The RAM-OP Workflow
The RAM-OP Workflow is summarised in the diagram below.
The oldr package provides functions to use for all steps
after data collection. These functions were developed specifically for
the data structure created by the EpiData
or the Open Data
Kit collection tools. The data structure produced by these
collection tools is shown by the dataset testSVY included
in the oldr package.
testSVY
#> # A tibble: 192 × 90
#> ad2 psu hh id d1 d2 d3 d4 d5 f1 f2a f2b f2c
#> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 1 201 1 1 1 67 2 5 2 3 2 1 1
#> 2 1 201 2 1 1 74 1 2 2 3 2 1 1
#> 3 1 201 3 1 1 60 1 2 2 2 2 2 2
#> 4 1 201 3 2 1 60 2 2 2 3 2 2 1
#> 5 1 201 4 1 1 85 2 5 2 3 2 1 1
#> 6 1 201 5 1 2 86 1 5 1 4 2 1 1
#> 7 1 201 6 1 1 80 1 5 2 3 2 1 1
#> 8 1 201 6 2 1 60 2 5 2 3 2 2 1
#> 9 1 201 7 1 1 62 1 2 2 2 2 1 1
#> 10 1 201 8 1 1 72 2 5 2 2 2 1 1
#> # ℹ 182 more rows
#> # ℹ 77 more variables: f2d <int>, f2e <int>, f2f <int>, f2g <int>, f2h <int>,
#> # f2i <int>, f2j <int>, f2k <int>, f2l <int>, f2m <int>, f2n <int>,
#> # f2o <int>, f2p <int>, f2q <int>, f2r <int>, f2s <int>, f3 <int>, f4 <int>,
#> # f5 <int>, f6 <int>, f7 <int>, a1 <int>, a2 <int>, a3 <int>, a4 <int>,
#> # a5 <int>, a6 <int>, a7 <int>, a8 <int>, k6a <int>, k6b <int>, k6c <int>,
#> # k6d <int>, k6e <int>, k6f <int>, ds1 <int>, ds2 <int>, ds3 <int>, …Processing and recoding data
Once RAM-OP data is collected, it will need to be processed and
recoded based on the definitions of the various indicators included in
RAM-OP. The oldr package provides a suite functions to
perform this processing and recoding. These functions and their syntax
can be easily remembered as the create_op_ functions as
their function names start with the create_ verb followed
by the op_ label and then followed by an indicator or
indicator set specific identifier or short name. Finally, an additional
tag for male or female can be added to the
main function to provide gender-specific outputs.
Currently, a standard RAM-OP can provide results for the 13 indicators or indicator sets for older people. The following table shows these indicators/indicator sets alongside the functions related to them:
| Indicator / Indicator Set | Related Functions |
|---|---|
| Demography and situation | create_op_demo;
create_op_demo_males;
create_op_demo_females |
| Food intake | create_op_food;
create_op_food_males;
create_op_food_females |
| Severe food insecurity | create_op_hunger;
create_op_hunger_males;
create_op_hunger_females |
| Disability | create_op_disability;
create_op_disability_males;
create_op_disability_females |
| Activities of daily living | create_op_adl;
create_op_adl_males;
create_op_adl_females |
| Mental health and well-being | create_op_mental;
create_op_mental_males;
create_op_mental_females |
| Dementia | create_op_dementia;
create_op_dementia_males;
create_op_dementia_females |
| Health and health-seeking behaviour | create_op_health;
create_op_health_males;
create_op_health_females |
| Sources of income | create_op_income;
create_op_income_males;
create_op_income_females |
| Water, sanitation, and hygiene | create_op_wash;
create_op_wash_males;
create_op_wash_females |
| Anthropometry and anthropometric screening coverage | create_op_anthro;
create_op_anthro_males;
create_op_anthro_females |
| Visual impairment | create_op_visual;
create_op_visual_males;
create_op_visual_females |
| Miscellaneous | create_op_misc;
create_op_misc_males;
create_op_misc_females |
A final function in the processing and recoding set -
create_op - is provided to perform the processing and
recoding of all indicators or indicator sets. This function allows for
the specification of which indicators or indicator sets to process and
recode which is useful for cases where not all the indicators or
indicator sets have been collected or if only specific indicators or
indicator sets need to be analysed or reported. This function also
specifies whether a specific gender subset of the data is needed.
For a standard RAM-OP implementation, this step is performed in R as follows:
## Process and recode all standard RAM-OP indicators in the testSVY dataset
create_op(svy = testSVY)which results in the following output:
#> # A tibble: 192 × 138
#> psu sex1 sex2 resp1 resp2 resp3 resp4 age ageGrp1 ageGrp2 ageGrp3
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 201 0 1 1 0 0 0 67 0 1 0
#> 2 201 1 0 1 0 0 0 74 0 0 1
#> 3 201 1 0 1 0 0 0 60 0 1 0
#> 4 201 0 1 1 0 0 0 60 0 1 0
#> 5 201 0 1 1 0 0 0 85 0 0 0
#> 6 201 1 0 0 1 0 0 86 0 0 0
#> 7 201 1 0 1 0 0 0 80 0 0 0
#> 8 201 0 1 1 0 0 0 60 0 1 0
#> 9 201 1 0 1 0 0 0 62 0 1 0
#> 10 201 0 1 1 0 0 0 72 0 0 1
#> # ℹ 182 more rows
#> # ℹ 127 more variables: ageGrp4 <dbl>, ageGrp5 <dbl>, marital1 <dbl>,
#> # marital2 <dbl>, marital3 <dbl>, marital4 <dbl>, marital5 <dbl>,
#> # marital6 <dbl>, alone <dbl>, MF <dbl>, DDS <dbl>, FG01 <dbl>, FG02 <dbl>,
#> # FG03 <dbl>, FG04 <dbl>, FG05 <dbl>, FG06 <dbl>, FG07 <dbl>, FG08 <dbl>,
#> # FG09 <dbl>, FG10 <dbl>, FG11 <dbl>, proteinRich <dbl>, pProtein <dbl>,
#> # aProtein <dbl>, pVitA <dbl>, aVitA <dbl>, xVitA <dbl>, ironRich <dbl>, …Estimating indicators
Once data has been processed and appropriate recoding for indicators has been performed, indicator estimates can now be calculated.
It is important to note that estimation procedures need to account for the sample design. All major statistical analysis software can do this (details vary). There are two things to note:
The RAM-OP sample is a two-stage sample. Subjects are sampled from a small number of primary sampling units (PSUs).
The RAM-OP sample is not prior weighted. This means that per-PSU sampling weights are needed. These are usually the populations of the PSU.
This sample design will need to be specified to statistical analysis software being used. If no weights are provided, then the analysis may produce estimates that place undue weight to observations from smaller communities with confidence intervals with lower than nominal coverage (i.e. they will be too narrow).
Blocked weighted bootstrap
The oldr package uses blocked weighted
bootstrap estimation approach:
Blocked : The block corresponds to the PSU or cluster.
Weighted : The RAM-OP sampling procedure does not use population proportional sampling to weight the sample prior to data collection as is done with SMART type surveys. This means that a posterior weighting procedure is required. The standard RAM-OP software uses a “roulette wheel” algorithm to weight (i.e. by population) the selection probability of PSUs in bootstrap replicates.
A total of m PSUs are sampled with-replacement from the
survey dataset where m is the number of PSUs in the survey
sample. Individual records within each PSU are then sampled
with-replacement. A total of n records are sampled
with-replacement from each of the selected PSUs where n is
the number of individual records in a selected PSU. The resulting
collection of records replicates the original survey in terms of both
sample design and sample size. A large number of replicate surveys are
taken (the standard RAM-OP software uses r = 399 replicate surveys but this
can be changed). The required statistic (e.g. the mean of an indicator
value) is applied to each replicate survey. The reported estimate
consists of the 50th (point estimate), 2.5th (lower 95% confidence
limit), and the 97.5th (upper 95% confidence limit) percentiles of the
distribution of the statistic observed across all replicate surveys. The
blocked weighted bootstrap procedure is outlined in the figure
below.
