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 |
| 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 5 145
#> 2 Area2 2015 73 184
#> 3 Area3 2015 73 129
#> 4 Area4 2015 4 142
#> 5 Area1 2016 16 114
#> 6 Area2 2016 87 125
#> 7 Area3 2016 16 118
#> 8 Area4 2016 55 141INPUT: 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 5 145 0.03448276 0.01481722 0.07817743 95%
#> 2 Area2 2015 73 184 0.39673913 0.32885777 0.46884397 95%
#> 3 Area3 2015 73 129 0.56589147 0.47968015 0.64829195 95%
#> 4 Area4 2015 4 142 0.02816901 0.01100774 0.07018631 95%
#> 5 Area1 2016 16 114 0.14035088 0.08827407 0.21587576 95%
#> 6 Area2 2016 87 125 0.69600000 0.61051568 0.76979669 95%
#> 7 Area3 2016 16 118 0.13559322 0.08521666 0.20894806 95%
#> 8 Area4 2016 55 141 0.39007092 0.31349773 0.47247515 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 5 145 0.03448276 0.009538616 0.1169550 99.8%
#> 2 Area2 2015 73 184 0.39673913 0.293046929 0.5106209 99.8%
#> 3 Area3 2015 73 129 0.56589147 0.431147591 0.6915522 99.8%
#> 4 Area4 2015 4 142 0.02816901 0.006822400 0.1089783 99.8%
#> 5 Area1 2016 16 114 0.14035088 0.067658665 0.2686399 99.8%
#> 6 Area2 2016 87 125 0.69600000 0.558758202 0.8054199 99.8%
#> 7 Area3 2016 16 118 0.13559322 0.065307615 0.2604445 99.8%
#> 8 Area4 2016 55 141 0.39007092 0.273999592 0.5200881 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 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 5 145 3.448276 1.481722 7.817743 95% percentage Wilson
#> 2 Area2 2015 73 184 39.673913 32.885777 46.884397 95% percentage Wilson
#> 3 Area3 2015 73 129 56.589147 47.968015 64.829195 95% percentage Wilson
#> 4 Area4 2015 4 142 2.816901 1.100774 7.018631 95% percentage Wilson
#> 5 Area1 2016 16 114 14.035088 8.827407 21.587576 95% percentage Wilson
#> 6 Area2 2016 87 125 69.600000 61.051568 76.979669 95% percentage Wilson
#> 7 Area3 2016 16 118 13.559322 8.521666 20.894806 95% percentage Wilson
#> 8 Area4 2016 55 141 39.007092 31.349773 47.247515 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 5 145 3.448276 0.9538616 11.69550 99.8% percentage Wilson
#> 2 Area2 2015 73 184 39.673913 29.3046929 51.06209 99.8% percentage Wilson
#> 3 Area3 2015 73 129 56.589147 43.1147591 69.15522 99.8% percentage Wilson
#> 4 Area4 2015 4 142 2.816901 0.6822400 10.89783 99.8% percentage Wilson
#> 5 Area1 2016 16 114 14.035088 6.7658665 26.86399 99.8% percentage Wilson
#> 6 Area2 2016 87 125 69.600000 55.8758202 80.54199 99.8% percentage Wilson
#> 7 Area3 2016 16 118 13.559322 6.5307615 26.04445 99.8% percentage Wilson
#> 8 Area4 2016 55 141 39.007092 27.3999592 52.00881 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 5 145 3.448276 0.9538616 11.69550
#> 2 Area2 2015 73 184 39.673913 29.3046929 51.06209
#> 3 Area3 2015 73 129 56.589147 43.1147591 69.15522
#> 4 Area4 2015 4 142 2.816901 0.6822400 10.89783
#> 5 Area1 2016 16 114 14.035088 6.7658665 26.86399
#> 6 Area2 2016 87 125 69.600000 55.8758202 80.54199
#> 7 Area3 2016 16 118 13.559322 6.5307615 26.04445
#> 8 Area4 2016 55 141 39.007092 27.3999592 52.00881
# default rate
phe_rate(df, obs, pop)
#> area year obs pop value lowercl uppercl confidence statistic
#> 1 Area1 2015 5 145 3448.276 1119.6458 8047.126 95% rate per 100000
#> 2 Area2 2015 73 184 39673.913 31096.7782 49884.675 95% rate per 100000
#> 3 Area3 2015 73 129 56589.147 44355.0945 71153.334 95% rate per 100000
