I have a table with columns
[Time, var1, var2, var3, var4...varN]
I need to calculate mean/SE per Time for each var1, var2...var n , and I want to do this programmatically for all variables, rather than 1 at a time which would involve a lot of copy-pasting.
Section 8.2.3 here https://tidyeval.tidyverse.org/dplyr.html is close to what I want but my below code:
x <- as.data.frame(matrix(nrow = 2, ncol = 3))
x[1,1] = 1
x[1,2] = 2
x[1,3] = 3
x[2,1] =4
x[2,2] = 5
x[2,3] = 6
names(x)[1] <- "time"
names(x)[2] <- "var1"
names(x)[3] <- "var2"
grouped_mean3 <- function(.data, ...) {
print(.data)
summary_vars <- enquos(...)
print(summary_vars)
summary_vars <- purrr::map(summary_vars, function(var) {
expr(mean(!!var, na.rm = TRUE))
})
print(summary_vars)
.data %>%
group_by(time)
summarise(!!!summary_vars) # Unquote-splice the list
}
grouped_mean3(x, var("var1"), var("var2"))
Yields
Error in !summary_vars : invalid argument type
And the original cause is "Must group by variables found in .data." and it finds a column that isn't in the dummy "x" that I generated for the purposes of testing. I have no idea what's happening, sadly.
How do I actually extract the mean from the new summary_vars and add it to the .data table? summary_vars becomes something like
[[1]]
mean(~var1, na.rm = TRUE)
[[2]]
mean(~var2, na.rm = TRUE)
Which seems close, but needs evaluation. How do I evaluate this? !!! wasn't working.
For what it's worth, I tried plugging the example in dplyr into this R engine https://rdrr.io/cran/dplyr/man/starwars.html and it didn't work either.
Help?
End goal would be a table along the lines of
[Time, var1mean, var2mean, var3mean, var4mean...]
Try this :
library(dplyr)
grouped_mean3 <- function(.data, ...) {
vars <- c(...)
.data %>%
group_by(time) %>%
summarise(across(all_of(vars), mean))
}
grouped_mean3(x, 'var1')
# time var1mean
# <dbl> <dbl>
#1 1 2
#2 4 5
grouped_mean3(x, 'var1', 'var2')
# time var1mean var2mean
# <dbl> <dbl> <dbl>
#1 1 2 3
#2 4 5 6
Perhaps this is what you are looking for?
x %>%
group_by(time) %>%
summarise_at(vars(starts_with('var')), ~mean(.,na.rm=T)) %>%
rename_at(vars(starts_with('var')),funs(paste(.,"mean"))) %>%
merge(x)
With your data (from your question) following is the output:
time var1mean var2mean var1 var2
1 1 2 3 2 3
2 4 5 6 5 6
Related
I am relatively new to R and I have been facing issues using dplyr inside functions. I have scrounged the forum, looked at all similar issues but I am unable to resolve my issue. I have tried to simplify my issue with the following example
df <- tibble(
g1 = c(1, 2, 3, 4, 5),
a = sample(5),
b = sample(5)
)
I want to write a function to calculate the sum of a and b as follows:
sum <- function(df, group_var, a, b) {
group_var <- enquo(group_var)
a <- enquo(a)
b <- enquo(b)
df.temp<- df %>%
group_by(g1) %>%
mutate(
sum = !!a + !!b
)
return(df.temp)
}
and I can call the function thru this line:
df2 <- sum(df, g1, a, b)
My issue is that I do not want to hard code the columns names in function call since the columns names "g1", "a" and "b" are likely to change. and hence, I have the columns names assigned from a config file (config.yml) to a variable.
