This vignette introduces the following functions from the PHEindicatormethods package and provides basic sample code to demonstrate their execution. The code included is based on the code provided within the ‘examples’ section of the function documentation. This vignette does not explain the methods applied in detail but these can (optionally) be output alongside the statistics or for a more detailed explanation, please see the references section of the function documentation.
This vignette covers the following core functions available within PHEindicatormethods:
| Function | Type | Description |
|---|---|---|
| phe_proportion | Non-aggregate | Performs a calculation on each row of data (unless data is grouped) |
| phe_rate | Non-aggregate | Performs a calculation on each row of data (unless data is grouped) |
| phe_mean | Aggregate | Performs a calculation on each grouping set |
| calculate_dsr | Aggregate, standardised | Performs a calculation on each grouping set and requires additional reference inputs |
| calculate_ISRatio | Aggregate, standardised | Performs a calculation on each grouping set and requires additional reference inputs |
| calculate_ISRate | Aggregate, standardised | Performs a calculation on each grouping set and requires additional reference inputs |
Other functions are introduced in separate vignettes.
The following code chunk creates a data frame containing observed number of events and populations for 4 geographical areas over 2 time periods that is used later to demonstrate the PHEindicatormethods package functions:
df <- data.frame(
area = rep(c("Area1","Area2","Area3","Area4"), 2),
year = rep(2015:2016, each = 4),
obs = sample(100, 2 * 4, replace = TRUE),
pop = sample(100:200, 2 * 4, replace = TRUE))
df
#> area year obs pop
#> 1 Area1 2015 1 197
#> 2 Area2 2015 71 198
#> 3 Area3 2015 76 129
#> 4 Area4 2015 60 122
#> 5 Area1 2016 64 182
#> 6 Area2 2016 16 194
#> 7 Area3 2016 69 200
#> 8 Area4 2016 52 125INPUT: The phe_proportion and phe_rate functions take a single data frame as input with columns representing the numerators and denominators for the statistic. Any other columns present will be retained in the output.
OUTPUT: The functions output the original data frame with additional columns appended. By default the additional columns are the proportion or rate, the lower 95% confidence limit, the upper 95% confidence limit, the confidence level, the statistic name and the method.
OPTIONS: The functions also accept additional arguments to specify the level of confidence, the multiplier and a reduced level of detail to be output.
Here are some example code chunks to demonstrate these two functions and the arguments that can optionally be specified
# default proportion
phe_proportion(df, obs, pop)
#> area year obs pop value lowercl uppercl confidence
#> 1 Area1 2015 1 197 0.005076142 0.0008966264 0.0281883 95%
#> 2 Area2 2015 71 198 0.358585859 0.2950605233 0.4274940 95%
#> 3 Area3 2015 76 129 0.589147287 0.5028661010 0.6702726 95%
#> 4 Area4 2015 60 122 0.491803279 0.4047061621 0.5794008 95%
#> 5 Area1 2016 64 182 0.351648352 0.2859970732 0.4234327 95%
#> 6 Area2 2016 16 194 0.082474227 0.0514016685 0.1297609 95%
#> 7 Area3 2016 69 200 0.345000000 0.2825979438 0.4132441 95%
#> 8 Area4 2016 52 125 0.416000000 0.3333590675 0.5036499 95%
#> statistic method
#> 1 proportion of 1 Wilson
#> 2 proportion of 1 Wilson
#> 3 proportion of 1 Wilson
#> 4 proportion of 1 Wilson
#> 5 proportion of 1 Wilson
#> 6 proportion of 1 Wilson
#> 7 proportion of 1 Wilson
#> 8 proportion of 1 Wilson
# specify confidence level for proportion
phe_proportion(df, obs, pop, confidence = 99.8)
#> area year obs pop value lowercl uppercl confidence
#> 1 Area1 2015 1 197 0.005076142 0.0004430205 0.05547352 99.8%
