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Solving parenthesis problem in README.md and documentation

tags/0.1.7
Adrien Boukobza 4 years ago
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2 changed files with 138 additions and 141 deletions
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@@ -75,31 +75,31 @@ When used on a data.frame, it returns a descriptive table:


``` r ``` r
iris %>% iris %>%
desctable
desctable()
``` ```


## N Mean/% sd Med IQR
## 1 Sepal.Length 150 NA NA 5.80 1.3
## 2 Sepal.Width 150 3.057333 0.4358663 NA NA
## 3 Petal.Length 150 NA NA 4.35 3.5
## 4 Petal.Width 150 NA NA 1.30 1.5
## 5 Species 150 NA NA NA NA
## 6 Species: setosa 50 33.333333 NA NA NA
## 7 Species: versicolor 50 33.333333 NA NA NA
## 8 Species: virginica 50 33.333333 NA NA NA
##   N % Mean sd Med IQR
## 1 Sepal.Length 150 NA NA NA 5.80 1.3
## 2 Sepal.Width 150 NA 3.057333 0.4358663 3.00 0.5
## 3 Petal.Length 150 NA NA NA 4.35 3.5
## 4 Petal.Width 150 NA NA NA 1.30 1.5
## 5 Species 150 NA NA NA NA NA
## 6 Species: setosa 50 33.33333 NA NA NA NA
## 7 Species: versicolor 50 33.33333 NA NA NA NA
## 8 Species: virginica 50 33.33333 NA NA NA NA


``` r ``` r
desctable(mtcars) desctable(mtcars)
``` ```


## N Mean sd Med IQR
## 1 mpg 32 20.090625 6.0269481 NA NA
##   N Mean sd Med IQR
## 1 mpg 32 20.090625 6.0269481 19.200 7.37500
## 2 cyl 32 NA NA 6.000 4.00000 ## 2 cyl 32 NA NA 6.000 4.00000
## 3 disp 32 NA NA 196.300 205.17500 ## 3 disp 32 NA NA 196.300 205.17500
## 4 hp 32 NA NA 123.000 83.50000 ## 4 hp 32 NA NA 123.000 83.50000
## 5 drat 32 3.596563 0.5346787 NA NA
## 5 drat 32 3.596563 0.5346787 3.695 0.84000
## 6 wt 32 NA NA 3.325 1.02875 ## 6 wt 32 NA NA 3.325 1.02875
## 7 qsec 32 17.848750 1.7869432 NA NA
## 7 qsec 32 17.848750 1.7869432 17.710 2.00750
## 8 vs 32 NA NA 0.000 1.00000 ## 8 vs 32 NA NA 0.000 1.00000
## 9 am 32 NA NA 0.000 1.00000 ## 9 am 32 NA NA 0.000 1.00000
## 10 gear 32 NA NA 4.000 1.00000 ## 10 gear 32 NA NA 4.000 1.00000
@@ -121,20 +121,20 @@ interactive html output with **DT** (`datatable`) are provided:


``` r ``` r
iris %>% iris %>%
desctable %>%
pander
desctable() %>%
pander()
``` ```


| | N | Mean/% | sd | Med | IQR |
| :----------- | :-- | :----- | :--- | :-- | :-- |
| Sepal.Length | 150 | | | 5.8 | 1.3 |
| Sepal.Width | 150 | 3.1 | 0.44 | | |
| Petal.Length | 150 | | | 4.3 | 3.5 |
| Petal.Width | 150 | | | 1.3 | 1.5 |
| **Species** | 150 | | | | |
| setosa | 50 | 33 | | | |
| versicolor | 50 | 33 | | | |
| virginica | 50 | 33 | | | |
| Â | N | % | Mean | sd | Med | IQR |
| :----------- | :-- | :- | :--- | :--- | :-- | :-- |
| Sepal.Length | 150 | | | | 5.8 | 1.3 |
| Sepal.Width | 150 | | 3.1 | 0.44 | 3 | 0.5 |
| Petal.Length | 150 | | | | 4.3 | 3.5 |
| Petal.Width | 150 | | | | 1.3 | 1.5 |
| **Species** | 150 | | | | | |
| setosa | 50 | 33 | | | | |
| versicolor | 50 | 33 | | | | |
| virginica | 50 | 33 | | | | |


<br> You need to load these two packages first (and prior to <br> You need to load these two packages first (and prior to
**desctable** for **DT**) if you want to use them. **desctable** for **DT**) if you want to use them.
@@ -194,22 +194,22 @@ You can also provide your own automatic function, which needs to
<!-- end list --> <!-- end list -->


``` r ``` r
# Strictly equivalent to iris %>% desctable %>% pander
# Strictly equivalent to iris %>% desctable() %>% pander()
iris %>% iris %>%
desctable(stats = stats_auto) %>% desctable(stats = stats_auto) %>%
pander
pander()
``` ```


| | N | Mean/% | sd | Med | IQR |
| :----------- | :-- | :----- | :--- | :-- | :-- |
| Sepal.Length | 150 | | | 5.8 | 1.3 |
| Sepal.Width | 150 | 3.1 | 0.44 | | |
| Petal.Length | 150 | | | 4.3 | 3.5 |
| Petal.Width | 150 | | | 1.3 | 1.5 |
| **Species** | 150 | | | | |
| setosa | 50 | 33 | | | |
| versicolor | 50 | 33 | | | |
| virginica | 50 | 33 | | | |
| Â | N | % | Mean | sd | Med | IQR |
| :----------- | :-- | :- | :--- | :--- | :-- | :-- |
| Sepal.Length | 150 | | | | 5.8 | 1.3 |
| Sepal.Width | 150 | | 3.1 | 0.44 | 3 | 0.5 |
| Petal.Length | 150 | | | | 4.3 | 3.5 |
| Petal.Width | 150 | | | | 1.3 | 1.5 |
| **Species** | 150 | | | | | |
| setosa | 50 | 33 | | | | |
| versicolor | 50 | 33 | | | | |
| virginica | 50 | 33 | | | | |