#> 4 Area4 2015 4 142 2816.901 767.5108 7212.386 95% rate per 100000
#> 5 Area1 2016 16 114 14035.088 8017.1131 22793.367 95% rate per 100000
#> 6 Area2 2016 87 125 69600.000 55745.1083 85852.300 95% rate per 100000
#> 7 Area3 2016 16 118 13559.322 7745.3465 22020.710 95% rate per 100000
#> 8 Area4 2016 55 141 39007.092 29383.5646 50774.057 95% rate per 100000
#> method
#> 1 Exact
#> 2 Byars
#> 3 Byars
#> 4 Exact
#> 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 5 145 3.448276 0.5099115 11.34810 99.8% rate per 100
#> 2 Area2 2015 73 184 39.673913 26.8450767 56.23698 99.8% rate per 100
#> 3 Area3 2015 73 129 56.589147 38.2906521 80.21398 99.8% rate per 100
#> 4 Area4 2015 4 142 2.816901 0.3017975 10.41841 99.8% rate per 100
#> 5 Area1 2016 16 114 14.035088 5.5850528 28.65934 99.8% rate per 100
#> 6 Area2 2016 87 125 69.600000 48.7830681 95.90284 99.8% rate per 100
#> 7 Area3 2016 16 118 13.559322 5.3957289 27.68783 99.8% rate per 100
#> 8 Area4 2016 55 141 39.007092 24.7315166 58.17287 99.8% rate per 100
#> method
#> 1 Exact
#> 2 Byars
#> 3 Byars
#> 4 Exact
#> 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 5 145 3.448276 0.5099115 11.34810
#> 2 Area2 2015 73 184 39.673913 26.8450767 56.23698
#> 3 Area3 2015 73 129 56.589147 38.2906521 80.21398
#> 4 Area4 2015 4 142 2.816901 0.3017975 10.41841
#> 5 Area1 2016 16 114 14.035088 5.5850528 28.65934
#> 6 Area2 2016 87 125 69.600000 48.7830681 95.90284
#> 7 Area3 2016 16 118 13.559322 5.3957289 27.68783
#> 8 Area4 2016 55 141 39.007092 24.7315166 58.17287These 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 155 600 0.258 0.225 0.295 95% proportion of 1 Wilson
#> 2 2016 174 498 0.349 0.309 0.392 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 155 600 25833. 21926. 30236. 95% rate per 100000 Byars
#> 2 2016 174 498 34940. 29941. 40535. 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
#> 1 329 8 34.38204 41.125 12.38089 69.86911 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 155 4 39.6 38.8 -163. 241. 99.8% mean
#> 2 2016 174 4 34.3 43.5 -132. 219. 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 155 4 39.6 38.8 -163. 241.
#> 2 2016 174 4 34.3 43.5 -132. 219.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 16 14525
#> 2 Area1 2006 Male 5 41 11517
#> 3 Area1 2006 Male 10 31 17932
#> 4 Area1 2006 Male 15 168 14798
#> 5 Area1 2006 Male 20 91 19848
#> 6 Area1 2006 Male 25 145 16485INPUT: 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_pop value lowercl uppercl confidence
#> <chr> <int> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
#> 1 Area1 2006 Female 2122 262014 788. 751. 825. 95%
#> 2 Area1 2006 Male 1883 305893 639. 608. 670. 95%
#> 3 Area1 2007 Female 2036 261799 815. 778. 853. 95%
#> 4 Area1 2007 Male 1997 289411 708. 674. 743. 95%
#> 5 Area1 2008 Female 1782 294098 651. 618. 684. 95%
#> 6 Area1 2008 Male 1461 252190 558. 528. 590. 95%
#> 7 Area1 2009 Female 2354 299239 787. 753. 823. 95%
#> 8 Area1 2009 Male 1752 285455 640. 610. 672. 95%
#> 9 Area1 2010 Female 2008 260319 840. 802. 879. 95%
#> 10 Area1 2010 Male 1842 277287 685. 653. 719. 95%
#> # ℹ 30 more rows
#> # ℹ 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 × 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 2122 262014 788. 751. 825.
#> 2 Area1 2006 Male 1883 305893 639. 608. 670.
#> 3 Area1 2007 Female 2036 261799 815. 778. 853.
#> 4 Area1 2007 Male 1997 289411 708. 674. 743.
