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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 27 156
#> 2 Area2 2015 36 145
#> 3 Area3 2015 29 120
#> 4 Area4 2015 79 123
#> 5 Area1 2016 53 101
#> 6 Area2 2016 46 188
#> 7 Area3 2016 91 156
#> 8 Area4 2016 43 190INPUT: 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 27 156 0.1730769 0.1217616 0.2401061 95% proportion of 1
#> 2 Area2 2015 36 145 0.2482759 0.1850655 0.3244797 95% proportion of 1
#> 3 Area3 2015 29 120 0.2416667 0.1738584 0.3255015 95% proportion of 1
#> 4 Area4 2015 79 123 0.6422764 0.5544397 0.7214953 95% proportion of 1
#> 5 Area1 2016 53 101 0.5247525 0.4282498 0.6194412 95% proportion of 1
#> 6 Area2 2016 46 188 0.2446809 0.1887455 0.3108413 95% proportion of 1
#> 7 Area3 2016 91 156 0.5833333 0.5048757 0.6577855 95% proportion of 1
#> 8 Area4 2016 43 190 0.2263158 0.1725838 0.2908953 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 27 156 0.1730769 0.09913737 0.2847328 99.8% proportion of 1
#> 2 Area2 2015 36 145 0.2482759 0.15532170 0.3723378 99.8% proportion of 1
#> 3 Area3 2015 29 120 0.2416667 0.14293143 0.3784872 99.8% proportion of 1
#> 4 Area4 2015 79 123 0.6422764 0.50296043 0.7610918 99.8% proportion of 1
#> 5 Area1 2016 53 101 0.5247525 0.37582448 0.6694041 99.8% proportion of 1
#> 6 Area2 2016 46 188 0.2446809 0.16170173 0.3523442 99.8% proportion of 1
#> 7 Area3 2016 91 156 0.5833333 0.46002112 0.6970316 99.8% proportion of 1
#> 8 Area4 2016 43 190 0.2263158 0.14694208 0.3318841 99.8% proportion of 1
#> method
#> 1 Wilson
#> 2 Wilson
#> 3 Wilson
#> 4 Wilson
#> 5 Wilson
#> 6 Wilson
#> 7 Wilson
#> 8 Wilson
# specify multiplier 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 27 156 17.30769 12.17616 24.01061 95% percentage Wilson
#> 2 Area2 2015 36 145 24.82759 18.50655 32.44797 95% percentage Wilson
#> 3 Area3 2015 29 120 24.16667 17.38584 32.55015 95% percentage Wilson
#> 4 Area4 2015 79 123 64.22764 55.44397 72.14953 95% percentage Wilson
#> 5 Area1 2016 53 101 52.47525 42.82498 61.94412 95% percentage Wilson
#> 6 Area2 2016 46 188 24.46809 18.87455 31.08413 95% percentage Wilson
#> 7 Area3 2016 91 156 58.33333 50.48757 65.77855 95% percentage Wilson
#> 8 Area4 2016 43 190 22.63158 17.25838 29.08953 95% percentage 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 27 156 17.30769 9.913737 28.47328
#> 2 Area2 2015 36 145 24.82759 15.532170 37.23378
#> 3 Area3 2015 29 120 24.16667 14.293143 37.84872
#> 4 Area4 2015 79 123 64.22764 50.296043 76.10918
#> 5 Area1 2016 53 101 52.47525 37.582448 66.94041
#> 6 Area2 2016 46 188 24.46809 16.170173 35.23442
#> 7 Area3 2016 91 156 58.33333 46.002112 69.70316
#> 8 Area4 2016 43 190 22.63158 14.694208 33.18841
# default rate
phe_rate(df, obs, pop)
#> area year obs pop value lowercl uppercl confidence statistic
#> 1 Area1 2015 27 156 17307.69 11403.18 25182.76 95% rate per 100000
#> 2 Area2 2015 36 145 24827.59 17386.52 34372.86 95% rate per 100000
#> 3 Area3 2015 29 120 24166.67 16181.45 34708.62 95% rate per 100000
#> 4 Area4 2015 79 123 64227.64 50847.87 80047.81 95% rate per 100000
#> 5 Area1 2016 53 101 52475.25 39304.83 68640.27 95% rate per 100000
#> 6 Area2 2016 46 188 24468.09 17912.11 32637.75 95% rate per 100000
