)# `reframe()` allows you to apply functions that return# an arbitrary number of rowsdf %>%reframe(x = intersect(x, table))#> # A tibble: 4 × 1#> x#> <chr>#> 1 a#> 2 b#> 3 f#> 4 d# Functions are applied per group, and each group can return a# different number of ...
Youcaneithersupplyonedataframeperargument,oralistofdataframesin thefirstargument. Whencolumn-binding,rowsarematchedbyposition,notvaluesoalldata framesmusthavethesamenumberofrows.Tomatchbyvalue,notposition, seeleft_joinetc.Whenrow-binding,columnsarematchedbyname,andany valuesthatdon’tmatchwillbefilledwith...
so the following code works without errordf %>% group_by(group) %>% slice_head(n =2)#> # A tibble: 5 × 2#> # Groups: group [3]#> group x#> <chr> <dbl>#> 1 a 0.427#> 2 b 0.403#> 3 b 0.976#> 4 c 0.449#> 5 c 0.755# When specifying the proportion of rows to i...
Fixed rare out-of-bounds memory write in slice() when negative indices beyond the number of rows were involved (#3073). select(), rename() and summarise() no longer change the grouped vars of the original data (#3038). nth(default = var), first(default = var) and last(default = va...
batters%>%arrange(desc(ba))#> # A tibble: 18,915 x 3#> playerID ba ab#> <chr> <dbl> <int>#> 1 abramge01 1 1#> 2 banisje01 1 1#> 3 bartocl01 1 1#> 4 bassdo01 1 1#> 5 berrijo01 1 1#> 6 birasst01 1 2#> # … with 1.891e+04 more rows ...
State)from_dplyr = summarize(mygroup,mean=mean(Avg.Spending.Per.Episode..Hospital.))from_data_table=hospital_spending_DT[,.(mean=mean(Avg.Spending.Per.Episode..Hospital.)), by=.(Hospital,State)]compare(from_dplyr,from_data_table, allowAll=TRUE)TRUE sorted renamed rows dropped row names ...
时间复杂度 O ( 2
number_of_rooms) airbnb_listings %>% filter(country == "France" & number_of_rooms > 3) # Filter by checking if a value exists in another set of values airbnb_listings %>% filter(country %in% c( "Japan", "France")) # Filter rows based on index of rows (e.g., first 3 rows)...
air_time/60,gain_per_hour=gain/hours)#> # A tibble: 336,776 x 3#> gain hours gain_per_hour#> <dbl> <dbl> <dbl>#> 1 -9 3.78 -2.38#> 2 -16 3.78 -4.23#> 3 -31 2.67 -11.6#> 4 17 3.05 5.57#> 5 19 1.93 9.83#> 6 -16 2.5 -6.4#> # … with 3.368e+05 more rows...
For joins, tidylog provides more detailed information. For any join, tidylog will show the number of rows that are only present in x (the first dataframe), only present in y (the second dataframe), and rows that have been matched. Numbers in parentheses indicate that these rows are not ...