Creating sequence of dates in a column in a dataframe in R - r

Question

I am having a data frame in R as follows:
df <- data.frame("Type" = c("Item A","Item B"), "Frequency" = c("Quarterly","Other"), "Date" = as.Date(c("2021-02-05","2021-05-05")),"endDate" = as.Date("2021-12-12"), stringsAsFactors = F)
I am trying to generate the sequence of dates between Date and endDate as each row. I am using the code below to generate the sequence
df <- df %>%
dplyr::mutate(id = 1:nrow(df),deliveryDate = ifelse(
df$Frequency == "Quarterly", list(seq(as.Date(df$Date), as.Date(df$endDate), by = "3 month")),
ifelse(df$Frequency == "Monthly", list(seq(as.Date(df$Date), as.Date(df$endDate), by = "month")),
ifelse(df$Frequency %in% c("Other"),list(seq(as.Date(df$Date), as.Date(df$Date), by = "month")),df$Date)))) %>%
tidyr::unnest(deliveryDate) %>%
dplyr::group_by(Type) %>%
dplyr::mutate(deliveryNumber = row_number()) %>%
dplyr::select(deliveryNumber,Type, Frequency, deliveryDate) %>%
TO be more descriptive, the sequence of date will be generated based on the frequency of the type. So to handle that case, I used dplyr::mutate().
But I am getting an error as follows:
Error: Problem with `mutate()` input `deliveryDate`.
x 'from' must be of length 1
ℹ Input `deliveryDate` is `ifelse(...)`.
Can anyone help me solve this issue in R? Thanks in advance!!!

Answer 1

You should consider a named vector:
library(tidyverse)
vec<-c(Quarterly = "3 months", Other = "month")
df %>%
rowwise() %>%
mutate(deliveryDate = list(seq(Date,endDate, by = vec[Frequency]))) %>%
unnest(deliveryDate)
# A tibble: 12 x 5
Type Frequency Date endDate deliveryDate
<chr> <chr> <date> <date> <date>
1 Item A Quarterly 2021-02-05 2021-12-12 2021-02-05
2 Item A Quarterly 2021-02-05 2021-12-12 2021-05-05
3 Item A Quarterly 2021-02-05 2021-12-12 2021-08-05
4 Item A Quarterly 2021-02-05 2021-12-12 2021-11-05
5 Item B Other 2021-05-05 2021-12-12 2021-05-05
6 Item B Other 2021-05-05 2021-12-12 2021-06-05
7 Item B Other 2021-05-05 2021-12-12 2021-07-05
8 Item B Other 2021-05-05 2021-12-12 2021-08-05
9 Item B Other 2021-05-05 2021-12-12 2021-09-05
10 Item B Other 2021-05-05 2021-12-12 2021-10-05
11 Item B Other 2021-05-05 2021-12-12 2021-11-05
12 Item B Other 2021-05-05 2021-12-12 2021-12-05

Answer 2

Use complete
df %>% group_by(Type) %>% mutate(DeliveryDate = Date,
Frequency = case_when(Frequency %in% "Quarterly"~ "quarter",
Frequency %in% "Monthly" ~ "month",
Frequency %in% "Weekly" ~ "week",
TRUE ~ "month")) %>%
complete(DeliveryDate = seq.Date(Date, endDate, by = Frequency)) %>%
fill(Frequency, Date, endDate)
# A tibble: 12 x 5
# Groups: Type [2]
Type DeliveryDate Frequency Date endDate
<chr> <date> <chr> <date> <date>
1 Item A 2021-02-05 quarter 2021-02-05 2021-12-12
2 Item A 2021-05-05 quarter 2021-02-05 2021-12-12
3 Item A 2021-08-05 quarter 2021-02-05 2021-12-12
4 Item A 2021-11-05 quarter 2021-02-05 2021-12-12
5 Item B 2021-05-05 month 2021-05-05 2021-12-12
6 Item B 2021-06-05 month 2021-05-05 2021-12-12
7 Item B 2021-07-05 month 2021-05-05 2021-12-12
8 Item B 2021-08-05 month 2021-05-05 2021-12-12
9 Item B 2021-09-05 month 2021-05-05 2021-12-12
10 Item B 2021-10-05 month 2021-05-05 2021-12-12
11 Item B 2021-11-05 month 2021-05-05 2021-12-12
12 Item B 2021-12-05 month 2021-05-05 2021-12-12

Answer 3

Here's one way. It's not clear what you want for "Other" as opposed to "month", so I set it here to "week".
Note that you don't need to reference the data frame when using mutate() since everything called in the function is set to the environment of the data frame. Also, look into using case_when() instead of using nested ifelse() calls.
library(tidyverse)
df %>%
mutate(Frequency2 = case_when(Frequency == "Quarterly" ~ "3 month",
Frequency == "Month" ~ "month",
TRUE ~ "week")) %>%
group_by(Type, Frequency2) %>%
nest() %>%
mutate(middates = map2(data, Frequency2, ~ seq.Date(min(.x$Date), max(.x$endDate), by = .y))) %>%
unnest(c(data, middates)) %>%
ungroup()
# A tibble: 36 x 6
Type Frequency Frequency2 Date endDate middates
<chr> <chr> <chr> <date> <date> <date>
1 Item A Quarterly 3 month 2021-02-05 2021-12-12 2021-02-05
2 Item A Quarterly 3 month 2021-02-05 2021-12-12 2021-05-05
3 Item A Quarterly 3 month 2021-02-05 2021-12-12 2021-08-05
4 Item A Quarterly 3 month 2021-02-05 2021-12-12 2021-11-05
5 Item B Other week 2021-05-05 2021-12-12 2021-05-05
6 Item B Other week 2021-05-05 2021-12-12 2021-05-12
7 Item B Other week 2021-05-05 2021-12-12 2021-05-19
8 Item B Other week 2021-05-05 2021-12-12 2021-05-26
9 Item B Other week 2021-05-05 2021-12-12 2021-06-02
10 Item B Other week 2021-05-05 2021-12-12 2021-06-09
# ... with 26 more rows