How to select the row with the maximum value in each group
In a dataset with multiple observations for each subject I want to take a subset with only the maximum data value for each record. For example, with a following dataset:
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Here's a
data.tablesolution:require(data.table) ## 1.9.2 group <- as.data.table(group)If you want to keep all the entries corresponding to max values of
ptwithin each group:group[group[, .I[pt == max(pt)], by=Subject]$V1] # Subject pt Event # 1: 1 5 2 # 2: 2 17 2 # 3: 3 5 2If you'd like just the first max value of
pt:group[group[, .I[which.max(pt)], by=Subject]$V1] # Subject pt Event # 1: 1 5 2 # 2: 2 17 2 # 3: 3 5 2In this case, it doesn't make a difference, as there aren't multiple maximum values within any group in your data.
讨论(0) -
Another
data.tablesolution:library(data.table) setDT(group)[, head(.SD[order(-pt)], 1), by = .(Subject)]讨论(0) -
Since {dplyr} v1.0.0 (May 2020) there is the new
slice_*syntax which supersedestop_n().See also https://dplyr.tidyverse.org/reference/slice.html.
library(tidyverse) ID <- c(1,1,1,2,2,2,2,3,3) Value <- c(2,3,5,2,5,8,17,3,5) Event <- c(1,1,2,1,2,1,2,2,2) group <- data.frame(Subject=ID, pt=Value, Event=Event) group %>% group_by(Subject) %>% slice_max(pt) #> # A tibble: 3 x 3 #> # Groups: Subject [3] #> Subject pt Event #> <dbl> <dbl> <dbl> #> 1 1 5 2 #> 2 2 17 2 #> 3 3 5 2Created on 2020-08-18 by the reprex package (v0.3.0.9001)
Session infosessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.0.2 Patched (2020-06-30 r78761) #> os macOS Catalina 10.15.6 #> system x86_64, darwin17.0 #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz Europe/Berlin #> date 2020-08-18 #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date lib source #> assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.0.0) #> backports 1.1.8 2020-06-17 [1] CRAN (R 4.0.1) #> blob 1.2.1 2020-01-20 [1] CRAN (R 4.0.0) #> broom 0.7.0 2020-07-09 [1] CRAN (R 4.0.2) #> cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.0.0) #> cli 2.0.2 2020-02-28 [1] CRAN (R 4.0.0) #> colorspace 1.4-1 2019-03-18 [1] CRAN (R 4.0.0) #> crayon 1.3.4 2017-09-16 [1] CRAN (R 4.0.0) #> DBI 1.1.0 2019-12-15 [1] CRAN (R 4.0.0) #> dbplyr 1.4.4 2020-05-27 [1] CRAN (R 4.0.0) #> digest 0.6.25 2020-02-23 [1] CRAN (R 4.0.0) #> dplyr * 1.0.1 2020-07-31 [1] CRAN (R 4.0.2) #> ellipsis 0.3.1 2020-05-15 [1] CRAN (R 4.0.0) #> evaluate 0.14 2019-05-28 [1] CRAN (R 4.0.0) #> fansi 0.4.1 2020-01-08 [1] CRAN (R 4.0.0) #> forcats * 0.5.0 2020-03-01 [1] CRAN (R 4.0.0) #> fs 1.5.0 2020-07-31 [1] CRAN (R 4.0.2) #> generics 0.0.2 2018-11-29 [1] CRAN (R 4.0.0) #> ggplot2 * 3.3.2 2020-06-19 [1] CRAN (R 4.0.1) #> glue 1.4.1 2020-05-13 [1] CRAN (R 