I have the following example, where I pass a simple dataframe to a function that summarizes a column. The name of the summarizing column, s, I would like to have as a parameter to the function:
df <- data.frame(id = c(1,1,1,1,1,2,2,2,2,2),
a=c(1:10),
b=c(10:19))
sum <- function(df, s){
df <- df %>%
group_by(id) %>%
summarize(s = sum(a))
return(df)
}
sum(df = df, s = "summarizing.column.label")
However, regardless of the value I set, the summarizing-column always get the same name s. Is there a way to alter it?
EDIT: The output I would like is:
sum(df = df, s = "summarizing.column.label")
id summarizing.column.label
<dbl> <int>
1 1.00 15
2 2.00 40
sum(df = df, s = "a")
id a
<dbl> <int>
1 1.00 15
2 2.00 40
If we are passing a quoted argument, then one option is after the summarise, we use rename_at
sumf <- function(df, s){
df %>%
group_by(id) %>%
summarize(a = sum(a))%>%
rename_at("a", ~ s)
}
sumf(df, s ="summarizing.column.label" )
# A tibble: 2 x 2
# id summarizing.column.label
# <dbl> <int>
#1 1.00 15
#2 2.00 40
sumf(df, s ="a" )
# A tibble: 2 x 2
# id a
# <dbl> <int>
#1 1.00 15
#2 2.00 40
Or another option is to make use of := with !!
sumf <- function(df, s){
df %>%
group_by(id) %>%
summarize(a = sum(a))%>%
rename(!! (s) := a)
}
sumf(df, s ="summarizing.column.label" )
# A tibble: 2 x 2
# id summarizing.column.label
# <dbl> <int>
#1 1.00 15
#2 2.00 40
Or within summarise
sumf <- function(df, s){
df %>%
group_by(id) %>%
summarise(!!(s) := sum(a))
}
sumf(df, s ="summarizing.column.label" )
Try this:
sum <- function(df, s){
df <- df %>%
group_by(id) %>%
summarize(!!s := sum(a))
return(df)
}
Related
I would like to use group_by( ) function with my customised function but the column names that goes within group_by would be defined in my function argument.
See a hypothetical example of what my data would look like:
data <- data.frame(ind = rep(c("A", "B", "C"), 4),
gender = rep(c("F", "M"), each = 6),
value = sample(1:100, 12))
And this is the result I would like to have:
result <- data %>%
group_by(ind, gender) %>%
mutate(value = mean(value)) %>%
distinct()
This is how I was trying to make my function to work:
myFunction <- function(data, set_group, variable){
result <- data %>%
group_by(get(set_group)) %>%
mutate(across(all_of(variable), ~ mean(.x, na.rm = TRUE))) %>%
distinct()
}
result3 <- myFunction(data, set_group = c("ind", "gender"), variable = c("value"))
result3
I want to allow that the user define as many set_group as needed and as many variable as needed. I tried using get( ) function, all_of( ) function and mget( ) function within group_by but none worked.
Does anyone know how can I code it?
Thank you!
We could use across within group_by
myFunction <- function(data, set_group, variable){
data %>%
group_by(across(all_of(set_group))) %>%
mutate(across(all_of(variable), ~ mean(.x, na.rm = TRUE))) %>%
ungroup %>%
distinct()
}
-testing
> myFunction(data, set_group = c("ind", "gender"), variable = c("value"))
# A tibble: 6 × 3
ind gender value
<chr> <chr> <dbl>
1 A F 43.5
2 B F 87.5
3 C F 67.5
4 A M 13
5 B M 43.5
6 C M 37.5
Another option is to convert to symbols and evaluate (!!!)
myFunction <- function(data, set_group, variable){
data %>%
group_by(!!! rlang::syms(set_group)) %>%
mutate(across(all_of(variable), ~ mean(.x, na.rm = TRUE))) %>%
ungroup %>%
distinct()
}
-testing
> myFunction(data, set_group = c("ind", "gender"), variable = c("value"))
# A tibble: 6 × 3
ind gender value
<chr> <chr> <dbl>
1 A F 43.5
2 B F 87.5
3 C F 67.5
4 A M 13
5 B M 43.5
6 C M 37.5
NOTE: get is used when there is a single object, for multiple objects mget can be used. But, it is better to use tidyverse functions
first time for me here, I'll try to explain you my problem as clearly as possible.
I'm working on erosion data contained in farms in the form of pixels (e.g. 1 farm = 10 pixels so 10 lines in my df), for this I have 4 df in a list, and I would like to calculate for each farm the mean of erosion. I thought about a loop on the name of erosion field but my problem is that my df don't have the exact name (either ERO13 or ERO17). I don't want to work the position of the field because it could change between the df, only with the name which is variable.
