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 14 148
#> 2 Area2 2015 76 174
#> 3 Area3 2015 98 125
#> 4 Area4 2015 97 109
#> 5 Area1 2016 1 150
#> 6 Area2 2016 45 150
#> 7 Area3 2016 65 105
#> 8 Area4 2016 23 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 14 148 0.094594595 0.057185755 0.15251625 95%
#> 2 Area2 2015 76 174 0.436781609 0.365238623 0.51105569 95%
#> 3 Area3 2015 98 125 0.784000000 0.703972958 0.84709190 95%
#> 4 Area4 2015 97 109 0.889908257 0.817377280 0.93589196 95%
#> 5 Area1 2016 1 150 0.006666667 0.001177801 0.03679284 95%
#> 6 Area2 2016 45 150 0.300000000 0.232408294 0.37757980 95%
#> 7 Area3 2016 65 105 0.619047619 0.523517011 0.70617488 95%
#> 8 Area4 2016 23 141 0.163120567 0.111224465 0.23288598 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 14 148 0.094594595 0.0430418185 0.1952930 99.8%
#> 2 Area2 2015 76 174 0.436781609 0.3268909758 0.5532504 99.8%
#> 3 Area3 2015 98 125 0.784000000 0.6523743704 0.8753123 99.8%
#> 4 Area4 2015 97 109 0.889908257 0.7642746772 0.9527252 99.8%
#> 5 Area1 2016 1 150 0.006666667 0.0005819015 0.0718065 99.8%
#> 6 Area2 2016 45 150 0.300000000 0.1992211268 0.4247201 99.8%
#> 7 Area3 2016 65 105 0.619047619 0.4685581525 0.7496880 99.8%
#> 8 Area4 2016 23 141 0.163120567 0.0890127738 0.2799657 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 14 148 9.4594595 5.7185755 15.251625 95% percentage
#> 2 Area2 2015 76 174 43.6781609 36.5238623 51.105569 95% percentage
#> 3 Area3 2015 98 125 78.4000000 70.3972958 84.709190 95% percentage
#> 4 Area4 2015 97 109 88.9908257 81.7377280 93.589196 95% percentage
#> 5 Area1 2016 1 150 0.6666667 0.1177801 3.679284 95% percentage
#> 6 Area2 2016 45 150 30.0000000 23.2408294 37.757980 95% percentage
#> 7 Area3 2016 65 105 61.9047619 52.3517011 70.617488 95% percentage
#> 8 Area4 2016 23 141 16.3120567 11.1224465 23.288598 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 14 148 9.4594595 4.30418185 19.52930
#> 2 Area2 2015 76 174 43.6781609 32.68909758 55.32504
#> 3 Area3 2015 98 125 78.4000000 65.23743704 87.53123
#> 4 Area4 2015 97 109 88.9908257 76.42746772 95.27252
#> 5 Area1 2016 1 150 0.6666667 0.05819015 7.18065
#> 6 Area2 2016 45 150 30.0000000 19.92211268 42.47201
#> 7 Area3 2016 65 105 61.9047619 46.85581525 74.96880
#> 8 Area4 2016 23 141 16.3120567 8.90127738 27.99657
# default rate
phe_rate(df, obs, pop)
#> area year obs pop value lowercl uppercl confidence
#> 1 Area1 2015 14 148 9459.4595 5167.25047 15872.296 95%
#> 2 Area2 2015 76 174 43678.1609 34412.14672 54670.448 95%
#> 3 Area3 2015 98 125 78400.0000 63647.38292 95545.409 95%
#> 4 Area4 2015 97 109 88990.8257 72163.80324 108562.320 95%
#> 5 Area1 2016 1 150 666.6667 16.87854 3714.429 95%
#> 6 Area2 2016 45 150 30000.0000 21880.16900 40143.308 95%
#> 7 Area3 2016 65 105 61904.7619 47774.46449 78903.941 95%
#> 8 Area4 2016 23 141 16312.0567 10337.14233 24477.170 95%
#> statistic method
#> 1 rate per 100000 Byars
#> 2 rate per 100000 Byars
#> 3 rate per 100000 Byars
#> 4 rate per 100000 Byars
#> 5 rate per 100000 Exact
#> 6 rate per 100000 Byars
#> 7 rate per 100000 Byars
#> 8 rate per 100000 Byars
# specify multiplier for rate and confidence level
phe_rate(df, obs, pop, confidence = 99.8, multiplier = 100)
#> area year obs pop value lowercl uppercl confidence statistic
