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 0.839 0.808 0.879 0.824 0.749 0.888 0.880
#> 2 resp2 0.104 0.0844 0.134 0.0882 0.0484 0.141 0.0965
#> 3 resp3 0.0365 0.0312 0.0625 0.0641 0.0288 0.0782 0.0167
#> 4 resp4 0.0104 0 0.0292 0.0235 0 0.0515 0
#> 5 age 71.1 69.2 72.2 71.6 70.0 73.0 70.7
#> 6 ageGrp1 0 0 0 0 0 0 0
#> 7 ageGrp2 0.521 0.433 0.590 0.487 0.38 0.551 0.523
#> 8 ageGrp3 0.260 0.176 0.305 0.261 0.184 0.350 0.225
#> 9 ageGrp4 0.208 0.101 0.275 0.208 0.141 0.286 0.211
#> 10 ageGrp5 0.0417 0.0115 0.0708 0.0429 0.0120 0.122 0.0180
#> # 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 2.84e-2 7.32e- 3 0.0527 9.33e- 3 1.06e- 3 0.0214 0.0301
#> 2 MAM 2.64e-2 6.87e- 3 0.0527 9.33e- 3 1.02e- 3 0.0214 0.0300
#> 3 SAM 9.98e-6 2.43e-17 0.00305 6.77e-19 6.30e-36 0.000161 0.00170
#> # 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… 0.839 0.808 0.879 0.824 0.749
#> 2 resp2 Survey Resp… Prop… 0.104 0.0844 0.134 0.0882 0.0484
#> 3 resp3 Survey Resp… Prop… 0.0365 0.0312 0.0625 0.0641 0.0288
#> 4 resp4 Survey Resp… Prop… 0.0104 0 0.0292 0.0235 0
#> 5 age Demography… Mean… Mean 71.1 69.2 72.2 71.6 70.0
#> 6 ageGrp1 Demography… Self… Prop… 0 0 0 0 0
#> 7 ageGrp2 Demography… Self… Prop… 0.521 0.433 0.590 0.487 0.38
#> 8 ageGrp3 Demography… Self… Prop… 0.260 0.176 0.305 0.261 0.184
#> 9 ageGrp4 Demography… Self… Prop… 0.208 0.101 0.275 0.208 0.141
#> 10 ageGrp5 Demography… Self… Prop… 0.0417 0.0115 0.0708 0.0429 0.0120
#> # 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 0.8385 0.8083 0.8792 0.8235 0.7489
#> 5 96 2 0.1042 0.0844 0.1344 0.0882 0.0484
#> 6 98 2 0.0365 0.0312 0.0625 0.0641 0.0288
#> 7 97 2 0.0104 0.0000 0.0292 0.0235 0.0000
#> 8
#> 9 Demography and situation
#> 10 ALL MALES
#> 11 INDICATOR TYPE EST LCL UCL EST LCL
#> 12 54 1 71.0677 69.2094 72.2031 71.5761 70.0141
#> 13 106 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 14 107 2 0.5208 0.4333 0.5896 0.4872 0.3800
#> 15 108 2 0.2604 0.1760 0.3052 0.2609 0.1835
#> 16 109 2 0.2083 0.1010 0.2750 0.2083 0.1407
#> 17 105 2 0.0417 0.0115 0.0708 0.0429 0.0120
#> 18 115 2 0.4010 0.3708 0.4854 1.0000 1.0000
#> 19 114 2 0.5990 0.5146 0.6292 0.0000 0.0000
#> 20 51 2 0.0260 0.0010 0.0500 0.0286 0.0000
#> 21 49 2 0.2969 0.2406 0.3615 0.5588 0.4482
#> 22 48 2 0.1042 0.0573 0.1406 0.1618 0.1059
#> 23 47 2 0.0729 0.0312 0.1104 0.0588 0.0057
#> 24 52 2 0.5000 0.4260 0.6010 0.1746 0.1059
#> 25 50 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 26 127 2 0.1510 0.1021 0.1812 0.1529 0.0984
#> 27
#> 28 Diet
#> 29 ALL MALES
#> 30 INDICATOR TYPE EST LCL UCL EST LCL
#> 31 53 1 2.5573 2.4969 2.6979 2.3971 2.2918
#> 32 25 1 4.5000 4.3125 4.7740 4.2949 4.1790
#> 33 14 2 0.9167 0.8906 0.9375 0.9059 0.8617
#> 34 23 2 0.4948 0.4281 0.5677 0.4722 0.3153
#> 35 18 2 0.5781 0.5281 0.6396 0.5397 0.4119
#> 36 20 2 0.0677 0.0271 0.0854 0.0278 0.0024
