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I'm having some trouble while I'm searching to specify parameters in custom function passed to .fns argument in dplyr's across.
Consider this code:
data(iris)
ref_col <- "Sepal.Length"
iris_summary <- iris %>%
group_by(Species) %>%
summarise(
Sepal.Length_max = max(Sepal.Length),
across(
Sepal.Width:Petal.Width,
~ .x[which.max(get(ref_col))]
)
)
This works properly. Then I need to replace lambda function with a custom function and then pass requested arguments inside across (in my code the custom function is more complex and it is not convenient to be embedded in dplyr piping). See following code:
ref_col <- "Sepal.Length"
get_which_max <- function(x, col_max) x[which.max(get(col_max))]
iris_summary <- iris %>%
group_by(Species) %>%
summarise(
Sepal.Length_max = max(Sepal.Length),
across(
Sepal.Width:Petal.Width,
~ get_which_max(.x, ref_col)
)
)
R is now giving error "object 'Sepal.Length' not found" as it is sercing for an object instead colname inside piping process. Anyone can help me to fix this problem?
We may either use cur_data() or pick (from the devel version of dplyr to select the column. Also, remove the get from inside the get_which_max
get_which_max <- function(x, col_max) x[which.max(col_max)]
iris_summary <- iris %>%
group_by(Species) %>%
summarise(
Sepal.Length_max = max(Sepal.Length),
across(
Sepal.Width:Petal.Width,
~ get_which_max(.x, cur_data()[[ref_col]])
)
)
-output
# A tibble: 3 × 5
Species Sepal.Length_max Sepal.Width Petal.Length Petal.Width
<fct> <dbl> <dbl> <dbl> <dbl>
1 setosa 5.8 4 1.2 0.2
2 versicolor 7 3.2 4.7 1.4
3 virginica 7.9 3.8 6.4 2
This question already has answers here:
Pass arguments to dplyr functions
(7 answers)
Closed 2 years ago.
I am having issues with pipes inside a custom function. Based on the previous posts, I understand that a pipe inside a function creates another level(?) which results in the error I'm getting (see below).
I'm hoping to write a summary function for a large data set with hundreds of numeric and categorical variables. I would like to have the option to use this on different data frames (with similar structure), always group by a certain factor variable and get summaries for multiple columns.
library(tidyverse)
data(iris)
iris %>% group_by(Species) %>% summarise(count = n(), mean = mean(Sepal.Length, na.rm = T))
# A tibble: 3 x 3
Species count mean
<fct> <int> <dbl>
1 setosa 50 5.01
2 versicolor 50 5.94
3 virginica 50 6.59
I'm hoping to create a function like this:
sum_cols <- function (df, col) {
df %>%
group_by(Species) %>%
summarise(count = n(),
mean = mean(col, na.rm = T))
}
And this is the error I'm getting:
sum_cols(iris, Sepal.Length)
Error in mean(col, na.rm = T) : object 'Petal.Width' not found
Called from: mean(col, na.rm = T)
I have had this problem for a while and even though I tried to get answers in a few previous posts, I haven't quite grasped why the problem occurs and how to get around it.
Any help would be greatly appreciated, thanks!
Try searching for non-standard evaluation (NSE).
You can use here {{}} to let R know that col is the column name in df.
library(dplyr)
library(rlang)
sum_cols <- function (df, col) {
df %>%
group_by(Species) %>%
summarise(count = n(), mean = mean({{col}}, na.rm = T))
}
sum_cols(iris, Sepal.Length)
# A tibble: 3 x 3
# Species count mean
# <fct> <int> <dbl>
#1 setosa 50 5.01
#2 versicolor 50 5.94
#3 virginica 50 6.59
If we do not have the latest rlang we can use the old method of enquo and !!
sum_cols <- function (df, col) {
df %>%
group_by(Species) %>%
summarise(count = n(), mean = mean(!!enquo(col), na.rm = T))
}
sum_cols(iris, Sepal.Length)
I'm trying to create a simple code that I can reuse over and over (with minimal adjustments) to be able to print a table of summary statistics.
