I am using dplyr's lag() function and I am trying to figure out not make NA (but take the original value instead) as the default value for the blank lagged cells.
Here is my code:
df <- data_frame(d1 = runif(10, 1, 5),
d2 = runif(10, 2, 6),
d3 = runif(10, 3, 7),
d4 = runif(10, 4, 8),
d5 = runif(10, 5, 9),
d6 = runif(10, 6, 10),
d7 = runif(10, 7, 11),
d8 = runif(10, 8, 12)) %>% rownames_to_column()
df %>%
gather(key = "col", value = "val", -"rowname") %>%
group_by(col) %>%
mutate(new_col = ifelse(val >= lag(val, 2) + lag(val, 2)*0.4, NA, val))
It doesn't work if I do this code (which, honestly, I quite expect):
df %>%
gather(key = "col", value = "val", -"rowname") %>%
group_by(col) %>%
mutate(new_col = if_else(val >= lag(val, 2, default = val) + lag(val, 2, default = val)*0.4, NA, val))
What am I missing so that I can arrive to this result?
rowname col val new_col
<chr> <chr> <dbl> <dbl>
1 1 d1 1.31 **1.31**
2 2 d1 4.10 **4.10**
3 3 d1 3.81 NA
4 4 d1 4.52 4.52
5 5 d1 3.89 3.89
6 6 d1 1.01 1.01
7 7 d1 2.68 2.68
8 8 d1 2.81 NA
9 9 d1 1.18 1.18
10 10 d1 1.19 1.19
# ... with 70 more rows
Appreciate any help!
You could replace the n lagged values with the original values.
library(dplyr)
n <- 2
df %>%
tidyr::pivot_longer(cols = -rowname, values_to = 'val', names_to = 'col') %>%
group_by(col) %>%
mutate(new_col = if_else(val >= lag(val, n) + lag(val, n)*0.4, NA_real_, val),
new_col = replace(new_col, 1:n, val[1:n]))