Mutating dummy variables in dplyr
If you want to do this with the pipe, you can do something like:
library(dplyr)
library(sjmisc)
mydf %>%
to_dummy(day, suffix = "label") %>%
bind_cols(mydf) %>%
select(x, day, everything())
Returns:
# A tibble: 9 x 9
x day day_Fri day_Mon day_Sat day_Sun day_Thurs day_Tues day_Wed
<fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 a Mon 0. 1. 0. 0. 0. 0. 0.
2 b Tues 0. 0. 0. 0. 0. 1. 0.
3 c Wed 0. 0. 0. 0. 0. 0. 1.
4 d Thurs 0. 0. 0. 0. 1. 0. 0.
5 e Fri 1. 0. 0. 0. 0. 0. 0.
6 f Sat 0. 0. 1. 0. 0. 0. 0.
7 g Sun 0. 0. 0. 1. 0. 0. 0.
8 h Fri 1. 0. 0. 0. 0. 0. 0.
9 i Mon 0. 1. 0. 0. 0. 0. 0.
With dplyr and tidyr we could do:
library(dplyr)
library(tidyr)
mydf %>%
mutate(var = 1) %>%
spread(day, var, fill = 0, sep = "_") %>%
left_join(mydf) %>%
select(x, day, everything())
And with base R we could do something like:
as.data.frame.matrix(table(rep(mydf$x, lengths(mydf$day)), unlist(mydf$day)))
Returns:
Fri Mon Sat Sun Thurs Tues Wed
a 0 1 0 0 0 0 0
b 0 0 0 0 0 1 0
c 0 0 0 0 0 0 1
d 0 0 0 0 1 0 0
e 1 0 0 0 0 0 0
f 0 0 1 0 0 0 0
g 0 0 0 1 0 0 0
h 1 0 0 0 0 0 0
i 0 1 0 0 0 0 0
use dplyr to create dummy variables
Try mutate:
> y <- data.frame(var1 = (-2):2)
> y %>% mutate(var2 = as.numeric(var1 > 0.5))
var1 var2
1 -2 0
2 -1 0
3 0 0
4 1 1
5 2 1
Update: dplyr now uses %>% in place of %.%
create a dummy variable (using mutate) based on a pattern in a character string
This works:
library(stringr)
iris%>% mutate(
anyV = ifelse(str_detect(Species, "v"), "withV", "noV"))
Sepal.Length Sepal.Width Petal.Length Petal.Width Species anyV
1 5.1 3.5 1.4 0.2 setosa noV
2 4.9 3.0 1.4 0.2 setosa noV
3 4.7 3.2 1.3 0.2 setosa noV
4 4.6 3.1 1.5 0.2 setosa noV
5 5.0 3.6 1.4 0.2 setosa noV
...
52 6.4 3.2 4.5 1.5 versicolor withV
53 6.9 3.1 4.9 1.5 versicolor withV
54 5.5 2.3 4.0 1.3 versicolor withV
55 6.5 2.8 4.6 1.5 versicolor withV
56 5.7 2.8 4.5 1.3 versicolor withV
57 6.3 3.3 4.7 1.6 versicolor withV
58 4.9 2.4 3.3 1.0 versicolor withV
59 6.6 2.9 4.6 1.3 versicolor withV
An alternative to nested ifelse statements:
iris%>% mutate(newVar = case_when(
str_detect(.$Species, "se") ~ "group1",
str_detect(.$Species, "ve") ~ "group2",
str_detect(.$Species, "vi") ~ "group3",
TRUE ~ as.character(.$Species)))
Mutate dummy variable with observation before and after
Here is an approach using dplyr:
library(dplyr)
df1 %>%
group_by(title, v1) %>%
mutate(summe = sum(volume)) %>%
group_by(title) %>%
mutate(dummy_volume = all(summe > 0)) %>%
select(-summe)
# A tibble: 15 x 5
# Groups: title [3]
title day volume v1 dummy_volume
<fct> <dbl> <dbl> <dbl> <lgl>
1 x 0 0 0 FALSE
2 x 1 0 0 FALSE
3 x 2 1 1 FALSE
4 x 3 1 1 FALSE
5 x 4 2 1 FALSE
6 y 0 3 0 FALSE
7 y 1 0 1 FALSE
8 y 2 0 1 FALSE
9 y 3 0 1 FALSE
10 y 4 0 1 FALSE
11 z 0 3 0 TRUE
12 z 1 3 0 TRUE
13 z 2 4 0 TRUE
14 z 3 2 1 TRUE
15 z 4 1 1 TRUE
With the Dummy coded as 0/1 as in your desired output:
df1 %>%
group_by(title, v1) %>%
mutate(summe = sum(volume)) %>%
group_by(title) %>%
mutate(dummy_volume = as.integer(all(summe > 0))) %>%
select(-summe)
# A tibble: 15 x 5
# Groups: title [3]
title day volume v1 dummy_volume
<fct> <dbl> <dbl> <dbl> <int>
1 x 0 0 0 0
2 x 1 0 0 0
3 x 2 1 1 0
4 x 3 1 1 0
5 x 4 2 1 0
6 y 0 3 0 0
7 y 1 0 1 0
8 y 2 0 1 0
9 y 3 0 1 0
10 y 4 0 1 0
11 z 0 3 0 1
12 z 1 3 0 1
13 z 2 4 0 1
14 z 3 2 1 1
15 z 4 1 1 1
creating a conditional dummy variable using dplyr and ifelse statements in R
You can use any to check if any value of manu_GDP is greater than 20.
