Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Physics
Chemistry
Biology
Mathematics
English
Economics
Psychology
Social Studies
Fashion Studies
Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
How to fill a data.table row with missing values in R?
Instead of filling missing values, we sometimes need to replace the data with missing values. This might be required in situations when missing values are coded with a number or the actual values are not useful or sensible for the data study. Also, we might want to replace the values with something else in the future.
Check out the below given examples to understand how we can fill data.table row with missing values.
Example 1
Following snippet creates a data.table object −
library(data.table) x1<-rnorm(20) x2<-rnorm(20) x3<-rnorm(20) DT1<-data.table(x1,x2,x3) DT1
The following data.table object is created −
x1 x2 x3 1: 0.359330986 0.20756943 -1.41681109 2: 3.030837814 -0.90236706 0.18453973 3: 0.094325185 -0.62989777 -0.76818543 4: -0.605302247 0.57308532 -0.26006696 5: 0.589317918 -0.48431919 0.29462134 6: 0.924100008 -1.43570087 -1.68280918 7: -1.211456354 0.86749925 1.39952788 8: 0.061726522 1.10284992 -0.61436950 9: -0.981008050 0.18694454 2.67624706 10: -1.386289393 1.60893091 2.05774337 11: -0.978221580 -0.20008714 -1.03519166 12: -1.553948892 -1.36348786 -0.37988549 13: -1.002634550 -1.00437648 0.44634500 14: 0.280758507 -0.76264247 -0.36987504 15: -0.002528128 1.68233987 0.16512468 16: 1.200738477 -0.13188273 -0.19674097 17: 1.062584867 0.66075529 -0.06017969 18: -0.956870759 0.92754861 0.91910574 19: -1.323343765 -2.20655283 1.18144943 20: -1.618360372 -0.02947935 -0.53886698
In order to fill the first row in DT1 with missing values, add the following code to the above snippet −
DT1[1,(names(DT1)):=.SD[NA]] DT1
Output
If you execute all the above given snippets as a single program, it generates the following output: −
x1 x2 x3 1: NA NA NA 2: 3.030837814 -0.90236706 0.18453973 3: 0.094325185 -0.62989777 -0.76818543 4: -0.605302247 0.57308532 -0.26006696 5: 0.589317918 -0.48431919 0.29462134 6: 0.924100008 -1.43570087 -1.68280918 7: -1.211456354 0.86749925 1.39952788 8: 0.061726522 1.10284992 -0.61436950 9: -0.981008050 0.18694454 2.67624706 10: -1.386289393 1.60893091 2.05774337 11: -0.978221580 -0.20008714 -1.03519166 12: -1.553948892 -1.36348786 -0.37988549 13: -1.002634550 -1.00437648 0.44634500 14: 0.280758507 -0.76264247 -0.36987504 15: -0.002528128 1.68233987 0.16512468 16: 1.200738477 -0.13188273 -0.19674097 17: 1.062584867 0.66075529 -0.06017969 18: -0.956870759 0.92754861 0.91910574 19: -1.323343765 -2.20655283 1.18144943 20: -1.618360372 -0.02947935 -0.53886698
Example 2
Following snippet creates a data.table object −
y1<-rpois(20,1) y2<-rpois(20,5) y3<-rpois(20,1) y4<-rpois(20,2) DT2<-data.table(y1,y2,y3,y4) DT2
The following data.table object is created −
y1 y2 y3 y4 1: 2 11 1 6 2: 1 4 1 3 3: 0 4 0 0 4: 2 7 2 1 5: 1 7 2 1 6: 1 5 1 1 7: 0 8 0 1 8: 3 6 2 3 9: 2 5 2 2 10: 1 8 0 3 11: 0 7 1 0 12: 1 3 1 2 13: 3 5 0 5 14: 0 0 2 2 15: 2 6 1 4 16: 0 7 2 2 17: 1 5 3 0 18: 1 6 0 1 19: 3 7 2 3 20: 0 5 0 1
In order to fill the fifth row in DT2 with missing values, add the following code to the above snippet −
DT2[5,(names(DT2)):=.SD[NA]] DT2
Output
If you execute all the above given snippets as a single program, it generates the following output: −
y1 y2 y3 y4 1: 2 11 1 6 2: 1 4 1 3 3: 0 4 0 0 4: 2 7 2 1 5: NA NA NA NA 6: 1 5 1 1 7: 0 8 0 1 8: 3 6 2 3 9: 2 5 2 2 10: 1 8 0 3 11: 0 7 1 0 12: 1 3 1 2 13: 3 5 0 5 14: 0 0 2 2 15: 2 6 1 4 16: 0 7 2 2 17: 1 5 3 0 18: 1 6 0 1 19: 3 7 2 3 20: 0 5 0 1
358 Views
