In YuanchenZhu2020/antgreens: Automatic Generation of Vegetable Report

蔬菜产品中农药（兽药）多残留风险分析

  

总体多残留风险

full_sample_multi <- spec_dataset(dims_comb_data, ana_dims, ana_threshold, "multi_residue")

(ref:full-sample-multi) 总体多残留指标

sp_dable(full_sample_multi, ref_text = "(ref:full-sample-multi)", filter = "none")

  表 \@ref(tab:full-sample-multi) 给出了 r year_range[1] - r year_range[2] 年蔬菜类产品的抽检样本量及多残留指标。可以看出，r year_range[1] - r year_range[2] 年共抽取样本 r full_sample_multi[["sample_size"]] 例，多残留检出率为 r full_sample_multi[["multi_detection_rate_percent"]]%，最大多残留检出数为 r full_sample_multi[["max_detection_num"]]，多残留超标率为 r full_sample_multi[["multi_defective_rate_percent"]]%，最大多残留超标数为 r full_sample_multi[["max_defective_num"]]。

各年份多残留风险

year_multi <- spec_dataset(dims_comb_data, ana_dims, ana_threshold, "multi_residue", year) |>
  factor_dims(year)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("year"))

(ref:year-multi) 各年份多残留指标

sp_dable(year_multi, ref_text = "(ref:year-multi)")

  表 \@ref(tab:year-multi) 给出了 r year_range[1] - r year_range[2] 年蔬菜类产品年抽检样本量及多残留指标，其图形展示见图 \@ref(fig:year-multi-det-bar-line-echart) 和图 \@ref(fig:year-multi-def-bar-line-echart)。可以看出，r year_range[1] - r year_range[2] 年蔬菜类产品年抽检样本量在 r text_range(year_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(year_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(year_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(year_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(year_multi, "max_defective_num") 之间。

bar_line_echart(
  year_multi, x_var = "year", 
  bar_var = "max_detection_num", line_var = "multi_detection_rate_percent", 
  use_y_upper_bound = TRUE
)

bar_line_echart(
  year_multi, x_var = "year", 
  bar_var = "max_defective_num", line_var = "multi_defective_rate_percent", 
  use_y_upper_bound = TRUE
)

各季度多残留风险

quarter_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  quarter
) |>
  factor_dims(quarter)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("quarter"))

(ref:quarter-multi) 各季度多残留指标

sp_dable(quarter_multi, ref_text = "(ref:quarter-multi)")

  表 \@ref(tab:quarter-multi) 给出了蔬菜类产品各季度抽检样本量及多残留指标，其图形展示见图 \@ref(fig:quarter-multi-det-bar-line-echart) 和图 \@ref(fig:quarter-multi-def-bar-line-echart)。可以看出，蔬菜类产品各季度抽检样本量在 r text_range(quarter_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(quarter_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(quarter_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(quarter_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(quarter_multi, "max_defective_num") 之间。

bar_line_echart(
  quarter_multi, x_var = "quarter", 
  bar_var = "max_detection_num", line_var = "multi_detection_rate_percent", 
  use_y_upper_bound = TRUE
)

bar_line_echart(
  quarter_multi, x_var = "quarter", 
  bar_var = "max_defective_num", line_var = "multi_defective_rate_percent", 
  use_y_upper_bound = TRUE
)

各省份多残留风险

province_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  province
) |>
  factor_dims(province)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("province"))

(ref:province-multi) 各省份多残留指标

sp_dable(province_multi, ref_text = "(ref:province-multi)")

  表 \@ref(tab:province-multi) 给出了蔬菜类产品在 r nrow(province) 个省市自治区的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:province-multi-det-rate-emap) - 图 \@ref(fig:province-multi-def-num-emap)。可以看出，各省市自治区蔬菜类产品抽检样本量在 r text_range(province_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(province_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(province_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(province_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(province_multi, "max_defective_num") 之间。

map_echart(
  province_multi |> shorten_province_name(), map_json = cn_province_map_json_2015,
  value_var = "multi_detection_rate_percent"
)

map_echart(
  province_multi |> shorten_province_name(), map_json = cn_province_map_json_2015,
  value_var = "max_detection_num", use_digits = FALSE
)

map_echart(
  province_multi |> shorten_province_name(), map_json = cn_province_map_json_2015,
  value_var = "multi_defective_rate_percent"
)

map_echart(
  province_multi |> shorten_province_name(), map_json = cn_province_map_json_2015,
  value_var = "max_detection_num", use_digits = FALSE
)

各蔬菜类别多残留风险

category_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  category
) |>
  factor_dims(category)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("category"))

(ref:category-multi) 各蔬菜类别多残留指标

sp_dable(category_multi, ref_text = "(ref:category-multi)")

