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 97 172
#> 2 Area2 2015 50 167
#> 3 Area3 2015 3 191
#> 4 Area4 2015 90 130
#> 5 Area1 2016 49 135
#> 6 Area2 2016 91 105
#> 7 Area3 2016 4 104
#> 8 Area4 2016 37 144INPUT: 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 97 172 0.56395349 0.489247841 0.63586486 95%
#> 2 Area2 2015 50 167 0.29940120 0.235086550 0.37273698 95%
#> 3 Area3 2015 3 191 0.01570681 0.005355862 0.04515422 95%
#> 4 Area4 2015 90 130 0.69230769 0.608401892 0.76517443 95%
#> 5 Area1 2016 49 135 0.36296296 0.286680886 0.44682811 95%
#> 6 Area2 2016 91 105 0.86666667 0.788564932 0.91888608 95%
#> 7 Area3 2016 4 104 0.03846154 0.015056813 0.09474751 95%
#> 8 Area4 2016 37 144 0.25694444 0.192543683 0.33397614 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 97 172 0.56395349 0.446808065 0.67437098 99.8%
#> 2 Area2 2015 50 167 0.29940120 0.203183253 0.41731986 99.8%
#> 3 Area3 2015 3 191 0.01570681 0.003159054 0.07437558 99.8%
#> 4 Area4 2015 90 130 0.69230769 0.557697323 0.80059839 99.8%
#> 5 Area1 2016 49 135 0.36296296 0.248091378 0.49594101 99.8%
#> 6 Area2 2016 91 105 0.86666667 0.733299440 0.93889883 99.8%
#> 7 Area3 2016 4 104 0.03846154 0.009329569 0.14522444 99.8%
#> 8 Area4 2016 37 144 0.25694444 0.162049529 0.38207153 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 97 172 56.395349 48.9247841 63.586486 95% percentage
#> 2 Area2 2015 50 167 29.940120 23.5086550 37.273698 95% percentage
#> 3 Area3 2015 3 191 1.570681 0.5355862 4.515422 95% percentage
#> 4 Area4 2015 90 130 69.230769 60.8401892 76.517443 95% percentage
#> 5 Area1 2016 49 135 36.296296 28.6680886 44.682811 95% percentage
#> 6 Area2 2016 91 105 86.666667 78.8564932 91.888608 95% percentage
#> 7 Area3 2016 4 104 3.846154 1.5056813 9.474751 95% percentage
#> 8 Area4 2016 37 144 25.694444 19.2543683 33.397614 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 97 172 56.395349 44.6808065 67.437098
#> 2 Area2 2015 50 167 29.940120 20.3183253 41.731986
#> 3 Area3 2015 3 191 1.570681 0.3159054 7.437558
#> 4 Area4 2015 90 130 69.230769 55.7697323 80.059839
#> 5 Area1 2016 49 135 36.296296 24.8091378 49.594101
#> 6 Area2 2016 91 105 86.666667 73.3299440 93.889883
#> 7 Area3 2016 4 104 3.846154 0.9329569 14.522444
#> 8 Area4 2016 37 144 25.694444 16.2049529 38.207153
# default rate
phe_rate(df, obs, pop)
#> area year obs pop value lowercl uppercl confidence statistic
#> 1 Area1 2015 97 172 56395.349 45731.7125 68798.215 95% rate per 100000
#> 2 Area2 2015 50 167 29940.120 22220.4194 39473.148 95% rate per 100000
#> 3 Area3 2015 3 191 1570.681 323.9121 4590.195 95% rate per 100000
#> 4 Area4 2015 90 130 69230.769 55668.1699 85097.279 95% rate per 100000
#> 5 Area1 2016 49 135 36296.296 26850.0523 47986.663 95% rate per 100000
#> 6 Area2 2016 91 105 86666.667 69776.6946 106408.585 95% rate per 100000
#> 7 Area3 2016 4 104 3846.154 1047.9475 9847.681 95% rate per 100000
#> 8 Area4 2016 37 144 25694.444 18088.8642 35417.428 95% rate per 100000
#> method
#> 1 Byars
#> 2 Byars
#> 3 Exact
#> 4 Byars
#> 5 Byars
#> 6 Byars
#> 7 Exact
