[R] Row exclude

[Val]

Thank you David for your help.

I just have one question on this. What is the purpose of  using the
"unique" function on this?
  (dat2 <- dat1[-unique(c(BadName, BadAge, BadWeight)), ])

I got the same result without using it.
       (dat2 <- dat1[-(c(BadName, BadAge, BadWeight)), ])

My concern is when I am applying this for the large data set the "unique"
function may consume resources(time  and memory).

Thank you.

On Sat, Jan 29, 2022 at 12:30 AM David Carlson <dcarlson using tamu.edu> wrote:

> Given that you know which columns should be numeric and which should be
> character, finding characters in numeric columns or numbers in character
> columns is not difficult. Your data frame consists of three character
> columns so you can use regular expressions as Bert mentioned. First you
> should strip the whitespace out of your data:
>
> dat1 <-read.table(text="Name, Age, Weight
>   Alex,  20,  13X
>   Bob,  25,  142
>   Carol, 24,  120
>   John,  3BC,  175
>   Katy,  35,  160
>   Jack3, 34,  140",sep=",", header=TRUE, stringsAsFactors=FALSE,
> strip.white=TRUE)
>
> Now check to see if all of the fields are character as expected.
>
> sapply(dat1, typeof)
> #        Name         Age      Weight
> # "character" "character" "character"
>
> Now identify character variables containing numbers and numeric variables
> containing characters:
>
> BadName <- which(grepl("[[:digit:]]", dat1$Name))
> BadAge <- which(grepl("[[:alpha:]]", dat1$Age))
> BadWeight <- which(grepl("[[:alpha:]]", dat1$Weight))
>
> Next remove those rows:
>
> (dat2 <- dat1[-unique(c(BadName, BadAge, BadWeight)), ])
> #    Name Age Weight
> #  2   Bob  25    142
> #  3 Carol  24    120
> #  5  Katy  35    160
>
> You still need to convert Age and Weight to numeric, e.g. dat2$Age <-
> as.numeric(dat2$Age).
>
> David Carlson
>
>
> On Fri, Jan 28, 2022 at 11:59 PM Bert Gunter <bgunter.4567 using gmail.com>
> wrote:
>
>> As character 'polluted' entries will cause a column to be read in (via
>> read.table and relatives) as factor or character data, this sounds like a
>> job for regular expressions. If you are not familiar with this subject,
>> time to learn. And, yes, ZjQcmQRYFpfptBannerStart
>> This Message Is From an External Sender
>> This message came from outside your organization.
>> ZjQcmQRYFpfptBannerEnd
>>
>> As character 'polluted' entries will cause a column to be read in (via
>> read.table and relatives) as factor or character data, this sounds like a
>> job for regular expressions. If you are not familiar with this subject,
>> time to learn. And, yes, some heavy lifting will be required.
>> See ?regexp for a start maybe? Or the stringr package?
>>
>> Cheers,
>> Bert
>>
>>
>>
>>
>> On Fri, Jan 28, 2022, 7:08 PM Val <valkremk using gmail.com> wrote:
>>
>> > Hi All,
>> >
>> > I want to remove rows that contain a character string in an integer
>> > column or a digit in a character column.
>> >
>> > Sample data
>> >
>> > dat1 <-read.table(text="Name, Age, Weight
>> >  Alex,  20,  13X
>> >  Bob,   25,  142
>> >  Carol, 24,  120
>> >  John,  3BC,  175
>> >  Katy,  35,  160
>> >  Jack3, 34,  140",sep=",",header=TRUE,stringsAsFactors=F)
>> >
>> > If the Age/Weight column contains any character(s) then remove
>> > if the Name  column contains an digit then remove that row
>> > Desired output
>> >
>> >    Name   Age weight
>> > 1   Bob     25    142
>> > 2   Carol   24    120
>> > 3   Katy    35    160
>> >
>> > Thank you,
>> >
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