  表 \@ref(tab:category-multi) 给出了 r nrow(category) 个蔬菜类别的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:category-multi-det-bar-line-echart) - 图 \@ref(fig:category-multi-num-scatter-echart)。可以看出，各蔬菜类别抽检样本量在 r text_range(category_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(category_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(category_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(category_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(category_multi, "max_defective_num") 之间。

bar_line_echart(
  category_multi, x_var = "category", 
  bar_var = "max_detection_num", line_var = "multi_detection_rate_percent", 
  use_y_upper_bound = TRUE
)

category_multi |> 
  e_charts(category) |> 
  e_pie(multi_detection_rate_percent, name = "多残留检出率（%）", roseType = "radius", center = c("25%", "50%"), radius = "45%") |> 
  e_pie(max_detection_num, name = "最大多残留检出数", roseType = "radius", center = c("75%", "50%"), radius = "45%") |> 
  e_tooltip(trigger = "item")

bar_line_echart(
  category_multi, x_var = "category", 
  bar_var = "max_defective_num", line_var = "multi_defective_rate_percent", 
  use_y_upper_bound = TRUE
)

category_multi |> 
  e_charts(category) |> 
  e_pie(multi_defective_rate_percent, name = "多残留超标率（%）", roseType = "radius", center = c("25%", "50%"), radius = "45%") |> 
  e_pie(max_defective_num, name = "最大多残留超标数", roseType = "radius", center = c("75%", "50%"), radius = "45%") |> 
  e_tooltip(trigger = "item")

scatter_echart(
  category_multi, 
  x_var = "max_detection_num", y_var = "multi_detection_rate_percent", label_var = "category",
  increment = 1, shrink_width = "70%"
)

scatter_echart(
  category_multi, 
  x_var = "max_defective_num", y_var = "multi_defective_rate_percent", label_var = "category",
  increment = 1, shrink_width = "70%"
)

scatter_echart(
  category_multi, 
  x_var = "multi_defective_rate_percent", y_var = "multi_detection_rate_percent", label_var = "category",
  increment = 1, shrink_width = "70%"
)

scatter_echart(
  category_multi, 
  x_var = "max_defective_num", y_var = "max_detection_num", label_var = "category",
  increment = 1, shrink_width = "70%"
)

各品种多残留风险

product_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  product
) |>
  factor_dims(product)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("product"))

(ref:product-multi) 各品种多残留指标

sp_dable(product_multi, ref_text = "(ref:product-multi)")

  表 \@ref(tab:product-multi) 给出了 r nrow(product) 个蔬菜品种的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:product-multi-det-rose-eplot) - 图 \@ref(fig:product-multi-num-scatter-echart)。可以看出，各蔬菜品种抽检样本量在 r text_range(product_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(product_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(product_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(product_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(product_multi, "max_defective_num") 之间。

product_multi |> 
  e_charts(product) |> 
  e_pie(multi_detection_rate_percent, name = "多残留检出率（%）", roseType = "radius", center = c("25%", "50%"), radius = "45%") |> 
  e_pie(max_detection_num, name = "最大多残留检出数", roseType = "radius", center = c("75%", "50%"), radius = "45%") |> 
  e_tooltip(trigger = "item")

scatter_echart(
  product_multi, 
  x_var = "max_detection_num", y_var = "multi_detection_rate_percent", label_var = "product",
  increment = 1, shrink_width = "70%"
)

product_multi |> 
  e_charts(product) |> 
  e_pie(multi_defective_rate_percent, name = "多残留超标率（%）", roseType = "radius", center = c("25%", "50%"), radius = "45%") |> 
  e_pie(max_defective_num, name = "最大多残留超标数", roseType = "radius", center = c("75%", "50%"), radius = "45%") |> 
  e_tooltip(trigger = "item")

scatter_echart(
  product_multi, 
  x_var = "max_defective_num", y_var = "multi_defective_rate_percent", label_var = "product",
  increment = 1, shrink_width = "70%"
)

scatter_echart(
  product_multi, 
  x_var = "multi_defective_rate_percent", y_var = "multi_detection_rate_percent", label_var = "product",
  increment = 1, shrink_width = "70%"
)

scatter_echart(
  product_multi, 
  x_var = "max_defective_num", y_var = "max_detection_num", label_var = "product",
  increment = 1, shrink_width = "70%"
)

“年份-季度”多残留风险

year_quarter_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  year, quarter
)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("year", "quarter"))

(ref:year-quarter-multi) 各年份各季度多残留指标

sp_dable(year_quarter_multi |> factor_dims(year, quarter), ref_text = "(ref:year-quarter-multi)")

  表 \@ref(tab:year-quarter-multi) 给出了 r year_range[1] - r year_range[2] 年每年四个季度的蔬菜类产品的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:year-quarter-multi-det-bar-line-echart) 和图 \@ref(fig:year-quarter-multi-def-bar-line-echart)。可以看出，各蔬菜品种抽检样本量在 r text_range(year_quarter_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(year_quarter_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(year_quarter_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(year_quarter_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(year_quarter_multi, "max_defective_num") 之间。

bar_line_echart(
  year_quarter_multi |> combine_year_quarter(), 
  x_var = "timeline", bar_var = "max_detection_num", line_var = "multi_detection_rate_percent",
  use_y_upper_bound = TRUE,
  x_axis_name = "时间", x_name_gap = 40,
  long_x_label = TRUE, x_label_width = 30
) |> e_labels(fontWeight = "bold", fontSize = 11)

bar_line_echart(
  year_quarter_multi |> combine_year_quarter(), 
  x_var = "timeline", bar_var = "max_defective_num", line_var = "multi_defective_rate_percent",
  use_y_upper_bound = TRUE,
  x_axis_name = "时间", x_name_gap = 40,
  long_x_label = TRUE, x_label_width = 30
) |> e_labels(fontWeight = "bold", fontSize = 11)

“年份-省份”多残留风险

year_province_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  year, province
) |>
  factor_dims(year, province)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("year", "province"))

(ref:year-province-multi) 各年份各省份多残留指标

sp_dable(year_province_multi, ref_text = "(ref:year-province-multi)")