#> 8 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 97 172 56.395349 40.3314147 76.44153 99.8% rate per 100
#> 2 Area2 2015 50 167 29.940120 18.5237775 45.49060 99.8% rate per 100
#> 3 Area3 2015 3 191 1.570681 0.0997557 6.83887 99.8% rate per 100
#> 4 Area4 2015 90 130 69.230769 48.8361086 94.89841 99.8% rate per 100
#> 5 Area1 2016 49 135 36.296296 22.3360064 55.37209 99.8% rate per 100
#> 6 Area2 2016 91 105 86.666667 61.2617114 118.59930 99.8% rate per 100
#> 7 Area3 2016 4 104 3.846154 0.4120696 14.22514 99.8% rate per 100
#> 8 Area4 2016 37 144 25.694444 14.5674857 41.63684 99.8% rate per 100
#> method
#> 1 Byars
#> 2 Byars
#> 3 Exact
#> 4 Byars
#> 5 Byars
#> 6 Byars
#> 7 Exact
#> 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 97 172 56.395349 40.3314147 76.44153
#> 2 Area2 2015 50 167 29.940120 18.5237775 45.49060
#> 3 Area3 2015 3 191 1.570681 0.0997557 6.83887
#> 4 Area4 2015 90 130 69.230769 48.8361086 94.89841
#> 5 Area1 2016 49 135 36.296296 22.3360064 55.37209
#> 6 Area2 2016 91 105 86.666667 61.2617114 118.59930
#> 7 Area3 2016 4 104 3.846154 0.4120696 14.22514
#> 8 Area4 2016 37 144 25.694444 14.5674857 41.63684These 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 240 660 0.364 0.328 0.401 95% proportion of 1 Wilson
#> 2 2016 181 488 0.371 0.329 0.415 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 240 660 36364. 31908. 41267. 95% rate per 100000 Byars
#> 2 2016 181 488 37090. 31883. 42905. 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
#> 1 421 8 37.66369 52.625 21.13737 84.11263 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 240 4 43.3 60 -161. 281. 99.8% mean
#> 2 2016 181 4 35.9 45.2 -138. 229. 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 240 4 43.3 60 -161. 281.
#> 2 2016 181 4 35.9 45.2 -138. 229.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 0 180 10251
#> 2 Area1 2006 Male 5 188 17675
#> 3 Area1 2006 Male 10 168 10268
#> 4 Area1 2006 Male 15 39 14612
#> 5 Area1 2006 Male 20 73 15771
#> 6 Area1 2006 Male 25 148 13248INPUT: 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 2017 266445 775. 739. 812. 95%
#> 2 Area1 2006 Male 2482 291977 859. 823. 896. 95%
#> 3 Area1 2007 Female 2110 283861 822. 785. 861. 95%
#> 4 Area1 2007 Male 1761 303140 611. 580. 644. 95%
#> 5 Area1 2008 Female 1607 298530 607. 576. 640. 95%
#> 6 Area1 2008 Male 2203 278454 783. 748. 819. 95%
#> 7 Area1 2009 Female 2085 290196 754. 720. 788. 95%
#> 8 Area1 2009 Male 2131 285076 752. 717. 788. 95%
#> 9 Area1 2010 Female 1573 259135 627. 595. 661. 95%
#> 10 Area1 2010 Male 1988 271954 746. 711. 783. 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 2017 266445 775. 739. 812.
#> 2 Area1 2006 Male 2482 291977 859. 823. 896.
#> 3 Area1 2007 Female 2110 283861 822. 785. 861.
#> 4 Area1 2007 Male 1761 303140 611. 580. 644.
#> 5 Area1 2008 Female 1607 298530 607. 576. 640.
#> 6 Area1 2008 Male 2203 278454 783. 748. 819.
#> 7 Area1 2009 Female 2085 290196 754. 720. 788.
#> 8 Area1 2009 Male 2131 285076 752. 717. 788.
#> 9 Area1 2010 Female 1573 259135 627. 595. 661.
#> 10 Area1 2010 Male 1988 271954 746. 711. 783.
