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.
library(PHEindicatormethods)
library(dplyr)This vignette covers the following functions available within the first release of the package (v1.0.8) but has been updated to apply to these functions in their latest release versions. If further functions are added to the package in future releases these will be explained elsewhere.
| 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 |
| phe_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 |
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 37 104
#> 2 Area2 2015 96 149
#> 3 Area3 2015 10 161
#> 4 Area4 2015 36 184
#> 5 Area1 2016 19 152
#> 6 Area2 2016 51 187
#> 7 Area3 2016 21 189
#> 8 Area4 2016 81 136INPUT: 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 statistic
#> 1 Area1 2015 37 104 0.3557692 0.27040433 0.4514095 95% proportion of 1
#> 2 Area2 2015 96 149 0.6442953 0.56468675 0.7166505 95% proportion of 1
#> 3 Area3 2015 10 161 0.0621118 0.03408441 0.1105482 95% proportion of 1
#> 4 Area4 2015 36 184 0.1956522 0.14480534 0.2589472 95% proportion of 1
#> 5 Area1 2016 19 152 0.1250000 0.08150359 0.1869837 95% proportion of 1
#> 6 Area2 2016 51 187 0.2727273 0.21395011 0.3406540 95% proportion of 1
#> 7 Area3 2016 21 189 0.1111111 0.07383069 0.1638851 95% proportion of 1
#> 8 Area4 2016 81 136 0.5955882 0.51157800 0.6743468 95% proportion of 1
#> method
#> 1 Wilson
#> 2 Wilson
#> 3 Wilson
#> 4 Wilson
#> 5 Wilson
#> 6 Wilson
#> 7 Wilson
#> 8 Wilson
# specify confidence level for proportion
phe_proportion(df, obs, pop, confidence=99.8)
#> area year obs pop value lowercl uppercl confidence statistic
#> 1 Area1 2015 37 104 0.3557692 0.22853378 0.5072643 99.8% proportion of 1
#> 2 Area2 2015 96 149 0.6442953 0.51779464 0.7534140 99.8% proportion of 1
#> 3 Area3 2015 10 161 0.0621118 0.02447767 0.1487830 99.8% proportion of 1
#> 4 Area4 2015 36 184 0.1956522 0.12128114 0.3000556 99.8% proportion of 1
#> 5 Area1 2016 19 152 0.1250000 0.06375981 0.2305743 99.8% proportion of 1
#> 6 Area2 2016 51 187 0.2727273 0.18498287 0.3825562 99.8% proportion of 1
#> 7 Area3 2016 21 189 0.1111111 0.05840020 0.2012304 99.8% proportion of 1
#> 8 Area4 2016 81 136 0.5955882 0.46345009 0.7151833 99.8% proportion of 1
#> method
#> 1 Wilson
#> 2 Wilson
#> 3 Wilson
#> 4 Wilson
#> 5 Wilson
#> 6 Wilson
#> 7 Wilson
#> 8 Wilson
# specify to output proportions as percentages
phe_proportion(df, obs, pop, multiplier=100)
#> area year obs pop value lowercl uppercl confidence statistic method
#> 1 Area1 2015 37 104 35.57692 27.040433 45.14095 95% percentage Wilson
#> 2 Area2 2015 96 149 64.42953 56.468675 71.66505 95% percentage Wilson
#> 3 Area3 2015 10 161 6.21118 3.408441 11.05482 95% percentage Wilson
#> 4 Area4 2015 36 184 19.56522 14.480534 25.89472 95% percentage Wilson
#> 5 Area1 2016 19 152 12.50000 8.150359 18.69837 95% percentage Wilson
#> 6 Area2 2016 51 187 27.27273 21.395011 34.06540 95% percentage Wilson
#> 7 Area3 2016 21 189 11.11111 7.383069 16.38851 95% percentage Wilson
#> 8 Area4 2016 81 136 59.55882 51.157800 67.43468 95% percentage Wilson
# specify level of detail to output for proportion
phe_proportion(df, obs, pop, confidence=99.8, multiplier=100)
#> area year obs pop value lowercl uppercl confidence statistic method
#> 1 Area1 2015 37 104 35.57692 22.853378 50.72643 99.8% percentage Wilson
#> 2 Area2 2015 96 149 64.42953 51.779464 75.34140 99.8% percentage Wilson
#> 3 Area3 2015 10 161 6.21118 2.447767 14.87830 99.8% percentage Wilson