The principal advantages of using a bootstrap estimator are:
Bootstrap estimators work well with small sample sizes.
The method is non-parametric and uses empirical rather than theoretical distributions. There are no assumptions of things like normality to worry about.
The method allows estimation of the sampling distribution of almost any statistic using only simple computational methods.
PROBIT estimator
The prevalence of GAM, MAM, and SAM are estimated using a PROBIT estimator. This type of estimator provides better precision than a classic estimator at small sample sizes as discussed in the following literature:
World Health Organisation, Physical Status: The use and interpretation of anthropometry. Report of a WHO expert committee, WHO Technical Report Series 854, WHO, Geneva, 1995
Dale NM, Myatt M, Prudhon C, Briend, A, “Assessment of the PROBIT approach for estimating the prevalence of global, moderate and severe acute malnutrition from population surveys”, Public Health Nutrition, 1–6. https://doi.org/10.1017/s1368980012003345, 2012
Blanton CJ, Bilukha, OO, “The PROBIT approach in estimating the prevalence of wasting: revisiting bias and precision”, Emerging Themes in Epidemiology, 10(1), 2013, p. 8
An estimate of GAM prevalence can be made using a classic estimator:
$$ \text{prevalence} ~ = ~ \frac{\text{Number of respondents with MUAC < 210}}{\text{Total number of respondents}} $$
On the other hand, the estimate of GAM prevalence made from the RAM-OP survey data is made using a PROBIT estimator. The PROBIT function is also known as the inverse cumulative distribution function. This function converts parameters of the distribution of an indicator (e.g. the mean and standard deviation of a normally distributed variable) into cumulative percentiles. This means that it is possible to use the normal PROBIT function with estimates of the mean and standard deviation of indicator values in a survey sample to predict (or estimate) the proportion of the population falling below a given threshold. For example, for data with a mean MUAC of 256 mm and a standard deviation of 28 mm the output of the normal PROBIT function for a threshold of 210 mm is 0.0502 meaning that 5.02% of the population are predicted (or estimated) to fall below the 210 mm threshold.
Both the classic and the PROBIT methods can be thought of as estimating area:
The principal advantage of the PROBIT approach is that the required sample size is usually smaller than that required to estimate prevalence with a given precision using the classic method.
The PROBIT method assumes that MUAC is a normally distributed variable. If this is not the case then the distribution of MUAC is transformed towards normality.
The prevalence of SAM is estimated in a similar way to GAM. The prevalence of MAM is estimated as the difference between the GAM and SAM prevalence estimates:
$$ \widehat{\text{GAM prevalence}} ~ = ~ \widehat{\text{GAM prevalence}} - \widehat{\text{SAM prevalence}} $$
Classic estimator
The function estimateClassic in oldr
implements the blocked weighted bootstrap classic estimator of RAM-OP.
This function uses the bootClassic statistic to estimate
indicator values.
The estimateClassic function is used for all the
standard RAM-OP indicators except for anthropometry. The function is
used as follows:
## Process and recode RAM-OP data (testSVY)
df <- create_op(svy = testSVY)
## Perform classic estimation on recoded data using appropriate weights provided by testPSU
classicDF <- estimate_classic(x = df, w = testPSU)This results in (using limited replicates to reduce computing time):
#> # A tibble: 136 × 10
#> INDICATOR EST.ALL LCL.ALL UCL.ALL EST.MALES LCL.MALES UCL.MALES EST.FEMALES
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 resp1 0.844 0.814 0.901 0.870 0.787 0.920 0.868
#> 2 resp2 0.104 0.0604 0.152 0.0519 0.0175 0.108 0.105
#> 3 resp3 0.0365 0.0146 0.0698 0.0519 0.0123 0.146 0.0275
#> 4 resp4 0.0104 0 0.0208 0.0135 0 0.0367 0
#> 5 age 71.1 69.8 71.9 71.1 68.9 72.1 71.3
#> 6 ageGrp1 0 0 0 0 0 0 0
#> 7 ageGrp2 0.5 0.45 0.602 0.542 0.452 0.650 0.513
#> 8 ageGrp3 0.245 0.179 0.309 0.247 0.201 0.326 0.229
#> 9 ageGrp4 0.208 0.161 0.232 0.138 0.0506 0.256 0.277
#> 10 ageGrp5 0.0417 0.0156 0.0562 0.0617 0.0148 0.121 0
#> # ℹ 126 more rows
#> # ℹ 2 more variables: LCL.FEMALES <dbl>, UCL.FEMALES <dbl>PROBIT estimator
The function estimateProbit in oldr
implements the blocked weighted bootstrap PROBIT estimator of RAM-OP.
This function uses the probit_GAM and the
probit_SAM statistic to estimate indicator values.
The estimateProbit function is used for only the
anthropometric indicators. The function is used as follows:
## Process and recode RAM-OP data (testSVY)
df <- create_op(svy = testSVY)
## Perform probit estimation on recoded data using appropriate weights provided by testPSU
probitDF <- estimate_probit(x = df, w = testPSU)This results in (using limited replicates to reduce computing time):
#> # A tibble: 3 × 10
#> INDICATOR EST.ALL LCL.ALL UCL.ALL EST.MALES LCL.MALES UCL.MALES EST.FEMALES
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 GAM 0.0303 5.06e-3 0.0436 9.63e- 3 1.99e- 3 1.46e-2 0.0383
#> 2 MAM 0.0302 4.56e-3 0.0415 9.63e- 3 1.99e- 3 1.46e-2 0.0365
#> 3 SAM 0.000346 1.27e-7 0.00254 1.10e-20 8.82e-60 8.30e-7 0.00365
#> # ℹ 2 more variables: LCL.FEMALES <dbl>, UCL.FEMALES <dbl>The two sets of estimates are then merged using the
merge_op function as follows:
which results in:
#> # A tibble: 139 × 13
#> INDICATOR GROUP LABEL TYPE EST.ALL LCL.ALL UCL.ALL EST.MALES LCL.MALES
#> <fct> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 resp1 Survey Resp… Prop… 0.844 0.814 0.901 0.870 0.787
#> 2 resp2 Survey Resp… Prop… 0.104 0.0604 0.152 0.0519 0.0175
#> 3 resp3 Survey Resp… Prop… 0.0365 0.0146 0.0698 0.0519 0.0123
#> 4 resp4 Survey Resp… Prop… 0.0104 0 0.0208 0.0135 0
#> 5 age Demography… Mean… Mean 71.1 69.8 71.9 71.1 68.9
#> 6 ageGrp1 Demography… Self… Prop… 0 0 0 0 0
#> 7 ageGrp2 Demography… Self… Prop… 0.5 0.45 0.602 0.542 0.452
#> 8 ageGrp3 Demography… Self… Prop… 0.245 0.179 0.309 0.247 0.201
#> 9 ageGrp4 Demography… Self… Prop… 0.208 0.161 0.232 0.138 0.0506
#> 10 ageGrp5 Demography… Self… Prop… 0.0417 0.0156 0.0562 0.0617 0.0148
#> # ℹ 129 more rows
#> # ℹ 4 more variables: UCL.MALES <dbl>, EST.FEMALES <dbl>, LCL.FEMALES <dbl>,
#> # UCL.FEMALES <dbl>Creating charts
Once indicators has been estimated, the outputs can then be used to
create relevant charts to visualise the results. A set of functions that
start with the verb chart_op_ is provided followed by the
indicator identifier to specify the type of indicator to visualise. The
output of the function is a PNG file saved in the specified filename
appended to the indicator identifier within the current working
directory or saved in the specified filename appended to the indicator
identifier in the specified directory path.
