I want to conditionally replace missing revenue up to 16th July 2017 with zero using tidyverse.
My Data
library(tidyverse)
library(lubridate)
df<- tribble(
~Date, ~Revenue,
"2017-07-01", 500,
"2017-07-02", 501,
"2017-07-03", 502,
"2017-07-04", 503,
"2017-07-05", 504,
"2017-07-06", 505,
"2017-07-07", 506,
"2017-07-08", 507,
"2017-07-09", 508,
"2017-07-10", 509,
"2017-07-11", 510,
"2017-07-12", NA,
"2017-07-13", NA,
"2017-07-14", NA,
"2017-07-15", NA,
"2017-07-16", NA,
"2017-07-17", NA,
"2017-07-18", NA,
"2017-07-19", NA,
"2017-07-20", NA
)
df$Date <- ymd(df$Date)
Date up to which I want to conditionally replace NAs
max.date <- ymd("2017-07-16")
Output I desire
# A tibble: 20 × 2
Date Revenue
<chr> <dbl>
1 2017-07-01 500
2 2017-07-02 501
3 2017-07-03 502
4 2017-07-04 503
5 2017-07-05 504
6 2017-07-06 505
7 2017-07-07 506
8 2017-07-08 507
9 2017-07-09 508
10 2017-07-10 509
11 2017-07-11 510
12 2017-07-12 0
13 2017-07-13 0
14 2017-07-14 0
15 2017-07-15 0
16 2017-07-16 0
17 2017-07-17 NA
18 2017-07-18 NA
19 2017-07-19 NA
20 2017-07-20 NA
The only way I could work this out was to split the df into several parts, update for NAs and then rbind the whole lot.
Could someone please help me do this efficiently using tidyverse.