Structuring code that is robust to updates in the data, changes in methodological specifications, etc., and can get to presentation-ready results in an automated way can mitigate errors caused by painful, tedious tasks–it can also be a huge time saver. The cheese package was designed with this philosophy in mind, heavily influenced by (and explicit dependencies on) tidy concepts. Its intention is to provide general tools for working with data and results during statistical analysis, in addition to a collection of functions designated for specific statistical tasks.

Heart disease dataset

This data comes from the UCI Machine Learning Repository, containing a collection of demographic and clinical characteristics from 303 patients. It was subsequently processed and cleaned into a format suitable for analysis–details of which can be found here.

#Look at the data
heart_disease

## # A tibble: 303 × 9
##      Age Sex    ChestPain           BP Cholesterol BloodSugar MaximumHR
##    <dbl> <fct>  <fct>            <dbl>       <dbl> <lgl>          <dbl>
##  1    63 Male   Typical angina     145         233 TRUE             150
##  2    67 Male   Asymptomatic       160         286 FALSE            108
##  3    67 Male   Asymptomatic       120         229 FALSE            129
##  4    37 Male   Non-anginal pain   130         250 FALSE            187
##  5    41 Female Atypical angina    130         204 FALSE            172
##  6    56 Male   Atypical angina    120         236 FALSE            178
##  7    62 Female Asymptomatic       140         268 FALSE            160
##  8    57 Female Asymptomatic       120         354 FALSE            163
##  9    63 Male   Asymptomatic       130         254 FALSE            147
## 10    53 Male   Asymptomatic       140         203 TRUE             155
## # ℹ 293 more rows
## # ℹ 2 more variables: ExerciseInducedAngina <fct>, HeartDisease <fct>

The functions that will be introduced are roughly in order of their development, as many build off of one another. Selection of columns is done with non-standard evaluation (NSE) or with tidyselect::select_helpers, where applicable.

Splitting and binding data

divide() is used to split up a data frame into a list of subsets. Suppose you want to split the example data set by sex and heart disease status:

div_dat <-
  heart_disease %>%
    divide(
      Sex,
      HeartDisease
    )
div_dat

## $Female
## $Female$No
## # A tibble: 72 × 7
##      Age ChestPain     BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
##    <dbl> <fct>      <dbl>       <dbl> <lgl>          <dbl> <fct>                
##  1    41 Atypical …   130         204 FALSE            172 No                   
##  2    57 Asymptoma…   120         354 FALSE            163 Yes                  
##  3    56 Atypical …   140         294 FALSE            153 No                   
##  4    48 Non-angin…   130         275 FALSE            139 No                   
##  5    58 Typical a…   150         283 TRUE             162 No                   
##  6    50 Non-angin…   120         219 FALSE            158 No                   
##  7    58 Non-angin…   120         340 FALSE            172 No                   
##  8    66 Typical a…   150         226 FALSE            114 No                   
##  9    69 Typical a…   140         239 FALSE            151 No                   
## 10    71 Atypical …   160         302 FALSE            162 No                   
## # ℹ 62 more rows
## 
## $Female$Yes
## # A tibble: 25 × 7
##      Age ChestPain     BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
##    <dbl> <fct>      <dbl>       <dbl> <lgl>          <dbl> <fct>                
##  1    62 Asymptoma…   140         268 FALSE            160 No                   
##  2    65 Asymptoma…   150         225 FALSE            114 No                   
##  3    61 Asymptoma…   130         330 FALSE            169 No                   
##  4    51 Asymptoma…   130         305 FALSE            142 Yes                  
##  5    62 Asymptoma…   160         164 FALSE            145 No                   
##  6    60 Asymptoma…   150         258 FALSE            157 No                   
##  7    61 Asymptoma…   145         307 FALSE            146 Yes                  
##  8    43 Asymptoma…   132         341 TRUE             136 Yes                  
##  9    62 Non-angin…   130         263 FALSE             97 No                   
## 10    63 Asymptoma…   150         407 FALSE            154 No                   
## # ℹ 15 more rows
## 
## 
## $Male
## $Male$No
## # A tibble: 92 × 7
##      Age ChestPain     BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
##    <dbl> <fct>      <dbl>       <dbl> <lgl>          <dbl> <fct>                
##  1    63 Typical a…   145         233 TRUE             150 No                   
##  2    37 Non-angin…   130         250 FALSE            187 No                   
##  3    56 Atypical …   120         236 FALSE            178 No                   
##  4    57 Asymptoma…   140         192 FALSE            148 No                   
##  5    44 Atypical …   120         263 FALSE            173 No                   
##  6    52 Non-angin…   172         199 TRUE             162 No                   
##  7    57 Non-angin…   150         168 FALSE            174 No                   
##  8    54 Asymptoma…   140         239 FALSE            160 No                   
##  9    49 Atypical …   130         266 FALSE            171 No                   
## 10    64 Typical a…   110         211 FALSE            144 Yes                  
## # ℹ 82 more rows
## 
## $Male$Yes
## # A tibble: 114 × 7
##      Age ChestPain     BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
##    <dbl> <fct>      <dbl>       <dbl> <lgl>          <dbl> <fct>                
##  1    67 Asymptoma…   160         286 FALSE            108 Yes                  
##  2    67 Asymptoma…   120         229 FALSE            129 Yes                  
##  3    63 Asymptoma…   130         254 FALSE            147 No                   
##  4    53 Asymptoma…   140         203 TRUE             155 Yes                  
##  5    56 Non-angin…   130         256 TRUE             142 Yes                  
##  6    48 Atypical …   110         229 FALSE            168 No                   
##  7    58 Atypical …   120         284 FALSE            160 No                   
##  8    58 Non-angin…   132         224 FALSE            173 No                   
##  9    60 Asymptoma…   130         206 FALSE            132 Yes                  
## 10    40 Asymptoma…   110         167 FALSE            114 Yes                  
## # ℹ 104 more rows

The default behavior is to continually split the data in a hierarchical structure based on the order of the input columns, and to remove them from the result. The keep argument can be used to retain the column(s) in the data frames.

fasten() allows you to take a list structure that contains data frames at an arbitrary depth, and roll them back up into a single data frame. This is useful when a statistical process needs to be mapped to many subsets, and you want to be able to easily collect the results without repeatedly binding data. You can call the function with the divided data from above:

div_dat %>%
  fasten()

