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 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 |
| phe_smr | Aggregate, standardised | Performs a calculation on each grouping set and requires additional reference inputs |
| phe_isr | 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 92 168
#> 2 Area2 2015 28 137
#> 3 Area3 2015 44 182
#> 4 Area4 2015 97 110
#> 5 Area1 2016 59 127
#> 6 Area2 2016 23 172
#> 7 Area3 2016 80 135
#> 8 Area4 2016 26 103INPUT: 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 92 168 0.5476190 0.47212935 0.6209797 95% proportion of 1
#> 2 Area2 2015 28 137 0.2043796 0.14535931 0.2795259 95% proportion of 1
#> 3 Area3 2015 44 182 0.2417582 0.18530912 0.3088834 95% proportion of 1
#> 4 Area4 2015 97 110 0.8818182 0.80824955 0.9296187 95% proportion of 1
#> 5 Area1 2016 59 127 0.4645669 0.38014301 0.5510714 95% proportion of 1
#> 6 Area2 2016 23 172 0.1337209 0.09078473 0.1926607 95% proportion of 1
#> 7 Area3 2016 80 135 0.5925926 0.50826068 0.6718008 95% proportion of 1
#> 8 Area4 2016 26 103 0.2524272 0.17847829 0.3441789 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 92 168 0.5476190 0.42959835 0.6605173 99.8% proportion of 1
#> 2 Area2 2015 28 137 0.2043796 0.11891920 0.3283667 99.8% proportion of 1
#> 3 Area3 2015 44 182 0.2417582 0.15817246 0.3510929 99.8% proportion of 1
#> 4 Area4 2015 97 110 0.8818182 0.75511680 0.9475208 99.8% proportion of 1
#> 5 Area1 2016 59 127 0.4645669 0.33512834 0.5989615 99.8% proportion of 1
#> 6 Area2 2016 23 172 0.1337209 0.07258592 0.2333886 99.8% proportion of 1
#> 7 Area3 2016 80 135 0.5925926 0.46003563 0.7129155 99.8% proportion of 1
#> 8 Area4 2016 26 103 0.2524272 0.14516538 0.4017008 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 92 168 54.76190 47.212935 62.09797 95% percentage Wilson
#> 2 Area2 2015 28 137 20.43796 14.535931 27.95259 95% percentage Wilson
#> 3 Area3 2015 44 182 24.17582 18.530912 30.88834 95% percentage Wilson
#> 4 Area4 2015 97 110 88.18182 80.824955 92.96187 95% percentage Wilson
#> 5 Area1 2016 59 127 46.45669 38.014301 55.10714 95% percentage Wilson
#> 6 Area2 2016 23 172 13.37209 9.078473 19.26607 95% percentage Wilson
#> 7 Area3 2016 80 135 59.25926 50.826068 67.18008 95% percentage Wilson
#> 8 Area4 2016 26 103 25.24272 17.847829 34.41789 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 92 168 54.76190 42.959835 66.05173 99.8% percentage Wilson
#> 2 Area2 2015 28 137 20.43796 11.891920 32.83667 99.8% percentage Wilson
#> 3 Area3 2015 44 182 24.17582 15.817246 35.10929 99.8% percentage Wilson
#> 4 Area4 2015 97 110 88.18182 75.511680 94.75208 99.8% percentage Wilson
#> 5 Area1 2016 59 127 46.45669 33.512834 59.89615 99.8% percentage Wilson
#> 6 Area2 2016 23 172 13.37209 7.258592 23.33886 99.8% percentage Wilson
#> 7 Area3 2016 80 135 59.25926 46.003563 71.29155 99.8% percentage Wilson
#> 8 Area4 2016 26 103 25.24272 14.516538 40.17008 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 92 168 54.76190 42.959835 66.05173
#> 2 Area2 2015 28 137 20.43796 11.891920 32.83667
#> 3 Area3 2015 44 182 24.17582 15.817246 35.10929
#> 4 Area4 2015 97 110 88.18182 75.511680 94.75208
#> 5 Area1 2016 59 127 46.45669 33.512834 59.89615
#> 6 Area2 2016 23 172 13.37209 7.258592 23.33886
#> 7 Area3 2016 80 135 59.25926 46.003563 71.29155
#> 8 Area4 2016 26 103 25.24272 14.516538 40.17008
# default rate
phe_rate(df, obs, pop)
#> area year obs pop value lowercl uppercl confidence statistic
#> 1 Area1 2015 92 168 54761.90 44144.645 67161.31 95% rate per 100000
#> 2 Area2 2015 28 137 20437.96 13577.873 29539.64 95% rate per 100000
#> 3 Area3 2015 44 182 24175.82 17564.489 32455.70 95% rate per 100000
#> 4 Area4 2015 97 110 88181.82 71507.769 107575.39 95% rate per 100000
#> 5 Area1 2016 59 127 46456.69 35362.950 59926.80 95% rate per 100000
