I'm sure it is something obvious since I'm an R novice, but I cannot figure out why the first approach is working while the second is not. Something is wrong with my use of "paste".
library(dplyr)
data(mtcars)
characteristics <- c('disp', 'hp')
summarise_at(df, .vars = vars(characteristics), mean)
characteristics <- paste('disp hp', collapse = ",")
summarise_at(df, .vars = vars(characteristics), mean)
If you want to summarise over disp and hp of mtcars why not use a simpler and more straigthforward approach, like so?
mtcars %>%
summarise(across(c('disp', 'hp'), mean))
disp hp
1 230.7219 146.6875
Of yourse, you can also 'feed' your vector into the across operation:
characteristics <- c('disp', 'hp')
mtcars %>%
summarise(across(characteristics, mean))
disp hp
1 230.7219 146.6875
Using summarise(across...)would also take into account that so-called scoped dplyr verbs have now essentially been superseded by across()
With help from a friend, I found the answer.
library('dplyr')
data(mtcars)
characteristics <- unlist(str_split('disp hp', ' '))
# the line above replaced characteristics <- paste('disp hp', collapse = ",")
summarise_at(mtcars, .vars = vars(characteristics), mean)
Related
Thanks for looking at this!
I want a function to build tables showing stats, such as the mean) for specific variables segrgated into groups.
Below is a start of a function that works up to a point! I use an example using the built in data for mtcars.
MeansbyGroup<-function(var){
M1<-mtcars %>% group_by(cyl)
n1=deparse(substitute(var))
r1<-transpose(M1 %>% summarise(disp=mean(var)))[2,]
}
# EXAMPLE using mtcars
df=MeansbyGroup(mtcars$disp)
df[nrow(df) + 1,] =MeansbyGroup(mtcars$drat)
df
# The above will output
V1 V2 V3
2 230.721875 230.721875 230.721875
2.1 3.596563 3.596563 3.596563
#which is not even the right means!
#below are the correct values...but I can't automate a table like I want
M1<-mtcars %>% group_by(cyl)
transpose(M1 %>% summarise(disp=mean(disp)))[2,]
transpose(M1 %>% summarise(disp=mean(drat)))[2,]
## Here is my desired output of means disaggregated into columns by the group "cyl"
## if the function worked right with the above example
V1 V2 V3
disp 105.1364 183.3143 353.1
drat 4.070909 3.585714 3.229286
As you will see, in the function I have "n1=deparse(substitute(var))" to capture the variable name which I would like to have in the first column, instead of 2 and 2.1 as shown in the example output.
I've tried a few techniques, but when I try to add n1 to the vector, it destroys the values of the means!
Also, I'd like to make the function more generalizable. For this example, I'd prefer the function call to look like MeansbyGroup(var,group,dataframe), which in the above example would be called by MeansbyGroup(disp,cyl,mtcars).
Thanks!
Here's how I would code your table outside of a function:
library(dplyr)
library(tibble)
mtcars %>%
group_by(cyl) %>%
summarize(across(c(disp, drat), mean)) %>%
column_to_rownames("cyl") %>%
t
# 4 6 8
# disp 105.136364 183.314286 353.100000
# drat 4.070909 3.585714 3.229286
Using across if you might have multiple variables is quite nice. Putting this inside a function, we will need to use deparse(substitute()) because column_to_rownames requires a string argument for the column. But for the others we can use the friendly {{:
foo = function(data, group, vars) {
grp_name = deparse(substitute(group))
data %>%
group_by({{group}}) %>%
summarize(across({{vars}}, mean)) %>%
column_to_rownames(grp_name) %>%
t
}
foo(data = mtcars, group = cyl, vars = c(disp, drat))
# 4 6 8
# disp 105.136364 183.314286 353.100000
# drat 4.070909 3.585714 3.229286
I'm working with a table for which I need to count the number of rows satisfying some criterion and I ended up with basically multiple repetitions of the same pipe differing only in the variable name.