#> 37 15 2 0.0260 0.0115 0.0552 0.0417 0.0146
#> 38 17 2 0.3333 0.2760 0.4021 0.4118 0.3560
#> 39 19 2 0.4062 0.3208 0.4677 0.3882 0.3473
#> 40 21 2 0.0104 0.0000 0.0354 0.0000 0.0000
#> 41 16 2 0.2292 0.1792 0.2385 0.2235 0.1821
#> 42 24 2 0.4688 0.4229 0.5250 0.3765 0.3484
#> 43 22 2 0.9792 0.9479 0.9948 0.9765 0.8934
#> 44
#> 45 Nutrients
#> 46 ALL MALES
#> 47 INDICATOR TYPE EST LCL UCL EST LCL
#> 48 88 2 0.4688 0.3656 0.5323 0.4706 0.3937
#> 49 89 2 0.4062 0.3208 0.4677 0.3882 0.3473
#> 50 87 2 0.1094 0.0740 0.1427 0.0706 0.0495
#> 51 83 2 0.6198 0.5635 0.6635 0.5647 0.4882
#> 52 2 2 0.0469 0.0229 0.0771 0.0513 0.0419
#> 53 3 2 0.6302 0.5740 0.6990 0.5897 0.5059
#> 54 42 2 0.6667 0.6042 0.7260 0.6324 0.5698
#> 55 9 2 0.0104 0.0000 0.0354 0.0000 0.0000
#> 56 140 2 0.5990 0.5594 0.6635 0.6824 0.5557
#> 57 135 2 0.6354 0.6021 0.6969 0.6957 0.5668
#> 58 137 2 0.8177 0.7708 0.8531 0.8095 0.6846
#> 59 138 2 0.5990 0.5594 0.6635 0.6824 0.5557
#> 60 139 2 0.8646 0.8333 0.8896 0.8857 0.7692
#> 61 136 2 0.3802 0.3344 0.4781 0.4487 0.3959
#> 62 134 2 0.3698 0.3333 0.4708 0.4471 0.3804
#> 63
#> 64 Food Security
#> 65 ALL MALES
#> 66 INDICATOR TYPE EST LCL UCL EST LCL
#> 67 45 2 0.7604 0.6948 0.8344 0.7460 0.6848
#> 68 60 2 0.1771 0.1125 0.2479 0.2206 0.0548
#> 69 113 2 0.0260 0.0167 0.0302 0.0353 0.0032
#> 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 0.9615 0.9327
#> 108 37 2 0.9896 0.9750 1.0000 0.9783 0.9415
#> 109 39 2 0.9896 0.9750 1.0000 0.9783 0.9415
#> 110 40 2 0.9740 0.9458 0.9969 0.9583 0.9194
#> 111 36 2 0.7396 0.6625 0.7635 0.7765 0.6855
#> 112 38 2 0.9896 0.9854 1.0000 0.9841 0.9647
#> 113 44 1 5.6667 5.5240 5.7260 5.6154 5.4631
#> 114 41 2 0.9792 0.9635 1.0000 0.9783 0.9415
#> 115 82 2 0.0052 0.0000 0.0250 0.0000 0.0000
#> 116 112 2 0.0104 0.0000 0.0250 0.0217 0.0120
#> 117 126 2 0.5833 0.5458 0.6250 0.5529 0.3871
#> 118 125 2 0.1198 0.0844 0.1656 0.1412 0.0974
#> 119
#> 120 Mental health
#> 121 ALL MALES
#> 122 INDICATOR TYPE EST LCL UCL EST LCL
#> 123 43 1 12.0729 11.1958 12.9740 12.1587 9.9867
#> 124 110 2 0.4427 0.4250 0.5406 0.5059 0.4143
#> 125 85 2 0.1875 0.1281 0.2219 0.1944 0.1246
#> 126
#> 127 Health
#> 128 ALL MALES
#> 129 INDICATOR TYPE EST LCL UCL EST LCL
#> 130 46 2 0.4219 0.3531 0.4750 0.3571 0.3378
#> 131 128 2 0.7468 0.7094 0.8086 0.6250 0.4884
#> 132 74 2 0.0625 0.0000 0.3187 0.2857 0.0222
#> 133 79 2 0.2500 0.2055 0.6253 0.3571 0.0250
#> 134 80 2 0.1667 0.0000 0.3621 0.0000 0.0000
#> 135 81 2 0.2083 0.1367 0.2682 0.1429 0.0000
#> 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 0.0800 0.0000 0.0000
#> 139 78 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 140 76 2 0.1765 0.0125 0.4267 0.2000 0.0267
#> 141 91 2 0.8750 0.8146 0.9281 0.8571 0.7741
#> 142 1 2 0.8466 0.7657 0.8619 0.7237 0.6617
#> 143 65 2 0.0870 0.0000 0.2162 0.1429 0.0000
#> 144 70 2 0.8750 0.5840 0.9097 0.7778 0.4667
