I've looked at other threads and tried to apply it to my code but have had no luck.
CDR3_post_challenge_unique_clonecount$participant_per_cdr3aa <- as.numeric(CDR3_post_challenge_unique_clonecount$cdr3aa)
participant_list <- unique(CDR3_post_challenge_unique_clonecount$cdr3aa)
for (c in participant_list)
{
CDR3_post_challenge_unique_clonecount$participant_per_cdr3aa[CDR3_post_challenge_unique_clonecount$cdr3aa == c] <- length(unique(CDR3_post_challenge_unique_clonecount$PartID[CDR3_post_challenge_unique_clonecount$cdr3aa == c]))
}
Here is a bit of the dataframe:
cdr3aa clonecount PartID
CAAGRAARGGSVPHWFDPF 1 S-1
CAALADSGSQTDAFDIA 1 S-1
CAFHAAYGSQHGLDVW 1 S-1
CAGGLAWLVDDW 1 S-1
CAGRWFFPW 1 S-1
CAGVKNGRGMDVW 1 S-1
I think you can replace the for loop with
CDR3_post_challenge_unique_clonecount$per3 <-
as.integer(
ave(CDR3_post_challenge_unique_clonecount$PartID,
CDR3_post_challenge_unique_clonecount$cdr3aa,
FUN = function(z) length(unique(z)))
)
I'll demonstrate with mtcars, using the follow analogs:
mtcars --> CDR3_post_challenge_unique_clonecount
cyl --> cdr3aa, the categorical variable in which we want to count PartID
drat --> PartID, the thing we want to count (uniquely) within each cdr3aa
mtcars$drat_per_cyl <- ave(mtcars$drat, mtcars$cyl, FUN = function(z) length(unique(z)))
mtcars
# mpg cyl disp hp drat wt qsec vs am gear carb drat_per_cyl
# Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 5
# Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 5
# Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 10
# Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 5
# Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 11
# Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 5
# Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 11
# Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 10
# Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 10
# Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 5
# Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 5
# Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 11
# Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 11
# Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 11
# Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 11
# Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 11
# Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 11
# Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 10
# Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 10
# Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 10
# Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 10
# Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 11
# AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 11
# Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 11
# Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 11
# Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 10
# Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 10
# Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 10
# Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 11
# Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 5
# Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 11
# Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 10
Notes:
ave is a little brain-dead in that the class of the return value is always the same as the class of the first argument. This means that one cannot count unique "character" and expect to get an integer, it is instead returned as a string. It's because of this that I wrap ave in as.integer(.).
ave returns a vector the same length as the input, with values corresponding 1-for-1 (meaning the order is relevant and preserved). In my example of mtcars, this means that it is effectively doing something like this:
ind4 <- which(mtcars$cyl == 4L)
ind4
# [1] 3 8 9 18 19 20 21 26 27 28 32
length(unique(mtcars$drat[ind4]))
# [1] 10
ind6 <- which(mtcars$cyl == 6L)
ind6
# [1] 1 2 4 6 10 11 30
length(unique(mtcars$drat[ind6]))
# [1] 5
### ...
but it will place the return value 10 in the ind4 positions of the return value. For example, because of my ind6, the return value will start with
c(5, 5, .., 5, .., 5, .., .., .., 5, 5, .., .....)
Because of ind4, it will contain
c(.., .., 10, .., .., .., .., 10, 10, .....)
(And same for cyl==8L.)
Related
It is a reference to this:
Alternatives to kmeans() for spotting small clusters for k=2
In the comments it is suggested something like this:
cc = ClusterR::GMM(mtcars, gaussian_comps = 2); predict(cc, mtcars)
But assume I have a tibble database db, I would like a code working like:
db %>%
mutate(cluster = clustering_function(selection())) -> db
Whereas selection is a selector of columns, and the clustering function put together the clustering methods and the assignment of predicted values.
I think your best bet in this situation is bind_cols():
library(tidyverse)
library(ClusterR)
#> Loading required package: gtools
cluster_fit <- ClusterR::GMM(mtcars, gaussian_comps = 2)
bind_cols(
mtcars,
.preds = predict(cluster_fit, mtcars)
)
#> mpg cyl disp hp drat wt qsec vs am gear carb .preds
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 1
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 1
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 2
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 1
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 1
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 1
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 1
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 2
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 2
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 2
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 2
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 2
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 2
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 1
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 2
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 1
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 1
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 2
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 1
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 2
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 1
Created on 2022-12-01 with reprex v2.0.2
I need to calculate row sums for a data frame except for the first 5 columns. The output will consist of these first 5 columns and the row sums.