4.0.0) #> gtable 0.3.0 2019-03-25 [1] CRAN (R 4.0.0) #> haven 2.3.1 2020-06-01 [1] CRAN (R 4.0.0) #> highr 0.8 2019-03-20 [1] CRAN (R 4.0.0) #> hms 0.5.3 2020-01-08 [1] CRAN (R 4.0.0) #> htmltools 0.5.0 2020-06-16 [1] CRAN (R 4.0.1) #> httr 1.4.2 2020-07-20 [1] CRAN (R 4.0.2) #> jsonlite 1.7.0 2020-06-25 [1] CRAN (R 4.0.2) #> knitr 1.29 2020-06-23 [1] CRAN (R 4.0.2) #> lifecycle 0.2.0 2020-03-06 [1] CRAN (R 4.0.0) #> lubridate 1.7.9 2020-06-08 [1] CRAN (R 4.0.1) #> magrittr 1.5 2014-11-22 [1] CRAN (R 4.0.0) #> modelr 0.1.8 2020-05-19 [1] CRAN (R 4.0.0) #> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.0.0) #> pillar 1.4.6 2020-07-10 [1] CRAN (R 4.0.2) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.0.0) #> purrr * 0.3.4 2020-04-17 [1] CRAN (R 4.0.0) #> R6 2.4.1 2019-11-12 [1] CRAN (R 4.0.0) #> Rcpp 1.0.5 2020-07-06 [1] CRAN (R 4.0.2) #> readr * 1.3.1 2018-12-21 [1] CRAN (R 4.0.0) #> readxl 1.3.1 2019-03-13 [1] CRAN (R 4.0.0) #> reprex 0.3.0.9001 2020-08-13 [1] Github (tidyverse/reprex@23a3462) #> rlang 0.4.7 2020-07-09 [1] CRAN (R 4.0.2) #> rmarkdown 2.3.3 2020-07-26 [1] Github (rstudio/rmarkdown@204aa41) #> rstudioapi 0.11 2020-02-07 [1] CRAN (R 4.0.0) #> rvest 0.3.6 2020-07-25 [1] CRAN (R 4.0.2) #> scales 1.1.1 2020-05-11 [1] CRAN (R 4.0.0) #> sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 4.0.2) #> stringi 1.4.6 2020-02-17 [1] CRAN (R 4.0.0) #> stringr * 1.4.0 2019-02-10 [1] CRAN (R 4.0.0) #> styler 1.3.2.9000 2020-07-05 [1] Github (pat-s/styler@51d5200) #> tibble * 3.0.3 2020-07-10 [1] CRAN (R 4.0.2) #> tidyr * 1.1.1 2020-07-31 [1] CRAN (R 4.0.2) #> tidyselect 1.1.0 2020-05-11 [1] CRAN (R 4.0.0) #> tidyverse * 1.3.0 2019-11-21 [1] CRAN (R 4.0.0) #> utf8 1.1.4 2018-05-24 [1] CRAN (R 4.0.0) #> vctrs 0.3.2 2020-07-15 [1] CRAN (R 4.0.2) #> withr 2.2.0 2020-04-20 [1] CRAN (R 4.0.0) #> xfun 0.16 2020-07-24 [1] CRAN (R 4.0.2) #> xml2 1.3.2 2020-04-23 [1] CRAN (R 4.0.0) #> yaml 2.2.1 2020-02-01 [1] CRAN (R 4.0.0) #> #> [1] /Users/pjs/Library/R/4.0/library #> [2] /Library/Frameworks/R.framework/Versions/4.0/Resources/library讨论(0) -
Another base solution
group_sorted <- group[order(group$Subject, -group$pt),] group_sorted[!duplicated(group_sorted$Subject),] # Subject pt Event # 1 5 2 # 2 17 2 # 3 5 2Order the data frame by
pt(descending) and then remove rows duplicated inSubject讨论(0) -
Here's another
data.tablesolution, sincewhich.maxdoes not work on characterslibrary(data.table) group <- data.table(Subject=ID, pt=Value, Event=Event) group[, .SD[order(pt, decreasing = TRUE) == 1], by = Subject]讨论(0) -
A shorter solution using
data.table:setDT(group)[, .SD[which.max(pt)], by=Subject] # Subject pt Event # 1: 1 5 2 # 2: 2 17 2 # 3: 3 5 2讨论(0)
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