#> 145 71 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 146 72 2 0.0417 0.0000 0.1588 0.0455 0.0000
#> 147 64 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 148 68 2 0.0303 0.0000 0.1428 0.0000 0.0000
#> 149 66 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 150 69 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 151 67 2 0.0000 0.0000 0.0000 0.0000 0.0000
#> 152 8 2 0.0156 0.0021 0.0333 0.0109 0.0000
#> 153 133 2 0.3958 0.2958 0.4677 0.4853 0.4212
#> 154 86 2 0.2865 0.2573 0.3333 0.2206 0.1671
#> 155
#> 156 Income
#> 157 ALL MALES
#> 158 INDICATOR TYPE EST LCL UCL EST LCL
#> 159 27 2 0.5469 0.4760 0.6500 0.6286 0.5619
#> 160 116 2 0.3594 0.2885 0.4885 0.4286 0.3495
#> 161 124 2 0.1146 0.0729 0.1396 0.2000 0.0725
#> 162 121 2 0.0312 0.0062 0.0552 0.0294 0.0022
#> 163 123 2 0.0469 0.0323 0.0823 0.0118 0.0000
#> 164 119 2 0.0052 0.0000 0.0240 0.0000 0.0000
#> 165 122 2 0.0052 0.0000 0.0302 0.0353 0.0024
#> 166 118 2 0.0104 0.0052 0.0396 0.0217 0.0000
#> 167 117 2 0.3385 0.2500 0.3688 0.2471 0.1942
#> 168 120 2 0.0104 0.0010 0.0208 0.0000 0.0000
#> 169
#> 170 WASH
#> 171 ALL MALES
#> 172 INDICATOR TYPE EST LCL UCL EST LCL
#> 173 34 2 0.5990 0.5260 0.6698 0.5694 0.5043
#> 174 100 2 0.6979 0.6146 0.7885 0.6250 0.5374
#> 175 33 2 0.2448 0.2104 0.3115 0.2471 0.1707
#> 176 32 2 0.2240 0.1969 0.3094 0.2471 0.1664
#> 177
#> 178 Relief
#> 179 ALL MALES
#> 180 INDICATOR TYPE EST LCL UCL EST LCL
#> 181 84 2 0.0260 0.0062 0.0740 0.0286 0.0110
#> 182 4 2 0.0469 0.0135 0.0792 0.0471 0.0146
#> 183 90 2 0.0208 0.0115 0.0500 0.0286 0.0000
#> 184
#> 185 Anthropometry
#> 186 ALL MALES
#> 187 INDICATOR TYPE EST LCL UCL EST LCL
#> 188 26 2 0.0284 0.0073 0.0527 0.0093 0.0011
#> 189 59 2 0.0264 0.0069 0.0527 0.0093 0.0010
#> 190 111 2 0.0000 0.0000 0.0030 0.0000 0.0000
#> X.7 X.8 X.9 X.10
#> 1
#> 2 FEMALES
#> 3 UCL EST LCL UCL
#> 4 0.8883 0.8803 0.8295 0.9334
#> 5 0.1413 0.0965 0.0630 0.1298
#> 6 0.0782 0.0167 0.0000 0.0357
#> 7 0.0515 0.0000 0.0000 0.0179
#> 8
#> 9
#> 10 FEMALES
#> 11 UCL EST LCL UCL
#> 12 72.9940 70.7241 69.5763 72.5032
#> 13 0.0000 0.0000 0.0000 0.0000
#> 14 0.5509 0.5231 0.4829 0.5868
#> 15 0.3504 0.2252 0.1438 0.2817
#> 16 0.2861 0.2105 0.1737 0.3104
#> 17 0.1218 0.0180 0.0000 0.0500
#> 18 1.0000 0.0000 0.0000 0.0000
#> 19 0.0000 1.0000 1.0000 1.0000
#> 20 0.0616 0.0485 0.0120 0.1142
#> 21 0.6627 0.1308 0.0811 0.1936
#> 22 0.2565 0.0769 0.0461 0.1396
#> 23 0.1389 0.0450 0.0018 0.1168
#> 24 0.2552 0.6667 0.5725 0.8008
#> 25 0.0000 0.0000 0.0000 0.0000
#> 26 0.2063 0.1250 0.0437 0.1600
#> 27
#> 28
#> 29 FEMALES
#> 30 UCL EST LCL UCL
#> 31 2.6982 2.6036 2.3240 2.7955
#> 32 4.7460 4.7184 4.5286 4.8805
#> 33 0.9683 0.9231 0.8732 0.9791
#> 34 0.5118 0.5586 0.5270 0.6017
#> 35 0.6600 0.6325 0.5665 0.7170
#> 36 0.0504 0.0684 0.0435 0.1237
#> 37 0.0588 0.0180 0.0000 0.0343
