In this vignette, we would like to discuss the similarities and differences between dplyr and rtable.
Much of the rtables framework focuses on tabulation/summarizing of data and then the visualization of the table. In this vignette, we focus on summarizing data using dplyr and contrast it to rtables. We won’t pay attention to the table visualization/markup and just derive the cell content.
Using dplyr to summarize data and gt to visualize the table is a good way if the tabulation is of a certain nature or complexity. However, there are tables such as the table created in the introduction vignette that take some effort to create with dplyr. Part of the effort is due to fact that when using dplyr the table data is stored in data.frames or tibbles which is not the most natural way to represent a table as we will show in this vignette.
If you know a more elegant way of deriving the table content with dplyr please let us know and we will update the vignette.
Here is the table and data used in the introduction vignette:
n <- 400
set.seed(1)
df <- tibble(
arm = factor(sample(c("Arm A", "Arm B"), n, replace = TRUE), levels = c("Arm A", "Arm B")),
country = factor(sample(c("CAN", "USA"), n, replace = TRUE, prob = c(.55, .45)), levels = c("CAN", "USA")),
gender = factor(sample(c("Female", "Male"), n, replace = TRUE), levels = c("Female", "Male")),
handed = factor(sample(c("Left", "Right"), n, prob = c(.6, .4), replace = TRUE), levels = c("Left", "Right")),
age = rchisq(n, 30) + 10
) %>% mutate(
weight = 35 * rnorm(n, sd = .5) + ifelse(gender == "Female", 140, 180)
)
lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("arm") %>%
split_cols_by("gender") %>%
split_rows_by("country") %>%
summarize_row_groups() %>%
split_rows_by("handed") %>%
summarize_row_groups() %>%
analyze("age", afun = mean, format = "xx.x")
tbl <- build_table(lyt, df)
tbl
# Arm A Arm B
# Female Male Female Male
# (N=96) (N=105) (N=92) (N=107)
# ————————————————————————————————————————————————————————————
# CAN 45 (46.9%) 64 (61.0%) 46 (50.0%) 62 (57.9%)
# Left 32 (33.3%) 42 (40.0%) 26 (28.3%) 37 (34.6%)
# mean 38.9 40.4 40.3 37.7
# Right 13 (13.5%) 22 (21.0%) 20 (21.7%) 25 (23.4%)
# mean 36.6 40.2 40.2 40.6
# USA 51 (53.1%) 41 (39.0%) 46 (50.0%) 45 (42.1%)
# Left 34 (35.4%) 19 (18.1%) 25 (27.2%) 25 (23.4%)
# mean 40.4 39.7 39.2 40.1
# Right 17 (17.7%) 22 (21.0%) 21 (22.8%) 20 (18.7%)
# mean 36.9 39.8 38.5 39.0We will start by deriving the first data cell on row 3 (note, row 1 and 2 have content cells, see the introduction vignette). Cell 3,1 contains the mean age for left handed & female Canadians in “Arm A”:
mean(df$age[df$country == "CAN" & df$arm == "Arm A" & df$gender == "Female" & df$handed == "Left"])
# [1] 38.86979or with dplyr:
df %>%
filter(country == "CAN", arm == "Arm A", gender == "Female", handed == "Left") %>%
summarise(mean_age = mean(age))
# # A tibble: 1 × 1
# mean_age
# <dbl>
# 1 38.9Further, dplyr gives us other verbs to easily get the average age of left handed Canadians for each group defined by the 4 columns:
df %>%
group_by(arm, gender) %>%
filter(country == "CAN", handed == "Left") %>%
summarise(mean_age = mean(age))
