Deleting Duplicate Data & Replacing missing values in certain areas - r

Question

So I have been looking at this code which is originally an excel sheet. Once the data set is put into R Studio then I have a few issues.
First of all I changed all the blank cells into NA once I run
CarparkData[is.na(CarparkData)] <- 0
it only changes the data which was originally NA not a blank cell.
Secondly deleting duplicate data, I used the following code and nothing happened.
library("dplyr")
install.packages("tidyverse")
library(tidyverse)
x <-CarparkData
duplicated(x)
x[duplicated(x),]
x[!duplicated(x),]
As I have a row for Date and Time I would like to use this as the column to delete the rows of duplicated data. As I have data which are the same but they are at different times compared to data which is the same and the date and time is the same.
And Thirdly Replacing missing Values
Some of the data has FULL written on it and I would like to home into one column and then change FULL to the number that is full in that specific car park, so changing the FULL cells in that column and not all the FULL cells.
Sample Data
> dput(head(CarparkData))
structure(list(Parnell = c(188L, 183L, 185L, 229L, 237L, 272L
), Ilac = c(665, 683, 694, 769, 786, 839), Jervis = c(421, 408,
403, 417, 423, 455), Arnotts = c(340, 344, 350, 359, 359, 355
), Malboro = c(160L, 160L, 156L, 157L, 173L, 207L), Abbey = c(0,
0, 0, 0, 0, 0), `Thomas Street` = c(173, 173, 173, 186, 189,
198), `Christ Church` = c(77, 76, 74, 73, 83, 91), Setanta = structure(c(24L,
23L, 23L, NA, NA, 46L), .Label = c("10", "100", "101", "102",
"103", "104", "107", "108", "110", "111", "112", "113", "114",
"115", "120", "123", "125", "128", "129", "131", "14", "17",
"19", "21", "24", "27", "28", "29", "30", "31", "32", "34", "36",
"39", "40", "44", "45", "47", "48", "51", "52", "53", "56", "57",
"6", "60", "63", "66", "67", "7", "70", "72", "74", "78", "79",
"80", "81", "82", "84", "85", "86", "89", "9", "91", "92", "93",
"94", "96", "98", "FULL"), class = "factor"), Dawson = c(70,
87, 83, 118, 122, 140), Trinity = c(142L, 143L, 145L, 165L, 167L,
191L), Greenrcs = structure(c(NA, 8L, 9L, NA, 4L, 5L), .Label = c("1125",
"157", "205", "250", "262", "264", "266", "267", "270", "296",
"305", "311", "319", "320", "324", "327", "342", "347", "350",
"353", "364", "371", "374", "375", "378", "379", "459", "463",
"591", "729", "754", "761", "879", "902", "903", "907", "911",
"913", "916", "917", "922", "931", "944", "955", "974", "985",
"FULL"), class = "factor"), Drury = c(148, 143, 147, 182, 193,
235), `Brown Thomas` = c(230, 231, 0, 267, 272, 293), `Date & Time` = structure(1:6, .Label = c("2019-03-19 13:43:33",
"2019-03-19 13:55:39", "2019-03-19 14:07:35", "2019-03-19 15:45:02",
"2019-03-19 16:00:02", "2019-03-19 16:45:03", "2019-03-19 17:00:02",
"2019-03-19 17:45:03", "2019-03-19 18:00:01", "2019-03-19 18:00:02",
"2019-03-19 18:45:03", "2019-03-19 19:00:01", "2019-03-19 19:00:02",
"2019-03-19 19:07:12", "2019-03-19 19:45:03", "2019-03-19 20:00:01",
"2019-03-19 20:00:02", "2019-03-19 20:45:03", "2019-03-19 21:00:01",
"2019-03-19 21:00:03", "2019-03-19 21:45:04", "2019-03-19 22:00:01",
"2019-03-19 22:00:03", "2019-03-19 22:45:04", "2019-03-19 23:00:01",
"2019-03-19 23:00:02", "2019-03-19 23:00:03", "2019-03-19 23:45:04",
"2019-03-20 00:00:01", "2019-03-20 00:00:02", "2019-03-20 00:00:03",
"2019-03-20 00:45:04", "2019-03-20 01:00:01", "2019-03-20 01:00:02",
"2019-03-20 01:00:03", "2019-03-20 01:45:04", "2019-03-20 02:00:01",
"2019-03-20 02:00:02", "2019-03-20 02:00:03", "2019-03-20 02:45:04",
"2019-03-20 03:00:01", "2019-03-20 03:00:02", "2019-03-20 03:00:03",
"2019-03-20 03:45:05", "2019-03-20 04:00:01", "2019-03-20 04:00:02",
"2019-03-20 04:00:04", "2019-03-20 04:45:05", "2019-03-20 05:00:01",
"2019-03-20 05:00:02",
Thanks.

Answer 1

First issue... if you want to explicity set all empty cells as NA you can have a custom function like the following:
empty_as_na <- function(x){
if("factor" %in% class(x)) x <- as.character(x) ## since ifelse wont work with factors
ifelse(as.character(x)!="", x, NA)
}
And then apply this function:
dplyr::mutate_all(df, .funs = empty_as_na)
where df is your data frame.
Second issue... for removing duplicate rows, you should look at dplyr::distinct()
Third issue... I did not get what the issue is... maybe you could clarify?
I am sorry, I can't give you a complete working example with the data you provided... but these functions should get you where you want.
EDIT
Solution for third issue based on comments...
Probably not the most elegant solution, but again, this is limited due to the not provided reprex.
Let df be your data frame, column_new your new column, column_number the column you mentioned that had numbers or FULL written, and column_carthe column where the cars are.
df %>%
mutate(
column_new = case_when(
column_number == "FULL" & column_car == "car_a" ~ 300,
column_number == "FULL" & column_car == "car_b" ~ 500,
TRUE ~ column_number
)
)