But when I use the variables, I run into multiple issues. Can someone guide me here please? For all column name references, I would ideally like to use variables. for e.g. I run into issues here in this code:
A.Key <- "a"
B.Key <- "b"
df2 <- sum(df, g1, A.Key, B.Key)
Thanks in advance and sorry if it has been answered before; I could not find it.
sum1 <- function(df, group_var,x,y) {
group_var <- enquo(group_var)
x = as.name(x)
y = as.name(y)
df.temp<- df %>%
group_by(!!group_var) %>%
mutate(
sum = !!enquo(x)+!!enquo(y)
)
return(df.temp)
}
sum1(df, g1, A.Key, B.Key)
# A tibble: 5 x 4
# Groups: g1 [5]
g1 a b sum
<dbl> <int> <int> <int>
1 1. 3 2 5
2 2. 2 1 3
3 3. 1 3 4
4 4. 4 4 8
5 5. 5 5 10
I am writing a function that computes the mean of a variable according to some grouping (g1 and g2). I would like the function to take care of the case when the user just wants to compute the mean across the groups, so the group argument will be empty.
I want a solution using tidyverse.
Suppose the following:
y = 1:4
g1 = c('a', 'a', 'b', 'b')
g2 = c(1,2,1,2)
MyData = data.frame(g1, g2, y)
MyFun = function(group){
group_sym = syms(group)
MyData %>%
group_by(!!!group_sym) %>%
summarise(mean = mean(y))
}
# this works well
MyFun(group = c('g1', 'g2'))
Now suppose I want the mean of y across all groups. I would like the function be able to treat something like
MyFun(group = '')
or
MyFun(group = NULL)
So ideally I would like the group argument to be empty / null and thus MyData would not be grouped. One solution could be to add a condition at the beginning of the function checking if the argument is empty and if TRUE write summarise without group_by. But this is not elegant and my real code is much longer than just a few lines.
Any idea?
1) Use {{...}} and use g1 in place of 'g1':
MyFun = function(group) {
MyData %>%
group_by({{group}}) %>%
summarise(mean = mean(y)) %>%
ungroup
}
MyFun(g1)
## # A tibble: 2 x 2
## g1 mean
## <fct> <dbl>
## 1 a 1.5
## 2 b 3.5
MyFun()
## # A tibble: 1 x 1
## mean
## <dbl>
## 1 2.5
2) This approach uses 'g1' as in the question.
MyFun = function(group) {
group <- if (missing(group)) 'All' else sym(group)
MyData %>%
group_by(!!group) %>%
summarise(mean = mean(y)) %>%
ungroup
}
MyFun('g1')
## # A tibble: 2 x 2
## g1 mean
## <fct> <dbl>
## 1 a 1.5
## 2 b 3.5
MyFun()
## # A tibble: 1 x 2
## `"All"` mean
## <chr> <dbl>
## 1 All 2.5
3) This also works and gives the same output as (2).
MyFun = function(...) {
group <- if (...length()) syms(...) else 'All'
MyData %>%
group_by(!!!group) %>%
summarise(mean = mean(y)) %>%
ungroup
}
MyFun('g1')
MyFun()
A different approach consists of creating a fake group (named 'across_group') in the data, in the case of group is missing.
MyFun = function(group) {
if (missing(group)) MyData$across_group = 1
group <- if (missing(group)) syms('across_group') else syms(group)
MyData %>%
group_by(!!!group) %>%
summarise(mean = mean(y)) %>%
ungroup
}
MyFun()
# A tibble: 1 x 2
across_group mean
<dbl> <dbl>
1 1 2.5
Sample data
dat <-
data.frame(Sim.Y1 = rnorm(10), Sim.Y2 = rnorm(10),
Sim.Y3 = rnorm(10), obsY = rnorm(10),
ID = sample(1:10, 10), ID_s = rep(1:2, each = 5))
For the following vector, I want to calculate the mean across ID_s
simVec <- c('Sim.Y1.cor','Sim.Y2.cor')
for(s in simVec){
simRef <- simVec[s]
simID <- unlist(strsplit(simRef, split = '.cor',fixed = T))[1]
# this works
dat %>% dplyr::group_by(ID_s) %>%
dplyr::summarise(meanMod = mean(Sim.Y1))
# this doesn't work
dat %>% dplyr::group_by(ID_s) %>%
dplyr::summarise(meanMod = mean(!!(simID)))
}
How do I refer a column in dplyr not by its explicit name?