#> 7 Area3 2016 91 156 58333.33 46965.08 71621.16 95% rate per 100000
#> 8 Area4 2016 43 190 22631.58 16376.93 30485.34 95% rate per 100000
#> method
#> 1 Byars
#> 2 Byars
#> 3 Byars
#> 4 Byars
#> 5 Byars
#> 6 Byars
#> 7 Byars
#> 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 method
#> 1 Area1 2015 27 156 17.30769 8.783552 30.29970 99.8% rate per 100 Byars
#> 2 Area2 2015 36 145 24.82759 13.952944 40.48648 99.8% rate per 100 Byars
#> 3 Area3 2015 29 120 24.16667 12.601525 41.53299 99.8% rate per 100 Byars
#> 4 Area4 2015 79 123 64.22764 44.173569 89.86280 99.8% rate per 100 Byars
#> 5 Area1 2016 53 101 52.47525 32.961126 78.81964 99.8% rate per 100 Byars
#> 6 Area2 2016 46 188 24.46809 14.799981 37.81279 99.8% rate per 100 Byars
#> 7 Area3 2016 91 156 58.33333 41.233844 79.82645 99.8% rate per 100 Byars
#> 8 Area4 2016 43 190 22.63158 13.429676 35.47490 99.8% rate per 100 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 27 156 17.30769 8.783552 30.29970
#> 2 Area2 2015 36 145 24.82759 13.952944 40.48648
#> 3 Area3 2015 29 120 24.16667 12.601525 41.53299
#> 4 Area4 2015 79 123 64.22764 44.173569 89.86280
#> 5 Area1 2016 53 101 52.47525 32.961126 78.81964
#> 6 Area2 2016 46 188 24.46809 14.799981 37.81279
#> 7 Area3 2016 91 156 58.33333 41.233844 79.82645
#> 8 Area4 2016 43 190 22.63158 13.429676 35.47490These 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 171 544 0.314 0.277 0.355 95% proportion of 1 Wilson
#> 2 2016 233 635 0.367 0.330 0.405 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 171 544 31434. 26899. 36515. 95% rate per 100000 Byars
#> 2 2016 233 635 36693. 32132. 41719. 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
#> 1 404 8 23.17634 50.5 31.12409 69.87591 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 171 4 24.5 42.8 -82.2 168. 99.8% mean
#> 2 2016 233 4 22.2 58.2 -55.3 172. 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 171 4 24.5 42.8 -82.2 168.
#> 2 2016 233 4 22.2 58.2 -55.3 172.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 0 195 15786
#> 2 Area1 2006 Male 5 159 18310
#> 3 Area1 2006 Male 10 129 14997
#> 4 Area1 2006 Male 15 121 18247
#> 5 Area1 2006 Male 20 77 10446
#> 6 Area1 2006 Male 25 46 15595INPUT: 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 1976 285485 712. 679. 746. 95%
#> 2 Area1 2006 Male 2067 292506 713. 680. 748. 95%
#> 3 Area1 2007 Female 2027 304681 670. 638. 702. 95%
#> 4 Area1 2007 Male 2110 292002 761. 726. 796. 95%
#> 5 Area1 2008 Female 1872 288328 706. 673. 740. 95%
#> 6 Area1 2008 Male 2172 284452 807. 771. 844. 95%
#> 7 Area1 2009 Female 1765 272181 651. 619. 684. 95%
#> 8 Area1 2009 Male 1935 280477 760. 725. 797. 95%
#> 9 Area1 2010 Female 2419 293242 924. 884. 965. 95%
#> 10 Area1 2010 Male 2137 266901 878. 839. 918. 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 1976 285485 712. 679. 746.
#> 2 Area1 2006 Male 2067 292506 713. 680. 748.
#> 3 Area1 2007 Female 2027 304681 670. 638. 702.
#> 4 Area1 2007 Male 2110 292002 761. 726. 796.
#> 5 Area1 2008 Female 1872 288328 706. 673. 740.
#> 6 Area1 2008 Male 2172 284452 807. 771. 844.
#> 7 Area1 2009 Female 1765 272181 651. 619. 684.
#> 8 Area1 2009 Male 1935 280477 760. 725. 797.
#> 9 Area1 2010 Female 2419 293242 924. 884. 965.
#> 10 Area1 2010 Male 2137 266901 878. 839. 918.