#> 2 Area2 2015 71 198 0.358585859 0.2620151420 0.46816975 99.8%
#> 3 Area3 2015 76 129 0.589147287 0.4536921053 0.71231350 99.8%
#> 4 Area4 2015 60 122 0.491803279 0.3576999292 0.62709667 99.8%
#> 5 Area1 2016 64 182 0.351648352 0.2521750632 0.46591353 99.8%
#> 6 Area2 2016 16 194 0.082474227 0.0393419348 0.16478300 99.8%
#> 7 Area3 2016 69 200 0.345000000 0.2503386597 0.45378858 99.8%
#> 8 Area4 2016 52 125 0.416000000 0.2905149497 0.55340870 99.8%
#> statistic method
#> 1 proportion of 1 Wilson
#> 2 proportion of 1 Wilson
#> 3 proportion of 1 Wilson
#> 4 proportion of 1 Wilson
#> 5 proportion of 1 Wilson
#> 6 proportion of 1 Wilson
#> 7 proportion of 1 Wilson
#> 8 proportion of 1 Wilson
# specify multiplier to output proportions as percentages
phe_proportion(df, obs, pop, multiplier = 100)
#> area year obs pop value lowercl uppercl confidence statistic
#> 1 Area1 2015 1 197 0.5076142 0.08966264 2.81883 95% percentage
#> 2 Area2 2015 71 198 35.8585859 29.50605233 42.74940 95% percentage
#> 3 Area3 2015 76 129 58.9147287 50.28661010 67.02726 95% percentage
#> 4 Area4 2015 60 122 49.1803279 40.47061621 57.94008 95% percentage
#> 5 Area1 2016 64 182 35.1648352 28.59970732 42.34327 95% percentage
#> 6 Area2 2016 16 194 8.2474227 5.14016685 12.97609 95% percentage
#> 7 Area3 2016 69 200 34.5000000 28.25979438 41.32441 95% percentage
#> 8 Area4 2016 52 125 41.6000000 33.33590675 50.36499 95% percentage
#> method
#> 1 Wilson
#> 2 Wilson
#> 3 Wilson
#> 4 Wilson
#> 5 Wilson
#> 6 Wilson
#> 7 Wilson
#> 8 Wilson
# specify multiplier for proportion, confidence level and remove metadata columns
phe_proportion(df, obs, pop, confidence = 99.8, multiplier = 100, type = "standard")
#> area year obs pop value lowercl uppercl
#> 1 Area1 2015 1 197 0.5076142 0.04430205 5.547352
#> 2 Area2 2015 71 198 35.8585859 26.20151420 46.816975
#> 3 Area3 2015 76 129 58.9147287 45.36921053 71.231350
#> 4 Area4 2015 60 122 49.1803279 35.76999292 62.709667
#> 5 Area1 2016 64 182 35.1648352 25.21750632 46.591353
#> 6 Area2 2016 16 194 8.2474227 3.93419348 16.478300
#> 7 Area3 2016 69 200 34.5000000 25.03386597 45.378858
#> 8 Area4 2016 52 125 41.6000000 29.05149497 55.340870
# default rate
phe_rate(df, obs, pop)
#> area year obs pop value lowercl uppercl confidence
#> 1 Area1 2015 1 197 507.6142 12.85168 2828.245 95%
#> 2 Area2 2015 71 198 35858.5859 28004.68471 45231.369 95%
#> 3 Area3 2015 76 129 58914.7287 46416.38394 73741.534 95%
#> 4 Area4 2015 60 122 49180.3279 37527.67173 63305.921 95%
#> 5 Area1 2016 64 182 35164.8352 27079.85468 44905.428 95%
#> 6 Area2 2016 16 194 8247.4227 4711.08707 13394.040 95%
#> 7 Area3 2016 69 200 34500.0000 26841.88252 43662.599 95%
#> 8 Area4 2016 52 125 41600.0000 31066.63356 54553.968 95%
#> statistic method
#> 1 rate per 100000 Exact
#> 2 rate per 100000 Byars
#> 3 rate per 100000 Byars
#> 4 rate per 100000 Byars
#> 5 rate per 100000 Byars
#> 6 rate per 100000 Byars
#> 7 rate per 100000 Byars
#> 8 rate per 100000 Byars
# specify multiplier for rate and confidence level
phe_rate(df, obs, pop, confidence = 99.8, multiplier = 100)
#> area year obs pop value lowercl uppercl confidence statistic
#> 1 Area1 2015 1 197 0.5076142 5.078682e-04 4.687012 99.8% rate per 100
#> 2 Area2 2015 71 198 35.8585859 2.412038e+01 51.068120 99.8% rate per 100
#> 3 Area3 2015 76 129 58.9147287 4.020077e+01 82.952531 99.8% rate per 100
#> 4 Area4 2015 60 122 49.1803279 3.184853e+01 72.157570 99.8% rate per 100
#> 5 Area1 2016 64 182 35.1648352 2.311658e+01 50.994333 99.8% rate per 100
#> 6 Area2 2016 16 194 8.2474227 3.281938e+00 16.841054 99.8% rate per 100
#> 7 Area3 2016 69 200 34.5000000 2.306344e+01 49.374341 99.8% rate per 100
#> 8 Area4 2016 52 125 41.6000000 2.600267e+01 62.717603 99.8% rate per 100
