,‘grocery’),market_squarefeet=c(120,342,220,110,342,220,110))
 
#get the sum of square feet in group by grouping market_place and market_type
print(aggregate(market$market_squarefeet, list(market$market_place,market$market_type), FUN=sum))

Result

We can see that the similar values from the two columns were grouped and returned the sum of the grouped values in the market_square feet column.

Method 2: Dplyr

Here, we use the group_by() function with the summarise_at() function which are available in the dplyr library to perform the group_by function with the sum operation.

Syntax

dataframe_object%>% group_by(grouping) %>% summarise_at(vars(grouped), list(name = sum))

Where:

  1. group_by() takes one parameter, i.e. grouping column
  2. summarise_at() takes two parameters:
  1. The first parameter takes the variable column (grouped) which returns the sum of the values per group.
  2. The second parameter takes the sum function through the list.

Finally, we first summarize with the sum and load it into the group. Then, we load the grouped column into the dataframe object.

It returns a tibble.

Example 1
In this example, we group the values in the market_place column and get the sum of the values in the market_squarefeet column grouped by the market_place column.

library(«dplyr»)
 
#get the sum of square feet in group by grouping market_place
print(market %>% group_by(market_place) %>%
summarise_at(vars(market_squarefeet), list(name = sum)))

Result

We can see that the similar values (Australia, India and USA) in the market_place column are grouped and returned the sum of the grouped values in the market_square feet column.

Example 2
In this example, we group the values in the market_type column and get the sum of the values in the market_squarefeet column grouped by the market_type  column.

library(«dplyr»)
 
#get the sum of square feet in group by grouping market_type
print(market %>% group_by(market_type) %>%
summarise_at(vars(market_squarefeet), list(name = sum)))

Result

We can see that the similar values (bar, grocery and restaurent) in the market_type column are grouped and returned the sum of the grouped values in the market_square feet column.

Conclusion

It is possible to group the single or multiple columns with the other numeric columns to return the sum of the numeric column using the aggregate() function. Similarly, we can use the groupby() fucniton with the summarise_at() function to group the similar values in a column and return the sum of the grouped values with respect to another column.



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In this R tutorial, we will see how to perform the aggregation operations by grouping the data and returning the media values for grouped rows.

This operation has to be performed on a dataframe. Let’s create the dataframe with seven rows and five columns.

#create a dataframe-market that has 7 rows and 5 columns.
market=data.frame(market_id=c(1,2,1,4,3,4,5),market_name=c(‘M1’,‘M2’,‘M3’,
‘M4’,‘M3’,‘M4’,‘M3’),market_place=c(‘India’,‘USA’,‘India’,‘Australia’,‘USA’,
‘India’,‘Australia’),market_type=c(‘grocery’,‘bar’,‘grocery’,‘restaurent’,
‘grocery’,‘bar’,‘grocery’),market_squarefeet=c(120,342,220,110,342,220,110))

#display the market dataframe
print(market)

Result

Now, we return the media values of a column by grouping the similar values in another column.

Method 1: Aggregate()

Here, we use the aggregate() function that takes three parameters.

Syntax

aggregate(dataframe_object$grouped, list(dataframe_object$grouping), FUN=mean)

Parameters

  1. The first parameter takes the variable column (grouped) which returns the mean values per group.
  2. The second parameter takes a single or multiple column (grouping) in a list such that the values are grouped in these columns.
  3. The third parameter takes FUN, which takes the mean function to return the media on the grouped values.

Example 1
In this example, we group the values in the market_place column and get the media values in the market_squarefeet column grouped by the market_place column.

#create a dataframe-market that has 7 rows and 5 columns.
market=data.frame(market_id=c(1,2,1,4,3,4,5),market_name=c(‘M1’,‘M2’,‘M3’,
‘M4’,‘M3’,‘M4’,‘M3’),market_place=c(‘India’,‘USA’,‘India’,‘Australia’,‘USA’,
‘India’,‘Australia’),market_type=c(‘grocery’,‘bar’,‘grocery’,‘restaurent’,
‘grocery’,‘bar’,‘grocery’),market_squarefeet=c(120,342,220,110,342,220,110))

#get the media of square feet in group by grouping market_place
print(aggregate(market$market_squarefeet, list(market$market_place), FUN=mean))

Result

We can see that the similar values (Australia, India and USA) in the market_place column are grouped and returned the mean of the grouped values in the market_square feet column.