## # A tibble: 303 × 7
##      Age ChestPain     BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
##    <dbl> <fct>      <dbl>       <dbl> <lgl>          <dbl> <fct>                
##  1    41 Atypical …   130         204 FALSE            172 No                   
##  2    57 Asymptoma…   120         354 FALSE            163 Yes                  
##  3    56 Atypical …   140         294 FALSE            153 No                   
##  4    48 Non-angin…   130         275 FALSE            139 No                   
##  5    58 Typical a…   150         283 TRUE             162 No                   
##  6    50 Non-angin…   120         219 FALSE            158 No                   
##  7    58 Non-angin…   120         340 FALSE            172 No                   
##  8    66 Typical a…   150         226 FALSE            114 No                   
##  9    69 Typical a…   140         239 FALSE            151 No                   
## 10    71 Atypical …   160         302 FALSE            162 No                   
## # ℹ 293 more rows

It was binded back to the original number of rows, but it lost the columns it was split by. The into argument can be used to handle this:

div_dat %>%
  fasten(
    into = c("Sex", "HeartDisease")
  )

## # A tibble: 303 × 9
##    Sex    HeartDisease   Age ChestPain       BP Cholesterol BloodSugar MaximumHR
##    <chr>  <chr>        <dbl> <fct>        <dbl>       <dbl> <lgl>          <dbl>
##  1 Female No              41 Atypical an…   130         204 FALSE            172
##  2 Female No              57 Asymptomatic   120         354 FALSE            163
##  3 Female No              56 Atypical an…   140         294 FALSE            153
##  4 Female No              48 Non-anginal…   130         275 FALSE            139
##  5 Female No              58 Typical ang…   150         283 TRUE             162
##  6 Female No              50 Non-anginal…   120         219 FALSE            158
##  7 Female No              58 Non-anginal…   120         340 FALSE            172
##  8 Female No              66 Typical ang…   150         226 FALSE            114
##  9 Female No              69 Typical ang…   140         239 FALSE            151
## 10 Female No              71 Atypical an…   160         302 FALSE            162
## # ℹ 293 more rows
## # ℹ 1 more variable: ExerciseInducedAngina <fct>

The positions of the into values always lineup with the level of the list hierarchy. So, for example, if you don’t care about retaining the heart disease status, you can do this:

div_dat %>%
  fasten(
    into = "Sex"
  )

## # A tibble: 303 × 8
##    Sex      Age ChestPain           BP Cholesterol BloodSugar MaximumHR
##    <chr>  <dbl> <fct>            <dbl>       <dbl> <lgl>          <dbl>
##  1 Female    41 Atypical angina    130         204 FALSE            172
##  2 Female    57 Asymptomatic       120         354 FALSE            163
##  3 Female    56 Atypical angina    140         294 FALSE            153
##  4 Female    48 Non-anginal pain   130         275 FALSE            139
##  5 Female    58 Typical angina     150         283 TRUE             162
##  6 Female    50 Non-anginal pain   120         219 FALSE            158
##  7 Female    58 Non-anginal pain   120         340 FALSE            172
##  8 Female    66 Typical angina     150         226 FALSE            114
##  9 Female    69 Typical angina     140         239 FALSE            151
## 10 Female    71 Atypical angina    160         302 FALSE            162
## # ℹ 293 more rows
## # ℹ 1 more variable: ExerciseInducedAngina <fct>

In contrast, if you want to forgo the sex indicator, empty strings will need to be used as placeholders so the names are applied at the correct levels.

div_dat %>%
  fasten(
    into = c("", "HeartDisease")
  )

## # A tibble: 303 × 8
##    HeartDisease   Age ChestPain           BP Cholesterol BloodSugar MaximumHR
##    <chr>        <dbl> <fct>            <dbl>       <dbl> <lgl>          <dbl>
##  1 No              41 Atypical angina    130         204 FALSE            172
##  2 No              57 Asymptomatic       120         354 FALSE            163
##  3 No              56 Atypical angina    140         294 FALSE            153
##  4 No              48 Non-anginal pain   130         275 FALSE            139
##  5 No              58 Typical angina     150         283 TRUE             162
##  6 No              50 Non-anginal pain   120         219 FALSE            158
##  7 No              58 Non-anginal pain   120         340 FALSE            172
##  8 No              66 Typical angina     150         226 FALSE            114
##  9 No              69 Typical angina     140         239 FALSE            151
## 10 No              71 Atypical angina    160         302 FALSE            162
## # ℹ 293 more rows
## # ℹ 1 more variable: ExerciseInducedAngina <fct>

Obviously, the classes of the split columns are not retained from the original data since the splitting and binding processes are independent.

Adjusting the depth

As shown above, the default behavior for these functions is to split or bind “as much as possible”. However, it can be useful to have control over the shape of the split. This is where the depth argument comes in. Suppose you want the same data frames at the leaves of the list, but only one level deep:

heart_disease %>%
  divide(
    Sex,
    HeartDisease,
    depth = 1
  )