#> 4 Area4 2015 36 184 19.56522 12.128114 30.00556 99.8% percentage Wilson
#> 5 Area1 2016 19 152 12.50000 6.375981 23.05743 99.8% percentage Wilson
#> 6 Area2 2016 51 187 27.27273 18.498287 38.25562 99.8% percentage Wilson
#> 7 Area3 2016 21 189 11.11111 5.840020 20.12304 99.8% percentage Wilson
#> 8 Area4 2016 81 136 59.55882 46.345009 71.51833 99.8% percentage Wilson
# specify level of detail to output for proportion 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 37 104 35.57692 22.853378 50.72643
#> 2 Area2 2015 96 149 64.42953 51.779464 75.34140
#> 3 Area3 2015 10 161 6.21118 2.447767 14.87830
#> 4 Area4 2015 36 184 19.56522 12.128114 30.00556
#> 5 Area1 2016 19 152 12.50000 6.375981 23.05743
#> 6 Area2 2016 51 187 27.27273 18.498287 38.25562
#> 7 Area3 2016 21 189 11.11111 5.840020 20.12304
#> 8 Area4 2016 81 136 59.55882 46.345009 71.51833
# default rate
phe_rate(df, obs, pop)
#> area year obs pop value lowercl uppercl confidence statistic
#> 1 Area1 2015 37 104 35576.92 25046.120 49039.52 95% rate per 100000
#> 2 Area2 2015 96 149 64429.53 52186.830 78680.26 95% rate per 100000
#> 3 Area3 2015 10 161 6211.18 2973.571 11423.27 95% rate per 100000
#> 4 Area4 2015 36 184 19565.22 13701.330 27087.31 95% rate per 100000
#> 5 Area1 2016 19 152 12500.00 7522.356 19521.28 95% rate per 100000
#> 6 Area2 2016 51 187 27272.73 20304.824 35859.34 95% rate per 100000
#> 7 Area3 2016 21 189 11111.11 6875.342 16985.31 95% rate per 100000
#> 8 Area4 2016 81 136 59558.82 47296.731 74027.08 95% rate per 100000
#> method
#> 1 Byars
#> 2 Byars
#> 3 Byars
#> 4 Byars
#> 5 Byars
#> 6 Byars
#> 7 Byars
#> 8 Byars
# specify rate parameters
phe_rate(df, obs, pop, confidence=99.8, multiplier=100)
#> area year obs pop value lowercl uppercl confidence statistic method
#> 1 Area1 2015 37 104 35.57692 20.170365 57.65101 99.8% rate per 100 Byars
#> 2 Area2 2015 96 149 64.42953 45.991324 87.46519 99.8% rate per 100 Byars
#> 3 Area3 2015 10 161 6.21118 1.811378 15.02716 99.8% rate per 100 Byars
#> 4 Area4 2015 36 184 19.56522 10.995526 31.90510 99.8% rate per 100 Byars
#> 5 Area1 2016 19 152 12.50000 5.440463 24.17448 99.8% rate per 100 Byars
#> 6 Area2 2016 51 187 27.27273 16.961482 41.27496 99.8% rate per 100 Byars
#> 7 Area3 2016 21 189 11.11111 5.071160 20.85551 99.8% rate per 100 Byars
#> 8 Area4 2016 81 136 59.55882 41.168127 82.99569 99.8% rate per 100 Byars
# specify rate parameters and reduce columns output 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 37 104 35.57692 20.170365 57.65101
#> 2 Area2 2015 96 149 64.42953 45.991324 87.46519
#> 3 Area3 2015 10 161 6.21118 1.811378 15.02716
#> 4 Area4 2015 36 184 19.56522 10.995526 31.90510
#> 5 Area1 2016 19 152 12.50000 5.440463 24.17448
#> 6 Area2 2016 51 187 27.27273 16.961482 41.27496
#> 7 Area3 2016 21 189 11.11111 5.071160 20.85551
#> 8 Area4 2016 81 136 59.55882 41.168127 82.99569These 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 179 598 0.299 0.264 0.337 95% proportion of 1 Wilson
#> 2 2016 172 664 0.259 0.227 0.294 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 179 598 29933. 25708. 34654. 95% rate per 100000 Byars
#> 2 2016 172 664 25904. 22177. 30077. 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
#> 1 351 8 30.57748 43.875 18.31159 69.43841 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 confi…¹ stati…² method
#> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 2015 179 4 36.4 44.8 -141. 231. 99.8% mean Stude…