#> 5 Area1 2008 Female 1782 294098 651. 618. 684.
#> 6 Area1 2008 Male 1461 252190 558. 528. 590.
#> 7 Area1 2009 Female 2354 299239 787. 753. 823.
#> 8 Area1 2009 Male 1752 285455 640. 610. 672.
#> 9 Area1 2010 Female 2008 260319 840. 802. 879.
#> 10 Area1 2010 Male 1842 277287 685. 653. 719.
#> # ℹ 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_pop value lowercl uppercl confidence
#> <chr> <int> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
#> 1 Area1 2006 Female 2122 262014 788. 751. 825. 95%
#> 2 Area1 2006 Male 1883 305893 639. 608. 670. 95%
#> 3 Area1 2007 Female 2036 261799 815. 778. 853. 95%
#> 4 Area1 2007 Male 1997 289411 708. 674. 743. 95%
#> 5 Area1 2008 Female 1782 294098 651. 618. 684. 95%
#> 6 Area1 2008 Male 1461 252190 558. 528. 590. 95%
#> 7 Area1 2009 Female 2354 299239 787. 753. 823. 95%
#> 8 Area1 2009 Male 1752 285455 640. 610. 672. 95%
#> 9 Area1 2010 Female 2008 260319 840. 802. 879. 95%
#> 10 Area1 2010 Male 1842 277287 685. 653. 719. 95%
#> # ℹ 30 more rows
#> # ℹ 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 × 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 Female 2122 262014 788. 751. 825. 95%
#> 2 Area1 2006 Male 1883 305893 639. 608. 670. 95%
#> 3 Area1 2007 Female 2036 261799 815. 778. 853. 95%
#> 4 Area1 2007 Male 1997 289411 708. 674. 743. 95%
#> 5 Area1 2008 Female 1782 294098 651. 618. 684. 95%
#> 6 Area1 2008 Male 1461 252190 558. 528. 590. 95%
#> 7 Area1 2009 Female 2354 299239 787. 753. 823. 95%
#> 8 Area1 2009 Male 1752 285455 640. 610. 672. 95%
#> 9 Area1 2010 Female 2008 260319 840. 802. 879. 95%
#> 10 Area1 2010 Male 1842 277287 685. 653. 719. 95%
#> # ℹ 30 more rows
#> # ℹ 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 × 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 Female 1585 205757 779. 740. 819. 95%
#> 2 Area1 2006 Male 1439 236639 624. 592. 658. 95%
#> 3 Area1 2007 Female 1743 203479 849. 808. 890. 95%
#> 4 Area1 2007 Male 1517 223282 697. 661. 735. 95%
#> 5 Area1 2008 Female 1420 229257 673. 637. 709. 95%
#> 6 Area1 2008 Male 1071 198871 542. 510. 576. 95%
#> 7 Area1 2009 Female 1825 231772 792. 755. 831. 95%
#> 8 Area1 2009 Male 1567 234471 668. 635. 702. 95%
#> 9 Area1 2010 Female 1673 210904 828. 787. 869. 95%
#> 10 Area1 2010 Male 1438 219113 667. 632. 703. 95%
#> # ℹ 30 more rows
#> # ℹ 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 × 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 4005 567907 701. 678. 725. 95% dsr per 1…
#> 2 Area1 2007 4033 551210 761. 736. 787. 95% dsr per 1…
#> 3 Area1 2008 3243 546288 597. 575. 619. 95% dsr per 1…
#> 4 Area1 2009 4106 584694 714. 692. 738. 95% dsr per 1…
#> 5 Area1 2010 3850 537606 754. 729. 779. 95% dsr per 1…
#> 6 Area2 2006 3636 574690 587. 566. 608. 95% dsr per 1…
#> 7 Area2 2007 4504 559264 823. 797. 849. 95% dsr per 1…
#> 8 Area2 2008 3648 554981 658. 635. 681. 95% dsr per 1…
#> 9 Area2 2009 3819 552990 732. 708. 756. 95% dsr per 1…
#> 10 Area2 2010 3948 556917 730. 706. 754. 95% dsr per 1…
#> 11 Area3 2006 3401 602772 584. 563. 605. 95% dsr per 1…