## $`Female|No`
## # A tibble: 72 × 7
##      Age ChestPain     BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
##    <dbl> <fct>      <dbl>       <dbl> <lgl>          <dbl> <fct>                
##  1    41 Atypical …   130         204 FALSE            172 No                   
##  2    57 Asymptoma…   120         354 FALSE            163 Yes                  
##  3    56 Atypical …   140         294 FALSE            153 No                   
##  4    48 Non-angin…   130         275 FALSE            139 No                   
##  5    58 Typical a…   150         283 TRUE             162 No                   
##  6    50 Non-angin…   120         219 FALSE            158 No                   
##  7    58 Non-angin…   120         340 FALSE            172 No                   
##  8    66 Typical a…   150         226 FALSE            114 No                   
##  9    69 Typical a…   140         239 FALSE            151 No                   
## 10    71 Atypical …   160         302 FALSE            162 No                   
## # ℹ 62 more rows
## 
## $`Male|No`
## # A tibble: 92 × 7
##      Age ChestPain     BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
##    <dbl> <fct>      <dbl>       <dbl> <lgl>          <dbl> <fct>                
##  1    63 Typical a…   145         233 TRUE             150 No                   
##  2    37 Non-angin…   130         250 FALSE            187 No                   
##  3    56 Atypical …   120         236 FALSE            178 No                   
##  4    57 Asymptoma…   140         192 FALSE            148 No                   
##  5    44 Atypical …   120         263 FALSE            173 No                   
##  6    52 Non-angin…   172         199 TRUE             162 No                   
##  7    57 Non-angin…   150         168 FALSE            174 No                   
##  8    54 Asymptoma…   140         239 FALSE            160 No                   
##  9    49 Atypical …   130         266 FALSE            171 No                   
## 10    64 Typical a…   110         211 FALSE            144 Yes                  
## # ℹ 82 more rows
## 
## $`Female|Yes`
## # A tibble: 25 × 7
##      Age ChestPain     BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
##    <dbl> <fct>      <dbl>       <dbl> <lgl>          <dbl> <fct>                
##  1    62 Asymptoma…   140         268 FALSE            160 No                   
##  2    65 Asymptoma…   150         225 FALSE            114 No                   
##  3    61 Asymptoma…   130         330 FALSE            169 No                   
##  4    51 Asymptoma…   130         305 FALSE            142 Yes                  
##  5    62 Asymptoma…   160         164 FALSE            145 No                   
##  6    60 Asymptoma…   150         258 FALSE            157 No                   
##  7    61 Asymptoma…   145         307 FALSE            146 Yes                  
##  8    43 Asymptoma…   132         341 TRUE             136 Yes                  
##  9    62 Non-angin…   130         263 FALSE             97 No                   
## 10    63 Asymptoma…   150         407 FALSE            154 No                   
## # ℹ 15 more rows
## 
## $`Male|Yes`
## # A tibble: 114 × 7
##      Age ChestPain     BP Cholesterol BloodSugar MaximumHR ExerciseInducedAngina
##    <dbl> <fct>      <dbl>       <dbl> <lgl>          <dbl> <fct>                
##  1    67 Asymptoma…   160         286 FALSE            108 Yes                  
##  2    67 Asymptoma…   120         229 FALSE            129 Yes                  
##  3    63 Asymptoma…   130         254 FALSE            147 No                   
##  4    53 Asymptoma…   140         203 TRUE             155 Yes                  
##  5    56 Non-angin…   130         256 TRUE             142 Yes                  
##  6    48 Atypical …   110         229 FALSE            168 No                   
##  7    58 Atypical …   120         284 FALSE            160 No                   
##  8    58 Non-angin…   132         224 FALSE            173 No                   
##  9    60 Asymptoma…   130         206 FALSE            132 Yes                  
## 10    40 Asymptoma…   110         167 FALSE            114 Yes                  
## # ℹ 104 more rows

You now have list with four elements containing the subsets–the names of which are the concatenated levels of the split columns (see the sep argument).

This argument also works when binding data. Going back to the original divided data frame, you may only want to bind the internal lists.

div_dat %>%
  fasten(
    into = "HeartDisease",
    depth = 1
  )

## $Female
## # A tibble: 97 × 8
##    HeartDisease   Age ChestPain           BP Cholesterol BloodSugar MaximumHR
##    <chr>        <dbl> <fct>            <dbl>       <dbl> <lgl>          <dbl>
##  1 No              41 Atypical angina    130         204 FALSE            172
##  2 No              57 Asymptomatic       120         354 FALSE            163
##  3 No              56 Atypical angina    140         294 FALSE            153
##  4 No              48 Non-anginal pain   130         275 FALSE            139
##  5 No              58 Typical angina     150         283 TRUE             162
##  6 No              50 Non-anginal pain   120         219 FALSE            158
##  7 No              58 Non-anginal pain   120         340 FALSE            172
##  8 No              66 Typical angina     150         226 FALSE            114
##  9 No              69 Typical angina     140         239 FALSE            151
## 10 No              71 Atypical angina    160         302 FALSE            162
## # ℹ 87 more rows
## # ℹ 1 more variable: ExerciseInducedAngina <fct>
## 
## $Male
## # A tibble: 206 × 8
##    HeartDisease   Age ChestPain           BP Cholesterol BloodSugar MaximumHR
##    <chr>        <dbl> <fct>            <dbl>       <dbl> <lgl>          <dbl>
##  1 No              63 Typical angina     145         233 TRUE             150
##  2 No              37 Non-anginal pain   130         250 FALSE            187
##  3 No              56 Atypical angina    120         236 FALSE            178
##  4 No              57 Asymptomatic       140         192 FALSE            148
##  5 No              44 Atypical angina    120         263 FALSE            173
##  6 No              52 Non-anginal pain   172         199 TRUE             162
##  7 No              57 Non-anginal pain   150         168 FALSE            174
##  8 No              54 Asymptomatic       140         239 FALSE            160
##  9 No              49 Atypical angina    130         266 FALSE            171
## 10 No              64 Typical angina     110         211 FALSE            144
## # ℹ 196 more rows
## # ℹ 1 more variable: ExerciseInducedAngina <fct>

Note that the positions of the into values are directly related to how shallow/deep you want the result to be.

The depth can also be controlled relative to the leaves by using negative integers. This can be useful if it is unclear how deep the list will be (or is). In the example above, suppose you didn’t know how deep the list was, but you knew the heart disease status was the most internal split, and thus wanted to bind it.

div_dat %>%
  fasten(
    into = "HeartDisease",
    depth = -1
  )

