How to remove empty rows after csv data import in r - r

Question

I combine several large csv files with arrival (ATA) and departure (ATD) times of objects. After combining the files I cannot remove <NA> rows using familiar methods. The origin may be in difference between Windows and Unix files in newline and carriage returns. But I don't want to alter the csv files. I want to be able to correct the data frame in R.
I combine several large csv files containing the same variables, e.g.:
# read csv files
df1 <- read.csv("data_1.csv", stringsAsFactors = FALSE)
df2 <- read.csv("data_2.csv", stringsAsFactors = FALSE)
df3 <- read.csv("data_3.csv", stringsAsFactors = FALSE)
# combine csv files
combidat <- rbind(df1, df2, df3)
# remove duplicate entries
combidat <- combidat[!duplicated(combidat), ]
To remove entries with <NA> in ID (first column variable), I use one of several:
combidat <- combidat[!is.na(combidat$ID),]
combidat <- combidat[complete.cases(combidat[ , 1]),]
combidat <- combidat[rowSums(is.na(combidat)) != ncol(combidat),]
I also found:
combidat <- combidat[-which(apply(combidat,1,function(x)all(is.na(x)))),]
But I cannot use this approach. If I do combidat becomes empty.
If I check the result:
combidat[is.na(combidat$ID),]
I get:
[1] ID ATA ATD object
<0 rows> (or 0-length row.names)
However, if I check on inconsistencies, i.e. departure times before arrival times:
combidat[(ATD<ATA),]
I get:
ID ATA ATD object
233 51586002 2016-03-14 09:44:00 2016-03-14 09:00:00 car718
798 54846070 2016-06-19 01:37:00 2016-04-07 23:59:00 car276
4126 56066767 2016-03-31 14:00:00 2016-03-30 07:00:00 car089
NA NA <NA> <NA> NA
NA.1 NA <NA> <NA> NA
NA.2 NA <NA> <NA> NA
NA.3 NA <NA> <NA> NA
NA.4 NA <NA> <NA> NA
NA.5 NA <NA> <NA> NA
NA.6 NA <NA> <NA> NA
NA.7 NA <NA> <NA> NA
What I hope to get is:
ID ATA ATD object
233 51586002 2016-03-14 09:44:00 2016-03-14 09:00:00 car718
798 54846070 2016-06-19 01:37:00 2016-04-07 23:59:00 car276
4126 56066767 2016-03-31 14:00:00 2016-03-30 07:00:00 car089
Any explanation what I do wrong and how to correct it, would be much appreciated.
[Addition June 28, 2019]
Something goes wrong with the imported csv files. Somehow newlines/carriage returns within a data field, get interpreted as end of record markers. I have juggled with 'quote':
df1 <- read.csv("data_1.csv", stringsAsFactors = FALSE, quote = "\"'")
And this has some impact, but I do not get it right.

Answer 1

As noted by gdevaux, maybe your "NA" are characters. In that case you could filter your data with the dplyr package (you can also do it with base R as you attempted):
library(dplyr)
combidat<- combidat%>% filter(!ID=="NA")
It may also have spaces, in that case you can trim in the importing or trim the column and retry the code above.
library(stringr)
combidat<- combidat%>%
mutate(ID=str_trim(ID, side = "both"))%>%
filter(!ID=="NA")
Lastly, it seems that your ID column is just composed of numeric ID. In that case you could make your column numeric and hence coerce the NA values to real NA.
combidat$ID<- as.numeric(combidat$ID)
combidat<- combidat%>% filter(!is.na(ID))
If values were coerced, R will tell you.

Answer 2

The two answers above would have helped you. Here is another approach to filter empty observation and variables from the data frame.
#install.packages("janitor")
library(janitor)
remove_empty(combidat, "rows")

Answer 3

combidat <- combidat[which(!is.na(combidat$ID)),]