@francojc showed us some tricks using subsetting and the complete.cases function. Here is another way to do the same thing with the tidyverse syntax
# Make Some Missing Data
missing_cars <- mtcars
missing_rows <- sample(size = 5, x = 1:nrow(missing_cars),
replace = T)
missing_columns <- sample(size = 5,x = 1:ncol(missing_cars),
replace = T)
missing_cars[missing_rows, missing_columns] <- NA
Now we have some missing data in our data frame
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
NA
NA
17.02
NA
NA
4
NA
Datsun 710
22.8
4
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
18.7
8
360.0
175
3.15
3.440
17.02
0
0
3
2
Valiant
18.1
6
225.0
105
NA
NA
20.22
NA
NA
3
NA
Duster 360
14.3
8
360.0
245
NA
NA
15.84
NA
NA
3
NA
Merc 240D
24.4
4
146.7
62
3.69
3.190
20.00
1
0
4
2
Merc 230
22.8
4
140.8
95
3.92
3.150
22.90
1
0
4
2
Merc 280
19.2
6
167.6
123
3.92
3.440
18.30
1
0
4
4
Merc 280C
17.8
6
167.6
123
3.92
3.440
18.90
1
0
4
4
Merc 450SE
16.4
8
275.8
180
3.07
4.070
17.40
0
0
3
3
Merc 450SL
17.3
8
275.8
180
3.07
3.730
17.60
0
0
3
3
Merc 450SLC
15.2
8
275.8
180
3.07
3.780
18.00
0
0
3
3
Cadillac Fleetwood
10.4
8
472.0
205
2.93
5.250
17.98
0
0
3
4
Lincoln Continental
10.4
8
460.0
215
3.00
5.424
17.82
0
0
3
4
Chrysler Imperial
14.7
8
440.0
230
3.23
5.345
17.42
0
0
3
4
Fiat 128
32.4
4
78.7
66
4.08
2.200
19.47
1
1
4
1
Honda Civic
30.4
4
75.7
52
4.93
1.615
18.52
1
1
4
2
Toyota Corolla
33.9
4
71.1
65
4.22
1.835
19.90
1
1
4
1
Toyota Corona
21.5
4
120.1
97
NA
NA
20.01
NA
NA
3
NA
Dodge Challenger
15.5
8
318.0
150
NA
NA
16.87
NA
NA
3
NA
AMC Javelin
15.2
8
304.0
150
3.15
3.435
17.30
0
0
3
2
Camaro Z28
13.3
8
350.0
245
3.73
3.840
15.41
0
0
3
4
Pontiac Firebird
19.2
8
400.0
175
3.08
3.845
17.05
0
0
3
2
Fiat X1-9
27.3
4
79.0
66
4.08
1.935
18.90
1
1
4
1
Porsche 914-2
26.0
4
120.3
91
4.43
2.140
16.70
0
1
5
2
Lotus Europa
30.4
4
95.1
113
3.77
1.513
16.90
1
1
5
2
Ford Pantera L
15.8
8
351.0
264
4.22
3.170
14.50
0
1
5
4
Ferrari Dino
19.7
6
145.0
175
3.62
2.770
15.50
0
1
5
6
Maserati Bora
15.0
8
301.0
335
3.54
3.570
14.60
0
1
5
8
Volvo 142E
21.4
4
121.0
109
4.11
2.780
18.60
1
1
4
2
Now we can remove the missing data using the filter verb with complete.cases. The only trick is adding a period, ., inside of the complete.cases function in order for the function to complete.
missing_cars %>%
filter(complete.cases(.))
Which removes those rows with missing data completely
@francojc showed us some tricks using subsetting and the
complete.casesfunction. Here is another way to do the same thing with the tidyverse syntaxNow we have some missing data in our data frame
Now we can remove the missing data using the
filterverb withcomplete.cases. The only trick is adding a period,., inside of thecomplete.casesfunction in order for the function to complete.Which removes those rows with missing data completely