## $Female
## # A tibble: 97 × 8
##    HeartDisease   Age ChestPain           BP Cholesterol BloodSugar MaximumHR
##    <chr>        <dbl> <fct>            <dbl>       <dbl> <lgl>          <dbl>
##  1 No              41 Atypical angina    130         204 FALSE            172
##  2 No              57 Asymptomatic       120         354 FALSE            163
##  3 No              56 Atypical angina    140         294 FALSE            153
##  4 No              48 Non-anginal pain   130         275 FALSE            139
##  5 No              58 Typical angina     150         283 TRUE             162
##  6 No              50 Non-anginal pain   120         219 FALSE            158
##  7 No              58 Non-anginal pain   120         340 FALSE            172
##  8 No              66 Typical angina     150         226 FALSE            114
##  9 No              69 Typical angina     140         239 FALSE            151
## 10 No              71 Atypical angina    160         302 FALSE            162
## # ℹ 87 more rows
## # ℹ 1 more variable: ExerciseInducedAngina <fct>
## 
## $Male
## # A tibble: 206 × 8
##    HeartDisease   Age ChestPain           BP Cholesterol BloodSugar MaximumHR
##    <chr>        <dbl> <fct>            <dbl>       <dbl> <lgl>          <dbl>
##  1 No              63 Typical angina     145         233 TRUE             150
##  2 No              37 Non-anginal pain   130         250 FALSE            187
##  3 No              56 Atypical angina    120         236 FALSE            178
##  4 No              57 Asymptomatic       140         192 FALSE            148
##  5 No              44 Atypical angina    120         263 FALSE            173
##  6 No              52 Non-anginal pain   172         199 TRUE             162
##  7 No              57 Non-anginal pain   150         168 FALSE            174
##  8 No              54 Asymptomatic       140         239 FALSE            160
##  9 No              49 Atypical angina    130         266 FALSE            171
## 10 No              64 Typical angina     110         211 FALSE            144
## # ℹ 196 more rows
## # ℹ 1 more variable: ExerciseInducedAngina <fct>

The new depth is one less than the input. In this case, the same result was achieved because of the symmetry.

Applying functions

The next set of functions are rooted in functional programming (i.e. purrr). In each instance, given a pre-defined function, you can easily control the scope of the data in which it is evaluated on. This is an incredibly useful and efficient philosophy for generating reusable code that is non-repetitive.

Pairwise combinations of column sets

Often times a statistical analysis is concerned with multiple outcomes/targets, though each one potentially uses the same set of explanatory variables. You could of course hard code the specific analysis steps for each outcome (e.g. formulas, etc.), but then the code becomes repetitive. You could also make separate data sets for each outcome with the associated predictors and then apply the same analysis process to each data set, but then you waste resources by storing multiple copies of the same data in memory. dish() allows you to distribute a function to various pairwise sets of columns from the same data set in a single call. The motivation described above is merely that–this is a general function that can be applied to any context.

As a simple first example, lets compute the Pearson correlation of blood pressure and cholesterol with all numeric columns:

heart_disease %>%
  dish(
    f = cor,
    left = c(BP, Cholesterol),
    right = where(is.numeric)
  )

## $BP
## $BP$Age
## [1] 0.2849459
## 
## $BP$BP
## [1] 1
## 
## $BP$Cholesterol
## [1] 0.1301201
## 
## $BP$MaximumHR
## [1] -0.04535088
## 
## 
## $Cholesterol
## $Cholesterol$Age
## [1] 0.2089503
## 
## $Cholesterol$BP
## [1] 0.1301201
## 
## $Cholesterol$Cholesterol
## [1] 1
## 
## $Cholesterol$MaximumHR
## [1] -0.003431832

The left argument controls which columns are entered in the first argument of f, and the right argument controls which columns are entered into the second. In the example, you’ll notice that the left columns were also included in the set of right columns, because they are, in fact, numeric(). If you didn’t want this, you can omit the right argument, which will then include everything not in left as the second argument of f:

heart_disease %>%
  dplyr::select(where(is.numeric)) %>% #Need to do this so only numeric columns are evaluated
  dish(
    f = cor,
    left = c(BP, Cholesterol)
  )

## $BP
## $BP$Age
## [1] 0.2849459
## 
## $BP$MaximumHR
## [1] -0.04535088
## 
## 
## $Cholesterol
## $Cholesterol$Age
## [1] 0.2089503
## 
## $Cholesterol$MaximumHR
## [1] -0.003431832

Now lets suppose you want to regress both blood pressure and cholesterol on all other variables in the data set. The each_ arguments allow you to control whether the column sets are entered into the function individually as vectors, or together in a single data frame. The former is the default, so you’ll need to use this argument here:

heart_disease %>%
  dish(
    f = function(y, x) lm(y ~ ., data = x),
    left = c(BP, Cholesterol),
    each_right = FALSE
  )

## $BP
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Coefficients:
##               (Intercept)                        Age  
##                  97.09264                    0.50748  
##                   SexMale   ChestPainAtypical angina  
##                  -3.95940                   -9.84788  
## ChestPainNon-anginal pain      ChestPainAsymptomatic  
##                  -9.56350                  -10.11788  
##            BloodSugarTRUE                  MaximumHR  
##                   6.70106                    0.09688  
##  ExerciseInducedAnginaYes            HeartDiseaseYes  
##                   1.72906                    6.00819  
## 
## 
## $Cholesterol
## 
## Call:
## lm(formula = y ~ ., data = x)
## 
## Coefficients:
##               (Intercept)                        Age  
##                  127.5108                     1.2471  
##                   SexMale   ChestPainAtypical angina  
##                  -23.6185                     8.8270  
## ChestPainNon-anginal pain      ChestPainAsymptomatic  
##                    5.7660                     8.0778  
##            BloodSugarTRUE                  MaximumHR  
##                   -0.7327                     0.3491  
##  ExerciseInducedAnginaYes            HeartDiseaseYes  
##                    7.8039                    12.5303

Subsets of data

stratiply() streamlines the familiar split() then purrr::map() approach by making it simple to select the stratification columns, and apply a function to each subset. Let’s run a multiple logistic regression model using heart disease as the outcome, stratified by sex:

heart_disease %>%
  stratiply(
    f = glm,
    by = Sex,
    formula = HeartDisease ~ . -ChestPain,
    family = "binomial"
  )

## $Female
## 
## Call:  .f(formula = ..1, family = "binomial", data = .x[[i]])
## 
## Coefficients:
##              (Intercept)                       Age                        BP  
##                 -6.95485                   0.03362                   0.04366  
##              Cholesterol            BloodSugarTRUE                 MaximumHR  
##                  0.00354                   0.47028                  -0.02465  
## ExerciseInducedAnginaYes  
##                  2.12039  
## 
## Degrees of Freedom: 96 Total (i.e. Null);  90 Residual
## Null Deviance:       110.7 
## Residual Deviance: 76.21     AIC: 90.21
## 
## $Male
## 
## Call:  .f(formula = ..1, family = "binomial", data = .x[[i]])
## 
## Coefficients:
##              (Intercept)                       Age                        BP  
##                 1.877142                  0.024853                  0.007710  
##              Cholesterol            BloodSugarTRUE                 MaximumHR  
##                 0.008718                 -0.390310                 -0.042231  
## ExerciseInducedAnginaYes  
##                 1.106387  
## 
## Degrees of Freedom: 205 Total (i.e. Null);  199 Residual
## Null Deviance:       283.2 
## Residual Deviance: 211.2     AIC: 225.2

