tidyverse_tips_R_101.R

#  _____  
# |  __ \ 
# | |__) |
# |  _  / 
# | | \ \ 
# |_|  \_\ Programming Language

# Some data 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)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ ggplot2 3.3.0     ✓ purrr   0.3.4
## ✓ tibble  3.0.1     ✓ dplyr   0.8.5
## ✓ tidyr   1.0.3     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

# 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.