The following shows how to produce the chart for ADLs saved with filename test appended at the start inside a temporary directory:
The resulting PNG file can be found in the temporary directory
and will look something like this:
Reporting estimates
Finally, estimates can be reported through report tables. The
report_op_table function facilitates this through the
following syntax:
The resulting CSV file is found in the temporary directory
and will look something like this:
#> X X.1 X.2 X.3 X.4 X.5 X.6
#> 1 Survey
#> 2 ALL MALES
#> 3 INDICATOR TYPE EST LCL UCL EST LCL
#> 4 99 2 0.8438 0.8135 0.9010 0.8701 0.7865
#> 5 96 2 0.1042 0.0604 0.1521 0.0519 0.0175
#> 6 98 2 0.0365 0.0146 0.0698 0.0519 0.0123
#> 7 97 2 0.0104 0.0000 0.0208 0.0135 0.0000
#> 8
#> 9 Demography and situation
#> 10 ALL MALES
#> 11 INDICATOR TYPE EST LCL UCL EST LCL
#> 12 54 1 71.1354 69.8094 71.9469 71.1190 68.8814
#> 13 106 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 14 107 2 0.5000 0.4500 0.6021 0.5422 0.4519
#> 15 108 2 0.2448 0.1792 0.3094 0.2468 0.2005
#> 16 109 2 0.2083 0.1615 0.2323 0.1375 0.0506
#> 17 105 2 0.0417 0.0156 0.0563 0.0617 0.0148
#> 18 115 2 0.4062 0.3521 0.4656 1.0000 1.0000
#> 19 114 2 0.5938 0.5344 0.6479 0.0000 0.0000
#> 20 51 2 0.0365 0.0260 0.0646 0.0130 0.0000
#> 21 49 2 0.2969 0.2573 0.3604 0.5663 0.4062
#> 22 48 2 0.1042 0.0573 0.1677 0.1625 0.0599
#> 23 47 2 0.0625 0.0219 0.0875 0.0723 0.0314
#> 24 52 2 0.4844 0.4344 0.5281 0.1875 0.0351
#> 25 50 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 26 127 2 0.1406 0.1208 0.1760 0.1351 0.0545
#> 27
#> 28 Diet
#> 29 ALL MALES
#> 30 INDICATOR TYPE EST LCL UCL EST LCL
#> 31 53 1 2.5625 2.3750 2.6531 2.4444 2.3321
#> 32 25 1 4.5365 4.3427 4.7271 4.4250 3.8745
#> 33 14 2 0.9062 0.8542 0.9302 0.9383 0.8562
#> 34 23 2 0.5521 0.4615 0.5615 0.4861 0.4006
#> 35 18 2 0.6094 0.5094 0.6698 0.5522 0.4607
#> 36 20 2 0.0417 0.0271 0.0646 0.0270 0.0048
#> 37 15 2 0.0208 0.0104 0.0479 0.0357 0.0000
#> 38 17 2 0.3385 0.2292 0.4010 0.4250 0.3413
#> 39 19 2 0.4375 0.3969 0.4740 0.4048 0.1651
#> 40 21 2 0.0260 0.0156 0.0396 0.0000 0.0000
#> 41 16 2 0.2240 0.1562 0.2604 0.2143 0.1761
#> 42 24 2 0.4740 0.4146 0.5667 0.3766 0.2816
#> 43 22 2 0.9792 0.9594 0.9927 0.9753 0.9281
#> 44
#> 45 Nutrients
#> 46 ALL MALES
#> 47 INDICATOR TYPE EST LCL UCL EST LCL
#> 48 88 2 0.4844 0.4240 0.5396 0.4524 0.2036
#> 49 89 2 0.4375 0.3969 0.4740 0.4048 0.1651
#> 50 87 2 0.0885 0.0604 0.1302 0.0779 0.0072
#> 51 83 2 0.6354 0.5406 0.6875 0.5714 0.4889
#> 52 2 2 0.0521 0.0271 0.0792 0.0370 0.0024
#> 53 3 2 0.6406 0.5844 0.7167 0.5946 0.5173
#> 54 42 2 0.6562 0.6115 0.7427 0.6418 0.4512
#> 55 9 2 0.0260 0.0156 0.0396 0.0000 0.0000
#> 56 140 2 0.6146 0.5354 0.7010 0.6429 0.5115
#> 57 135 2 0.6823 0.6000 0.7292 0.6548 0.5290
#> 58 137 2 0.8281 0.7781 0.8792 0.7160 0.6514
#> 59 138 2 0.6146 0.5354 0.7010 0.6429 0.5115
#> 60 139 2 0.8646 0.8292 0.9146 0.8571 0.7556
#> 61 136 2 0.4010 0.2833 0.4615 0.4625 0.3771
#> 62 134 2 0.3854 0.2604 0.4531 0.4625 0.3771
#> 63
#> 64 Food Security
#> 65 ALL MALES
#> 66 INDICATOR TYPE EST LCL UCL EST LCL
#> 67 45 2 0.8125 0.7510 0.8802 0.7273 0.7161
#> 68 60 2 0.1354 0.0802 0.1917 0.2078 0.1504
#> 69 113 2 0.0312 0.0062 0.0875 0.0241 0.0000
#> 70
#> 71 Disability (WG)
#> 72 ALL MALES
#> 73 INDICATOR TYPE EST LCL UCL EST LCL
#> 74 129 2 1.0000 1.0000 1.0000 1.0000 1.0000
#> 75 130 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 76 131 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 77 132 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 78 28 2 1.0000 1.0000 1.0000 1.0000 1.0000
#> 79 29 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 80 30 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 81 31 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 82 55 2 1.0000 1.0000 1.0000 1.0000 1.0000
#> 83 56 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 84 57 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 85 58 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 86 92 2 1.0000 1.0000 1.0000 1.0000 1.0000
#> 87 93 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 88 94 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 89 95 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 90 101 2 1.0000 1.0000 1.0000 1.0000 1.0000
#> 91 102 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 92 103 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 93 104 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 94 10 2 1.0000 1.0000 1.0000 1.0000 1.0000
#> 95 11 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 96 12 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 97 13 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 98 63 2 1.0000 1.0000 1.0000 1.0000 1.0000