Adding multiple stratification columns will produce a deeper list:

heart_disease %>%
  stratiply(
    f = glm,
    by = c(Sex, ExerciseInducedAngina),
    formula = HeartDisease ~ . -ChestPain,
    family = "binomial"
  )

## $Female
## $Female$No
## 
## Call:  .f(formula = ..1, family = "binomial", data = .x[[i]])
## 
## Coefficients:
##    (Intercept)             Age              BP     Cholesterol  BloodSugarTRUE  
##       -9.58123         0.01027         0.07213         0.00406         0.65807  
##      MaximumHR  
##       -0.02540  
## 
## Degrees of Freedom: 74 Total (i.e. Null);  69 Residual
## Null Deviance:       62.53 
## Residual Deviance: 49.93     AIC: 61.93
## 
## $Female$Yes
## 
## Call:  .f(formula = ..1, family = "binomial", data = .x[[i]])
## 
## Coefficients:
##    (Intercept)             Age              BP     Cholesterol  BloodSugarTRUE  
##       3.850121        0.049450        0.012214        0.001458        0.945153  
##      MaximumHR  
##      -0.056538  
## 
## Degrees of Freedom: 21 Total (i.e. Null);  16 Residual
## Null Deviance:       28.84 
## Residual Deviance: 23.36     AIC: 35.36
## 
## 
## $Male
## $Male$No
## 
## Call:  .f(formula = ..1, family = "binomial", data = .x[[i]])
## 
## Coefficients:
##    (Intercept)             Age              BP     Cholesterol  BloodSugarTRUE  
##       2.728309        0.053058       -0.003653        0.005387       -1.106214  
##      MaximumHR  
##      -0.042036  
## 
## Degrees of Freedom: 128 Total (i.e. Null);  123 Residual
## Null Deviance:       174 
## Residual Deviance: 142.3     AIC: 154.3
## 
## $Male$Yes
## 
## Call:  .f(formula = ..1, family = "binomial", data = .x[[i]])
## 
## Coefficients:
##    (Intercept)             Age              BP     Cholesterol  BloodSugarTRUE  
##        0.70070        -0.01348         0.03561         0.01342         1.09404  
##      MaximumHR  
##       -0.04531  
## 
## Degrees of Freedom: 76 Total (i.e. Null);  71 Residual
## Null Deviance:       75.94 
## Residual Deviance: 60.53     AIC: 72.53

Distributing to specific classes

typly() allows you to apply a function to columns of a data frame whose type inherits at least one (or none) of the specified candidates. It is similar to purrr::map_if(), but uses methods::is() for determining types (see some_type()), and only returns the results for the selected columns:

heart_disease %>%
  typly(
    f = mean,
    types = c("numeric", "logical")
  )

## $Age
## [1] 54.43894
## 
## $BP
## [1] 131.6898
## 
## $Cholesterol
## [1] 246.6931
## 
## $BloodSugar
## [1] 0.1485149
## 
## $MaximumHR
## [1] 149.6073

You can use the negated argument to apply the function to columns that are not any of the listed types:

heart_disease %>%
  typly(
    f = table,
    types = c("numeric", "logical"),
    negated = TRUE,
    useNA = "always"
  )

## $Sex
## 
## Female   Male   <NA> 
##     97    206      0 
## 
## $ChestPain
## 
##   Typical angina  Atypical angina Non-anginal pain     Asymptomatic 
##               23               50               86              144 
##             <NA> 
##                0 
## 
## $ExerciseInducedAngina
## 
##   No  Yes <NA> 
##  204   99    0 
## 
## $HeartDisease
## 
##   No  Yes <NA> 
##  164  139    0

Populating key-value pairs into custom string templates

absorb() allows you to create collections of strings that use keys as placeholders, which are then populated by the values. This is useful for setting up results of your analysis to be presented in a specific format that is independent of the data set at hand. Here’s a simple example:

absorb(
  key = c("mean", "sd", "var"),
  value = c("10", "2", "4"),
  text = 
    c(
      "MEAN: mean, SD: sd",
      "VAR: var = sd^2",
      MEAN = "mean"
    )
)

##                                                  MEAN 
## "MEAN: 10, SD: 2"    "VAR: 4 = 2^2"              "10"

The input text is scanned to look for the keys (interpreted as regular expressions) and are replaced with the corresponding values.

Let’s look at a more useful example. Suppose we have a collection summary statistics for patient age by angina and heart disease status:

age_stats <-
  heart_disease %>%
  dplyr::group_by(
    ExerciseInducedAngina,
    HeartDisease
  ) %>%
  dplyr::summarise(
    mean = round(mean(Age), 2),
    sd = round(sd(Age), 2),
    median = median(Age),
    iqr = IQR(Age),
    .groups = "drop"
  ) %>%
  tidyr::gather(
    key = "key",
    value = "value",
    -ExerciseInducedAngina,
    -HeartDisease
  )
age_stats

## # A tibble: 16 × 4
##    ExerciseInducedAngina HeartDisease key    value
##    <fct>                 <fct>        <chr>  <dbl>
##  1 No                    No           mean   52.4 
##  2 No                    Yes          mean   57.1 
##  3 Yes                   No           mean   53.6 
##  4 Yes                   Yes          mean   56.2 
##  5 No                    No           sd      9.68
##  6 No                    Yes          sd      7.56
##  7 Yes                   No           sd      8.54
##  8 Yes                   Yes          sd      8.27
##  9 No                    No           median 52   
## 10 No                    Yes          median 58   
## 11 Yes                   No           median 53   
## 12 Yes                   Yes          median 57   
## 13 No                    No           iqr    15   
## 14 No                    Yes          iqr    10   
## 15 Yes                   No           iqr    12.5 
## 16 Yes                   Yes          iqr     8.25

Next, you can specify the string format you want to nicely summarize the information in each group:

age_stats %>%
  dplyr::group_by(
    ExerciseInducedAngina,
    HeartDisease
  ) %>%
  dplyr::summarise(
    Summary = 
      absorb(
        key = key,
        value = value,
        text = 
          c(
            "mean (sd) | median (iqr)"
          )
      ),
    .groups = "drop"
  )

## # A tibble: 4 × 3
##   ExerciseInducedAngina HeartDisease Summary                 
##   <fct>                 <fct>        <chr>                   
## 1 No                    No           52.42 (9.68) | 52 (15)  
## 2 No                    Yes          57.1 (7.56) | 58 (10)   
## 3 Yes                   No           53.61 (8.54) | 53 (12.5)
## 4 Yes                   Yes          56.24 (8.27) | 57 (8.25)

Of course, this is much more useful when the summary data is already in the format of age_stats.

