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Corrected Spansih translations Jordi

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This commit is contained in:
Nik Verweel 2026-02-23 11:34:15 +01:00
parent fd0e0d0cc6
commit f583ea838b
2 changed files with 96 additions and 95 deletions
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191
r_app/90_CI_report_with_kpis_agronomic_support.Rmd
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@ -8,7 +8,7 @@ params:
borders: FALSE borders: FALSE
ci_plot_type: "both" ci_plot_type: "both"
colorblind_friendly: TRUE colorblind_friendly: TRUE
lang: "es" lang: "en"
facet_by_season: FALSE facet_by_season: FALSE
x_axis_unit: "days" x_axis_unit: "days"
output: output:
@ -503,12 +503,13 @@ t <- function(key) {
if (key %in% names(tr)) { if (key %in% names(tr)) {
txt <- glue(tr[key], .envir = parent.frame()) txt <- glue(tr[key], .envir = parent.frame())
txt <- gsub("\n", " \n", txt) txt <- gsub("\n", " \n", txt)
return(enc2utf8(as.character(txt))) return(enc2utf8(as.character(txt)))
} else if (key == "" | is.na(key)) { } else if (is.na(key)) {
return(t("NA"))
} else if (key == "") {
return("") return("")
} else { } else {
return(paste0("[", key, "]")) return(paste0(key))
} }
} }
``` ```
@ -538,7 +539,7 @@ if (exists("summary_tables") && !is.null(summary_tables) && length(summary_table
# 1. Uniformity insights - group by interpretation # 1. Uniformity insights - group by interpretation
if (!is.null(summary_tables$uniformity) && nrow(summary_tables$uniformity) > 0) { if (!is.null(summary_tables$uniformity) && nrow(summary_tables$uniformity) > 0) {
cat(t("field_unif"), "\n\n") cat("\n", t("field_unif"))
uniformity_counts <- summary_tables$uniformity %>% uniformity_counts <- summary_tables$uniformity %>%
dplyr::select(interpretation, count = field_count) dplyr::select(interpretation, count = field_count)
@ -546,14 +547,14 @@ if (exists("summary_tables") && !is.null(summary_tables) && length(summary_table
status <- uniformity_counts$interpretation[i] status <- uniformity_counts$interpretation[i]
count <- uniformity_counts$count[i] count <- uniformity_counts$count[i]
if (!is.na(status) && !is.na(count) && count > 0) { if (!is.na(status) && !is.na(count) && count > 0) {
cat("-", t("unif_status"), "\n\n") cat(" -", t("unif_status"))
} }
} }
} }
# 2. Area change insights - group by interpretation # 2. Area change insights - group by interpretation
if (!is.null(summary_tables$area_change) && nrow(summary_tables$area_change) > 0) { if (!is.null(summary_tables$area_change) && nrow(summary_tables$area_change) > 0) {
cat(t("field_area"), "\n\n") cat("\n\n", t("field_area"))
area_counts <- summary_tables$area_change %>% area_counts <- summary_tables$area_change %>%
dplyr::select(interpretation, count = field_count) dplyr::select(interpretation, count = field_count)
@ -561,14 +562,14 @@ if (exists("summary_tables") && !is.null(summary_tables) && length(summary_table