#> 6 Area2 2016 23 172 13372.09 8474.053 20065.59 95% rate per 100000
#> 7 Area3 2016 80 135 59259.26 46987.312 73754.19 95% rate per 100000
#> 8 Area4 2016 26 103 25242.72 16485.200 36987.90 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
#> 1 Area1 2015 92 168 54.76190 38.787820 74.81532 99.8% rate per 100
#> 2 Area2 2015 28 137 20.43796 10.517905 35.44235 99.8% rate per 100
#> 3 Area3 2015 44 182 24.17582 14.441203 37.71066 99.8% rate per 100
#> 4 Area4 2015 97 110 88.18182 63.063667 119.52675 99.8% rate per 100
#> 5 Area1 2016 59 127 46.45669 29.964550 68.37318 99.8% rate per 100
#> 6 Area2 2016 23 172 13.37209 6.355108 24.45284 99.8% rate per 100
#> 7 Area3 2016 80 135 59.25926 40.859655 82.74312 99.8% rate per 100
#> 8 Area4 2016 26 103 25.24272 12.621892 44.63484 99.8% rate per 100
#> method
#> 1 Byars
#> 2 Byars
#> 3 Byars
#> 4 Byars
#> 5 Byars
#> 6 Byars
#> 7 Byars
#> 8 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 92 168 54.76190 38.787820 74.81532
#> 2 Area2 2015 28 137 20.43796 10.517905 35.44235
#> 3 Area3 2015 44 182 24.17582 14.441203 37.71066
#> 4 Area4 2015 97 110 88.18182 63.063667 119.52675
#> 5 Area1 2016 59 127 46.45669 29.964550 68.37318
#> 6 Area2 2016 23 172 13.37209 6.355108 24.45284
#> 7 Area3 2016 80 135 59.25926 40.859655 82.74312
#> 8 Area4 2016 26 103 25.24272 12.621892 44.63484These 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 x 9
#> # Groups: year [2]
#> year obs pop value lowercl uppercl confidence statistic method
#> <int> <int> <int> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 2015 261 597 0.437 0.398 0.477 95% proportion of 1 Wilson
#> 2 2016 188 537 0.350 0.311 0.391 95% proportion of 1 Wilson
# default rate - grouped
df %>%
group_by(year) %>%
phe_rate(obs, pop)
#> # A tibble: 2 x 9
#> # Groups: year [2]
#> year obs pop value lowercl uppercl confidence statistic method
#> <int> <int> <int> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 2015 261 597 43719. 38575. 49357. 95% rate per 100000 Byars
#> 2 2016 188 537 35009. 30183. 40387. 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
#> 1 449 8 30.42291 56.125 30.69081 81.55919 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 x 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 261 4 34.5 65.2 -111. 241. 99.8% mean
#> 2 2016 188 4 27.4 47 -92.9 187. 99.8% mean
#> # ... with 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 x 7
#> # Groups: year [2]
#> year value_sum value_count stdev value lowercl uppercl
#> <int> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 2015 261 4 34.5 65.2 -111. 241.
#> 2 2016 188 4 27.4 47 -92.9 187.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 54 15659
#> 2 Area1 2006 Male 5 175 11467
#> 3 Area1 2006 Male 10 84 16941
#> 4 Area1 2006 Male 15 162 18434
#> 5 Area1 2006 Male 20 52 10299
#> 6 Area1 2006 Male 25 169 16142INPUT: 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 x 11
#> # Groups: area, year, sex [40]
#> area year sex total_count total_pop value lowercl uppercl confidence
#> <chr> <int> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
#> 1 Area1 2006 Fema~ 1716 305697 590. 561. 620. 95%
#> 2 Area1 2006 Male 1820 265371 703. 669. 738. 95%
#> 3 Area1 2007 Fema~ 2021 295895 665. 635. 697. 95%
#> 4 Area1 2007 Male 1966 290233 601. 572. 632. 95%
#> 5 Area1 2008 Fema~ 2057 276281 802. 764. 840. 95%
#> 6 Area1 2008 Male 1936 289989 650. 618. 682. 95%
#> 7 Area1 2009 Fema~ 1403 270814 526. 496. 557. 95%
#> 8 Area1 2009 Male 2070 282766 791. 756. 829. 95%
#> 9 Area1 2010 Fema~ 1254 272712 527. 496. 559. 95%
#> 10 Area1 2010 Male 2694 273206 1061. 1018. 1104. 95%
#> # ... with 30 more rows, and 2 more variables: statistic <chr>, method <chr>
# 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 x 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 1716 305697 590. 561. 620.