Concatenating repetitive keys

In the case where the key pattern found in the string template is repeated, its values are collapsed into a concatenated string:

absorb(
  key = age_stats$key,
  value = age_stats$value,
  text = c("(mean) / (sd)", "median")
)

## [1] "(52.42_57.1_53.61_56.24) / (9.68_7.56_8.54_8.27)"
## [2] "52_58_53_57"

The sep argument can be used to customize how values are separated.

Evaluating strings as expressions

It may be useful in some cases to evaluate the resulting string as an expression. You can setup the prior example so that the result contains valid expressions:

absorb(
  key = age_stats$key,
  value = age_stats$value,
  text = c("(mean) / (sd)", "median"),
  sep = "+",
  evaluate = TRUE,
  trace = TRUE
)

## (mean) / (sd) median 
## (mean) / (sd) median 
## (52.42+57.1+53.61+56.24) / (sd) median 
## (52.42+57.1+53.61+56.24) / (sd) 52+58+53+57

## [[1]]
## [1] 6.442584
## 
## [[2]]
## [1] 220

These statistics are not entirely useful here, but you get the point.

Traversing lists to identify objects

depths() and depths_string() are used for traversing a list structure to find the elements that satisfy a predicate. The former produces a vector of the unique depths that contain at least one element where predicate is true, and the latter creates a string representation of the traversal with the actual positions.

#Make a divided data frame
div_dat1 <-
  heart_disease %>%
  divide(
    Sex,
    HeartDisease
  )

#Find the depths of the data frames
div_dat1 %>%
  depths(
    predicate = is.data.frame
  )

## [1] 2

div_dat1 %>%
  depths_string(
    predicate = is.data.frame
  )

## [1] "{1{-1,-2},2{-1,-2}}"

In the string result, the brackets represent the level of the list, and the integers represent the index at that level, which are negative if predicate is true for that element.

By default, the algorithm continues when rlang::is_bare_list() so that the traversal can end with list-like objects. However, the bare argument can be used to traverse deeper into the list:

div_dat1 %>%
  depths_string(
    predicate = is.data.frame,
    bare = FALSE
  )

## [1] "{1{-1{1,2,3,4,5,6,7},-2{1,2,3,4,5,6,7}},2{-1{1,2,3,4,5,6,7},-2{1,2,3,4,5,6,7}}}"

Now it also evaluated the columns of each data frame in the list, though it still found the correct positions where the predicate held.

Distorting relationships between columns

muddle() can be used to randomly shuffle columns in a data frame. This is a convenient way to remove confounding when exploring the effects of a variable on an outcome.

set.seed(123)
heart_disease %>%
  muddle()

## # A tibble: 303 × 9
##      Age Sex    ChestPain           BP Cholesterol BloodSugar MaximumHR
##    <dbl> <fct>  <fct>            <dbl>       <dbl> <lgl>          <dbl>
##  1    43 Female Non-anginal pain   130         214 FALSE            120
##  2    44 Male   Asymptomatic       130         204 FALSE            143
##  3    68 Female Asymptomatic       128         242 FALSE            125
##  4    35 Female Asymptomatic       120         269 FALSE            163
##  5    45 Female Atypical angina    138         240 FALSE            169
##  6    54 Male   Asymptomatic       128         209 FALSE            182
##  7    61 Male   Asymptomatic       100         258 FALSE            152
##  8    57 Female Asymptomatic       152         229 FALSE            142
##  9    67 Male   Non-anginal pain   110         283 FALSE            138
## 10    51 Female Atypical angina    125         204 TRUE             161
## # ℹ 293 more rows
## # ℹ 2 more variables: ExerciseInducedAngina <fct>, HeartDisease <fct>

All columns are permuted by default. Use the at argument to only shuffle certain ones:

heart_disease %>%
  muddle(
    at = Age
  )

## # A tibble: 303 × 9
##      Age Sex    ChestPain           BP Cholesterol BloodSugar MaximumHR
##    <dbl> <fct>  <fct>            <dbl>       <dbl> <lgl>          <dbl>
##  1    47 Male   Typical angina     145         233 TRUE             150
##  2    49 Male   Asymptomatic       160         286 FALSE            108
##  3    59 Male   Asymptomatic       120         229 FALSE            129
##  4    35 Male   Non-anginal pain   130         250 FALSE            187
##  5    44 Female Atypical angina    130         204 FALSE            172
##  6    29 Male   Atypical angina    120         236 FALSE            178
##  7    45 Female Asymptomatic       140         268 FALSE            160
##  8    59 Female Asymptomatic       120         354 FALSE            163
##  9    46 Male   Asymptomatic       130         254 FALSE            147
## 10    54 Male   Asymptomatic       140         203 TRUE             155
## # ℹ 293 more rows
## # ℹ 2 more variables: ExerciseInducedAngina <fct>, HeartDisease <fct>

Additional arguments can be passed to sample() as well. For example, you might want five random values from each column:

heart_disease %>%
  muddle(
    size = 5
  )

## # A tibble: 5 × 9
##     Age Sex    ChestPain           BP Cholesterol BloodSugar MaximumHR
##   <dbl> <fct>  <fct>            <dbl>       <dbl> <lgl>          <dbl>
## 1    54 Female Asymptomatic       136         275 TRUE             163
## 2    62 Male   Asymptomatic       125         271 FALSE            195
## 3    52 Male   Non-anginal pain   150         286 FALSE            105
## 4    64 Male   Asymptomatic       106         273 FALSE            163
## 5    54 Male   Atypical angina    120         199 FALSE            182
## # ℹ 2 more variables: ExerciseInducedAngina <fct>, HeartDisease <fct>

Spanning keys and values across the columns

stretch() is for spanning keys and values across the columns. It is similar to tidyr::spread(), but allows for any number of keys and values to be selected. It’s functionality is more so targeted at setting up your results to be presented in a table, rather than for general data manipulation. Thus, the resulting column names and orderings are setup to make it a seamless transition.