status <- area_counts$interpretation[i] status <- area_counts$interpretation[i]
count <- area_counts$count[i] count <- area_counts$count[i]
if (!is.na(status) && !is.na(count) && count > 0) { if (!is.na(status) && !is.na(count) && count > 0) {
cat("-", t("area_status"), "\n\n") cat(" -", t("area_status"))
} }
} }
} }
# 3. Growth trend insights - group by trend_interpretation # 3. Growth trend insights - group by trend_interpretation
if (!is.null(summary_tables$growth_decline) && nrow(summary_tables$growth_decline) > 0) { if (!is.null(summary_tables$growth_decline) && nrow(summary_tables$growth_decline) > 0) {
cat(t("growth_trend"), "\n\n") cat("\n\n", t("growth_trend"))
growth_counts <- summary_tables$growth_decline %>% growth_counts <- summary_tables$growth_decline %>%
dplyr::select(trend_interpretation, count = field_count) dplyr::select(trend_interpretation, count = field_count)
@ -576,14 +577,14 @@ if (exists("summary_tables") && !is.null(summary_tables) && length(summary_table
trend <- growth_counts$trend_interpretation[i] trend <- growth_counts$trend_interpretation[i]
count <- growth_counts$count[i] count <- growth_counts$count[i]
if (!is.na(trend) && !is.na(count) && count > 0) { if (!is.na(trend) && !is.na(count) && count > 0) {
cat("-", t("trend_status"), "\n\n") cat(" -", t("trend_status"))
} }
} }
} }
# 4. Patchiness insights - group by patchiness_risk # 4. Patchiness insights - group by patchiness_risk
if (!is.null(summary_tables$patchiness) && nrow(summary_tables$patchiness) > 0) { if (!is.null(summary_tables$patchiness) && nrow(summary_tables$patchiness) > 0) {
cat(t("patch_risk"), "\n\n") cat("\n\n", t("patch_risk"))
patchiness_counts <- summary_tables$patchiness %>% patchiness_counts <- summary_tables$patchiness %>%
dplyr::select(patchiness_risk, count = field_count) dplyr::select(patchiness_risk, count = field_count)
@ -591,14 +592,14 @@ if (exists("summary_tables") && !is.null(summary_tables) && length(summary_table
risk <- patchiness_counts$patchiness_risk[i] risk <- patchiness_counts$patchiness_risk[i]
count <- patchiness_counts$count[i] count <- patchiness_counts$count[i]
if (!is.na(risk) && !is.na(count) && count > 0) { if (!is.na(risk) && !is.na(count) && count > 0) {
cat("-", t("patch_status"), "\n\n") cat(" -", t("patch_status"))
} }
} }
} }
# 5. Total fields analyzed # 5. Total fields analyzed
total_fields <- sum(summary_tables$uniformity$field_count, na.rm = TRUE) total_fields <- sum(summary_tables$uniformity$field_count, na.rm = TRUE)
cat(t("tot_fields_analyzed")) cat("\n\n", t("tot_fields_analyzed"))
} else { } else {
cat(t("kpi_na")) cat(t("kpi_na"))
@ -1455,14 +1456,14 @@ tryCatch({
if (nrow(field_kpi) > 0) { if (nrow(field_kpi) > 0) {
# Format KPIs as compact single line (no interpretations, just values) # Format KPIs as compact single line (no interpretations, just values)
kpi_parts <- c( kpi_parts <- c(
sprintf("**CV:** %.2f", field_kpi$CV), sprintf("**%s:** %.2f", t("cv_value"), field_kpi$CV),