A reproducible example creates a table with M and SD for the variable V1 broken down by group:
data <- as.data.frame(cbind(1:100, sample(1:2), rnorm(100), rnorm(100)))
names(data) <- c("ID", "Group", "V1", "V2")
library(dplyr)
descriptives <- data %>% group_by(Group) %>%
summarize(
Mean = mean(V2)
, SD = sd(V2)
)
descriptives
I'd like to modify this function so that it will compute M and SD for all variables in my dataset.
I'd like to be able to replace the call to V1 with something like vars which is just a list of all the variables in my dataset; in this example, V1 and V2. But usually I have like 100 variables.
The reason I'd like it to work this way is so that I can do something very easy like:
vars <- names(data[3:4])
and very quickly select the columns for which I want summary statistics.
A few things for my wishlist:
M and SD for a given variable should be next to eachother and I'd like to add a column above each pair with the variable name.
I'd like the end product to look something like
I'd like to use dplyr, but I'm open to other options.
I'd also like to learn how I could switch the rows and columns of the table so that the variables are on separate rows and each group has a column (or two columns, one for M and one for SD). Like this:
Close, but no cigar:
The newish summarise(across()) kind of helps:
dplyr::group_by(df, Group) %>%
dplyr::summarise(dplyr::across(.cols = c(V1, V2), .fns = c(mean, sd)))
But I don't know how to scale it without making multiple table and using rbind() to stack them.
I really like the format of table1() (vignette), but from what I can tell I can only stratify the column M/SDs by another variable. I really wish I could just add additional grouping variables on.
There is a limitation in the ordering, but if we use select, then can reorder on the substring on the column names
library(dplyr)
library(stringr)
data %>%
group_by(Group) %>%
summarise_at(vars(vars), list(Mean = mean, SD = sd)) %>%
select(Group, order(str_remove(names(.)[-1], "_.*")) + 1)
# A tibble: 2 x 5
# Group V1_Mean V1_SD V2_Mean V2_SD
# <dbl> <dbl> <dbl> <dbl> <dbl>
#1 1 0.165 0.915 0.146 1.16
#2 2 0.308 1.31 -0.00711 0.854
I had a similar question here, and got some really useful and simple answers using tidyverse. In the end a really robust approach was made, which I wrapped in a function and use regularly.
library(tidyverse)
baseline_table <- function(data, variables, grouping_var) {
data %>%
group_by(!!sym(grouping_var)) %>%
summarise(
across(
all_of(variables),
~ paste0(mean(.) %>% round(2), "(±", sd(.) %>% round(2), ")")
)
) %>% pivot_longer(
cols = -grouping_var,
names_to = "variable"
) %>% pivot_wider(
names_from = grouping_var
)
}
It takes three arguments, data, variables and the grouping_var - all of which are rather self explanatory.
Here is a test using mtcars with a 2 level and 3 level grouping var.
