Prepare Sahel binary variables for plotting.

Usage

sahel_prep_gg_binary(data, ..., alpha = 0.05)

Arguments

data: A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr)
...: Passed to group_by(), variables or computations to group by. Computations are always done on the ungrouped data frame. To perform computations on the grouped data, you need to use a separate mutate() step before the group_by(). Computations are not allowed in nest_by(). In ungroup(), variables to remove from the grouping.
alpha: Signifiicance level for confidence interval

Value

A dataframe ready for plotting. Computes the

Examples

sahel_sim_long_binary <- tidyr::gather(sahel_sim, key = "variable",
                                       value = "value", has_children,
                                       owns_animals, female_head)
sahel_prep_gg_binary(sahel_sim_long_binary, country_names, variable,
                       alpha = 0.05)
#> # A tibble: 12 × 7
#> # Groups:   country_names [4]
#>    country_names variable      mean    sd      se     t     CI
#>    <chr>         <chr>        <dbl> <dbl>   <dbl> <dbl>  <dbl>
#>  1 Burkina Faso  female_head  0.147 0.355 0.00915  1.96 0.0180
#>  2 Burkina Faso  has_children 0.789 0.408 0.0105   1.96 0.0207
#>  3 Burkina Faso  owns_animals 0.753 0.431 0.0111   1.96 0.0218
#>  4 Mauritania    female_head  0.147 0.354 0.00792  1.96 0.0155
#>  5 Mauritania    has_children 0.788 0.408 0.00913  1.96 0.0179
#>  6 Mauritania    owns_animals 0.758 0.429 0.00959  1.96 0.0188
#>  7 Niger         female_head  0.138 0.345 0.00814  1.96 0.0160
#>  8 Niger         has_children 0.794 0.404 0.00953  1.96 0.0187
#>  9 Niger         owns_animals 0.742 0.438 0.0103   1.96 0.0202
#> 10 Senegal       female_head  0.134 0.340 0.00681  1.96 0.0133
#> 11 Senegal       has_children 0.800 0.400 0.00800  1.96 0.0157
#> 12 Senegal       owns_animals 0.744 0.437 0.00873  1.96 0.0171