This question already has answers here:
dplyr mutate rowSums calculations or custom functions
(7 answers)
Closed 4 years ago.
I'd to make a new variable which represents the sum (or other function) of many other variables which all start with "prefix_". Is there a way to do this neatly using these select() helpers (e.g. starts_with())?
I don't think mutate_at() works for this since I'm just trying to create a single new variable based on many existing variables.
My attempt:
df %<>%
mutate(newvar = sum(vars(starts_with("prefix_"))))
This of course doesn't work. Many thanks!
A reproducible example:
mtcars %<>%
rename("prefix_mpg" = mpg) %>%
rename("prefix_cyl" = cyl) %>%
mutate(newvar = sum(var(starts_with("prefix_"))))
Intended output would be mtcars$newvar which is the sum of prefix_mpg and prefix_cyl. Of course I could just explicitly name mpg and cyl but in my actual case it's a long list of variables, too long to name conveniently.
We can use starts_with with the select call and put them in the rowSums function. . is a way to specify the object from the output of the previous pipe operation.
library(dplyr)
mtcars %>%
rename(prefix_mpg = mpg, prefix_cyl = cyl) %>%
mutate(newvar = rowSums(select(., starts_with("prefix_"))))
Related
This question already has answers here:
dplyr groups not working with dollar sign data$column syntax
(1 answer)
Why does summarize or mutate not work with group_by when I load `plyr` after `dplyr`?
(2 answers)
Closed last year.
I'm trying to do the average and correlation for some variables sorted gender. I don't think my group_by function is working, for some reason.
data(PSID1982, package ="AER" )
PSID1982 %>%
group_by(gender) %>%
summarise(avgeduc = mean(PSID1982$education), avgexper = mean(PSID1982$experience), avgwage= mean(PSID1982$wage),cor_wagvseduc = cor( x=PSID1982$wage, y= PSID1982$education))
The result is just the summary statistics of the entire group, not broken up into different genders.
Your syntax is correct but when you are using pipes and dplyr functions you do not need to call the column name using PSID1982$Column_Name. You just use the name of the column as follows:
PSID1982 %>%
group_by(gender) %>%
summarise(avgeduc = mean(education),
avgexper = mean(experience),
avgwage= mean(wage),
cor_wagvseduc = cor( x=wage, y= education))
This question already has answers here:
Mean per group in a data.frame [duplicate]
(8 answers)
Closed 4 years ago.
I am trying to get better in using pipes %>% in dplyr package. I understand that the whole point of using pipes (%>%) is that it replaces the first argument in a function by the one connected by pipe. That is, in this example:
area = rep(c(3:7), 5) + rnorm(5)
Pipes
area %>%
mean
equal normal function
`mean(area)`.
My problem is when it gets to a dataframe. I would like to split dataframe in a list of dataframes, and than calculate means per area columns. But, I can't figure out how to call the column instead of the dataframe?
I know that I can get means by year simply by aggregate(area~ year, df, mean) but I would like to practice pipes instead.
Thank you!
# Dummy data
set.seed(13)
df<-data.frame(year = rep(c(1:5), each = 5),
area = rep(c(3:7), each = 5) + rnorm(1))
# Calculate means.
# Neither `mean(df$area)`, `mean("area")` or `mean[area]` does not work. How to call the column correctly?
df %>%
split(df$year) %>%
mean
This?
df %>%
group_by(year) %>%
summarise(Mean=mean(area))
We need to extract the column from the list of data.frames in split. One option is to loop through the list with map, and summarise the 'area'.
df %>%
split(.$year) %>%
map_df(~ .x %>%
summarise(area = mean(area)))
I am trying to use dplyr to apply a function to a data frame that is grouped using the group_by function. I am applying a function to each row of the grouped data using do(). I would like to obtain the value of the group_by variable so that I might use it in a function call.
So, effectively, I have-
tmp <-
my_data %>%
group_by(my_grouping_variable) %>%
do(my_function_call(data.frame(x = .$X, y = .$Y),
GROUP_BY_VARIABLE)
I'm sure that I could call unique and get it...
do(my_function_call(data.frame(x = .$X, y = .$Y),
unique(.$my_grouping_variable))
But, it seems clunky and would inefficiently call unique for every grouping value.
Is there a way to get the value of the group_by variable in dplyr?
I'm going to prematurely say sorry if this is a crazy easy thing to answer. I promise that I've exhaustively searched for an answer.
First, if necessary, check if it's a grouped data frame: inherits(data, "grouped_df").
If you want the subsets of data frames, you could nest the groups:
mtcars %>% group_by(cyl) %>% nest()
Usually, you won't nest within the pipe-chain, but check in your function:
your_function(.x) <- function(x) {
if(inherits(x, "grouped_df")) x <- nest(x)
}
Your function should then iterate over the list-column data with all grouped subsets. If you use a function within mutate, e.g.
mtcars %>% group_by(cyl) %>% mutate(abc = your_function_call(.x))
then note that your function directly receives the values for each group, passed as class structure. It's a bit difficult to explain, just try it out and debug your_function_call step by step...
You can use groups(), however a SE version of this does not exist so I'm unsure of its use in programming.
library(dplyr)
df <- mtcars %>% group_by(cyl, mpg)
groups(df)
[[1]]
cyl
[[2]]
mpg
This question already has answers here:
dplyr: How to use group_by inside a function?
(4 answers)
Closed 6 years ago.
I want to set the column for grouping a data frame into a variable and then group and summarise the data frame based on it, i.e.
require(dplyr)
var <- colnames(mtcars)[10]
summaries <- mtcars %>% dplyr::group_by(var) %>% dplyr::summarise_each(funs(mean))
such that I can simply change var and use the second line without changing anything. Unfortunately my solution does not work as group_by asks the column name and not a variable.
Use group_by_, which takes arguments as character strings:
require(dplyr)
var <- colnames(mtcars)[10]
summaries <- mtcars %>% dplyr::group_by_(var) %>% dplyr::summarise_each(funs(mean))
(Maybe resources on standard vs non-standard evaluation would be of interest: http://adv-r.had.co.nz/Computing-on-the-language.html)
This question already has answers here:
Pass a vector of variable names to arrange() in dplyr
(6 answers)
Closed 7 years ago.
Often I'll want to select a subset of variables where the subset is the result of a function. In this simple case, I first get all the variable names which pertain to width characteristics
library(dplyr)
library(magrittr)
data(iris)
width.vars <- iris %>%
names %>%
extract(grep(".Width", .))
Which returns:
>width.vars
[1] "Sepal.Width" "Petal.Width"
It would be useful to be able to use these returns as a way to select columns (and while I'm aware that contains() and its siblings exist, there are plenty of more complicated subsets I would like to perform, and this example is made trivial for the purpose of this example.
If I was to attempt to use this function as a way to select columns, the following happens:
iris %>%
select(Species,
width.vars)
Error: All select() inputs must resolve to integer column positions.
The following do not:
* width.vars
How can I use dplyr::select with a vector of variable names stored as strings?
Within dplyr, most commands have an alternate version that ends with a '_' that accept strings as input; in this case, select_. These are typically what you have to use when you are utilizing dplyr programmatically.
iris %>% select_(.dots=c("Species",width.vars))
First of all, you can do the selection in dplyr with
iris %>% select(Species, contains(".Width"))
No need to create the vector of names separately. But if you did have a list of columns as string names, you could do
width.vars <- c("Sepal.Width", "Petal.Width")
iris %>% select(Species, one_of(width.vars))
See the ?select help page for all the available options.