Description
Creates one or more columns summarizing the columns of tbl_teradata. Tibbles with groups created by 'group_by()' and 'group_by_time()' will result in one or more rows in the output for each group based on the aggregate operation used in summarise. Tibbles without groups will result in one or more rows in the output based on the aggregate operation.
Usage
## S3 method for class 'tbl_teradata'
summarise(.data, ...)
Arguments
.data |
Required Argument. |
... |
Name-value pairs of summary functions. The name specifies the name of the column in the result tbl_teradata. The value specifies an expression that returns a single value like ‘min(x)', 'n()', or 'sum(is.na(y))' where ’x' and 'y' are the column names. |
Value
A 'tbl_teradata' object.
See Also
summarise
function in the dplyr package for some other
examples of summarise.
Examples
# Get remote data source connection.
con <- td_get_context()$connection
# Load the required tables.
loadExampleData("time_series_example", "ocean_buoys_seq")
# Create object of class "tbl_teradata".
df_seq <- tbl(con, "ocean_buoys_seq")
# Example 1: Get the minimum of the column 'temperature', grouped by timebucket
# timebucket duration of 30 minutes and the column 'buoyid'.
# Grouping by timebucket duration of 30 minutes and 'buoyid'.
seq_group1 <- df_seq %>% group_by_time(timebucket.duration = "30m",
value.expression = "buoyid")
# Applying min() aggregation on grouped tbl object.
seq_group1 %>% summarise(min_temp = min(temperature))
# Example 2: Get the minimum of the column 'temperature' grouped by the
# column 'buoyid'.
# Grouping by column 'buoyid'.
seq_group2 <- df_seq %>% group_by(buoyid)
# Applying min() aggregation on grouped tbl object.
seq_group2 %>% summarise(min_temp = min(temperature))