  表 \@ref(tab:year-province-multi) 给出了 r year_range[1] - r year_range[2] 年 r nrow(province) 个省市自治区的蔬菜类产品的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:year-province-multi-det-rate-emap) 和图 \@ref(fig:year-province-multi-def-num-emap)。可以看出，各年份各省市自治区的抽检样本量在 r text_range(year_province_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(year_province_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(year_province_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(year_province_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(year_province_multi, "max_defective_num") 之间。

map_echart(
  year_province_multi |> shorten_province_name(), map_json = cn_province_map_json_2015,
  value_var = "multi_detection_rate_percent", timeline_var = "year"
)

map_echart(
  year_province_multi |> shorten_province_name(), map_json = cn_province_map_json_2015,
  value_var = "max_detection_num", use_digits = FALSE, timeline_var = "year"
)

map_echart(
  year_province_multi |> shorten_province_name(), map_json = cn_province_map_json_2015,
  value_var = "multi_defective_rate_percent", timeline_var = "year"
)

map_echart(
  year_province_multi |> shorten_province_name(), map_json = cn_province_map_json_2015,
  value_var = "max_detection_num", use_digits = FALSE, timeline_var = "year"
)

“年份-蔬菜类别”多残留风险

year_category_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  year, category
) |>
  factor_dims(year, category)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("year", "category"))

(ref:year-category-multi) 各年份各蔬菜类别多残留指标

sp_dable(year_category_multi, ref_text = "(ref:year-category-multi)")

  表 \@ref(tab:year-category-multi) 给出了 r year_range[1] - r year_range[2] 年 r nrow(category) 个蔬菜类别的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:year-category-multi-det-bar-line-echart) - 图 \@ref(fig:year-category-multi-num-scatter-echart)。可以看出，各年份各蔬菜类别的抽检样本量在 r text_range(year_category_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(year_category_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(year_category_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(year_category_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(year_category_multi, "max_defective_num") 之间。

bar_line_echart(
  year_category_multi, timeline_var = "year", x_var = "category", 
  bar_var = "max_detection_num", line_var = "multi_detection_rate_percent", 
  use_y_upper_bound = TRUE
)

bar_line_echart(
  year_category_multi, timeline_var = "year", x_var = "category", 
  bar_var = "max_defective_num", line_var = "multi_defective_rate_percent", 
  use_y_upper_bound = TRUE
)

scatter_timeline_echart(
  year_category_multi, timeline_var = "year",
  x_var = "category", y_var = "multi_detection_rate_percent", size_var = "max_detection_num"
)

scatter_timeline_echart(
  year_category_multi, timeline_var = "year",
  x_var = "category", y_var = "multi_defective_rate_percent", size_var = "max_defective_num"
)

bar_line_echart(
  year_category_multi, timeline_var = "category", x_var = "year", 
  bar_var = "max_detection_num", line_var = "multi_detection_rate_percent", 
  use_y_upper_bound = TRUE
) |>
  e_timeline_opts(
    label = list(interval = 0, width = 50, overflow = "break"),
    padding = 0
  )

bar_line_echart(
  year_category_multi, timeline_var = "category", x_var = "year", 
  bar_var = "max_defective_num", line_var = "multi_defective_rate_percent", 
  use_y_upper_bound = TRUE
) |>
  e_timeline_opts(
    label = list(interval = 0, width = 50, overflow = "break"),
    padding = 0
  )

“年份-品种”多残留风险

year_product_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  year, product
) |>
  factor_dims(year, product)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("year", "product"))

(ref:year-product-multi) 各年份各品种多残留指标

sp_dable(year_product_multi, ref_text = "(ref:year-product-multi)")

  表 \@ref(tab:year-product-multi) 给出了 r year_range[1] - r year_range[2] 年 r nrow(product) 个蔬菜品种的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:year-product-multi-det-scatter-echart) 和图 \@ref(fig:year-product-multi-def-scatter-echart)。可以看出，各年份各蔬菜品种的抽检样本量在 r text_range(year_product_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(year_product_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(year_product_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(year_product_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(year_product_multi, "max_defective_num") 之间。

scatter_timeline_echart(
  year_product_multi, timeline_var = "year", 
  x_var = "product", y_var = "multi_detection_rate_percent", size_var = "max_detection_num",
  x_label_width = 1
)

scatter_timeline_echart(
  year_product_multi, timeline_var = "year", 
  x_var = "product", y_var = "multi_defective_rate_percent", size_var = "max_defective_num",
  x_label_width = 1
)

“季度-省份”多残留风险

quarter_province_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  quarter, province
) |>
  factor_dims(quarter, province)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("quarter", "province"))

(ref:quarter-province-multi) 各季度各省份多残留指标

sp_dable(quarter_province_multi, ref_text = "(ref:quarter-province-multi)")

  表 \@ref(tab:quarter-province-multi) 给出了 r nrow(province) 个省市自治区在四个季度的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:quarter-province-multi-det-rate-emap) - 图 \@ref(fig:quarter-province-multi-def-num-emap)。可以看出，各季度各省份的抽检样本量在 r text_range(quarter_province_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(quarter_province_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(quarter_province_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(quarter_province_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(quarter_province_multi, "max_defective_num") 之间。

map_echart(
  quarter_province_multi |> shorten_province_name(), map_json = cn_province_map_json_2015,
  value_var = "multi_detection_rate_percent", timeline_var = "quarter"
)

map_echart(
  quarter_province_multi |> shorten_province_name(), map_json = cn_province_map_json_2015,
  value_var = "max_detection_num", use_digits = FALSE, timeline_var = "quarter"
)

map_echart(
  quarter_province_multi |> shorten_province_name(), map_json = cn_province_map_json_2015,
  value_var = "multi_defective_rate_percent", timeline_var = "quarter"
)

map_echart(
  quarter_province_multi |> shorten_province_name(), map_json = cn_province_map_json_2015,
  value_var = "max_detection_num", use_digits = FALSE, timeline_var = "quarter"
)