#> 99 5 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 100 6 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 101 7 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 102 62 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 103
#> 104 Activities of daily living
#> 105 ALL MALES
#> 106 INDICATOR TYPE EST LCL UCL EST LCL
#> 107 35 2 0.9792 0.9365 0.9844 0.9740 0.8944
#> 108 37 2 0.9896 0.9552 0.9948 0.9870 0.9280
#> 109 39 2 0.9896 0.9552 0.9948 0.9870 0.9280
#> 110 40 2 0.9583 0.9312 0.9740 0.9610 0.9255
#> 111 36 2 0.7344 0.6781 0.7771 0.7531 0.6046
#> 112 38 2 0.9896 0.9719 1.0000 1.0000 0.9588
#> 113 44 1 5.6302 5.4594 5.7052 5.7024 5.2750
#> 114 41 2 0.9844 0.9469 0.9896 0.9870 0.9280
#> 115 82 2 0.0104 0.0000 0.0344 0.0000 0.0000
#> 116 112 2 0.0104 0.0010 0.0281 0.0130 0.0000
#> 117 126 2 0.5833 0.5490 0.6427 0.5405 0.4574
#> 118 125 2 0.1146 0.0802 0.1448 0.1528 0.1117
#> 119
#> 120 Mental health
#> 121 ALL MALES
#> 122 INDICATOR TYPE EST LCL UCL EST LCL
#> 123 43 1 12.2552 11.8490 13.2135 10.9012 8.6286
#> 124 110 2 0.5000 0.4583 0.5375 0.4054 0.2626
#> 125 85 2 0.1823 0.1198 0.2552 0.1500 0.0671
#> 126
#> 127 Health
#> 128 ALL MALES
#> 129 INDICATOR TYPE EST LCL UCL EST LCL
#> 130 46 2 0.4531 0.4042 0.4781 0.3506 0.2965
#> 131 128 2 0.7000 0.5964 0.8020 0.5556 0.4590
#> 132 74 2 0.2083 0.1120 0.3389 0.0769 0.0000
#> 133 79 2 0.3636 0.1833 0.6283 0.5000 0.0400
#> 134 80 2 0.1000 0.0000 0.2111 0.0000 0.0000
#> 135 81 2 0.1111 0.0000 0.2758 0.2308 0.0222
#> 136 73 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 137 77 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 138 75 2 0.0000 0.0000 0.1569 0.0000 0.0000
#> 139 78 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 140 76 2 0.1212 0.0569 0.3407 0.1667 0.0000
#> 141 91 2 0.8698 0.8250 0.9156 0.8916 0.6958
#> 142 1 2 0.8114 0.7667 0.8681 0.7162 0.5678
#> 143 65 2 0.0938 0.0000 0.2628 0.0000 0.0000
#> 144 70 2 0.8400 0.5350 0.9044 0.9048 0.5571
#> 145 71 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 146 72 2 0.0345 0.0000 0.1973 0.0571 0.0000
#> 147 64 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 148 68 2 0.0263 0.0000 0.1705 0.0000 0.0000
#> 149 66 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 150 69 2 0.0000 0.0000 0.0250 0.0000 0.0000
#> 151 67 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 152 8 2 0.0156 0.0062 0.0500 0.0123 0.0000
#> 153 133 2 0.3802 0.3208 0.4542 0.4881 0.4472
#> 154 86 2 0.2865 0.2344 0.3667 0.2537 0.1729
#> 155
#> 156 Income
#> 157 ALL MALES
#> 158 INDICATOR TYPE EST LCL UCL EST LCL
#> 159 27 2 0.5833 0.4594 0.6135 0.5974 0.5619
#> 160 116 2 0.3698 0.2646 0.4500 0.4805 0.3432
#> 161 124 2 0.1198 0.0521 0.1396 0.2090 0.1621
#> 162 121 2 0.0208 0.0062 0.0479 0.0494 0.0000
#> 163 123 2 0.0677 0.0312 0.1021 0.0135 0.0000
#> 164 119 2 0.0000 0.0000 0.0094 0.0000 0.0000
#> 165 122 2 0.0000 0.0000 0.0188 0.0361 0.0000
#> 166 118 2 0.0104 0.0052 0.0427 0.0130 0.0000
#> 167 117 2 0.3073 0.2812 0.4365 0.2840 0.1586
#> 168 120 2 0.0052 0.0000 0.0156 0.0123 0.0000
#> 169
#> 170 WASH
#> 171 ALL MALES
#> 172 INDICATOR TYPE EST LCL UCL EST LCL
#> 173 34 2 0.5781 0.4844 0.6844 0.6071 0.4739
#> 174 100 2 0.6875 0.5760 0.7750 0.6190 0.5135
#> 175 33 2 0.2135 0.1333 0.2813 0.2727 0.1973
#> 176 32 2 0.1927 0.1292 0.2792 0.2727 0.1973
#> 177
#> 178 Relief
#> 179 ALL MALES
#> 180 INDICATOR TYPE EST LCL UCL EST LCL
#> 181 84 2 0.0365 0.0260 0.0594 0.0270 0.0000
#> 182 4 2 0.0521 0.0104 0.0917 0.0370 0.0024
#> 183 90 2 0.0208 0.0062 0.0531 0.0299 0.0024
#> 184
#> 185 Anthropometry
#> 186 ALL MALES
#> 187 INDICATOR TYPE EST LCL UCL EST LCL
#> 188 26 2 0.0303 0.0051 0.0436 0.0096 0.0020
#> 189 59 2 0.0302 0.0046 0.0415 0.0096 0.0020
#> 190 111 2 0.0003 0.0000 0.0025 0.0000 0.0000
#> X.7 X.8 X.9 X.10
#> 1
#> 2 FEMALES
#> 3 UCL EST LCL UCL
#> 4 0.9202 0.8679 0.8084 0.9018
#> 5 0.1076 0.1048 0.0665 0.1609
#> 6 0.1460 0.0275 0.0000 0.0471
#> 7 0.0367 0.0000 0.0000 0.0232
#> 8
#> 9
#> 10 FEMALES
#> 11 UCL EST LCL UCL
#> 12 72.1408 71.3204 69.6729 72.0494
#> 13 0.0000 0.0000 0.0000 0.0000
#> 14 0.6499 0.5133 0.4687 0.5484
#> 15 0.3264 0.2288 0.1564 0.2777
#> 16 0.2557 0.2768 0.1943 0.3117
#> 17 0.1214 0.0000 0.0000 0.0343
#> 18 1.0000 0.0000 0.0000 0.0000
#> 19 0.0000 1.0000 1.0000 1.0000
#> 20 0.0710 0.0357 0.0268 0.0569
#> 21 0.6855 0.1619 0.0736 0.2105
#> 22 0.2618 0.0660 0.0368 0.1034
#> 23 0.1187 0.0286 0.0188 0.0730
#> 24 0.2938 0.6696 0.6602 0.7792
#> 25 0.0000 0.0000 0.0000 0.0000
#> 26 0.1917 0.1262 0.0938 0.1665
#> 27
#> 28
#> 29 FEMALES
#> 30 UCL EST LCL UCL
#> 31 2.7896 2.6415 2.5068 2.7752
#> 32 4.9325 4.5138 4.4365 4.9077
#> 33 0.9642 0.9143 0.8872 0.9929
#> 34 0.6097 0.5189 0.4807 0.6863
