Check for alluvial structure and convert between alluvial formats

Source: R/alluvial-data.r

Alluvial plots consist of multiple horizontally-distributed columns (axes) representing factor variables, vertical divisions (strata) of these axes representing these variables' values; and splines (alluvial flows) connecting vertical subdivisions (lodes) within strata of adjacent axes representing subsets or amounts of observations that take the corresponding values of the corresponding variables. This function checks a data frame for either of two types of alluvial structure:

is_lodes_form(
  data,
  key,
  value,
  id,
  weight = NULL,
  site = NULL,
  logical = TRUE,
  silent = FALSE
)

is_alluvia_form(
  data,
  ...,
  axes = NULL,
  weight = NULL,
  logical = TRUE,
  silent = FALSE
)

to_lodes_form(
  data,
  ...,
  axes = NULL,
  key = "x",
  value = "stratum",
  id = "alluvium",
  diffuse = FALSE,
  discern = FALSE
)

to_alluvia_form(data, key, value, id, distill = FALSE)

Arguments

data

A data frame.
key, value, id

In to_lodes_form, handled as in tidyr::gather() and used to name the new axis (key), stratum (value), and alluvium (identifying) variables. In to_alluvia_form, handled as in tidyr::spread() and used to identify the fields of data to be used as the axis (key), stratum (value), and alluvium (identifying) variables.
weight

Optional field of data, handled using rlang::enquo(), to be used as heights or depths of the alluvia or lodes.
site

Optional vector of fields of data, handled using rlang::enquos(), to be used to group rows before testing for duplicate and missing id-axis pairings. Variables intended for faceting should be passed to site.
logical

Defunct. Whether to return a logical value or a character string indicating the type of alluvial structure ("none", "lodes", or "alluvia").
silent

Whether to print messages.
...

Used in is_alluvia_form and to_lodes_form as in dplyr::select() to determine axis variables, as an alternative to axes. Ignored when axes is provided.
axes

In *_alluvia_form, handled as in dplyr::select() and used to identify the field(s) of data to be used as axes.
diffuse

Fields of data, handled using tidyselect::vars_select(), to merge into the reshapen data by id. They must be a subset of the axis variables. Alternatively, a logical value indicating whether to merge all (TRUE) or none (FALSE) of the axis variables.
discern

Logical value indicating whether to suffix values of the variables used as axes that appear at more than one variable in order to distinguish their factor levels. This forces the levels of the combined factor variable value to be in the order of the axes.
distill

A logical value indicating whether to include variables, other than those passed to key and value, that vary within values of id. Alternatively, a function (or its name) to be used to distill each such variable to a single value. In addition to existing functions, distill accepts the character values "first" (used if distill is TRUE), "last", and "most" (which returns the first modal value).

Details

  • One row per lode, wherein each row encodes a subset or amount of observations having a specific profile of axis values, a key field encodes the axis, a value field encodes the value within each axis, and a id column identifies multiple lodes corresponding to the same subset or amount of observations. is_lodes_form tests for this structure.

  • One row per alluvium, wherein each row encodes a subset or amount of observations having a specific profile of axis values and a set axes of fields encodes its values at each axis variable. is_alluvia_form tests for this structure.

to_lodes_form takes a data frame with several designated variables to be used as axes in an alluvial plot, and reshapes the data frame so that the axis variable names constitute a new factor variable and their values comprise another. Other variables' values will be repeated, and a row-grouping variable can be introduced. This function invokes tidyr::gather().

to_alluvia_form takes a data frame with axis and axis value variables to be used in an alluvial plot, and reshape the data frame so that the axes constitute separate variables whose values are given by the value variable. This function invokes tidyr::spread().

See also

Other alluvial data manipulation: self-adjoin

Examples

# Titanic data in alluvia format
titanic_alluvia <- as.data.frame(Titanic)
head(titanic_alluvia)
#>   Class    Sex   Age Survived Freq
#> 1   1st   Male Child       No    0
#> 2   2nd   Male Child       No    0
#> 3   3rd   Male Child       No   35
#> 4  Crew   Male Child       No    0
#> 5   1st Female Child       No    0
#> 6   2nd Female Child       No    0
is_alluvia_form(titanic_alluvia,
                weight = "Freq")
#> [1] TRUE
# Titanic data in lodes format
titanic_lodes <- to_lodes_form(titanic_alluvia,
                               key = "x", value = "stratum", id = "alluvium",
                               axes = 1:4)
head(titanic_lodes)
#>   Freq alluvium     x stratum
#> 1    0        1 Class     1st
#> 2    0        2 Class     2nd
#> 3   35        3 Class     3rd
#> 4    0        4 Class    Crew
#> 5    0        5 Class     1st
#> 6    0        6 Class     2nd
is_lodes_form(titanic_lodes,
              key = "x", value = "stratum", id = "alluvium",
              weight = "Freq")
#> [1] TRUE
# again in lodes format, this time diffusing the `Class` variable
titanic_lodes2 <- to_lodes_form(titanic_alluvia,
                                key = variable, value = value,
                                id = cohort,
                                1:3, diffuse = Class)
head(titanic_lodes2)
#>   cohort Class Survived Freq variable value
#> 1      1   1st       No    0    Class   1st
#> 2      1   1st       No    0      Sex  Male
#> 3      1   1st       No    0      Age Child
#> 4      2   2nd       No    0    Class   2nd
#> 5      2   2nd       No    0      Sex  Male
#> 6      2   2nd       No    0      Age Child
is_lodes_form(titanic_lodes2,
              key = variable, value = value, id = cohort,
              weight = Freq)
#> [1] TRUE
# use `site` to separate data before lode testing
is_lodes_form(titanic_lodes2,
              key = variable, value = value, id = Class,
              weight = Freq)
#> Duplicated id-axis pairings.
#> [1] FALSE
is_lodes_form(titanic_lodes2,
              key = variable, value = value, id = Class,
              weight = Freq, site = cohort)
#> [1] TRUE

