README.md
In MethodsConsultants/tibbletest: Clean Descriptives Tables with Significance Tests by Group
tibbletest
Installation
Install the development version from GitHub with:
devtools::install_github("MethodsConsultants/tibbletest")
Usage
propensity_weighting
library(tibbletest)
example_dat %>%
add_propensity_weights(
treatment = "treat",
ivs = c("age", "sugar_factor", "gender")
)
## Error: df[[treatment]] contains 1 missing values
example_dat %>%
tidyr::drop_na(treat, age) %>%
add_propensity_weights(
treatment = "treat",
ivs = c("age", "sugar_factor", "gender")
) %>%
dplyr::glimpse()
## Observations: 495
## Variables: 10
## $ gender <fct> male, female, male, female, male, female, fe...
## $ age <int> 80, 60, 8, 61, 53, 73, 6, 26, 63, 76, 31, 18...
## $ sugar_factor <dbl> 0.89372538, 0.83360390, 0.22198429, 0.665734...
## $ treat <fct> ice cream, candy, candy, candy, candy, ice c...
## $ happiness <fct> happy, happy, happy, happy, happy, happy, ha...
## $ happy <fct> Yes, Yes, yes, Yes, Yes, Yes, yes, Yes, Yes,...
## $ weight <dbl> 1.3788863, 0.9021300, 1.3989398, 1.3817288, ...
## $ no_weight <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ treat2 <chr> "pizza", "pizza", "ice cream", "pizza", "ice...
## $ propensity_weight <dbl> 0.9032606, 0.8181604, 0.8252251, 1.0340621, ...
descriptives
example_dat %>%
descriptives(
treatment = "treat",
variables = c("age", "sugar_factor", "gender", "happiness", "happy")
)
## # A tibble: 9 x 5
## Variable Label candy `ice cream` `P Value`
## <chr> <chr> <chr> <chr> <dbl>
## 1 gender female 124 (48.25%) 110 (45.27%) 0.563
## 2 gender male 133 (51.75%) 133 (54.73%) 0.563
## 3 happiness happy 185 (76.76%) 181 (78.7%) 0.694
## 4 happiness sad 56 (23.24%) 49 (21.3%) 0.694
## 5 happy no 56 (23.24%) 49 (21.3%) 0.855
## 6 happy yes 47 (19.5%) 44 (19.13%) 0.855
## 7 happy Yes 138 (57.26%) 137 (59.57%) 0.855
## 8 age "" 42.26 (22.62) 42.39 (21.61) 0.946
## 9 sugar_factor "" 0.46 (0.3) 0.52 (0.29) 0.0129
example_dat %>%
dplyr::select(treat, age, gender) %>%
descriptives(
treatment = "treat"
)
## # A tibble: 3 x 5
## Variable Label candy `ice cream` `P Value`
## <chr> <chr> <chr> <chr> <dbl>
## 1 gender female 124 (48.25%) 110 (45.27%) 0.563
## 2 gender male 133 (51.75%) 133 (54.73%) 0.563
## 3 age "" 42.26 (22.62) 42.39 (21.61) 0.946
example_dat %>%
dplyr::select(treat, weight, age, gender) %>%
descriptives(
treatment = "treat",
weights = "weight"
)
## Weights were normalized to a mean of 1 to preserve sample size in significance tests
## # A tibble: 3 x 5
## Variable Label candy `ice cream` `P Value`
## <chr> <chr> <chr> <chr> <dbl>
## 1 gender female 48.65% 44.97% 0.475
## 2 gender male 51.35% 55.03% 0.475
## 3 age "" 42.17 (22.55) 42.43 (21.93) 0.897
example_dat %>%
descriptives(
treatment = "treat",
variables = c("age", "sugar_factor", "gender", "happiness", "happy"),