#> 38 0.5306 0.2632 0.2037 0.3380
#> 39 0.5283 0.4359 0.3368 0.5069
#> 40 0.0378 0.0439 0.0274 0.1004
#> 41 0.2799 0.2167 0.1757 0.2393
#> 42 0.5076 0.5470 0.4850 0.6444
#> 43 1.0000 0.9833 0.9102 1.0000
#> 44
#> 45
#> 46 FEMALES
#> 47 UCL EST LCL UCL
#> 48 0.5518 0.5167 0.4265 0.5758
#> 49 0.5283 0.4359 0.3368 0.5069
#> 50 0.1153 0.1462 0.1136 0.1791
#> 51 0.6306 0.6486 0.5999 0.7277
#> 52 0.0682 0.0574 0.0410 0.1080
#> 53 0.6659 0.6923 0.6378 0.7637
#> 54 0.6855 0.7049 0.6286 0.7760
#> 55 0.0378 0.0439 0.0274 0.1004
#> 56 0.7536 0.6019 0.5058 0.6384
#> 57 0.7578 0.6810 0.5582 0.7321
#> 58 0.8849 0.8462 0.7947 0.8957
#> 59 0.7536 0.6019 0.5058 0.6384
#> 60 0.9724 0.8583 0.8231 0.9000
#> 61 0.5376 0.3462 0.2613 0.4070
#> 62 0.5329 0.3462 0.2613 0.3931
#> 63
#> 64
#> 65 FEMALES
#> 66 UCL EST LCL UCL
#> 67 0.9093 0.8198 0.7397 0.8780
#> 68 0.2751 0.1316 0.0736 0.1913
#> 69 0.0650 0.0291 0.0104 0.0648
#> 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 0.9779 0.9825 0.9643 1.0000
#> 108 0.9880 1.0000 0.9928 1.0000
#> 109 0.9880 1.0000 0.9928 1.0000
#> 110 0.9880 0.9667 0.9171 0.9965
#> 111 0.8471 0.7000 0.5721 0.7635
#> 112 0.9978 1.0000 1.0000 1.0000
#> 113 5.7183 5.6583 5.4943 5.6958
#> 114 0.9880 0.9833 0.9346 0.9985
#> 115 0.0000 0.0167 0.0015 0.0654
#> 116 0.0585 0.0000 0.0000 0.0000
#> 117 0.6430 0.6639 0.5227 0.7475
#> 118 0.2030 0.1081 0.0239 0.1787
#> 119
#> 120
#> 121 FEMALES
#> 122 UCL EST LCL UCL
#> 123 13.5763 12.4923 11.0184 13.5120
#> 124 0.5914 0.4912 0.3915 0.5615
#> 125 0.2659 0.2167 0.1756 0.3415
#> 126
#> 127
#> 128 FEMALES
#> 129 UCL EST LCL UCL
#> 130 0.4559 0.5315 0.4442 0.5942
#> 131 0.7898 0.8356 0.7231 0.8936
#> 132 0.3950 0.0833 0.0000 0.1516
#> 133 0.6333 0.4615 0.4167 0.8167
#> 134 0.0000 0.2105 0.0000 0.4286
#> 135 0.5400 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 0.0842
#> 139 0.0000 0.0000 0.0000 0.0000
#> 140 0.4533 0.1538 0.0000 0.4500
#> 141 0.9010 0.8769 0.7759 0.9304
#> 142 0.8786 0.8673 0.8102 0.9807
#> 143 0.5121 0.0714 0.0000 0.2974
#> 144 0.9299 0.8500 0.6762 1.0000
#> 145 0.0000 0.0000 0.0000 0.0000
#> 146 0.2786 0.0000 0.0000 0.0000
#> 147 0.0000 0.0000 0.0000 0.0000
#> 148 0.0000 0.0000 0.0000 0.2014
#> 149 0.0000 0.0000 0.0000 0.0000
#> 150 0.0000 0.0000 0.0000 0.0615
#> 151 0.0000 0.0000 0.0000 0.0000
#> 152 0.0305 0.0175 0.0085 0.0498
#> 153 0.6005 0.3398 0.2425 0.4241
#> 154 0.3316 0.3246 0.2026 0.4127
#> 155
#> 156
#> 157 FEMALES
#> 158 UCL EST LCL UCL
#> 159 0.7006 0.5154 0.4480 0.6444
#> 160 0.6026 0.3301 0.2100 0.3751
#> 161 0.2867 0.0417 0.0171 0.0962
#> 162 0.1135 0.0000 0.0000 0.0219
#> 163 0.0235 0.0680 0.0369 0.0850
#> 164 0.0000 0.0082 0.0000 0.0303
#> 165 0.0560 0.0000 0.0000 0.0000
#> 166 0.0508 0.0000 0.0000 0.0338
#> 167 0.3894 0.3761 0.2806 0.4300
#> 168 0.0336 0.0000 0.0000 0.0334
#> 169
#> 170
#> 171 FEMALES
#> 172 UCL EST LCL UCL
