把空白细胞改成“ NA”

I'm assuming you are talking about row 5 column "sex." It could be the case that in the data2.csv file, the cell contains a space and hence is not considered empty by R.

Also, I noticed that in row 5 columns "axles" and "door", the original values read from data2.csv are string "NA". You probably want to treat those as na.strings as well. To do this,

dat2 <- read.csv("data2.csv", header=T, na.strings=c("","NA"))

EDIT:

I downloaded your data2.csv. Yes, there is a space in row 5 column "sex". So you want

na.strings=c(""," ","NA")

You can use gsub to replace multiple mutations of empty, like "" or a space, to be NA:

data= data.frame(cats=c('', ' ', 'meow'), dogs=c("woof", " ", NA))
apply(data, 2, function(x) gsub("^$|^ $", NA, x))

A more eye-friendly solution using dplyr would be

require(dplyr)


## fake blank cells
iris[1,1]=""


## define a helper function
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)
}


## transform all columns
iris %>% mutate_each(funs(empty_as_na))

To apply the correction to just a subset of columns you can specify columns of interest using dplyr's column matching syntax. Example:mutate_each(funs(empty_as_na), matches("Width"), Species)

In case you table contains dates you should consider using a more typesafe version of ifelse

I recently ran into similar issues, and this is what worked for me.

If the variable is numeric, then a simple df$Var[df$Var == ""] <- NA should suffice. But if the variable is a factor, then you need to convert it to the character first, then replace "" cells with the value you want, and convert it back to factor. So case in point, your Sex variable, I assume it would be a factor and if you want to replace the empty cell, I would do the following:

df$Var <- as.character(df$Var)
df$Var[df$Var==""] <- NA
df$Var <- as.factor(df$Var)

Couldn't you just use

dat <- read.csv("data2.csv",na.strings=" ",header=TRUE)

should convert all blanks to NA as the data are read in be sure to put a space between your quotation

Call dplyr package by installing from cran in r

library(dplyr)


(file)$(colname)<-sub("-",NA,file$colname)

It will convert all the blank cell in a particular column as NA

If the column contains "-", "", 0 like this change it in code according to the type of blank cell

E.g. if I get a blank cell like "" instead of "-", then use this code:

(file)$(colname)<-sub("", NA, file$colname)

My function takes into account factor, character vector and potential attributes, if you use haven or foreign package to read external files. Also it allows matching different self-defined na.strings. To transform all columns, simply use lappy: df[] = lapply(df, blank2na, na.strings=c('','NA','na','N/A','n/a','NaN','nan'))

See more the comments:

#' Replaces blank-ish elements of a factor or character vector to NA
#' @description Replaces blank-ish elements of a factor or character vector to NA
#' @param x a vector of factor or character or any type
#' @param na.strings case sensitive strings that will be coverted to NA. The function will do a trimws(x,'both') before conversion. If NULL, do only trimws, no conversion to NA.
#' @return Returns a vector trimws (always for factor, character) and NA converted (if matching na.strings). Attributes will also be kept ('label','labels', 'value.labels').
#' @seealso \code{\link{ez.nan2na}}
#' @export
blank2na = function(x,na.strings=c('','.','NA','na','N/A','n/a','NaN','nan')) {
if (is.factor(x)) {
lab = attr(x, 'label', exact = T)
labs1 <- attr(x, 'labels', exact = T)
labs2 <- attr(x, 'value.labels', exact = T)


# trimws will convert factor to character
x = trimws(x,'both')
if (! is.null(lab)) lab = trimws(lab,'both')
if (! is.null(labs1)) labs1 = trimws(labs1,'both')
if (! is.null(labs2)) labs2 = trimws(labs2,'both')


if (!is.null(na.strings)) {
# convert to NA
x[x %in% na.strings] = NA
# also remember to remove na.strings from value labels
labs1 = labs1[! labs1 %in% na.strings]
labs2 = labs2[! labs2 %in% na.strings]
}


# the levels will be reset here
x = factor(x)


if (! is.null(lab)) attr(x, 'label') <- lab
if (! is.null(labs1)) attr(x, 'labels') <- labs1
if (! is.null(labs2)) attr(x, 'value.labels') <- labs2
} else if (is.character(x)) {
lab = attr(x, 'label', exact = T)
labs1 <- attr(x, 'labels', exact = T)
labs2 <- attr(x, 'value.labels', exact = T)


