note - code is working as exptected (all testthat tests pass), but there are some issues with the documentation/library dependencies in the code, causing the build to fail...

distransam

Randomly samples a time series groups and plates/sites to get equal N's for both plates/sites and samples from each group

Takes a data frame containing a time series, a column name for the grouping variable, a column name for the plates/sites variable, and a column name for the sample id's. Returns one with equal N's for both plates/sites and samples from each group.

For unequal site numbers, it finds the minimum site number from the smallest group, and randomly selects that number of sites from each group. For unequal sample sizes between sites, it finds the minimum sample size from the smallest site, and randomly selects that number of samples from each group. The returned object is a dataframe, with all the same columns as the original data frame.

note - that because this package uses dplyr & %>% both distransam & dplyr libraries must be loaded

Installation

devtools::install_github("ttimbers/distransam")

Quick demo

Create some data:

# create test data
group <- c(rep('Strain1', 28),
           rep('Strain2', 46))

plate <- c(rep('Plate1', 10),
           rep('Plate2', 12),
           rep('Plate3', 6),
           rep('Plate4', 12),
           rep('Plate5', 12),
           rep('Plate6', 10),
           rep('Plate7', 12))

id <- c(rep(1, 5), rep(2, 5),
        rep(1, 4), rep(2, 4), rep(3, 4),
        rep(1, 3), rep(2, 3),
        rep(1, 4), rep(2, 4), rep(3, 4),
        rep(1, 4), rep(2, 4), rep(3, 4),
        rep(1, 5), rep(2, 5),
        rep(1, 4), rep(2, 4), rep(3, 4))

time <- c(rep(seq(1:5), 2),
          rep(seq(1:4), 3),
          rep(seq(1:3), 2),
          rep(seq(1:4), 3),
          rep(seq(1:4), 3),
          rep(seq(1:5), 2),
          rep(seq(1:4), 3))

measurement <- c(rnorm(28, mean = 5, sd = 0.5),
                 rnorm(46, mean = 6.5, sd = 0.5))

test_data <- data.frame(group, plate, id, time, measurement)
head(test_data)
#>     group  plate id time measurement
#> 1 Strain1 Plate1  1    1    4.653315
#> 2 Strain1 Plate1  1    2    5.635409
#> 3 Strain1 Plate1  1    3    4.573239
#> 4 Strain1 Plate1  1    4    4.811544
#> 5 Strain1 Plate1  1    5    4.712574
#> 6 Strain1 Plate1  2    1    4.657307

Use distransam to get a new, randomly sampled dataframe where N's are equal for each plate, and strain:

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(purrr)
#> 
#> Attaching package: 'purrr'
#> The following objects are masked from 'package:dplyr':
#> 
#>     contains, order_by
library(tidyr)
library(distransam)

sampled_test_data <- distransam(test_data, 'group', 'plate', 'id')
#>                         stat value
#> 1          Number of Strains     2
#> 2 Min N of samples per plate     2
#> 3 Min N of plates per strain     3

Compare test_data to sampled_test_data:

str(test_data)
#> 'data.frame':    74 obs. of  5 variables:
#>  $ group      : Factor w/ 2 levels "Strain1","Strain2": 1 1 1 1 1 1 1 1 1 1 ...
#>  $ plate      : Factor w/ 7 levels "Plate1","Plate2",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ id         : num  1 1 1 1 1 2 2 2 2 2 ...
#>  $ time       : int  1 2 3 4 5 1 2 3 4 5 ...
#>  $ measurement: num  4.65 5.64 4.57 4.81 4.71 ...

str(sampled_test_data)
#> Classes 'tbl_df', 'tbl' and 'data.frame':    50 obs. of  5 variables:
#>  $ group      : Factor w/ 2 levels "Strain1","Strain2": 1 1 1 1 1 1 1 1 1 1 ...
#>  $ plate      : Factor w/ 7 levels "Plate1","Plate2",..: 2 2 2 2 2 2 2 2 3 3 ...
#>  $ id         : num  2 2 2 2 1 1 1 1 1 1 ...
#>  $ time       : int  1 2 3 4 1 2 3 4 1 2 ...
#>  $ measurement: num  4.42 5.56 5.16 4.36 4.74 ...