#> 2 Area1 2006 Male 1820 265371 703. 669. 738.
#> 3 Area1 2007 Female 2021 295895 665. 635. 697.
#> 4 Area1 2007 Male 1966 290233 601. 572. 632.
#> 5 Area1 2008 Female 2057 276281 802. 764. 840.
#> 6 Area1 2008 Male 1936 289989 650. 618. 682.
#> 7 Area1 2009 Female 1403 270814 526. 496. 557.
#> 8 Area1 2009 Male 2070 282766 791. 756. 829.
#> 9 Area1 2010 Female 1254 272712 527. 496. 559.
#> 10 Area1 2010 Male 2694 273206 1061. 1018. 1104.
#> # ... 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 x 11
#> # Groups: area, year, sex [40]
#> area year sex total_count total_pop value lowercl uppercl confidence
#> <chr> <int> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
#> 1 Area1 2006 Fema~ 1716 305697 590. 561. 620. 95%
#> 2 Area1 2006 Male 1820 265371 703. 669. 738. 95%
#> 3 Area1 2007 Fema~ 2021 295895 665. 635. 697. 95%
#> 4 Area1 2007 Male 1966 290233 601. 572. 632. 95%
#> 5 Area1 2008 Fema~ 2057 276281 802. 764. 840. 95%
#> 6 Area1 2008 Male 1936 289989 650. 618. 682. 95%
#> 7 Area1 2009 Fema~ 1403 270814 526. 496. 557. 95%
#> 8 Area1 2009 Male 2070 282766 791. 756. 829. 95%
#> 9 Area1 2010 Fema~ 1254 272712 527. 496. 559. 95%
#> 10 Area1 2010 Male 2694 273206 1061. 1018. 1104. 95%
#> # ... with 30 more rows, and 2 more variables: statistic <chr>, method <chr>
# 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 x 11
#> # Groups: area, year, sex [40]
#> area year sex total_count total_pop value lowercl uppercl confidence
#> <chr> <int> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
#> 1 Area1 2006 Fema~ 1716 305697 590. 561. 620. 95%
#> 2 Area1 2006 Male 1820 265371 703. 669. 738. 95%
#> 3 Area1 2007 Fema~ 2021 295895 665. 635. 697. 95%
#> 4 Area1 2007 Male 1966 290233 601. 572. 632. 95%
#> 5 Area1 2008 Fema~ 2057 276281 802. 764. 840. 95%
#> 6 Area1 2008 Male 1936 289989 650. 618. 682. 95%
#> 7 Area1 2009 Fema~ 1403 270814 526. 496. 557. 95%
#> 8 Area1 2009 Male 2070 282766 791. 756. 829. 95%
#> 9 Area1 2010 Fema~ 1254 272712 527. 496. 559. 95%
#> 10 Area1 2010 Male 2694 273206 1061. 1018. 1104. 95%
#> # ... with 30 more rows, and 2 more variables: statistic <chr>, method <chr>
# 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 x 11
#> # Groups: area, year, sex [40]
#> area year sex total_count total_pop value lowercl uppercl confidence
#> <chr> <int> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
#> 1 Area1 2006 Fema~ 1438 252522 590. 559. 622. 95%
#> 2 Area1 2006 Male 1416 218146 681. 645. 718. 95%
#> 3 Area1 2007 Fema~ 1602 242161 662. 630. 696. 95%
#> 4 Area1 2007 Male 1313 224706 563. 532. 595. 95%
#> 5 Area1 2008 Fema~ 1612 215664 809. 769. 850. 95%
#> 6 Area1 2008 Male 1507 225263 662. 628. 697. 95%
#> 7 Area1 2009 Fema~ 1054 207128 533. 501. 567. 95%
#> 8 Area1 2009 Male 1625 215894 786. 748. 826. 95%
#> 9 Area1 2010 Fema~ 1064 218006 550. 516. 585. 95%
#> 10 Area1 2010 Male 2251 217491 1080. 1034. 1127. 95%
#> # ... with 30 more rows, and 2 more variables: statistic <chr>, method <chr>
# 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 x 10
#> # Groups: area, year [20]
#> area year total_count total_pop value lowercl uppercl confidence statistic
#> <chr> <int> <int> <int> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 Area1 2006 3536 571068 635. 613. 657. 95% dsr per ~