### Statistical functions ### Statistical functions


@@ -225,10 +225,10 @@ As mentioned above, they need to be used inside a named list, such as
``` r ``` r
mtcars %>% mtcars %>%
desctable(stats = list("N" = length, "Mean" = mean, "SD" = sd)) %>% desctable(stats = list("N" = length, "Mean" = mean, "SD" = sd)) %>%
pander
pander()
``` ```


| | N | Mean | SD |
| Â | N | Mean | SD |
| :--- | :- | :--- | :--- | | :--- | :- | :--- | :--- |
| mpg | 32 | 20 | 6 | | mpg | 32 | 20 | 6 |
| cyl | 32 | 6.2 | 1.8 | | cyl | 32 | 6.2 | 1.8 |
@@ -293,10 +293,10 @@ iris %>%
desctable(stats = list("N" = length, desctable(stats = list("N" = length,
"%/Mean" = is.factor ~ percent | (is.normal ~ mean), "%/Mean" = is.factor ~ percent | (is.normal ~ mean),
"Median" = is.normal ~ NA | median)) %>% "Median" = is.normal ~ NA | median)) %>%
pander
pander()
``` ```


| | N | %/Mean | Median |
| Â | N | %/Mean | Median |
| :----------- | :-- | :----- | :----- | | :----------- | :-- | :----- | :----- |
| Sepal.Length | 150 | | 5.8 | | Sepal.Length | 150 | | 5.8 |
| Sepal.Width | 150 | 3.1 | | | Sepal.Width | 150 | 3.1 | |
@@ -333,15 +333,14 @@ package:
## list(N = length, `%` = percent) ## list(N = length, `%` = percent)
## else if (!fact & normal & nonnormal) ## else if (!fact & normal & nonnormal)
## list(N = length, Mean = is.normal ~ mean, sd = is.normal ~ ## list(N = length, Mean = is.normal ~ mean, sd = is.normal ~
## sd, Med = is.normal ~ NA | median, IQR = is.normal ~
## NA | IQR)
## sd, Med = stats::median, IQR = is.factor ~ NA | IQR)
## else if (!fact & normal & !nonnormal) ## else if (!fact & normal & !nonnormal)
## list(N = length, Mean = mean, sd = stats::sd) ## list(N = length, Mean = mean, sd = stats::sd)
## else if (!fact & !normal & nonnormal) ## else if (!fact & !normal & nonnormal)
## list(N = length, Med = stats::median, IQR = IQR) ## list(N = length, Med = stats::median, IQR = IQR)
## else stats_default(data) ## else stats_default(data)
## } ## }
## <bytecode: 0x55d9b10cfdf0>
## <bytecode: 0x0000000018eb1900>
## <environment: namespace:desctable> ## <environment: namespace:desctable>


### Labels ### Labels
@@ -374,24 +373,24 @@ mtlabels <- c(mpg = "Miles/(US) gallon",
mtcars %>% mtcars %>%
dplyr::mutate(am = factor(am, labels = c("Automatic", "Manual"))) %>% dplyr::mutate(am = factor(am, labels = c("Automatic", "Manual"))) %>%
desctable(labels = mtlabels) %>% desctable(labels = mtlabels) %>%
pander
pander()
``` ```


| | N | Mean/% | sd | Med | IQR |
| :---------------------- | :- | :----- | :--- | :-- | :-- |
| Miles/(US) gallon | 32 | 20 | 6 | | |
| Number of cylinders | 32 | | | 6 | 4 |
| Displacement (cu.in.) | 32 | | | 196 | 205 |
| Gross horsepower | 32 | | | 123 | 84 |
| Rear axle ratio | 32 | 3.6 | 0.53 | | |
| Weight (1000 lbs) | 32 | | | 3.3 | 1 |
| ¼ mile time | 32 | 18 | 1.8 | | |
| V/S | 32 | | | 0 | 1 |
| **Transmission** | 32 | | | | |
| Automatic | 19 | 59 | | | |
| Manual | 13 | 41 | | | |
| Number of forward gears | 32 | | | 4 | 1 |
| Number of carburetors | 32 | | | 2 | 2 |
| Â | N | % | Mean | sd | Med | IQR |
| :---------------------- | :- | :- | :--- | :--- | :-- | :--- |
| Miles/(US) gallon | 32 | | 20 | 6 | 19 | 7.4 |
| Number of cylinders | 32 | | | | 6 | 4 |
| Displacement (cu.in.) | 32 | | | | 196 | 205 |
| Gross horsepower | 32 | | | | 123 | 84 |
| Rear axle ratio | 32 | | 3.6 | 0.53 | 3.7 | 0.84 |
| Weight (1000 lbs) | 32 | | | | 3.3 | 1 |
| ¼ mile time | 32 | | 18 | 1.8 | 18 | 2 |
| V/S | 32 | | | | 0 | 1 |
| **Transmission** | 32 | | | | | |
| Automatic | 19 | 59 | | | | |
| Manual | 13 | 41 | | | | |
| Number of forward gears | 32 | | | | 4 | 1 |
| Number of carburetors | 32 | | | | 2 | 2 |


<br> <br>


@@ -409,24 +408,24 @@ It uses the well known `group_by` function from **dplyr**:
``` r ``` r
iris %>% iris %>%
group_by(Species) %>% group_by(Species) %>%
desctable -> iris_by_Species
desctable() -> iris_by_Species


iris_by_Species iris_by_Species
``` ```


