How to pass column name as parameter to function in dplyr? - r

I want to do the same as here but with dplyr and one more column.
I want to selecting a column via a string variable, but on top I also want to select a second column normally.
I need this because I have a function which selects a couple of columns by a given parameters.
I have the following code as an example:
library(dplyr)
data(cars)
x <- "speed"
cars %>% select_(x, dist)


You can use quote() for the dist column
x <- "speed"
cars %>% select_(x, quote(dist)) %>% head
# speed dist
# 1 4 2
# 2 4 10
# 3 7 4
# 4 7 22
# 5 8 16
# 6 9 10


I know I'm a little late to this one, but I figured I would add it for others.
x <- "speed"
cars %>% select(one_of(x),dist) %>% head()
## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
OR this would work too
cars %>% select(one_of(c(x,'dist')))

Related

How to calculate mean for specific columns in r?

The goal: I want to create 2 new columns by using R.
1 column which shows the mean of each row (but only calculating specific columns - only the mean of the columns which do not contain the string "_X")
1 column which shows the mean of each row (but only calculating specific columns - only the mean of the columns which do contain the string "_X").
For example:
phone1 phone1_X phon2 phone2_X phone3 phone3_X
1 2 3 4 5 6
2 4 6 8 10 12
Results:
Mean_of_none_X
3 (1+3+5)/3
6 (2+5+10)3
Mean_of_X
4
8
Thank you!

Try using rowMeans and grep over the column names to include/exclude certain columns:
# only "_x"
rowMeans(df[,grep("_x",colnames(df))])
# No "_x"
rowMeans(df[,-grep("_x",colnames(df))])
Output:
#> # only "_x"
#> rowMeans(df[,grep("_x",colnames(df))])
#[1] 4 8
#> # No "_x"
#> rowMeans(df[,-grep("_x",colnames(df))])
#[1] 3 6

Try this
> lapply(split.default(df, endsWith(names(df), "_X")), rowMeans)
$`FALSE`
[1] 3 6
$`TRUE`
[1] 4 8

library(dplyr)
df %>%
rowwise() %>%
mutate(x_mean = mean(c_across(contains('_X'))),
notx_mean = mean(c_across(!contains('_X') & !contains('_mean'))))

Combining two columns using shared values in first column

I am trying to adjust the formatting of a data set. My current set looks like this, in two columns. The first column is a "cluster" and the second column "name" contains values within each cluster:
Cluster Name
A 1
A 2
A 3
B 4
B 5
C 2
C 6
C 7
And I'd like a list that is, one column wherein all the values from column 2 are listed under the associated cluster from column 1 in a single column:
Cluster A
1
2
3
Cluster B
4
5
Cluster C
2
6
7
I've been trying in R and Excel with no luck for the last few hours. Any ideas?

Using a trick with tidyr::nest :
library(dplyr)
library(tidyr)
df %>% mutate(Cluster = paste0("Cluster_",Cluster)) %>% nest(Name) %>% t %>% unlist %>% as.data.frame
# .
# 1 Cluster_A
# 2 1
# 3 2
# 4 3
# 5 Cluster_B
# 6 4
# 7 5
# 8 Cluster_C
# 9 2
# 10 6
# 11 7

sum up certain variables (columns) by variable names

i want to sum up certain variables (columns in a data frame).
I would like to select those variables by parts of their names.
The complex thing is that i have various conditions. So, using a single contains from dplyr does not work.
Here is an example:
ab_yy <- c(1:5)
bc_yy <- c(5:9)
cd_yy <- c(2:6)
de_xx <- c(3:7)
ab_yy bc_yy cd_yy de_xx
1 1 5 2 3
2 2 6 3 4
3 3 7 4 5
4 4 8 5 6
5 5 9 6 7
dat <- data.frame(ab_yy,bc_yy,cd_yy,de_xx)
#sum up all variables that contain yy and certain extra conditions
#may look something like this: rowSums(select(dat, contains(("yy&ab")|("yy&bc")) ) )
desired result:
6 8 10 12 14

EDIT: Fixed, sorry, low on caffeine
If you want to use dplyr, try using matches:
library(dplyr)
dat %>%
select(matches("*yy", )) %>%
select(matches("ab*|bc*")) %>%
rowSums(.)
[1] 6 8 10 12 14

I don't think that it's the best way but u can do it like that with a grepl:
rowSums(dat[,grepl(pattern = "ab.*yy|bc.*yy",colnames(dat))==T])

