This function takes a dataframe of BSA data, a list of grouping conditions, and a list of sample columns, and returns a dataframe in which the data has been grouped according to the specified conditions. It performs several operations including grouping, summarizing, uniting, and arranging.
Arguments
- df_with_meta
A dataframe that contains BSA data.
- grouping_conditions
A vector of column names that specifies the grouping conditions.
- sample_columns
A vector of column names that specifies the sample columns.
Value
A dataframe in which the data has been grouped, summarized, united, and arranged according to the specified conditions and columns.
Examples
df_with_meta <- data.frame(
A = rep(c("group1", "group2"), each = 5),
CHR = rep("CHR1", 10),
POS = rep(c(1,5,10,20,30), times=2),
REF_Allele = rep('A', 10),
ALT1 = rep('G', 10),
RealDepth = runif(10, 1, 10),
Depth = runif(10, 1, 10),
Reference = runif(10, 1, 10),
Alternative1 = runif(10, 1, 10)
)
grouping_conditions <- c('CHR','POS','REF_Allele','ALT1')
sample_columns <- c("RealDepth", "Depth", "Reference", "Alternative1")
collapse_bsa_data(df_with_meta, grouping_conditions, sample_columns)
#> # A tibble: 5 × 9
#> CHR POS REF_Allele ALT1 sample RealDepth Depth Reference Alternative1
#> <chr> <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 CHR1 1 A G 6.9242927… 6.92 11.6 11.3 6.17
#> 2 CHR1 5 A G 13.988993… 14.0 7.21 8.57 10.4
#> 3 CHR1 10 A G 10.014753… 10.0 2.88 13.4 13.2
#> 4 CHR1 20 A G 10.010813… 10.0 8.38 10.0 9.35
#> 5 CHR1 30 A G 9.0192885… 9.02 14.4 17.0 8.26