## Species: setosa (n=50) / N Species: setosa (n=50) / Mean
##   Species: setosa (n=50) / N Species: setosa (n=50) / Mean
## 1 Sepal.Length 50 5.006 ## 1 Sepal.Length 50 5.006
## 2 Sepal.Width 50 3.428 ## 2 Sepal.Width 50 3.428
## 3 Petal.Length 50 NA ## 3 Petal.Length 50 NA
## 4 Petal.Width 50 NA ## 4 Petal.Width 50 NA
## Species: setosa (n=50) / sd Species: setosa (n=50) / Med ## Species: setosa (n=50) / sd Species: setosa (n=50) / Med
## 1 0.3524897 NA
## 2 0.3790644 NA
## 1 0.3524897 5.0
## 2 0.3790644 3.4
## 3 NA 1.5 ## 3 NA 1.5
## 4 NA 0.2 ## 4 NA 0.2
## Species: setosa (n=50) / IQR Species: versicolor (n=50) / N1 ## Species: setosa (n=50) / IQR Species: versicolor (n=50) / N1
## 1 NA 50
## 2 NA 50
## 1 0.400 50
## 2 0.475 50
## 3 0.175 50 ## 3 0.175 50
## 4 0.100 50 ## 4 0.100 50
## Species: versicolor (n=50) / Mean1 Species: versicolor (n=50) / sd1 ## Species: versicolor (n=50) / Mean1 Species: versicolor (n=50) / sd1
@@ -435,25 +434,25 @@ iris_by_Species
## 3 4.260 0.4699110 ## 3 4.260 0.4699110
## 4 NA NA ## 4 NA NA
## Species: versicolor (n=50) / Med1 Species: versicolor (n=50) / IQR1 ## Species: versicolor (n=50) / Med1 Species: versicolor (n=50) / IQR1
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 1.3 0.3
## 1 5.90 0.700
## 2 2.80 0.475
## 3 4.35 0.600
## 4 1.30 0.300
## Species: virginica (n=50) / N2 Species: virginica (n=50) / Mean2 ## Species: virginica (n=50) / N2 Species: virginica (n=50) / Mean2
## 1 50 6.588 ## 1 50 6.588
## 2 50 2.974 ## 2 50 2.974
## 3 50 5.552 ## 3 50 5.552
## 4 50 NA ## 4 50 NA
## Species: virginica (n=50) / sd2 Species: virginica (n=50) / Med2 ## Species: virginica (n=50) / sd2 Species: virginica (n=50) / Med2
## 1 0.6358796 NA
## 2 0.3224966 NA
## 3 0.5518947 NA
## 4 NA 2
## 1 0.6358796 6.50
## 2 0.3224966 3.00
## 3 0.5518947 5.55
## 4 NA 2.00
## Species: virginica (n=50) / IQR2 tests / p ## Species: virginica (n=50) / IQR2 tests / p
## 1 NA 1.505059e-28
## 2 NA 4.492017e-17
## 3 NA 4.803974e-29
## 4 0.5 3.261796e-29
## 1 0.675 1.505059e-28
## 2 0.375 4.492017e-17
## 3 0.775 4.803974e-29
## 4 0.500 3.261796e-29
## tests / test ## tests / test
## 1 . %>% oneway.test(var.equal = F) ## 1 . %>% oneway.test(var.equal = F)
## 2 . %>% oneway.test(var.equal = T) ## 2 . %>% oneway.test(var.equal = T)
@@ -480,23 +479,23 @@ str(iris_by_Species)
## $ Variables :'data.frame': 4 obs. of 1 variable: ## $ Variables :'data.frame': 4 obs. of 1 variable:
## ..$ Variables: chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" ## ..$ Variables: chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width"
## $ Species: setosa (n=50) :'data.frame': 4 obs. of 5 variables: ## $ Species: setosa (n=50) :'data.frame': 4 obs. of 5 variables:
## ..$ N : num [1:4] 50 50 50 50
## ..$ N : int [1:4] 50 50 50 50
## ..$ Mean: num [1:4] 5.01 3.43 NA NA ## ..$ Mean: num [1:4] 5.01 3.43 NA NA
## ..$ sd : num [1:4] 0.352 0.379 NA NA ## ..$ sd : num [1:4] 0.352 0.379 NA NA
## ..$ Med : num [1:4] NA NA 1.5 0.2
## ..$ IQR : num [1:4] NA NA 0.175 0.1
## ..$ Med : num [1:4] 5 3.4 1.5 0.2
## ..$ IQR : num [1:4] 0.4 0.475 0.175 0.1
## $ Species: versicolor (n=50):'data.frame': 4 obs. of 5 variables: ## $ Species: versicolor (n=50):'data.frame': 4 obs. of 5 variables:
## ..$ N : num [1:4] 50 50 50 50
## ..$ N : int [1:4] 50 50 50 50
## ..$ Mean: num [1:4] 5.94 2.77 4.26 NA ## ..$ Mean: num [1:4] 5.94 2.77 4.26 NA
## ..$ sd : num [1:4] 0.516 0.314 0.47 NA ## ..$ sd : num [1:4] 0.516 0.314 0.47 NA
## ..$ Med : num [1:4] NA NA NA 1.3
## ..$ IQR : num [1:4] NA NA NA 0.3
## ..$ Med : num [1:4] 5.9 2.8 4.35 1.3
## ..$ IQR : num [1:4] 0.7 0.475 0.6 0.3
## $ Species: virginica (n=50) :'data.frame': 4 obs. of 5 variables: ## $ Species: virginica (n=50) :'data.frame': 4 obs. of 5 variables:
## ..$ N : num [1:4] 50 50 50 50
## ..$ N : int [1:4] 50 50 50 50
## ..$ Mean: num [1:4] 6.59 2.97 5.55 NA ## ..$ Mean: num [1:4] 6.59 2.97 5.55 NA
## ..$ sd : num [1:4] 0.636 0.322 0.552 NA ## ..$ sd : num [1:4] 0.636 0.322 0.552 NA
## ..$ Med : num [1:4] NA NA NA 2
## ..$ IQR : num [1:4] NA NA NA 0.5
## ..$ Med : num [1:4] 6.5 3 5.55 2
## ..$ IQR : num [1:4] 0.675 0.375 0.775 0.5
## $ tests :'data.frame': 4 obs. of 2 variables: ## $ tests :'data.frame': 4 obs. of 2 variables:
## ..$ p : num [1:4] 1.51e-28 4.49e-17 4.80e-29 3.26e-29 ## ..$ p : num [1:4] 1.51e-28 4.49e-17 4.80e-29 3.26e-29
## ..$ test: chr [1:4] ". %>% oneway.test(var.equal = F)" ". %>% oneway.test(var.equal = T)" "kruskal.test" "kruskal.test" ## ..$ test: chr [1:4] ". %>% oneway.test(var.equal = F)" ". %>% oneway.test(var.equal = T)" "kruskal.test" "kruskal.test"
@@ -508,11 +507,11 @@ You can specify groups based on any variable, not only factors:
