Using tidyverse and pipes how do I assign fixed rows - r

Question

Given this dataframe
X1 X2
2001 NA
abc 10
def 12
xo 13
2002 NA
abc 10
efd 22
dd 23
2005 NA
a 30
All the years have NA in X2. My goal is to get this data frame to become
X1 X2 Date
abc 10 2001
def 12 2001
xo 13 2001
abc 10 2002
efd 22 2002
dd 23 2002
a 30 2005
That is, the years became their own column and the NA's have been dropped
What I tried
a = read_csv("given.csv")
a %>% mutate(Date = ifelse(is.na(X2), X1, NA))
This turns the first dataframe to
X1 X2 Date
2001 NA 2001
abc 10 NA
def 12 NA
xo 13 NA
2002 NA 2002
abc 10 NA
efd 22 NA
dd 23 NA
2005 NA 2005
a 30 NA
I'm not sure how to replace the NA of the date column into the upper value for each year. After that I think i can just drop_na and it will be like i would want it

Answer 1

Another option:
library(dplyr)
library(zoo)
a %>%
mutate(Date = na.locf(case_when(is.na(X2) ~ X1))) %>%
na.omit
Output:
X1 X2 Date
2 abc 10 2001
3 def 12 2001
4 xo 13 2001
6 abc 10 2002
7 efd 22 2002
8 dd 23 2002
10 a 30 2005
If you want to reset row numbers just use filter(!is.na(X2)) instead of na.omit.
P.S. You can of course just load tidyverse and do something like:
library(tidyverse)
a %>%
mutate(Date = case_when(is.na(X2) ~ X1)) %>%
fill(Date) %>%
drop_na
.. however note that fill is quite slow compared to the na.locf function from zoo.

Answer 2

We can create a grouping column based on the occurrence of numbers only elements (\\d+) in 'X1', get the cumulative sum, create the 'Date' as the first element of 'X1', ungroup and remove the NA rows
library(dplyr)
library(stringr)
a %>%
group_by(grp = cumsum(str_detect(X1, '^\\d+$'))) %>%
mutate(Date = first(X1)) %>%
ungroup %>%
select(-grp) %>%
na.omit
# A tibble: 7 x 3
# X1 X2 Date
# <chr> <int> <chr>
#1 abc 10 2001
#2 def 12 2001
#3 xo 13 2001
#4 abc 10 2002
#5 efd 22 2002
#6 dd 23 2002
#7 a 30 2005
Or using data.table with zoo
library(data.table)
library(zoo)
na.omit(setDT(a)[, Date := na.locf(fifelse(is.na(X2), X1, NA_character_))])
data
a <- structure(list(X1 = c("2001", "abc", "def", "xo", "2002", "abc",
"efd", "dd", "2005", "a"), X2 = c(NA, 10L, 12L, 13L, NA, 10L,
22L, 23L, NA, 30L)), class = "data.frame", row.names = c(NA,
-10L))