Last updated: 2022-08-03
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Knit directory: cassavabaseembrapa/
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library(tidyverse)-- Attaching packages --------------------------------------- tidyverse 1.3.1 --v ggplot2 3.3.6 v purrr 0.3.4
v tibble 3.1.7 v dplyr 1.0.9
v tidyr 1.2.0 v stringr 1.4.0
v readr 2.1.2 v forcats 0.5.1-- Conflicts ------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()library(readxl)
trials_in_cassava_base <-
read_excel("data/Trials-in-cassava-base.xlsx") ## Trials already in cassavabase
acessions_in_cassava_base <- read_excel("data/sinonimous.xlsx") ## accesses and synonyms already in cassavabase
abreviation_trials <-
read_excel("data/locais e siglas dos experimentos 2021.xlsx") ## trial abbreviations
my_col_types = c(
"numeric",
rep("text", 7),
rep("numeric", 69),
rep("text", 4),
rep("numeric", 8),
"text"
) ## Classes of the columns of the complete dataset
trial_to_insert <-
read_excel("data/plot_name.xlsx", na = "NA", col_types = my_col_types) ## Complete datasettrials_in_cassava_base <- trials_in_cassava_base %>%
mutate_if(is.character, as.factor) %>%
mutate_if(is.numeric, as.factor) %>%
select(where( ~ n_distinct(.) > 1))
trial_to_insert <- trial_to_insert %>%
mutate_if(is.character, as.factor) %>%
mutate_if(is.numeric, as.factor) %>%
select(where( ~ n_distinct(.) > 1))
abreviation_trials <- abreviation_trials %>%
mutate(Local = toupper(Local)) %>%
mutate_if(is.character, as.factor) %>%
mutate_if(is.numeric, as.factor) %>%
select(where( ~ n_distinct(.) > 1))MUNICÍPIO, LOCAL, CAMPO and
BREEDING STAGEresumo_mun <- trial_to_insert %>%
group_by(Município, Local, Campo, `Breeding stage`) %>%
tally()
writexl::write_xlsx(resumo_mun, "data/resumo_mun.xlsx")trial_to_insert and
abreviation_trials datasets according to
LOCAL.trial_to_insert2 <- trial_to_insert %>%
left_join(abreviation_trials, by = "Local")waxy,
16cm and 8MP experiments. This to create the
trial_name and trial_name_suggest, with this
information. Then we put together in the dataset
trial_names_differ these experiments.trial1 <- trial_to_insert2 %>%
group_by(Ano, `Breeding stage`, Abbreviation, Campo, Waxy) %>%
tally() %>%
filter(Waxy == "SIM") %>%
mutate(
trial_name = paste(
"BR",
`Breeding stage`,
as.numeric(as.character(Ano)) - 2001,
Abbreviation,
sep = "."
),
trial_name_suggest = paste(
"BR",
paste(`Breeding stage`, "Wx", sep = ""),
as.numeric(as.character(Ano)) - 2001,
Abbreviation,
sep = "."
)
) %>%
ungroup()
trial2 <- trial_to_insert2 %>%
group_by(Ano, `Breeding stage`, Abbreviation, Campo) %>%
tally() %>%
filter(str_detect(as.character(Campo), "16CM")) %>%
mutate(
trial_name = paste(
"BR",
`Breeding stage`,
as.numeric(as.character(Ano)) - 2001,
Abbreviation,
sep = "."
),
trial_name_suggest = paste(
"BR",
paste(`Breeding stage`, "16CM", sep = ""),
as.numeric(as.character(Ano)) - 2001,
Abbreviation,
sep = "."
)
) %>%
ungroup()
trial3 <- trial_to_insert2 %>%
group_by(Ano, `Breeding stage`, Abbreviation, Campo) %>%
tally() %>%
filter(str_detect(as.character(Campo), "8MP")) %>%
mutate(
trial_name = paste(
"BR",
`Breeding stage`,
as.numeric(as.character(Ano)) - 2001,
Abbreviation,
sep = "."
),
trial_name_suggest = paste(
"BR",
paste(`Breeding stage`, "8MP", sep = ""),
as.numeric(as.character(Ano)) - 2001,
Abbreviation,
sep = "."
)
) %>%
ungroup()
trial_names_differ <- trial1 %>%
full_join(trial2) %>%
full_join(trial3)Joining, by = c("Ano", "Breeding stage", "Abbreviation", "Campo", "n",
"trial_name", "trial_name_suggest")
Joining, by = c("Ano", "Breeding stage", "Abbreviation", "Campo", "n",
"trial_name", "trial_name_suggest")trial_to_insert2 dataset with the
trial_names_differ. Then we corrected the names of the
genotypes by adding BR, we created the other
trial_name and the trial_name_suggest. We
check which trials and accesses are already in cassavabase, creating the
trial_inserted_in_cassava and acces_in_cassava
columns and also check that the trial names are as we suggest. Finally
we create the plot_name_suggest column.trial_to_insert3 <- trial_to_insert2 %>%
full_join(trial_names_differ[-6]) %>%
mutate(
Genotipo = str_replace_all(Genotipo, c("2011" = "BR-11", "2012" = "BR-12")),
trial_name = if_else(
is.na(trial_name),
paste(
"BR",
`Breeding stage`,
as.numeric(as.character(Ano)) - 2001,
Abbreviation,
sep = "."
