Returns all indicators and data for a given time period and area. Also returns time series data for all time periods available. If tags are specified, only indicators which have one of the specified tags will be returned.
Details
CVD Prevent API documentation: Indicator
See also
cvd_indicator_list(), cvd_indicator_metric_list(),
cvd_indicator_tags(), cvd_indicator_details(), cvd_indicator_sibling(),
cvd_indicator_child_data(), cvd_indicator_data(), cvd_indicator_metric_data(),
cvd_indicator_raw_data(), cvd_indicator_nationalarea_metric_data(),
cvd_indicator_priority_groups(), cvd_indicator_pathway_group(), #
cvd_indicator_group(), cvd_indicator_metric_timeseries(),
cvd_indicator_person_timeseries(), cvd_indicator_metric_systemlevel_comparison(),
cvd_indicator_metric_area_breakdown()
Examples
# Returns a list of named tibbles. To use we need to unpack the list:
return_list <- cvd_indicator(time_period_id = 17, area_id = 1103)
# See what the list contains
return_list |> summary()
#> Length Class Mode
#> indicators 14 tbl_df list
#> metric_categories 7 tbl_df list
#> metric_data 19 tbl_df list
#> timeseries_data 7 tbl_df list
# extract the indicators
indicators <- return_list$indicators
indicators |>
dplyr::select(IndicatorID, IndicatorCode, IndicatorShortName) |>
dplyr::arrange(IndicatorID) |>
dplyr::slice_head(n = 4)
#> # A tibble: 4 × 3
#> IndicatorID IndicatorCode IndicatorShortName
#> <int> <chr> <chr>
#> 1 2 CVDP002HYP Hypertension: treatment to recommended age specific…
#> 2 3 CVDP003HYP Hypertension: treatment to recommended age specific…
#> 3 4 CVDP004HYP Hypertension: BP monitoring
#> 4 7 CVDP002AF AF: treatment with anticoagulants
# extract the metric categories
categories <- return_list$metric_categories
categories |>
dplyr::filter(IndicatorID == 7, MetricCategoryID %in% c(7, 8)) |>
dplyr::select(IndicatorID, MetricCategoryTypeName,
CategoryAttribute, MetricCategoryName, MetricID)
#> # A tibble: 2 × 5
#> IndicatorID MetricCategoryTypeName CategoryAttribute MetricCategoryName
#> <int> <chr> <chr> <chr>
#> 1 7 Age group Male 40-59
#> 2 7 Age group Female 40-59
#> # ℹ 1 more variable: MetricID <int>
# extract metric data
metric_data <- return_list$metric_data
metric_data |>
dplyr::filter(MetricID %in% c(126, 132)) |>
dplyr::select(MetricID, Value, Numerator, Denominator)
#> # A tibble: 2 × 4
#> MetricID Value Numerator Denominator
#> <int> <dbl> <dbl> <dbl>
#> 1 126 100 15 15
#> 2 132 100 10 10
# extract the time series data
timeseries_data <- return_list$timeseries_data
timeseries_data |>
dplyr::filter(MetricID %in% c(126, 132), !is.na(Value))
#> # A tibble: 19 × 7
#> MetricID EndDate Median StartDate TimePeriodID TimePeriodName Value
#> <int> <chr> <dbl> <chr> <int> <chr> <dbl>
#> 1 126 Tue, 31 Mar 2020… 84.6 Mon, 01 … 1 To March 2020 100
#> 2 126 Wed, 31 Mar 2021… 85.7 Mon, 01 … 2 To March 2021 100
#> 3 126 Thu, 30 Sep 2021… 86.7 Mon, 01 … 3 To September … 94.7
#> 4 126 Thu, 31 Mar 2022… 87.0 Mon, 01 … 4 To March 2022 94.7
#> 5 126 Thu, 30 Jun 2022… 86.7 Mon, 01 … 5 To June 2022 88.9
#> 6 126 Fri, 30 Sep 2022… 87.5 Mon, 01 … 6 To September … 90
#> 7 126 Sat, 31 Dec 2022… 88.2 Mon, 01 … 7 To December 2… 94.1
#> 8 126 Fri, 31 Mar 2023… 89.5 Mon, 01 … 8 To March 2023 94.7
#> 9 126 Fri, 30 Jun 2023… 89.2 Mon, 01 … 9 To June 2023 94.7
#> 10 126 Sun, 31 Dec 2023… 90 Mon, 01 … 15 To December 2… 94.1
#> 11 126 Sun, 31 Mar 2024… 90.9 Mon, 01 … 17 To March 2024 100
#> 12 132 Thu, 31 Mar 2022… 81.8 Mon, 01 … 4 To March 2022 100
#> 13 132 Thu, 30 Jun 2022… 81.8 Mon, 01 … 5 To June 2022 100
#> 14 132 Fri, 30 Sep 2022… 81.8 Mon, 01 … 6 To September … 100
#> 15 132 Sat, 31 Dec 2022… 83.3 Mon, 01 … 7 To December 2… 100
#> 16 132 Fri, 31 Mar 2023… 83.3 Mon, 01 … 8 To March 2023 100
#> 17 132 Fri, 30 Jun 2023… 84.6 Mon, 01 … 9 To June 2023 100
#> 18 132 Sun, 31 Dec 2023… 84.6 Mon, 01 … 15 To December 2… 100
#> 19 132 Sun, 31 Mar 2024… 88.1 Mon, 01 … 17 To March 2024 100
# indicators are searcheable by one or more Tag.
return_list <-
cvd_indicator(time_period_id = 17, area_id = 3, tag_id = c(3, 4))