#> # 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 1570 222259 755. 717. 795. 95%
#> 2 Area1 2006 Male 1842 226309 839. 801. 879. 95%
#> 3 Area1 2007 Female 1707 215448 838. 797. 879. 95%
#> 4 Area1 2007 Male 1389 229806 619. 586. 654. 95%
#> 5 Area1 2008 Female 1309 228223 613. 579. 648. 95%
#> 6 Area1 2008 Male 1665 213212 775. 737. 814. 95%
#> 7 Area1 2009 Female 1855 230066 789. 753. 827. 95%
#> 8 Area1 2009 Male 1459 225044 712. 676. 751. 95%
#> 9 Area1 2010 Female 1195 201975 590. 557. 626. 95%
#> 10 Area1 2010 Male 1443 213375 724. 686. 762. 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 1189 112241
#> 2 5 771 123596
#> 3 10 780 112929
#> 4 15 691 135211
#> 5 20 603 123704
#> 6 25 826 115815Here 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 2017 1828. 1.10 1.06 1.15 95%
#> 2 Area1 2006 Male 2482 2017. 1.23 1.18 1.28 95%
#> 3 Area1 2007 Female 2110 1986. 1.06 1.02 1.11 95%
#> 4 Area1 2007 Male 1761 2137. 0.824 0.786 0.863 95%
#> 5 Area1 2008 Female 1607 2053. 0.783 0.745 0.822 95%
#> 6 Area1 2008 Male 2203 1928. 1.14 1.10 1.19 95%
#> 7 Area1 2009 Female 2085 2051. 1.02 0.973 1.06 95%
#> 8 Area1 2009 Male 2131 1993. 1.07 1.02 1.12 95%
#> 9 Area1 2010 Female 1573 1815. 0.867 0.824 0.911 95%
#> 10 Area1 2010 Male 1988 1881. 1.06 1.01 1.10 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 2017 1828. 1.10 1.06 1.15
#> 2 Area1 2006 Male 2482 2017. 1.23 1.18 1.28
#> 3 Area1 2007 Female 2110 1986. 1.06 1.02 1.11
#> 4 Area1 2007 Male 1761 2137. 0.824 0.786 0.863
#> 5 Area1 2008 Female 1607 2053. 0.783 0.745 0.822
#> 6 Area1 2008 Male 2203 1928. 1.14 1.10 1.19
#> 7 Area1 2009 Female 2085 2051. 1.02 0.973 1.06
#> 8 Area1 2009 Male 2131 1993. 1.07 1.02 1.12
#> 9 Area1 2010 Female 1573 1815. 0.867 0.824 0.911
#> 10 Area1 2010 Male 1988 1881. 1.06 1.01 1.10
#> # 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… 2017 1828. 692. 764. 731. 798. 95%
#> 2 Area1 2006 Male 2482 2017. 692. 852. 818. 886. 95%
#> 3 Area1 2007 Fema… 2110 1986. 692. 735. 704. 767. 95%
#> 4 Area1 2007 Male 1761 2137. 692. 570. 544. 598. 95%
#> 5 Area1 2008 Fema… 1607 2053. 692. 542. 515. 569. 95%
#> 6 Area1 2008 Male 2203 1928. 692. 791. 758. 825. 95%
#> 7 Area1 2009 Fema… 2085 2051. 692. 703. 674. 734. 95%
#> 8 Area1 2009 Male 2131 1993. 692. 740. 709. 772. 95%
#> 9 Area1 2010 Fema… 1573 1815. 692. 600. 570. 630. 95%
#> 10 Area1 2010 Male 1988 1881. 692. 731. 699. 764. 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 2017 1828. 692. 764. 731. 798.
#> 2 Area1 2006 Male 2482 2017. 692. 852. 818. 886.
#> 3 Area1 2007 Female 2110 1986. 692. 735. 704. 767.
#> 4 Area1 2007 Male 1761 2137. 692. 570. 544. 598.
#> 5 Area1 2008 Female 1607 2053. 692. 542. 515. 569.
#> 6 Area1 2008 Male 2203 1928. 692. 791. 758. 825.
#> 7 Area1 2009 Female 2085 2051. 692. 703. 674. 734.
#> 8 Area1 2009 Male 2131 1993. 692. 740. 709. 772.
#> 9 Area1 2010 Female 1573 1815. 692. 600. 570. 630.
#> 10 Area1 2010 Male 1988 1881. 692. 731. 699. 764.
#> # ℹ 30 more rows