The goal of analysistools is to provide tools to analyse dataset collected with ODK. The main function is create_analysis.
All create_analysis_* fuctions will take a survey design object as input and will return a long result table with the analysis key.
The analysis key is the unique identifier of the analysis. The format is the following:
analysis type @/@ analysis variable %/% analysis variable value @/@ grouping variable %/% grouping variable value
analysis type @/@ dependent variable %/% dependent variable value @/@ independent variable %/% independent variable value
If there are two or more grouping variables it would look like that
- analysis type @/@ analysis variable %/% analysis variable value @/@ grouping variable 1 %/% grouping variable value 1 -/- grouping variable 2 %/% grouping variable value 2
There are 3 types of separators:
@/@ will separate the top level information: analysis type, the analysis (dependent) variable information and the grouping (independent) variable
%/% will separate the analysis and grouping information: it will separate the variable name and the variable value
-/- will separate 2 variables in case there are multiple variable in either the analysis or grouping sets.
The current analysis types available are :
- mean
- median
- prop_select_one: proportion for select one
- prop_select_multiple: proportion for select multiple
- ratio
Installation
You can install the development version of analysistools from GitHub with:
# install.packages("devtools")
devtools::install_github("impact-initiatives/analysistools")Example
How to add weights
shorter_df <- analysistools_MSNA_template_data[, c(
"admin1",
"admin2",
"expenditure_debt",
"income_v1_salaried_work",
"wash_drinkingwatersource",
grep("edu_learning_conditions_reasons_v1", names(analysistools_MSNA_template_data), value = T)
)]
example_sample <- data.frame(
strata = c("admin1a", "admin1b", "admin1c"),
population = c(30000, 50000, 80000)
)
weighted_shorter_df <- shorter_df %>%
add_weights(example_sample,
strata_column_dataset = "admin1",
strata_column_sample = "strata",
population_column = "population"
)
weighted_shorter_df[, c("admin1", "weights")] %>% head()
#> admin1 weights
#> 1 admin1b 1.157407
#> 2 admin1c 1.190476
#> 3 admin1c 1.190476
#> 4 admin1c 1.190476
#> 5 admin1c 1.190476
#> 6 admin1c 1.190476How to perform a descriptive analysis (mean, median, proportions)
The create_analysis function needs a survey design from srvyr.
example_design <- srvyr::as_survey(weighted_shorter_df, strata = admin1, weights = weights)If only the design is provided, it will perform mean, median and proportions.
ex1_results <- create_analysis(design = example_design, sm_separator = "/")
#> Joining with `by = join_by(type)`
#> Joining with `by = join_by(admin1)`
#> Joining with `by = join_by(admin2)`
#> Joining with `by = join_by(wash_drinkingwatersource)`It should return an object with 3 elements: - the results table (in a long format and analysis key), - the dataset used, - the list of analysis performed.