# trimws will convert factor to character
x = trimws(x,'both')
if (! is.null(lab)) lab = trimws(lab,'both')
if (! is.null(labs1)) labs1 = trimws(labs1,'both')
if (! is.null(labs2)) labs2 = trimws(labs2,'both')


if (!is.null(na.strings)) {
# convert to NA
x[x %in% na.strings] = NA
# also remember to remove na.strings from value labels
labs1 = labs1[! labs1 %in% na.strings]
labs2 = labs2[! labs2 %in% na.strings]
}


if (! is.null(lab)) attr(x, 'label') <- lab
if (! is.null(labs1)) attr(x, 'labels') <- labs1
if (! is.null(labs2)) attr(x, 'value.labels') <- labs2
} else {
x = x
}
return(x)
}

While many options above function well, I found coercion of non-target variables to chr problematic. Using ifelse and grepl within lapply resolves this off-target effect (in limited testing). Using slarky's regular expression in grepl:

set.seed(42)
x1 <- sample(c("a","b"," ", "a a", NA), 10, TRUE)
x2 <- sample(c(rnorm(length(x1),0, 1), NA), length(x1), TRUE)


df <- data.frame(x1, x2, stringsAsFactors = FALSE)

The problem of coercion to character class:

df2 <- lapply(df, function(x) gsub("^$|^ $", NA, x))
lapply(df2, class)

$x1 [1] "character"

[1] "character"

Resolution with use of ifelse:

df3 <- lapply(df, function(x) ifelse(grepl("^$|^ $", x)==TRUE, NA, x))
lapply(df3, class)

$x1 [1] "character"

[1] "numeric"

As of (dplyr 1.0.0) we can use across()

Note: Sometimes using NA within a variable causes issues and you might need to specify the type of NA - for example for this case it would be NA_character_. For nested ifelse() statements you can use case_when().

For all columns:

dat <- dat %>%
mutate(across(everything(), ~ifelse(.=="", NA, as.character(.))))

For individual columns:

dat <- dat %>%
mutate(across(c("Age","Gender"), ~ifelse(.=="", NA, as.character(.))))

As of (dplyr 0.8.0 above) the way this should be written has changed. Before it was, funs() in .funs (funs(name = f(.)). Instead of funs, now we use list (list(name = ~f(.)))

Note that there is also a much simpler way to list the column names ! (both the name of the column and column index work)

dat <- dat %>%
mutate_at(.vars = c("Age","Gender"),
.funs = list(~ifelse(.=="", NA, as.character(.))))

Original Answer:

You can also use mutate_at in dplyr

dat <- dat %>%
mutate_at(vars(colnames(.)),
.funs = funs(ifelse(.=="", NA, as.character(.))))

Select individual columns to change:

dat <- dat %>%
mutate_at(vars(colnames(.)[names(.) %in% c("Age","Gender")]),
.funs = funs(ifelse(.=="", NA, as.character(.))))

This should do the trick

dat <- dat %>% mutate_all(na_if,"")

For those wondering about a solution using the data.table way, here is one I wrote a function for, available on my Github:

library(devtools)
source_url("https://github.com/YoannPa/Miscellaneous/blob/master/datatable_pattern_substitution.R?raw=TRUE")
dt.sub(DT = dat2, pattern = "^$|^ $",replacement = NA)
dat2

The function goes through each column, to identify which column contains pattern matches. Then gsub() is aplied only on columns containing matches for the pattern "^$|^ $", to substitutes matches by NAs.

I will keep improving this function to make it faster.

Updated Answer Some additional options that I find useful building on the great options from @camnesia leveraging dplyr's across():

Using na_if()

mutate(across(c("Age","Gender"), ~na_if(., ""))).

Also perhaps worth noting to people who wander in, in addition to specifying the columns with c(""), you can use dplyr selectors:

mutate(across(starts_with("x_"), ~na_if(., ""))).

Finally, I also like replace() here if you have multiple values you want to replace with NA:

Using replace():

mutate(across(everything(), ~replace(., . %in% c("N.A.", "NA", "N/A", ""), NA)))

Original Answer I suspect everyone has an answer already, though in case someone comes looking, dplyr na_if() would be (from my perspective) the more efficient of those mentioned:

# Import CSV, convert all 'blank' cells to NA
dat <- read.csv("data2.csv") %>% na_if("")

Here is an additional approach leveraging readr's read_delim function. I just picked-up (probably widely know, but I'll archive here for future users). This is very straight forward and more versatile than the above, as you can capture all types of blank and NA related values in your csv file:

dat <- read_csv("data2.csv", na = c("", "NA", "N/A"))

Note the underscore in readr's version versus Base R "." in read_csv.

Hopefully this helps someone who wanders upon the post!

This works for me.

dataset <- read.csv(file = "data.csv",header=TRUE,fill = T,na.strings = "")

I think data.table is your best bet (for code simplicity and speed). The following would replace all blanks in a dataset called "data" with NA:

data[data==""] <- NA

It is also possible to use mutate with case_when:

dat <- dat %>% mutate(sex = case_when(sex == "" ~ "NA",TRUE ~ sex))