#> 35 0.6649 0.6038 0.5435 0.6490
#> 36 0.1372 0.0381 0.0113 0.1089
#> 37 0.1102 0.0194 0.0018 0.0396
#> 38 0.5631 0.2400 0.1812 0.3226
#> 39 0.5310 0.4476 0.3483 0.4896
#> 40 0.0468 0.0459 0.0119 0.0917
#> 41 0.2857 0.2095 0.1456 0.2792
#> 42 0.4592 0.5421 0.5049 0.5828
#> 43 1.0000 0.9709 0.9476 1.0000
#> 44
#> 45
#> 46 FEMALES
#> 47 UCL EST LCL UCL
#> 48 0.5779 0.5085 0.4406 0.5524
#> 49 0.5310 0.4476 0.3483 0.4896
#> 50 0.2847 0.1102 0.0461 0.2190
#> 51 0.6528 0.6449 0.5840 0.6795
#> 52 0.1517 0.0642 0.0284 0.1198
#> 53 0.7039 0.6636 0.5916 0.7158
#> 54 0.7833 0.6505 0.6200 0.7093
#> 55 0.0468 0.0459 0.0119 0.0917
#> 56 0.8452 0.5840 0.4574 0.6190
#> 57 0.8452 0.6240 0.5256 0.6806
#> 58 0.9524 0.8349 0.7794 0.8682
#> 59 0.8452 0.5840 0.4574 0.6190
#> 60 0.9762 0.8571 0.8015 0.8870
#> 61 0.5837 0.2768 0.2255 0.4313
#> 62 0.5837 0.2679 0.2146 0.3939
#> 63
#> 64
#> 65 FEMALES
#> 66 UCL EST LCL UCL
#> 67 0.8087 0.8165 0.7234 0.8486
#> 68 0.2707 0.1359 0.0753 0.1988
#> 69 0.0906 0.0095 0.0000 0.0622
#> 70
#> 71
#> 72 FEMALES
#> 73 UCL EST LCL UCL
#> 74 1.0000 1.0000 1.0000 1.0000
#> 75 0.0000 0.0000 0.0000 0.0000
#> 76 0.0000 0.0000 0.0000 0.0000
#> 77 0.0000 0.0000 0.0000 0.0000
#> 78 1.0000 1.0000 1.0000 1.0000
#> 79 0.0000 0.0000 0.0000 0.0000
#> 80 0.0000 0.0000 0.0000 0.0000
#> 81 0.0000 0.0000 0.0000 0.0000
#> 82 1.0000 1.0000 1.0000 1.0000
#> 83 0.0000 0.0000 0.0000 0.0000
#> 84 0.0000 0.0000 0.0000 0.0000
#> 85 0.0000 0.0000 0.0000 0.0000
#> 86 1.0000 1.0000 1.0000 1.0000
#> 87 0.0000 0.0000 0.0000 0.0000
#> 88 0.0000 0.0000 0.0000 0.0000
#> 89 0.0000 0.0000 0.0000 0.0000
#> 90 1.0000 1.0000 1.0000 1.0000
#> 91 0.0000 0.0000 0.0000 0.0000
#> 92 0.0000 0.0000 0.0000 0.0000
#> 93 0.0000 0.0000 0.0000 0.0000
#> 94 1.0000 1.0000 1.0000 1.0000
#> 95 0.0000 0.0000 0.0000 0.0000
#> 96 0.0000 0.0000 0.0000 0.0000
#> 97 0.0000 0.0000 0.0000 0.0000
#> 98 1.0000 1.0000 1.0000 1.0000
#> 99 0.0000 0.0000 0.0000 0.0000
#> 100 0.0000 0.0000 0.0000 0.0000
#> 101 0.0000 0.0000 0.0000 0.0000
#> 102 0.0000 0.0000 0.0000 0.0000
#> 103
#> 104
#> 105 FEMALES
#> 106 UCL EST LCL UCL
#> 107 0.9976 0.9908 0.9647 1.0000
#> 108 1.0000 1.0000 0.9929 1.0000
#> 109 1.0000 1.0000 0.9929 1.0000
#> 110 1.0000 0.9576 0.9270 0.9821
#> 111 0.8492 0.6990 0.6076 0.7745
#> 112 1.0000 1.0000 1.0000 1.0000
#> 113 5.7377 5.6604 5.5297 5.7293
#> 114 1.0000 0.9760 0.9444 1.0000
#> 115 0.0000 0.0240 0.0000 0.0556
#> 116 0.0720 0.0000 0.0000 0.0000
#> 117 0.6992 0.6075 0.4596 0.6668
#> 118 0.2669 0.0826 0.0654 0.1336
#> 119
#> 120
#> 121 FEMALES
#> 122 UCL EST LCL UCL
#> 123 12.4571 12.7431 11.9183 13.7337
#> 124 0.4913 0.5140 0.4762 0.6301
#> 125 0.2905 0.2358 0.1529 0.2606
#> 126
#> 127
#> 128 FEMALES
#> 129 UCL EST LCL UCL
#> 130 0.4611 0.4762 0.4196 0.5479
#> 131 0.8074 0.8431 0.7509 0.8909
#> 132 0.2333 0.1250 0.0000 0.3167
#> 133 0.6500 0.3750 0.1471 0.5943
#> 134 0.0000 0.3333 0.1267 0.6200
#> 135 0.6442 0.0000 0.0000 0.0000
#> 136 0.0000 0.0000 0.0000 0.0000
#> 137 0.0000 0.0000 0.0000 0.0000
#> 138 0.0000 0.0000 0.0000 0.2286
#> 139 0.0000 0.0000 0.0000 0.0000
#> 140 0.7567 0.0000 0.0000 0.3357
#> 141 0.9293 0.8879 0.8387 0.9115
#> 142 0.8108 0.8692 0.7851 0.8926
#> 143 0.1925 0.0714 0.0000 0.1835
#> 144 1.0000 0.8182 0.7171 0.9833
#> 145 0.0000 0.0000 0.0000 0.0000
#> 146 0.4417 0.0000 0.0000 0.0000
#> 147 0.0000 0.0000 0.0000 0.0000
#> 148 0.0000 0.0714 0.0000 0.2078
#> 149 0.0000 0.0000 0.0000 0.0000
#> 150 0.0000 0.0000 0.0000 0.0571
#> 151 0.0000 0.0000 0.0000 0.0000
#> 152 0.0291 0.0283 0.0162 0.0517
#> 153 0.5161 0.3458 0.2817 0.4176
#> 154 0.3316 0.3429 0.2619 0.4493
#> 155
#> 156
#> 157 FEMALES
#> 158 UCL EST LCL UCL
#> 159 0.7472 0.5524 0.4189 0.5833
#> 160 0.5443 0.3010 0.2140 0.3642
#> 161 0.3105 0.0254 0.0083 0.0834
#> 162 0.0950 0.0000 0.0000 0.0161
#> 163 0.0717 0.0763 0.0348 0.1312
#> 164 0.0000 0.0160 0.0000 0.0267
#> 165 0.1109 0.0000 0.0000 0.0000
#> 166 0.1097 0.0189 0.0000 0.0360
#> 167 0.4698 0.3585 0.2829 0.4194
#> 168 0.0248 0.0160 0.0000 0.0271
#> 169
#> 170
#> 171 FEMALES
#> 172 UCL EST LCL UCL
#> 173 0.6335 0.6117 0.5612 0.7284
#> 174 0.6915 0.7358 0.6491 0.8076
#> 175 0.3759 0.2569 0.1438 0.3039
#> 176 0.3663 0.2569 0.1232 0.3021
#> 177
#> 178
#> 179 FEMALES
#> 180 UCL EST LCL UCL
#> 181 0.0961 0.0280 0.0018 0.0549
#> 182 0.1254 0.0536 0.0268 0.0647
#> 183 0.0618 0.0275 0.0018 0.0549
#> 184
#> 185
#> 186 FEMALES
#> 187 UCL EST LCL UCL
#> 188 0.0146 0.0383 0.0231 0.0818
#> 189 0.0146 0.0365 0.0202 0.0776
#> 190 0.0000 0.0037 0.0002 0.0117The RAM-OP workflow in R using pipe operators
The oldr package functions were designed in such a way
that they can be piped to each other to provide the desired output.
Below we use the base R pipe operator |>.