# `summarise()` has grouped output by 'arm'. You can override using the `.groups`
# argument.
# # A tibble: 4 × 3
# # Groups: arm [2]
# arm gender mean_age
# <fct> <fct> <dbl>
# 1 Arm A Female 38.9
# 2 Arm A Male 40.4
# 3 Arm B Female 40.3
# 4 Arm B Male 37.7We can further get to all the average age cell values with:
average_age <- df %>%
group_by(arm, gender, country, handed) %>%
summarise(mean_age = mean(age))
# `summarise()` has grouped output by 'arm', 'gender', 'country'. You can
# override using the `.groups` argument.
average_age
# # A tibble: 16 × 5
# # Groups: arm, gender, country [8]
# arm gender country handed mean_age
# <fct> <fct> <fct> <fct> <dbl>
# 1 Arm A Female CAN Left 38.9
# 2 Arm A Female CAN Right 36.6
# 3 Arm A Female USA Left 40.4
# 4 Arm A Female USA Right 36.9
# 5 Arm A Male CAN Left 40.4
# 6 Arm A Male CAN Right 40.2
# 7 Arm A Male USA Left 39.7
# 8 Arm A Male USA Right 39.8
# 9 Arm B Female CAN Left 40.3
# 10 Arm B Female CAN Right 40.2
# 11 Arm B Female USA Left 39.2
# 12 Arm B Female USA Right 38.5
# 13 Arm B Male CAN Left 37.7
# 14 Arm B Male CAN Right 40.6
# 15 Arm B Male USA Left 40.1
# 16 Arm B Male USA Right 39.0In rtable syntax, we need the following code to get to the same content:
As mentioned in the introduction to this vignette, please ignore the difference in arranging and formatting the data: it’s possible to condense the rtable more and it is possible to make the tibble look more like the reference table using the gt R package.
In terms of tabulation for this example there was arguably not much added by rtables over dplyr.
Unlike in rtables the different levels of summarization are discrete computations in dplyr which we will then need to combine
We first focus on the count and percentage information for handedness within each country (for each arm-gender pair), along with the analysis row mean values:
c_h_df <- df %>%
group_by(arm, gender, country, handed) %>%
summarize(mean = mean(age), c_h_count = n()) %>%
## we need the sum below to *not* be by country, so that we're dividing by the column counts
ungroup(country) %>%
# now the `handed` grouping has been removed, therefore we can calculate percent now:
mutate(n_col = sum(c_h_count), c_h_percent = c_h_count / n_col)
# `summarise()` has grouped output by 'arm', 'gender', 'country'. You can
# override using the `.groups` argument.
c_h_df
# # A tibble: 16 × 8
# # Groups: arm, gender [4]
# arm gender country handed mean c_h_count n_col c_h_percent
# <fct> <fct> <fct> <fct> <dbl> <int> <int> <dbl>
# 1 Arm A Female CAN Left 38.9 32 96 0.333
# 2 Arm A Female CAN Right 36.6 13 96 0.135
# 3 Arm A Female USA Left 40.4 34 96 0.354
# 4 Arm A Female USA Right 36.9 17 96 0.177
# 5 Arm A Male CAN Left 40.4 42 105 0.4
# 6 Arm A Male CAN Right 40.2 22 105 0.210
# 7 Arm A Male USA Left 39.7 19 105 0.181
# 8 Arm A Male USA Right 39.8 22 105 0.210
# 9 Arm B Female CAN Left 40.3 26 92 0.283
# 10 Arm B Female CAN Right 40.2 20 92 0.217
# 11 Arm B Female USA Left 39.2 25 92 0.272
# 12 Arm B Female USA Right 38.5 21 92 0.228
# 13 Arm B Male CAN Left 37.7 37 107 0.346
# 14 Arm B Male CAN Right 40.6 25 107 0.234
# 15 Arm B Male USA Left 40.1 25 107 0.234
# 16 Arm B Male USA Right 39.0 20 107 0.187which has 16 rows (cells) like the average_age data frame defined above. Next, we will derive the group information for countries:
c_df <- df %>%
group_by(arm, gender, country) %>%
summarize(c_count = n()) %>%
# now the `handed` grouping has been removed, therefore we can calculate percent now:
mutate(n_col = sum(c_count), c_percent = c_count / n_col)
