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 find the log10 of each value in columns if some columns are categorical in R data frame?
To find the log10 of each value if some columns are categorical in R data frame, we can follow the below steps −
First of all, create a data frame.
Then, use numcolwise function from plyr package to find the log10 if some columns are categorical.
Example
Create the data frame
Let’s create a data frame as shown below −
Level<-sample(c("low","medium","high"),25,replace=TRUE)
Group<-sample(c("first","second"),25,replace=TRUE)
Score<-sample(1:50,25)
Demand<-sample(1:100,25)
df<-data.frame(Level,Group,Score,Demand)
dfOutput
On executing, the above script generates the below output(this output will vary on your system due to randomization) −
Level Group Score Demand 1 low second 30 9 2 high first 42 48 3 medium first 14 88 4 high second 27 63 5 high first 37 97 6 medium first 38 39 7 high second 13 18 8 high first 16 14 9 low second 40 90 10 medium first 25 53 11 low second 48 78 12 high second 28 64 13 low first 22 32 14 medium first 1 31 15 high first 2 21 16 low second 17 26 17 high first 5 41 18 medium first 49 85 19 low second 45 29 20 low first 9 49 21 medium first 31 44 22 medium first 29 46 23 high second 10 36 24 low second 21 33 25 low second 35 40
Find the log10 if some columns are categorical
Using numcolwise function from plyr package to find the log10 of each value in numerical columns in the data frame df −
Level<-sample(c("low","medium","high"),25,replace=TRUE)
Group<-sample(c("first","second"),25,replace=TRUE)
Score<-sample(1:50,25)
Demand<-sample(1:100,25)
df<-data.frame(Level,Group,Score,Demand)
library(plyr)
numcolwise(log10)(df)Output
Score Demand 1 1.2552725 1.851258 2 1.3424227 1.477121 3 1.6334685 1.041393 4 1.6020600 1.986772 5 1.6532125 1.944483 6 1.5910646 1.934498 7 1.5440680 1.897627 8 1.2787536 1.977724 9 1.4913617 1.857332 10 1.5051500 0.602060 11 1.5682017 1.505150 12 1.3979400 1.230449 13 0.8450980 1.681241 14 0.6989700 1.748188 15 1.6720979 0.903090 16 1.4623980 1.863323 17 1.0000000 1.799341 18 1.1139434 1.380211 19 1.5563025 1.982271 20 0.9542425 0.698970 21 0.7781513 1.633468 22 1.0413927 1.707570 23 1.5797836 1.792392 24 1.6901961 1.556303 25 1.6989700 1.806180
Updated on: 10-Nov-2021
83 Views
Advertisements