FIlter the top 50 values from multiple colums in R

Question

I got an R dataframe with several hundred rows and a total of 6 columns. The first column is my row description, the second one a numeric variable and the columns 3 to 6 are my scientific measurements of my four different treatments (thus four different columns).

I would now like to get the highest 50 value across all rows of all four columns containing the numeric measurements with the corresponding first two columns attached.

So far I've tried:

mat.df <- slice_max(mat.df, mat.df[,c(3:6)], n=50, prop, with_ties = TRUE)

but this does not give my the highest 50 results from all four columns.

Any ideas?

Any help would be greatly appreciated. Thanks in advance! :)

Max

Answer 1

EDITED to generate some data like yours more rows and similar values. Notice that order_by is actually a formula and you can adjust whether you want absolute values or something else

library(dplyr)

mat.df %>% 
  slice_max(order_by = ControlAliette + ControlLuna + ControlMovento + ControlSerenade, 
            n = 50, 
            with_ties = FALSE)
#>    Genus Intercept ControlAliette ControlLuna ControlMovento ControlSerenade
#> 1      w  6.435287    0.056526489 -0.08267739    0.079620300      1.09842783
#> 2      c  6.079201    0.558397153 -0.16290829    0.554822034      0.11534424
#> 3      m  6.192000    0.241815318 -0.11150510    0.088925592      0.82798422
#> 4      x  5.392598    0.105206847  0.11572941    0.043688356      0.73782691
#> 5      s  6.446660    0.650486353 -0.02469053    0.191842753      0.17521118
#> 6      n  5.296171    0.366777627 -0.20989214    0.749322631      0.05003869
#> 7      q  5.282046   -0.218363044  0.13508214    0.720711704      0.26279334
#> 8      r  6.149206    0.581825377 -0.17562109   -0.090843851      0.58309241
#> 9      h  6.925687    0.417453587 -0.20572095    0.321314117      0.36169818
#> 10     m  5.691742    0.285472121 -0.05085905    0.137731401      0.51481684
#> 11     c  5.145111    0.002638383  0.28201133    0.049834715      0.54417127
#> 12     c  5.307666    0.073090636  0.06610125    0.426498084      0.30129010
#> 13     w  5.260718    0.048086123  0.07908467    0.358431527      0.35255379
#> 14     m  6.211825    0.180772350  0.44305053    0.445286789     -0.26957951
#> 15     x  5.227344    0.225538161  0.16067200    0.002541409      0.37038387
#> 16     u  5.723938   -0.332155838 -0.12333414    0.454581607      0.75518647
#> 17     t  5.152110   -0.282558292 -0.03498565    0.531423513      0.53623335
#> 18     a  6.526487    0.229463088  0.36144873    0.377937318     -0.23017497
#> 19     k  6.757341    0.516884599  0.49018540   -0.021555273     -0.25513704
#> 20     v  5.446486   -0.064045063  0.04287848    0.563663731      0.15906521
#> 21     o  5.070087    0.170030563  0.10599479    0.398073757      0.02670369
#> 22     t  5.586618   -0.162407261  0.30959608    0.784672641     -0.23755880
#> 23     w  6.184066    0.295550746  0.23812025   -0.117498910      0.26837228
#> 24     d  5.848479    0.033128367  0.12548966    0.114049621      0.38727744
#> 25     b  5.544807    0.007965195  0.00866282    0.335334827      0.28700183
#> 26     n  5.251765    0.444732965 -0.09725644   -0.387806585      0.67485325
#> 27     x  6.123222    0.158880473 -0.40684437   -0.110981338      0.99074654
#> 28     g  5.259382   -0.154187237  0.30824362   -0.365527503      0.84178062
#> 29     g  6.496157   -0.053255524  0.18689232    0.181498712      0.31483353
#> 30     q  6.770110   -0.162705871  0.03580742    0.513900306      0.24142638
#> 31     r  5.901752   -0.062622025 -0.05764049   -0.101135091      0.81935090
#> 32     d  6.549394    0.380371776  0.02137438   -0.131768823      0.29908691
#> 33     b  6.675606   -0.130378318 -0.07211543    0.397251508      0.37068326
#> 34     s  5.110820    0.229168985  0.24135752    0.049987817     -0.01836014
#> 35     j  5.944663    0.251562062 -0.09888048    0.237664053      0.10494435
#> 36     m  6.547734    0.112588977 -0.12150620   -0.149065617      0.64185923
#> 37     r  6.560016    0.010965496 -0.26502303    0.162376862      0.55101225
#> 38     h  5.922249    0.398995408  0.31074350   -0.164695748     -0.09995690
#> 39     t  6.708726   -0.081686217  0.32626594    0.487625193     -0.32319336
#> 40     e  6.483050    0.455495878 -0.15995120   -0.014532759      0.12664553
#> 41     q  6.945471    0.032406517  0.02001101    0.652315538     -0.29794823
#> 42     s  5.800246    0.081368176  0.44097023    0.359698727     -0.49149988
#> 43     h  6.186769   -0.127186068  0.10296196   -0.117877929      0.52542571
#> 44     t  5.799786    0.359494314  0.30821149    0.213104574     -0.50623447
#> 45     o  5.592515   -0.072610242 -0.09783531    0.496258542      0.04371708
#> 46     x  5.605788    0.095883060  0.18309971    0.247253420     -0.15940040
#> 47     l  5.124376   -0.082027992  0.29219376    0.245457652     -0.09446580
#> 48     x  5.993785    0.081067454 -0.31210067    0.180427362      0.40128945
#> 49     i  6.691422   -0.169410111  0.33240079   -0.238838985      0.41678546
#> 50     q  6.706284    0.460427014 -0.34032290   -0.285672344      0.50624378

