Created
May 29, 2020 17:53
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R script with some tidyverse selection and filtering tips.
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# _____ | |
# | __ \ | |
# | |__) | | |
# | _ / | |
# | | \ \ | |
# |_| \_\ Programming Language | |
# Some data selection and filtering tips using R programming. | |
# And the tidyverse library to filter and subset our data. | |
# Exercise from 'R Programming 101' youtube channel (org. Greg Martin). | |
# Library used | |
library(tidyverse) | |
# Tidtverse builtin data set | |
# Many characteristics about some animals species. | |
view(msleep) | |
# Selecting and filtering | |
# Example I | |
my_data <- msleep %>% | |
select(name, sleep_total) %>% | |
filter(sleep_total > 18) | |
# Example II | |
my_data <- msleep %>% | |
select(name, order, bodywt, sleep_total) %>% | |
filter(order == "Primates", bodywt > 20) | |
# Example III | |
my_data <- msleep %>% | |
select(name, sleep_total) %>% | |
filter(name %in% c("Cow", "Dog", "Horse")) | |
# Example IV | |
my_data <- msleep %>% | |
select(name, sleep_total) %>% | |
filter(between(sleep_total, 16, 18)) | |
# Example V | |
my_data <- msleep %>% | |
select(name, sleep_total) %>% | |
filter(near(sleep_total, 17, tol = 0.5)) | |
# Example VI | |
my_data <- msleep %>% | |
select(name, conservation, sleep_total) %>% | |
filter(is.na(conservation)) #locating the missing values. | |
# Example VII | |
my_data <- msleep %>% | |
select(name, conservation, sleep_total) %>% | |
filter(!is.na(conservation)) #seeing data without the missing values. |
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URL from original tutorial R Programming 101