R rearrange data

I have a bunch of texts written by the same person, and I'm trying to estimate the templates they use for each text. The way I'm going about this is:
create a TermDocumentMatrix for all the texts
take the raw Euclidean distance of each pair
cut out any pair greater than X distance (10 for the sake of argument)
flatten the forest
return one example of each template with some summarized stats
I'm able to get to the point of having the distance pairs, but I am unable to convert the dist instance to something I can work with. There is a reproducible example at the bottom.
The data in the dist instance looks like this:
The row and column names correspond to indexes in the original list of texts which I can use to do achieve step 5.
What I have been trying to get out of this is a sparse matrix with col name, row name, value.
col, row, value
1 2 14.966630
1 3 12.449900
1 4 13.490738
1 5 12.688578
1 6 12.369317
2 3 12.449900
2 4 13.564660
2 5 12.922848
2 6 12.529964
3 4 5.385165
3 5 5.830952
3 6 5.830952
4 5 7.416198
4 6 7.937254
5 6 7.615773
From this point I would be comfortable cutting out all pairs greater than my cutoff and flattening the forest, i.e. returning 3 templates in this example, a group containing only document 1, a group containing only document 2 and a third group containing documents 3, 4, 5, and 6.
I have tried a bunch of things from creating a matrix out of this and then trying to make it sparse, to directly using the vector inside of the dist class, and I just can't seem to figure it out.
Reproducible example:
tdm <- matrix(c(1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,1,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,3,1,2,2,2,3,2,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,1,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,4,1,1,1,1,1,0,0,1,1,1,1,0,1,0,0,0,0,0,0,1,1,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,1,1,1,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,2,0,0,1,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,1,0,1,1,1,1,0,1,0,0,0,0,0,0,1,1,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,1,0,0,1,1,1,1,0,1,0,1,0,0,2,0,0,0,0,0,1,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,3,1,1,1,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,1,1,1,0,0,1,1,1,1,0,0,0,1,0,0,2,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,3,1,1,1,1,0,1,0,0,0,0,1,2,0,1,1,0,0,0,0,1,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,1,1,1,1,0,1,0,0,0,0,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,1,1,1,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,1,1,0,1,0,0,0,0,0,1,1,1,2,1,1,1,0,0,0,0,1,2,2,1,1,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,1,1,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,1,1,1,0,2,0,0,0,0,0,0,1,1,1,1,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,2,0,2,2,3,2,1,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,1,1,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,1,1,1,1,1,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,0,1,0,0,0,0,1,0,1,1,1,1,1,0,0,1,1,1,0,0,1,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,1,2,1,1,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,1,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,3,0,1,1,1,1,0,0,1,0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,4,2,4,6,4,3,1,0,1,2,1,1,0,1,0,0,0,0,2,0,0,0,0,0,0,1,1,1,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,1,1,1,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,2,1,2,2,2,2,1,0,1,2,1,1,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,2,2,2,2,2,2,3,3,4,5,3,1,2,1,1,1,1,1,1,0,0,0,0,3,3,0,0,1,1,0,1,0,0,0,0), nrow=6)
rownames(tdm) <- 1:6
colnames(tdm) <- paste("term", 1:229, sep="")
tdm.dist <- dist(tdm)
# I'm stuck turning tdm.dist into what I have shown

A classic approach to turn a "matrix"-like object to a [row, col, value] "data.frame" is the as.data.frame(as.table(.)) route. Specifically here, we need:
subset(as.data.frame(as.table(as.matrix(tdm.dist))), as.numeric(Var1) < as.numeric(Var2))
But that includes way too many coercions and creation of a larger object only to be subset immediately.
Since dist stores its values in a "lower.tri"angle form we could use combn to generate the row/col indices and cbind with the "dist" object:
data.frame(do.call(rbind, combn(attr(tdm.dist, "Size"), 2, simplify = FALSE)), c(tdm.dist))
Also, "Matrix" package has some flexibility that, along its memory efficiency in creating objects, could be used here:
library(Matrix)
tmp = combn(attr(tdm.dist, "Size"), 2)
summary(sparseMatrix(i = tmp[2, ], j = tmp[1, ], x = c(tdm.dist),
dims = rep_len(attr(tdm.dist, "Size"), 2), symmetric = TRUE))
Additionally, among different functions that handle "dist" objects,
cutree(hclust(tdm.dist), h = 10)
#1 2 3 4 5 6
#1 2 3 3 3 3
groups by specifying the cut height.