#> 12 Area3 2007 4240 575772 751. 727. 775. 95% dsr per 1…
#> 13 Area3 2008 4068 565843 721. 698. 745. 95% dsr per 1…
#> 14 Area3 2009 3746 549496 712. 688. 737. 95% dsr per 1…
#> 15 Area3 2010 3176 538633 567. 546. 589. 95% dsr per 1…
#> 16 Area4 2006 4183 555916 756. 732. 780. 95% dsr per 1…
#> 17 Area4 2007 3179 552813 594. 572. 616. 95% dsr per 1…
#> 18 Area4 2008 4032 544503 742. 717. 767. 95% dsr per 1…
#> 19 Area4 2009 4334 577014 783. 758. 807. 95% dsr per 1…
#> 20 Area4 2010 3617 572747 653. 631. 676. 95% dsr per 1…
#> # ℹ 1 more variable: method <chr>INPUT: 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 0 753 115387
#> 2 5 813 110861
#> 3 10 747 137533
#> 4 15 901 107845
#> 5 20 752 127510
#> 6 25 899 119024Here 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 2122 1753. 1.21 1.16 1.26 95%
#> 2 Area1 2006 Male 1883 2042. 0.922 0.881 0.965 95%
#> 3 Area1 2007 Female 2036 1736. 1.17 1.12 1.22 95%
#> 4 Area1 2007 Male 1997 1960. 1.02 0.975 1.06 95%
#> 5 Area1 2008 Female 1782 1917. 0.930 0.887 0.974 95%
#> 6 Area1 2008 Male 1461 1672. 0.874 0.830 0.920 95%
#> 7 Area1 2009 Female 2354 2008. 1.17 1.13 1.22 95%
#> 8 Area1 2009 Male 1752 1870. 0.937 0.894 0.982 95%
#> 9 Area1 2010 Female 2008 1736. 1.16 1.11 1.21 95%
#> 10 Area1 2010 Male 1842 1842. 1.00 0.955 1.05 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 2122 1753. 1.21 1.16 1.26
#> 2 Area1 2006 Male 1883 2042. 0.922 0.881 0.965
#> 3 Area1 2007 Female 2036 1736. 1.17 1.12 1.22
#> 4 Area1 2007 Male 1997 1960. 1.02 0.975 1.06
#> 5 Area1 2008 Female 1782 1917. 0.930 0.887 0.974
#> 6 Area1 2008 Male 1461 1672. 0.874 0.830 0.920
#> 7 Area1 2009 Female 2354 2008. 1.17 1.13 1.22
#> 8 Area1 2009 Male 1752 1870. 0.937 0.894 0.982
#> 9 Area1 2010 Female 2008 1736. 1.16 1.11 1.21
#> 10 Area1 2010 Male 1842 1842. 1.00 0.955 1.05
#> # 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… 2122 1753. 662. 801. 767. 836. 95%
#> 2 Area1 2006 Male 1883 2042. 662. 610. 583. 638. 95%
#> 3 Area1 2007 Fema… 2036 1736. 662. 776. 743. 810. 95%
#> 4 Area1 2007 Male 1997 1960. 662. 674. 645. 704. 95%
#> 5 Area1 2008 Fema… 1782 1917. 662. 615. 587. 644. 95%
#> 6 Area1 2008 Male 1461 1672. 662. 578. 549. 609. 95%
#> 7 Area1 2009 Fema… 2354 2008. 662. 775. 744. 807. 95%
#> 8 Area1 2009 Male 1752 1870. 662. 620. 591. 650. 95%
#> 9 Area1 2010 Fema… 2008 1736. 662. 765. 732. 800. 95%
#> 10 Area1 2010 Male 1842 1842. 662. 662. 632. 692. 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 2122 1753. 662. 801. 767. 836.
#> 2 Area1 2006 Male 1883 2042. 662. 610. 583. 638.
#> 3 Area1 2007 Female 2036 1736. 662. 776. 743. 810.
#> 4 Area1 2007 Male 1997 1960. 662. 674. 645. 704.
#> 5 Area1 2008 Female 1782 1917. 662. 615. 587. 644.
#> 6 Area1 2008 Male 1461 1672. 662. 578. 549. 609.
#> 7 Area1 2009 Female 2354 2008. 662. 775. 744. 807.
#> 8 Area1 2009 Male 1752 1870. 662. 620. 591. 650.
#> 9 Area1 2010 Female 2008 1736. 662. 765. 732. 800.
#> 10 Area1 2010 Male 1842 1842. 662. 662. 632. 692.
#> # ℹ 30 more rows