names(ex1_results)
#> [1] "results_table" "dataset" "loa"
ex1_results[["results_table"]] %>% head()
#> # A tibble: 6 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 prop_select_o… admin1 admin1a <NA> <NA> 0.188
#> 2 prop_select_o… admin1 admin1b <NA> <NA> 0.313
#> 3 prop_select_o… admin1 admin1c <NA> <NA> 0.5
#> 4 prop_select_o… admin2 admin2a <NA> <NA> 0.284
#> 5 prop_select_o… admin2 admin2b <NA> <NA> 0.385
#> 6 prop_select_o… admin2 admin2c <NA> <NA> 0.331
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <dbl>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>
ex1_results[["loa"]] %>% head()
#> analysis_type analysis_var group_var level
#> 1 prop_select_one admin1 <NA> 0.95
#> 2 prop_select_one admin2 <NA> 0.95
#> 3 mean expenditure_debt <NA> 0.95
#> 4 median expenditure_debt <NA> 0.95
#> 5 mean income_v1_salaried_work <NA> 0.95
#> 6 median income_v1_salaried_work <NA> 0.95Grouping variables
The group_var can be used to defined the different grouping, independent variables. For example: - one variable
ex2_results <- create_analysis(design = srvyr::as_survey(shorter_df), group_var = "admin1", sm_separator = "/")
#> Joining with `by = join_by(type)`
#> Joining with `by = join_by(admin1)`
#> Joining with `by = join_by(admin2)`
#> Joining with `by = join_by(wash_drinkingwatersource)`
#> ■■■■■■■■■■■■■■■ 47% | ETA: 1s
#> Joining with `by = join_by(admin1, admin2)`
#> ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 1s
#> Joining with `by = join_by(admin1, wash_drinkingwatersource)`
#> ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 93% | ETA: 0s
ex2_results[["loa"]]
#> analysis_type analysis_var group_var level
#> 1 prop_select_one admin1 <NA> 0.95
#> 2 prop_select_one admin2 <NA> 0.95
#> 3 mean expenditure_debt <NA> 0.95
#> 4 median expenditure_debt <NA> 0.95
#> 5 mean income_v1_salaried_work <NA> 0.95
#> 6 median income_v1_salaried_work <NA> 0.95
#> 7 prop_select_one wash_drinkingwatersource <NA> 0.95
#> 8 prop_select_multiple edu_learning_conditions_reasons_v1 <NA> 0.95
#> 9 prop_select_one admin2 admin1 0.95
#> 10 mean expenditure_debt admin1 0.95
#> 11 median expenditure_debt admin1 0.95
#> 12 mean income_v1_salaried_work admin1 0.95
#> 13 median income_v1_salaried_work admin1 0.95
#> 14 prop_select_one wash_drinkingwatersource admin1 0.95
#> 15 prop_select_multiple edu_learning_conditions_reasons_v1 admin1 0.95- two variables separately
ex3_results <- create_analysis(design = srvyr::as_survey(shorter_df), group_var = c("admin1", "admin2"), sm_separator = "/")
#> Joining with `by = join_by(type)`
#> Joining with `by = join_by(admin1)`
#> Joining with `by = join_by(admin2)`
#> Joining with `by = join_by(wash_drinkingwatersource)`
#> ■■■■■■■■■■■ 32% | ETA: 2s
#> ■■■■■■■■■■■■ 36% | ETA: 2s
#> Joining with `by = join_by(admin1, admin2)`
#> ■■■■■■■■■■■■■ 41% | ETA: 2s
#> ■■■■■■■■■■■■■■■ 45% | ETA: 2s
#> ■■■■■■■■■■■■■■■■■ 55% | ETA: 2s
#> Joining with `by = join_by(admin1, wash_drinkingwatersource)`
#> ■■■■■■■■■■■■■■■■■■■■ 64% | ETA: 2s
#> ■■■■■■■■■■■■■■■■■■■■■ 68% | ETA: 2s
#> Joining with `by = join_by(admin2, admin1)`
#> ■■■■■■■■■■■■■■■■■■■■■■■ 73% | ETA: 2s
#> ■■■■■■■■■■■■■■■■■■■■■■■■ 77% | ETA: 1s
#> ■■■■■■■■■■■■■■■■■■■■■■■■■■■ 86% | ETA: 1s
#> Joining with `by = join_by(admin2, wash_drinkingwatersource)`
#> ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 95% | ETA: 0s
ex3_results[["loa"]]
#> analysis_type analysis_var group_var level
#> 1 prop_select_one admin1 <NA> 0.95
#> 2 prop_select_one admin2 <NA> 0.95
#> 3 mean expenditure_debt <NA> 0.95
#> 4 median expenditure_debt <NA> 0.95
#> 5 mean income_v1_salaried_work <NA> 0.95
#> 6 median income_v1_salaried_work <NA> 0.95
#> 7 prop_select_one wash_drinkingwatersource <NA> 0.95
#> 8 prop_select_multiple edu_learning_conditions_reasons_v1 <NA> 0.95