Piped operation to get output estimates table
testSVY |>
create_op() |>
estimate_op(w = testPSU, replicates = 9) |>
report_op_table(filename = file.path(tempdir(), "TEST"))This results in a CSV file TEST.report.csv in the
temporary directory
with the following structure:
#> X X.1 X.2 X.3 X.4 X.5
#> 1 Survey
#> 2 ALL MALES
#> 3 INDICATOR TYPE EST LCL UCL EST
#> 4 99 2 85.9375 82.3958 87.5000 81.8182
#> 5 96 2 9.8958 7.9167 10.8333 8.9744
#> 6 98 2 4.1667 1.8750 6.4583 6.4103
#> 7 97 2 0.0000 0.0000 1.0417 1.2821
#> 8
#> 9 Demography and situation
#> 10 ALL MALES
#> 11 INDICATOR TYPE EST LCL UCL EST
#> 12 54 1 71.5938 70.3135 73.4292 71.9882
#> 13 106 2 0.0000 0.0000 0.0000 0.0000
#> 14 107 2 48.4375 42.9167 56.5625 48.0519
#> 15 108 2 21.8750 18.4375 26.2500 28.5714
#> 16 109 2 21.8750 17.9167 31.5625 18.2927
#> 17 105 2 4.1667 1.6667 9.3750 7.6923
#> 18 115 2 41.1458 34.8958 50.1042 100.0000
#> 19 114 2 58.8542 49.8958 65.1042 0.0000
#> 20 51 2 3.6458 1.5625 7.3958 1.2987
#> 21 49 2 30.2083 25.6250 41.2500 50.0000
#> 22 48 2 12.5000 8.4375 16.5625 14.2857
#> 23 47 2 6.7708 3.8542 7.2917 13.4146
#> 24 52 2 47.3958 36.2500 50.7292 18.2927
#> 25 50 2 0.0000 0.0000 0.0000 0.0000
#> 26 127 2 13.0208 8.9583 16.4583 16.8831
#> 27
#> 28 Diet
#> 29 ALL MALES
#> 30 INDICATOR TYPE EST LCL UCL EST
#> 31 53 1 2.5573 2.4531 2.6479 2.5385
#> 32 25 1 4.5521 4.3417 4.7406 4.5584
#> 33 14 2 91.6667 86.2500 95.8333 92.9412
#> 34 23 2 54.6875 50.6250 58.2292 51.2821
#> 35 18 2 57.2917 52.3958 64.1667 58.4416
#> 36 20 2 5.7292 4.1667 8.5417 3.5294
#> 37 15 2 1.5625 0.6250 5.7292 4.8780
#> 38 17 2 33.8542 28.5417 36.6667 44.1558
#> 39 19 2 39.0625 35.7292 49.2708 41.0256
#> 40 21 2 3.6458 2.0833 4.0625 0.0000
#> 41 16 2 21.3542 17.0833 26.3542 22.0779
#> 42 24 2 48.9583 44.5833 55.5208 44.7059
#> 43 22 2 96.3542 93.4375 97.3958 98.7013
#> 44
#> 45 Nutrients
#> 46 ALL MALES
#> 47 INDICATOR TYPE EST LCL UCL EST
#> 48 88 2 45.8333 41.9792 55.0000 45.1220
#> 49 89 2 39.0625 35.7292 49.2708 41.0256
#> 50 87 2 10.9375 8.4375 15.5208 9.0909
#> 51 83 2 59.8958 52.1875 63.5417 57.1429
#> 52 2 2 5.7292 3.2292 8.1250 5.1948
#> 53 3 2 62.5000 54.1667 66.5625 62.3377
#> 54 42 2 65.1042 60.1042 70.1042 64.6341
#> 55 9 2 3.6458 2.0833 4.0625 0.0000
#> 56 140 2 58.8542 57.3958 66.1458 68.8312
#> 57 135 2 63.5417 60.0000 72.1875 71.4286
#> 58 137 2 80.2083 73.9583 87.7083 82.9268
#> 59 138 2 58.8542 57.3958 66.1458 68.8312
#> 60 139 2 87.5000 79.1667 90.0000 90.9091
#> 61 136 2 38.0208 34.8958 42.7083 46.7532
#> 62 134 2 37.5000 33.5417 41.3542 46.7532
#> 63
#> 64 Food Security
#> 65 ALL MALES
#> 66 INDICATOR TYPE EST LCL UCL EST
#> 67 45 2 77.0833 72.1875 83.1250 76.9231
#> 68 60 2 18.7500 13.2292 21.1458 20.5128
#> 69 113 2 2.6042 0.5208 4.4792 2.5641
#> 70
#> 71 Disability (WG)
#> 72 ALL MALES
#> 73 INDICATOR TYPE EST LCL UCL EST
#> 74 129 2 100.0000 100.0000 100.0000 100.0000
#> 75 130 2 0.0000 0.0000 0.0000 0.0000
#> 76 131 2 0.0000 0.0000 0.0000 0.0000
#> 77 132 2 0.0000 0.0000 0.0000 0.0000
#> 78 28 2 100.0000 100.0000 100.0000 100.0000
#> 79 29 2 0.0000 0.0000 0.0000 0.0000
#> 80 30 2 0.0000 0.0000 0.0000 0.0000
#> 81 31 2 0.0000 0.0000 0.0000 0.0000
#> 82 55 2 100.0000 100.0000 100.0000 100.0000
#> 83 56 2 0.0000 0.0000 0.0000 0.0000
#> 84 57 2 0.0000 0.0000 0.0000 0.0000
#> 85 58 2 0.0000 0.0000 0.0000 0.0000
#> 86 92 2 100.0000 100.0000 100.0000 100.0000
#> 87 93 2 0.0000 0.0000 0.0000 0.0000
#> 88 94 2 0.0000 0.0000 0.0000 0.0000
#> 89 95 2 0.0000 0.0000 0.0000 0.0000
#> 90 101 2 100.0000 100.0000 100.0000 100.0000
#> 91 102 2 0.0000 0.0000 0.0000 0.0000
#> 92 103 2 0.0000 0.0000 0.0000 0.0000
#> 93 104 2 0.0000 0.0000 0.0000 0.0000
#> 94 10 2 100.0000 100.0000 100.0000 100.0000
#> 95 11 2 0.0000 0.0000 0.0000 0.0000
#> 96 12 2 0.0000 0.0000 0.0000 0.0000
#> 97 13 2 0.0000 0.0000 0.0000 0.0000
#> 98 63 2 100.0000 100.0000 100.0000 100.0000
#> 99 5 2 0.0000 0.0000 0.0000 0.0000
#> 100 6 2 0.0000 0.0000 0.0000 0.0000
#> 101 7 2 0.0000 0.0000 0.0000 0.0000
#> 102 62 2 0.0000 0.0000 0.0000 0.0000
#> 103
#> 104 Activities of daily living
#> 105 ALL MALES
#> 106 INDICATOR TYPE EST LCL UCL EST
#> 107 35 2 97.9167 94.7917 99.3750 96.1538
#> 108 37 2 98.9583 97.3958 100.0000 97.4359
#> 109 39 2 98.9583 97.3958 100.0000 97.4359
#> 110 40 2 96.3542 93.5417 97.8125 96.1039
#> 111 36 2 73.4375 68.5417 78.5417 75.6410
#> 112 38 2 99.4792 98.9583 100.0000 98.5714
#> 113 44 1 5.6458 5.5760 5.7177 5.6286
#> 114 41 2 97.3958 95.6250 98.9583 97.4359
#> 115 82 2 1.0417 0.0000 3.0208 0.0000
#> 116 112 2 1.0417 0.0000 2.3958 2.5641
#> 117 126 2 58.8542 54.4792 66.1458 50.6494
#> 118 125 2 10.9375 7.3958 14.4792 11.5385
#> 119
#> 120 Mental health
#> 121 ALL MALES
#> 122 INDICATOR TYPE EST LCL UCL EST
#> 123 43 1 12.0990 11.1583 13.0000 11.3636
#> 124 110 2 47.9167 41.5625 53.1250 42.8571
