This is my code
col_vec <- vector(length = 11)
my_df <- mtcars
for (i in 1:11) {
col_vec[i]<- glue::glue("col{i}")
colnames(my_df) <- col_vec
}
I want to do this bellow for all columns but I would like to use glue package:
my_df %>% mutate(up_down = as.numeric(col1) - lag(as.numeric(col1),
default = as.numeric(col1)[1]))
I tried this, but it didnt work:
for (i in 1:11) {
my_df %>% mutate(glue::glue("up_down_{i} = as.numeric(col{i}) - lag(as.numeric(col{i}),
default = as.numeric(col{i})[1]))"))
}
The output should give me for each col_* a column that calculate the difference between rows.
So, I would have 22 columns as a result
CodePudding user response：
The below answer should work,
Basically, we use mutate() and across() to create new columns, that take a lag difference of a column and apply that across to each numeric column. We then use the .names argument in the across to give each new lag column a new name.
my_df %>%
mutate(across(where(is.numeric),list(lag=~.x-lag(.x)),.names= "{.col}_{.fn}"))
CodePudding user response：
We can use across more easily without having to assign with glue on the lhs. The new columns are automatically added once we modify the .names
library(dplyr)
my_df2 <- my_df %>%
mutate(across(starts_with('col'), ~ .x -
lag(.x, default = first(.x)), .names = "up_down_{.col}"))
-output
> head(my_df2)
col1 col2 col3 col4 col5 col6 col7 col8 col9 col10 col11 up_down_col1 up_down_col2 up_down_col3 up_down_col4 up_down_col5
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 0.0 0 0 0 0.00
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 0.0 0 0 0 0.00
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 1.8 -2 -52 -17 -0.05
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 -1.4 2 150 17 -0.77
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 -2.7 2 102 65 0.07
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 -0.6 -2 -135 -70 -0.39
up_down_col6 up_down_col7 up_down_col8 up_down_col9 up_down_col10 up_down_col11
Mazda RX4 0.000 0.00 0 0 0 0
Mazda RX4 Wag 0.255 0.56 0 0 0 0
Datsun 710 -0.555 1.59 1 0 0 -3
Hornet 4 Drive 0.895 0.83 0 -1 -1 0
Hornet Sportabout 0.225 -2.42 -1 0 0 1
Valiant 0.020 3.20 1 0 0 -1
If we want to use a for loop, it would need assignment (<-) to original dataset for creating new columns. In addition, we need to evaluate (!!) while assigning with :=) (instead of eval/parse)
my_df3 <- my_df
for(i in 1:11) {
my_df3 <- my_df3 %>%
mutate(!! glue::glue("up_down_{i}") :=
!! rlang::sym(glue::glue("col{i}")) -
lag(!!rlang::sym(glue::glue("col{i}")), default =
first(!!rlang::sym(glue::glue("col{i}")))))
}
-output
> head(my_df3)
col1 col2 col3 col4 col5 col6 col7 col8 col9 col10 col11 up_down_1 up_down_2 up_down_3 up_down_4 up_down_5 up_down_6 up_down_7
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 0.0 0 0 0 0.00 0.000 0.00
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 0.0 0 0 0 0.00 0.255 0.56
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 1.8 -2 -52 -17 -0.05 -0.555 1.59
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 -1.4 2 150 17 -0.77 0.895 0.83
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 -2.7 2 102 65 0.07 0.225 -2.42
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 -0.6 -2 -135 -70 -0.39 0.020 3.20
up_down_8 up_down_9 up_down_10 up_down_11
Mazda RX4 0 0 0 0
Mazda RX4 Wag 0 0 0 0
Datsun 710 1 0 0 -3
Hornet 4 Drive 0 -1 -1 0
Hornet Sportabout -1 0 0 1
Valiant 1 0 0 -1
> dim(my_df3)
[1] 32 22