#> 1 Area1 2015 14 148 9.4594595 3.483328e+00 20.204526 99.8% rate per 100
#> 2 Area2 2015 76 174 43.6781609 2.980402e+01 61.499290 99.8% rate per 100
#> 3 Area3 2015 98 125 78.4000000 5.617104e+01 106.107951 99.8% rate per 100
#> 4 Area4 2015 97 109 88.9908257 6.364223e+01 120.623331 99.8% rate per 100
#> 5 Area1 2016 1 150 0.6666667 6.670002e-04 6.155609 99.8% rate per 100
#> 6 Area2 2016 45 150 30.0000000 1.803481e+01 46.574638 99.8% rate per 100
#> 7 Area3 2016 65 105 61.9047619 4.083834e+01 89.524087 99.8% rate per 100
#> 8 Area4 2016 23 141 16.3120567 7.752330e+00 29.828996 99.8% rate per 100
#> method
#> 1 Byars
#> 2 Byars
#> 3 Byars
#> 4 Byars
#> 5 Exact
#> 6 Byars
#> 7 Byars
#> 8 Byars
# specify multiplier for rate, confidence level and remove metadata columns
phe_rate(df, obs, pop, type = "standard", confidence = 99.8, multiplier = 100)
#> area year obs pop value lowercl uppercl
#> 1 Area1 2015 14 148 9.4594595 3.483328e+00 20.204526
#> 2 Area2 2015 76 174 43.6781609 2.980402e+01 61.499290
#> 3 Area3 2015 98 125 78.4000000 5.617104e+01 106.107951
#> 4 Area4 2015 97 109 88.9908257 6.364223e+01 120.623331
#> 5 Area1 2016 1 150 0.6666667 6.670002e-04 6.155609
#> 6 Area2 2016 45 150 30.0000000 1.803481e+01 46.574638
#> 7 Area3 2016 65 105 61.9047619 4.083834e+01 89.524087
#> 8 Area4 2016 23 141 16.3120567 7.752330e+00 29.828996These 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 285 556 0.513 0.471 0.554 95% proportion of 1 Wilson
#> 2 2016 134 546 0.245 0.211 0.283 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 285 556 51259. 45480. 57569. 95% rate per 100000 Byars
#> 2 2016 134 546 24542. 20563. 29067. 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
#> 1 419 8 37.4545 52.375 21.06226 83.68774 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 285 4 39.5 71.2 -130. 273. 99.8% mean
#> 2 2016 134 4 27.6 33.5 -108. 175. 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 285 4 39.5 71.2 -130. 273.
#> 2 2016 134 4 27.6 33.5 -108. 175.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 98 19501
#> 2 Area1 2006 Male 5 86 10608
#> 3 Area1 2006 Male 10 105 19243
#> 4 Area1 2006 Male 15 9 19771
#> 5 Area1 2006 Male 20 116 10870
#> 6 Area1 2006 Male 25 107 14685INPUT: 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 2052 285877 792. 756. 829. 95%
#> 2 Area1 2006 Male 1614 289807 663. 629. 699. 95%
#> 3 Area1 2007 Female 2118 291239 746. 713. 780. 95%
#> 4 Area1 2007 Male 1963 281201 719. 686. 754. 95%
#> 5 Area1 2008 Female 1880 273984 716. 682. 751. 95%
#> 6 Area1 2008 Male 2127 300111 706. 674. 738. 95%
#> 7 Area1 2009 Female 1747 283821 580. 550. 610. 95%
#> 8 Area1 2009 Male 2159 281163 811. 774. 849. 95%
#> 9 Area1 2010 Female 1877 278715 732. 697. 768. 95%
#> 10 Area1 2010 Male 1810 286097 668. 635. 702. 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 2052 285877 792. 756. 829.
#> 2 Area1 2006 Male 1614 289807 663. 629. 699.
#> 3 Area1 2007 Female 2118 291239 746. 713. 780.
#> 4 Area1 2007 Male 1963 281201 719. 686. 754.
#> 5 Area1 2008 Female 1880 273984 716. 682. 751.
#> 6 Area1 2008 Male 2127 300111 706. 674. 738.
#> 7 Area1 2009 Female 1747 283821 580. 550. 610.
#> 8 Area1 2009 Male 2159 281163 811. 774. 849.
#> 9 Area1 2010 Female 1877 278715 732. 697. 768.
#> 10 Area1 2010 Male 1810 286097 668. 635. 702.
