Removing Rows in R Based on Values in a Single Column

R: Deleting rows based on a value in a column from a large data set in R

I suggest you learn how to use dplyr, and other packages in the tidyverse. I find them to be an indispensable tool in cleaning data.

Here's how I would use dplyr to filter out both Texas and New York in your data set:

library(dplyr)
customers = filter(customers, State != "TX" & State != "NY")

Alternatively,

customers = filter(customers, !(State %in% c("TX", "NY")))

Removing rows in R based on values in a single column

You could also use the subset() function.

a <- matrix(1:9, nrow=3)  
threshhold <- 8  
subset(a, a[ , 3] < threshhold)  

How to remove row if it has a NA value in one certain column

The easiest solution is to use is.na():

df[!is.na(df$B), ]

which gives you:

   A B  C
1 NA 2 NA
2  1 2  3
4  1 2  3

How can I delete rows if a column contains a certain value?

It's better to think "how do I create an object in the form I want", than "how do I manipulate this object in place".
So you can use the following syntax:

df <- df[!df$classification == "D1" | df$classification == "RD", ]

or, the slightly more easy to maintain:

df <- df[!df$classification %in% c("D1", "RD"), ]

Remove rows from a single-column data frame

Try adding the drop = FALSE option:

R> df[-(length(df[,1])), , drop = FALSE]
  a
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9

R: removing rows based on row value in a column of a data frame

EDIT I reproduced your error, you need to add the drop = FALSE option in your subsetting to get a data.frame as result and not a vector :

df_a <- structure(list(order..new..i...2.ncol.new..i..... = c(620L, 2851L, 1972L, 565L, 1025L, 2509L)), row.names = c("J.TYMO", "J.TTMO", "J.NTT", "J.ABOT", "J.NNDO", "J.SFTB"), class = "data.frame")
str(df_a)
#> 'data.frame':    6 obs. of  1 variable:
#>  $ order..new..i...2.ncol.new..i.....: int  620 2851 1972 565 1025 2509
names(df_a) <- "V1"

df_a[df_a[[1]] <= 1000 , , drop = FALSE]
#>         V1
#> J.TYMO 620
#> J.ABOT 565

OLD ANSWER

The best with-row-names-dataset I though of was the mtcars dataset. Building from that I found that adding a comma in your call solves the problem :

dfr <- head(mtcars)
dfr
#>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
#> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
#> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
#> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
#> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
#> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
#> Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

as.data.frame(dfr[dfr[1]<20 , ])
#>                    mpg cyl disp  hp drat   wt  qsec vs am gear carb
#> Hornet Sportabout 18.7   8  360 175 3.15 3.44 17.02  0  0    3    2
#> Valiant           18.1   6  225 105 2.76 3.46 20.22  1  0    3    1

Thus with your peculiar a object, if it is a data.frame the answer should be :

as.data.frame(a[a[1] <= 333 , ])

remove R Dataframe rows based on zero values in one column

Just subset the data frame based on the value in the No_of_Mails column:

df[df$No_of_Mails != 0, ]

Demo

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