baseline_table(
data = mtcars,
variables = c("mpg", "hp"),
grouping_var = "am"
)
# A tibble: 2 x 3
variable `0` `1`
<chr> <chr> <chr>
1 mpg 17.15(±3.83) 24.39(±6.17)
2 hp 160.26(±53.91) 126.85(±84.06)
baseline_table(
data = mtcars,
variables = c("mpg", "hp"),
grouping_var = "cyl"
)
# A tibble: 2 x 4
variable `4` `6` `8`
<chr> <chr> <chr> <chr>
1 mpg 26.66(±4.51) 19.74(±1.45) 15.1(±2.56)
2 hp 82.64(±20.93) 122.29(±24.26) 209.21(±50.98)
It works out of the box, and are applicable to all data, below I used iris,
baseline_table(
data = iris,
variables = c("Sepal.Length", "Sepal.Width"),
grouping_var = "Species"
)
# A tibble: 2 x 4
variable setosa versicolor virginica
<chr> <chr> <chr> <chr>
1 Sepal.Length 5.01(±0.35) 5.94(±0.52) 6.59(±0.64)
2 Sepal.Width 3.43(±0.38) 2.77(±0.31) 2.97(±0.32)
Of course; some grouping variables are not directly suited for this. Namely cyl but it does serve as a good example though. but you can recode your grouping variables accordingly,
baseline_table(
data = mtcars %>% mutate(cyl = paste(cyl, "Cylinders", sep = " ")),
variables = c("mpg", "hp"),
grouping_var = "cyl"
)
# A tibble: 2 x 4
variable `4 Cylinders` `6 Cylinders` `8 Cylinders`
<chr> <chr> <chr> <chr>
1 mpg 26.66(±4.51) 19.74(±1.45) 15.1(±2.56)
2 hp 82.64(±20.93) 122.29(±24.26) 209.21(±50.98)
You can also modify the function to include descriptive strings, about the values,
baseline_table <- function(data, variables, grouping_var) {
# Generate the table;
tmpTable <- data %>%
group_by(!!sym(grouping_var)) %>%
summarise(
across(
all_of(variables),
~ paste0(mean(.) %>% round(2), "(±", sd(.) %>% round(2), ")")
)
) %>% pivot_longer(
cols = -grouping_var,
names_to = "variable"
) %>% pivot_wider(
names_from = grouping_var
)
# Generate Descriptives dynamically
tmpDesc <- tmpTable[1,] %>% mutate(
across(.fns = ~ paste("Mean (±SD)"))
) %>% mutate(
variable = ""
)
bind_rows(
tmpDesc,
tmpTable
)
}
Granted, this extension is a bit awkward - but it is nonetheless still robust. The output is,
# A tibble: 3 x 4
variable `4 Cylinders` `6 Cylinders` `8 Cylinders`
<chr> <chr> <chr> <chr>
1 "" Mean (±SD) Mean (±SD) Mean (±SD)
2 "mpg" 26.66(±4.51) 19.74(±1.45) 15.1(±2.56)
3 "hp" 82.64(±20.93) 122.29(±24.26) 209.21(±50.98)
Update: Ive rewritten the function for added flexibility as noted in the comments.
library(tidyverse)
baseline_table <- function(data, variables, grouping_var) {
data %>%
group_by(!!!syms(grouping_var)) %>%
summarise(
across(
all_of(variables),
~ paste0(mean(.) %>% round(2), "(±", sd(.) %>% round(2), ")")
)
) %>% unite(
"grouping",
all_of(grouping_var)
) %>% pivot_longer(
cols = -"grouping",
names_to = "variables"
) %>% pivot_wider(
names_from = "grouping"
)
}
It works in the same way, and outputs the same, unless there is more than one grouping_var,
baseline_table(
mtcars,
variables = c("hp", "mpg"),
grouping_var = c("am", "cyl")
)
# A tibble: 2 x 7
variables `0_4` `0_6` `0_8` `1_4` `1_6` `1_8`
<chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 hp 84.67(±19.66) 115.25(±9.18) 194.17(±33.36) 81.88(±22.66) 131.67(±37.53) 299.5(±50.2)
2 mpg 22.9(±1.45) 19.12(±1.63) 15.05(±2.77) 28.08(±4.48) 20.57(±0.75) 15.4(±0.57)
In the updated function I used unite with a default seperator. Clearly, you can modify this to suit your needs such that the colnames says, for example, 4 Cylinder (Automatic) 6 Cylinder (Automatic) etc.