library(dplyr)
df %>%
group_by(country) %>%
summarise(new_dummy = as.integer(any(manu_GDP > 20, na.rm = TRUE)))
If you want to maintain the number of rows in the data use mutate instead of summarise.
Creating dummy variables as counts using tidyverse/dplyr
using reshape2 but you could pretty much use any package that lets you reformat from long to wide
library(reshape2)
df = dcast(fruitData,ID~FRUIT,length)
> df
ID apple banana grape
1 1 2 1 0
2 2 1 0 1
3 3 1 0 0
Transforming dummy variables to single column in R
We could first define a custom function that allows us to recode dummy variables based on their variable names, below called var_nm2value.
This function takes the values of the variables as x argument. In dplyr::across this is the .x part. And it takes a list of name-value pairs as value_ls argument. The function just loops over the list of name-value pairs, checks if the name in value_ls is found in the variable name. To do this it uses grepl on dplyr::cur_column(). If we have a match then we replace all 1s with the value from our value_ls and we return all other values, that is the zeros, as is.
Then we can define a list of recode values, below recode_ls.
Finally, we use purrr::map_dfc in a dplyr::summarise where we use the variable strings we want to create "age" and "chol_test", then ii) select only columns which contain this string, and in each iteration we iii) apply dplyr::across to recode the values, iv) pipe the result in a do.call to get the max and finally v) recode 0s to NA:
# custom function to recode 0/1 dummy variables based on their variable name an
var_nm2value <- function(x, values_ls) {
for (val in seq_along(values_ls)) {
if(grepl(names(values_ls)[val], dplyr::cur_column())) {
return(ifelse(x == 1L, values_ls[[val]], x))
}
}
}
# define list of recode values
recode_ls <- list(low = 1, medium = 2, high = 3)
library(tidyverse)
# apply functions to data.frame
df1 %>%
summarise(race = race,
gender = gender,
map_dfc(set_names(c("age", "chol_test")), # i)
function(x) {
select(., contains(x)) %>% # ii)
summarise("{x}" := across(everything(), var_nm2value, recode_ls) %>% # iii)
do.call("pmax", .) %>% # iv)
ifelse(. == 0, NA, .))} # v)
))
#> race gender age chol_test
#> 1 white 0 1 NA
#> 2 white 0 1 NA
#> 3 white 1 1 NA
#> 4 black 1 2 NA
#> 5 white 0 3 3
#> 6 black 0 2 1
Created on 2022-01-03 by the reprex package (v0.3.0)
Using dplyr to gather dummy variables
This can be done using the 'tidyverse' library - specificially 'tidyr' and 'dplyr'. The following code produces the output you are after.
library(tidyverse)
type %>% gather(TypeOfCar, Count) %>% filter(Count >= 1) %>% select(TypeOfCar)
Output:
TypeOfCar
<chr>
1 convertible
2 convertible
3 convertible
4 convertible
5 coupe
6 sedan
Hopefully this solves your problem, let me know if any changes are needed! Thanks.
How to generate a set of dummy variables dependent on values in several other columns with same prefix in R?
Does this help you?
library(tidyverse)
document <- data.frame(
stringsAsFactors = FALSE,
ID = c(1L, 2L, 3L, 4L),
Name = c("Contract XYZ","Agreement ABC",
"Document 123","Empty Space"),
Year = c(2000L, 2003L, 2003L, 2004L),
K1 = c("transport", "pens", "elephants", "music"),
K2 = c("elephants", "music", NA, NA),
K50 = c(NA, NA, NA, "transport")
)
document %>%
pivot_longer(starts_with("K")) %>%
select(-name) %>%
filter(! is.na(value)) %>%
mutate(has_property = 1) %>%
pivot_wider(names_from = value, values_from = has_property)
#> # A tibble: 4 x 7
#> ID Name Year transport elephants pens music
#> <int> <chr> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 1 Contract XYZ 2000 1 1 NA NA
#> 2 2 Agreement ABC 2003 NA NA 1 1
#> 3 3 Document 123 2003 NA 1 NA NA
#> 4 4 Empty Space 2004 1 NA NA 1
Created on 2021-09-21 by the reprex package (v2.0.1)
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