Filtering Data in R 10 Tips -tidyverse package
filtering data in r, In this tutorial describes how to filter or extract data frame rows based on certain criteria.
It’s the process of getting your raw data transformed into a format that’s easier to work with for analysis.
In this tutorial, you will learn the filter R functions from the tidyverse package.
The main idea is to showcase different ways of filtering from the data set.
Filtering data is one of the common tasks in the data analysis process. When you want to remove or extract a part of the data use tidyverse package ’filter()’ function.
Load Library
library(tidyverse)
str(msleep)tibble[,11] [83 x 11] (S3: tbl_df/tbl/data.frame) $ name : chr [1:83] "Cheetah" "Owl monkey" "Mountain beaver" "Greater short-tailed shrew" … $ genus : chr [1:83] "Acinonyx" "Aotus" "Aplodontia" "Blarina" … $ vore : chr [1:83] "carni" "omni" "herbi" "omni" … $ order : chr [1:83] "Carnivora" "Primates" "Rodentia" "Soricomorpha" … $ conservation: chr [1:83] "lc" NA "nt" "lc" … $ sleep_total : num [1:83] 12.1 17 14.4 14.9 4 14.4 8.7 7 10.1 3 … $ sleep_rem : num [1:83] NA 1.8 2.4 2.3 0.7 2.2 1.4 NA 2.9 NA … $ sleep_cycle : num [1:83] NA NA NA 0.133 0.667 … $ awake : num [1:83] 11.9 7 9.6 9.1 20 9.6 15.3 17 13.9 21 … $ brainwt : num [1:83] NA 0.0155 NA 0.00029 0.423 NA NA NA 0.07 0.0982 … $ bodywt : num [1:83] 50 0.48 1.35 0.019 600 …
We’ll use the R built-in msleep data set, which we will use for a different types of filtering.
It’s estimated that as much as 75% of a data scientist’s time is spent data cleaning & reshaping. To be an effective data scientist, you need to learn fastest way handling these things.
Example 1
Sentiment analysis in R » Complete Tutorial »
data1<-msleep %>%
select(name,sleep_total) %>%
filter(sleep_total>15)Output:-
name sleep_total 1 Owl monkey 17 2 Long-nosed armadillo 17.4 3 North American Opossum 18 4 Big brown bat 19.7 5 Thick-tailed opposum 19.4 6 Little brown bat 19.9 7 Tiger 15.8 8 Giant armadillo 18.1 9 Arctic ground squirrel 16.6 10 Golden-mantled ground squirrel 15.9 11 Eastern american chipmunk 15.8 12 Tenrec 15.6
Example 2
Correlation Analysis Different Types of Plots in R »
data2<-msleep %>%
select(name,sleep_total) %>%
filter(!sleep_total>15)Output:-
name sleep_total 1 Cheetah 12.1 2 Mountain beaver 14.4 3 Greater short-tailed shrew 14.9 4 Cow 4 5 Three-toed sloth 14.4 6 Northern fur seal 8.7 7 Vesper mouse 7 8 Dog 10.1 9 Roe deer 3 10 Goat 5.3
Example 3
Power analysis in Statistics with R »
data3<-msleep %>%
select(name,order,bodywt,sleep_total) %>%
filter(order=="Primates", bodywt>15)Output:-
name order bodywt sleep_total 1 Human Primates 62 8 2 Chimpanzee Primates 52.2 9.7 3 Baboon Primates 25.2 9.4
Example 4
Principal component analysis (PCA) in R »
data4<-msleep %>%
select(name, order, bodywt,sleep_total) %>%
filter(order=="Primates" | bodywt>15)Output:-
name order bodywt sleep_total 1 Cheetah Carnivora 50 12.1 2 Owl monkey Primates 0.48 17 3 Cow Artiodactyla 600 4 4 Northern fur seal Carnivora 20.5 8.7 5 Goat Artiodactyla 33.5 5.3 6 Grivet Primates 4.75 10 7 Asian elephant Proboscidea 2547 3.9 8 Horse Perissodactyla 521 2.9 9 Donkey Perissodactyla 187 3.1 10 Patas monkey Primates 10 10.9
Example 5
data5<-msleep %>%
select(name,sleep_total) %>%
filter(name=="Cow" |
name=="Dog"|
name=="Goat")Output:-
name sleep_total 1 Cow 4 2 Dog 10.1 3 Goat 5.3
Example 6
Stock Prediction-Intraday Trading » With High Accuracy »
data6<-msleep %>%
select(name, sleep_total) %>%
filter(name %in% c("Cow","Dog","Goat"))Output:-
name sleep_total 1 Cow 4 2 Dog 10.1 3 Goat 5.3
Example 7
data7<-msleep %>%
select(name, sleep_total) %>%
filter(between(sleep_total,16,18))Output:-
name sleep_total 1 Owl monkey 17 2 Long-nosed armadillo 17.4 3 North American Opossum 18 4 Arctic ground squirrel 16.6
Example 8
KNN Algorithm Machine Learning » Classification & Regression »
data8<-msleep %>%
select(name, sleep_total) %>%
filter(near(sleep_total,17, tol=0.5))Output:-
name sleep_total 1 Owl monkey 17 2 Long-nosed armadillo 17.4 3 Arctic ground squirrel 16.6
Example 9
data9<-msleep %>%
select(name, conservation,sleep_total) %>%
filter(is.na(conservation))Output:-
name conservation sleep_total 1 "Owl monkey" NA 17 2 "Three-toed sloth" NA 14.4 3 "Vesper mouse" NA 7 4 "African giant pouched rat" NA 8.3 5 "Western american chipmunk" NA 14.9 6 "Galago" NA 9.8 7 "Human" NA 8 8 "Macaque" NA 10.1 9 "Vole " NA 12.8 10 "Little brown bat" NA 19.9
Example 10
data10<-msleep %>%
select(name, conservation,sleep_total) %>%
filter(!is.na(conservation)) Output:-
name conservation sleep_total 1 Cheetah lc 12.1 2 Mountain beaver nt 14.4 3 Greater short-tailed shrew lc 14.9 4 Cow domesticated 4 5 Northern fur seal vu 8.7 6 Dog domesticated 10.1 7 Roe deer lc 3 8 Goat lc 5.3 9 Guinea pig domesticated 9.4 10 Grivet lc 10
Market Basket Analysis in R » What Goes With What »