#> 125 85 2 21.3542 16.0417 27.1875 16.6667
#> 126
#> 127 Health
#> 128 ALL MALES
#> 129 INDICATOR TYPE EST LCL UCL EST
#> 130 46 2 45.3125 41.1458 57.8125 33.3333
#> 131 128 2 69.9029 65.9740 85.5696 65.3846
#> 132 74 2 14.2857 3.7607 28.6804 15.3846
#> 133 79 2 36.3636 29.3088 49.2308 30.0000
#> 134 80 2 11.1111 0.7143 25.0182 0.0000
#> 135 81 2 9.0909 1.2903 27.0154 27.2727
#> 136 73 2 0.0000 0.0000 0.0000 0.0000
#> 137 77 2 0.0000 0.0000 0.0000 0.0000
#> 138 75 2 2.7778 0.0000 13.7363 0.0000
#> 139 78 2 0.0000 0.0000 0.0000 0.0000
#> 140 76 2 18.7500 0.0000 35.8120 23.0769
#> 141 91 2 88.0208 86.0417 93.9583 85.7143
#> 142 1 2 81.3253 78.1451 84.4393 75.7576
#> 143 65 2 12.1212 0.7407 26.7552 7.6923
#> 144 70 2 80.6452 59.8793 98.6667 88.8889
#> 145 71 2 0.0000 0.0000 0.0000 0.0000
#> 146 72 2 2.9412 0.0000 10.9402 5.2632
#> 147 64 2 0.0000 0.0000 0.0000 0.0000
#> 148 68 2 3.2258 0.0000 9.9698 0.0000
#> 149 66 2 0.0000 0.0000 0.0000 0.0000
#> 150 69 2 0.0000 0.0000 10.0940 0.0000
#> 151 67 2 0.0000 0.0000 0.0000 0.0000
#> 152 8 2 1.5625 0.6250 5.2083 1.2195
#> 153 133 2 41.6667 35.5208 45.2083 47.0588
#> 154 86 2 29.6875 24.8958 37.0833 24.6753
#> 155
#> 156 Income
#> 157 ALL MALES
#> 158 INDICATOR TYPE EST LCL UCL EST
#> 159 27 2 54.1667 49.1667 58.7500 64.2857
#> 160 116 2 34.8958 24.6875 40.5208 45.4545
#> 161 124 2 9.8958 6.1458 13.3333 22.0779
#> 162 121 2 3.1250 0.4167 3.9583 4.8780
#> 163 123 2 5.7292 4.6875 10.9375 2.5974
#> 164 119 2 0.5208 0.0000 1.0417 0.0000
#> 165 122 2 1.0417 0.0000 3.2292 2.4390
#> 166 118 2 1.5625 0.2083 3.4375 2.5974
#> 167 117 2 31.2500 25.4167 37.1875 28.5714
#> 168 120 2 1.0417 0.0000 2.6042 0.0000
#> 169
#> 170 WASH
#> 171 ALL MALES
#> 172 INDICATOR TYPE EST LCL UCL EST
#> 173 34 2 61.4583 56.8750 68.5417 59.7403
#> 174 100 2 70.8333 66.6667 76.9792 66.2338
#> 175 33 2 26.0417 18.7500 33.0208 24.6753
#> 176 32 2 24.4792 17.8125 32.8125 24.6753
#> 177
#> 178 Relief
#> 179 ALL MALES
#> 180 INDICATOR TYPE EST LCL UCL EST
#> 181 84 2 4.1667 2.7083 7.7083 2.5974
#> 182 4 2 3.1250 0.8333 8.5417 4.2857
#> 183 90 2 2.6042 1.1458 4.6875 1.2821
#> 184
#> 185 Anthropometry
#> 186 ALL MALES
#> 187 INDICATOR TYPE EST LCL UCL EST
#> 188 26 2 1.1634 0.5139 4.5287 1.1262
#> 189 59 2 1.1306 0.4477 4.3533 1.1262
#> 190 111 2 0.0205 0.0001 0.1775 0.0000
#> X.6 X.7 X.8 X.9 X.10
#> 1
#> 2 FEMALES
#> 3 LCL UCL EST LCL UCL
#> 4 77.1229 90.4416 83.4862 78.0782 88.8856
#> 5 4.1159 11.4683 11.0169 5.4089 15.0605
#> 6 1.9221 9.0909 3.4783 0.0000 10.1519
#> 7 0.0000 4.8818 0.0000 0.0000 1.6921
#> 8
#> 9
#> 10 FEMALES
#> 11 LCL UCL EST LCL UCL
#> 12 70.4453 73.9901 70.5164 68.7955 71.7261
#> 13 0.0000 0.0000 0.0000 0.0000 0.0000
#> 14 40.0000 54.1242 54.6296 50.5235 62.2660
#> 15 21.3568 37.1795 21.1864 16.9147 30.4048
#> 16 13.2468 21.0970 16.3934 11.6645 25.8255
#> 17 2.5708 15.1688 2.6549 0.1852 9.2209
#> 18 100.0000 100.0000 0.0000 0.0000 0.0000
#> 19 0.0000 0.0000 100.0000 100.0000 100.0000
#> 20 0.0000 7.9786 2.6087 0.1695 10.5811
#> 21 44.2990 66.7532 16.8142 7.5326 21.6797
#> 22 12.3202 23.8442 5.6911 2.1920 9.3036
#> 23 5.4379 18.3922 5.9322 1.6712 10.1175
#> 24 9.8610 26.6667 65.5462 61.4034 79.5292
#> 25 0.0000 0.0000 0.0000 0.0000 0.0000
#> 26 11.6883 22.2857 11.3043 7.7203 16.4454
#> 27
#> 28
#> 29 FEMALES
#> 30 LCL UCL EST LCL UCL
#> 31 2.2032 2.7143 2.7034 2.5414 2.8911
#> 32 3.8779 4.8205 4.6696 4.4841 4.9175
#> 33 84.9351 97.9854 92.1739 82.3649 94.7940
#> 34 32.1558 59.8934 56.4815 47.2189 63.3860
#> 35 47.3552 64.7686 66.9492 56.0262 74.5783
#> 36 0.0000 11.1455 8.1301 4.2594 12.3063
#> 37 0.0000 10.6494 0.9174 0.1681 5.8077
#> 38 38.0260 52.3077 27.5229 21.5509 34.5347
#> 39 28.2078 46.7380 41.6667 37.7340 49.6610
#> 40 0.0000 1.2987 4.2017 0.4878 6.4696
#> 41 15.3247 33.5065 21.2389 10.8301 26.9763
#> 42 21.3506 53.2468 54.6218 45.6541 67.7035
#> 43 91.8442 99.7561 98.2609 92.4168 99.8305
#> 44
#> 45
#> 46 FEMALES
#> 47 LCL UCL EST LCL UCL
#> 48 32.3636 53.2068 51.3043 46.5814 56.0120
#> 49 28.2078 46.7380 41.6667 37.7340 49.6610
#> 50 0.0000 16.5834 13.9344 8.6927 19.0850
#> 51 50.0519 68.8911 66.9492 62.1347 77.7669
#> 52 0.0000 10.9091 5.6911 4.3982 9.1243
#> 53 52.9351 71.2454 70.3390 62.4188 78.7670
#> 54 57.4286 73.2468 75.0000 68.3344 83.2925
#> 55 0.0000 1.2987 4.2017 0.4878 6.4696
#> 56 58.1818 78.1870 59.0164 54.8353 63.8983
#> 57 58.1818 78.6432 62.9630 59.1228 68.1356
#> 58 69.3553 90.1329 88.9831 83.3623 92.6749
#> 59 58.1818 78.1870 59.0164 54.8353 63.8983
#> 60 81.4026 95.2097 89.3443 83.6932 93.6427
#> 61 38.5455 58.2917 34.7458 30.6795 40.1155
#> 62 38.5455 56.4103 34.1463 30.3093 38.9010
#> 63
#> 64
#> 65 FEMALES
#> 66 LCL UCL EST LCL UCL
#> 67 65.4736 80.5195 78.6885 68.6439 86.4407
#> 68 17.1429 25.4479 15.4472 8.9831 25.7719
#> 69 0.0000 9.1036 1.6807 0.0000 3.3536