To illustrate, lets first collect the parameter estimates and standard errors from multiple regression models using blood pressure and cholesterol as outcomes, stratified by sex:

mod_results <-
  heart_disease %>%
  
  #For each sex
  stratiply(
    by = Sex,
    f =
      ~.x %>%
      
      #Collect coefficients and standard errors for model estimates on multiple outcomes
      dish(
        f = function(y, x) {
          model <- lm(y ~ ., data = x)
          tibble::tibble(
            Parameter = names(model$coefficients),
            Estimate = model$coefficients,
            SE = sqrt(diag(vcov(model)))
          )
        },
        left = c(BP, Cholesterol),
        each_right = FALSE
      ) 
  ) %>%
  
  #Gather results into a single data frame
  fasten(
    into = c("Sex", "Outcome")
  )
mod_results

## # A tibble: 36 × 5
##    Sex    Outcome     Parameter                 Estimate      SE
##    <chr>  <chr>       <chr>                        <dbl>   <dbl>
##  1 Female BP          (Intercept)                 93.7   24.7   
##  2 Female BP          Age                          0.533  0.210 
##  3 Female BP          ChestPainAtypical angina   -16.3    9.73  
##  4 Female BP          ChestPainNon-anginal pain  -15.3    9.15  
##  5 Female BP          ChestPainAsymptomatic      -14.2    9.64  
##  6 Female BP          BloodSugarTRUE               6.92   5.53  
##  7 Female BP          MaximumHR                    0.123  0.0993
##  8 Female BP          ExerciseInducedAnginaYes     8.00   4.89  
##  9 Female BP          HeartDiseaseYes             12.1    5.21  
## 10 Female Cholesterol (Intercept)                -12.1   91.6   
## # ℹ 26 more rows

Now lets span the estimates and standard errors across the columns for both outcomes:

straught1 <-
  mod_results %>%
  stretch(
    key = Outcome,
    value = c(Estimate, SE)
  )
straught1

## # A tibble: 18 × 6
##    Sex    Parameter      BP_Estimate   BP_SE Cholesterol_Estimate Cholesterol_SE
##    <chr>  <chr>                <dbl>   <dbl>                <dbl>          <dbl>
##  1 Female (Intercept)        93.7    24.7                 -12.1           91.6  
##  2 Female Age                 0.533   0.210                 2.30           0.779
##  3 Female BloodSugarTRUE      6.92    5.53                 24.6           20.6  
##  4 Female ChestPainAsym…    -14.2     9.64                 34.0           35.8  
##  5 Female ChestPainAtyp…    -16.3     9.73                 22.7           36.1  
##  6 Female ChestPainNon-…    -15.3     9.15                 34.1           34.0  
##  7 Female ExerciseInduc…      8.00    4.89                  8.14          18.2  
##  8 Female HeartDiseaseY…     12.1     5.21                  5.88          19.3  
##  9 Female MaximumHR           0.123   0.0993                0.720          0.369
## 10 Male   (Intercept)       105.     14.7                 151.            38.7  
## 11 Male   Age                 0.480   0.142                 0.765          0.374
## 12 Male   BloodSugarTRUE      4.96    3.12                 -6.85           8.23 
## 13 Male   ChestPainAsym…    -10.0     4.25                  3.38          11.2  
## 14 Male   ChestPainAtyp…     -8.61    4.68                  9.55          12.3  
## 15 Male   ChestPainNon-…     -6.99    4.31                 -0.802         11.4  
## 16 Male   ExerciseInduc…     -1.08    2.78                  6.88           7.33 
## 17 Male   HeartDiseaseY…      3.07    2.80                 13.4            7.39 
## 18 Male   MaximumHR           0.0392  0.0601                0.239          0.158

The sep argument controls how the new column names are concatenated.

Now suppose you wanted to also span sex across the columns. It can just be added to the keys:

straught2 <-
  mod_results %>%
  stretch(
    key = c(Sex, Outcome),
    value = c(Estimate, SE)
  )
straught2

## # A tibble: 9 × 9
##   Parameter               Female_BP_Estimate Female_BP_SE Female_Cholesterol_E…¹
##   <chr>                                <dbl>        <dbl>                  <dbl>
## 1 (Intercept)                         93.7        24.7                   -12.1  
## 2 Age                                  0.533       0.210                   2.30 
## 3 BloodSugarTRUE                       6.92        5.53                   24.6  
## 4 ChestPainAsymptomatic              -14.2         9.64                   34.0  
## 5 ChestPainAtypical angi…            -16.3         9.73                   22.7  
## 6 ChestPainNon-anginal p…            -15.3         9.15                   34.1  
## 7 ExerciseInducedAnginaY…              8.00        4.89                    8.14 
## 8 HeartDiseaseYes                     12.1         5.21                    5.88 
## 9 MaximumHR                            0.123       0.0993                  0.720
## # ℹ abbreviated name: ¹Female_Cholesterol_Estimate
## # ℹ 5 more variables: Female_Cholesterol_SE <dbl>, Male_BP_Estimate <dbl>,
## #   Male_BP_SE <dbl>, Male_Cholesterol_Estimate <dbl>,
## #   Male_Cholesterol_SE <dbl>

Notice how the resulting columns are positioned. They are sequentially sorted starting with the outer-most key in a hierarchical manner. Since there are multiple value columns in this example, their names are also appended to the result in the order in which they were requested.

