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)
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 |
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. |
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 .
Tiago Olivoto tiagoolivoto@gmail.com
# \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")#>#> # 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# }