Create multiple new variables that are calculated from existing variables
I'm trying to figure out how to create multiple new variables that are calculated using variables currently in my dataset
Here's some example data
library(tidyverse)
df <- data.frame(
a1 = rnorm(100),
a2 = rnorm(100),
b1 = rnorm(100),
b2 = rnorm(100),
c1 = rnorm(100),
c2 = rnorm(100)
)
Essentially, I want to create a new variable for each a, b, c pair that divides a1 by a2, b1 by b2, etc. For example:
df <- df %>%
mutate(a3 = a1/a2)
The variables in my dataset don't follow these naming conventions, so I feel like I need to assign names to vectors:
numerators <- c('a1', 'b1', 'c1')
denominators <- c('a2', 'b2', 'c2')
And then creating a new variable would follow the basic convention
mutate(newvars = numerators/denominators)
But I'm stuck as to how to actually do this. Any help would be much appreciated -- thanks!
r dplyr iteration purrr
add a comment |
I'm trying to figure out how to create multiple new variables that are calculated using variables currently in my dataset
Here's some example data
library(tidyverse)
df <- data.frame(
a1 = rnorm(100),
a2 = rnorm(100),
b1 = rnorm(100),
b2 = rnorm(100),
c1 = rnorm(100),
c2 = rnorm(100)
)
Essentially, I want to create a new variable for each a, b, c pair that divides a1 by a2, b1 by b2, etc. For example:
df <- df %>%
mutate(a3 = a1/a2)
The variables in my dataset don't follow these naming conventions, so I feel like I need to assign names to vectors:
numerators <- c('a1', 'b1', 'c1')
denominators <- c('a2', 'b2', 'c2')
And then creating a new variable would follow the basic convention
mutate(newvars = numerators/denominators)
But I'm stuck as to how to actually do this. Any help would be much appreciated -- thanks!
r dplyr iteration purrr
add a comment |
I'm trying to figure out how to create multiple new variables that are calculated using variables currently in my dataset
Here's some example data
library(tidyverse)
df <- data.frame(
a1 = rnorm(100),
a2 = rnorm(100),
b1 = rnorm(100),
b2 = rnorm(100),
c1 = rnorm(100),
c2 = rnorm(100)
)
Essentially, I want to create a new variable for each a, b, c pair that divides a1 by a2, b1 by b2, etc. For example:
df <- df %>%
mutate(a3 = a1/a2)
The variables in my dataset don't follow these naming conventions, so I feel like I need to assign names to vectors:
numerators <- c('a1', 'b1', 'c1')
denominators <- c('a2', 'b2', 'c2')
And then creating a new variable would follow the basic convention
mutate(newvars = numerators/denominators)
But I'm stuck as to how to actually do this. Any help would be much appreciated -- thanks!
r dplyr iteration purrr
I'm trying to figure out how to create multiple new variables that are calculated using variables currently in my dataset
Here's some example data
library(tidyverse)
df <- data.frame(
a1 = rnorm(100),
a2 = rnorm(100),
b1 = rnorm(100),
b2 = rnorm(100),
c1 = rnorm(100),
c2 = rnorm(100)
)
Essentially, I want to create a new variable for each a, b, c pair that divides a1 by a2, b1 by b2, etc. For example:
df <- df %>%
mutate(a3 = a1/a2)
The variables in my dataset don't follow these naming conventions, so I feel like I need to assign names to vectors:
numerators <- c('a1', 'b1', 'c1')
denominators <- c('a2', 'b2', 'c2')
And then creating a new variable would follow the basic convention
mutate(newvars = numerators/denominators)
But I'm stuck as to how to actually do this. Any help would be much appreciated -- thanks!
r dplyr iteration purrr
r dplyr iteration purrr
asked Nov 14 '18 at 0:57
EkholmeEkholme
1325
1325
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1 Answer
1
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votes
Here is an option to split
the dataset into a list
of data.frame based on the column name pattern, then reduce
it by dividing elementwise on each pair of columns in each of the dataset and bind with the original dataset
library(tidyverse)
df %>%
split.default(sub("\d+", "", names(.))) %>%
map_df(reduce, `/`) %>%
rename_all(~paste0(., 3)) %>%
bind_cols(df, .)
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Here is an option to split
the dataset into a list
of data.frame based on the column name pattern, then reduce
it by dividing elementwise on each pair of columns in each of the dataset and bind with the original dataset
library(tidyverse)
df %>%
split.default(sub("\d+", "", names(.))) %>%
map_df(reduce, `/`) %>%
rename_all(~paste0(., 3)) %>%
bind_cols(df, .)
add a comment |
Here is an option to split
the dataset into a list
of data.frame based on the column name pattern, then reduce
it by dividing elementwise on each pair of columns in each of the dataset and bind with the original dataset
library(tidyverse)
df %>%
split.default(sub("\d+", "", names(.))) %>%
map_df(reduce, `/`) %>%
rename_all(~paste0(., 3)) %>%
bind_cols(df, .)
add a comment |
Here is an option to split
the dataset into a list
of data.frame based on the column name pattern, then reduce
it by dividing elementwise on each pair of columns in each of the dataset and bind with the original dataset
library(tidyverse)
df %>%
split.default(sub("\d+", "", names(.))) %>%
map_df(reduce, `/`) %>%
rename_all(~paste0(., 3)) %>%
bind_cols(df, .)
Here is an option to split
the dataset into a list
of data.frame based on the column name pattern, then reduce
it by dividing elementwise on each pair of columns in each of the dataset and bind with the original dataset
library(tidyverse)
df %>%
split.default(sub("\d+", "", names(.))) %>%
map_df(reduce, `/`) %>%
rename_all(~paste0(., 3)) %>%
bind_cols(df, .)
answered Nov 14 '18 at 4:07
akrunakrun
410k13199274
410k13199274
add a comment |
add a comment |
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