Note that your particular task can be performed without any non-standard evaluation by using summarize_at(), which works directly with strings:
simIDs <- stringr::str_split(simVec, ".cor") %>% purrr::map_chr(1)
# [1] "Sim.Y1" "Sim.Y2"
dat %>% dplyr::group_by(ID_s) %>% dplyr::summarise_at(simIDs, mean)
# # A tibble: 2 x 3
# ID_s Sim.Y1 Sim.Y2
# <int> <dbl> <dbl>
# 1 1 0.494 -0.0522
# 2 2 -0.104 -0.370
A custom suffix can also be supplied through the named list:
dat %>% dplyr::group_by(ID_s) %>% dplyr::summarise_at(simIDs, list(m=mean))
# # A tibble: 2 x 3
# ID_s Sim.Y1_m Sim.Y2_m <--- Note the _m suffix
# <int> <dbl> <dbl>
# 1 1 0.494 -0.0522
# 2 2 -0.104 -0.370
First, you have to use seq_along() if you want to index you vector with s.
Second, you are missing sym().
This should work:
simVec <- c('Sim.Y1.cor','Sim.Y3.cor')
for(s in seq_along(simVec)){
simRef <- simVec[s]
simID <- unlist(strsplit(simRef, split = '.cor',fixed = T))[1]
# this works
dat %>% dplyr::group_by(ID_s) %>%
dplyr::summarise(meanMod = mean(Sim.Y1))
# this doesn't work
dat %>% dplyr::group_by(ID_s) %>%
dplyr::summarise(meanMod = mean(!!sym(simID)))
}
edit: no Typo
Try this
library(dplyr)
dat %>% group_by(ID) %>%
summarise(mean_y1 =mean(Sim.Y1),
mean_y2 =mean(Sim.Y2),
mean_y3 =mean(Sim.Y3),
mean_obsY = mean(obsY))
I understand the question to be, how do you get a column without referencing the column name, i.e. using the index instead.
Let me know if my understanding is incorrect.
If not, I believe the easiest way would be as per below.
> df1 <- data.frame(ID_s=c('a','b','c'),Val=c('a1','b1','c1'))
> df1
ID_s Val
1 a a1
2 b b1
3 c c1
> df1[,1]
[1] a b c
Levels: a b c
If you want to save that as a dataframe, can be extended as per below:
cc <- data.frame(ID_s=df1[,1])
Hope this helps!
I am relatively new to R and I have been facing issues using dplyr inside functions. I have scrounged the forum, looked at all similar issues but I am unable to resolve my issue. I have tried to simplify my issue with the following example
df <- tibble(
g1 = c(1, 2, 3, 4, 5),
a = sample(5),
b = sample(5)
)
I want to write a function to calculate the sum of a and b as follows:
sum <- function(df, group_var, a, b) {
group_var <- enquo(group_var)
a <- enquo(a)
b <- enquo(b)
df.temp<- df %>%
group_by(g1) %>%
mutate(
sum = !!a + !!b
)
return(df.temp)
}
and I can call the function thru this line:
df2 <- sum(df, g1, a, b)
My issue is that I do not want to hard code the columns names in function call since the columns names "g1", "a" and "b" are likely to change. and hence, I have the columns names assigned from a config file (config.yml) to a variable.
But when I use the variables, I run into multiple issues. Can someone guide me here please? For all column name references, I would ideally like to use variables. for e.g. I run into issues here in this code:
A.Key <- "a"
B.Key <- "b"
df2 <- sum(df, g1, A.Key, B.Key)
Thanks in advance and sorry if it has been answered before; I could not find it.
sum1 <- function(df, group_var,x,y) {
group_var <- enquo(group_var)
x = as.name(x)
y = as.name(y)
df.temp<- df %>%
group_by(!!group_var) %>%
mutate(
sum = !!enquo(x)+!!enquo(y)
)
return(df.temp)
}
sum1(df, g1, A.Key, B.Key)
# A tibble: 5 x 4
# Groups: g1 [5]
g1 a b sum
<dbl> <int> <int> <int>
1 1. 3 2 5
2 2. 2 1 3
3 3. 1 3 4
4 4. 4 4 8
5 5. 5 5 10
I am kind of new to R and programming in general. I am currently strugling with a piece of code for data transformation and hope someone can take a little bit of time to help me.