# With pander output # With pander output
mtcars %>% mtcars %>%
group_by(cyl) %>% group_by(cyl) %>%
desctable %>%
pander
desctable() %>%
pander()
``` ```


| | cyl: 4 (n=11)<br/>N | <br/>Med | <br/>IQR | cyl: 6 (n=7)<br/>N1 | <br/>Med1 | <br/>IQR1 | cyl: 8 (n=14)<br/>N2 | <br/>Med2 | <br/>IQR2 | tests<br/>p | <br/>test |
| Â | cyl: 4 (n=11)<br/>N | <br/>Med | <br/>IQR | cyl: 6 (n=7)<br/>N1 | <br/>Med1 | <br/>IQR1 | cyl: 8 (n=14)<br/>N2 | <br/>Med2 | <br/>IQR2 | tests<br/>p | <br/>test |
| :--- | :------------------ | :------- | :------- | :------------------ | :-------- | :-------- | :------------------- | :-------- | :-------- | :---------- | :----------- | | :--- | :------------------ | :------- | :------- | :------------------ | :-------- | :-------- | :------------------- | :-------- | :-------- | :---------- | :----------- |
| mpg | 11 | 26 | 7.6 | 7 | 20 | 2.4 | 14 | 15 | 1.8 | 2.6e-06 | kruskal.test | | mpg | 11 | 26 | 7.6 | 7 | 20 | 2.4 | 14 | 15 | 1.8 | 2.6e-06 | kruskal.test |
| disp | 11 | 108 | 42 | 7 | 168 | 36 | 14 | 350 | 88 | 1.6e-06 | kruskal.test | | disp | 11 | 108 | 42 | 7 | 168 | 36 | 14 | 350 | 88 | 1.6e-06 | kruskal.test |
@@ -530,20 +529,20 @@ Also with conditions:
``` r ``` r
iris %>% iris %>%
group_by(Petal.Length > 5) %>% group_by(Petal.Length > 5) %>%
desctable %>%
pander
desctable() %>%
pander()
``` ```


| | Petal.Length \> 5: FALSE (n=108)<br/>N | <br/>Mean/% | <br/>sd | <br/>Med | <br/>IQR | Petal.Length \> 5: TRUE (n=42)<br/>N1 | <br/>Mean/%1 | <br/>sd1 | <br/>Med1 | <br/>IQR1 | tests<br/>p | <br/>test |
| :----------- | :------------------------------------- | :---------- | :------ | :------- | :------- | :------------------------------------ | :----------- | :------- | :-------- | :-------- | :---------- | :---------- |
| Sepal.Length | 108 | | | 5.5 | 1 | 42 | | | 6.7 | 0.85 | 1.6e-15 | wilcox.test |
| Sepal.Width | 108 | 3.1 | 0.48 | | | 42 | | | 3 | 0.4 | 0.69 | wilcox.test |
| Petal.Length | 108 | | | 3.5 | 3 | 42 | | | 5.6 | 0.67 | 2.1e-21 | wilcox.test |
| Petal.Width | 108 | | | 1 | 1.2 | 42 | 2.1 | 0.28 | | | 1.6e-19 | wilcox.test |
| **Species** | 108 | | | | | 42 | | | | | 2.5e-26 | fisher.test |
| setosa | 50 | 46 | | | | 0 | 0 | | | | | |
| versicolor | 49 | 45 | | | | 1 | 2.4 | | | | | |
| virginica | 9 | 8.3 | | | | 41 | 98 | | | | | |
| Â | Petal.Length \> 5: FALSE (n=108)<br/>N | <br/>% | <br/>Mean | <br/>sd | <br/>Med | <br/>IQR | Petal.Length \> 5: TRUE (n=42)<br/>N1 | <br/>%1 | <br/>Mean1 | <br/>sd1 | <br/>Med1 | <br/>IQR1 | tests<br/>p | <br/>test |
| :----------- | :------------------------------------- | :----- | :-------- | :------ | :------- | :------- | :------------------------------------ | :------ | :--------- | :------- | :-------- | :-------- | :---------- | :---------- |
| Sepal.Length | 108 | | | | 5.5 | 1 | 42 | | | | 6.7 | 0.85 | 1.6e-15 | wilcox.test |
| Sepal.Width | 108 | | 3.1 | 0.48 | 3 | 0.6 | 42 | | | | 3 | 0.4 | 0.69 | wilcox.test |
| Petal.Length | 108 | | | | 3.5 | 3 | 42 | | | | 5.6 | 0.67 | 2.1e-21 | wilcox.test |
| Petal.Width | 108 | | | | 1 | 1.2 | 42 | | 2.1 | 0.28 | 2.1 | 0.47 | 1.6e-19 | wilcox.test |
| **Species** | 108 | | | | | | 42 | | | | | | 2.5e-26 | fisher.test |
| setosa | 50 | 46 | | | | | 0 | 0 | | | | | | |
| versicolor | 49 | 45 | | | | | 1 | 2.4 | | | | | | |
| virginica | 9 | 8.3 | | | | | 41 | 98 | | | | | | |


<br> <br>


@@ -553,11 +552,11 @@ And even on multiple nested groups:
mtcars %>% mtcars %>%
dplyr::mutate(am = factor(am, labels = c("Automatic", "Manual"))) %>% dplyr::mutate(am = factor(am, labels = c("Automatic", "Manual"))) %>%
group_by(vs, am, cyl) %>% group_by(vs, am, cyl) %>%
desctable %>%
pander
desctable() %>%
pander()
``` ```