#> method
#> 1 Exact
#> 2 Byars
#> 3 Byars
#> 4 Byars
#> 5 Byars
#> 6 Byars
#> 7 Byars
#> 8 Byars
# specify multiplier for rate, confidence level and remove metadata columns
phe_rate(df, obs, pop, type = "standard", confidence = 99.8, multiplier = 100)
#> area year obs pop value lowercl uppercl
#> 1 Area1 2015 1 197 0.5076142 5.078682e-04 4.687012
#> 2 Area2 2015 71 198 35.8585859 2.412038e+01 51.068120
#> 3 Area3 2015 76 129 58.9147287 4.020077e+01 82.952531
#> 4 Area4 2015 60 122 49.1803279 3.184853e+01 72.157570
#> 5 Area1 2016 64 182 35.1648352 2.311658e+01 50.994333
#> 6 Area2 2016 16 194 8.2474227 3.281938e+00 16.841054
#> 7 Area3 2016 69 200 34.5000000 2.306344e+01 49.374341
#> 8 Area4 2016 52 125 41.6000000 2.600267e+01 62.717603These functions can also return aggregate data if the input dataframes are grouped:
# default proportion - grouped
df %>%
group_by(year) %>%
phe_proportion(obs, pop)
#> # A tibble: 2 × 9
#> # Groups: year [2]
#> year obs pop value lowercl uppercl confidence statistic method
#> <int> <int> <int> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 2015 208 646 0.322 0.287 0.359 95% proportion of 1 Wilson
#> 2 2016 201 701 0.287 0.254 0.321 95% proportion of 1 Wilson
# default rate - grouped
df %>%
group_by(year) %>%
phe_rate(obs, pop)
#> # A tibble: 2 × 9
#> # Groups: year [2]
#> year obs pop value lowercl uppercl confidence statistic method
#> <int> <int> <int> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 2015 208 646 32198. 27971. 36884. 95% rate per 100000 Byars
#> 2 2016 201 701 28673. 24846. 32923. 95% rate per 100000 Byars
The remaining functions aggregate the rows in the input data frame to produce a single statistic. It is also possible to calculate multiple statistics in a single execution of these functions if the input data frame is grouped - for example by indicator ID, geographic area or time period (or all three). The output contains only the grouping variables and the values calculated by the function - any additional unused columns provided in the input data frame will not be retained in the output.
The df test data generated earlier can be used to demonstrate phe_mean:
INPUT: The phe_mean function take a single data frame as input with a column representing the numbers to be averaged.
OUTPUT: By default, the function outputs one row per grouping set containing the grouping variable values (if applicable), the mean, the lower 95% confidence limit, the upper 95% confidence limit, the confidence level, the statistic name and the method.
OPTIONS: The function also accepts additional arguments to specify the level of confidence and a reduced level of detail to be output.
Here are some example code chunks to demonstrate the phe_mean function and the arguments that can optionally be specified
# default mean
phe_mean(df,obs)
#> value_sum value_count stdev value lowercl uppercl confidence statistic
#> 1 409 8 27.58073 51.125 28.06694 74.18306 95% mean
#> method
#> 1 Student's t-distribution
# multiple means in a single execution with 99.8% confidence
df %>%
group_by(year) %>%
phe_mean(obs, confidence = 0.998)
#> # A tibble: 2 × 10
#> # Groups: year [2]
#> year value_sum value_count stdev value lowercl uppercl confidence statistic
#> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 2015 208 4 34.7 52 -125. 229. 99.8% mean
#> 2 2016 201 4 23.9 50.2 -71.9 172. 99.8% mean
#> # ℹ 1 more variable: method <chr>
# multiple means in a single execution with 99.8% confidence and data-only output
df %>%
group_by(year) %>%
phe_mean(obs, type = "standard", confidence = 0.998)
#> # A tibble: 2 × 7
#> # Groups: year [2]
#> year value_sum value_count stdev value lowercl uppercl
#> <int> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 2015 208 4 34.7 52 -125. 229.