sprintf("**Mean CI:** %.2f", field_kpi$Mean_CI) sprintf("**%s:** %.2f", t("mean_ci"), field_kpi$Mean_CI)
) )
# Add Weekly_CI_Change if available (note: capital C and I) # Add Weekly_CI_Change if available (note: capital C and I)
if (!is.null(field_kpi$Weekly_CI_Change) && !is.na(field_kpi$Weekly_CI_Change)) { if (!is.null(field_kpi$Weekly_CI_Change) && !is.na(field_kpi$Weekly_CI_Change)) {
change_sign <- ifelse(field_kpi$Weekly_CI_Change >= 0, "+", "") change_sign <- ifelse(field_kpi$Weekly_CI_Change >= 0, "+", "")
kpi_parts <- c(kpi_parts, sprintf("**Δ CI:** %s%.2f", change_sign, field_kpi$Weekly_CI_Change)) kpi_parts <- c(kpi_parts, sprintf("**Δ%s:** %s%.2f", t("CI"), change_sign, field_kpi$Weekly_CI_Change))
} }
# Compact trend display with symbols # Compact trend display with symbols
@ -1474,17 +1475,17 @@ tryCatch({
grepl("Strong decline|Severe", field_kpi$Trend_Interpretation, ignore.case = TRUE) ~ "↓↓", grepl("Strong decline|Severe", field_kpi$Trend_Interpretation, ignore.case = TRUE) ~ "↓↓",
TRUE ~ field_kpi$Trend_Interpretation TRUE ~ field_kpi$Trend_Interpretation
) )
kpi_parts <- c(kpi_parts, sprintf("**Trend:** %s", trend_compact)) kpi_parts <- c(kpi_parts, sprintf("**%s:** %s", t("Trend"), trend_compact))
if (!is.na(field_kpi$TCH_Forecasted) && field_kpi$TCH_Forecasted > 0) { if (!is.na(field_kpi$TCH_Forecasted) && field_kpi$TCH_Forecasted > 0) {
kpi_parts <- c(kpi_parts, sprintf("**Yield:** %.1f t/ha", field_kpi$TCH_Forecasted)) kpi_parts <- c(kpi_parts, sprintf("**%s:** %.1f t/ha", t("Yield"), field_kpi$TCH_Forecasted))
} }
kpi_parts <- c( kpi_parts <- c(
kpi_parts, kpi_parts,
sprintf("**Gap:** %.0f", field_kpi$Gap_Score), sprintf("**%s:** %.0f%%", t("Gaps"), field_kpi$Gap_Score),
sprintf("**Patchiness:** %s", field_kpi$Patchiness_Risk), sprintf("**%s:** %s", t("Patchiness"), t(field_kpi$Patchiness_Risk)),
sprintf("**Decline:** %s", field_kpi$Decline_Severity) sprintf("**%s:** %s", t("Decline"), t(field_kpi$Decline_Severity))
) )
cat(paste(kpi_parts, collapse = " | "), "\n\n") # Double newline for markdown paragraph break cat(paste(kpi_parts, collapse = " | "), "\n\n") # Double newline for markdown paragraph break
@ -1667,86 +1668,74 @@ img_path <- ifelse(file.exists(target_img), target_img, "CI_graph_example.png")
``` ```
<div align="center"> <div align="center">
![`r t("ci_caption")`](`r img_path`) ![`r t("ci_caption")`](`r img_path`){width=4in}
</div> </div>
`r t("sec_iii_1")` `r t("sec_iii_1")`
- `r t("kpi_i")` - `r t("kpi_i")`
- `r t("kpi_i_metric")` - `r t("kpi_i_metric")`
- `r t("kpi_i_calc")` - `r t("kpi_i_calc")`
- `r t("kpi_categories")` - `r t("kpi_categories")`
- `r t("kpi_i_excellent")` - `r t("kpi_i_excellent")`
- `r t("kpi_i_good")` - `r t("kpi_i_good")`
- `r t("kpi_i_accept")` - `r t("kpi_i_accept")`
- `r t("kpi_i_poor")` - `r t("kpi_i_poor")`
- `r t("kpi_i_verypoor")` - `r t("kpi_i_verypoor")`
- `r t("kpi_i_why")` - `r t("kpi_i_why")`
- `r t("kpi_ii")`