mat.df %>% 
  slice_max(order_by = abs(ControlAliette) + abs(ControlLuna) + abs(ControlMovento) + abs(ControlSerenade), 
            n = 50, 
            with_ties = FALSE)
#>    Genus Intercept ControlAliette  ControlLuna ControlMovento ControlSerenade
#> 1      w  6.163855    -0.01602246 -0.139432692     1.11086916     -0.66393207
#> 2      z  6.344662    -0.56700045  0.132946953    -0.64180401      0.57921853
#> 3      v  6.388810    -0.77792884 -0.187690390    -0.26208884      0.64532993
#> 4      g  5.179393     0.12719675  0.488018448    -0.94983749      0.15642056
#> 5      d  5.475559    -0.10763699 -0.251141467    -0.32858552     -0.99127322
#> 6      g  5.259382    -0.15418724  0.308243618    -0.36552750      0.84178062
#> 7      x  6.123222     0.15888047 -0.406844369    -0.11098134      0.99074654
#> 8      u  5.723938    -0.33215584 -0.123334142     0.45458161      0.75518647
#> 9      v  6.623464    -0.51131603  0.201377070     0.30133355     -0.59963009
#> 10     n  5.251765     0.44473297 -0.097256441    -0.38780658      0.67485325
#> 11     q  6.706284     0.46042701 -0.340322896    -0.28567234      0.50624378
#> 12     p  6.111853    -0.27896210  0.104354182    -0.68234909     -0.52491266
#> 13     i  5.024287     0.01636362 -0.392316890     0.68981739     -0.40043887
#> 14     t  5.586618    -0.16240726  0.309596080     0.78467264     -0.23755880
#> 15     r  6.424762    -0.27752663  0.263024479     0.55815724     -0.37006130
#> 16     l  6.219644     0.47414209 -0.133076705    -0.16541590     -0.68872026
#> 17     p  5.256076    -0.30643889 -0.364943892    -0.39541292     -0.37877951
#> 18     r  6.149206     0.58182538 -0.175621089    -0.09084385      0.58309241
#> 19     f  6.971580     0.14013934  0.252297801    -0.67945341      0.35465739
#> 20     c  6.079201     0.55839715 -0.162908293     0.55482203      0.11534424
#> 21     t  5.799786     0.35949431  0.308211494     0.21310457     -0.50623447
#> 22     t  5.152110    -0.28255829 -0.034985649     0.53142351      0.53623335
#> 23     n  5.296171     0.36677763 -0.209892141     0.74932263      0.05003869
#> 24     s  5.800246     0.08136818  0.440970230     0.35969873     -0.49149988
#> 25     m  5.734308    -0.50240458 -0.294817028    -0.27977801     -0.29563500
#> 26     m  6.211825     0.18077235  0.443050531     0.44528679     -0.26957951
#> 27     q  5.282046    -0.21836304  0.135082142     0.72071170      0.26279334
#> 28     k  6.724188    -0.21118584  0.313290229    -0.20714810     -0.60087592
#> 29     a  5.937355     0.27092841  0.089738018    -0.37856526     -0.57958569
#> 30     w  6.435287     0.05652649 -0.082677388     0.07962030      1.09842783
#> 31     x  6.437799     0.63411108 -0.330220416    -0.06455209     -0.28624028
#> 32     h  6.925687     0.41745359 -0.205720947     0.32131412      0.36169818
#> 33     q  6.912286     0.27655245 -0.285097288     0.24735970     -0.48033045
#> 34     k  6.757341     0.51688460  0.490185399    -0.02155527     -0.25513704
#> 35     h  5.782539    -0.12631548 -0.053706678    -0.48881997     -0.61186837
#> 36     a  5.053820     0.21205072 -0.532491520     0.29227655      0.23504310
#> 37     m  6.192000     0.24181532 -0.111505098     0.08892559      0.82798422
#> 38     e  5.241864    -0.63895794 -0.244007611     0.24505811      0.13904614
#> 39     m  5.494797    -0.17277764 -0.002460733    -0.41205105     -0.66710689
#> 40     n  5.178992    -0.50196726 -0.123846277     0.38787254     -0.21968941
#> 41     m  6.469092    -0.47968618 -0.161308327    -0.54584551     -0.04310575
#> 42     t  6.708726    -0.08168622  0.326265943     0.48762519     -0.32319336
#> 43     r  6.076358     0.22051300 -0.293593812     0.22617724     -0.47817442
#> 44     a  6.526487     0.22946309  0.361448730     0.37793732     -0.23017497
#> 45     j  6.292186    -0.65160300  0.201960978    -0.07772339     -0.25767368
#> 46     j  5.818572     0.04629336 -0.108579717    -0.36723803      0.66664097
#> 47     k  5.048105    -0.33966657  0.246071452    -0.47745520     -0.11651867
#> 48     o  6.491382    -0.16081949  0.091642534     0.31591590     -0.59354074
#> 49     v  6.795716    -0.03606983  0.008426413     0.26267357     -0.85435700
#> 50     x  6.265645    -0.41563103  0.165907772    -0.50745799      0.07073063

Your data simulated

set.seed(2020)
mat.df <- data.frame(
Genus = sample(letters, size = 200, replace = TRUE),
Intercept = runif(n = 200, min = 5, max = 7),
ControlAliette = rnorm(n = 200, mean = 0, sd = .25),
ControlLuna = rnorm(n = 200, mean = 0, sd = .2),
ControlMovento = rnorm(n = 200, mean = 0, sd = .3), 
ControlSerenade = rnorm(n = 200, mean = 0, sd = .4)
)