| | vs: 0 (n=18)<br/>am: Automatic (n=12)<br/>cyl: 8 (n=12)<br/>N | <br/><br/><br/>Med | <br/><br/><br/>IQR | <br/><br/>tests<br/>p | <br/><br/><br/>test | <br/>am: Manual (n=6)<br/>cyl: 4 (n=1)<br/>N3 | <br/><br/><br/>Med3 | <br/><br/><br/>IQR3 | <br/><br/>cyl: 6 (n=3)<br/>N1 | <br/><br/><br/>Med1 | <br/><br/><br/>IQR1 | <br/><br/>cyl: 8 (n=2)<br/>N2 | <br/><br/><br/>Med2 | <br/><br/><br/>IQR2 | <br/><br/>tests<br/>p1 | <br/><br/><br/>test1 | vs: 1 (n=14)<br/>am: Automatic (n=7)<br/>cyl: 4 (n=3)<br/>N4 | <br/><br/><br/>Med4 | <br/><br/><br/>IQR4 | <br/><br/>cyl: 6 (n=4)<br/>N11 | <br/><br/><br/>Med11 | <br/><br/><br/>IQR11 | <br/><br/>tests<br/>p2 | <br/><br/><br/>test2 | <br/>am: Manual (n=7)<br/>cyl: 4 (n=7)<br/>N21 | <br/><br/><br/>Med21 | <br/><br/><br/>IQR21 | <br/><br/>tests<br/>p11 | <br/><br/><br/>test11 |
| Â | vs: 0 (n=18)<br/>am: Automatic (n=12)<br/>cyl: 8 (n=12)<br/>N | <br/><br/><br/>Med | <br/><br/><br/>IQR | <br/><br/>tests<br/>p | <br/><br/><br/>test | <br/>am: Manual (n=6)<br/>cyl: 4 (n=1)<br/>N3 | <br/><br/><br/>Med3 | <br/><br/><br/>IQR3 | <br/><br/>cyl: 6 (n=3)<br/>N1 | <br/><br/><br/>Med1 | <br/><br/><br/>IQR1 | <br/><br/>cyl: 8 (n=2)<br/>N2 | <br/><br/><br/>Med2 | <br/><br/><br/>IQR2 | <br/><br/>tests<br/>p1 | <br/><br/><br/>test1 | vs: 1 (n=14)<br/>am: Automatic (n=7)<br/>cyl: 4 (n=3)<br/>N4 | <br/><br/><br/>Med4 | <br/><br/><br/>IQR4 | <br/><br/>cyl: 6 (n=4)<br/>N11 | <br/><br/><br/>Med11 | <br/><br/><br/>IQR11 | <br/><br/>tests<br/>p2 | <br/><br/><br/>test2 | <br/>am: Manual (n=7)<br/>cyl: 4 (n=7)<br/>N21 | <br/><br/><br/>Med21 | <br/><br/><br/>IQR21 | <br/><br/>tests<br/>p11 | <br/><br/><br/>test11 |
| :--- | :------------------------------------------------------------ | :----------------- | :----------------- | :-------------------- | :------------------ | :-------------------------------------------- | :------------------ | :------------------ | :---------------------------- | :------------------ | :------------------ | :---------------------------- | :------------------ | :------------------ | :--------------------- | :------------------- | :----------------------------------------------------------- | :------------------ | :------------------ | :----------------------------- | :------------------- | :------------------- | :--------------------- | :------------------- | :--------------------------------------------- | :------------------- | :------------------- | :---------------------- | :-------------------- | | :--- | :------------------------------------------------------------ | :----------------- | :----------------- | :-------------------- | :------------------ | :-------------------------------------------- | :------------------ | :------------------ | :---------------------------- | :------------------ | :------------------ | :---------------------------- | :------------------ | :------------------ | :--------------------- | :------------------- | :----------------------------------------------------------- | :------------------ | :------------------ | :----------------------------- | :------------------- | :------------------- | :--------------------- | :------------------- | :--------------------------------------------- | :------------------- | :------------------- | :---------------------- | :-------------------- |
| mpg | 12 | 15 | 2.6 | | no.test | 1 | 26 | 0 | 3 | 21 | 0.65 | 2 | 15 | 0.4 | 0.11 | kruskal.test | 3 | 23 | 1.5 | 4 | 19 | 1.7 | 0.057 | wilcox.test | 7 | 30 | 6.3 | | no.test | | mpg | 12 | 15 | 2.6 | | no.test | 1 | 26 | 0 | 3 | 21 | 0.65 | 2 | 15 | 0.4 | 0.11 | kruskal.test | 3 | 23 | 1.5 | 4 | 19 | 1.7 | 0.057 | wilcox.test | 7 | 30 | 6.3 | | no.test |
| disp | 12 | 355 | 113 | | no.test | 1 | 120 | 0 | 3 | 160 | 7.5 | 2 | 326 | 25 | 0.11 | kruskal.test | 3 | 141 | 13 | 4 | 196 | 66 | 0.05 | wilcox.test | 7 | 79 | 24 | | no.test | | disp | 12 | 355 | 113 | | no.test | 1 | 120 | 0 | 3 | 160 | 7.5 | 2 | 326 | 25 | 0.11 | kruskal.test | 3 | 141 | 13 | 4 | 196 | 66 | 0.05 | wilcox.test | 7 | 79 | 24 | | no.test |
@@ -622,22 +621,21 @@ You can also provide your own automatic function, which needs to
- return a single-term formula containing a statistical test function. - return a single-term formula containing a statistical test function.