#> 9 prop_select_one admin2 admin1 0.95
#> 10 mean expenditure_debt admin1 0.95
#> 11 median expenditure_debt admin1 0.95
#> 12 mean income_v1_salaried_work admin1 0.95
#> 13 median income_v1_salaried_work admin1 0.95
#> 14 prop_select_one wash_drinkingwatersource admin1 0.95
#> 15 prop_select_multiple edu_learning_conditions_reasons_v1 admin1 0.95
#> 16 prop_select_one admin1 admin2 0.95
#> 17 mean expenditure_debt admin2 0.95
#> 18 median expenditure_debt admin2 0.95
#> 19 mean income_v1_salaried_work admin2 0.95
#> 20 median income_v1_salaried_work admin2 0.95
#> 21 prop_select_one wash_drinkingwatersource admin2 0.95
#> 22 prop_select_multiple edu_learning_conditions_reasons_v1 admin2 0.95- two variables combined
ex4_results <- create_analysis(design = srvyr::as_survey(shorter_df), group_var = "admin1, admin2", sm_separator = "/")
#> Joining with `by = join_by(type)`
#> Joining with `by = join_by(admin1)`
#> Joining with `by = join_by(admin2)`
#> Joining with `by = join_by(wash_drinkingwatersource)`
#> ■■■■■■■■■■■■■■■■■■■■■■■■■■■ 86% | ETA: 0s
#> Joining with `by = join_by(admin1, admin2, wash_drinkingwatersource)`
#> ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 93% | ETA: 0s
ex4_results[["loa"]]
#> analysis_type analysis_var group_var level
#> 1 prop_select_one admin1 <NA> 0.95
#> 2 prop_select_one admin2 <NA> 0.95
#> 3 mean expenditure_debt <NA> 0.95
#> 4 median expenditure_debt <NA> 0.95
#> 5 mean income_v1_salaried_work <NA> 0.95
#> 6 median income_v1_salaried_work <NA> 0.95
#> 7 prop_select_one wash_drinkingwatersource <NA> 0.95
#> 8 prop_select_multiple edu_learning_conditions_reasons_v1 <NA> 0.95
#> 9 mean expenditure_debt admin1, admin2 0.95
#> 10 median expenditure_debt admin1, admin2 0.95
#> 11 mean income_v1_salaried_work admin1, admin2 0.95
#> 12 median income_v1_salaried_work admin1, admin2 0.95
#> 13 prop_select_one wash_drinkingwatersource admin1, admin2 0.95
#> 14 prop_select_multiple edu_learning_conditions_reasons_v1 admin1, admin2 0.95How to perform a descriptive analysis with a list of analysis
ex5_results <- create_analysis(design = srvyr::as_survey(shorter_df), loa = analysistools_MSNA_template_loa, sm_separator = "/")
#> Joining with `by = join_by(admin1)`
#> Joining with `by = join_by(wash_drinkingwatersource)`
#> ■■■■■■■■■■■■■■■■■■■■■■■■■■ 85% | ETA: 0s
#> Joining with `by = join_by(admin1, wash_drinkingwatersource)`
#> ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 92% | ETA: 0s
ex5_results[["loa"]]
#> analysis_type analysis_var group_var level
#> 1 prop_select_one admin1 <NA> 0.95
#> 2 mean income_v1_salaried_work <NA> 0.95
#> 3 median income_v1_salaried_work <NA> 0.95
#> 4 mean expenditure_debt <NA> 0.95
#> 5 median expenditure_debt <NA> 0.95
#> 6 prop_select_one wash_drinkingwatersource <NA> 0.95
#> 7 prop_select_multiple edu_learning_conditions_reasons_v1 <NA> 0.95
#> 8 mean income_v1_salaried_work admin1 0.95
#> 9 median income_v1_salaried_work admin1 0.95
#> 10 mean expenditure_debt admin1 0.95
#> 11 median expenditure_debt admin1 0.95
#> 12 prop_select_one wash_drinkingwatersource admin1 0.95
#> 13 prop_select_multiple edu_learning_conditions_reasons_v1 admin1 0.95How to perform specfic analysis
Mean
This is a basic example which shows you how to calculate the mean:
somedata <- data.frame(
aa = 1:10,
bb = rep(c("a", "b"), 5),
weights = rep(c(.5, 1.5), 5),
stratas = rep(c("strata_a", "strata_b"), 5)
)
me_design <- srvyr::as_survey(somedata)
create_analysis_mean(me_design, analysis_var = "aa")
#> # A tibble: 1 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 mean aa <NA> <NA> <NA> 5.5
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>
create_analysis_mean(me_design, group_var = "bb", analysis_var = "aa")