# `summarise()` has grouped output by 'arm', 'gender'. You can override using the
# `.groups` argument.
c_df
# # A tibble: 8 × 6
# # Groups: arm, gender [4]
# arm gender country c_count n_col c_percent
# <fct> <fct> <fct> <int> <int> <dbl>
# 1 Arm A Female CAN 45 96 0.469
# 2 Arm A Female USA 51 96 0.531
# 3 Arm A Male CAN 64 105 0.610
# 4 Arm A Male USA 41 105 0.390
# 5 Arm B Female CAN 46 92 0.5
# 6 Arm B Female USA 46 92 0.5
# 7 Arm B Male CAN 62 107 0.579
# 8 Arm B Male USA 45 107 0.421Finally, we left_join() the two levels of summary to get a data.frame containing the full set of values which make up the body of our table (note, however, they are not in the same order):
full_dplyr <- left_join(c_h_df, c_df) %>% ungroup()
# Joining with `by = join_by(arm, gender, country, n_col)`Alternatively, we could calculate only the counts in c_h_df, and use mutate() after the left_join() to divide the counts by the n_col values which are more naturally calculated within c_df. This would simplify c_h_df’s creation somewhat by not requiring the explicit ungroup(), but it prevents each level of summarization from being a self-contained set of computations.
The rtables call in contrast is:
lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("arm") %>%
split_cols_by("gender") %>%
split_rows_by("country") %>%
summarize_row_groups() %>%
split_rows_by("handed") %>%
summarize_row_groups() %>%
analyze("age", afun = mean, format = "xx.x")
tbl <- build_table(lyt, df)
tbl
# Arm A Arm B
# Female Male Female Male
# (N=96) (N=105) (N=92) (N=107)
# ————————————————————————————————————————————————————————————
# CAN 45 (46.9%) 64 (61.0%) 46 (50.0%) 62 (57.9%)
# Left 32 (33.3%) 42 (40.0%) 26 (28.3%) 37 (34.6%)
# mean 38.9 40.4 40.3 37.7
# Right 13 (13.5%) 22 (21.0%) 20 (21.7%) 25 (23.4%)
# mean 36.6 40.2 40.2 40.6
# USA 51 (53.1%) 41 (39.0%) 46 (50.0%) 45 (42.1%)
# Left 34 (35.4%) 19 (18.1%) 25 (27.2%) 25 (23.4%)
# mean 40.4 39.7 39.2 40.1
# Right 17 (17.7%) 22 (21.0%) 21 (22.8%) 20 (18.7%)
# mean 36.9 39.8 38.5 39.0We can now spot check that the values are the same
frm_rtables_h <- cell_values(
tbl,
rowpath = c("country", "CAN", "handed", "Right", "@content"),
colpath = c("arm", "Arm B", "gender", "Female")
)[[1]]
frm_rtables_h
# [1] 20.0000000 0.2173913
frm_dplyr_h <- full_dplyr %>%
filter(country == "CAN" & handed == "Right" & arm == "Arm B" & gender == "Female") %>%
select(c_h_count, c_h_percent)
frm_dplyr_h
# # A tibble: 1 × 2
# c_h_count c_h_percent
# <int> <dbl>
# 1 20 0.217
frm_rtables_c <- cell_values(
tbl,
rowpath = c("country", "CAN", "@content"),
colpath = c("arm", "Arm A", "gender", "Male")
)[[1]]
frm_rtables_c
# [1] 64.0000000 0.6095238
frm_dplyr_c <- full_dplyr %>%
filter(country == "CAN" & arm == "Arm A" & gender == "Male") %>%
select(c_count, c_percent)
frm_dplyr_c
# # A tibble: 2 × 2
# c_count c_percent
# <int> <dbl>
# 1 64 0.610
# 2 64 0.610Further, the rtable syntax has hopefully also become a bit more straightforward to derive the cell values than with dplyr for this particular table.
In this vignette learned that:
dplyr and data.frame or tibble as data structure
dplyr keeps simple things simplertables streamlines the construction of complex tablesWe recommend that you continue reading the clinical_trials vignette where we create a number of more advanced tables using layouts.
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