I am aiming to normalize the length of vectors for averaging within a tidy data set. Using approx seems to be way to go but I can't make it work efficiently in tidyverse. One issue is probably related to resizing within a dataframe. Here's a reproducible example:
# create reproducible dataset
i = 80
I = 110
id = rep("AA", I+i)
event = rep("event1", I+i)
sub_event = NA
sub_event[1:i] = 1
sub_event[i+1:I] = 2
sub_event = as.factor(sub_event)
y1 = sin(seq(0, 5*pi, length.out = i))
y2 = sin(seq(0, 5*pi, length.out = I))
y3 = cos(seq(0, 5*pi, length.out = i))
y4 = cos(seq(0, 5*pi, length.out = I))
var1 = c(y1,y2)
var2 = c(y3,y4)
df1 <- data.frame(id, event, sub_event,var1, var2)
df2 <- df1
df2$event = "event2"
df <- rbind(df1, df2)
temp <- df
temp$id = "BB"
df <- rbind(df, temp)
# create a "time" vector for sub_event
df <- df %>%
group_by(id, event, sub_event) %>%
mutate(sub_event_time = seq_along(var1)) %>%
select(id, event, sub_event, sub_event_time, everything()) %>%
ungroup()
Plot var1
# plot
ggplot(df,
aes(x=sub_event_time, y=var1, colour = sub_event)) +
geom_point() +
geom_path() +
facet_wrap(id~event)
I want transform/resample data to obtain length of var1 for each sub_events to be the length of the longest sub_event within each event for each id.
For instance we want: length of var1 for event 1 sub event 1 = length of var1 for event 1 sub event 2 (which is the longest).
Here's an attempt:
# attempt for var1 only
aim.df <- df %>%
ungroup() %>%
select(-var2) %>%
group_by(id, event) %>%
mutate(max_sub_event_time = max(sub_event_time)) %>%
mutate(var1 = approx(var1, n = max_sub_event_time)$y)
This returns the following error:
Error in mutate_impl(.data, dots) :
Column `var1` must be length 190 (the group size) or one, not 110
In addition: Warning messages:
1: In if (n <= 0) stop("'approx' requires n >= 1") :
the condition has length > 1 and only the first element will be used
2: In seq.int(x[1L], x[nx], length.out = n) :
first element used of 'length.out' argument
Any ideas ?
steps...
group_by(id, event, sub_event)
sub_event_time
since it will be irrelevant once you add observations summarise
the result of the approx
function as a list column (you will have to convert var1
and max_sub_event_time
to appropriate input for approx
) unnest
the resulting list column group_by(id, event, sub_event)
again and add a new sub_event_time
code...
library(dplyr)
library(tidyr)
df %>%
ungroup() %>%
select(-var2) %>%
group_by(id, event) %>%
mutate(max_sub_event_time = max(sub_event_time)) %>%
group_by(id, event, sub_event) %>%
select(-sub_event_time) %>%
summarise(var1_int = list(approx(as.numeric(var1), n = first(max_sub_event_time))$y)) %>%
unnest() %>%
group_by(id, event, sub_event) %>%
mutate(sub_event_time = row_number())
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