#> 173 0.7061 0.5923 0.5483 0.6580
#> 174 0.7313 0.7027 0.6227 0.7739
#> 175 0.3365 0.2393 0.1970 0.3438
#> 176 0.3365 0.2083 0.1672 0.3173
#> 177
#> 178
#> 179 FEMALES
#> 180 UCL EST LCL UCL
#> 181 0.1064 0.0385 0.0174 0.0645
#> 182 0.0630 0.0692 0.0206 0.1132
#> 183 0.0659 0.0328 0.0086 0.0532
#> 184
#> 185
#> 186 FEMALES
#> 187 UCL EST LCL UCL
#> 188 0.0214 0.0301 0.0035 0.0471
#> 189 0.0214 0.0300 0.0015 0.0452
#> 190 0.0002 0.0017 0.0002 0.0054The 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 83.8542 80.8333 86.3542 83.3333
#> 5 96 2 10.4167 7.0833 13.8542 5.9524
#> 6 98 2 4.1667 1.7708 7.0833 3.7500
#> 7 97 2 1.0417 0.1042 3.3333 3.7037
#> 8
#> 9 Demography and situation
#> 10 ALL MALES
#> 11 INDICATOR TYPE EST LCL UCL EST
#> 12 54 1 70.7396 69.7271 72.2406 71.0897
#> 13 106 2 0.0000 0.0000 0.0000 0.0000
#> 14 107 2 55.2083 45.5208 58.7500 56.2500
#> 15 108 2 25.5208 22.3958 28.3333 23.5294
#> 16 109 2 15.1042 9.3750 25.9375 14.1026
#> 17 105 2 4.6875 2.3958 7.1875 6.7568
#> 18 115 2 38.5417 31.6667 44.7917 100.0000
#> 19 114 2 61.4583 55.2083 68.3333 0.0000
#> 20 51 2 3.6458 1.6667 5.8333 1.1905
#> 21 49 2 30.7292 23.2292 39.2708 52.9412
#> 22 48 2 9.8958 8.3333 12.6042 17.2840
#> 23 47 2 5.7292 4.2708 10.2083 12.5000
#> 24 52 2 48.4375 43.3333 53.1250 15.0000
#> 25 50 2 0.0000 0.0000 0.0000 0.0000
#> 26 127 2 13.0208 6.3542 16.8750 13.7500
#> 27
#> 28 Diet
#> 29 ALL MALES
#> 30 INDICATOR TYPE EST LCL UCL EST
#> 31 53 1 2.5885 2.4625 2.6948 2.4568
#> 32 25 1 4.5625 4.4750 4.8177 4.5952
#> 33 14 2 91.6667 87.6042 97.8125 92.8571
#> 34 23 2 52.6042 48.6458 61.8750 52.5000
#> 35 18 2 58.8542 53.4375 65.3125 58.9744
#> 36 20 2 4.6875 3.1250 9.6875 3.7500
#> 37 15 2 2.6042 1.1458 5.6250 3.7500
#> 38 17 2 32.2917 29.1667 38.5417 43.7500
#> 39 19 2 41.6667 34.5833 47.0833 40.4762
#> 40 21 2 2.6042 1.0417 5.3125 0.0000
#> 41 16 2 22.3958 18.0208 25.4167 25.9259
#> 42 24 2 52.6042 41.7708 55.5208 42.3077
#> 43 22 2 97.9167 95.9375 99.4792 97.4359
#> 44
#> 45 Nutrients
#> 46 ALL MALES
#> 47 INDICATOR TYPE EST LCL UCL EST
#> 48 88 2 46.3542 39.4792 53.2292 48.5294
#> 49 89 2 41.6667 34.5833 47.0833 40.4762
#> 50 87 2 11.4583 5.6250 18.2292 9.5238
#> 51 83 2 63.5417 56.7708 66.1458 60.0000
#> 52 2 2 5.7292 2.1875 10.4167 3.7500
#> 53 3 2 64.5833 57.6042 70.5208 62.5000
#> 54 42 2 65.6250 61.7708 71.9792 58.3333
#> 55 9 2 2.6042 1.0417 5.3125 0.0000
#> 56 140 2 58.8542 54.3750 67.3958 66.6667
#> 57 135 2 64.0625 58.1250 70.2083 71.2500
#> 58 137 2 80.7292 76.5625 86.0417 82.5000
#> 59 138 2 58.8542 54.3750 67.3958 66.6667
#> 60 139 2 84.8958 81.4583 91.8750 91.2500
#> 61 136 2 37.5000 32.2917 43.8542 46.4286
#> 62 134 2 37.5000 31.8750 41.2500 46.4286
#> 63
#> 64 Food Security
#> 65 ALL MALES
#> 66 INDICATOR TYPE EST LCL UCL EST
#> 67 45 2 78.1250 68.4375 82.2917 71.4286
#> 68 60 2 16.1458 13.6458 27.2917 25.0000