This function will be used on every variable and every grouping factor This function will be used on every variable and every grouping factor
to determine the appropriate
test.
to determine the appropriate test.


``` r ``` r
# Strictly equivalent to iris %>% group_by(Species) %>% desctable %>% pander # Strictly equivalent to iris %>% group_by(Species) %>% desctable %>% pander
iris %>% iris %>%
group_by(Species) %>% group_by(Species) %>%
desctable(tests = tests_auto) %>% desctable(tests = tests_auto) %>%
pander
pander()
``` ```


| | Species: setosa (n=50)<br/>N | <br/>Mean | <br/>sd | <br/>Med | <br/>IQR | Species: versicolor (n=50)<br/>N1 | <br/>Mean1 | <br/>sd1 | <br/>Med1 | <br/>IQR1 | Species: virginica (n=50)<br/>N2 | <br/>Mean2 | <br/>sd2 | <br/>Med2 | <br/>IQR2 | tests<br/>p | <br/>test |
| Â | Species: setosa (n=50)<br/>N | <br/>Mean | <br/>sd | <br/>Med | <br/>IQR | Species: versicolor (n=50)<br/>N1 | <br/>Mean1 | <br/>sd1 | <br/>Med1 | <br/>IQR1 | Species: virginica (n=50)<br/>N2 | <br/>Mean2 | <br/>sd2 | <br/>Med2 | <br/>IQR2 | tests<br/>p | <br/>test |
| :----------- | :--------------------------- | :-------- | :------ | :------- | :------- | :-------------------------------- | :--------- | :------- | :-------- | :-------- | :------------------------------- | :--------- | :------- | :-------- | :-------- | :---------- | :-------------------------------- | | :----------- | :--------------------------- | :-------- | :------ | :------- | :------- | :-------------------------------- | :--------- | :------- | :-------- | :-------- | :------------------------------- | :--------- | :------- | :-------- | :-------- | :---------- | :-------------------------------- |
| Sepal.Length | 50 | 5 | 0.35 | | | 50 | 5.9 | 0.52 | | | 50 | 6.6 | 0.64 | | | 1.5e-28 | . %\>% oneway.test(var.equal = F) |
| Sepal.Width | 50 | 3.4 | 0.38 | | | 50 | 2.8 | 0.31 | | | 50 | 3 | 0.32 | | | 4.5e-17 | . %\>% oneway.test(var.equal = T) |
| Petal.Length | 50 | | | 1.5 | 0.18 | 50 | 4.3 | 0.47 | | | 50 | 5.6 | 0.55 | | | 4.8e-29 | kruskal.test |
| Sepal.Length | 50 | 5 | 0.35 | 5 | 0.4 | 50 | 5.9 | 0.52 | 5.9 | 0.7 | 50 | 6.6 | 0.64 | 6.5 | 0.67 | 1.5e-28 | . %\>% oneway.test(var.equal = F) |
| Sepal.Width | 50 | 3.4 | 0.38 | 3.4 | 0.48 | 50 | 2.8 | 0.31 | 2.8 | 0.48 | 50 | 3 | 0.32 | 3 | 0.38 | 4.5e-17 | . %\>% oneway.test(var.equal = T) |
| Petal.Length | 50 | | | 1.5 | 0.18 | 50 | 4.3 | 0.47 | 4.3 | 0.6 | 50 | 5.6 | 0.55 | 5.5 | 0.78 | 4.8e-29 | kruskal.test |
| Petal.Width | 50 | | | 0.2 | 0.1 | 50 | | | 1.3 | 0.3 | 50 | | | 2 | 0.5 | 3.3e-29 | kruskal.test | | Petal.Width | 50 | | | 0.2 | 0.1 | 50 | | | 1.3 | 0.3 | 50 | | | 2 | 0.5 | 3.3e-29 | kruskal.test |


<br> <br>
@@ -664,19 +662,19 @@ iris %>%
group_by(Petal.Length > 5) %>% group_by(Petal.Length > 5) %>%
desctable(tests = list(.auto = tests_auto, desctable(tests = list(.auto = tests_auto,
Species = ~chisq.test)) %>% Species = ~chisq.test)) %>%
pander
pander()
``` ```


| | Petal.Length \> 5: FALSE (n=108)<br/>N | <br/>Mean/% | <br/>sd | <br/>Med | <br/>IQR | Petal.Length \> 5: TRUE (n=42)<br/>N1 | <br/>Mean/%1 | <br/>sd1 | <br/>Med1 | <br/>IQR1 | tests<br/>p | <br/>test |
| :----------- | :------------------------------------- | :---------- | :------ | :------- | :------- | :------------------------------------ | :----------- | :------- | :-------- | :-------- | :---------- | :---------- |
| Sepal.Length | 108 | | | 5.5 | 1 | 42 | | | 6.7 | 0.85 | 1.6e-15 | wilcox.test |
| Sepal.Width | 108 | 3.1 | 0.48 | | | 42 | | | 3 | 0.4 | 0.69 | wilcox.test |
| Petal.Length | 108 | | | 3.5 | 3 | 42 | | | 5.6 | 0.67 | 2.1e-21 | wilcox.test |
| Petal.Width | 108 | | | 1 | 1.2 | 42 | 2.1 | 0.28 | | | 1.6e-19 | wilcox.test |
| **Species** | 108 | | | | | 42 | | | | | 2.7e-24 | chisq.test |
| setosa | 50 | 46 | | | | 0 | 0 | | | | | |
| versicolor | 49 | 45 | | | | 1 | 2.4 | | | | | |
| virginica | 9 | 8.3 | | | | 41 | 98 | | | | | |
| Â | Petal.Length \> 5: FALSE (n=108)<br/>N | <br/>% | <br/>Mean | <br/>sd | <br/>Med | <br/>IQR | Petal.Length \> 5: TRUE (n=42)<br/>N1 | <br/>%1 | <br/>Mean1 | <br/>sd1 | <br/>Med1 | <br/>IQR1 | tests<br/>p | <br/>test |
| :----------- | :------------------------------------- | :----- | :-------- | :------ | :------- | :------- | :------------------------------------ | :------ | :--------- | :------- | :-------- | :-------- | :---------- | :---------- |
| Sepal.Length | 108 | | | | 5.5 | 1 | 42 | | | | 6.7 | 0.85 | 1.6e-15 | wilcox.test |
| Sepal.Width | 108 | | 3.1 | 0.48 | 3 | 0.6 | 42 | | | | 3 | 0.4 | 0.69 | wilcox.test |
| Petal.Length | 108 | | | | 3.5 | 3 | 42 | | | | 5.6 | 0.67 | 2.1e-21 | wilcox.test |
| Petal.Width | 108 | | | | 1 | 1.2 | 42 | | 2.1 | 0.28 | 2.1 | 0.47 | 1.6e-19 | wilcox.test |
| **Species** | 108 | | | | | | 42 | | | | | | 2.7e-24 | chisq.test |
| setosa | 50 | 46 | | | | | 0 | 0 | | | | | | |
| versicolor | 49 | 45 | | | | | 1 | 2.4 | | | | | | |
| virginica | 9 | 8.3 | | | | | 41 | 98 | | | | | | |


<br> <br>


@@ -686,10 +684,10 @@ mtcars %>%
group_by(am) %>% group_by(am) %>%
desctable(tests = list(.default = ~wilcox.test, desctable(tests = list(.default = ~wilcox.test,
mpg = ~t.test)) %>% mpg = ~t.test)) %>%
pander
pander()
``` ```