#> # A tibble: 2 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 mean aa <NA> bb a 5
#> 2 mean aa <NA> bb b 6
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>
me_design_w <- srvyr::as_survey(somedata, weights = weights)
create_analysis_mean(me_design_w, analysis_var = "aa")
#> # A tibble: 1 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 mean aa <NA> <NA> <NA> 5.75
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>
create_analysis_mean(me_design_w, group_var = "bb", analysis_var = "aa")
#> # A tibble: 2 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 mean aa <NA> bb a 5
#> 2 mean aa <NA> bb b 6
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>Median
This is a basic example which shows you how to calculate the median:
somedata <- data.frame(
aa = 1:10,
bb = rep(c("a", "b"), 5),
weights = rep(c(.5, 1.5), 5),
stratas = rep(c("strata_a", "strata_b"), 5)
)
me_design <- srvyr::as_survey(somedata)
create_analysis_median(me_design, analysis_var = "aa")
#> # A tibble: 1 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 median aa <NA> <NA> <NA> 5
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>
create_analysis_median(me_design, group_var = "bb", analysis_var = "aa")
#> # A tibble: 2 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 median aa <NA> bb a 5
#> 2 median aa <NA> bb b 6
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>
me_design_w <- srvyr::as_survey(somedata, weights = weights)
create_analysis_median(me_design_w, analysis_var = "aa")
#> # A tibble: 1 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 median aa <NA> <NA> <NA> 6
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>
create_analysis_median(me_design_w, group_var = "bb", analysis_var = "aa")
#> # A tibble: 2 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 median aa <NA> bb a 5
#> 2 median aa <NA> bb b 6
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>Proportion
Select one
This is a basic example which shows you how to calculate the proportion for select one:
somedata <- data.frame(
groups = sample(c("group_a", "group_b"),
size = 100,
replace = TRUE
),
value = sample(c("a", "b", "c"),
size = 100, replace = TRUE,
prob = c(.6, .4, .1)
)
)
create_analysis_prop_select_one(srvyr::as_survey(somedata, strata = groups),
group_var = NA,
analysis_var = "value",
level = .95
)
#> Joining with `by = join_by(value)`
#> # A tibble: 3 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 prop_select_o… value a <NA> <NA> 0.52
#> 2 prop_select_o… value b <NA> <NA> 0.38
#> 3 prop_select_o… value c <NA> <NA> 0.1
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>
create_analysis_prop_select_one(srvyr::as_survey(somedata, strata = groups),
group_var = "groups",
analysis_var = "value",
level = .95
)
#> Joining with `by = join_by(groups, value)`
#> # A tibble: 6 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 prop_select_… value a groups group_a 0.549
#> 2 prop_select_… value b groups group_a 0.373
#> 3 prop_select_… value c groups group_a 0.0784
#> 4 prop_select_… value a groups group_b 0.490
#> 5 prop_select_… value b groups group_b 0.388
#> 6 prop_select_… value c groups group_b 0.122
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>Select multiple
somedata <- data.frame(
groups = sample(c("group_a", "group_b"), size = 100, replace = T),
smvar = rep(NA_character_, 100),
smvar.option1 = sample(c(TRUE, FALSE), size = 100, replace = T, prob = c(.7, .3)),
smvar.option2 = sample(c(TRUE, FALSE), size = 100, replace = T, prob = c(.6, .4)),
smvar.option3 = sample(c(TRUE, FALSE), size = 100, replace = T, prob = c(.1, .9)),
smvar.option4 = sample(c(TRUE, FALSE), size = 100, replace = T, prob = c(.8, .2)),
uuid = 1:100 %>% as.character()
) %>%
cleaningtools::recreate_parent_column(uuid = "uuid", sm_separator = ".")