“季度-蔬菜类别”多残留风险

quarter_category_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  quarter, category
) |>
  factor_dims(quarter, category)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("quarter", "category"))

(ref:quarter-category-multi) 各季度各蔬菜类别多残留指标

sp_dable(quarter_category_multi, ref_text = "(ref:quarter-category-multi)")

  表 \@ref(tab:quarter-category-multi) 给出了 r nrow(category) 个蔬菜类别在四个季度的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:quarter-category-multi-det-bar-line-echart) - 图 \@ref(fig:quarter-category-multi-num-scatter-echart)。可以看出，各季度各蔬菜类别的抽检样本量在 r text_range(quarter_category_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(quarter_category_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(quarter_category_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(quarter_category_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(quarter_category_multi, "max_defective_num") 之间。

bar_line_echart(
  quarter_category_multi, timeline_var = "quarter", x_var = "category", 
  bar_var = "max_detection_num", line_var = "multi_detection_rate_percent", 
  use_y_upper_bound = TRUE
)

bar_line_echart(
  quarter_category_multi, timeline_var = "quarter", x_var = "category", 
  bar_var = "max_defective_num", line_var = "multi_defective_rate_percent", 
  use_y_upper_bound = TRUE
)

scatter_timeline_echart(
  quarter_category_multi, timeline_var = "quarter",
  x_var = "category", y_var = "multi_detection_rate_percent", size_var = "max_detection_num"
)

scatter_timeline_echart(
  quarter_category_multi, timeline_var = "quarter",
  x_var = "category", y_var = "multi_defective_rate_percent", size_var = "max_defective_num"
)

bar_line_echart(
  quarter_category_multi, timeline_var = "category", x_var = "quarter", 
  bar_var = "max_detection_num", line_var = "multi_detection_rate_percent", 
  use_y_upper_bound = TRUE
) |>
  e_timeline_opts(
    label = list(interval = 0, width = 50, overflow = "break"),
    padding = 0
  )

bar_line_echart(
  quarter_category_multi, timeline_var = "category", x_var = "quarter", 
  bar_var = "max_defective_num", line_var = "multi_defective_rate_percent", 
  use_y_upper_bound = TRUE
) |>
  e_timeline_opts(
    label = list(interval = 0, width = 50, overflow = "break"),
    padding = 0
  )

“季度-品种”多残留风险

quarter_product_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  quarter, product
) |>
  factor_dims(quarter, product)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("quarter", "product"))

(ref:quarter-product-multi) 各季度各品种多残留指标

sp_dable(quarter_product_multi, ref_text = "(ref:quarter-product-multi)")

  表 \@ref(tab:quarter-product-multi) 给出了 r nrow(product) 个蔬菜品种在四个季度的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:quarter-product-multi-det-bar-line-echart) 和图 \@ref(fig:quarter-product-multi-def-bar-line-echart)。可以看出，各季度各蔬菜品种的抽检样本量在 r text_range(quarter_product_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(quarter_product_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(quarter_product_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(quarter_product_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(quarter_product_multi, "max_defective_num") 之间。

scatter_timeline_echart(
  quarter_product_multi, timeline_var = "quarter", 
  x_var = "product", y_var = "multi_detection_rate_percent", size_var = "max_detection_num",
  x_label_width = 1
)

scatter_timeline_echart(
  quarter_product_multi, timeline_var = "quarter", 
  x_var = "product", y_var = "multi_defective_rate_percent", size_var = "max_defective_num",
  x_label_width = 1
)

“省份-蔬菜类别”多残留风险

province_category_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  province, category
) |>
  factor_dims(province, category)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("province", "category"))

(ref:province-category-multi) 各省份各蔬菜类别多残留指标

sp_dable(province_category_multi, ref_text = "(ref:province-category-multi)")

  表 \@ref(tab:province-category-multi) 给出了 r nrow(province) 个省市自治区的 r nrow(category) 个蔬菜类别的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:province-category-multi-det-rate-emap) - 图 \@ref(fig:province-category-multi-def-num-emap)。可以看出，各省市自治区各蔬菜类别的抽检样本量在 r text_range(province_category_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(province_category_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(province_category_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(province_category_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(province_category_multi, "max_defective_num") 之间。

map_echart(
  province_category_multi |> shorten_province_name(), 
  map_json = cn_province_map_json_2015,
  timeline_var = "category", value_var = "multi_detection_rate_percent"
) |> e_timeline_opts(controlStyle = list(showPlayBtn = FALSE))

map_echart(
  province_category_multi |> shorten_province_name(), 
  map_json = cn_province_map_json_2015,
  timeline_var = "category", value_var = "max_detection_num"
) |> e_timeline_opts(controlStyle = list(showPlayBtn = FALSE))

map_echart(
  province_category_multi |> shorten_province_name(), 
  map_json = cn_province_map_json_2015,
  timeline_var = "category", value_var = "multi_defective_rate_percent"
) |> e_timeline_opts(controlStyle = list(showPlayBtn = FALSE))

map_echart(
  province_category_multi |> shorten_province_name(), 
  map_json = cn_province_map_json_2015,
  timeline_var = "category", value_var = "max_defective_num"
) |> e_timeline_opts(controlStyle = list(showPlayBtn = FALSE))