# curriculum data in lodes format
data(majors)
head(majors)
#>   student semester curriculum
#> 1       1    CURR1   Painting
#> 2       2    CURR1   Painting
#> 3       6    CURR1   Sculpure
#> 4       8    CURR1   Painting
#> 5       9    CURR1   Sculpure
#> 6      10    CURR1   Painting
is_lodes_form(majors,
              key = "semester", value = "curriculum", id = "student")
#> [1] TRUE
# curriculum data in alluvia format
majors_alluvia <- to_alluvia_form(majors,
                                  key = "semester", value = "curriculum",
                                  id = "student")
head(majors_alluvia)
#>   student    CURR1       CURR3       CURR5    CURR7    CURR9   CURR11   CURR13
#> 1       1 Painting    Painting    Painting Painting Painting Painting Painting
#> 2       2 Painting    Painting    Painting Painting Painting Painting     <NA>
#> 3       6 Sculpure    Sculpure    Painting Painting Painting Painting Painting
#> 4       8 Painting    Painting    Painting Painting     <NA> Painting Painting
#> 5       9 Sculpure Art History Art History Painting Painting Painting Painting
#> 6      10 Painting    Painting    Painting Painting Painting Painting     <NA>
#>     CURR15
#> 1 Painting
#> 2     <NA>
#> 3 Painting
#> 4 Painting
#> 5 Painting
#> 6     <NA>
is_alluvia_form(majors_alluvia, tidyselect::starts_with("CURR"))
#> Missing alluvia for some stratum combinations.
#> [1] TRUE

# distill variables that vary within `id` values
set.seed(1)
majors$hypo_grade <- LETTERS[sample(5, size = nrow(majors), replace = TRUE)]
majors_alluvia2 <- to_alluvia_form(majors,
                                   key = "semester", value = "curriculum",
                                   id = "student",
                                   distill = "most")
#> Distilled variables: hypo_grade
head(majors_alluvia2)
#>   student hypo_grade    CURR1       CURR3       CURR5    CURR7    CURR9
#> 1       1          A Painting    Painting    Painting Painting Painting
#> 2       2          D Painting    Painting    Painting Painting Painting
#> 3       6          B Sculpure    Sculpure    Painting Painting Painting
#> 4       8          B Painting    Painting    Painting Painting     <NA>
#> 5       9          E Sculpure Art History Art History Painting Painting
#> 6      10          A Painting    Painting    Painting Painting Painting
#>     CURR11   CURR13   CURR15
#> 1 Painting Painting Painting
#> 2 Painting     <NA>     <NA>
#> 3 Painting Painting Painting
#> 4 Painting Painting Painting
#> 5 Painting Painting Painting
#> 6 Painting     <NA>     <NA>

# options to distinguish strata at different axes
gg <- ggplot(majors_alluvia,
             aes(axis1 = CURR1, axis2 = CURR7, axis3 = CURR13))
gg +
  geom_alluvium(aes(fill = as.factor(student)), width = 2/5, discern = TRUE) +
  geom_stratum(width = 2/5, discern = TRUE) +
  geom_text(stat = "stratum", discern = TRUE, aes(label = after_stat(stratum)))

gg +
  geom_alluvium(aes(fill = as.factor(student)), width = 2/5, discern = FALSE) +
  geom_stratum(width = 2/5, discern = FALSE) +
  geom_text(stat = "stratum", discern = FALSE, aes(label = after_stat(stratum)))

# warning when inappropriate
ggplot(majors[majors$semester %in% paste0("CURR", c(1, 7, 13)), ],
       aes(x = semester, stratum = curriculum, alluvium = student,
           label = curriculum)) +
  geom_alluvium(aes(fill = as.factor(student)), width = 2/5, discern = TRUE) +
  geom_stratum(width = 2/5, discern = TRUE) +
  geom_text(stat = "stratum", discern = TRUE)
#> Warning: Data is already in lodes format, so `discern` will be ignored.
#> Warning: Data is already in lodes format, so `discern` will be ignored.
#> Warning: Data is already in lodes format, so `discern` will be ignored.
#> Warning: Removed 1 rows containing missing values (geom_text).