#> 2 2016 172 4 29.3 43 -106. 192. 99.8% mean Stude…
#> # … with abbreviated variable names ¹confidence, ²statistic
# 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 179 4 36.4 44.8 -141. 231.
#> 2 2016 172 4 29.3 43 -106. 192.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 0 117 13514
#> 2 Area1 2006 Male 5 168 18639
#> 3 Area1 2006 Male 10 107 12462
#> 4 Area1 2006 Male 15 21 14494
#> 5 Area1 2006 Male 20 168 11097
#> 6 Area1 2006 Male 25 57 15297INPUT: The minimum input requirement for the phe_dsr function is a single data frame with columns representing the numerators and denominators for each standardisation category. This is sufficient if the data is:
The 2013 European Standard Population is provided within the package in vector form (esp2013) and is used by default by this function. Alternative standard populations can be used but must be provided by the user. When the function joins a standard population vector to the input data frame it does this by position so it is important that the data is sorted accordingly. This is a user responsibility.
The function can also accept standard populations provided as a column within the input data frame.
standard populations provided as a vector - the vector 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
standard populations provided as a column within the input data frame - the standard populations can be appended to the input data frame by the user prior to execution of the function - if the data is grouped to generate multiple dsrs then the standard populations will need to be repeated and appended to the data rows for every grouping set.
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: If standard populations are being provided as a column within the input data frame then the user must specify this using the stdpoptype argument as the function expects a vector by default. The function also accepts additional arguments to specify the standard populations, the level of confidence, the multiplier and a reduced level of detail to be output.
Here are some example code chunks to demonstrate the phe_dsr function and the arguments that can optionally be specified
# calculate separate dsrs for each area, year and sex
df_std %>%
group_by(area, year, sex) %>%
phe_dsr(obs, pop)
#> # A tibble: 40 × 11
#> # Groups: area, year, sex [40]
#> area year sex total_count total_…¹ value lowercl uppercl confi…² stati…³
#> <chr> <int> <chr> <int> <int> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 Area1 2006 Female 1482 285827 515. 487. 544. 95% dsr pe…
#> 2 Area1 2006 Male 2022 269373 734. 699. 770. 95% dsr pe…
#> 3 Area1 2007 Female 1980 276436 774. 737. 812. 95% dsr pe…
#> 4 Area1 2007 Male 1785 282487 641. 609. 674. 95% dsr pe…
#> 5 Area1 2008 Female 1752 291891 632. 601. 664. 95% dsr pe…
#> 6 Area1 2008 Male 2482 279738 898. 859. 938. 95% dsr pe…
#> 7 Area1 2009 Female 2033 305778 707. 674. 741. 95% dsr pe…
#> 8 Area1 2009 Male 1765 288388 613. 582. 645. 95% dsr pe…
#> 9 Area1 2010 Female 1935 285218 670. 638. 703. 95% dsr pe…
#> 10 Area1 2010 Male 1851 259257 766. 728. 805. 95% dsr pe…
#> # … with 30 more rows, 1 more variable: method <chr>, and abbreviated variable
#> # names ¹total_pop, ²confidence, ³statistic
# calculate separate dsrs for each area, year and sex and drop metadata fields from output
df_std %>%
group_by(area, year, sex) %>%
phe_dsr(obs, pop, type="standard")
#> # A tibble: 40 × 8
#> # Groups: area, year, sex [40]
#> area year sex total_count total_pop value lowercl uppercl
#> <chr> <int> <chr> <int> <int> <dbl> <dbl> <dbl>
#> 1 Area1 2006 Female 1482 285827 515. 487. 544.