),
trial_name
),
trial_name_suggest = if_else(is.na(trial_name_suggest), trial_name, trial_name_suggest),
trial_inserted_in_cassava = if_else(
trial_name %in% levels(trials_in_cassava_base$`Trial name`) |
Campo %in% levels(trials_in_cassava_base$`Trial name`) |
trial_name_suggest %in% levels(trials_in_cassava_base$`Trial name`),
"yes",
"no"
) ,
correct_trial_inserted_in_cassava = if_else(
trial_name_suggest %in% levels(trials_in_cassava_base$`Trial name`),
"yes",
"no"
),
acces_in_cassava = if_else(
Genotipo %in% acessions_in_cassava_base$`germplasmName (Stock_Name)`,
"yes",
"no"
),
plot_name_suggest =
paste(trial_name_suggest, Genotipo, Plot, sep = "-")
)Joining, by = c("Ano", "Breeding stage", "Campo", "Waxy", "Abbreviation")trials <- trial_to_insert3 %>%
group_by(
Ano,
`Breeding stage`,
Abbreviation,
Campo,
trial_name,
trial_name_suggest,
trial_inserted_in_cassava,
correct_trial_inserted_in_cassava
) %>%
tally()
writexl::write_xlsx(trials, "data/resumo_trials4.xlsx")acess <- trial_to_insert3 %>%
group_by(Genotipo, acces_in_cassava) %>%
tally()
writexl::write_xlsx(acess, "data/resumo_acess3.xlsx")cultivars are used to create the
is_control column in the final dataset of the trials.cultivars<-c("Aipim Abacate",
"Baianinha-MS",
"BRS Caipira",
"BRS CS01",
"BRS Dourada",
"BRS Formosa",
"BRS Gema de Ovo",
"BRS Jari",
"BRS Kiriris",
"BRS Mulatinha",
"BRS Novo Horizonte",
"BRS Poti Branca",
"BRS Tapioqueira",
"BRS Verdinha",
"Cacau",
"Cascuda",
"Cigana Preta",
"Correntao",
"Corrente",
"Eucalipto",
"Fecula Branca",
"IAC-12",
"IAC-14",
"IAC-576",
"IAC-90",
"Mani Branca",
"Nega Maluca",
"Olho Junto",
"Pioneira",
"Salangor",
"Tailandia",
"Valencia",
"Vassoura Preta"
)for(i in levels(factor(trial_to_insert3$trial_name_suggest))) {
creating_trials <- trial_to_insert3 %>%
group_by(plot_name_suggest,
Genotipo,
Plot,
Bloco,
row_number,
col_number) %>%
filter(trial_inserted_in_cassava == "no" &
trial_name_suggest == i) %>%
mutate(
is_a_control = if_else(Genotipo %in% cultivars, 1, 0))%>%
select(plot_name_suggest,
Genotipo,
Plot,
Bloco,
is_a_control,
row_number,
col_number)
creating_trials["is_a_control"][creating_trials["is_a_control"] == 0] <- NA
colnames(creating_trials) <-
c(
"plot_name",
"accession_name",
"plot_number",
"block_number",
"is_a_control",
"row_number",
"col_number"
)
writexl::write_xlsx(creating_trials, paste("output/", i, ".xlsx", sep =""))
}creating_acess <- trial_to_insert3 %>%
filter(trial_inserted_in_cassava == "no") %>%
select(plot_name_suggest, 13:72) %>%
rename(observationunit_name = plot_name_suggest)
writexl::write_xlsx(creating_acess, "output/acess_pheno.xlsx")
sessionInfo()R version 4.1.3 (2022-03-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)
Matrix products: default
locale:
[1] LC_COLLATE=Portuguese_Brazil.1252 LC_CTYPE=Portuguese_Brazil.1252
[3] LC_MONETARY=Portuguese_Brazil.1252 LC_NUMERIC=C
[5] LC_TIME=Portuguese_Brazil.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] readxl_1.4.0 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9
[5] purrr_0.3.4 readr_2.1.2 tidyr_1.2.0 tibble_3.1.7
[9] ggplot2_3.3.6 tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8.3 lubridate_1.8.0 getPass_0.2-2 ps_1.6.0
[5] assertthat_0.2.1 rprojroot_2.0.3 digest_0.6.29 utf8_1.2.2
[9] R6_2.5.1 cellranger_1.1.0 backports_1.4.1 reprex_2.0.1
[13] evaluate_0.15 httr_1.4.3 pillar_1.7.0 rlang_1.0.4
[17] rstudioapi_0.13 whisker_0.4 callr_3.7.0 jquerylib_0.1.4
[21] rmarkdown_2.14 munsell_0.5.0 broom_1.0.0 compiler_4.1.3
[25] httpuv_1.6.5 modelr_0.1.8 xfun_0.30 pkgconfig_2.0.3
[29] htmltools_0.5.2 tidyselect_1.1.2 fansi_1.0.3 withr_2.5.0
[33] crayon_1.5.1 tzdb_0.3.0 dbplyr_2.2.1 later_1.3.0
[37] grid_4.1.3 jsonlite_1.8.0 gtable_0.3.0 lifecycle_1.0.1
[41] DBI_1.1.3 git2r_0.30.1 magrittr_2.0.3 scales_1.2.0
[45] writexl_1.4.0 cli_3.3.0 stringi_1.7.6 fs_1.5.2
[49] promises_1.2.0.1 xml2_1.3.3 bslib_0.3.1 ellipsis_0.3.2
[53] generics_0.1.3 vctrs_0.4.1 tools_4.1.3 glue_1.6.2
[57] hms_1.1.1 processx_3.5.3 fastmap_1.1.0 yaml_2.3.5
[61] colorspace_2.0-3 rvest_1.0.2 knitr_1.39 haven_2.5.0
[65] sass_0.4.1