#> 2 Area1 2007 3987 586128 631. 610. 653. 95% dsr per ~
#> 3 Area1 2008 3993 566270 718. 694. 743. 95% dsr per ~
#> 4 Area1 2009 3473 553580 652. 629. 675. 95% dsr per ~
#> 5 Area1 2010 3948 545918 784. 758. 810. 95% dsr per ~
#> 6 Area2 2006 4462 583452 759. 735. 782. 95% dsr per ~
#> 7 Area2 2007 3261 558711 649. 626. 673. 95% dsr per ~
#> 8 Area2 2008 3767 541450 724. 700. 748. 95% dsr per ~
#> 9 Area2 2009 4373 594046 738. 715. 762. 95% dsr per ~
#> 10 Area2 2010 3827 547360 697. 674. 721. 95% dsr per ~
#> 11 Area3 2006 4421 623195 750. 727. 774. 95% dsr per ~
#> 12 Area3 2007 3574 558936 640. 618. 662. 95% dsr per ~
#> 13 Area3 2008 4076 595022 692. 669. 715. 95% dsr per ~
#> 14 Area3 2009 4076 562820 742. 717. 767. 95% dsr per ~
#> 15 Area3 2010 3802 584059 658. 636. 681. 95% dsr per ~
#> 16 Area4 2006 3966 597001 686. 663. 709. 95% dsr per ~
#> 17 Area4 2007 3262 550467 576. 555. 597. 95% dsr per ~
#> 18 Area4 2008 3935 609070 683. 661. 706. 95% dsr per ~
#> 19 Area4 2009 3129 564811 584. 562. 606. 95% dsr per ~
#> 20 Area4 2010 3861 588521 710. 687. 734. 95% dsr per ~
#> # ... with 1 more variable: method <chr>INPUT: Unlike the phe_dsr function, there is no default standard or reference data for the phe_smr and phe_isr 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 smrs/isrs 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 (isr only), the smr or isr, 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 x 3
#> ageband obs pop
#> <dbl> <int> <int>
#> 1 0 888 140943
#> 2 5 1065 127287
#> 3 10 832 126762
#> 4 15 896 120984
#> 5 20 846 127930
#> 6 25 934 129497Here are some example code chunks to demonstrate the phe_smr function and the arguments that can optionally be specified
# calculate separate smrs for each area, year and sex
df_std %>%
group_by(area, year, sex) %>%
phe_smr(obs, pop, df_ref$obs, df_ref$pop)
#> # A tibble: 40 x 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 Fema~ 1716 2103. 0.816 0.778 0.855 95%
#> 2 Area1 2006 Male 1820 1820. 1.00 0.954 1.05 95%
#> 3 Area1 2007 Fema~ 2021 2034. 0.994 0.951 1.04 95%
#> 4 Area1 2007 Male 1966 2030. 0.968 0.926 1.01 95%
#> 5 Area1 2008 Fema~ 2057 1922. 1.07 1.02 1.12 95%
#> 6 Area1 2008 Male 1936 1991. 0.972 0.930 1.02 95%
#> 7 Area1 2009 Fema~ 1403 1871. 0.750 0.711 0.790 95%
#> 8 Area1 2009 Male 2070 1982. 1.04 1.00 1.09 95%
#> 9 Area1 2010 Fema~ 1254 1895. 0.662 0.625 0.699 95%
#> 10 Area1 2010 Male 2694 1894. 1.42 1.37 1.48 95%
#> # ... with 30 more rows, and 2 more variables: statistic <chr>, method <chr>
# calculate the same smrs by appending the reference data to the data frame
df_std %>%
mutate(refobs = rep(df_ref$obs,40),
refpop = rep(df_ref$pop,40)) %>%
group_by(area, year, sex) %>%
phe_smr(obs, pop, refobs, refpop, refpoptype="field")
#> # A tibble: 40 x 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 Fema~ 1716 2103. 0.816 0.778 0.855 95%
#> 2 Area1 2006 Male 1820 1820. 1.00 0.954 1.05 95%
#> 3 Area1 2007 Fema~ 2021 2034. 0.994 0.951 1.04 95%
#> 4 Area1 2007 Male 1966 2030. 0.968 0.926 1.01 95%
#> 5 Area1 2008 Fema~ 2057 1922. 1.07 1.02 1.12 95%
#> 6 Area1 2008 Male 1936 1991. 0.972 0.930 1.02 95%
#> 7 Area1 2009 Fema~ 1403 1871. 0.750 0.711 0.790 95%
