Unnesting tibble columns: "Wide" data summaries with dplyr v1.0.0 - r

Question

I'd like to produce "wide" summary tables of data in this sort of format:
---- Centiles ----
Param Group Mean SD 25% 50% 75%
Height 1 x.xx x.xxx x.xx x.xx x.xx
2 x.xx x.xxx x.xx x.xx x.xx
3 x.xx x.xxx x.xx x.xx x.xx
Weight 1 x.xx x.xxx x.xx x.xx x.xx
2 x.xx x.xxx x.xx x.xx x.xx
3 x.xx x.xxx x.xx x.xx x.xx
I can do that in dplyr 0.8.x. I can do it generically, with a function that can handle arbitrary grouping variables with arbitrary numbers of levels and arbitrary statistics summarising arbitrary numbers of variables with arbitrary names. I get that level of flexibility by making my data tidy. That's not what this question is about.
First, some toy data:
set.seed(123456)
toy <- tibble(
Group=rep(1:3, each=5),
Height=1.65 + rnorm(15, 0, 0.1),
Weight= 75 + rnorm(15, 0, 10)
) %>%
pivot_longer(
values_to="Value",
names_to="Parameter",
cols=c(Height, Weight)
)
Now, a simple summary function, and a helper:
quibble2 <- function(x, q = c(0.25, 0.5, 0.75)) {
tibble(Value := quantile(x, q), "Quantile" := q)
}
mySummary <- function(data, ...) {
data %>%
group_by(Parameter, Group) %>%
summarise(..., .groups="drop")
}
So I can say things like
summary <- mySummary(toy, Q=quibble2(Value), Mean=mean(Value, na.rm=TRUE), SD=sd(Value, na.rm=TRUE))
summary %>% head()
Giving
# A tibble: 6 x 5
Parameter Group Q$Value $Quantile Mean SD
<chr> <int> <dbl> <dbl> <dbl> <dbl>
1 Height 1 1.45 0.25 1.54 0.141
2 Height 1 1.49 0.5 1.54 0.141
3 Height 1 1.59 0.75 1.54 0.141
4 Height 2 1.64 0.25 1.66 0.0649
5 Height 2 1.68 0.5 1.66 0.0649
6 Height 2 1.68 0.75 1.66 0.0649
So that's the summary I need, but it's in long format. And Q is a df-col. It's a tibble:
is_tibble(summary$Q)
[1] TRUE
So pivot_wider doesn't seem to work. I can use nest_by() to get to a one-row-per-group format:
toySummary <- summary %>% nest_by(Group, Mean, SD)
toySummary
# Rowwise: Group, Mean, SD
Group Mean SD data
<int> <dbl> <dbl> <list<tbl_df[,2]>>
1 1 1.54 0.141 [3 × 2]
2 1 78.8 10.2 [3 × 2]
3 2 1.66 0.0649 [3 × 2]
4 2 82.9 9.09 [3 × 2]
5 3 1.63 0.100 [3 × 2]
6 3 71.0 10.8 [3 × 2]
But now the format of the centiles is even more complicated:
> toySummary$data[1]
<list_of<
tbl_df<
Parameter: character
Q :
tbl_df<
Value : double
Quantile: double
>
>
>[1]>
[[1]]
# A tibble: 3 x 2
Parameter Q$Value $Quantile
<chr> <dbl> <dbl>
1 Height 1.45 0.25
2 Height 1.49 0.5
3 Height 1.59 0.75
It looks like a list, so I guess some form of lapply would probably work, but is there a neater, tidy, solution that I've not spotted yet? I've discovered several new verbs that I didn't know abou whilst researching this question (chop, pack, rowwise(), nest_by and such) but none seem to give me what I want: ideally, a tibble with 6 rows (defined by unique Group and Parameter combinations) and columns for Mean, SD, Q25, Q50 and Q75.
To clarify in response to the first two proposed answers: getting the exact numbers that my toy example generates is less important than finding a generic technique for moving from the df-col(s) that summarise returns in dplyr v1.0.0 to a wide data summary of the general form that my example illustrates.

Answer 1

revised answer
Here is my revised answer. This time, I rewrote your quibble2 function with enframe and pivot_wider so that it returns a tibble with three rows.
This will again lead to a df-col in your summary tibble, and now we can use unpack directly, without using pivot_wider to get the expected outcome.
This should generalize on centiles etc. as well.
library(tidyverse)
set.seed(123456)
toy <- tibble(
Group=rep(1:3, each=5),
Height=1.65 + rnorm(15, 0, 0.1),
Weight= 75 + rnorm(15, 0, 10)
) %>%
pivot_longer(
values_to="Value",
names_to="Parameter",
cols=c(Height, Weight)
)
quibble2 <- function(x, q = c(0.25, 0.5, 0.75)) {
pivot_wider(enframe(quantile(x, q)),
names_from = name,
values_from = value)
}
mySummary <- function(data, ...) {
data %>%
group_by(Parameter, Group) %>%
summarise(..., .groups="drop")
}
summary <- mySummary(toy, Q=quibble2(Value), Mean=mean(Value, na.rm=TRUE), SD=sd(Value, na.rm=TRUE))
summary %>%
unpack(Q)
#> # A tibble: 6 x 7
#> Parameter Group `25%` `50%` `75%` Mean SD
#> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Height 1 1.62 1.66 1.73 1.70 0.108
#> 2 Height 2 1.73 1.77 1.78 1.76 0.105
#> 3 Height 3 1.55 1.64 1.76 1.65 0.109
#> 4 Weight 1 75.6 80.6 84.3 80.0 9.05
#> 5 Weight 2 75.4 76.9 79.6 77.4 7.27
#> 6 Weight 3 70.7 75.2 82.0 76.3 6.94
Created on 2020-06-13 by the reprex package (v0.3.0)
Second approach
without changing quibble2, we would need to first call unpack and then pivot_wider. This should scale as well.
library(tidyverse)
set.seed(123456)
toy <- tibble(
Group=rep(1:3, each=5),
Height=1.65 + rnorm(15, 0, 0.1),
Weight= 75 + rnorm(15, 0, 10)
) %>%
pivot_longer(
values_to="Value",
names_to="Parameter",
cols=c(Height, Weight)
)
quibble2 <- function(x, q = c(0.25, 0.5, 0.75)) {
tibble(Value := quantile(x, q), "Quantile" := q)
}
mySummary <- function(data, ...) {
data %>%
group_by(Parameter, Group) %>%
summarise(..., .groups="drop")
}
summary <- mySummary(toy, Q=quibble2(Value), Mean=mean(Value, na.rm=TRUE), SD=sd(Value, na.rm=TRUE))
summary %>%
unpack(Q) %>%
pivot_wider(names_from = Quantile, values_from = Value)
#> # A tibble: 6 x 7
#> Parameter Group Mean SD `0.25` `0.5` `0.75`
#> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Height 1 1.70 0.108 1.62 1.66 1.73
#> 2 Height 2 1.76 0.105 1.73 1.77 1.78
#> 3 Height 3 1.65 0.109 1.55 1.64 1.76
#> 4 Weight 1 80.0 9.05 75.6 80.6 84.3
#> 5 Weight 2 77.4 7.27 75.4 76.9 79.6
#> 6 Weight 3 76.3 6.94 70.7 75.2 82.0
Created on 2020-06-13 by the reprex package (v0.3.0)
generalized approach
I tried to figure out a more general approach by rewriting the mySummary function. Now it will convert automatically those outputs to df-cols which return a vector or a named vector. It will also wrap list automatically around expressions if necessary.
Then, I defined a function widen which will widen the df as much as possible, by preserving rows, including calling broom::tidy on supported list-columns.
The approach is not perfect, and could be extended by including unnest_wider in the widen function.
Note, that I changed the grouping in the example to be able to use t.test as another example output.
library(tidyverse)
set.seed(123456)
toy <- tibble(
Group=rep(1:3, each=5),
Height=1.65 + rnorm(15, 0, 0.1),
Weight= 75 + rnorm(15, 0, 10)
) %>%
pivot_longer(
values_to="Value",
names_to="Parameter",
cols=c(Height, Weight)
)
# modified summary function
mySummary <- function(data, ...) {
fns <- rlang::enquos(...)
fns <- map(fns, function(x) {
res <- rlang::eval_tidy(x, data = data)
if ( ((is.vector(res) || is.factor(res)) && length(res) == 1) ||
("list" %in% class(res) && is.list(res)) ||
rlang::call_name(rlang::quo_get_expr(x)) == "list") {
x
}
else if ((is.vector(res) || is.factor(res)) && length(res) > 1) {
x_expr <- as.character(list(rlang::quo_get_expr(x)))
x_expr <- paste0(
"pivot_wider(enframe(",
x_expr,
"), names_from = name, values_from = value)"
)
x <- rlang::quo_set_expr(x, str2lang(x_expr))
x
} else {
x_expr <- as.character(list(rlang::quo_get_expr(x)))
x_expr <- paste0("list(", x_expr,")")
x <- rlang::quo_set_expr(x, str2lang(x_expr))
x
}
})
data %>%
group_by(Parameter) %>%
summarise(!!! fns, .groups="drop")
}
# A function to automatically widen the df as much as possible while preserving rows
widen <- function(df) {
df_cols <- names(df)[map_lgl(df, is.data.frame)]
df <- unpack(df, all_of(df_cols), names_sep = "_")
try_tidy <- function(x) {
tryCatch({
broom::tidy(x)
}, error = function(e) {
x
})
}
df <- df %>% rowwise() %>% mutate(across(where(is.list), try_tidy))
ungroup(df)
}
# if you want to specify function arguments for convenience use purrr::partial
quantile3 <- partial(quantile, x = , q = c(.25, .5, .75))
summary <- mySummary(toy,
Q = quantile3(Value),
R = range(Value),
T_test = t.test(Value),
Mean = mean(Value, na.rm=TRUE),
SD = sd(Value, na.rm=TRUE)
)
summary
#> # A tibble: 2 x 6
#> Parameter Q$`0%` $`25%` $`50%` $`75%` $`100%` R$`1` $`2` T_test Mean SD
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <list> <dbl> <dbl>
#> 1 Height 1.54 1.62 1.73 1.77 1.90 1.54 1.90 <htest> 1.70 0.109
#> 2 Weight 67.5 72.9 76.9 83.2 91.7 67.5 91.7 <htest> 77.9 7.40
widen(summary)
#> # A tibble: 2 x 11
#> Parameter `Q_0%` `Q_25%` `Q_50%` `Q_75%` `Q_100%` R_1 R_2 T_test$estimate
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Height 1.54 1.62 1.73 1.77 1.90 1.54 1.90 1.70
#> 2 Weight 67.5 72.9 76.9 83.2 91.7 67.5 91.7 77.9
#> # … with 9 more variables: $statistic <dbl>, $p.value <dbl>, $parameter <dbl>,
#> # $conf.low <dbl>, $conf.high <dbl>, $method <chr>, $alternative <chr>,
#> # Mean <dbl>, SD <dbl>
Created on 2020-06-14 by the reprex package (v0.3.0)

Answer 2

What if you change quibble2 to return a list, and then use unnest_wider?
quibble2 <- function(x, q = c(0.25, 0.5, 0.75)) {
list(quantile(x, q))
}
mySummary(toy, Q=quibble2(Value), Mean=mean(Value, na.rm=TRUE), SD=sd(Value, na.rm=TRUE)) %>%
unnest_wider(Q)
# A tibble: 6 x 7
Parameter Group `25%` `50%` `75%` Mean SD
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Height 1 1.62 1.66 1.73 1.70 0.108
2 Height 2 1.73 1.77 1.78 1.76 0.105
3 Height 3 1.55 1.64 1.76 1.65 0.109
4 Weight 1 75.6 80.6 84.3 80.0 9.05
5 Weight 2 75.4 76.9 79.6 77.4 7.27
6 Weight 3 70.7 75.2 82.0 76.3 6.94