Many datasets are a matrix with two tables attached: survey answers with information about the respondents and the questions, gene expression with gene and sample annotations, sales per shop and product. Kept as three separate objects, every filter or reordering has to be applied to all three at once, and it is easy to end up with a matrix that no longer lines up with its metadata.
tidymatrix keeps the three pieces together in one object
and lets you manipulate it with the dplyr verbs you already know. Much
like tidygraph does
for networks, you activate() the part you want to work on
(rows, columns or the matrix itself) and the rest follows along.
# install.packages("devtools")
devtools::install_github("raivokolde/tidymatrix")The package ships with big5, a simulated personality
survey: 400 people answered 30 statements on a 1–5 scale, and each
statement measures one of the Big Five personality traits.
library(tidymatrix)
library(dplyr, warn.conflicts = FALSE)
tm <- tidymatrix(big5_responses, big5_respondents, big5_items)
tm
#> # A tidymatrix: 400 x 30 matrix
#> # Active: matrix
#> #
#> # Row data: 400 rows x 8 columns
#> # Column data: 30 rows x 5 columns
#> #
#> # Matrix preview:
#> E1 E2 E3 E4 E5 E6 A1 A2 A3 A4 A5 A6 C1 C2 C3 C4 C5 C6 N1 N2 N3 N4 N5 N6 O1
#> R001 4 3 5 3 3 4 3 5 3 4 4 3 1 2 1 2 4 5 3 3 3 2 3 2 2
#> R002 2 4 2 2 3 4 3 2 3 3 2 4 3 4 3 2 4 4 5 4 5 5 2 2 5
#> R003 2 2 3 4 4 3 3 3 4 5 4 3 2 3 1 3 4 4 4 4 3 2 4 2 1
#> R004 2 3 3 3 3 4 3 2 3 2 2 3 3 2 4 2 4 4 5 4 3 3 1 1 4
#> R005 1 2 2 1 5 5 5 5 5 5 2 1 2 5 4 3 2 2 4 2 3 4 3 2 2
#> R006 1 3 3 3 4 3 5 4 5 3 3 2 3 3 1 4 5 5 5 5 5 5 1 1 2
#> O2 O3 O4 O5 O6
#> R001 4 1 3 2 3
#> R002 3 4 5 3 1
#> R003 4 1 2 5 4
#> R004 3 4 4 2 2
#> R005 3 2 3 4 3
#> R006 1 1 1 3 5Filtering the metadata subsets the matrix to match. Here we drop respondents who rushed through the survey and keep the neuroticism items in the order they were asked:
tm_clean <- tm |>
activate(rows) |>
filter(completion_min > 3.5) |>
activate(columns) |>
filter(trait == "Neuroticism") |>
arrange(position)
tm_clean
#> # A tidymatrix: 388 x 6 matrix
#> # Active: columns
#> #
#> # Row data: 388 rows x 8 columns
#> # Column data: 6 rows x 5 columns
#> #
#> # Active data (columns):
#> item_id trait reversed item_text position
#> 1 N1 Neuroticism FALSE I get stressed out easily. 4
#> 2 N2 Neuroticism FALSE I worry about things. 9
#> 3 N3 Neuroticism FALSE My mood changes often. 14
#> 4 N4 Neuroticism FALSE I get irritated easily. 19
#> 5 N5 Neuroticism TRUE I stay calm under pressure. 24
#> 6 N6 Neuroticism TRUE I seldom feel blue. 29Operations on the matrix can use the metadata too. With rows active,
transform_matrix() gets one respondent’s answers at a time,
and extra arguments can refer to item metadata, here to flip the
reverse-keyed items. add_stats() stores per-respondent
summaries back into the row metadata:
tm_clean |>
activate(rows) |>
transform_matrix(\(x, flip) ifelse(flip, 6L - x, x), flip = reversed) |>
add_stats(mean) |>
as_tibble() |>
select(respondent_id, age, gender, neuroticism = mean)
#> # A tibble: 388 x 4
#> respondent_id age gender neuroticism
#> <chr> <int> <chr> <dbl>
#> 1 R001 52 Female 3
#> 2 R002 32 Female 4.5
#> 3 R003 66 Male 3.17
#> 4 R004 42 Male 4.17
#> 5 R005 48 Female 3.33
#> 6 R006 62 Female 5
#> 7 R007 26 Female 4.17
#> 8 R008 38 Female 5
#> 9 R009 55 Male 3
#> 10 R010 57 Female 2.33
#> # i 378 more rowsAnalyses such as PCA and clustering store their results in the metadata and keep the full result object for later:
tm |>
activate(columns) |>
compute_prcomp(n_components = 2) |>
compute_hclust(k = 5, method = "ward.D2") |>
list_analyses()
#> [1] "column_pca" "column_hclust"filter(),
select(), mutate(), arrange(),
slice(), group_by() and
summarise(), plus all six joins.transform_matrix(),
scale(), center(),
log_transform(), clip_values(),
t(), add_stats().compute_across().as_tibble(),
to_long() for ggplot2, and plot_pheatmap() for
annotated heatmaps.The Get started vignette is a quick tour of all of these, with links to a detailed vignette for each topic.