Say I want to know how many cars are better than Valiant in mtcars on each of the variables there. An example of the code with two variables is below:
library(tidyverse)
reference <- mtcars %>%
slice(6)
mpg <- mtcars %>%
filter(mpg > reference$mpg) %>%
count() %>%
pull()
cyl <- mtcars %>%
filter(cyl > reference$cyl) %>%
count() %>%
pull()
tibble(mpg, cyl)
Except, suppose I need to do it for like 100 variables so there must be a more optimal way to just repeat the process.
What would be the way to rewrite the code above in an optimal way (maybe, using map() or anything else that works with pipes nicely so that the result would be a tibble with the counts for all the variables in mtcars?
I feel the solution should be very easy but I'm stuck.
Thank you!
Or:
library(tidyverse)
map_dfc(mtcars, ~sum(.x[6] < .x))
map2_dfc(mtcars, reference, ~sum(.y < .x))
You could use summarise + across to count observations greater than a certain value in each column.
library(dplyr)
mtcars %>%
summarise(across(everything(), ~ sum(. > .[6])))
# mpg cyl disp hp drat wt qsec vs am gear carb
# 1 18 14 15 22 30 11 1 0 13 17 25
base solution:
# (1)
colSums(mtcars > mtcars[rep(6, nrow(mtcars)), ])
# (2)
colSums(sweep(as.matrix(mtcars), 2, mtcars[6, ], ">"))
# mpg cyl disp hp drat wt qsec vs am gear carb
# 18 14 15 22 30 11 1 0 13 17 25
You can do it in a loop for example. Like this:
library(tidyverse)
reference <- mtcars %>%
slice(6)
# Empty list to save outcome
list_outcome <- list()
# Get the columnnames to loop over
loop_var <- colnames(reference)
for(i in loop_var){
nr <- mtcars %>%
filter(mtcars[, i] > reference[, i]) %>%
count() %>%
pull()
# Save every iteration in the loop as the ith element of the list
list_outcome[[i]] <- data.frame(Variable = i, Value = nr)
}
# combine all the data frames in the list to one final data frame
df_result <- do.call(rbind, list_outcome)
I'm using this pretty nice code to perform a multiple t.test keeping the independent variable constant!
data(mtcars)
library(dplyr)
vars_to_test <- c("disp","hp","drat","wt","qsec")
iv <- "vs"
mtcars %>%
summarise_each_(
funs_(
sprintf("stats::t.test(.[%s == 0], .[%s == 1])$p.value",iv,iv)
),
vars = vars_to_test)
Unfortunately, dplyr was updated and I've been facing this report
summarise_each() is deprecated. Use summarise_all(),
summarise_at() or summarise_if() instead. To map funs over a
selection of variables, use summarise_at()
When i change the code for _all, at or _if, this function doest not work any more. I'm looking for some advice and thanks much for your support.
Thanks
Instead of creating a string expression with sprintf and then evaluating it, we can use the evaluate the 'vs' converting it to symbol and then evaluate it
library(dplyr)
mtcars %>%
summarise_at(vars(vars_to_test), funs(
try(stats::t.test(.[(!! rlang::sym(iv)) == 0], .[(!! rlang::sym(iv)) == 1])$p.value)
))
# disp hp drat wt qsec
#1 2.476526e-06 1.819806e-06 0.01285342 0.0007281397 3.522404e-06
If we really wanted to parse an expression, use the rlang_parse_expr and rlang::eval_tidy along with sym
library(rlang)
eval_tidy(parse_expr("mtcars %>% summarise_at(vars(vars_to_test),
funs(t.test(.[(!!sym(iv))==0],
.[(!!sym(iv))==1])$p.value ))"))
# disp hp drat wt qsec
#1 2.476526e-06 1.819806e-06 0.01285342 0.0007281397 3.522404e-06
I've got this one-liner:
aggregate(. ~ gear,mtcars[mtcars$mpg>20,c('gear','mpg')],mean)
Now I want to filter by mpg>25 like his:
aggregate(. ~ gear,mtcars[mtcars$mpg>20,c('gear','mpg')],mean)[mpg>25,]
But now I get some 1743 lines of NA entries
any ideas?