I tried this:
df1$rowsums <- rowSums(df1[,-c(1:5)], na.rm= T)
But I get this error message:
Error in rowSums(df1[, c(1:5)], na.rm = T) : 'x' must be numeric
without data my guess is, that the columns you are using are not numeric. Then it will be hard to calculate the rowsum. Make sure, that columns you use for summing (except 1:5) are indeed numeric, then the following code should work:
library(tidyverse)
df2 <- df1[,-c(1:5)] %>%
rowwise() %>%
mutate(rowsum = sum(c_across(everything()), na.rm = T))
df_result <- cbind(df1[,c(1:5)], df2$rowsum)
EDIT: I added na.rm = T (dont know if necessary). And you might want to rename the resulting "df2$rowsum" column of the resulting df_result dataframe this can be done using
df_result <- df_result %>% rename(rowsum_name = "df2$rowsum")
You could select the columns except the first 5 by -c(1:5) and use rowSums like this (I use mtcars as an example):
library(dplyr)
mtcars %>%
mutate(rowsums = select(., -c(1:5)) %>%
rowSums(na.rm = TRUE))
#> mpg cyl disp hp drat wt qsec vs am gear carb rowsums
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 28.080
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 28.895
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 27.930
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 27.655
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 25.460
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 28.680
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 26.410
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 30.190
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 33.050
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 30.740
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 31.340
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 27.470
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 27.330
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 27.780
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 30.230
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 30.244
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 29.765
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 28.670
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 28.135
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 28.735
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 27.475
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 25.390
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 25.735
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 26.250
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 25.895
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 27.835
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 26.840
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 27.413
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 27.670
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 30.270
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 32.170
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 29.380
Created on 2022-07-09 by the reprex package (v2.0.1)
I was wondering if there might be a way to specify the name of a new variable used in mutate() (argument mutate_nm = "bb") inside the foo() function below?
library(tidyverse)
foo <- function(data = mtcars, mutate_nm, val = 12){
mutate(mutate_nm = val)
}
#==== EXAMPLE OF USE:
foo(mutate_nm = "bb") Doesn't produce a dataframe with a new column named `"bb"`
We need to use := with !! or {{}}. Also, mutate by itself doesn't create any column i.e. need the data argument as well
foo <- function(data = mtcars, mutate_nm, val = 12){
data %>%
mutate({{mutate_nm}} := val)
#or use
#mutate(!! mutate_nm := val)
}
-testing
> foo(mtcars, "bb")
mpg cyl disp hp drat wt qsec vs am gear carb bb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 12
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 12
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 12
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 12
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 12
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 12
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 12
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 12
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 12
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 12
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 12
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 12
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 12
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 12
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 12
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 12
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 12
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 12
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 12
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 12
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 12
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 12
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 12
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 12
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 12
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 12
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 12
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 12
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 12
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 12
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 12
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 12
I want to assert that one column in my data is always greater than the other column using the assertr package. As an example let's day that mtcars mpg should always be greater than cyl. Here is what I've tried but it throws an error. Am I making a simple mistake?
library(assertr)
greater_than <- function(x, y){if(x <= y) return(FALSE)}
assert(mtcars, greater_than, x = mpg, y = cyl)
> Error in improper.predicate(x) : argument "y" is missing, with no default
I don't think you want assert - instead I think you want assert_rows. That means you need a row reduction function (takes a row and results in a single value) in addition to the predicate function. Here the reduction function just finds the difference between the first two columns of a data frame. Then the last argument in assert_rows tells it to essentially use a data frame only consisting of mpg and cyl (in that order) for passing to the row reduction function.
I will say, the documentation is not great for this package. I had to go to their GitHub and then consult the code of assert_rows directly to come up with this answer.
library(assertr)
greater_than_0 <- function(x){if(x <= 0) return(FALSE)}
row_redux <- function(df){df[[1]] - df[[2]]}
assert_rows(mtcars, row_redux, greater_than_0, c(mpg, cyl))
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
Created on 2019-09-17 by the reprex package (v0.3.0)
I have a dataframe with a list-column which itself contains dataframes (see below). Essentially, I am trying to add values from another column in the parent dataframe into the smaller dataframe by creating another column.
This is a simplified example- my real application is more complex.
library(tidyverse)
# What I am trying to do: add column "a" to dataframe within the list column
add_column(mtcars, a = 1)
#> mpg cyl disp hp drat wt qsec vs am gear carb a
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 1
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 1
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 1
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 1
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 1
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 1
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 1
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 1
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 1
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 1
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 1
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 1
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 1
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 1
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 1
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 1
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 1
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 1
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 1
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 1
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 1
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 1
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 1
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 1
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 1
Then create list-column:
(df <- tibble(data = rep(list(mtcars), times = 3), a = 1:3))
#> # A tibble: 3 x 2
#> data a
#> <list> <int>
#> 1 <data.frame [32 x 11]> 1
#> 2 <data.frame [32 x 11]> 2
#> 3 <data.frame [32 x 11]> 3
But this doesn't work:
df %>%
rowwise() %>%
modify_at("data", ~ add_column(., a = a))
# Error in eval_tidy(xs[[i]], unique_output): object 'a' not found
We may use
df %>% mutate(data = data %>% map2(a, ~add_column(.x, a = .y)))
In this way we start by mutating a column as usual, but then recognising that it's a list we use map2 along with the a column.