Here's a example :
df1 <- data.frame(ID = c(1,1,2), ERO13 = c(2,4,6))
df2 <- data.frame(ID = c(4,4,6), ERO17 = c(4,5,12))
lst_df <- list(df1,df2)
for (df in lst_df){
cur_df <- df
cur_df <- cur_df %>%
group_by(ID) %>%
summarise(current_name_of_erosion_field = mean(current_name_of_erosion_field))
}
I tried with
for (df in lst_df){
cur_df <- df
cur_camp <- names(cur_df)[2]
cur_df <- cur_df %>%
group_by(ID) %>%
summarise(cur_camp = mean(cur_camp))
}
but first doesn't work because it's a string character and not a variable containing the string character and it works with the position.
How can I build the current_name_of_erosion_field here ?
We may convert it to symbol and evaluate (!!) or may pass the string across. Also, as we are using a for loop, make sure to create a list to store the output. Also, to assign from an object created, use := with !!
out <- vector('list', length(lst_df))
for (i in seq_along(lst_df)){
cur_df <- lst_df[[i]]
cur_camp <- names(cur_df)[2]
cur_df <- cur_df %>%
group_by(ID) %>%
summarise(!!cur_camp := mean(!! sym(cur_camp)))
out[[i]] <- cur_df
}
-output
> out
[[1]]
# A tibble: 2 × 2
ID ERO13
<dbl> <dbl>
1 1 3
2 2 6
[[2]]
# A tibble: 2 × 2
ID ERO17
<dbl> <dbl>
1 4 4.5
2 6 12
Or may use across
out <- vector('list', length(lst_df))
for (i in seq_along(lst_df)){
cur_df <- lst_df[[i]]
cur_camp <- names(cur_df)[2]
cur_df <- cur_df %>%
group_by(ID) %>%
summarise(across(all_of(cur_camp), mean))
out[[i]] <- cur_df
}
-output
> out
[[1]]
# A tibble: 2 × 2
ID ERO13
<dbl> <dbl>
1 1 3
2 2 6
[[2]]
# A tibble: 2 × 2
ID ERO17
<dbl> <dbl>
1 4 4.5
2 6 12
A slightly different approach would be to bind the dataframes and use pivot_longer to separate the erosion name from the erosion value. Then you can take the mean of the values without having to specify the name.
library(tidyverse)
df1 <- data.frame(ID = c(1,1,2), ERO13 = c(2,4,6))
df2 <- data.frame(ID = c(4,4,6), ERO17 = c(4,5,12))
bind_rows(df1, df2) %>%
pivot_longer(starts_with('ERO'),
names_to = 'ERO',
values_drop_na = TRUE) %>%
group_by(ID, ERO) %>%
summarize(value = mean(value))
#> `summarise()` has grouped output by 'ID'. You can override using the `.groups` argument.
#> # A tibble: 4 x 3
#> # Groups: ID [4]
#> ID ERO value
#> <dbl> <chr> <dbl>
#> 1 1 ERO13 3
#> 2 2 ERO13 6
#> 3 4 ERO17 4.5
#> 4 6 ERO17 12
Created on 2022-01-14 by the reprex package (v2.0.0)
My code is dirty.
if condition smaller than two, names = unpopular.
df <- data.frame(vote=c("A","A","A","B","B","B","B","B","B","C","D"),
val=c(rep(1,11))
)
df %>% group_by(vote) %>% summarise(val=sum(val))
out
vote val
<fct> <dbl>
1 A 3
2 B 6
3 C 1
4 D 1
but I need
vote val
<fct> <dbl>
1 A 3
2 B 6
3 unpopular 2
my idea is
df2 <- df %>% group_by(vote) %>% summarise(val=sum(val))
df2$vote[df2$val < 2] <- "unpop"
df2 %>% group_by....
it's not cool.
do you know any cool & helpful function ?