nonparametric = c("age")
)
## # A tibble: 9 x 5
## Variable Label candy `ice cream` `P Value`
## <chr> <chr> <chr> <chr> <dbl>
## 1 gender female 124 (48.25%) 110 (45.27%) 0.563
## 2 gender male 133 (51.75%) 133 (54.73%) 0.563
## 3 happiness happy 185 (76.76%) 181 (78.7%) 0.694
## 4 happiness sad 56 (23.24%) 49 (21.3%) 0.694
## 5 happy no 56 (23.24%) 49 (21.3%) 0.855
## 6 happy yes 47 (19.5%) 44 (19.13%) 0.855
## 7 happy Yes 138 (57.26%) 137 (59.57%) 0.855
## 8 sugar_factor "" 0.46 (0.3) 0.52 (0.29) 0.0129
## 9 age "" 42 [22, 62] 41 [26, 61] 0.888
format_tbl
univariate <- example_dat %>%
descriptives(
variables = c("age", "sugar_factor", "gender", "happiness", "happy")
)
univariate
## # A tibble: 9 x 3
## Variable Label Statistics
## <chr> <chr> <chr>
## 1 gender female 235 (46.91%)
## 2 gender male 266 (53.09%)
## 3 happiness happy 367 (77.75%)
## 4 happiness sad 105 (22.25%)
## 5 happy no 105 (22.25%)
## 6 happy yes 91 (19.28%)
## 7 happy Yes 276 (58.47%)
## 8 age "" 42.3 (22.1)
## 9 sugar_factor "" 0.49 (0.3)
univariate %>%
format_tbl()
## # A tibble: 12 x 3
## Variable Label `Statistics (N=501)`
## <chr> <chr> <chr>
## 1 gender "" ""
## 2 "" female 235 (46.91%)
## 3 "" male 266 (53.09%)
## 4 happiness "" ""
## 5 "" happy 367 (77.75%)
## 6 "" sad 105 (22.25%)
## 7 happy "" ""
## 8 "" no 105 (22.25%)
## 9 "" yes 91 (19.28%)
## 10 "" Yes 276 (58.47%)
## 11 age "" 42.3 (22.1)
## 12 sugar_factor "" 0.49 (0.3)
bivariate <- example_dat %>%
descriptives(
treatment = "treat",
variables = c("age", "sugar_factor", "gender", "happiness", "happy")
)
bivariate
## # A tibble: 9 x 5
## Variable Label candy `ice cream` `P Value`
## <chr> <chr> <chr> <chr> <dbl>
## 1 gender female 124 (48.25%) 110 (45.27%) 0.563
## 2 gender male 133 (51.75%) 133 (54.73%) 0.563
## 3 happiness happy 185 (76.76%) 181 (78.7%) 0.694
## 4 happiness sad 56 (23.24%) 49 (21.3%) 0.694
## 5 happy no 56 (23.24%) 49 (21.3%) 0.855
## 6 happy yes 47 (19.5%) 44 (19.13%) 0.855
## 7 happy Yes 138 (57.26%) 137 (59.57%) 0.855
## 8 age "" 42.26 (22.62) 42.39 (21.61) 0.946
## 9 sugar_factor "" 0.46 (0.3) 0.52 (0.29) 0.0129
bivariate %>%
format_tbl()
## # A tibble: 12 x 5
## Variable Label `candy (N=257)` `ice cream (N=243)` `P Value`
## <chr> <chr> <chr> <chr> <chr>
## 1 gender "" "" "" 0.563
## 2 "" female 124 (48.25%) 110 (45.27%) ""
## 3 "" male 133 (51.75%) 133 (54.73%) ""
## 4 happiness "" "" "" 0.694
## 5 "" happy 185 (76.76%) 181 (78.7%) ""
## 6 "" sad 56 (23.24%) 49 (21.3%) ""
## 7 happy "" "" "" 0.855
## 8 "" no 56 (23.24%) 49 (21.3%) ""
## 9 "" yes 47 (19.5%) 44 (19.13%) ""
## 10 "" Yes 138 (57.26%) 137 (59.57%) ""
## 11 age "" 42.26 (22.62) 42.39 (21.61) 0.946
## 12 sugar_factor "" 0.46 (0.3) 0.52 (0.29) 0.013
MethodsConsultants/tibbletest documentation built on June 28, 2021, 5:22 p.m.