#> 69 113 2 2.0833 0.7292 3.4375 4.7619
#> 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 96.0417 98.4375 93.7500
#> 108 37 2 98.9583 97.5000 99.8958 95.5882
#> 109 39 2 98.9583 97.5000 99.8958 95.5882
#> 110 40 2 96.8750 94.4792 97.9167 95.0617
#> 111 36 2 74.4792 70.4167 80.9375 77.3810
#> 112 38 2 100.0000 98.9583 100.0000 98.5294
#> 113 44 1 5.6562 5.6312 5.7135 5.5476
#> 114 41 2 98.4375 96.5625 98.9583 95.5882
#> 115 82 2 0.5208 0.0000 3.2292 0.0000
#> 116 112 2 1.0417 0.0000 1.9792 4.4118
#> 117 126 2 59.8958 52.5000 65.4167 52.5641
#> 118 125 2 11.4583 5.7292 13.9583 15.3846
#> 119
#> 120 Mental health
#> 121 ALL MALES
#> 122 INDICATOR TYPE EST LCL UCL EST
#> 123 43 1 12.4062 11.0552 13.0219 11.0256
#> 124 110 2 48.9583 42.2917 56.4583 38.2353
#> 125 85 2 19.7917 15.7292 28.1250 11.5385
#> 126
#> 127 Health
#> 128 ALL MALES
#> 129 INDICATOR TYPE EST LCL UCL EST
#> 130 46 2 47.3958 37.9167 50.9375 33.7500
#> 131 128 2 77.0833 70.5495 87.7460 70.3704
#> 132 74 2 9.0909 0.0000 21.7778 0.0000
#> 133 79 2 31.8182 15.6364 61.4286 37.5000
#> 134 80 2 15.0000 4.7619 35.9544 0.0000
#> 135 81 2 13.6364 3.7963 32.6154 28.5714
#> 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 0.0000 0.0000 0.0000 0.0000
#> 139 78 2 0.0000 0.0000 0.0000 0.0000
#> 140 76 2 15.0000 1.4286 54.0909 28.5714
#> 141 91 2 87.5000 78.0208 89.4792 85.0000
#> 142 1 2 84.2767 78.8335 86.1946 78.3333
#> 143 65 2 4.5455 0.0000 11.8222 0.0000
#> 144 70 2 90.9091 74.1818 96.3736 84.2105
#> 145 71 2 0.0000 0.0000 0.0000 0.0000
#> 146 72 2 0.0000 0.0000 6.5143 15.3846
#> 147 64 2 0.0000 0.0000 0.0000 0.0000
#> 148 68 2 0.0000 0.0000 7.5765 0.0000
#> 149 66 2 0.0000 0.0000 0.0000 0.0000
#> 150 69 2 0.0000 0.0000 14.5455 0.0000
#> 151 67 2 0.0000 0.0000 0.0000 0.0000
#> 152 8 2 2.0833 1.0417 5.8333 0.0000
#> 153 133 2 39.0625 34.0625 48.3333 44.4444
#> 154 86 2 34.3750 30.7292 39.0625 25.0000
#> 155
#> 156 Income
#> 157 ALL MALES
#> 158 INDICATOR TYPE EST LCL UCL EST
#> 159 27 2 54.6875 50.8333 65.8333 61.7284
#> 160 116 2 34.3750 30.0000 44.3750 47.2973
#> 161 124 2 9.3750 4.7917 18.8542 22.5000
#> 162 121 2 1.5625 0.5208 3.5417 6.4103
#> 163 123 2 4.6875 2.9167 8.2292 2.4691
#> 164 119 2 0.5208 0.0000 2.7083 0.0000
#> 165 122 2 1.0417 0.1042 2.9167 2.7027
#> 166 118 2 3.1250 0.2083 4.5833 1.1905
#> 167 117 2 32.8125 27.0833 44.6875 30.0000
#> 168 120 2 1.5625 0.0000 2.5000 1.2500
#> 169
#> 170 WASH
#> 171 ALL MALES
#> 172 INDICATOR TYPE EST LCL UCL EST
#> 173 34 2 65.6250 56.5625 69.7917 61.2500
#> 174 100 2 72.9167 63.2292 79.2708 70.2703
#> 175 33 2 28.1250 18.5417 34.3750 30.7692
#> 176 32 2 27.0833 17.1875 34.2708 29.4872
#> 177
#> 178 Relief
#> 179 ALL MALES
#> 180 INDICATOR TYPE EST LCL UCL EST
#> 181 84 2 3.1250 0.6250 5.9375 4.7619
#> 182 4 2 3.1250 1.1458 6.2500 1.2821
#> 183 90 2 2.0833 0.6250 4.5833 3.5714