Example 2
In this example, we group the values in the market_type column and get the media values in the market_squarefeet column grouped by the market_type  column.

#create a dataframe-market that has 7 rows and 5 columns.
market=data.frame(market_id=c(1,2,1,4,3,4,5),market_name=c(‘M1’,‘M2’,‘M3’,
‘M4’,‘M3’,‘M4’,‘M3’),market_place=c(‘India’,‘USA’,‘India’,‘Australia’,‘USA’,
‘India’,‘Australia’),market_type=c(‘grocery’,‘bar’,‘grocery’,‘restaurent’,
‘grocery’,‘bar’,‘grocery’),market_squarefeet=c(120,342,220,110,342,220,110))

#get the media of square feet in group by grouping market_type
print(aggregate(market$market_squarefeet, list(market$market_type), FUN=mean))

Result

We can see that the similar values (bar, grocery, and restaurent) in the market_type column are grouped and returned the mean of the grouped values in the market_square feet column.

Example 3
In this example, we group the values in the market_type and market_place columns and get the media values in the market_squarefeet column grouped by the market_type and market_place columns.

#create a dataframe-market that has 7 rows and 5 columns.
market=data.frame(market_id=c(1,2,1,4,3,4,5),market_name=c(‘M1’,‘M2’,‘M3’,
‘M4’,‘M3’,‘M4’,‘M3’),market_place=c(‘India’,‘USA’,‘India’,‘Australia’,‘USA’,
‘India’,‘Australia’),market_type=c(‘grocery’,‘bar’,‘grocery’,‘restaurent’,
‘grocery’,‘bar’,‘grocery’),market_squarefeet=c(120,342,220,110,342,220,110))

#get the media of square feet in group by grouping market_place and market_type
print(aggregate(market$market_squarefeet, list(market$market_place,market$market_type), FUN=mean))

Result

We can see that the similar values from the two columns were grouped and returned the mean of the grouped values in the market_square feet column.

Method 2: Dplyr

Here, we use the group_by with summarise_at() which are available in the dplyr library to perform the group_by with the mean operation.

Syntax

dataframe_object%>% group_by(grouping) %>% summarise_at(vars(grouped), list(name = mean))

Where:

group_by() takes one parameter, i.e. grouping column

summarise_at()  takes two parameters:

  1. The first parameter takes the variable column (grouped) which returns the mean values per group.
  2. The second parameter takes the mean function through the list.

Finally, we first summarize with the mean and load into the group. Then, we load the grouped column into the dataframe object.

It returns a tibble.

Example 1
In this example, we group the values in the market_place column and get the media values in the market_squarefeet column grouped by the market_place column.

library(«dplyr»)

#get the media of square feet in group by grouping market_place
print(market %>% group_by(market_place) %>%
summarise_at(vars(market_squarefeet), list(name = mean)))

Result

We can see that the similar values (Australia, India and USA) in the market_place column are grouped and returned the mean of the grouped values in the market_square feet column.

Example 2
In this example, we group the values in the market_type column and get the media values in the market_squarefeet column grouped by the market_type  column.

library(«dplyr»)

#get the media of square feet in group by grouping market_type
print(market %>% group_by(market_type) %>%
summarise_at(vars(market_squarefeet), list(name = mean)))

Result

We can see that the similar values (bar, grocery, and restaurent) in the market_type column are grouped and returned the mean of the grouped values in the market_square feet column.

Conclusion

It is possible to group the single or multiple columns with other numeric columns to return the mean of the numeric column using the aggregate() function. Similarly, we can use the groupby() function with the summarise_at() function to group the similar values in a column and return the media of the grouped values with respect to another column.



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