Slight variation of your original code, you could use across() more simply/flexibly if you specify you don't want the ID (or the already-grouped Group) column, but rather everything else:
data %>%
group_by(Group) %>%
summarize(across(-ID, .fns = list(Mean = mean, SD = sd), .names = "{.col}_{.fn}"))
# A tibble: 2 x 5
Group V1_Mean V1_SD V2_Mean V2_SD
<dbl> <dbl> <dbl> <dbl> <dbl>
1 1 -0.0167 0.979 0.145 1.02
2 2 0.119 1.11 -0.277 1.05
EDIT:
If you want to create your (first) goal exactly, you can use the gt package to make an html table with column spanners:
data %>%
group_by(Group) %>%
summarize(across(-ID, .fns = list(Mean = mean, SD = sd), .names = "{.col}_{.fn}")) %>%
gt::gt() %>%
gt::tab_spanner_delim("_") %>%
gt::fmt_number(-Group, decimals = 2)
As to your other question, you could alternately do something like this to get the combined & transposed variation:
data %>%
group_by(Group) %>%
summarize(across(-ID, .fns = ~paste0(
sprintf("%.2f", mean(.x)),
sprintf(" (%.2f)", sd(.x))))) %>%
t() %>%
as.data.frame()
V1 V2
Group 1 2
V1 -0.02 (0.98) 0.12 (1.11)
V2 0.15 (1.02) -0.28 (1.05)
Outside dplyr, you could use the tables package which allows to create summary statistics out of a table formula:
library(tables)
vars <- c("V1","V2")
vars <- paste(vars, collapse="+")
table <- as.formula(paste("(group = factor(Group)) ~ (", vars ,")*(mean+sd)"))
table
# (group = factor(Group)) ~ (V1 + V2) * (mean + sd)
tables::tabular(table, data = data)
# V1 V2
# group mean sd mean sd
# 1 -0.15759 0.9771 0.1405 1.0697
# 2 0.05084 0.9039 -0.1470 0.9949
One way to make a nice summary table is to use a package called gtsummary (note I am a co-author on this package just as an FYI). Below I am just formatting the data a little bit in data2 and dropping the ID variable. Then it is a two line call to gtsummary to summarize your data. The by statement is what stratifies the table, and in the statistics input I am simply telling to show the mean and sd, by default gtsummary will show median q1-q3. This table can be rendered in all markdown options (word, pdf, html).
library(dplyr)
library(gtsummary)
data <- as.data.frame(cbind(1:100, sample(1:2), rnorm(100), rnorm(100)))
names(data) <- c("ID", "Group", "V1", "V2")
data2 <- data %>%
mutate(Group = ifelse(Group == 1, "Group Var1","Group Var2")) %>%
select(-ID)
tbl_summary(data2, by = Group,
statistic = all_continuous()~ "{mean} ({sd})")
If you want more than one strata but do not want to use tbl_strata you can combine two variables into one column and use that in the by statement. You can unite() as many variables as you want (although maybe not reccomended)
trial %>%
tidyr::unite(col = "trt_grade", trt, grade, sep = ", ") %>%
select(age, marker,stage,trt_grade) %>%
tbl_summary(by = c(trt_grade))
A data.table option
dcast(
setDT(data)[,
c(
.(Meas = c("M", "Sd")),
lapply(.SD, function(x) c(mean(x), sd(x)))
),
Group,
.SDcols = patterns("V\\d")
], Group ~ Meas,
value.var = c("V1", "V2")
)
gives
Group V1_M V1_Sd V2_M V2_Sd
1: 1 -0.2392583 1.097343 -0.08048455 0.7851212
2: 2 0.1059716 1.011769 -0.23356373 0.9927975
You can also use base R:
# using do.call to make the result a data.frame
do.call(
data.frame
# here you aggregate for all the functions you need
,(aggregate(. ~ Group, data = data[,-1], FUN = function(x) c(mn = mean(x), sd = sd(x))))
)
This leads to something like this:
Group V1.mn V1.sd V2.mn V2.sd
1 1 0.1239868 1.008214 0.07215481 1.026059
2 2 -0.2324611 1.048230 0.11348897 1.071467
If you want a fancier table, kableExtra could really help. Note, the %>% should be imported also in kableExtra, but in case, from R 4.1 you can use |> instead of it:
library(kableExtra)
# data manipulation as above, note the [,-1] to remove the Group column
do.call(
data.frame
,(aggregate(. ~ Group, data = data[,-1], FUN = function(x) c(mn = mean(x), sd = sd(x)))))[,-1] %>%
# here you define as a kable, and give the names you want to columns
kbl(col.names = rep(c('mean','sd'),2) ) %>%
# some formatting
kable_paper() %>%
# adding the first header
add_header_above(c( "Group 1" = 2, "Group 2" = 2)) %>%
# another header if you need it
add_header_above(c( "Big group" = 4))
And you can find much more to make great tables.
