How to rewrite the same code with across function - r

Question

I scripted the following code
out %>% group_by(tests0, GROUP) %>%
summarise(
mean0 = mean(score0, na.rm = T),
stderr0 = std.error(score0, na.rm = T),
mean7 = mean(score7, na.rm = T),
stederr7 = std.error(score7, na.rm = T),
diff.std.mean = t.test(score0, score7, paired = T)$estimate,
p.value = t.test(score0, score7, paired = T)$p.value,
)
and I have obtained the following output
tests0 GROUP mean0 stderr0 mean7 stederr7 diff.std.mean p.value
<fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 ADAS_CogT0 CONTROL 12.6 0.525 13.6 0.662 -1.15 0.00182
2 ADAS_CogT0 TRAINING 14.0 0.613 12.6 0.570 1.40 0.00295
3 PVF_T0 CONTROL 32.1 1.22 31.3 1.45 0.498 0.636
4 PVF_T0 TRAINING 31.6 1.37 34.3 1.51 -2.48 0.0102
5 ROCF_CT0 CONTROL 29.6 0.893 30.3 0.821 -0.180 0.835
6 ROCF_CT0 TRAINING 30.1 0.906 29.5 0.929 0.489 0.615
7 ROCF_IT0 CONTROL 12.8 0.563 12.2 0.683 0.580 0.356
8 ROCF_IT0 TRAINING 10.9 0.735 12.3 0.768 -1.44 0.0238
9 ROCF_RT0 CONTROL 12.1 0.725 12.5 0.797 -0.370 0.598
10 ROCF_RT0 TRAINING 10.5 0.746 10.9 0.742 -0.534 0.370
11 SVF_T0 CONTROL 35.5 1.05 34 1.15 1.42 0.107
12 SVF_T0 TRAINING 34.1 1.04 32.9 1.16 0.962 0.231
In case I would like to do the same via across function, What am i supposed to do to achieve the same results, shown into the code above? Actaully I am in trouble becase I was drawing some example from the answer published under this question Reproduce a complex table with double headesrs, but I was not able to suit it properly.
Here the dataset
Below you could find the way I would like to obtain the same. It ius a method requiring for .x manipulation.
out %>%
group_by(across(all_of(tests0, GROUP))) %>% summarise(across(starts_with('score'),
list(mean = ~ mean(.x,na.rm = T),
stderr = ~ std.error(.x, na.rm = TRUE),
diff.std.mean = ~ t.test(.x, na.rm = T)))$estimate,
p.value = ~ t.test(.x, na.rm = T)))$p.value)),.groups = "drop")

Answer 1

You can use the argument .names in across():
library(dplyr)
out %>%
group_by(tests0, GROUP) %>%
summarize(across(c(score0, score7), sd, na.rm = TRUE, .names = "sd_{.col}"),
across(c(score0, score7), mean, na.rm = TRUE, .names = "mean_{.col}"),
diff.std.mean = t.test(score0, score7, paired = T)$estimate,
p.value = t.test(score0, score7, paired = T)$p.value) %>%
ungroup()
#> `summarise()` has grouped output by 'tests0'. You can override using the `.groups` argument.
#> # A tibble: 2 x 8
#> tests0 GROUP sd_score0 sd_score7 mean_score0 mean_score7 diff.std.mean p.value
#> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ADAS_~ CONT~ 3.72 4.81 12.5 13.5 -1.24 0.00471
#> 2 ADAS_~ TRAI~ 4.55 4.15 14.0 12.6 1.40 0.00295
Created on 2021-11-26 by the reprex package (v2.0.1)
EDIT
If you prefer a list it would be easier to determine the separate parts and then bind them together:
library(data.table)
by <- c("tests0", "GROUP")
out_dt <- data.table::data.table(out)
means <- out_dt[, sapply(.SD, function(x) list(mean = mean(x, na.rm = TRUE))),
by = by, .SDcols = patterns("^score")]
sds <- out_dt[, sapply(.SD, function(x) list(sd = sd(x, na.rm = TRUE))),
by = by, .SDcols = patterns("^score")]
t_est <- out_dt[, .(diff.std.mean = t.test(score0, score7, paired = T)$estimate), by = by]
tpvalue <- out_dt[, .(p.value = t.test(score0, score7, paired = T)$p.value), by = by]
list(means = means, sds = sds, diff.std.mean = t_est, p.value = tpvalue)

Answer 2

Here is another approach you may want to consider. First I took your code and cut and pasted it into a function. Abstracting the column names and removing the dependency on the plotrix package for calculating the standard error are the only changes.
g <- function (df)
{
nms <- c(names(df)[1:2],
paste0('mean', sub(".*[a-z]","",names(df)[3])),
paste0('stderr', sub(".*[a-z]","",names(df)[3])),
paste0('mean', sub(".*[a-z]","",names(df)[4])),
paste0('stderr', sub(".*[a-z]","",names(df)[4])),
'diff.std.mean', 'p.value')
z <- df %>% group_by(df[,1:2]) %>%
summarize(
x1 = mean(pull(df[,3]), na.rm = T),
x2 = sd(pull(df[,3]), na.rm=T) / sqrt(sum(!is.na(pull(df[,3])))),
x3 = mean(pull(df[,4]), na.rm = T),
x4 = sd(pull(df[,4]), na.rm=T) / sqrt(sum(!is.na(pull(df[,4])))),
x5 = t.test(pull(df[,3]), pull(df[,4]), paired = T)$estimate,
x6 = t.test(pull(df[,3]), pull(df[,4]), paired = T)$p.value)
colnames(z) <- nms
return(z)
}
Then, because the test data only had one level of a factor and insufficient sample size for the plotrix::std.error function that you used, I introduced variation in the 'test0' factor, doubled the sample size, and dropped the unused levels because they would cause iterations on empty frames. In addition I added a score8 to show how you could run on other variables.
s <- t %>% mutate(tests0 = case_when(Education <= 8 ~ 'ADAS_CogTO', T ~ 'PVF_T0'),
score8 = score0 + score7)
q <- rbind(s, s)
fct_drop(q$tests0)
Then I split the frame by the factor levels, applied the function to each of the splits, then remerged the data back together inside a function that allows you to manipulate the score and group variables. I assumed 2 each, which is safe with the score variables since your are doing a paired t-test, and it is easily extendible with the group variables (if you simply move the score variables to positions 1 and 2, and use all remaining variables passed to the function as group variables).
h <- function(df, group_vars, score_vars)
{
z <- df %>% select(group_vars, score_vars)
z <- z %>% group_by(z[,1:2]) %>%
group_map( ~ g(.x), .keep = T) %>%
bind_rows()
}
Note that if you desire to apply this to other data, you only need to change the columns passed to the group and score variables. Should be fairly easy to alter that if you want to as well, just thought this was a good framework for what you seem to be trying to do. Think about how you handle the case where test0 is null and test7 is non-null (or vice-versa) since these observations are included in come of your summary statistics, but necessarily excluded from the t-test. Good luck.
x <- h(q, c("tests0", "GROUP"), c("score0", "score7")) %>%
group_by(tests0) %>%
pivot_wider(id_cols = tests0,
names_from = GROUP,
values_from = c("mean0","stderr0","mean7","stderr7",
'diff.std.mean', 'p.value'))

Answer 3

I don't have a function called std.error so I've used sd, but of course you can change it.
library(dplyr)
library(readr)
out %>%
group_by(tests0, GROUP) %>%
summarise(
across(c(score0, score7), list(mean = mean, stderr = sd), na.rm = TRUE,
.names = '{.fn}{parse_number(.col)}'),
with(t.test(score0, score7, paired = T),
tibble(diff.std.mean = estimate,
p.value)))
# # A tibble: 2 × 8
# tests0 GROUP mean0 stderr0 mean7 stderr7 diff.std.mean p.value
# <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 ADAS_CogT0 CONTROL 12.5 3.72 13.5 4.81 -1.24 0.00471
# 2 ADAS_CogT0 TRAINING 14.0 4.55 12.6 4.15 1.40 0.00295
In reality I would just put the above code in a function that takes an x and y argument and then run fun(df, x = score0, y = score7). But, just for fun, if you must use .x and .y, here's one way (although imo it would be a little silly to do this)
df %>%
group_by(tests0, GROUP) %>%
select(starts_with('score')) %>%
summarise(
across(everything(), list(mean = mean, stderr = sd), na.rm = TRUE,
.names = '{.fn}{parse_number(.col)}'),
across(everything(), list(list)) %>%
pmap_dfr(~ t.test(.x, .y, paired = TRUE)[c('estimate', 'p.value')]) %>%
transmute(diff.std.mean = estimate, p.value))
# # A tibble: 2 × 8
# # Groups: tests0 [1]
# tests0 GROUP mean0 stderr0 mean7 stderr7 diff.std.mean p.value
# <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 ADAS_CogT0 CONTROL 12.5 3.72 13.5 4.81 -1.24 0.00471
# 2 ADAS_CogT0 TRAINING 14.0 4.55 12.6 4.15 1.40 0.00295

Answer 4

I thought of a possible workaround (that may or may not help) by using across() "manually", without applying functions one column at a time. The resulting output is a data.frame with list columns that are deeply nested, so unnest() will come in handy. I also used possibly() to address the case when two columns are not present, remember that across() can match any number of columns and t.test() needs x and y arguments.
Code:
library(tidyverse)
data <-
df %>%
group_by(tests0, GROUP) %>%
summarize(
all = list(across(starts_with("score")) %>%
{
tibble(
ttest = data.frame(possibly(~ reduce(., ~ t.test(.x, .y, paired = TRUE))[c("estimate", 'p.value')], NA)(.)),
means = data.frame(map(., ~ mean(.x, na.rm = TRUE)) %>% set_names(., str_replace(names(.), "\\D+", "mean"))),
stderrs = data.frame(map(., ~ sd(.x, na.rm = TRUE)) %>% set_names(., str_replace(names(.), "\\D+", "stederr")))
)
})
)
#> `summarise()` has grouped output by 'tests0'. You can override using the `.groups` argument.
data %>%
unnest(all) %>%
unnest(-c("tests0", "GROUP"))
#> # A tibble: 2 × 8
#> # Groups: tests0 [1]
#> tests0 GROUP estimate p.value mean0 mean7 stederr0 stederr7
#> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ADAS_CogT0 CONTROL -1.24 0.00471 12.5 13.5 3.72 4.81
#> 2 ADAS_CogT0 TRAINING 1.40 0.00295 14.0 12.6 4.55 4.15
Created on 2021-11-29 by the reprex package (v2.0.1)