Below a reproducible exemple :
# Data
a <- c(rnorm(12, 20))
b <- c(rnorm(12, 25))
f1 <- rep(c("X","Y","Z"), each=4) #family
f2 <- rep(x = c(0,1,50,100), 3) #reference and test levels
dt <- data.frame(f1=factor(f1), f2=factor(f2), a,b)
#library loading
library(tidyverse)
Goal : Compute all values (a,b) using a reference value. Calculation should be : a/a_ref with a_ref = a when f2=0 depending on the family (f1 can be X,Y or Z).
I tried to solve this by using this code :
test <- filter(dt, f2!=0) %>% group_by(f1) %>%
mutate("a/a_ref"=a/(filter(dt, f2==0) %>% group_by(f1) %>% distinct(a) %>% pull))
I get :
test results
as you can see a is divided by a_ref. But my script seems to recycle the use of reference values (a_ref) regardless of the family f1.
Do you have any suggestion so A is computed with regard of the family (f1) ?
Thank you for reading !
EDIT
I found a way to do it 'manualy'
filter(dt, f1=="X") %>% mutate("a/a_ref"=a/(filter(dt, f1=="X" & f2==0) %>% distinct(a) %>% pull()))
f1 f2 a b a/a_ref
1 X 0 21.77605 24.53115 1.0000000
2 X 1 20.17327 24.02512 0.9263973
3 X 50 19.81482 25.58103 0.9099366
4 X 100 19.90205 24.66322 0.9139422
the problem is that I'd have to update the code for each variable and family and thus is not a clean way to do it.
# use this to reproduce the same dataset and results
set.seed(5)
# Data
a <- c(rnorm(12, 20))
b <- c(rnorm(12, 25))
f1 <- rep(c("X","Y","Z"), each=4) #family
f2 <- rep(x = c(0,1,50,100), 3) #reference and test levels
dt <- data.frame(f1=factor(f1), f2=factor(f2), a,b)
#library loading
library(tidyverse)
dt %>%
group_by(f1) %>% # for each f1 value
mutate(a_ref = a[f2 == 0], # get the a_ref and add it in each row
"a/a_ref" = a/a_ref) %>% # divide a and a_ref
ungroup() %>% # forget the grouping
filter(f2 != 0) # remove rows where f2 == 0
# # A tibble: 9 x 6
# f1 f2 a b a_ref `a/a_ref`
# <fctr> <fctr> <dbl> <dbl> <dbl> <dbl>
# 1 X 1 21.38436 24.84247 19.15914 1.1161437
# 2 X 50 18.74451 23.92824 19.15914 0.9783583
# 3 X 100 20.07014 24.86101 19.15914 1.0475490
# 4 Y 1 19.39709 22.81603 21.71144 0.8934042
# 5 Y 50 19.52783 25.24082 21.71144 0.8994260
# 6 Y 100 19.36463 24.74064 21.71144 0.8919090
# 7 Z 1 20.13811 25.94187 19.71423 1.0215013
# 8 Z 50 21.22763 26.46796 19.71423 1.0767671
# 9 Z 100 19.19822 25.70676 19.71423 0.9738257
You can do this for more than one variable using:
dt %>%
group_by(f1) %>%
mutate_at(vars(a:b), funs(./.[f2 == 0])) %>%
ungroup()
Or generally use vars(a:z) to use all variables between a and z as long as they are one after the other in your dataset.
Another solution could be using mutate_if like:
dt %>%
group_by(f1) %>%
mutate_if(is.numeric, funs(./.[f2 == 0])) %>%
ungroup()
Where the function will be applied to all numeric variables you have. The variables f1 and f2 will be factor variables, so it just excludes those ones.