| | am: Automatic (n=19)<br/>N | <br/>Med | <br/>IQR | am: Manual (n=13)<br/>N1 | <br/>Med1 | <br/>IQR1 | tests<br/>p | <br/>test |
| Â | am: Automatic (n=19)<br/>N | <br/>Med | <br/>IQR | am: Manual (n=13)<br/>N1 | <br/>Med1 | <br/>IQR1 | tests<br/>p | <br/>test |
| :--- | :------------------------- | :------- | :------- | :----------------------- | :-------- | :-------- | :---------- | :---------- | | :--- | :------------------------- | :------- | :------- | :----------------------- | :-------- | :-------- | :---------- | :---------- |
| mpg | 19 | 17 | 4.2 | 13 | 23 | 9.4 | 0.0014 | t.test | | mpg | 19 | 17 | 4.2 | 13 | 23 | 9.4 | 0.0014 | t.test |
| cyl | 19 | 8 | 2 | 13 | 4 | 2 | 0.0039 | wilcox.test | | cyl | 19 | 8 | 2 | 13 | 4 | 2 | 0.0039 | wilcox.test |
@@ -736,10 +734,10 @@ mtcars %>%
"Sum of squares" = function(x) sum(x^2), "Sum of squares" = function(x) sum(x^2),
"Q1" = . %>% quantile(prob = .25), "Q1" = . %>% quantile(prob = .25),
"Q3" = purrr::partial(quantile, probs = .75))) %>% "Q3" = purrr::partial(quantile, probs = .75))) %>%
pander
pander()
``` ```


| | N | Sum of squares | Q1 | Q3 |
| Â | N | Sum of squares | Q1 | Q3 |
| :--- | :- | :------------- | :-- | :-- | | :--- | :- | :------------- | :-- | :-- |
| mpg | 32 | 14042 | 15 | 23 | | mpg | 32 | 14042 | 15 | 23 |
| cyl | 32 | 1324 | 4 | 8 | | cyl | 32 | 1324 | 4 | 8 |
@@ -765,21 +763,20 @@ iris %>%
Sepal.Width = ~function(f) oneway.test(f, var.equal = F), Sepal.Width = ~function(f) oneway.test(f, var.equal = F),
Petal.Length = ~. %>% oneway.test(var.equal = T), Petal.Length = ~. %>% oneway.test(var.equal = T),
Sepal.Length = ~purrr::partial(oneway.test, var.equal = T))) %>% Sepal.Length = ~purrr::partial(oneway.test, var.equal = T))) %>%
pander
pander()
``` ```