#> groups
#> smvar
#> smvar.option1
#> smvar.option2
#> smvar.option3
#> smvar.option4
#> groups
#> smvar.option1
#> smvar.option2
#> smvar.option3
#> smvar.option4
#> groups
#> smvar
#> smvar.option1
#> smvar.option2
#> smvar.option3
#> smvar.option4
somedata <- somedata$data_with_fix_concat
create_analysis_prop_select_multiple(srvyr::as_survey(somedata),
group_var = NA,
analysis_var = "smvar",
level = 0.95
)
#> # A tibble: 5 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 prop_select_… smvar option1 <NA> <NA> 0.694
#> 2 prop_select_… smvar option2 <NA> <NA> 0.622
#> 3 prop_select_… smvar option3 <NA> <NA> 0.143
#> 4 prop_select_… smvar option4 <NA> <NA> 0.806
#> 5 prop_select_… smvar NA <NA> <NA> NA
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <dbl>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>
create_analysis_prop_select_multiple(srvyr::as_survey(somedata),
group_var = "groups",
analysis_var = "smvar",
level = 0.95
)
#> # A tibble: 9 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 prop_select_… smvar option1 groups group_a 0.76
#> 2 prop_select_… smvar option2 groups group_a 0.58
#> 3 prop_select_… smvar option3 groups group_a 0.04
#> 4 prop_select_… smvar option4 groups group_a 0.74
#> 5 prop_select_… smvar option1 groups group_b 0.625
#> 6 prop_select_… smvar option2 groups group_b 0.667
#> 7 prop_select_… smvar option3 groups group_b 0.25
#> 8 prop_select_… smvar option4 groups group_b 0.875
#> 9 prop_select_… smvar NA groups group_b NA
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <dbl>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>Ratios
This is a basic example which shows you how to calculate the ratio between 2 numeric variables:
school_ex <- data.frame(
hh = c("hh1", "hh2", "hh3", "hh4"),
num_children = c(3, 0, 2, NA),
num_enrolled = c(3, NA, 0, NA),
num_attending = c(1, NA, NA, NA),
group = c("a", "a", "b", "b")
)
me_design <- srvyr::as_survey(school_ex)Default value will give a ratio of 0.2 as there are 1 child out of 5 attending school. In the hh3, the NA is present because there is a skip logic, there cannot be a child attending as none are enrolled. The number of household counted, n, is equal to 2, as there are 2 households only having child.
create_analysis_ratio(me_design,
analysis_var_numerator = "num_attending",
analysis_var_denominator = "num_children"
)
#> # A tibble: 1 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 ratio num_attendin… NA %/% NA <NA> <NA> 0.2
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>If numerator_NA_to_0 is set to FALSE, ratio will be 1/3, as hh3 with 2 children and NA for attending will be removed with the na.rm = T inside the survey_ratio calculation. The number of household used in the calculation is 1.
create_analysis_ratio(me_design,
analysis_var_numerator = "num_attending",
analysis_var_denominator = "num_children",
numerator_NA_to_0 = FALSE
)
#> Warning: There were 2 warnings in `dplyr::summarise()`.
#> The first warning was:
#> ℹ In argument: `srvyr::survey_ratio(...)`.
#> Caused by warning in `qt()`:
#> ! NaNs produced
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
#> # A tibble: 1 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 ratio num_attendin… NA %/% NA <NA> <NA> 0.333
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>If filter_denominator_0 is set to FALSE, ratio will be 0.2 as there are 1 child out of 5 attending school. In the hh3, the NA is present because there is a skip logic, there cannot be a child attending as none are enrolled. The number of household counted, n, is equal to 3 instead 2. The household with 0 child is counted in the n.
create_analysis_ratio(me_design,
analysis_var_numerator = "num_attending",
analysis_var_denominator = "num_children",
numerator_NA_to_0 = FALSE
)
#> Warning: There were 2 warnings in `dplyr::summarise()`.