We can do a double grouping
library(dplyr)
df %>%
group_by(vote) %>%
summarise(val=sum(val)) %>%
group_by(vote = replace(vote, val <2, 'unpop')) %>%
summarise(val = sum(val))
-output
# A tibble: 3 x 2
# vote val
# <chr> <dbl>
#1 A 3
#2 B 6
#3 unpop 2
Or another option with rowsum
df %>%
group_by(vote = replace(vote, vote %in%
names(which((rowsum(val, vote) < 2)[,1])), 'unpopular')) %>%
summarise(val = sum(val))
Or using fct_lump_n from forcats
library(forcats)
df %>%
group_by(vote = fct_lump_n(vote, 2, other_level = "unpop")) %>%
summarise(val = sum(val))
# A tibble: 3 x 2
# vote val
# <fct> <dbl>
#1 A 3
#2 B 6
#3 unpop 2
Or using table
df %>%
group_by(vote = replace(vote,
vote %in% names(which(table(vote) < 2)), 'unpop')) %>%
summarise(val = sum(val))
If you want to vote based on sum of val in base R you can do this as :
aggregate(val~vote, transform(aggregate(val~vote, df, sum),
vote = replace(vote, val < 2, 'unpop')), sum)
# vote val
#1 A 3
#2 B 6
#3 unpop 2
I have the following function to describe a variable
library(dplyr)
describe = function(.data, variable){
args <- as.list(match.call())
evalue = eval(args$variable, .data)
summarise(.data,
'n'= length(evalue),
'mean' = mean(evalue),
'sd' = sd(evalue))
}
I want to use dplyr for describing the variable.
set.seed(1)
df = data.frame(
'g' = sample(1:3, 100, replace=T),
'x1' = rnorm(100),
'x2' = rnorm(100)
)
df %>% describe(x1)
# n mean sd
# 1 100 -0.01757949 0.9400179
The problem is that when I try to apply the same descrptive using function group_by the describe function is not applied in each group
df %>% group_by(g) %>% describe(x1)
# # A tibble: 3 x 4
# g n mean sd
# <int> <int> <dbl> <dbl>
# 1 1 100 -0.01757949 0.9400179
# 2 2 100 -0.01757949 0.9400179
# 3 3 100 -0.01757949 0.9400179
How would you change the function to obtain what is desired using an small number of modifications?
You need tidyeval:
describe = function(.data, variable){
evalue = enquo(variable)
summarise(.data,
'n'= length(!!evalue),
'mean' = mean(!!evalue),
'sd' = sd(!!evalue))
}
df %>% group_by(g) %>% describe(x1)
# A tibble: 3 x 4
g n mean sd
<int> <int> <dbl> <dbl>
1 1 27 -0.23852862 1.0597510
2 2 38 0.11327236 0.8470885
3 3 35 0.01079926 0.9351509
The dplyr vignette 'Programming with dplyr' has a thorough description of using enquo and !!
Edit:
In response to Axeman's comment, I'm not 100% why the group_by and describe does not work here.
However, using debugonce with the funciton in it's original form
debugonce(describe)
df %>% group_by(g) %>% describe(x1)
one can see that evalue is not grouped and is just a numeric vector of length 100.
Base NSE appears to work, too:
describe <- function(data, var){
var_q <- substitute(var)
data %>%
summarise(n = n(),
mean = mean(eval(var_q)),
sd = sd(eval(var_q)))
}
df %>% describe(x1)
n mean sd
1 100 -0.1266289 1.006795
df %>% group_by(g) %>% describe(x1)
# A tibble: 3 x 4
g n mean sd
<int> <int> <dbl> <dbl>
1 1 33 -0.1379206 1.107412
2 2 29 -0.4869704 0.748735
3 3 38 0.1581745 1.020831
Say I have a data frame like this:
group1 <- c('a','a','a','a','a','a','b','b','b','b','b','b','b','b')
group2 <- c('x','y','x','y','x','y','x','y','x','y','x','y','x','y')
value <- round(runif(14, min=0, max=1), digits = 2)
df1 <- as.data.frame(cbind(group1,group2,value))
df1$value <- as.numeric(df1$value)
It is easy to get a new data frame with only the maximum values of each group, by using the dplyr package and summarise function:
df2 <- summarise(group_by(df1,group1),max_v = max(value))
But what I want is a new data frame with the 3 maximum values of each group, doing something like that:
df2 <- summarise(group_by(df1,group1),max_v = max(value),max2_v = secondmax(value),max3_v = thirdmax(value))
Is there a way to do that without using the sort function ?
We can use arrange/slice/spread way to get this
library(dplyr)
library(tidyr)
df1 %>%
group_by(group1) %>%
arrange(desc(value)) %>%
slice(seq_len(3)) %>%
mutate(Max = paste0("max_", row_number())) %>%
select(-group2) %>%
spread(Max, value)
# A tibble: 2 x 4
# Groups: group1 [2]
# group1 max_1 max_2 max_3
#* <fctr> <dbl> <dbl> <dbl>
#1 a 0.84 0.69 0.41
#2 b 0.89 0.72 0.54
data
df1 <- data.frame(group1,group2,value)