![]() If you're working with large data, it maybe also be helpful to set auto_index = TRUE. You can use semi_join(x, y, copy = TRUE) to upload the indices of interest to a temporary table in the same database as x, and then perform a efficient semi join in the database. This is useful if you've downloaded a summarised dataset and determined a subset of interest that you now want the full data for. If you specify copy = TRUE, dplyr will copy the y table into the same location as the x variable. X and y don't have to be tables in the same database. | setdiff(x, y) | SELECT * FROM x EXCEPT SELECT * FROM y | union(x, y) | SELECT * FROM x UNION SELECT * FROM y | intersect(x, y)| SELECT * FROM x INTERSECT SELECT * FROM y | anti_join() | SELECT * FROM x WHERE NOT EXISTS (SELECT 1 FROM y WHERE x.a = y.a) | semi_join() | SELECT * FROM x WHERE EXISTS (SELECT 1 FROM y WHERE x.a = y.a) ![]() | full_join() | SELECT * FROM x FULL JOIN y ON x.a = y.a | right_join() | SELECT * FROM x RIGHT JOIN y ON x.a = y.a | left_join() | SELECT * FROM x LEFT JOIN y ON x.a = y.a | inner_join() | SELECT * FROM x JOIN y ON x.a = y.a To see how individual window functions are translated to SQL, we can again use translate_sql():įlights %>% group_by ( month, day ) %>% summarise ( delay = mean ( dep_delay )) %>% show_query () Rolling: BETWEEN 2 PRECEEDING AND 2 FOLLOWINGĭplyr generates the frame clause based on whether your using a recycled.Cumulative: BETWEEN UNBOUND PRECEEDING AND CURRENT ROW.Recycled: BETWEEN UNBOUND PRECEEDING AND UNBOUND FOLLOWING.They select between aggregation variants: Of the many possible specifications, there are only three that commonly To include all preceding rows (in SQL, "unbounded preceding"), 0 means theĬurrent row ("current row"), and Inf means all following rows ("unbounded There are three special values: -Inf means The frame clause provides two offsets which determine To the window function, describing which rows (relative to the current row) The frame clause defines which rows, or frame, that are passed Needed, some databases fail with an error message while others return Whenever you're thinking about before and after in SQL, you must always tell Variables to rank by, but it's also needed for cumulative functions and lead. This is important for the ranking functions since it specifies which The order clause controls the ordering (when it makes a difference). It is possible for different window functions toīe partitioned into different groups, but not all databases support it, and It plays an analogous role to GROUP BY for aggregate functions,Īnd group_by() in dplyr. The partition clause specifies how the window function is broken down Last, count, min, max, sum, avg and stddev. Support the ranking functions, lead, lag, nth, first, Support for window functions varies from database to database, but most The expression is a combination of variable names and window functions. Things get a little trickier with window functions, because SQL's window functions are considerably more expressive than the specific variants provided by base R or dplyr. Translate_sql ( glob ( x, y )) translate_sql ( x %like% "ab%" ) Window functions # This means the essence of simple calls like mean(x) will be translated accurately, but more complicated calls like mean(x, trim = 0.5, na.rm = TRUE) will raise an error: Databases automatically drop NULLs (their equivalent of missing values), whereas in R you have to ask nicely. R's mean() also provides a trim option for computing trimmed means this is something that databases do not provide. For example, in R, in order to get a higher level of numerical accuracy, mean() loops through the data twice. In fact, even for functions that exist both in databases and R, you shouldn't expect results to be identical database programmers have different priorities than R core programmers. The goal of dplyr is to provide a semantic rather than a literal translation: what you mean rather than what is done. Perfect translation is not possible because databases don't have all the functions that R does. coerce types: as.numeric, as.integer, as.character.string functions: tolower, toupper, trimws, nchar, substr.basic aggregations: mean, sum, min, max, sd, var.boolean operations: &, &, |, ||, !, xor.Log, log10, round, sign, sin, sinh, sqrt, tan, tanh math functions: abs, acos, acosh, asin, asinh, atan, atan2,Ītanh, ceiling, cos, cosh, cot, coth, exp, floor,.Translate_sql ( if ( x > 5 ) "big" else "small" )ĭplyr knows how to convert the following R functions to SQL:
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