#> 184
#> 185 Anthropometry
#> 186 ALL MALES
#> 187 INDICATOR TYPE EST LCL UCL EST
#> 188 26 2 3.0186 2.3935 5.0943 1.0258
#> 189 59 2 3.0176 2.3900 5.0549 1.0258
#> 190 111 2 0.0328 0.0002 0.1506 0.0000
#> X.6 X.7 X.8 X.9 X.10
#> 1
#> 2 FEMALES
#> 3 LCL UCL EST LCL UCL
#> 4 79.8332 92.7857 85.3448 81.2672 90.4249
#> 5 1.5811 12.0194 12.2807 8.7453 14.0373
#> 6 1.6667 13.8553 2.7273 0.0000 8.2476
#> 7 0.2500 6.4103 0.0000 0.0000 0.8882
#> 8
#> 9
#> 10 FEMALES
#> 11 LCL UCL EST LCL UCL
#> 12 68.9115 71.6172 70.0252 69.1766 72.5471
#> 13 0.0000 0.0000 0.0000 0.0000 0.0000
#> 14 42.5559 65.0833 56.3025 43.9221 62.5670
#> 15 18.8095 39.5135 20.4918 17.7614 25.4150
#> 16 6.5119 19.9506 22.6891 15.9226 32.2468
#> 17 2.7143 8.9744 0.8621 0.0000 5.9040
#> 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 3.5769 4.2017 0.3509 9.3322
#> 21 38.5843 63.9286 14.5455 8.8096 23.3063
#> 22 13.1789 24.5238 10.3448 2.9692 13.4953
#> 23 1.5385 17.2487 3.6364 1.9458 7.3051
#> 24 8.3077 31.3306 68.1818 59.0277 71.4037
#> 25 0.0000 0.0000 0.0000 0.0000 0.0000
#> 26 3.5219 18.8095 14.0351 6.9875 16.7647
#> 27
#> 28
#> 29 FEMALES
#> 30 LCL UCL EST LCL UCL
#> 31 2.3710 2.6981 2.6555 2.4516 2.9343
#> 32 4.2795 5.0373 4.6364 4.4790 5.0878
#> 33 88.2190 98.5362 90.4762 85.1137 95.5672
#> 34 45.4405 66.2462 58.1967 50.9794 69.5567
#> 35 38.9560 68.9030 64.5455 51.0468 73.7532
#> 36 0.5000 8.5352 6.8966 4.2234 12.2640
#> 37 0.5128 10.1871 1.7241 0.0000 5.7818
#> 38 31.8654 49.1358 25.2174 18.5178 34.2063
#> 39 31.8708 49.7619 40.0000 32.0614 51.4633
#> 40 0.0000 1.2637 4.5455 2.7592 12.3975
#> 41 17.6891 30.6154 19.6721 15.0988 29.7619
#> 42 28.0672 53.2564 55.1724 45.4783 63.1780
#> 43 90.5742 100.0000 98.2759 94.2506 100.0000
#> 44
#> 45
#> 46 FEMALES
#> 47 LCL UCL EST LCL UCL
#> 48 38.9487 57.2365 50.9091 43.7719 58.8796
#> 49 31.8708 49.7619 40.0000 32.0614 51.4633
#> 50 1.2500 17.6043 14.6552 8.6192 22.3957
#> 51 45.4212 70.6471 68.6957 54.5197 77.9449
#> 52 0.5128 10.4435 7.1429 4.2704 15.0446
#> 53 48.1868 71.1471 70.0000 57.3030 80.0919
#> 54 51.2821 71.6049 70.5882 58.3744 77.8530
#> 55 0.0000 1.2637 4.5455 2.7592 12.3975
#> 56 56.1614 78.0695 57.2727 49.5801 64.3509
#> 57 58.5068 79.6268 66.3934 54.1053 69.4755
#> 58 69.5023 86.0406 85.0877 80.8374 90.7376
#> 59 56.1614 78.0695 57.2727 49.5801 64.3509
#> 60 81.1573 92.8042 86.8421 83.8608 91.1462
#> 61 36.9679 55.2819 34.7826 25.6740 41.2126
#> 62 36.9679 53.9540 34.7826 23.2918 39.9015
#> 63
#> 64
#> 65 FEMALES
#> 66 LCL UCL EST LCL UCL
#> 67 61.1574 74.1026 77.3913 74.6364 86.2125
#> 68 20.7206 29.7097 13.9344 10.5973 18.1008
#> 69 0.2564 9.4012 3.4783 0.3279 5.1478
#> 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 89.6786 97.4359 98.2456 93.7205 100.0000
#> 108 91.5714 100.0000 100.0000 96.1646 100.0000
#> 109 91.5714 100.0000 100.0000 96.1646 100.0000