#> 2 2016 201 4 23.9 50.2 -71.9 172.The following code chunk creates a data frame containing observed number of events and populations by age band for 4 areas, 5 time periods and 2 sexes:
df_std <- data.frame(
area = rep(c("Area1", "Area2", "Area3", "Area4"), each = 19 * 2 * 5),
year = rep(2006:2010, each = 19 * 2),
sex = rep(rep(c("Male", "Female"), each = 19), 5),
ageband = rep(c(0, 5,10,15,20,25,30,35,40,45,
50,55,60,65,70,75,80,85,90), times = 10),
obs = sample(200, 19 * 2 * 5 * 4, replace = TRUE),
pop = sample(10000:20000, 19 * 2 * 5 * 4, replace = TRUE))
head(df_std)
#> area year sex ageband obs pop
#> 1 Area1 2006 Male 0 146 18698
#> 2 Area1 2006 Male 5 84 16357
#> 3 Area1 2006 Male 10 107 16938
#> 4 Area1 2006 Male 15 77 16600
#> 5 Area1 2006 Male 20 36 11298
#> 6 Area1 2006 Male 25 188 10454INPUT: The minimum input requirement for the
calculate_dsr function is a single data frame with columns representing
the numerators and denominators and standard populations for each
standardisation category. The standard populations must be appended to
the input data frame by the user prior to execution of the function. The
2013 European Standard Population is provided within the package in
vector form (esp2013), which you can join to your dataset.
Alternative standard populations can be used but must be provided by the
user.
OUTPUT: By default, the function outputs one row per grouping set containing the grouping variable values, the total count, the total population, the dsr, the lower 95% confidence limit, the upper 95% confidence limit, the confidence level, the statistic name and the method.
OPTIONS: The function also accepts additional arguments to specify the level of confidence, the multiplier and a reduced level of detail to be output. It is also possible to calculate CIs when we can’t assume events are independent - further details can be found in the DSR vignette.
Here are some example code chunks to demonstrate the calculate_dsr function and the arguments that can optionally be specified
# Append the standard populations to the data frame
# calculate separate dsrs for each area, year and sex
df_std %>%
mutate(refpop = rep(esp2013, 40)) %>%
group_by(area, year, sex) %>%
calculate_dsr(obs,pop, stdpop = refpop)
#> # A tibble: 40 × 11
#> area year sex total_count total_pop value lowercl uppercl confidence
#> <chr> <int> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
#> 1 Area1 2006 Female 1593 293006 575. 546. 606. 95%
#> 2 Area1 2006 Male 1917 268666 717. 683. 752. 95%
#> 3 Area1 2007 Female 2228 240152 1009. 965. 1054. 95%
#> 4 Area1 2007 Male 1643 266019 611. 579. 645. 95%
#> 5 Area1 2008 Female 1744 303557 636. 605. 668. 95%
#> 6 Area1 2008 Male 1782 279438 651. 619. 684. 95%
#> 7 Area1 2009 Female 1944 293929 651. 620. 683. 95%
#> 8 Area1 2009 Male 1687 292927 607. 576. 639. 95%
#> 9 Area1 2010 Female 1897 280573 651. 619. 684. 95%
#> 10 Area1 2010 Male 1743 297686 587. 557. 618. 95%
#> # ℹ 30 more rows
#> # ℹ 2 more variables: statistic <chr>, method <chr>
# Append the standard populations to the data frame
# calculate separate dsrs for each area, year and sex and drop metadata fields from output
df_std %>%
mutate(refpop = rep(esp2013, 40)) %>%
group_by(area, year, sex) %>%
calculate_dsr(obs,pop, stdpop = refpop, type = "standard")
#> # A tibble: 40 × 8
#> area year sex total_count total_pop value lowercl uppercl
#> <chr> <int> <chr> <int> <int> <dbl> <dbl> <dbl>
#> 1 Area1 2006 Female 1593 293006 575. 546. 606.
#> 2 Area1 2006 Male 1917 268666 717. 683. 752.
#> 3 Area1 2007 Female 2228 240152 1009. 965. 1054.
#> 4 Area1 2007 Male 1643 266019 611. 579. 645.
#> 5 Area1 2008 Female 1744 303557 636. 605. 668.
#> 6 Area1 2008 Male 1782 279438 651. 619. 684.
#> 7 Area1 2009 Female 1944 293929 651. 620. 683.
#> 8 Area1 2009 Male 1687 292927 607. 576. 639.
#> 9 Area1 2010 Female 1897 280573 651. 619. 684.
#> 10 Area1 2010 Male 1743 297686 587. 557. 618.