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tibbletest
Installation
Install the development version from GitHub with:
devtools::install_github("MethodsConsultants/tibbletest")
Usage
propensity_weighting
library(tibbletest)
example_dat %>%
add_propensity_weights(
treatment = "treat",
ivs = c("age", "sugar_factor", "gender")
)
## Error: df[[treatment]] contains 1 missing values
example_dat %>%
tidyr::drop_na(treat, age) %>%
add_propensity_weights(
treatment = "treat",
ivs = c("age", "sugar_factor", "gender")
) %>%
dplyr::glimpse()
## Observations: 495
## Variables: 10
## $ gender <fct> male, female, male, female, male, female, fe...
## $ age <int> 80, 60, 8, 61, 53, 73, 6, 26, 63, 76, 31, 18...
## $ sugar_factor <dbl> 0.89372538, 0.83360390, 0.22198429, 0.665734...
## $ treat <fct> ice cream, candy, candy, candy, candy, ice c...
## $ happiness <fct> happy, happy, happy, happy, happy, happy, ha...
## $ happy <fct> Yes, Yes, yes, Yes, Yes, Yes, yes, Yes, Yes,...
## $ weight <dbl> 1.3788863, 0.9021300, 1.3989398, 1.3817288, ...
## $ no_weight <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ treat2 <chr> "pizza", "pizza", "ice cream", "pizza", "ice...
## $ propensity_weight <dbl> 0.9032606, 0.8181604, 0.8252251, 1.0340621, ...
descriptives
example_dat %>%
descriptives(
treatment = "treat",
variables = c("age", "sugar_factor", "gender", "happiness", "happy")
)
## # A tibble: 9 x 5
## Variable Label candy `ice cream` `P Value`
## <chr> <chr> <chr> <chr> <dbl>
## 1 gender female 124 (48.25%) 110 (45.27%) 0.563
## 2 gender male 133 (51.75%) 133 (54.73%) 0.563
## 3 happiness happy 185 (76.76%) 181 (78.7%) 0.694
## 4 happiness sad 56 (23.24%) 49 (21.3%) 0.694
## 5 happy no 56 (23.24%) 49 (21.3%) 0.855
## 6 happy yes 47 (19.5%) 44 (19.13%) 0.855
## 7 happy Yes 138 (57.26%) 137 (59.57%) 0.855
## 8 age "" 42.26 (22.62) 42.39 (21.61) 0.946
## 9 sugar_factor "" 0.46 (0.3) 0.52 (0.29) 0.0129
example_dat %>%
dplyr::select(treat, age, gender) %>%
descriptives(
treatment = "treat"
)
## # A tibble: 3 x 5
## Variable Label candy `ice cream` `P Value`
## <chr> <chr> <chr> <chr> <dbl>
## 1 gender female 124 (48.25%) 110 (45.27%) 0.563
## 2 gender male 133 (51.75%) 133 (54.73%) 0.563
## 3 age "" 42.26 (22.62) 42.39 (21.61) 0.946
example_dat %>%
dplyr::select(treat, weight, age, gender) %>%
descriptives(
treatment = "treat",
weights = "weight"
)
## Weights were normalized to a mean of 1 to preserve sample size in significance tests
## # A tibble: 3 x 5
## Variable Label candy `ice cream` `P Value`
## <chr> <chr> <chr> <chr> <dbl>
## 1 gender female 48.65% 44.97% 0.475
## 2 gender male 51.35% 55.03% 0.475
## 3 age "" 42.17 (22.55) 42.43 (21.93) 0.897
example_dat %>%
descriptives(
treatment = "treat",
variables = c("age", "sugar_factor", "gender", "happiness", "happy"),