#> 110 88.3382 99.7619 97.3684 93.9007 99.6721
#> 111 72.7059 87.9286 66.3934 59.0016 78.3354
#> 112 93.0123 100.0000 100.0000 100.0000 100.0000
#> 113 5.3825 5.7275 5.5826 5.5070 5.7463
#> 114 91.5714 100.0000 98.2143 93.1621 100.0000
#> 115 0.0000 0.0000 1.7857 0.0000 6.8379
#> 116 0.0000 8.4286 0.0000 0.0000 0.0000
#> 117 40.4286 71.8124 63.4783 52.8581 68.7768
#> 118 6.2291 21.3929 12.1739 5.4078 17.1264
#> 119
#> 120
#> 121 FEMALES
#> 122 LCL UCL EST LCL UCL
#> 123 8.2778 12.6125 12.5238 11.8375 12.9857
#> 124 28.4303 50.7500 50.9091 45.9430 56.8067
#> 125 10.0513 27.3552 22.8070 18.1976 32.3302
#> 126
#> 127
#> 128 FEMALES
#> 129 LCL UCL EST LCL UCL
#> 130 26.6281 38.7179 52.5862 45.9030 60.4333
#> 131 52.0348 87.9273 75.0000 68.6631 86.4301
#> 132 0.0000 42.8571 7.1429 0.0000 34.9206
#> 133 0.0000 50.0000 35.7143 25.7143 67.1111
#> 134 0.0000 0.0000 20.0000 2.8571 50.0000
#> 135 0.0000 65.8333 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 4.7619 0.0000 22.8571
#> 139 0.0000 0.0000 0.0000 0.0000 0.0000
#> 140 0.0000 53.6364 20.0000 1.9048 27.5630
#> 141 75.7231 90.9487 89.6552 87.9394 92.9106
#> 142 72.5684 84.1576 84.9057 77.6662 89.9553
#> 143 0.0000 19.3162 7.1429 0.0000 27.9487
#> 144 72.5275 100.0000 73.6842 65.6410 100.0000
#> 145 0.0000 0.0000 0.0000 0.0000 0.0000
#> 146 0.0000 27.4725 0.0000 0.0000 0.0000
#> 147 0.0000 0.0000 0.0000 0.0000 0.0000
#> 148 0.0000 0.0000 3.8462 0.0000 28.3333
#> 149 0.0000 0.0000 0.0000 0.0000 0.0000
#> 150 0.0000 0.0000 0.0000 0.0000 14.7368
#> 151 0.0000 0.0000 0.0000 0.0000 0.0000
#> 152 0.0000 2.3454 0.8772 0.1681 6.2863
#> 153 36.5714 56.4054 30.7018 25.6727 38.4156
#> 154 16.9231 38.2073 35.7143 27.3747 38.1526
#> 155
#> 156
#> 157 FEMALES
#> 158 LCL UCL EST LCL UCL
#> 159 54.3590 71.3385 50.4348 46.4532 59.4015
#> 160 39.2986 56.2143 30.4348 19.9754 37.3183
#> 161 12.2222 30.3649 4.3103 1.0591 5.9558
#> 162 2.7143 8.2051 0.8696 0.0000 3.6423
#> 163 0.0000 4.8108 8.6207 2.5761 14.3049
#> 164 0.0000 0.0000 0.8621 0.0000 2.5631
#> 165 0.0000 5.9524 0.0000 0.0000 0.0000
#> 166 0.0000 7.0656 0.8621 0.0000 2.6646
#> 167 23.4615 36.3348 34.4828 32.0746 42.5945
#> 168 0.0000 3.3700 0.0000 0.0000 1.8117
#> 169
#> 170
#> 171 FEMALES
#> 172 LCL UCL EST LCL UCL
#> 173 48.8235 71.4359 57.3913 50.0000 73.1047
#> 174 56.2325 75.1187 69.8276 62.5202 84.1270
#> 175 18.0392 38.0192 20.1754 16.3821 28.1755
#> 176 18.0392 36.1795 20.1681 13.6453 25.9248
#> 177
#> 178
#> 179 FEMALES
#> 180 LCL UCL EST LCL UCL
#> 181 0.2564 8.4136 3.5714 2.6144 7.9859
#> 182 0.2381 8.7580 6.7227 1.6207 9.3253
#> 183 0.2564 5.2767 2.4590 0.8104 6.0345
#> 184
#> 185
#> 186 FEMALES
#> 187 LCL UCL EST LCL UCL
#> 188 0.0035 2.2857 4.2406 0.7788 5.4436
#> 189 0.0035 2.2752 4.0131 0.7234 5.2531
#> 190 0.0000 0.0105 0.1037 0.0000 1.0286Piped 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:
