We have a data frame from a CSV file. The data frame DF
has columns that contain observed values and a column (VaR2
) that contains the date at which a measurement has been taken. If the date was not recorded, the CSV file contains the value NA
, for missing data.
Var1 Var2
10 2010/01/01
20 NA
30 2010/03/01
We would like to use the subset command to define a new data frame new_DF
such that it only contains rows that have an NA'
value from the column (VaR2
). In the example given, only Row 2 will be contained in the new DF
.
The command
new_DF<-subset(DF,DF$Var2=="NA")
does not work, the resulting data frame has no row entries.
If in the original CSV file the Value NA
are exchanged with NULL
, the same command produces the desired result: new_DF<-subset(DF,DF$Var2=="NULL")
.
How can I get this method working, if for the character string the value NA
is provided in the original CSV file?
Prints all the rows with NA data:
tmp <- data.frame(c(1,2,3),c(4,NA,5));
tmp[round(which(is.na(tmp))/ncol(tmp)),]
Never use =='NA' to test for missing values. Use is.na()
instead. This should do it:
new_DF <- DF[rowSums(is.na(DF)) > 0,]
or in case you want to check a particular column, you can also use
new_DF <- DF[is.na(DF$Var),]
In case you have NA character values, first run
Df[Df=='NA'] <- NA
to replace them with missing values.
complete.cases
gives TRUE
when all values in a row are not NA
DF[!complete.cases(DF), ]
new_data <- data %>% filter_all(any_vars(is.na(.)))
This should create a new data frame (new_data
) with only the missing values in it.
Works best to keep a track of values that you might later drop because they had some columns with missing observations (NA).
Try changing this:
new_DF<-dplyr::filter(DF,is.na(Var2))
NA is a special value in R, do not mix up the NA value with the "NA" string. Depending on the way the data was imported, your "NA" and "NULL" cells may be of various type (the default behavior is to convert "NA" strings to NA values, and let "NULL" strings as is).
If using read.table() or read.csv(), you should consider the "na.strings" argument to do clean data import, and always work with real R NA values.
An example, working in both cases "NULL" and "NA" cells :
DF <- read.csv("file.csv", na.strings=c("NA", "NULL"))
new_DF <- subset(DF, is.na(DF$Var2))
Source: Stackoverflow.com