#> # 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 1858 234685 843. 804. 884. 95%
#> 2 Area1 2006 Male 1399 227444 701. 663. 740. 95%
#> 3 Area1 2007 Female 1727 227760 758. 722. 795. 95%
#> 4 Area1 2007 Male 1508 219439 712. 676. 749. 95%
#> 5 Area1 2008 Female 1626 220197 749. 712. 787. 95%
#> 6 Area1 2008 Male 1696 235777 717. 682. 752. 95%
#> 7 Area1 2009 Female 1204 226379 543. 512. 576. 95%
#> 8 Area1 2009 Male 1684 221656 819. 779. 860. 95%
#> 9 Area1 2010 Female 1606 218730 752. 714. 791. 95%
#> 10 Area1 2010 Male 1430 217660 687. 651. 724. 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 0 780 122561
#> 2 5 877 100412
#> 3 10 498 124111
#> 4 15 680 118401
#> 5 20 938 131301
#> 6 25 578 111035Here 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 2052 1890. 1.09 1.04 1.13 95%
#> 2 Area1 2006 Male 1614 1904. 0.848 0.807 0.890 95%
#> 3 Area1 2007 Female 2118 1981. 1.07 1.02 1.12 95%
#> 4 Area1 2007 Male 1963 1856. 1.06 1.01 1.11 95%
#> 5 Area1 2008 Female 1880 1822. 1.03 0.986 1.08 95%
#> 6 Area1 2008 Male 2127 2041. 1.04 0.998 1.09 95%
#> 7 Area1 2009 Female 1747 1906. 0.917 0.874 0.961 95%
#> 8 Area1 2009 Male 2159 1869. 1.15 1.11 1.20 95%
#> 9 Area1 2010 Female 1877 1859. 1.01 0.964 1.06 95%
#> 10 Area1 2010 Male 1810 1932. 0.937 0.894 0.981 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 2052 1890. 1.09 1.04 1.13
#> 2 Area1 2006 Male 1614 1904. 0.848 0.807 0.890
#> 3 Area1 2007 Female 2118 1981. 1.07 1.02 1.12
#> 4 Area1 2007 Male 1963 1856. 1.06 1.01 1.11
#> 5 Area1 2008 Female 1880 1822. 1.03 0.986 1.08
#> 6 Area1 2008 Male 2127 2041. 1.04 0.998 1.09
#> 7 Area1 2009 Female 1747 1906. 0.917 0.874 0.961
#> 8 Area1 2009 Male 2159 1869. 1.15 1.11 1.20
#> 9 Area1 2010 Female 1877 1859. 1.01 0.964 1.06
#> 10 Area1 2010 Male 1810 1932. 0.937 0.894 0.981
#> # 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… 2052 1890. 666. 723. 692. 755. 95%
#> 2 Area1 2006 Male 1614 1904. 666. 565. 537. 593. 95%
#> 3 Area1 2007 Fema… 2118 1981. 666. 712. 682. 743. 95%
#> 4 Area1 2007 Male 1963 1856. 666. 704. 674. 736. 95%
#> 5 Area1 2008 Fema… 1880 1822. 666. 687. 656. 719. 95%
#> 6 Area1 2008 Male 2127 2041. 666. 694. 665. 724. 95%
#> 7 Area1 2009 Fema… 1747 1906. 666. 611. 582. 640. 95%
#> 8 Area1 2009 Male 2159 1869. 666. 769. 737. 802. 95%
#> 9 Area1 2010 Fema… 1877 1859. 666. 672. 642. 703. 95%
#> 10 Area1 2010 Male 1810 1932. 666. 624. 596. 653. 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 2052 1890. 666. 723. 692. 755.
#> 2 Area1 2006 Male 1614 1904. 666. 565. 537. 593.
#> 3 Area1 2007 Female 2118 1981. 666. 712. 682. 743.
#> 4 Area1 2007 Male 1963 1856. 666. 704. 674. 736.
#> 5 Area1 2008 Female 1880 1822. 666. 687. 656. 719.
#> 6 Area1 2008 Male 2127 2041. 666. 694. 665. 724.
#> 7 Area1 2009 Female 1747 1906. 666. 611. 582. 640.
#> 8 Area1 2009 Male 2159 1869. 666. 769. 737. 802.
#> 9 Area1 2010 Female 1877 1859. 666. 672. 642. 703.
#> 10 Area1 2010 Male 1810 1932. 666. 624. 596. 653.
#> # ℹ 30 more rows