In case, you can also try something like this:
do.call(data.frame,
aggregate(. ~ Group, data = data[,-1], FUN = function(x) paste0(round(mean(x),2),' (', round(sd(x),2),')'))
) %>%
kbl() %>%
kable_paper()
That leads to:
I have this situation where I need a different summary function based on a condition.
For example, using iris, say for some reason I wanted the sum of the petal width if the species was setosa, otherwise I wanted the mean of the petal width.
Naively, I wrote this using case_when, which does not work:
iris <- tibble::as_tibble(iris)
iris %>%
group_by(Species) %>%
summarise(pwz = case_when(
Species == "setosa" ~ sum(Petal.Width, na.rm = TRUE),
TRUE ~ mean(Petal.Width, na.rm = TRUE)))
Error in summarise_impl(.data, dots) :
Column pwz must be length 1 (a summary value), not 50
I eventually found something like this, summarizing using each method, and then in a mutate picking which one I actually wanted:
iris %>%
group_by(Species) %>%
summarise(pws = sum(Petal.Width, na.rm = TRUE),
pwm = mean(Petal.Width, na.rm = TRUE)) %>%
mutate(pwz = case_when(
Species == "setosa" ~ pws,
TRUE ~ pwm)) %>%
select(-pws, -pwm)
But that seems more than a bit awkward with creating all these summarized values and only picking one at the end, especially when my real case_when is a lot more complicated. Can I not use case_when inside of summarise? Do I have my syntax wrong? Any help is appreciated!
Edit: I suppose I should have pointed out that I have multiple conditions/functions (just assume I've got, depending on the variable, some that need mean, sum, max, min, or other summary).
This is pretty easy with data.table
library(data.table)
iris2 <- as.data.table(iris)
iris2[, if(Species == 'setosa') sum(Petal.Width)
else mean(Petal.Width)
, by = Species]
More concisely, but maybe not as clear
iris2[, ifelse(Species == 'setosa', sum, mean)(Petal.Width)
, by = Species]
With dplyr you can do
iris %>%
group_by(Species) %>%
summarise(pwz = if_else(first(Species == "setosa")
, sum(Petal.Width)
, mean(Petal.Width)))
Note:
I'm thinking it probably makes more sense to "spread" your data with tidyr::spread so that each day has a column for temperature, rainfall, etc. Then you can use summarise in the usual way.
Why not calculate at the row level first, then summarize?
iris %>% group_by(Species) %>% mutate(pwz = case_when(
Species == "setosa" ~ sum(Petal.Width, na.rm = TRUE),
TRUE ~ mean(Petal.Width, na.rm = TRUE))) %>%
summarize(pwz= first(pwz))
# A tibble: 3 x 2
Species pwz
<fctr> <dbl>
1 setosa 12.300
2 versicolor 1.326
3 virginica 2.026
data(iris)
library(dplyr)
sum_species <- c('setosa')
iris %>%
group_by(Species) %>%
summarise(pwz_sum = sum(Petal.Width, na.rm=T),
pwz_mean= mean(Petal.Width, na.rm=T)) %>%
ungroup() %>%
mutate(pwz = if_else(Species %in% sum_species, pwz_sum, pwz_mean))
You could always do something like this if you want to put everything in the summarise function. But it's no less complicated than your original workaround:
iris %>%
group_by(Species) %>%
summarise(pwz =
sum(Petal.Width, na.rm = TRUE)*
(1/n()*mean(Species != "setosa") +
mean(Species == "setosa")))
You could split your data.frame and then use map2_dfr to apply a different function on each part and stitch the results back together:
library(tidyverse) # purrr & dplyr
iris %>%
arrange(Species=="setosa") %>%
split(.,.$Species=="setosa") %>%
map2_dfr(c(mean,sum),~.x %>% group_by(Species) %>% summarize_at("Petal.Width",.y))
# # A tibble: 3 x 2
# Species Petal.Width
# <fctr> <dbl>
# 1 versicolor 1.326
# 2 virginica 2.026
# 3 setosa 12.300
I have a dataset for which I want to summarise by mean, but also calculate the max to just 1 of the variables.
