[Experimental]

Useful function for data organization before statistical analysis

  • add_seq_block(): Add a column with sequential block numeration in multi-environment data sets.

  • recode_factor(): Recode a factor column. A sequential numbering (with possible prefix) is used to identify each level.

  • df_to_selegen_54(): Given a multi-environment data with environment, genotype, and replication, format the data to be used in the Selegen software (model 54).

add_seq_block(data, env, rep, new_factor = BLOCK, prefix = "", verbose = TRUE)

recode_factor(data, factor, new_factor = CODE, prefix = "", verbose = TRUE)

df_to_selegen_54(data, env, gen, rep, verbose = TRUE)

Arguments

data

A data frame.

env

The name of the column that contains the levels of the environments.

rep

The name of the column that contains the levels of the replications/blocks.

new_factor

The name of the new column created.

prefix

An optional prefix to bind with the new factor.

verbose

Logical argument. If verbose = FALSE the code will run silently.

factor

A column to recode.

gen

The name of the column that contains the levels of the genotypes, that will be treated as random effect.

References

Resende, M.D. V. 2016. Software Selegen-REML/BLUP: a useful tool for plant breeding. Crop Breed. Appl. Biotechnol. 16(4): 330–339. doi: 10.1590/1984-70332016v16n4a49 .

Author

Tiago Olivoto tiagoolivoto@gmail.com

Examples

# \donttest{ library(metan) df_ge <- ge_simula(ngen = 2, nenv = 3, nrep = 2) %>% add_cols(ENV = c(rep("CACIQUE", 4), rep("FREDERICO", 4), rep("SANTA_MARIA", 4))) df_ge
#> # A tibble: 12 x 4 #> ENV GEN REP V1 #> <chr> <fct> <fct> <dbl> #> 1 CACIQUE H1 B1 86.5 #> 2 CACIQUE H1 B2 80.8 #> 3 CACIQUE H2 B1 96.8 #> 4 CACIQUE H2 B2 98.5 #> 5 FREDERICO H1 B1 85.7 #> 6 FREDERICO H1 B2 104. #> 7 FREDERICO H2 B1 91.6 #> 8 FREDERICO H2 B2 97.8 #> 9 SANTA_MARIA H1 B1 90.4 #> 10 SANTA_MARIA H1 B2 99.5 #> 11 SANTA_MARIA H2 B1 103. #> 12 SANTA_MARIA H2 B2 103.
# Add sequential block numbering over environments add_seq_block(df_ge, ENV, REP, prefix = "B")
#> The data `df_ge` has been arranged according to the `ENV` and `REP` columns.
#> # A tibble: 12 x 5 #> ENV GEN REP BLOCK V1 #> <chr> <fct> <fct> <chr> <dbl> #> 1 CACIQUE H1 B1 B1 86.5 #> 2 CACIQUE H2 B1 B1 96.8 #> 3 CACIQUE H1 B2 B2 80.8 #> 4 CACIQUE H2 B2 B2 98.5 #> 5 FREDERICO H1 B1 B3 85.7 #> 6 FREDERICO H2 B1 B3 91.6 #> 7 FREDERICO H1 B2 B4 104. #> 8 FREDERICO H2 B2 B4 97.8 #> 9 SANTA_MARIA H1 B1 B5 90.4 #> 10 SANTA_MARIA H2 B1 B5 103. #> 11 SANTA_MARIA H1 B2 B6 99.5 #> 12 SANTA_MARIA H2 B2 B6 103.
# Recode the 'ENV' column to "ENV1", "ENV2", and so on. recode_factor(df_ge, factor = ENV, prefix = "ENV", new_factor = ENV_CODE)
#> Error in list2(...): objeto 'ENV' não encontrado
# Format the data to be used in the Selegen software (model 54) df <- df_to_selegen_54(df_ge, ENV, GEN, REP) %>% recode_factor(ENV, prefix = "E", new_factor = ENV)
#> Error in list2(...): objeto 'ENV' não encontrado
# }