#> The first warning was:
#> ℹ In argument: `srvyr::survey_ratio(...)`.
#> Caused by warning in `qt()`:
#> ! NaNs produced
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
#> # A tibble: 1 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 ratio num_attendin… NA %/% NA <NA> <NA> 0.333
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>For weigths and group:
set.seed(8988)
somedata <- data.frame(
groups = rep(c("a", "b"), 50),
children_518 = sample(0:5, 100, replace = TRUE),
children_enrolled = sample(0:5, 100, replace = TRUE)
) %>%
dplyr::mutate(children_enrolled = ifelse(children_enrolled > children_518,
children_518,
children_enrolled
))
somedata[["weights"]] <- ifelse(somedata$groups == "a", 1.33, .67)
create_analysis_ratio(srvyr::as_survey(somedata, weights = weights, strata = groups),
group_var = NA,
analysis_var_numerator = "children_enrolled",
analysis_var_denominator = "children_518",
level = 0.95
)
#> # A tibble: 1 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 ratio children_enr… NA %/% NA <NA> <NA> 0.639
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>
create_analysis_ratio(srvyr::as_survey(somedata, weights = weights, strata = groups),
group_var = "groups",
analysis_var_numerator = "children_enrolled",
analysis_var_denominator = "children_518",
level = 0.95
)
#> # A tibble: 2 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 ratio children_enr… NA %/% NA groups a 0.670
#> 2 ratio children_enr… NA %/% NA groups b 0.578
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>How to review results
The logic behind reviewing analysis is to compare the results from 2 independent analysis of the same variables using the review_analysis.
In this example, the results table to be review and the dataset are loaded.
results_to_review <- analysistools::analysistools_MSNA_template_with_ratio_results_table$results_table
dataset_to_analyse <- analysistools::analysistools_MSNA_template_dataThe list of analysis from the results can be reproduced with create_loa_from_results and the analysis key. This loa can be used to create a new analysis to be compared with.
me_loa <- create_loa_from_results(results_to_review)
me_analysis <- create_analysis(srvyr::as_survey(dataset_to_analyse),
loa = me_loa,
sm_separator = "/")
#> Joining with `by = join_by(admin1)`
#> Joining with `by = join_by(wash_drinkingwatersource)`
#> ■■■■■■■■■■■■■■■ 47% | ETA: 2s
#> ■■■■■■■■■■■■■■■■■■■ 60% | ETA: 1s
#> ■■■■■■■■■■■■■■■■■■■■■ 67% | ETA: 1s
#> ■■■■■■■■■■■■■■■■■■■■■■■■■ 80% | ETA: 1s
#> ■■■■■■■■■■■■■■■■■■■■■■■■■■■ 87% | ETA: 0s
#> Joining with `by = join_by(admin1, wash_drinkingwatersource)`
#> ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 93% | ETA: 0sThe new results and the results to be reviewed are bound together by the analysis_key.