- `r t("kpi_ii_calc")`
- `r t("kpi_categories")`
- `r t("kpi_ii_rapid")`
- `r t("kpi_ii_positive")`
- `r t("kpi_ii_stable")`
- `r t("kpi_ii_declining")`
- `r t("kpi_ii_rapid_decline")`
- `r t("kpi_ii_why")`
- `r t("kpi_iii")`
- `r t("kpi_iii_applies")`
- `r t("kpi_iii_method")`
- `r t("kpi_iii_input")`
- `r t("kpi_iii_output")`
- `r t("kpi_iii_why")`
- `r t("kpi_iv")`
- `r t("kpi_iv_calc")`
- `r t("kpi_categories")`
- `r t("kpi_iv_strong")`
- `r t("kpi_iv_weak")`
- `r t("kpi_iv_sli_decline")`
- `r t("kpi_iv_mod_decline")`
- `r t("kpi_iv_str_decline")`
- `r t("kpi_iv_why")`
- `r t("kpi_v")`
- `r t("kpi_v_met1")`
- `r t("kpi_v_form")`
- `r t("kpi_v_range")`
- `r t("kpi_v_interpretation")`
- `r t("kpi_v_met2")`
- `r t("kpi_v_met2_range")`
- `r t("kpi_v_thresh")`
- `r t("kpi_v_risk")`
- `r t("kpi_v_minimal")`
- `r t("kpi_v_low")`
- `r t("kpi_v_medium")`
- `r t("kpi_v_high")`
- `r t("kpi_v_why")`
- `r t("unif_v_patch")` - `r t("kpi_ii")`
- `r t("unif")` - `r t("kpi_ii_calc")`
- `r t("patch")` - `r t("kpi_categories")`
- `r t("kpi_ii_rapid")`
- `r t("kpi_ii_positive")`
- `r t("kpi_ii_stable")`
- `r t("kpi_ii_declining")`
- `r t("kpi_ii_rapid_decline")`
- `r t("kpi_ii_why")`
- `r t("kpi_iii")`
- `r t("kpi_iii_applies")`
- `r t("kpi_iii_method")`
- `r t("kpi_iii_input")`
- `r t("kpi_iii_output")`
- `r t("kpi_iii_why")`
- `r t("kpi_iv")`
- `r t("kpi_iv_calc")`
- `r t("kpi_categories")`
- `r t("kpi_iv_strong")`
- `r t("kpi_iv_weak")`
- `r t("kpi_iv_sli_decline")`
- `r t("kpi_iv_mod_decline")`
- `r t("kpi_iv_str_decline")`
- `r t("kpi_iv_why")`
- `r t("kpi_v")`
- `r t("kpi_v_met1")`
- `r t("kpi_v_form")`
- `r t("kpi_v_range")`
- `r t("kpi_v_interpretation")`
- `r t("kpi_v_met2")`
- `r t("kpi_v_met2_range")`
- `r t("kpi_v_thresh")`
- `r t("kpi_v_risk")`
- `r t("kpi_v_minimal")`
- `r t("kpi_v_low")`
- `r t("kpi_v_medium")`
- `r t("kpi_v_high")`
- `r t("kpi_v_why")`
- `r t("kpi_vi")`
`r t("practical_example")` - `r t("kpi_vi_calc")`
- `r t("kpi_vi_identify")`
- `r t("field_a")` - `r t("kpi_vi_calculates")`
- `r t("field_b")` - `r t("kpi_vi_example")`
- `r t("kpi_vi_scores")`
`r t("scouting")` - `r t("kpi_vi_0")`
- `r t("kpi_vi_10")`
- `r t("kpi_vi")` - `r t("kpi_vi_25")`
- `r t("kpi_vi_calc")` - `r t("kpi_vi_why")`
- `r t("kpi_vi_identify")`
- `r t("kpi_vi_calculates")`
- `r t("kpi_vi_example")`
- `r t("kpi_vi_scores")`
- `r t("kpi_vi_0")`
- `r t("kpi_vi_10")`
- `r t("kpi_vi_25")`
- `r t("kpi_vi_why")`
`r t("sec_iii_2_to_6")` `r t("sec_iii_2_to_6")`
@ -1754,6 +1743,18 @@ img_path <- ifelse(file.exists(target_img), target_img, "CI_graph_example.png")
`r t("hist_benchmark")` `r t("hist_benchmark")`
`r t("unif_v_patch")`
- `r t("unif")`
- `r t("patch")`
`r t("practical_example")`
- `r t("field_a")`
- `r t("field_b")`
`r t("scouting")`
\newpage \newpage
`r t("metadata")` `r t("metadata")`

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r_app/translations/translations.xlsx
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