#> 2 Area1 2006 Male 2022 269373 734. 699. 770.
#> 3 Area1 2007 Female 1980 276436 774. 737. 812.
#> 4 Area1 2007 Male 1785 282487 641. 609. 674.
#> 5 Area1 2008 Female 1752 291891 632. 601. 664.
#> 6 Area1 2008 Male 2482 279738 898. 859. 938.
#> 7 Area1 2009 Female 2033 305778 707. 674. 741.
#> 8 Area1 2009 Male 1765 288388 613. 582. 645.
#> 9 Area1 2010 Female 1935 285218 670. 638. 703.
#> 10 Area1 2010 Male 1851 259257 766. 728. 805.
#> # … with 30 more rows
# calculate same specifying standard population in vector form
df_std %>%
group_by(area, year, sex) %>%
phe_dsr(obs, pop, stdpop = esp2013)
#> # A tibble: 40 × 11
#> # Groups: area, year, sex [40]
#> area year sex total_count total_…¹ value lowercl uppercl confi…² stati…³
#> <chr> <int> <chr> <int> <int> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 Area1 2006 Female 1482 285827 515. 487. 544. 95% dsr pe…
#> 2 Area1 2006 Male 2022 269373 734. 699. 770. 95% dsr pe…
#> 3 Area1 2007 Female 1980 276436 774. 737. 812. 95% dsr pe…
#> 4 Area1 2007 Male 1785 282487 641. 609. 674. 95% dsr pe…
#> 5 Area1 2008 Female 1752 291891 632. 601. 664. 95% dsr pe…
#> 6 Area1 2008 Male 2482 279738 898. 859. 938. 95% dsr pe…
#> 7 Area1 2009 Female 2033 305778 707. 674. 741. 95% dsr pe…
#> 8 Area1 2009 Male 1765 288388 613. 582. 645. 95% dsr pe…
#> 9 Area1 2010 Female 1935 285218 670. 638. 703. 95% dsr pe…
#> 10 Area1 2010 Male 1851 259257 766. 728. 805. 95% dsr pe…
#> # … with 30 more rows, 1 more variable: method <chr>, and abbreviated variable
#> # names ¹total_pop, ²confidence, ³statistic
# calculate the same dsrs by appending the standard populations to the data frame
df_std %>%
mutate(refpop = rep(esp2013,40)) %>%
group_by(area, year, sex) %>%
phe_dsr(obs,pop, stdpop=refpop, stdpoptype="field")
#> # A tibble: 40 × 11
#> # Groups: area, year, sex [40]
#> area year sex total_count total_…¹ value lowercl uppercl confi…² stati…³
#> <chr> <int> <chr> <int> <int> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 Area1 2006 Female 1482 285827 515. 487. 544. 95% dsr pe…
#> 2 Area1 2006 Male 2022 269373 734. 699. 770. 95% dsr pe…
#> 3 Area1 2007 Female 1980 276436 774. 737. 812. 95% dsr pe…
#> 4 Area1 2007 Male 1785 282487 641. 609. 674. 95% dsr pe…
#> 5 Area1 2008 Female 1752 291891 632. 601. 664. 95% dsr pe…
#> 6 Area1 2008 Male 2482 279738 898. 859. 938. 95% dsr pe…
#> 7 Area1 2009 Female 2033 305778 707. 674. 741. 95% dsr pe…
#> 8 Area1 2009 Male 1765 288388 613. 582. 645. 95% dsr pe…
#> 9 Area1 2010 Female 1935 285218 670. 638. 703. 95% dsr pe…
#> 10 Area1 2010 Male 1851 259257 766. 728. 805. 95% dsr pe…
#> # … with 30 more rows, 1 more variable: method <chr>, and abbreviated variable
#> # names ¹total_pop, ²confidence, ³statistic
# calculate for under 75s by filtering out records for 75+ from input data frame and standard population
df_std %>%
filter(ageband <= 70) %>%
group_by(area, year, sex) %>%