Here is another solution with base functions:
subset(aggregate(. ~ gear,mtcars[mtcars$mpg>20,c('gear','mpg')],mean), mpg>25)
and here is a solution with data.table
library(data.table)
M <- data.table(mtcars)
M[mpg>20, .(mpg=mean(mpg)), by=gear][mpg>25]
A dplyr solution could be:
mtcars %>% group_by(gear) %>% filter(mpg>25) %>% summarise(mean(mpg))
or rather:
mtcars %>% group_by(gear) %>% filter(mpg>20) %>%
summarise(mean(mpg)) %>% filter(`mean(mpg)`>25)
You can't (and I don't get lots of NAs, I correctly get an error because mpg isn't found. Did you do something previously that allows mpg to be found?). R doesn't work the way you are trying to force it.
You will need
tmp <- aggregate(. ~ gear,
data = subset(mtcars, mpg > 20, select = c('gear','mpg')),
FUN = mean)
with(tmp, tmp[mpg > 25, ])
> with(tmp, tmp[mpg > 25, ])
gear mpg
2 4 25.74
3 5 28.20
Reading the documentation for do() in dplyr, I've been impressed by the ability to create regression models for groups of data and was wondering whether it would be possible to replicate it using different independent variables rather than groups of data.
So far I've tried
require(dplyr)
data(mtcars)
models <- data.frame(var = c("cyl", "hp", "wt"))
models <- models %>% do(mod = lm(mpg ~ as.name(var), data = mtcars))
Error in as.vector(x, "symbol") :
cannot coerce type 'closure' to vector of type 'symbol'
models <- models %>% do(mod = lm(substitute(mpg ~ i, as.name(.$var)), data = mtcars))
Error in substitute(mpg ~ i, as.name(.$var)) :
invalid environment specified
The desired final output would be something like
var slope standard_error_slope
1 cyl -2.87 0.32
2 hp -0.07 0.01
3 wt -5.34 0.56
I'm aware that something similar is possible using a lapply approach, but find the apply family largely inscrutable. Is there a dplyr solution?
There's nothing particularly complicated about the approach in the linked page. The use of substitute and as.name is a bit arcane, but that's easily rectified.
varlist <- names(mtcars)[-1]
models <- lapply(varlist, function(x) {
form <- formula(paste("mpg ~", x))
lm(form, data=mtcars)
})
dplyr is not the be-all and end-all of R programming. I'd suggest getting familiar with the *apply functions as they'll be of use in many situations where dplyr doesn't work.
This isn't pure "dplyr", but rather, "dplyr" + "tidyr" + "data.table". Still, I think it should be pretty easily readable.
library(data.table)
library(dplyr)
library(tidyr)
mtcars %>%
gather(var, val, cyl:carb) %>%
as.data.table %>%
.[, as.list(summary(lm(mpg ~ val))$coefficients[2, 1:2]), by = var]
# var Estimate Std. Error
# 1: cyl -2.87579014 0.322408883
# 2: disp -0.04121512 0.004711833
# 3: hp -0.06822828 0.010119304
# 4: drat 7.67823260 1.506705108
# 5: wt -5.34447157 0.559101045
# 6: qsec 1.41212484 0.559210130
# 7: vs 7.94047619 1.632370025
# 8: am 7.24493927 1.764421632
# 9: gear 3.92333333 1.308130699
# 10: carb -2.05571870 0.568545640
If you really just wanted a few variables, start with a vector, not a data.frame.
models <- c("cyl", "hp", "wt")
mtcars %>%
select_(.dots = c("mpg", models)) %>%
gather(var, val, -mpg) %>%
as.data.table %>%
.[, as.list(summary(lm(mpg ~ val))$coefficients[2, 1:2]), by = var]
# var Estimate Std. Error
# 1: cyl -2.87579014 0.3224089
# 2: hp -0.06822828 0.0101193
# 3: wt -5.34447157 0.5591010