“省份-品种”多残留风险

province_product_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  province, product
) |>
  factor_dims(province, product)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("province", "product"))

(ref:province-product-multi) 各省份各品种多残留指标

sp_dable(province_product_multi, ref_text = "(ref:province-product-multi)")

  表 \@ref(tab:province-product-multi) 给出了 r nrow(province) 个省市自治区的 r nrow(product) 个蔬菜品种的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:province-product-multi-det-rate-eheatmap) - 图 \@ref(fig:province-product-multi-def-num-eheatmap)。可以看出，各省市自治区各蔬菜品种的抽检样本量在 r text_range(province_product_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(province_product_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(province_product_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(province_product_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(province_product_multi, "max_defective_num") 之间。

heatmap_echart(
  province_product_multi |> shorten_province_name(), 
  x_var = "product", y_var = "province", value_var = "multi_detection_rate_percent"
)

heatmap_echart(
  province_product_multi |> shorten_province_name(), 
  x_var = "product", y_var = "province", value_var = "max_detection_num"
)

heatmap_echart(
  province_product_multi |> shorten_province_name(), 
  x_var = "product", y_var = "province", value_var = "multi_defective_rate_percent"
)

heatmap_echart(
  province_product_multi |> shorten_province_name(), 
  x_var = "product", y_var = "province", value_var = "max_defective_num"
)

“年份-季度-省份”多残留风险

year_quarter_province_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  year, quarter, province
)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("year", "quarter", "province"))

(ref:year-quarter-province-multi) 各年份各季度各省份多残留指标

sp_dable(
  year_quarter_province_multi |> factor_dims(year, quarter, province), 
  ref_text = "(ref:year-quarter-province-multi)"
)

  表 \@ref(tab:year-quarter-province-multi) 给出了 r year_range[1] 年第一季度到 r year_range[2] 年第四季度 r nrow(province) 个省市自治区的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:province-product-multi-det-rate-eheatmap) - 图 \@ref(fig:year-quarter-province-multi-def-num-eheatmap)。可以看出，各年份各季度各省市自治区的抽检样本量在 r text_range(year_quarter_province_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(year_quarter_province_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(year_quarter_province_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(year_quarter_province_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(year_quarter_province_multi, "max_defective_num") 之间。

map_echart(
  year_quarter_province_multi |> combine_year_quarter() |> shorten_province_name(), 
  map_json = cn_province_map_json_2015,
  value_var = "multi_detection_rate_percent", timeline_var = "timeline",
  interval = 3, timeline_label_width = 60
)

heatmap_echart(
  year_quarter_province_multi |> 
    combine_year_quarter() |> 
    shorten_province_name() |>
    factor_dims(timeline, province), 
  x_var = "province", y_var = "timeline", value_var = "multi_detection_rate_percent",
  y_axis_name = "年份-季度"
)

map_echart(
  year_quarter_province_multi |> combine_year_quarter() |> shorten_province_name(), 
  map_json = cn_province_map_json_2015,
  value_var = "max_detection_num", use_digits = FALSE, timeline_var = "timeline",
  interval = 3, timeline_label_width = 60
)

heatmap_echart(
  year_quarter_province_multi |> 
    combine_year_quarter() |> 
    shorten_province_name() |>
    factor_dims(timeline, province), 
  x_var = "province", y_var = "timeline", value_var = "max_detection_num",
  y_axis_name = "年份-季度"
)

map_echart(
  year_quarter_province_multi |> combine_year_quarter() |> shorten_province_name(), 
  map_json = cn_province_map_json_2015,
  value_var = "multi_defective_rate_percent", timeline_var = "timeline",
  interval = 3, timeline_label_width = 60
)

heatmap_echart(
  year_quarter_province_multi |> 
    combine_year_quarter() |> 
    shorten_province_name() |>
    factor_dims(timeline, province), 
  x_var = "province", y_var = "timeline", value_var = "multi_defective_rate_percent",
  y_axis_name = "年份-季度"
)

map_echart(
  year_quarter_province_multi |> combine_year_quarter() |> shorten_province_name(), 
  map_json = cn_province_map_json_2015,
  value_var = "max_detection_num", use_digits = FALSE, timeline_var = "timeline",
  interval = 3, timeline_label_width = 60
)

heatmap_echart(
  year_quarter_province_multi |> 
    combine_year_quarter() |> 
    shorten_province_name() |>
    factor_dims(timeline, province), 
  x_var = "province", y_var = "timeline", value_var = "max_detection_num",
  y_axis_name = "年份-季度"
)

“年份-季度-蔬菜类别”多残留风险

year_quarter_category_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  year, quarter, category
)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("year", "quarter", "category"))

(ref:year-quarter-category-multi) 各年份各季度各蔬菜类别多残留指标

sp_dable(
  year_quarter_category_multi |> factor_dims(year, quarter, category), 
  ref_text = "(ref:year-quarter-category-multi)"
)