phe_dsr(obs, pop, stdpop = esp2013[1:15])
#> # A tibble: 40 × 11
#> # Groups: area, year, sex [40]
#> area year sex total_count total_…¹ value lowercl uppercl confi…² stati…³
#> <chr> <int> <chr> <int> <int> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 Area1 2006 Female 1189 234243 523. 493. 554. 95% dsr pe…
#> 2 Area1 2006 Male 1451 210700 719. 681. 758. 95% dsr pe…
#> 3 Area1 2007 Female 1422 221255 736. 697. 777. 95% dsr pe…
#> 4 Area1 2007 Male 1233 225911 573. 541. 607. 95% dsr pe…
#> 5 Area1 2008 Female 1433 238125 638. 604. 672. 95% dsr pe…
#> 6 Area1 2008 Male 1822 215570 886. 844. 930. 95% dsr pe…
#> 7 Area1 2009 Female 1662 236502 716. 681. 753. 95% dsr pe…
#> 8 Area1 2009 Male 1240 225003 588. 555. 622. 95% dsr pe…
#> 9 Area1 2010 Female 1522 222696 691. 656. 727. 95% dsr pe…
#> 10 Area1 2010 Male 1410 188156 773. 732. 815. 95% dsr pe…
#> # … with 30 more rows, 1 more variable: method <chr>, and abbreviated variable
#> # names ¹total_pop, ²confidence, ³statistic
# calculate separate dsrs for persons for each area and year)
df_std %>%
group_by(area, year, ageband) %>%
summarise(obs = sum(obs),
pop = sum(pop),
.groups = "drop_last") %>%
phe_dsr(obs,pop)
#> # A tibble: 20 × 10
#> # Groups: area, year [20]
#> area year total_count total_…¹ value lowercl uppercl confi…² stati…³ method
#> <chr> <int> <int> <int> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 Area1 2006 3504 555200 626. 604. 649. 95% dsr pe… Dobson
#> 2 Area1 2007 3765 558923 678. 655. 702. 95% dsr pe… Dobson
#> 3 Area1 2008 4234 571629 737. 714. 761. 95% dsr pe… Dobson
#> 4 Area1 2009 3798 594166 645. 623. 667. 95% dsr pe… Dobson
#> 5 Area1 2010 3786 544475 701. 677. 726. 95% dsr pe… Dobson
#> 6 Area2 2006 3784 584731 667. 645. 690. 95% dsr pe… Dobson
#> 7 Area2 2007 4203 557600 755. 731. 779. 95% dsr pe… Dobson
#> 8 Area2 2008 3562 602957 637. 615. 660. 95% dsr pe… Dobson
#> 9 Area2 2009 3488 571701 622. 601. 645. 95% dsr pe… Dobson
#> 10 Area2 2010 3754 560347 692. 668. 715. 95% dsr pe… Dobson
#> 11 Area3 2006 3948 565913 712. 689. 736. 95% dsr pe… Dobson
#> 12 Area3 2007 4088 573314 683. 661. 706. 95% dsr pe… Dobson
#> 13 Area3 2008 3835 556410 736. 712. 761. 95% dsr pe… Dobson
#> 14 Area3 2009 3240 590352 575. 555. 596. 95% dsr pe… Dobson
#> 15 Area3 2010 2886 584177 496. 476. 515. 95% dsr pe… Dobson
#> 16 Area4 2006 3926 607962 689. 666. 713. 95% dsr pe… Dobson
#> 17 Area4 2007 3990 582280 709. 686. 733. 95% dsr pe… Dobson
#> 18 Area4 2008 3659 572429 618. 597. 640. 95% dsr pe… Dobson
#> 19 Area4 2009 3619 539841 680. 657. 704. 95% dsr pe… Dobson
#> 20 Area4 2010 3565 569012 618. 596. 640. 95% dsr pe… Dobson
#> # … with abbreviated variable names ¹total_pop, ²confidence, ³statisticINPUT: Unlike the phe_dsr function, there is no default standard or reference data for the calculate_ISRatio and calculate_ISRate functions. 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 0 749 132885