#> 8 Area1 2009 Male 2070 1982. 1.04 1.00 1.09 95%
#> 9 Area1 2010 Fema~ 1254 1895. 0.662 0.625 0.699 95%
#> 10 Area1 2010 Male 2694 1894. 1.42 1.37 1.48 95%
#> # ... with 30 more rows, and 2 more variables: statistic <chr>, method <chr>
# calculate separate smrs for each year and drop metadata columns from output
df_std %>%
group_by(year, ageband) %>%
summarise(obs = sum(obs),
pop = sum(pop),
.groups = "drop_last") %>%
phe_smr(obs, pop, df_ref$obs, df_ref$pop, type="standard")
#> # A tibble: 5 x 6
#> # Groups: year [5]
#> year observed expected value lowercl uppercl
#> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 2006 16385 16385 1 0.985 1.02
#> 2 2007 14084 15606. 0.902 0.888 0.917
#> 3 2008 15771 15983. 0.987 0.971 1.00
#> 4 2009 15051 15760. 0.955 0.940 0.970
#> 5 2010 15438 15636. 0.987 0.972 1.00The phe_isr 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 isrs for each area, year and sex
df_std %>%
group_by(area, year, sex) %>%
phe_isr(obs, pop, df_ref$obs, df_ref$pop)
#> # A tibble: 40 x 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~ 1716 2103. 690. 563. 537. 590. 95%
#> 2 Area1 2006 Male 1820 1820. 690. 690. 658. 722. 95%
#> 3 Area1 2007 Fema~ 2021 2034. 690. 686. 656. 716. 95%
#> 4 Area1 2007 Male 1966 2030. 690. 668. 639. 698. 95%
#> 5 Area1 2008 Fema~ 2057 1922. 690. 739. 707. 771. 95%
#> 6 Area1 2008 Male 1936 1991. 690. 671. 641. 702. 95%
#> 7 Area1 2009 Fema~ 1403 1871. 690. 517. 491. 545. 95%
#> 8 Area1 2009 Male 2070 1982. 690. 721. 690. 752. 95%
#> 9 Area1 2010 Fema~ 1254 1895. 690. 456. 432. 482. 95%
#> 10 Area1 2010 Male 2694 1894. 690. 981. 945. 1019. 95%
#> # ... with 30 more rows, and 2 more variables: statistic <chr>, method <chr>
# calculate the same isrs by appending the reference data to the data frame
df_std %>%
mutate(refobs = rep(df_ref$obs,40),
refpop = rep(df_ref$pop,40)) %>%
group_by(area, year, sex) %>%
phe_isr(obs, pop, refobs, refpop, refpoptype="field")
#> # A tibble: 40 x 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~ 1716 2103. 690. 563. 537. 590. 95%
#> 2 Area1 2006 Male 1820 1820. 690. 690. 658. 722. 95%
#> 3 Area1 2007 Fema~ 2021 2034. 690. 686. 656. 716. 95%
#> 4 Area1 2007 Male 1966 2030. 690. 668. 639. 698. 95%
#> 5 Area1 2008 Fema~ 2057 1922. 690. 739. 707. 771. 95%
#> 6 Area1 2008 Male 1936 1991. 690. 671. 641. 702. 95%
#> 7 Area1 2009 Fema~ 1403 1871. 690. 517. 491. 545. 95%
#> 8 Area1 2009 Male 2070 1982. 690. 721. 690. 752. 95%
#> 9 Area1 2010 Fema~ 1254 1895. 690. 456. 432. 482. 95%
#> 10 Area1 2010 Male 2694 1894. 690. 981. 945. 1019. 95%
#> # ... with 30 more rows, and 2 more variables: statistic <chr>, method <chr>
# calculate separate isrs for each year and drop metadata columns from output
df_std %>%
group_by(year, ageband) %>%
summarise(obs = sum(obs),
pop = sum(pop),
.groups = "drop_last") %>%
phe_isr(obs, pop, df_ref$obs, df_ref$pop, type="standard")
#> # A tibble: 5 x 7
#> # Groups: year [5]
#> year observed expected ref_rate value lowercl uppercl
#> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2006 16385 16385 690. 690. 679. 701.
#> 2 2007 14084 15606. 690. 623. 612. 633.
#> 3 2008 15771 15983. 690. 681. 670. 692.
#> 4 2009 15051 15760. 690. 659. 648. 670.
#> 5 2010 15438 15636. 690. 681. 671. 692.