  表 \@ref(tab:year-quarter-category-multi) 给出了 r year_range[1] 年第一季度到 r year_range[2] 年第四季度 r nrow(category) 个蔬菜类别的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:year-quarter-category-multi-det-bar-line-echart) - 图 \@ref(fig:year-quarter-category-multi-num-scatter-echart)。可以看出，各年份各季度各蔬菜类别的抽检样本量在 r text_range(year_quarter_category_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(year_quarter_category_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(year_quarter_category_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(year_quarter_category_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(year_quarter_category_multi, "max_defective_num") 之间。

bar_line_echart(
  year_quarter_category_multi |> combine_year_quarter() |> factor_dims(timeline, category), 
  timeline_var = "timeline", x_var = "category", 
  bar_var = "max_detection_num", line_var = "multi_detection_rate_percent", 
  use_y_upper_bound = TRUE
)

bar_line_echart(
  year_quarter_category_multi |> combine_year_quarter() |> factor_dims(timeline, category), 
  timeline_var = "timeline", x_var = "category", 
  bar_var = "max_defective_num", line_var = "multi_defective_rate_percent", 
  use_y_upper_bound = TRUE
)

scatter_timeline_echart(
  year_quarter_category_multi |> combine_year_quarter() |> factor_dims(timeline, category), 
  timeline_var = "timeline",
  x_var = "category", y_var = "multi_detection_rate_percent", size_var = "max_detection_num",
  symbol_size = c(1,50), interval = 3, timeline_label_width = 60
)

scatter_timeline_echart(
  year_quarter_category_multi |> combine_year_quarter() |> factor_dims(timeline, category),
  timeline_var = "timeline",
  x_var = "category", y_var = "multi_defective_rate_percent", size_var = "max_defective_num",
  symbol_size = c(1,50), interval = 3, timeline_label_width = 60
)

bar_line_echart(
  year_quarter_category_multi |> combine_year_quarter() |> factor_dims(timeline, category),
  timeline_var = "category", 
  x_var = "timeline", bar_var = "max_detection_num", line_var = "multi_detection_rate_percent", 
  x_axis_name = "年份-季度", use_y_upper_bound = TRUE,
  long_x_label = TRUE, x_label_width = 30
) |>
  e_timeline_opts(
    label = list(interval = 0, width = 50, overflow = "break"),
    padding = 0
  )

bar_line_echart(
  year_quarter_category_multi |> combine_year_quarter() |> factor_dims(timeline, category),
  timeline_var = "category", 
  x_var = "timeline", bar_var = "max_defective_num", line_var = "multi_defective_rate_percent", 
  x_axis_name = "年份-季度", use_y_upper_bound = TRUE,
  long_x_label = TRUE, x_label_width = 30
) |>
  e_timeline_opts(
    label = list(interval = 0, width = 50, overflow = "break"),
    padding = 0
  )

“年份-季度-品种”多残留风险

year_quarter_product_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  year, quarter, product
) 

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("year", "quarter", "product"))

(ref:year-quarter-product-multi) 各年份各季度各品种多残留指标

sp_dable(
  year_quarter_product_multi |> factor_dims(year, quarter, product), 
  ref_text = "(ref:year-quarter-product-multi)"
)

  表 \@ref(tab:year-quarter-product-multi) 给出了 r year_range[1] 年第一季度到 r year_range[2] 年第四季度 r nrow(product) 个蔬菜品种的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:year-quarter-product-multi-det-rate-eheatmap) - 图 \@ref(fig:year-quarter-product-multi-def-num-eheatmap)。可以看出，各年份各季度各蔬菜品种的抽检样本量在 r text_range(year_quarter_product_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(year_quarter_product_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(year_quarter_product_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(year_quarter_product_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(year_quarter_product_multi, "max_defective_num") 之间。

heatmap_echart(
  year_quarter_product_multi |> combine_year_quarter() |> factor_dims(product),
  x_var = "product", y_var = "timeline", value_var = "multi_detection_rate_percent",
  y_axis_name = "年份-季度"
)

heatmap_echart(
  year_quarter_product_multi |> combine_year_quarter() |> factor_dims(product),
  x_var = "product", y_var = "timeline", value_var = "max_detection_num",
  y_axis_name = "年份-季度"
)

heatmap_echart(
  year_quarter_product_multi |> combine_year_quarter() |> factor_dims(product),
  x_var = "product", y_var = "timeline", value_var = "multi_defective_rate_percent",
  y_axis_name = "年份-季度"
)

heatmap_echart(
  year_quarter_product_multi |> combine_year_quarter() |> factor_dims(product),
  x_var = "product", y_var = "timeline", value_var = "max_defective_num",
  y_axis_name = "年份-季度"
)

“年份-省份-蔬菜类别”多残留风险

year_province_category_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  year, province, category
) |> 
  factor_dims(year, province, category)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("year", "province", "category"))

(ref:year-province-category-multi) 各年份各省份各蔬菜类别多残留指标

sp_dable(year_province_category_multi, ref_text = "(ref:year-province-category-multi)")

  表 \@ref(tab:year-province-category-multi) 给出了 r year_range[1] - r year_range[2] 年 r nrow(province) 个省市自治区的 r nrow(category) 个蔬菜类别的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:2016-province-category-multi-det-rate-eheatmap) - 图 \@ref(fig:2020-province-category-multi-def-num-eheatmap)。可以看出，各年份各省市自治区各蔬菜类别的抽检样本量在 r text_range(year_province_category_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(year_province_category_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(year_province_category_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(year_province_category_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(year_province_category_multi, "max_defective_num") 之间。

heatmap_echart(
  year_province_category_multi |> filter(year == 2016) |> shorten_province_name(),
  x_var = "province", y_var = "category", value_var = "multi_detection_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2017) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "multi_detection_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2018) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "multi_detection_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2019) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "multi_detection_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2020) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "multi_detection_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2016) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "max_detection_num",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2017) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "max_detection_num",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2018) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "max_detection_num",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2019) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "max_detection_num",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2020) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "max_detection_num",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2016) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "multi_defective_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2017) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "multi_defective_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2018) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "multi_defective_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2019) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "multi_defective_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2020) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "multi_defective_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2016) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "max_defective_num",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2017) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "max_defective_num",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2018) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "max_defective_num",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2019) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "max_defective_num",
  height = "300%"
)

heatmap_echart(
  year_province_category_multi |> filter(year == 2020) |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "max_defective_num",
  height = "300%"
)