#> 2 5 757 115939
#> 3 10 841 127102
#> 4 15 826 131611
#> 5 20 1056 117404
#> 6 25 861 122266Here 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 stati…¹
#> <chr> <int> <chr> <int> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 Area1 2006 Female 1482 1845. 0.803 0.763 0.845 95% indire…
#> 2 Area1 2006 Male 2022 1766. 1.14 1.10 1.20 95% indire…
#> 3 Area1 2007 Female 1980 1816. 1.09 1.04 1.14 95% indire…
#> 4 Area1 2007 Male 1785 1860. 0.960 0.916 1.01 95% indire…
#> 5 Area1 2008 Female 1752 1912. 0.916 0.874 0.960 95% indire…
#> 6 Area1 2008 Male 2482 1831. 1.36 1.30 1.41 95% indire…
#> 7 Area1 2009 Female 2033 2021. 1.01 0.963 1.05 95% indire…
#> 8 Area1 2009 Male 1765 1892. 0.933 0.890 0.978 95% indire…
#> 9 Area1 2010 Female 1935 1882. 1.03 0.983 1.08 95% indire…
#> 10 Area1 2010 Male 1851 1674. 1.11 1.06 1.16 95% indire…
#> # … with 30 more rows, 1 more variable: method <chr>, and abbreviated variable
#> # name ¹statistic
# 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 1482 1845. 0.803 0.763 0.845
#> 2 Area1 2006 Male 2022 1766. 1.14 1.10 1.20
#> 3 Area1 2007 Female 1980 1816. 1.09 1.04 1.14
#> 4 Area1 2007 Male 1785 1860. 0.960 0.916 1.01
#> 5 Area1 2008 Female 1752 1912. 0.916 0.874 0.960
#> 6 Area1 2008 Male 2482 1831. 1.36 1.30 1.41
#> 7 Area1 2009 Female 2033 2021. 1.01 0.963 1.05
#> 8 Area1 2009 Male 1765 1892. 0.933 0.890 0.978
#> 9 Area1 2010 Female 1935 1882. 1.03 0.983 1.08
#> 10 Area1 2010 Male 1851 1674. 1.11 1.06 1.16
#> # … with 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 confide…¹
#> <chr> <int> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 Area1 2006 Female 1482 1845. 655. 526. 500. 554. 95%
#> 2 Area1 2006 Male 2022 1766. 655. 750. 718. 784. 95%
#> 3 Area1 2007 Female 1980 1816. 655. 715. 683. 747. 95%
#> 4 Area1 2007 Male 1785 1860. 655. 629. 600. 659. 95%
#> 5 Area1 2008 Female 1752 1912. 655. 600. 573. 629. 95%
#> 6 Area1 2008 Male 2482 1831. 655. 888. 853. 924. 95%
#> 7 Area1 2009 Female 2033 2021. 655. 659. 631. 689. 95%
#> 8 Area1 2009 Male 1765 1892. 655. 611. 583. 641. 95%
#> 9 Area1 2010 Female 1935 1882. 655. 674. 644. 704. 95%
#> 10 Area1 2010 Male 1851 1674. 655. 724. 692. 758. 95%
#> # … with 30 more rows, 2 more variables: statistic <chr>, method <chr>, and
#> # abbreviated variable name ¹confidence
# 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 1482 1845. 655. 526. 500. 554.
#> 2 Area1 2006 Male 2022 1766. 655. 750. 718. 784.
#> 3 Area1 2007 Female 1980 1816. 655. 715. 683. 747.
#> 4 Area1 2007 Male 1785 1860. 655. 629. 600. 659.
#> 5 Area1 2008 Female 1752 1912. 655. 600. 573. 629.
#> 6 Area1 2008 Male 2482 1831. 655. 888. 853. 924.
#> 7 Area1 2009 Female 2033 2021. 655. 659. 631. 689.
#> 8 Area1 2009 Male 1765 1892. 655. 611. 583. 641.
#> 9 Area1 2010 Female 1935 1882. 655. 674. 644. 704.
#> 10 Area1 2010 Male 1851 1674. 655. 724. 692. 758.
#> # … with 30 more rows