#> 70
#> 71
#> 72 FEMALES
#> 73 LCL UCL EST LCL UCL
#> 74 100.0000 100.0000 100.0000 100.0000 100.0000
#> 75 0.0000 0.0000 0.0000 0.0000 0.0000
#> 76 0.0000 0.0000 0.0000 0.0000 0.0000
#> 77 0.0000 0.0000 0.0000 0.0000 0.0000
#> 78 100.0000 100.0000 100.0000 100.0000 100.0000
#> 79 0.0000 0.0000 0.0000 0.0000 0.0000
#> 80 0.0000 0.0000 0.0000 0.0000 0.0000
#> 81 0.0000 0.0000 0.0000 0.0000 0.0000
#> 82 100.0000 100.0000 100.0000 100.0000 100.0000
#> 83 0.0000 0.0000 0.0000 0.0000 0.0000
#> 84 0.0000 0.0000 0.0000 0.0000 0.0000
#> 85 0.0000 0.0000 0.0000 0.0000 0.0000
#> 86 100.0000 100.0000 100.0000 100.0000 100.0000
#> 87 0.0000 0.0000 0.0000 0.0000 0.0000
#> 88 0.0000 0.0000 0.0000 0.0000 0.0000
#> 89 0.0000 0.0000 0.0000 0.0000 0.0000
#> 90 100.0000 100.0000 100.0000 100.0000 100.0000
#> 91 0.0000 0.0000 0.0000 0.0000 0.0000
#> 92 0.0000 0.0000 0.0000 0.0000 0.0000
#> 93 0.0000 0.0000 0.0000 0.0000 0.0000
#> 94 100.0000 100.0000 100.0000 100.0000 100.0000
#> 95 0.0000 0.0000 0.0000 0.0000 0.0000
#> 96 0.0000 0.0000 0.0000 0.0000 0.0000
#> 97 0.0000 0.0000 0.0000 0.0000 0.0000
#> 98 100.0000 100.0000 100.0000 100.0000 100.0000
#> 99 0.0000 0.0000 0.0000 0.0000 0.0000
#> 100 0.0000 0.0000 0.0000 0.0000 0.0000
#> 101 0.0000 0.0000 0.0000 0.0000 0.0000
#> 102 0.0000 0.0000 0.0000 0.0000 0.0000
#> 103
#> 104
#> 105 FEMALES
#> 106 LCL UCL EST LCL UCL
#> 107 93.5231 99.7403 98.1651 95.1915 98.9859
#> 108 94.8685 100.0000 100.0000 98.5205 100.0000
#> 109 94.8685 100.0000 100.0000 98.5205 100.0000
#> 110 91.6440 100.0000 95.7627 91.4896 97.4615
#> 111 69.3506 87.9457 68.8525 61.8217 78.7205
#> 112 94.8685 100.0000 100.0000 100.0000 100.0000
#> 113 5.4300 5.7844 5.6174 5.4989 5.7325
#> 114 94.8685 100.0000 97.3913 93.4381 98.3165
#> 115 0.0000 0.0000 2.6087 1.6835 6.5619
#> 116 0.0000 5.1315 0.0000 0.0000 0.0000
#> 117 39.5844 67.1029 60.1770 58.3036 73.7893
#> 118 6.7753 25.9645 6.7227 4.1532 14.5887
#> 119
#> 120
#> 121 FEMALES
#> 122 LCL UCL EST LCL UCL
#> 123 10.0320 12.8529 12.0325 10.1200 13.0577
#> 124 33.2040 54.4935 46.6102 34.0873 57.2405
#> 125 10.3896 25.9540 20.1835 16.6600 25.2198
#> 126
#> 127
#> 128 FEMALES
#> 129 LCL UCL EST LCL UCL
#> 130 29.9397 42.5714 46.3415 38.8828 56.4550
#> 131 48.8000 76.7050 82.3529 71.2594 87.6468
#> 132 2.0000 33.5354 20.0000 0.0000 43.6364
#> 133 0.0000 60.8974 45.4545 28.4848 86.6667
#> 134 0.0000 0.0000 0.0000 0.0000 38.6667
#> 135 3.3333 60.0000 0.0000 0.0000 0.0000
#> 136 0.0000 0.0000 0.0000 0.0000 0.0000
#> 137 0.0000 0.0000 0.0000 0.0000 0.0000
#> 138 0.0000 0.0000 0.0000 0.0000 20.2778
#> 139 0.0000 0.0000 0.0000 0.0000 0.0000
#> 140 1.6667 60.0000 11.1111 0.0000 26.8182
#> 141 82.9648 91.4805 87.3950 83.3482 94.4878
#> 142 67.8125 80.5882 87.2727 79.2674 94.0856
#> 143 0.9524 26.3866 12.5000 0.0000 55.7143
#> 144 66.0504 95.2381 85.7143 16.5714 98.0000
#> 145 0.0000 0.0000 0.0000 0.0000 0.0000
#> 146 0.0000 20.0000 0.0000 0.0000 0.0000
#> 147 0.0000 0.0000 0.0000 0.0000 0.0000
#> 148 0.0000 0.0000 0.0000 0.0000 38.6667
#> 149 0.0000 0.0000 0.0000 0.0000 0.0000
#> 150 0.0000 0.0000 0.0000 0.0000 0.0000
#> 151 0.0000 0.0000 0.0000 0.0000 0.0000
#> 152 0.0000 2.5907 1.7391 0.0000 3.1785
#> 153 41.8182 57.6923 32.1101 28.2081 44.3879
#> 154 19.6098 31.5385 27.8261 19.4722 36.8905
#> 155
#> 156
#> 157 FEMALES
#> 158 LCL UCL EST LCL UCL
#> 159 59.1275 72.7473 49.1525 41.7961 58.6254
#> 160 34.8052 59.3440 32.4074 25.1420 40.9016
#> 161 11.6364 27.0130 3.6697 1.0660 8.6052
#> 162 0.5128 8.5714 0.8475 0.0000 3.2336
#> 163 0.0000 8.7379 8.4746 5.7890 10.2901
#> 164 0.0000 0.0000 0.8475 0.0000 3.7602
#> 165 0.2353 6.2204 0.0000 0.0000 0.0000
#> 166 0.2597 6.1672 0.9259 0.0000 6.5220
#> 167 13.1897 41.0115 32.1101 27.3351 36.4443
#> 168 0.0000 3.5897 0.0000 0.0000 2.3420
#> 169
#> 170
#> 171 FEMALES
#> 172 LCL UCL EST LCL UCL
#> 173 55.3782 67.1029 62.8319 47.9975 68.0713
#> 174 61.0665 75.0583 74.3363 63.1597 85.9587
#> 175 15.4500 37.1107 25.6637 17.8958 32.3063
#> 176 15.4500 37.1107 24.5763 16.9064 30.2194
#> 177
#> 178
#> 179 FEMALES
#> 180 LCL UCL EST LCL UCL
#> 181 0.0000 9.5312 2.6549 1.0169 10.0403
#> 182 0.0000 10.9091 3.6697 2.4757 7.5832
#> 183 0.0000 7.6673 1.6949 0.1695 4.1287
#> 184
#> 185
#> 186 FEMALES
#> 187 LCL UCL EST LCL UCL
#> 188 0.1332 1.7095 5.9846 2.5175 7.3639
#> 189 0.1332 1.7095 5.7387 2.0151 7.3552
#> 190 0.0000 0.0330 0.0562 0.0010 0.5495Piped operation to get output an HTML report
If the preferred output is a report with combined charts and tables of results, the following piped operations can be performed:
testSVY |>
create_op() |>
estimate_op(w = testPSU, replicates = 9) |>
report_op_html(
svy = testSVY, filename = file.path(tempdir(), "ramOPreport")
)which results in an HTML file saved in the specified output directory that looks something like this:
