I am trying to get rid of the missing values of a dataset using
na.rm = TRUE
However, it continues having the missing values and it only works when I use
na.omit(data)
How can I make the first code work?
This is what I have and that doesn't work:
edu2018$mathtest[edu2018$mathtest < 0] <- NA
summary(edu2018$mathtest, na.rm=TRUE)
It shows that there are 104 NA's in the dataset
The code that works is:
edu2018$mathtest[edu2018$mathtest < 0] <- NA
edu2018 <- na.omit(edu2018)
summary(edu2018$mathtest)
The median and mean it returns for these two codes are different.
CodePudding user response：
Does the following work?
library(tidyverse)
edu2018New <- edu2018 |>
drop_na() |>
summarize(mean = mean(mathtest),
median = median(mathtest))
edu2018New
CodePudding user response：
The problem with your code is that you are using na.omit across the entire dataset and not just the variable you want. na.omit will remove any rows with NA, not just the rows with NA in the column you are interested. See the example below:
test <- mtcars
test$mpg[test$mpg < 20] <- NA
test$cyl[test$cyl < 6] <- NA
test
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 NA 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout NA 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant NA 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 NA 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 NA 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 NA 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 NA 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C NA 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE NA 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL NA 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC NA 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood NA 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental NA 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial NA 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 NA 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 NA 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 NA 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 NA 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger NA 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin NA 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 NA 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird NA 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 NA 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 NA 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 NA 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L NA 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino NA 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora NA 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 NA 121.0 109 4.11 2.780 18.60 1 1 4 2
summary(test$mpg)
#> Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
#> 21.00 21.43 23.60 25.48 29.62 33.90 18
summary(na.omit(test$mpg))
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 21.00 21.43 23.60 25.48 29.62 33.90
test2 <- na.omit(test)
summary(test2$mpg)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 21.00 21.00 21.00 21.13 21.20 21.40
Notice that when we do not remove the NA's and when we remove the NA's for the variable of interest, we get the same mean and medians. When we remove all the NA's from the full dataset, we get a different result because there are other columns that have NA; therefore, we remove some of the rows with non-NA values for mpg.