binded_results <- results_to_review %>%
dplyr::full_join(me_analysis$results_table, by ="analysis_key")
review_results <- review_analysis(binded_results,
stat_columns_to_review = c("stat.x", "stat_low.x", "stat_upp.x"),
stat_columns_to_compare_with = c("stat.y", "stat_low.y", "stat_upp.y"))
review_results$review_table %>%
dplyr::group_by(stat) %>%
dplyr::summarise(prop_correct = mean(review_check))
#> # A tibble: 3 × 2
#> stat prop_correct
#> <chr> <dbl>
#> 1 stat.x 1
#> 2 stat_low.x 1
#> 3 stat_upp.x 1
review_results$review_table %>%
dplyr::group_by(stat, review_comment) %>%
dplyr::tally(sort = T)
#> # A tibble: 3 × 3
#> # Groups: stat [3]
#> stat review_comment n
#> <chr> <glue> <int>
#> 1 stat.x Same results 147
#> 2 stat_low.x Same results 147
#> 3 stat_upp.x Same results 147
review_results$review_table %>%
dplyr::filter(!review_check) %>%
dplyr::select(analysis_type,analysis_var,group_var) %>%
dplyr::distinct()
#> [1] analysis_type analysis_var group_var
#> <0 rows> (or 0-length row.names)
analysis_key_column <- c("mean @/@ income %/% NA @/@ NA %/% NA",
"prop_select_one @/@ water_source %/% tap_water @/@ district %/% district_a",
"prop_select_one @/@ water_source %/% tap_water @/@ district %/% district_a -/- population %/% displaced",
"prop_select_multiple @/@ source_information %/% relatives @/@ NA %/% NA")
test_analysis_results <- data.frame(
test = c(
"test equality",
"test difference",
"test Missing in y",
"test Missing in x"
),
stat_col.x = c(0, 1, 2, NA),
stat_col.y = c(0, 2, NA, 3),
analysis_key = analysis_key_column
)
review_results2 <- review_analysis(test_analysis_results,
stat_columns_to_review = "stat_col.x",
stat_columns_to_compare_with = "stat_col.y")
review_results2$review_table %>%
dplyr::group_by(stat) %>%
dplyr::summarise(prop_correct = mean(review_check))
#> # A tibble: 1 × 2
#> stat prop_correct
#> <chr> <dbl>
#> 1 stat_col.x 0.25
review_results2$review_table %>%
dplyr::group_by(stat, review_comment) %>%
dplyr::tally(sort = T)
#> # A tibble: 4 × 3
#> # Groups: stat [1]
#> stat review_comment n
#> <chr> <glue> <int>
#> 1 stat_col.x Different results 1
#> 2 stat_col.x Missing in stat_col.x 1
#> 3 stat_col.x Missing in stat_col.y 1
#> 4 stat_col.x Same results 1
review_results2$review_table %>%
dplyr::filter(!review_check) %>%
dplyr::select(review_check, analysis_type,analysis_var,group_var) %>%
dplyr::distinct()
#> review_check analysis_type analysis_var group_var
#> 1 FALSE prop_select_one water_source district
#> 2 FALSE prop_select_one water_source district %/% population
#> 3 FALSE prop_select_multiple source_information NAConverting the analysis index into a table
This is is how to turn the analysis index into a table
resultstable <- data.frame(analysis_index = c(
"mean @/@ v1 %/% NA @/@ NA %/% NA",
"mean @/@ v1 %/% NA @/@ gro %/% A",
"mean @/@ v1 %/% NA @/@ gro %/% B"
))
key_table <- create_analysis_key_table(resultstable, "analysis_index")
key_table
#> # A tibble: 3 × 8
#> analysis_index analysis_type analysis_var_1 analysis_var_value_1 group_var_1
#> <chr> <chr> <chr> <chr> <chr>
#> 1 mean @/@ v1 %/%… mean v1 NA NA
#> 2 mean @/@ v1 %/%… mean v1 NA gro
#> 3 mean @/@ v1 %/%… mean v1 NA gro
#> # ℹ 3 more variables: group_var_value_1 <chr>, nb_analysis_var <dbl>,
#> # nb_group_var <dbl>You can then unite the analysis and grouping variables if needed.
unite_variables(key_table)
#> # A tibble: 3 × 8
#> analysis_index analysis_type analysis_var analysis_var_value group_var
#> <chr> <chr> <chr> <chr> <chr>
#> 1 mean @/@ v1 %/% NA @/… mean v1 NA NA
#> 2 mean @/@ v1 %/% NA @/… mean v1 NA gro
#> 3 mean @/@ v1 %/% NA @/… mean v1 NA gro
#> # ℹ 3 more variables: group_var_value <chr>, nb_analysis_var <dbl>,
#> # nb_group_var <dbl>Code of Conduct
Please note that the analysistools project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.