#> # ℹ 30 more rows
# calculate for under 75s by filtering out records for 75+ from input data frame and standard population
df_std %>%
filter(ageband <= 70) %>%
mutate(refpop = rep(esp2013[1:15], 40)) %>%
group_by(area, year, sex) %>%
calculate_dsr(obs, pop, stdpop = refpop)
#> # A tibble: 40 × 11
#> area year sex total_count total_pop value lowercl uppercl confidence
#> <chr> <int> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
#> 1 Area1 2006 Female 1298 238633 580. 549. 613. 95%
#> 2 Area1 2006 Male 1532 216555 717. 681. 755. 95%
#> 3 Area1 2007 Female 1907 195161 1042. 994. 1092. 95%
#> 4 Area1 2007 Male 1268 208013 615. 580. 650. 95%
#> 5 Area1 2008 Female 1516 236978 660. 627. 694. 95%
#> 6 Area1 2008 Male 1452 219591 665. 631. 701. 95%
#> 7 Area1 2009 Female 1473 231710 638. 605. 673. 95%
#> 8 Area1 2009 Male 1233 227568 587. 554. 621. 95%
#> 9 Area1 2010 Female 1280 229185 588. 555. 623. 95%
#> 10 Area1 2010 Male 1356 231197 598. 566. 631. 95%
#> # ℹ 30 more rows
#> # ℹ 2 more variables: statistic <chr>, method <chr>INPUT: These functions take a single data frame as input, with columns representing the numerators and denominators for each standardisation category, plus reference numerators and denominators for each standardisation category.
The reference data can either be provided in a separate data frame/vectors or as columns within the input data frame:
reference data provided as a data frame or as vectors - the data frame/vectors and the input data frame must both contain rows for the same standardisation categories, and both must be sorted, within each grouping set, by these standardisation categories in the same order.
reference data provided as columns within the input data frame - the reference numerators and denominators can be appended to the input data frame prior to execution of the function - if the data is grouped to generate multiple indirectly standardised rates or ratios then the reference data will need to be repeated and appended to the data rows for every grouping set.
OUTPUT: By default, the functions output one row per grouping set containing the grouping variable values, the observed and expected counts, the reference rate (ISRate only), the indirectly standardised rate or ratio, the lower 95% confidence limit, and the upper 95% confidence limit, the confidence level, the statistic name and the method.
OPTIONS: If reference data are being provided as columns within the input data frame then the user must specify this as the function expects vectors by default. The function also accepts additional arguments to specify the level of confidence, the multiplier and a reduced level of detail to be output.
The following code chunk creates a data frame containing the reference data - this example uses the all area data for persons in the baseline year:
df_ref <- df_std %>%
filter(year == 2006) %>%
group_by(ageband) %>%
summarise(obs = sum(obs),
pop = sum(pop),
.groups = "drop_last")
head(df_ref)
#> # A tibble: 6 × 3
#> ageband obs pop
#> <dbl> <int> <int>
#> 1 0 1159 125028
#> 2 5 897 137404
#> 3 10 700 111525
#> 4 15 711 109126
#> 5 20 632 106639
#> 6 25 972 110482Here are some example code chunks to demonstrate the calculate_ISRatio function and the arguments that can optionally be specified
# calculate separate smrs for each area, year and sex
# standardised against the all-year, all-sex, all-area reference data
df_std %>%
group_by(area, year, sex) %>%
calculate_ISRatio(obs, pop, df_ref$obs, df_ref$pop)
#> # A tibble: 40 × 11
#> # Groups: area, year, sex [40]
#> area year sex observed expected value lowercl uppercl confidence
#> <chr> <int> <chr> <int> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 Area1 2006 Female 1593 1910. 0.834 0.794 0.876 95%
#> 2 Area1 2006 Male 1917 1775. 1.08 1.03 1.13 95%
#> 3 Area1 2007 Female 2228 1602. 1.39 1.33 1.45 95%
#> 4 Area1 2007 Male 1643 1735. 0.947 0.902 0.994 95%
#> 5 Area1 2008 Female 1744 1979. 0.881 0.840 0.924 95%
#> 6 Area1 2008 Male 1782 1848. 0.964 0.920 1.01 95%
#> 7 Area1 2009 Female 1944 1930. 1.01 0.963 1.05 95%
#> 8 Area1 2009 Male 1687 1910. 0.883 0.842 0.927 95%
#> 9 Area1 2010 Female 1897 1818. 1.04 0.997 1.09 95%
#> 10 Area1 2010 Male 1743 1963. 0.888 0.847 0.931 95%
#> # ℹ 30 more rows
#> # ℹ 2 more variables: statistic <chr>, method <chr>
# calculate the same smrs by appending the reference data to the data frame
# and drop metadata columns from output
df_std %>%
mutate(refobs = rep(df_ref$obs,40),
refpop = rep(df_ref$pop,40)) %>%
group_by(area, year, sex) %>%
calculate_ISRatio(obs, pop, refobs, refpop, refpoptype = "field",
type = "standard")
#> # A tibble: 40 × 8
#> # Groups: area, year, sex [40]
#> area year sex observed expected value lowercl uppercl
#> <chr> <int> <chr> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 Area1 2006 Female 1593 1910. 0.834 0.794 0.876
#> 2 Area1 2006 Male 1917 1775. 1.08 1.03 1.13
#> 3 Area1 2007 Female 2228 1602. 1.39 1.33 1.45
#> 4 Area1 2007 Male 1643 1735. 0.947 0.902 0.994
#> 5 Area1 2008 Female 1744 1979. 0.881 0.840 0.924
#> 6 Area1 2008 Male 1782 1848. 0.964 0.920 1.01
#> 7 Area1 2009 Female 1944 1930. 1.01 0.963 1.05
#> 8 Area1 2009 Male 1687 1910. 0.883 0.842 0.927
#> 9 Area1 2010 Female 1897 1818. 1.04 0.997 1.09
#> 10 Area1 2010 Male 1743 1963. 0.888 0.847 0.931
#> # ℹ 30 more rowsThe calculate_ISRate function works exactly the same way but instead of expressing the result as a ratio of the observed and expected rates the result is expressed as a rate and the reference rate is also provided. Here are some examples:
# calculate separate indirectly standardised rates for each area, year and sex
# standardised against the all-year, all-sex, all-area reference data
df_std %>%
group_by(area, year, sex) %>%
calculate_ISRate(obs, pop, df_ref$obs, df_ref$pop)
#> # A tibble: 40 × 12
#> # Groups: area, year, sex [40]
#> area year sex observed expected ref_rate value lowercl uppercl confidence
#> <chr> <int> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 Area1 2006 Fema… 1593 1910. 655. 546. 520. 574. 95%
#> 2 Area1 2006 Male 1917 1775. 655. 708. 676. 740. 95%
#> 3 Area1 2007 Fema… 2228 1602. 655. 911. 873. 950. 95%
#> 4 Area1 2007 Male 1643 1735. 655. 620. 591. 651. 95%
#> 5 Area1 2008 Fema… 1744 1979. 655. 577. 551. 605. 95%
#> 6 Area1 2008 Male 1782 1848. 655. 632. 603. 662. 95%
#> 7 Area1 2009 Fema… 1944 1930. 655. 660. 631. 690. 95%
#> 8 Area1 2009 Male 1687 1910. 655. 579. 551. 607. 95%
#> 9 Area1 2010 Fema… 1897 1818. 655. 683. 653. 715. 95%
#> 10 Area1 2010 Male 1743 1963. 655. 582. 555. 610. 95%
#> # ℹ 30 more rows
#> # ℹ 2 more variables: statistic <chr>, method <chr>
# calculate the same indirectly standardised rates by appending the reference data to the data frame
# and drop metadata columns from output
df_std %>%
mutate(refobs = rep(df_ref$obs,40),
refpop = rep(df_ref$pop,40)) %>%
group_by(area, year, sex) %>%
calculate_ISRate(obs, pop, refobs, refpop, refpoptype = "field",
type = "standard")
#> # A tibble: 40 × 9
#> # Groups: area, year, sex [40]
#> area year sex observed expected ref_rate value lowercl uppercl
#> <chr> <int> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Area1 2006 Female 1593 1910. 655. 546. 520. 574.
#> 2 Area1 2006 Male 1917 1775. 655. 708. 676. 740.
#> 3 Area1 2007 Female 2228 1602. 655. 911. 873. 950.
#> 4 Area1 2007 Male 1643 1735. 655. 620. 591. 651.
#> 5 Area1 2008 Female 1744 1979. 655. 577. 551. 605.
#> 6 Area1 2008 Male 1782 1848. 655. 632. 603. 662.
#> 7 Area1 2009 Female 1944 1930. 655. 660. 631. 690.
#> 8 Area1 2009 Male 1687 1910. 655. 579. 551. 607.
#> 9 Area1 2010 Female 1897 1818. 655. 683. 653. 715.
#> 10 Area1 2010 Male 1743 1963. 655. 582. 555. 610.
#> # ℹ 30 more rows