This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.

3/4 In Bronze Reduced Pressure Zone Assembly Backflow Preventer, Quarter Turn Shutoff Valves_This 3/4 inch reduced pressure zone assembly backflow preventer helps prevent the flow of polluted water from entering into the potable water supply due to backsiphonage and or backpressure in accordance with national plumbing codes and water authority requirements. Its compact modular design facilitates easy maintenance with a single access cover for in-line repairs. It is ideal for protection in continuous pressure piping applications and at cross-connections identified as health hazards and for contaminants at the service line entrance. This assembly consists of a bronze body, NPT end connections, quarter turn ball valve shutoffs with tee handles, top mounted test cocks, two in-line independent check valves, captured springs, and replaceable check seats with an internal relief valve for reduced space requirements._Replaceable seats for an economical repair_Captured springs for safe maintenance_Quarter turn full port resilient seated ball valve shutoffs_NPT inlet and outlet connections_Bronze body construction for durability_Single access cover and modular check construction for the ease of maintenance_Large body passages provides a low pressure drop_Test cocks are screwdriver slotted_Internal relief valve for reduced installation clearances_Specifications_Backflow Preventer Type Reduced Pressure Zone Assembly_Body Finish Bronze_Body Material Bronze_Body Pattern Inline_Brand Watts_Buy American Act Compliant YES_Bypass No_Cap And Tether No_Disc Material Silicone_EAN 098268802214_Elbow Fitting No_GDS Status Registered_Hazard Protection Level Health Hazard_Hot And Harsh Water No_Hydrant Connection No_Inlet Connection Size 3/4 IN_Inlet Connection Size-Metric 20 MM_Inlet Connection Type Female Threaded_Inlet Shutoff Valve Type Quarter Turn Ball Valve_Inlet Thread Type NPT_Lead Free NO_Maximum Temperature-Fahrenheit 180_Maximum Working Pressure-BAR 12.10_Maximum Working Pressure-PSI 175_Meter Type No Meter_Minimum Temperature - Celsius 0.50_Minimum Temperature-Fahrenheit 33_Outlet Connection Size 3/4 IN_Outlet Connection Type Female Threaded_Outlet Shutoff Valve Type Quarter Turn Ball Valve_Outlet Thread Type NPT_Part Number 0063030_Polymer Coating No_Pressure Operation Continuous_Protection Type Backpressure and Backsiphonage_Seat Material Engineered Plastic_Series 009_Strainer No_Tradename None_Type Kit

Expressions that return a logical value, and are defined in terms of the variables in .data. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept.

Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by(). For details and examples, see ?dplyr_by.

Relevant when the .data input is grouped. If .preserve = FALSE (the default), the grouping structure is recalculated based on the resulting data, otherwise the grouping is kept as is.

A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.

In the ungrouped version, filter() compares the value of mass in each row to the global average (taken over the whole data set), keeping only the rows with mass greater than this global average. In contrast, the grouped version calculates the average mass separately for each gender group, and keeps rows with mass greater than the relevant within-gender average.

The filter() function is used to subset the rows of .data, applying the expressions in ... to the column values to determine which rows should be retained. It can be applied to both grouped and ungrouped data (see group_by() and ungroup()). However, dplyr is not yet smart enough to optimise the filtering operation on grouped datasets that do not need grouped calculations. For this reason, filtering is often considerably faster on ungrouped data.

The filter() function is used to subset a data frame, retaining all rows that satisfy your conditions. To be retained, the row must produce a value of TRUE for all conditions. Note that when a condition evaluates to NA the row will be dropped, unlike base subsetting with [.

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Because filtering expressions are computed within groups, they may yield different results on grouped tibbles. This will be the case as soon as an aggregating, lagging, or ranking function is involved. Compare this ungrouped filtering: