Summarize information by group in data table in R - r

Question

I'm trying to get multiple summary statistics in R grouped by Team. I used code like below, but output is not what I want.
please point me in a better direction. Thanks!
set.seed(77)
data <- data.frame(Team =sample(c("A","B"),30, replace=TRUE),
gender=sample(c("female","male"),30, replace=TRUE),
Age =sample(c(0:100),30, replace=T))
dat <- data %>%
group_by(Team, gender) %>%
dplyr::summarize_all(list(my_mean = mean,
my_sum = sum,
my_sd = sd)) %>%
as.data.frame()
df <- data %>%
group_by(Team) %>%
summarize(total = n(gender),
mean = mean(Age),
Max_Age = max(Age),
Min_Age = min(Age),
sd = sd(Age),
)
I want to get like this pic.

Answer 1

You may need to create the dataframe for the summary statistics of age per Team (age_summary in the example below) and that for the count of Team members per gender and Team (gender_summary in the example below), and then merge them into one dataframe (say summary_df).
library(tidyverse)
set.seed(77)
data <- data.frame(
Team = sample(c("A", "B"), 30, replace = TRUE),
gender = sample(c("female", "male"), 30, replace = TRUE),
Age = sample(c(0:100), 30, replace = T)
)
age_summary <- data %>%
group_by(Team) %>%
summarize(
mean = mean(Age),
Max = max(Age),
Min = min(Age),
sd = sd(Age)
) %>%
column_to_rownames("Team") %>%
t() %>%
as_tibble(
rownames = "age_summary"
)
gender_summary <- data %>%
group_by(Team) %>%
count(gender) %>%
ungroup() %>%
pivot_wider(names_from = Team, values_from = n)
summary_df <- full_join(
age_summary,
gender_summary
) %>%
mutate(
"item" = if_else(
is.na(gender),
"Age",
"Sex"
)
) %>%
unite("summary", c(age_summary, gender), na.rm = TRUE, remove = FALSE) %>%
relocate(item, .before = 1) %>%
select(-c(age_summary, gender))
# # A tibble: 6 × 4
# item summary A B
# <chr> <chr> <dbl> <dbl>
# 1 Age mean 45.6 57.8
# 2 Age Max 92 82
# 3 Age Min 5 14
# 4 Age sd 30.1 22.1
# 5 Sex female 8 9
# 6 Sex male 7 6