| | Species: setosa (n=50)<br/>N | <br/>Mean | <br/>sd | <br/>Med | <br/>IQR | Species: versicolor (n=50)<br/>N1 | <br/>Mean1 | <br/>sd1 | <br/>Med1 | <br/>IQR1 | Species: virginica (n=50)<br/>N2 | <br/>Mean2 | <br/>sd2 | <br/>Med2 | <br/>IQR2 | tests<br/>p | <br/>test |
| Â | Species: setosa (n=50)<br/>N | <br/>Mean | <br/>sd | <br/>Med | <br/>IQR | Species: versicolor (n=50)<br/>N1 | <br/>Mean1 | <br/>sd1 | <br/>Med1 | <br/>IQR1 | Species: virginica (n=50)<br/>N2 | <br/>Mean2 | <br/>sd2 | <br/>Med2 | <br/>IQR2 | tests<br/>p | <br/>test |
| :----------- | :--------------------------- | :-------- | :------ | :------- | :------- | :-------------------------------- | :--------- | :------- | :-------- | :-------- | :------------------------------- | :--------- | :------- | :-------- | :-------- | :---------- | :----------------------------------------- | | :----------- | :--------------------------- | :-------- | :------ | :------- | :------- | :-------------------------------- | :--------- | :------- | :-------- | :-------- | :------------------------------- | :--------- | :------- | :-------- | :-------- | :---------- | :----------------------------------------- |
| Sepal.Length | 50 | 5 | 0.35 | | | 50 | 5.9 | 0.52 | | | 50 | 6.6 | 0.64 | | | 1.7e-31 | purrr::partial(oneway.test, var.equal = T) |
| Sepal.Width | 50 | 3.4 | 0.38 | | | 50 | 2.8 | 0.31 | | | 50 | 3 | 0.32 | | | 1.4e-14 | function(f) oneway.test(f, var.equal = F) |
| Petal.Length | 50 | | | 1.5 | 0.18 | 50 | 4.3 | 0.47 | | | 50 | 5.6 | 0.55 | | | 2.9e-91 | . %\>% oneway.test(var.equal = T) |
| Sepal.Length | 50 | 5 | 0.35 | 5 | 0.4 | 50 | 5.9 | 0.52 | 5.9 | 0.7 | 50 | 6.6 | 0.64 | 6.5 | 0.67 | 1.7e-31 | purrr::partial(oneway.test, var.equal = T) |
| Sepal.Width | 50 | 3.4 | 0.38 | 3.4 | 0.48 | 50 | 2.8 | 0.31 | 2.8 | 0.48 | 50 | 3 | 0.32 | 3 | 0.38 | 1.4e-14 | function(f) oneway.test(f, var.equal = F) |
| Petal.Length | 50 | | | 1.5 | 0.18 | 50 | 4.3 | 0.47 | 4.3 | 0.6 | 50 | 5.6 | 0.55 | 5.5 | 0.78 | 2.9e-91 | . %\>% oneway.test(var.equal = T) |
| Petal.Width | 50 | | | 0.2 | 0.1 | 50 | | | 1.3 | 0.3 | 50 | | | 2 | 0.5 | 3.3e-29 | kruskal.test | | Petal.Width | 50 | | | 0.2 | 0.1 | 50 | | | 1.3 | 0.3 | 50 | | | 2 | 0.5 | 3.3e-29 | kruskal.test |


<br> <br>


This allows you to modulate the behavior of `desctable` in every detail, This allows you to modulate the behavior of `desctable` in every detail,
such as using paired tests, or non *htest*
tests.
such as using paired tests, or non *htest* tests.


``` r ``` r
# This is a contrived example, which would be better solved with a dedicated function # This is a contrived example, which would be better solved with a dedicated function
@@ -791,10 +788,10 @@ bladder %>%
group_by(rx) %>% group_by(rx) %>%
desctable(tests = list(.default = ~wilcox.test, desctable(tests = list(.default = ~wilcox.test,
surv = ~. %>% survdiff %>% .$chisq %>% pchisq(1, lower.tail = F) %>% list(p.value = .))) %>% surv = ~. %>% survdiff %>% .$chisq %>% pchisq(1, lower.tail = F) %>% list(p.value = .))) %>%
pander
pander()
``` ```


| | rx: 1 (n=188)<br/>N | <br/>Med | <br/>IQR | rx: 2 (n=152)<br/>N1 | <br/>Med1 | <br/>IQR1 | tests<br/>p | <br/>test |
| Â | rx: 1 (n=188)<br/>N | <br/>Med | <br/>IQR | rx: 2 (n=152)<br/>N1 | <br/>Med1 | <br/>IQR1 | tests<br/>p | <br/>test |
| :----- | :------------------ | :------- | :------- | :------------------- | :-------- | :-------- | :---------- | :--------------------------------------------------------------------------------- | | :----- | :------------------ | :------- | :------- | :------------------- | :-------- | :-------- | :---------- | :--------------------------------------------------------------------------------- |
| id | 188 | 24 | 24 | 152 | 66 | 19 | 1.3e-56 | wilcox.test | | id | 188 | 24 | 24 | 152 | 66 | 19 | 1.3e-56 | wilcox.test |
| number | 188 | 1 | 2 | 152 | 1 | 2 | 0.62 | wilcox.test | | number | 188 | 1 | 2 | 152 | 1 | 2 | 0.62 | wilcox.test |
@@ -802,4 +799,4 @@ bladder %>%
| stop | 188 | 23 | 20 | 152 | 25 | 28 | 0.17 | wilcox.test | | stop | 188 | 23 | 20 | 152 | 25 | 28 | 0.17 | wilcox.test |
| event | 188 | 0 | 1 | 152 | 0 | 1 | 0.02 | wilcox.test | | event | 188 | 0 | 1 | 152 | 0 | 1 | 0.02 | wilcox.test |
| enum | 188 | 2.5 | 1.5 | 152 | 2.5 | 1.5 | 1 | wilcox.test | | enum | 188 | 2.5 | 1.5 | 152 | 2.5 | 1.5 | 1 | wilcox.test |
| surv | 376 | | | 304 | | | 0.023 | . %\>% survdiff %\>% .$chisq %\>% pchisq(1, lower.tail = F) %\>% list(p.value = .) |
| surv | 188 | | | 152 | | | 0.023 | . %\>% survdiff %\>% .$chisq %\>% pchisq(1, lower.tail = F) %\>% list(p.value = .) |

+ 3
- 3
man/desctable.Rd View File

@@ -69,10 +69,10 @@ The output is a desctable object, which is a list of named dataframes that can b


\examples{ \examples{
iris \%>\% iris \%>\%
desctable
desctable()


# Does the same as stats_auto here # Does the same as stats_auto here
iris \%>\%
iris \%>\%
desctable(stats = list("N" = length, desctable(stats = list("N" = length,
"\%/Mean" = is.factor ~ percent | (is.normal ~ mean), "\%/Mean" = is.factor ~ percent | (is.normal ~ mean),
"sd" = is.normal ~ sd, "sd" = is.normal ~ sd,
@@ -92,7 +92,7 @@ iris \%>\%
# With nested grouping, on arbitrary variables # With nested grouping, on arbitrary variables
mtcars \%>\% mtcars \%>\%
group_by(vs, cyl) \%>\% group_by(vs, cyl) \%>\%
desctable
desctable()


# With grouping on a condition, and choice of tests # With grouping on a condition, and choice of tests
iris \%>\% iris \%>\%


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