That's how I've done a very similar thing in the past using dplyr and tidyr packages.
You can run the chained (%>%) script row by row to see how the dataset is updated step by step.
tdm <- matrix(c(1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,1,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,3,1,2,2,2,3,2,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,1,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,4,1,1,1,1,1,0,0,1,1,1,1,0,1,0,0,0,0,0,0,1,1,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,1,1,1,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,2,0,0,1,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,1,1,0,1,1,1,1,0,1,0,0,0,0,0,0,1,1,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,1,0,0,1,1,1,1,0,1,0,1,0,0,2,0,0,0,0,0,1,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,3,1,1,1,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,1,1,1,0,0,1,1,1,1,0,0,0,1,0,0,2,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,3,1,1,1,1,0,1,0,0,0,0,1,2,0,1,1,0,0,0,0,1,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,1,1,1,1,0,1,0,0,0,0,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,1,1,1,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,1,1,0,1,0,0,0,0,0,1,1,1,2,1,1,1,0,0,0,0,1,2,2,1,1,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,1,1,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,1,1,1,0,2,0,0,0,0,0,0,1,1,1,1,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,2,0,2,2,3,2,1,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,0,0,0,1,1,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,2,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,2,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,2,1,1,1,1,1,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0,1,1,1,1,1,1,0,0,0,0,0,1,0,0,0,0,1,0,1,1,1,1,1,0,0,1,1,1,0,0,1,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,1,2,1,1,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,1,1,0,2,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,3,0,1,1,1,1,0,0,1,0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,4,2,4,6,4,3,1,0,1,2,1,1,0,1,0,0,0,0,2,0,0,0,0,0,0,1,1,1,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,2,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,1,1,1,1,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,2,1,2,2,2,2,1,0,1,2,1,1,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,2,2,2,2,2,2,3,3,4,5,3,1,2,1,1,1,1,1,1,0,0,0,0,3,3,0,0,1,1,0,1,0,0,0,0), nrow=6)
rownames(tdm) <- 1:6
colnames(tdm) <- paste("term", 1:229, sep="")
tdm.dist <- dist(tdm)
library(dplyr)
library(tidyr)
tdm.dist %>%
as.matrix() %>% # update dist object to a matrix
data.frame() %>% # update matrix to a data frame
setNames(nm = 1:ncol(.)) %>% # update column names
mutate(names1 = 1:nrow(.)) %>% # use rownames as a variable
gather(names2, value , -names1) %>% # reshape data
filter(names1 <= names2) # keep the values only once
# names1 names2 value
# 1 1 1 0.000000
# 2 1 2 14.966630
# 3 2 2 0.000000
# 4 1 3 12.449900
# 5 2 3 12.449900
# 6 3 3 0.000000
# 7 1 4 13.490738
# 8 2 4 13.564660
# 9 3 4 5.385165
# 10 4 4 0.000000
# 11 1 5 12.688578
# 12 2 5 12.922848
# 13 3 5 5.830952
# 14 4 5 7.416198
# 15 5 5 0.000000
# 16 1 6 12.369317
# 17 2 6 12.529964
# 18 3 6 5.830952
# 19 4 6 7.937254
# 20 5 6 7.615773
# 21 6 6 0.000000

standard evaluation version of dplyr filter appears to be ignored

I'm trying to write a function in which a user will specify a dataframe and a
column name (string) and have the string be passed to the dplyr filter function.
I am confident that I am supposed to be using filter_, but I cannot seem to get it to behave as expected.
Consider the dataframe, data below. If I want to pipe it to the NSE version of filter this is accomplished as shown below
data <- data.frame(column1 = c(-1:10))
data %>% filter(column1 >= 0)
# column1
# 1 0
# 2 1
# 3 2
# 4 3
# 5 4
# 6 5
# 7 6
# 8 7
# 9 8
# 10 9
# 11 10
However, when I attempt to implement this within a function, the filter appears to be ignored as shown below.
filter_fn <- function(d_in,column){
criteria <- interp(~ as.name(column) >= 0)
d_out = d_in %>% filter_(criteria)
return(d_out)
}
filter_fn(data, "column1")
# column1
# 1 -1
# 2 0
# 3 1
# 4 2
# 5 3
# 6 4
# 7 5
# 8 6
# 9 7
# 10 8
# 11 9
# 12 10
This isn't a nuance of passing the params through a function either as the exact same result is returned from.
data %>% filter_(interp(~ as.name(column) >= 0))
QUESTION: How can I write a function with the desired parameters that will filter a dataframe using dplyr?

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