“年份-省份-品种”多残留风险

year_province_product_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  year, province, product
) |> 
  factor_dims(year, province, product)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("year", "province", "product"))

(ref:year-province-product-multi) 各年份各省份各品种多残留指标

sp_dable(year_province_product_multi, ref_text = "(ref:year-province-product-multi)")

  表 \@ref(tab:year-province-product-multi) 给出了 r year_range[1] - r year_range[2] 年 r nrow(province) 个省市自治区的 r nrow(product) 个蔬菜品种的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:2016-province-product-multi-det-rate-eheatmap) - 图 \@ref(fig:2020-province-product-multi-def-num-eheatmap)。可以看出，各年份各省市自治区各蔬菜品种的抽检样本量在 r text_range(year_province_product_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(year_province_product_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(year_province_product_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(year_province_product_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(year_province_product_multi, "max_defective_num") 之间。

heatmap_echart(
  year_province_product_multi |> filter(year == 2016) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "multi_detection_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2017) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "multi_detection_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2018) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "multi_detection_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2019) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "multi_detection_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2020) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "multi_detection_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2016) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "max_detection_num",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2017) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "max_detection_num",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2018) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "max_detection_num",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2019) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "max_detection_num",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2020) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "max_detection_num",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2016) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "multi_defective_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2017) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "multi_defective_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2018) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "multi_defective_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2019) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "multi_defective_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2020) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "multi_defective_rate_percent",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2016) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "max_defective_num",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2017) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "max_defective_num",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2018) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "max_defective_num",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2019) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "max_defective_num",
  height = "300%"
)

heatmap_echart(
  year_province_product_multi |> filter(year == 2020) |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "max_defective_num",
  height = "300%"
)

“季度-省份-蔬菜类别”多残留风险

quarter_province_category_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  quarter, province, category
) |>
  factor_dims(quarter, province, category)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("quarter", "province", "category"))

(ref:quarter-province-category-multi) 各季度各省份各蔬菜类别多残留指标

sp_dable(quarter_province_category_multi, ref_text = "(ref:quarter-province-category-multi)")

  表 \@ref(tab:quarter-province-category-multi) 给出了r nrow(province) 个省市自治区的 r nrow(category) 个蔬菜类别在四个季度的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:Q1-province-category-multi-det-rate-eheatmap) - 图 \@ref(fig:Q4-province-category-multi-def-num-eheatmap)，需要注意的是，豆类在各季度各省份的抽检样本量均低于阈值，不参与分析，因此图中只有其余 7 种蔬菜类别。可以看出，各季度各省市自治区各蔬菜类别的抽检样本量在 r text_range(quarter_province_category_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(quarter_province_category_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(quarter_province_category_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(quarter_province_category_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(quarter_province_category_multi, "max_defective_num") 之间。

heatmap_echart(
  quarter_province_category_multi |> filter(quarter == "第一季度") |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "multi_detection_rate_percent",
  height = "300%"
)

heatmap_echart(
  quarter_province_category_multi |> filter(quarter == "第二季度") |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "multi_detection_rate_percent",
  height = "300%"
)

heatmap_echart(
  quarter_province_category_multi |> filter(quarter == "第三季度") |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "multi_detection_rate_percent",
  height = "300%"
)

heatmap_echart(
  quarter_province_category_multi |> filter(quarter == "第四季度") |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "multi_detection_rate_percent",
  height = "300%"
)

heatmap_echart(
  quarter_province_category_multi |> filter(quarter == "第一季度") |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "max_detection_num",
  height = "300%"
)

heatmap_echart(
  quarter_province_category_multi |> filter(quarter == "第二季度") |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "max_detection_num",
  height = "300%"
)

heatmap_echart(
  quarter_province_category_multi |> filter(quarter == "第三季度") |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "max_detection_num",
  height = "300%"
)

heatmap_echart(
  quarter_province_category_multi |> filter(quarter == "第四季度") |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "max_detection_num",
  height = "300%"
)

heatmap_echart(
  quarter_province_category_multi |> filter(quarter == "第一季度") |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "multi_defective_rate_percent",
  height = "300%"
)

heatmap_echart(
  quarter_province_category_multi |> filter(quarter == "第二季度") |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "multi_defective_rate_percent",
  height = "300%"
)

heatmap_echart(
  quarter_province_category_multi |> filter(quarter == "第三季度") |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "multi_defective_rate_percent",
  height = "300%"
)

heatmap_echart(
  quarter_province_category_multi |> filter(quarter == "第四季度") |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "multi_defective_rate_percent",
  height = "300%"
)

heatmap_echart(
  quarter_province_category_multi |> filter(quarter == "第一季度") |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "max_defective_num",
  height = "300%"
)

heatmap_echart(
  quarter_province_category_multi |> filter(quarter == "第二季度") |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "max_defective_num",
  height = "300%"
)

heatmap_echart(
  quarter_province_category_multi |> filter(quarter == "第三季度") |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "max_defective_num",
  height = "300%"
)

heatmap_echart(
  quarter_province_category_multi |> filter(quarter == "第四季度") |> shorten_province_name(), 
  x_var = "province", y_var = "category", value_var = "max_defective_num",
  height = "300%"
)