Let me start with an example of what I would like to achieve:
iris %>%
group_by(Species) %>%
filter(Sepal.Length > 5) %>%
summarise_at("Sepal.Length:Petal.Width",funs(mean))
which give me the following result
# A tibble: 3 × 5
Species Sepal.Length Sepal.Width Petal.Length Petal.Width
<fctr> <dbl> <dbl> <dbl> <dbl>
1 setosa 5.8 4.4 1.9 0.5
2 versicolor 7.0 3.4 5.1 1.8
3 virginica 7.9 3.8 6.9 2.5
Is there an easy way to add, for example, max(Petal.Width)to summarise?
So far I have tried the following:
iris %>%
group_by(Species) %>%
filter(Sepal.Length > 5) %>%
summarise_at("Sepal.Length:Petal.Width",funs(mean)) %>%
mutate(Max.Petal.Width = max(iris$Petal.Width))
But with this approach I lose both the group_by and the filter from the code above and gives the wrong results.
The only solution I have been able to achieve is the following:
iris %>%
group_by(Species) %>%
filter(Sepal.Length > 5) %>%
summarise_at("Sepal.Length:Petal.Width",funs(mean,max)) %>%
select(Species:Petal.Width_mean,Petal.Width_max) %>%
rename(Max.Petal.Width = Petal.Width_max) %>%
rename_(.dots = setNames(names(.), gsub("_.*$","",names(.))))
Which is a bit convoluted and involves a lot of typing to just add a column with a different summarisation.
Thank you
Although this is an old question, it remains an interesting problem for which I have two solutions that I believe should be available to whoever finds this page.
Solution one
My own take:
mapply(summarise_at,
.vars = lst(names(iris)[!names(iris)%in%"Species"], "Petal.Width"),
.funs = lst(mean, max),
MoreArgs = list(.tbl = iris %>% group_by(Species) %>% filter(Sepal.Length > 5)))
%>% reduce(merge, by = "Species")
# Species Sepal.Length Sepal.Width Petal.Length Petal.Width.x Petal.Width.y
# 1 setosa 5.314 3.714 1.509 0.2773 0.5
# 2 versicolor 5.998 2.804 4.317 1.3468 1.8
# 3 virginica 6.622 2.984 5.573 2.0327 2.5
Solution two
An elegant solution using package purrr from the tidyverse itself, inspired by this discussion:
list(.vars = lst(names(iris)[!names(iris)%in%"Species"], "Petal.Width"),
.funs = lst("mean" = mean, "max" = max)) %>%
pmap(~ iris %>% group_by(Species) %>% filter(Sepal.Length > 5) %>% summarise_at(.x, .y))
%>% reduce(inner_join, by = "Species")
+ + + # A tibble: 3 x 6
Species Sepal.Length Sepal.Width Petal.Length Petal.Width.x Petal.Width.y
<fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 setosa 5.31 3.71 1.51 0.277 0.5
2 versicolor 6.00 2.80 4.32 1.35 1.8
3 virginica 6.62 2.98 5.57 2.03 2.5
Short discussion
The data.frame and tibble are the desired result, the last column being the max of petal.width and the other ones the means (by group and filter) of all other columns.
Both solutions hinge on three realizations:
summarise_at accepts as arguments two lists, one of n variables and one of m functions, and applies all m functions to all n variables, therefore producing m X n vectors in a tibble. The solution might thus imply forcing this function to loop in some way across "couples" formed by all variables to which we want one specific function to be applied and the one function, then another group of variables and their own function, and so on!
