Find the smallest value under conditions about the index (NA output possible) - r

Question

Question:
I am using dplyr to do data analysis in R, and I come across the following problem.
My data frame is like this:
item day val
1 A 1 90
2 A 2 100
3 A 3 110
4 A 5 80
5 A 8 70
6 B 1 75
7 B 3 65
The data frame is already arranged in item, day. Now I want to mutate a new column, with each row being the smallest value of the same group AND having the day to be within the next 2 days.
For the example above, I want the resulting data frame to be:
item day val output
1 A 1 90 100 # the smaller of 100 and 110
2 A 2 100 110 # the only value within 2 days
3 A 3 110 80 # the only value within 2 days
4 A 5 80 NA # there is no data within 2 days
5 A 8 70 NA # there is no data within 2 days
6 B 1 75 65 # the only value within 2 days
7 B 3 65 NA # there is no data within 2 days
I understand that I will probably use group_by and mutate, but how to write the inside function in order to achieve my desired result?
Any help is greatly appreciated. Let me know if you need me to clarify anything. Thank you!

Answer 1

Try this:
df %>%
# arrange(item, day) %>% # if not already arranged
# take note of the next two values & corresponding difference in days
group_by(item) %>%
mutate(val.1 = lead(val),
day.1 = lead(day) - day,
val.2 = lead(val, 2),
day.2 = lead(day, 2) - day) %>%
ungroup() %>%
# if the value is associated with a day more than 2 days away, change it to NA
mutate(val.1 = ifelse(day.1 %in% c(1, 2), val.1, NA),
val.2 = ifelse(day.2 %in% c(1, 2), val.2, NA)) %>%
# calculate output normally
group_by(item, day) %>%
mutate(output = min(val.1, val.2, na.rm = TRUE)) %>%
ungroup() %>%
# arrange results
select(item, day, val, output) %>%
mutate(output = ifelse(output == Inf, NA, output)) %>%
arrange(item, day)
# A tibble: 7 x 4
item day val output
<fctr> <int> <int> <dbl>
1 A 1 90 100
2 A 2 100 110
3 A 3 110 80.0
4 A 5 80 NA
5 A 8 70 NA
6 B 1 75 65.0
7 B 3 65 NA
Data:
df <- read.table(text = " item day val
1 A 1 90
2 A 2 100
3 A 3 110
4 A 5 80
5 A 8 70
6 B 1 75
7 B 3 65", header = TRUE)

Answer 2

We can use complete from the tidyr package to complete the dataset by day, and then use lead from dplyr and rollapply from zoo to find the minimum of the next two days.
library(dplyr)
library(tidyr)
library(zoo)
DF2 <- DF %>%
group_by(item) %>%
complete(day = full_seq(day, period = 1)) %>%
mutate(output = rollapply(lead(val), width = 2, FUN = min, na.rm = TRUE,
fill = NA, align = "left")) %>%
drop_na(val) %>%
ungroup() %>%
mutate(output = ifelse(output == Inf, NA, output))
DF2
# # A tibble: 7 x 4
# item day val output
# <chr> <dbl> <int> <dbl>
# 1 A 1.00 90 100
# 2 A 2.00 100 110
# 3 A 3.00 110 80.0
# 4 A 5.00 80 NA
# 5 A 8.00 70 NA
# 6 B 1.00 75 65.0
# 7 B 3.00 65 NA
DATA
DF <- read.table(text = "item day val
1 A 1 90
2 A 2 100
3 A 3 110
4 A 5 80
5 A 8 70
6 B 1 75
7 B 3 65",
header = TRUE, stringsAsFactors = FALSE)

Answer 3

We'll create a dataset with modified day, so we can left join it on the original dataset, keeping only minimum value.
df %>%
left_join(
bind_rows(mutate(.,day=day-1),mutate(.,day=day-2)) %>% rename(output=val)) %>%
group_by(item,day,val) %>%
summarize_at("output",min) %>%
ungroup
# # A tibble: 7 x 4
# item day val output
# <fctr> <dbl> <int> <dbl>
# 1 A 1 90 100
# 2 A 2 100 110
# 3 A 3 110 80
# 4 A 5 80 NA
# 5 A 8 70 NA
# 6 B 1 75 65
# 7 B 3 65 NA
data
df <- read.table(text = " item day val
1 A 1 90
2 A 2 100
3 A 3 110
4 A 5 80
5 A 8 70
6 B 1 75
7 B 3 65", header = TRUE)