nonparametric = c("age")
)
## # A tibble: 9 x 5
## Variable Label candy `ice cream` `P Value`
## <chr> <chr> <chr> <chr> <dbl>
## 1 gender female 124 (48.25%) 110 (45.27%) 0.563
## 2 gender male 133 (51.75%) 133 (54.73%) 0.563
## 3 happiness happy 185 (76.76%) 181 (78.7%) 0.694
## 4 happiness sad 56 (23.24%) 49 (21.3%) 0.694
## 5 happy no 56 (23.24%) 49 (21.3%) 0.855
## 6 happy yes 47 (19.5%) 44 (19.13%) 0.855
## 7 happy Yes 138 (57.26%) 137 (59.57%) 0.855
## 8 sugar_factor "" 0.46 (0.3) 0.52 (0.29) 0.0129
## 9 age "" 42 [22, 62] 41 [26, 61] 0.888
format_tbl
univariate <- example_dat %>%
descriptives(
variables = c("age", "sugar_factor", "gender", "happiness", "happy")
)
univariate
## # A tibble: 9 x 3
## Variable Label Statistics
## <chr> <chr> <chr>
## 1 gender female 235 (46.91%)
## 2 gender male 266 (53.09%)
## 3 happiness happy 367 (77.75%)
## 4 happiness sad 105 (22.25%)
## 5 happy no 105 (22.25%)
## 6 happy yes 91 (19.28%)
## 7 happy Yes 276 (58.47%)
## 8 age "" 42.3 (22.1)
## 9 sugar_factor "" 0.49 (0.3)
univariate %>%
format_tbl()
## # A tibble: 12 x 3
## Variable Label `Statistics (N=501)`
## <chr> <chr> <chr>
## 1 gender "" ""
## 2 "" female 235 (46.91%)
## 3 "" male 266 (53.09%)
## 4 happiness "" ""
## 5 "" happy 367 (77.75%)
## 6 "" sad 105 (22.25%)
## 7 happy "" ""
## 8 "" no 105 (22.25%)
## 9 "" yes 91 (19.28%)
## 10 "" Yes 276 (58.47%)
## 11 age "" 42.3 (22.1)
## 12 sugar_factor "" 0.49 (0.3)
bivariate <- example_dat %>%
descriptives(
treatment = "treat",
variables = c("age", "sugar_factor", "gender", "happiness", "happy")
)
bivariate
## # A tibble: 9 x 5
## Variable Label candy `ice cream` `P Value`
## <chr> <chr> <chr> <chr> <dbl>
## 1 gender female 124 (48.25%) 110 (45.27%) 0.563
## 2 gender male 133 (51.75%) 133 (54.73%) 0.563
## 3 happiness happy 185 (76.76%) 181 (78.7%) 0.694
## 4 happiness sad 56 (23.24%) 49 (21.3%) 0.694
## 5 happy no 56 (23.24%) 49 (21.3%) 0.855
## 6 happy yes 47 (19.5%) 44 (19.13%) 0.855
## 7 happy Yes 138 (57.26%) 137 (59.57%) 0.855
## 8 age "" 42.26 (22.62) 42.39 (21.61) 0.946
## 9 sugar_factor "" 0.46 (0.3) 0.52 (0.29) 0.0129
bivariate %>%
format_tbl()
## # A tibble: 12 x 5
## Variable Label `candy (N=257)` `ice cream (N=243)` `P Value`
## <chr> <chr> <chr> <chr> <chr>
## 1 gender "" "" "" 0.563
## 2 "" female 124 (48.25%) 110 (45.27%) ""
## 3 "" male 133 (51.75%) 133 (54.73%) ""
## 4 happiness "" "" "" 0.694
## 5 "" happy 185 (76.76%) 181 (78.7%) ""
## 6 "" sad 56 (23.24%) 49 (21.3%) ""
## 7 happy "" "" "" 0.855
## 8 "" no 56 (23.24%) 49 (21.3%) ""
## 9 "" yes 47 (19.5%) 44 (19.13%) ""
## 10 "" Yes 138 (57.26%) 137 (59.57%) ""
## 11 age "" 42.26 (22.62) 42.39 (21.61) 0.946
## 12 sugar_factor "" 0.46 (0.3) 0.52 (0.29) 0.013
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