Stacking headers in a table

grable() stands for “graded table”. It is used to stack headers into a knitr::kable() in an automated fashion by iteratively parsing the column names by the specified delimiter. The result of stretch() was purposely made to flow directly into this function.

straught1 %>%
  grable(
    at = -c(Sex, Parameter)
  )

		BP		Cholesterol
Sex	Parameter	Estimate	SE	Estimate	SE
Female	(Intercept)	93.7187493	24.6536900	-12.1297785	91.6044593
Female	Age	0.5330329	0.2096016	2.2975419	0.7788061
Female	BloodSugarTRUE	6.9178015	5.5329199	24.5796821	20.5583885
Female	ChestPainAsymptomatic	-14.1810178	9.6423236	34.0195680	35.8274902
Female	ChestPainAtypical angina	-16.3247037	9.7289213	22.6736038	36.1492568
Female	ChestPainNon-anginal pain	-15.3471449	9.1541813	34.1239627	34.0137249
Female	ExerciseInducedAnginaYes	7.9952541	4.8947727	8.1371920	18.1872573
Female	HeartDiseaseYes	12.0913966	5.2058240	5.8795266	19.3430149
Female	MaximumHR	0.1226586	0.0992801	0.7201695	0.3688901
Male	(Intercept)	105.2923383	14.6933205	150.9743256	38.7467039
Male	Age	0.4800104	0.1417168	0.7654107	0.3737111
Male	BloodSugarTRUE	4.9616363	3.1197639	-6.8540853	8.2269061
Male	ChestPainAsymptomatic	-10.0474949	4.2547960	3.3795322	11.2200180
Male	ChestPainAtypical angina	-8.6148546	4.6798629	9.5540927	12.3409315
Male	ChestPainNon-anginal pain	-6.9875791	4.3056911	-0.8015539	11.3542299
Male	ExerciseInducedAnginaYes	-1.0827067	2.7780388	6.8846691	7.3257672
Male	HeartDiseaseYes	3.0740495	2.8029917	13.4020228	7.3915687
Male	MaximumHR	0.0391572	0.0600935	0.2387579	0.1584683

The at argument specifies which columns should be spanned in the header. Let’s try it with the two-key example from above:

straught2 %>%
  grable(
    at = -Parameter
  )

	Female				Male
	BP		Cholesterol		BP		Cholesterol
Parameter	Estimate	SE	Estimate	SE	Estimate	SE	Estimate	SE
(Intercept)	93.7187493	24.6536900	-12.1297785	91.6044593	105.2923383	14.6933205	150.9743256	38.7467039
Age	0.5330329	0.2096016	2.2975419	0.7788061	0.4800104	0.1417168	0.7654107	0.3737111
BloodSugarTRUE	6.9178015	5.5329199	24.5796821	20.5583885	4.9616363	3.1197639	-6.8540853	8.2269061
ChestPainAsymptomatic	-14.1810178	9.6423236	34.0195680	35.8274902	-10.0474949	4.2547960	3.3795322	11.2200180
ChestPainAtypical angina	-16.3247037	9.7289213	22.6736038	36.1492568	-8.6148546	4.6798629	9.5540927	12.3409315
ChestPainNon-anginal pain	-15.3471449	9.1541813	34.1239627	34.0137249	-6.9875791	4.3056911	-0.8015539	11.3542299
ExerciseInducedAnginaYes	7.9952541	4.8947727	8.1371920	18.1872573	-1.0827067	2.7780388	6.8846691	7.3257672
HeartDiseaseYes	12.0913966	5.2058240	5.8795266	19.3430149	3.0740495	2.8029917	13.4020228	7.3915687
MaximumHR	0.1226586	0.0992801	0.7201695	0.3688901	0.0391572	0.0600935	0.2387579	0.1584683

The tables can then be piped through subsequent customized processing with kableExtra tools.

Univariate analysis

descriptives() produces a data frame in long format with a collection of descriptive statistics.

heart_disease %>%
  descriptives()

## # A tibble: 111 × 9
##    fun_eval fun_key   col_ind col_lab    val_ind val_lab val_dbl val_chr val_cbn
##    <chr>    <chr>       <int> <chr>        <int> <chr>     <dbl> <chr>   <chr>  
##  1 all      available       1 Age              1 <NA>        303 <NA>    303    
##  2 all      available       2 Sex              1 <NA>        303 <NA>    303    
##  3 all      available       3 ChestPain        1 <NA>        303 <NA>    303    
##  4 all      available       4 BP               1 <NA>        303 <NA>    303    
##  5 all      available       5 Cholester…       1 <NA>        303 <NA>    303    
##  6 all      available       6 BloodSugar       1 <NA>        303 <NA>    303    
##  7 all      available       7 MaximumHR        1 <NA>        303 <NA>    303    
##  8 all      available       8 ExerciseI…       1 <NA>        303 <NA>    303    
##  9 all      available       9 HeartDise…       1 <NA>        303 <NA>    303    
## 10 all      class           1 Age              1 <NA>         NA numeric numeric
## # ℹ 101 more rows

univariate_associations() is a special case of dish() specifically for computing association metrics of one or more responses with a collection of predictors.

heart_disease %>%
  univariate_associations(
    f = list(AIC = function(y, x) AIC(lm(y ~ x))),
    responses = c(BP, Cholesterol)
  )

## # A tibble: 14 × 3
##    response    predictor               AIC
##    <chr>       <chr>                 <dbl>
##  1 BP          Age                   2577.
##  2 BP          Sex                   2602.
##  3 BP          ChestPain             2598.
##  4 BP          BloodSugar            2593.
##  5 BP          MaximumHR             2602.
##  6 BP          ExerciseInducedAngina 2602.
##  7 BP          HeartDisease          2596.
##  8 Cholesterol Age                   3243.
##  9 Cholesterol Sex                   3244.
## 10 Cholesterol ChestPain             3259.
## 11 Cholesterol BloodSugar            3257.
## 12 Cholesterol MaximumHR             3257.
## 13 Cholesterol ExerciseInducedAngina 3256.
## 14 Cholesterol HeartDisease          3255.

univariate_table() allows you to create a custom descriptive table for a data set. It uses almost every function in the package across its span of capabilities.

heart_disease %>%
  univariate_table()

Variable	Level	Summary
Age		56 (48, 61)
Sex	Female	97 (32.01%)
Sex	Male	206 (67.99%)
ChestPain	Typical angina	23 (7.59%)
	Atypical angina	50 (16.5%)
	Non-anginal pain	86 (28.38%)
	Asymptomatic	144 (47.52%)
BP		130 (120, 140)
Cholesterol		241 (211, 275)
MaximumHR		153 (133.5, 166)
ExerciseInducedAngina	No	204 (67.33%)
ExerciseInducedAngina	Yes	99 (32.67%)
HeartDisease	No	164 (54.13%)
HeartDisease	Yes	139 (45.87%)

See vignette("describe") for a more in-depth introduction to these functions.

Making cheese