Re: [心得] dplyr filter and slice

看板R_Language作者celestialgod (天)時間9年前 (2016/01/01 22:18)推噓2(2推 0噓 2→)

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※ 引述《memphis (讓你喜歡這世界~)》之銘言： : 當你有一個 data.frame (如下) : ID col_a col_b : 01 01 2 : 01 02 1 : 02 05 3 : 02 NA 4 : 03 NA 2 : 03 NA 3 : ID=c('01','01','02','02','03','03') : col_a <- c('01','02','05',NA,NA,NA) : col_b <- c(2,1,3,4,2,3) : m <- data.frame(ID, col_a, col_b, stringsAsFactors=F) : #### : 1. 想要挑每組 col_a 最小 : m %>% group_by(ID) %>% summarize(min_a = min(col_a, na.rm=T)) : ID min_a : (chr) (chr) : 1 01 01 : 2 02 05 : 3 03 NA : Warning message: : In min(c(NA_character_, NA_character_), na.rm = TRUE) : : no non-missing arguments, returning NA : #### : 2. 想要挑每組 col_a 最小時的 col_b : m %>% group_by(ID) %>% filter(col_a = min(col_a)) : Error: filter condition does not evaluate to a logical vector. : m %>% group_by(ID) %>% filter(rank(col_a, ties.method='first')==1) : ID col_a col_b : (chr) (chr) (dbl) : 1 01 01 2 : 2 02 05 3 : 3 03 NA 2 : #### : 3. 想要挑每組 col_a 最小時的 col_b (較快) : m %>% group_by(ID) %>% slice(which.min(col_a)) : ID col_a col_b : (chr) (chr) (dbl) : 1 01 01 2 : 2 02 05 3 : #### : 歡迎討論各種例外狀況 : 有些時候取大取小, 只接受數字, 有時候又可以自己轉換 : 有些時候文字可以比大小, 其中有些格子是空格會無法比, 要轉成NA library(data.table) library(plyr) library(dplyr) library(purrr) library(magrittr) library(microbenchmark) set.seed(10) N = 1e5 numGroup = 100 maxID = 5000 DT = data.table(group = sample(sprintf('A%02i', 1:numGroup), N, TRUE), value = sample(1:1000, N, TRUE)) %>% tbl_dt(FALSE) %>% mutate(ID = sprintf('%02i', sample(1:maxID, N, TRUE)), group = factor(group, levels = sprintf('A%02i', 1:numGroup))) %>% select(ID, group, value) %>% distinct(ID, group) # distinct 是為了避免有同ID下有同樣組別之情形 dplyr_slice = function() DT %>% group_by(ID) %>% slice(which.min(group)) dplyr_filter_rank = function() DT %>% group_by(ID) %>% filter(rank(group, ties.method='first')==1) dplyr_filter_row_number = function() DT %>% group_by(ID) %>% filter(row_number(group) == 1) dplyr_arrange = function() DT %>% arrange(ID, group, value) %>% group_by(ID) %>% summarise(group = group[1], value = value[1]) purrr_split_map = function() DT %>% split(.$ID) %>% map(~ .[which.min(.$group), ]) %>% bind_rows plyr_ddply = function() ddply(DT, .(ID), function(x) x[which.min(x$group), ]) plyr_ddply_filter = function() ddply(DT, .(ID), function(x){ x %>% filter(row_number(group) == 1) }) all.equal(dplyr_slice() %>% arrange(ID), dplyr_filter_rank() %>% arrange(ID)) # TRUE all.equal(dplyr_slice() %>% arrange(ID), dplyr_filter_row_number() %>% arrange(ID)) # TRUE all.equal(dplyr_slice() %>% arrange(ID), dplyr_arrange() %>% arrange(ID)) # TRUE all.equal(dplyr_slice() %>% arrange(ID), purrr_split_map() %>% arrange(ID)) # TRUE all.equal(dplyr_slice() %>% arrange(ID), plyr_ddply() %>% arrange(ID)) # TRUE all.equal(dplyr_slice() %>% arrange(ID), plyr_ddply_filter() %>% arrange(ID)) # TRUE microbenchmark(dplyr_slice(), dplyr_filter_rank(), dplyr_filter_row_number(), dplyr_arrange(), purrr_split_map(), plyr_ddply(), plyr_ddply_filter(), times = 20L ) # Unit: milliseconds # expr min lq mean # dplyr_slice() 1347.66226 1401.43846 1464.60871 # dplyr_filter_rank() 449.52015 453.61900 471.02026 # dplyr_filter_row_number() 433.67680 468.01415 480.40588 # dplyr_arrange() 41.23724 42.51137 46.32983 # purrr_split_map() 3198.05461 3274.40538 3403.10413 # plyr_ddply() 1344.17546 1397.38130 1455.63575 # plyr_ddply_filter() 3894.99410 4013.59103 4117.50118 # Unit: milliseconds # expr median uq max neval # dplyr_slice() 1435.9503 1516.38167 1665.08106 20 # dplyr_filter_rank() 463.6700 480.17833 526.91304 20 # dplyr_filter_row_number() 480.6615 492.46276 531.02535 20 # dplyr_arrange() 46.4951 50.18027 52.71638 20 # purrr_split_map() 3372.4479 3488.43514 3755.94195 20 # plyr_ddply() 1460.6096 1500.80851 1587.33094 20 # plyr_ddply_filter() 4114.9551 4196.85084 4461.57974 20 這樣測試下來，直接排序，再取第一個是最快的方法 rank跟row_number差不多快 slice跟ddply差不多快 -- ※ 發信站: 批踢踢實業坊(ptt.cc), 來自: 140.109.73.232 ※ 文章網址: https://www.ptt.cc/bbs/R_Language/M.1451657895.A.273.html