Now, what does the above in R? What does force an operation to corresponding elements of two lists? Functions such as mapply or the family of functions map2, pmap and variations thereof from dplyr's tidyverse fellow purrr. Both accept two lists of l elements and perform a given operation on corresponding elements (matched by position) of the two lists.
Because the product is not a tibble or a data.frame, but a list, you
simply need to use reduce with inner_join or just merge.
Note that the means I obtain are different from those of the OP, but they are the means I obtain with his reproducible example as well (maybe we have two different versions of the iris dataset?).
If you wanted to do something more complex like that, you could write your own version of summarize_at. With this version you supply triplets of column names, functions, and naming rules. For example
Here's a rough start
my_summarise_at<-function (.tbl, ...)
{
dots <- list(...)
stopifnot(length(dots)%%3==0)
vars <- do.call("append", Map(function(.cols, .funs, .name) {
cols <- select_colwise_names(.tbl, .cols)
funs <- as.fun_list(.funs, .env = parent.frame())
val<-colwise_(.tbl, funs, cols)
names <- sapply(names(val), function(x) gsub("%", x, .name))
setNames(val, names)
}, dots[seq_along(dots)%%3==1], dots[seq_along(dots)%%3==2], dots[seq_along(dots)%%3==0]))
summarise_(.tbl, .dots = vars)
}
environment(my_summarise_at)<-getNamespace("dplyr")
And you can call it with
iris %>%
group_by(Species) %>%
filter(Sepal.Length > 5) %>%
my_summarise_at("Sepal.Length:Petal.Width", mean, "%_mean",
"Petal.Width", max, "%_max")
For the names we just replace the "%" with the default name. The idea is just to dynamically build the summarize_ expression. The summarize_at function is really just a convenience wrapper around that basic function.
If you are trying to do everything with dplyr (which might be easier to remember), then you can leverage the new across function which will be available from dplyr 1.0.0.
iris %>%
group_by(Species) %>%
filter(Sepal.Length > 5) %>%
summarize(across(Sepal.Length:Petal.Width, mean)) %>%
cbind(iris %>%
group_by(Species) %>%
summarize(across(Petal.Width, max)) %>%
select(-Species)
)
It shows that the only difficulty is to combine two calculations on the same column Petal.Width on a grouped variable - you have to do the grouping again but can nest it into the cbind.
This returns correctly the result:
Species Sepal.Length Sepal.Width Petal.Length Petal.Width Petal.Width
1 setosa 5.313636 3.713636 1.509091 0.2772727 0.6
2 versicolor 5.997872 2.804255 4.317021 1.3468085 1.8
3 virginica 6.622449 2.983673 5.573469 2.0326531 2.5
If the task would not specify two calculations but only one on the same column Petal.Width, then this could be elegantly written as:
iris %>%
group_by(Species) %>%
filter(Sepal.Length > 5) %>%
summarize(
across(Sepal.Length:Petal.Length, mean),
across(Petal.Width, max)
)
I was looking for something similar and tried the following. It works well and much easier to read than the suggested solutions.
iris %>%
group_by(Species) %>%
filter(Sepal.Length > 5) %>%
summarise(MeanSepalLength=mean(Sepal.Length),
MeanSepalWidth = mean(Sepal.Width),
MeanPetalLength=mean(Petal.Length),
MeanPetalWidth=mean(Petal.Width),
MaxPetalWidth=max(Petal.Width))
# A tibble: 3 x 6
Species MeanSepalLength MeanSepalWidth MeanPetalLength MeanPetalWidth MaxPetalWidth
<fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 setosa 5.01 3.43 1.46 0.246 0.6
2 versicolor 5.94 2.77 4.26 1.33 1.8
3 virginica 6.59 2.97 5.55 2.03 2.5
In summarise() part, define your column name and give your column to summarise inside your function of choice.