) ) %>% summarise ( Max_Count = max ( Count ), Min_Count = min ( Count ) ) monthly_ped #> # A tsibble: 95 x 4 #> # Key: Sensor #> Sensor Year_Month Max_Count Min_Count #> #> 1 Birrarung Marr 20 1 #> 2 Birrarung Marr 201 1 #> 3 Birrarung Marr 20 1 #> 4 Birrarung Marr 20 1 #> 5 Birrarung Marr 20 1 #> 6 Birrarung Marr 20 0 #> 7 Birrarung Marr 204 1 #> 8 Birrarung Marr 20 0 #> 9 Birrarung Marr 20 0 #> 10 Birrarung Marr 20 1 #> # … with 85 more rows index ( monthly_ped ) #> Year_Month # Using existing variable pedestrian %>% group_by_key ( ) %>% index_by ( Date ) %>% summarise ( Max_Count = max ( Count ), Min_Count = min ( Count ) ) #> # A tsibble: 2,752 x 4 #> # Key: Sensor #> Sensor Date Max_Count Min_Count #> #> 1 Birrarung Marr 1630 44 #> 2 Birrarung Marr 352 1 #> 3 Birrarung Marr 226 3 #> 4 Birrarung Marr 852 4 #> 5 Birrarung Marr 1427 3 #> 6 Birrarung Marr 937 5 #> 7 Birrarung Marr 708 4 #> 8 Birrarung Marr 568 9 #> 9 Birrarung Marr 1629 5 #> 10 Birrarung Marr 2439 10 #> # … with 2,742 more rows # Attempt to aggregate to 4-hour interval, with the effects of DST pedestrian %>% group_by_key ( ) %>% index_by (Date_Time4 = ~ lubridate :: floor_date (. ![]() Notice that the result of n = n() in the output is 1 for each row printed. To illustrate, we'll sum the values of vs, am. If one needs to use R functions to calculate values across columns within a row, one can use the rowwise() function to prevent mutate() from using multiple rows in the functions on the right hand side of equations within mutate(). The result of summarise() is one row for each combination of variables in the group_by() specification in the pipeline, and the column(s) for the summarized data. Resultĭplyr::summarise() is useful if one wants to summarise the data without adding additional column(s) to the input data frame in the pipeline. To illustrate, we'll calculate percentage of total displacement for V versus straight engines, print the results, and print the sum of pct_disp to illustrate that it equals 100 for V engines. The mutate() approach is useful when one needs to calculate percentage values row by row where the denominator of the percentage is a sum of a column within a combination of by groups. We'll illustrate by calculating cond_disp from the original post, and include n to count the number of rows included in the summary data. ![]() This eliminates the need for conditional logic in mutate() as specified in the original question. As noted in the comments, one can use group_by() to break the inputs on the right hand side functions into subgroups. ![]() Dplyr::mutate() will take multiple rows as inputs to functions on the right hand side of the equation(s) that are arguments to mutate().
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