“季度-省份-品种”多残留风险

quarter_province_product_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  quarter, province, product
) |>
  factor_dims(quarter, province, product)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("quarter", "province", "product"))

(ref:quarter-province-product-multi) 各季度各省份各品种多残留指标

sp_dable(quarter_province_product_multi, ref_text = "(ref:quarter-province-product-multi)")

  表 \@ref(tab:quarter-province-product-multi) 给出了 r nrow(province) 个省市自治区的 r nrow(product) 个蔬菜品种在四个季度的抽检样本量及多残留指标，其图形展示见图 \@ref(fig:Q1-province-product-multi-det-rate-eheatmap) - 图 \@ref(fig:Q4-province-product-multi-def-num-eheatmap)。可以看出，各季度各省市自治区各蔬菜品种的抽检样本量在 r text_range(quarter_province_product_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(quarter_province_product_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(quarter_province_product_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(quarter_province_product_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(quarter_province_product_multi, "max_defective_num") 之间。

heatmap_echart(
  quarter_province_product_multi |> filter(quarter == "第一季度") |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "multi_detection_rate_percent",
  height = "300%"
)

heatmap_echart(
  quarter_province_product_multi |> filter(quarter == "第二季度") |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "multi_detection_rate_percent",
  height = "300%"
)

heatmap_echart(
  quarter_province_product_multi |> filter(quarter == "第三季度") |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "multi_detection_rate_percent",
  height = "300%"
)

heatmap_echart(
  quarter_province_product_multi |> filter(quarter == "第四季度") |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "multi_detection_rate_percent",
  height = "300%"
)

heatmap_echart(
  quarter_province_product_multi |> filter(quarter == "第一季度") |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "max_detection_num",
  height = "300%"
)

heatmap_echart(
  quarter_province_product_multi |> filter(quarter == "第二季度") |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "max_detection_num",
  height = "300%"
)

heatmap_echart(
  quarter_province_product_multi |> filter(quarter == "第三季度") |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "max_detection_num",
  height = "300%"
)

heatmap_echart(
  quarter_province_product_multi |> filter(quarter == "第四季度") |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "max_detection_num",
  height = "300%"
)

heatmap_echart(
  quarter_province_product_multi |> filter(quarter == "第一季度") |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "multi_defective_rate_percent",
  height = "300%"
)

heatmap_echart(
  quarter_province_product_multi |> filter(quarter == "第二季度") |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "multi_defective_rate_percent",
  height = "300%"
)

heatmap_echart(
  quarter_province_product_multi |> filter(quarter == "第三季度") |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "multi_defective_rate_percent",
  height = "300%"
)

heatmap_echart(
  quarter_province_product_multi |> filter(quarter == "第四季度") |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "multi_defective_rate_percent",
  height = "300%"
)

heatmap_echart(
  quarter_province_product_multi |> filter(quarter == "第一季度") |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "max_defective_num",
  height = "300%"
)

heatmap_echart(
  quarter_province_product_multi |> filter(quarter == "第二季度") |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "max_defective_num",
  height = "300%"
)

heatmap_echart(
  quarter_province_product_multi |> filter(quarter == "第三季度") |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "max_defective_num",
  height = "300%"
)

heatmap_echart(
  quarter_province_product_multi |> filter(quarter == "第四季度") |> shorten_province_name(), 
  x_var = "province", y_var = "product", value_var = "max_defective_num",
  height = "300%"
)

“年份-季度-省份-蔬菜类别”多残留风险

year_quarter_province_category_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  year, quarter, province, category
) |>
  factor_dims(year, quarter, province, category)

r text_threshold(threshold_data = ana_threshold, dims = ana_dims, ana_dims = c("year", "quarter", "province", "category"))

(ref:year-quarter-province-category-multi) 各年份各季度各省份各蔬菜类别多残留指标

sp_dable(year_quarter_province_category_multi, ref_text = "(ref:year-quarter-province-category-multi)")

  表 \@ref(tab:year-quarter-province-category-multi) 给出了 r year_range[1] 年第一季度到 r year_range[2] 年第四季度 r nrow(province) 个省市自治区的 r nrow(category) 个蔬菜类别在四个季度的抽检样本量及多残留指标。可以看出，各年份各季度各省市自治区各蔬菜类别的抽检样本量在 r text_range(year_quarter_province_category_multi, "sample_size") 之间，多残留检出率稳定在 r text_range(year_quarter_province_category_multi, "multi_detection_rate_percent") 之间，最大多残留检出数稳定在 r text_range(year_quarter_province_category_multi, "max_detection_num") 之间，多残留超标率稳定在 r text_range(year_quarter_province_category_multi, "multi_defective_rate_percent") 之间，最大多残留超标数稳定在 r text_range(year_quarter_province_category_multi, "max_defective_num") 之间。

`r if (FALSE) '

“年份-季度-省份-品种”多残留风险（不分析）

'`   

year_quarter_province_product_multi <- spec_dataset(
  dims_comb_data, ana_dims, ana_threshold, "multi_residue", 
  year, quarter, province, product
) |>
  factor_dims(year, quarter, province, product)

(ref:year-quarter-province-product-multi) 各年份各季度各省份各品种多残留指标

sp_dable(year_quarter_province_product_multi, ref_text = "(ref:year-quarter-province-product-multi)")

YuanchenZhu2020/antgreens documentation built on Dec. 18, 2021, 8:20 p.m.