The goal of teethr (pronounced teether) is to provide a set of tools to calculate dental indices, such as caries ratio, and calculus index, and facilitate visualisation of data related to dental diseases.
You can install the development version of teethr like so:
devtools::install_github("bbartholdy/teethr")First, dental data are combined with demographic data and converted to
long format with dental_longer(). dental_longer() also adds
information on tooth type and position that can be used to calculate
tooth- and position-specific indices.
library(teethr)
library(dplyr)
mb11_calculus_long <- mb11_calculus %>%
dental_longer(-id, names_sep = "_") %>% # make longer all columns except 'id'
dental_join() # add tooth information (position, type, etc.)
inner_join(mb11_demography, by = "id") # combine with age and sex
mb11_calculus_long#> Joining with `by = join_by(tooth)`
#> # A tibble: 3,936 × 12
#> id tooth surface score region position side class type quadrant age
#> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 MB131 11 bucc 0 maxilla anterior right incis… i1 UA ma
#> 2 MB131 11 lin 0 maxilla anterior right incis… i1 UA ma
#> 3 MB131 11 ip 1 maxilla anterior right incis… i1 UA ma
#> 4 MB131 12 bucc 0 maxilla anterior right incis… i2 UA ma
#> 5 MB131 12 lin 0 maxilla anterior right incis… i2 UA ma
#> 6 MB131 12 ip 1 maxilla anterior right incis… i2 UA ma
#> 7 MB131 13 bucc 0 maxilla anterior right canine c UA ma
#> 8 MB131 13 lin 0 maxilla anterior right canine c UA ma
#> 9 MB131 13 ip 0 maxilla anterior right canine c UA ma
#> 10 MB131 14 bucc 1 maxilla posterior right premo… pm1 UP ma
#> # ℹ 3,926 more rows
#> # ℹ 1 more variable: sex <chr>
If you need to switch between tooth notations, you can use
dental_recode(). Currently available notations are FDI, Standards, and
text.
mb11_calculus_long %>%
dental_recode(col = tooth, from = "FDI", to = "text") %>%
select(id, tooth)
#> # A tibble: 3,936 × 2
#> id tooth
#> <chr> <chr>
#> 1 MB131 URI1
#> 2 MB131 URI1
#> 3 MB131 URI1
#> 4 MB131 URI2
#> 5 MB131 URI2
#> 6 MB131 URI2
#> 7 MB131 URC1
#> 8 MB131 URC1
#> 9 MB131 URC1
#> 10 MB131 URP1
#> # ℹ 3,926 more rowsThe long-format data can be used to calculate indices, such as the
dental calculus index using the calculus_index() function. The
function also provides the number of surfaces scored, number of teeth,
and sum of scores for verification. The original method recommends
grouping the dentition by quadrant to obtain four indices. This can be
done for the whole sample,
mb11_calculus_long %>%
group_by(quadrant) %>%
calculus_index()
#> previously defined groups (quadrant) were used. If that was not expected, use `ungroup()` on the data frame before using the function.
#> # A tibble: 4 × 4
#> quadrant n score_sum index
#> <chr> <int> <dbl> <dbl>
#> 1 LA 632 631 0.998
#> 2 LP 857 513 0.599
#> 3 UA 554 308 0.556
#> 4 UP 813 541 0.665or by sex,
mb11_calculus_long %>%
# groups can also be added directly to calculus_index()
calculus_index(sex, quadrant) %>%
select(sex, quadrant, index) # remove unnecessary outputs
#> previously defined groups (sex,quadrant) were used. If that was not expected, use `ungroup()` on the data frame before using the function.
#> # A tibble: 16 × 3
#> sex quadrant index
#> <chr> <chr> <dbl>
#> 1 f LA 0.778
#> 2 f LP 0.345
#> 3 f UA 0.0556
#> 4 f UP 0.5
#> 5 m LA 1.01
#> 6 m LP 0.582
#> 7 m UA 0.545
#> 8 m UP 0.630
#> 9 pf LA 0.667
#> 10 pf LP 0.667
#> 11 pf UA 0.154
#> 12 pf UP 0.688
#> 13 pm LA 1.03
#> 14 pm LP 0.678
#> 15 pm UA 0.671
#> 16 pm UP 0.782or by age and sex.
mb11_calculus_long %>%
calculus_index(sex, age, quadrant) %>%
select(sex, quadrant, index) # remove unnecessary outputs
#> previously defined groups (sex,age,quadrant) were used. If that was not expected, use `ungroup()` on the data frame before using the function.
#> Warning in dental_index(., score = {: 1 rows removed because of no teeth
#> present in groupings.
#> # A tibble: 39 × 3
#> sex quadrant index
#> <chr> <chr> <dbl>
#> 1 f LA 0.778
#> 2 f LP 0.345
#> 3 f UA 0.0556
#> 4 f UP 0.5
#> 5 m LA 0.9
#> 6 m LP 0.167
#> 7 m UA 0.167
#> 8 m UP 0.296
#> 9 m LA 0.930
#> 10 m LP 0.549
#> # ℹ 29 more rowsDental caries ratios can be calculated using the count_caries() and
dental_ratio() functions.
mb11_caries_long <- mb11_caries %>%
dental_longer(-id) %>%
dental_join()
#> Joining with `by = join_by(tooth)`
caries_ratios <- mb11_caries_long %>%
count_caries(caries = score, no_lesion = "none") %>% # convert location to lesion count
group_by(tooth) %>%
dental_ratio(count = caries_count)
#> previously defined groups (tooth) were used. If that was not expected, use `ungroup()` on the data frame before using the function.
caries_ratios
#> # A tibble: 32 × 4
#> tooth n count ratio
#> <chr> <int> <dbl> <dbl>
#> 1 11 35 4 0.114
#> 2 12 31 4 0.129
#> 3 13 35 7 0.2
#> 4 14 34 3 0.0882
#> 5 15 23 5 0.217
#> 6 16 32 7 0.219
#> 7 17 25 7 0.28
#> 8 18 22 9 0.409
#> 9 21 28 2 0.0714
#> 10 22 26 2 0.0769
#> # ℹ 22 more rowsA plot can be created for dental calculus indices per quadrant
library(ggplot2)
mb11_calculus_long %>%
calculus_index(id, age, quadrant) %>%
ggplot(aes(x = age, y = index)) +
geom_violin(aes(fill = age)) +
geom_boxplot(width = 0.2) +
scale_fill_viridis_d() +
facet_wrap(~ quadrant) +
theme_bw()
#> previously defined groups (id,age,quadrant) were used. If that was not expected, use `ungroup()` on the data frame before using the function.
#> Warning in dental_index(., score = {: 1 rows removed because of no teeth
#> present in groupings.
And the built-in dental_arcade_mapping data frame can be used to map
teeth to geom_sf() and create fun plots like this one:
library(sf)
#> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE
caries_ratios %>%
dental_recode(tooth, "FDI", "text") %>% # notation needs to be text
left_join(as_tibble(dental_arcade_mapping), by = "tooth") %>%
ggplot(aes(fill = ratio, geometry = geometry)) +
geom_sf() +
theme_void()