#> # 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 1613 227232 716. 681. 752. 95%
#> 2 Area1 2006 Male 1541 222402 722. 685. 760. 95%
#> 3 Area1 2007 Female 1484 235090 667. 632. 702. 95%
#> 4 Area1 2007 Male 1688 226660 772. 735. 811. 95%
#> 5 Area1 2008 Female 1594 231582 714. 679. 751. 95%
#> 6 Area1 2008 Male 1797 227822 825. 786. 865. 95%
#> 7 Area1 2009 Female 1361 223702 635. 601. 670. 95%
#> 8 Area1 2009 Male 1722 216161 806. 767. 846. 95%
#> 9 Area1 2010 Female 2020 215807 978. 935. 1023. 95%
#> 10 Area1 2010 Male 1748 201405 891. 849. 934. 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 0 970 113564
#> 2 5 971 103121
#> 3 10 831 112802
#> 4 15 865 128519
#> 5 20 952 111298
#> 6 25 400 129930Here 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 1976 1921. 1.03 0.984 1.08 95%
#> 2 Area1 2006 Male 2067 1971. 1.05 1.00 1.09 95%
#> 3 Area1 2007 Female 2027 2100. 0.965 0.924 1.01 95%
#> 4 Area1 2007 Male 2110 1984. 1.06 1.02 1.11 95%
#> 5 Area1 2008 Female 1872 1957. 0.957 0.914 1.00 95%
#> 6 Area1 2008 Male 2172 1958. 1.11 1.06 1.16 95%
#> 7 Area1 2009 Female 1765 1848. 0.955 0.911 1.00 95%
#> 8 Area1 2009 Male 1935 1869. 1.04 0.990 1.08 95%
#> 9 Area1 2010 Female 2419 2013. 1.20 1.15 1.25 95%
#> 10 Area1 2010 Male 2137 1799. 1.19 1.14 1.24 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 1976 1921. 1.03 0.984 1.08
#> 2 Area1 2006 Male 2067 1971. 1.05 1.00 1.09
#> 3 Area1 2007 Female 2027 2100. 0.965 0.924 1.01
#> 4 Area1 2007 Male 2110 1984. 1.06 1.02 1.11
#> 5 Area1 2008 Female 1872 1957. 0.957 0.914 1.00
#> 6 Area1 2008 Male 2172 1958. 1.11 1.06 1.16
#> 7 Area1 2009 Female 1765 1848. 0.955 0.911 1.00
#> 8 Area1 2009 Male 1935 1869. 1.04 0.990 1.08
#> 9 Area1 2010 Female 2419 2013. 1.20 1.15 1.25
#> 10 Area1 2010 Male 2137 1799. 1.19 1.14 1.24
#> # 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… 1976 1921. 671. 690. 660. 721. 95%
#> 2 Area1 2006 Male 2067 1971. 671. 703. 673. 734. 95%
#> 3 Area1 2007 Fema… 2027 2100. 671. 647. 619. 676. 95%
#> 4 Area1 2007 Male 2110 1984. 671. 713. 683. 744. 95%
#> 5 Area1 2008 Fema… 1872 1957. 671. 641. 613. 671. 95%
#> 6 Area1 2008 Male 2172 1958. 671. 744. 713. 776. 95%
#> 7 Area1 2009 Fema… 1765 1848. 671. 640. 611. 671. 95%
#> 8 Area1 2009 Male 1935 1869. 671. 694. 664. 726. 95%
#> 9 Area1 2010 Fema… 2419 2013. 671. 806. 774. 839. 95%
#> 10 Area1 2010 Male 2137 1799. 671. 797. 763. 831. 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 1976 1921. 671. 690. 660. 721.
#> 2 Area1 2006 Male 2067 1971. 671. 703. 673. 734.
#> 3 Area1 2007 Female 2027 2100. 671. 647. 619. 676.
#> 4 Area1 2007 Male 2110 1984. 671. 713. 683. 744.
#> 5 Area1 2008 Female 1872 1957. 671. 641. 613. 671.
#> 6 Area1 2008 Male 2172 1958. 671. 744. 713. 776.
#> 7 Area1 2009 Female 1765 1848. 671. 640. 611. 671.
#> 8 Area1 2009 Male 1935 1869. 671. 694. 664. 726.
#> 9 Area1 2010 Female 2419 2013. 671. 806. 774. 839.
#> 10 Area1 2010 Male 2137 1799. 671. 797. 763. 831.
#> # ℹ 30 more rowsThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.