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| Original file line number | Diff line number | Diff line change |
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| @@ -1,40 +1,40 @@ | ||
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| prep_raw <- formulario |> | ||
| dplyr::select(rowid, tema = quais_temas_voce_gostaria_que_fossem_abordados_em_eventos) |> | ||
| tidyr::separate_longer_delim(tema, ",") |> | ||
| dplyr::mutate(tema = stringr::str_trim(tema), quantidade_temas = dplyr::n_distinct(tema)) |> | ||
| dplyr::group_by(rowid) |> | ||
| dplyr::mutate(quantidade_temas_selecionados = dplyr::n()) |> | ||
| dplyr::ungroup() |> | ||
| dplyr::mutate(prop_qtd_temas = quantidade_temas_selecionados / quantidade_temas) |> | ||
| dplyr::filter(prop_qtd_temas < 0.5) |> | ||
| tidyr::drop_na(tema) | ||
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| prep <- prep_raw |> | ||
| dplyr::mutate(valor = 1) |> | ||
| dplyr::select(rowid, valor, tema) |> | ||
| tidyr::pivot_wider(names_from = tema, values_from = valor, values_fill = 0) |> | ||
| dplyr::mutate(dplyr::across(.cols = everything(), .fns = as.factor)) |> | ||
| dplyr::select(-rowid) | ||
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| # resultado_mca <- FactoMineR::MCA(prep, graph = TRUE) | ||
| # | ||
| # factoextra::fviz_mca_biplot(resultado_mca) | ||
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| cluster <- hclust(dist(prep)) | ||
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| factoextra::fviz_dend(cluster, k = 3) | ||
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| grupos <- factoextra::hcut(prep, k = 3) | ||
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| cluster_df <- tibble::tibble(cluster = grupos$cluster) |> | ||
| tibble::rowid_to_column() |> | ||
| dplyr::left_join(prep_raw, by = "rowid") |> | ||
| dplyr::group_by(rowid) |> | ||
| dplyr::mutate(quantidade_temas_selecionados = dplyr::n()) |> | ||
| dplyr::ungroup() | ||
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| cluster_df |> | ||
| dplyr::group_by(cluster) |> | ||
| dplyr::count(tema) | ||
| # | ||
| # prep_raw <- formulario |> | ||
| # dplyr::select(rowid, tema = quais_temas_voce_gostaria_que_fossem_abordados_em_eventos) |> | ||
| # tidyr::separate_longer_delim(tema, ",") |> | ||
| # dplyr::mutate(tema = stringr::str_trim(tema), quantidade_temas = dplyr::n_distinct(tema)) |> | ||
| # dplyr::group_by(rowid) |> | ||
| # dplyr::mutate(quantidade_temas_selecionados = dplyr::n()) |> | ||
| # dplyr::ungroup() |> | ||
| # dplyr::mutate(prop_qtd_temas = quantidade_temas_selecionados / quantidade_temas) |> | ||
| # dplyr::filter(prop_qtd_temas < 0.5) |> | ||
| # tidyr::drop_na(tema) | ||
| # | ||
| # prep <- prep_raw |> | ||
| # dplyr::mutate(valor = 1) |> | ||
| # dplyr::select(rowid, valor, tema) |> | ||
| # tidyr::pivot_wider(names_from = tema, values_from = valor, values_fill = 0) |> | ||
| # dplyr::mutate(dplyr::across(.cols = everything(), .fns = as.factor)) |> | ||
| # dplyr::select(-rowid) | ||
| # | ||
| # # resultado_mca <- FactoMineR::MCA(prep, graph = TRUE) | ||
| # # | ||
| # # factoextra::fviz_mca_biplot(resultado_mca) | ||
| # | ||
| # | ||
| # cluster <- hclust(dist(prep)) | ||
| # | ||
| # factoextra::fviz_dend(cluster, k = 3) | ||
| # | ||
| # grupos <- factoextra::hcut(prep, k = 3) | ||
| # | ||
| # cluster_df <- tibble::tibble(cluster = grupos$cluster) |> | ||
| # tibble::rowid_to_column() |> | ||
| # dplyr::left_join(prep_raw, by = "rowid") |> | ||
| # dplyr::group_by(rowid) |> | ||
| # dplyr::mutate(quantidade_temas_selecionados = dplyr::n()) |> | ||
| # dplyr::ungroup() | ||
| # | ||
| # cluster_df |> | ||
| # dplyr::group_by(cluster) |> | ||
| # dplyr::count(tema) |
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