Publications by Ozkan Gelincik
Cleaning Data in R
1 Common Data problems In this section, we will talk about Diagnosing dirty data Side effects of dirty data (bugs!) Cleaning data Data Type Constraints knitr::include_graphics("./dataTypeConstraints.png") Let’s load the bike_share_rides dataset and use the glimpse() function—from the dplyr package—review the data install.packages("dplyr", ...
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Working with Dates and Times in R
1 Introduction to Dates Difference countries use different formats but there is a global numeric date format called ISO 8601: YYYY-MM-DD Values are ordered from the largest to smallest unit of time Each has a fixed number of digits, must be padded with leading zeros Either, no separator for computers, or - in dates 1st of January 2011: 2011-01-01 ...
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Introduction to Regression in R
1 A Tale of Two Variables Let’s start with installing and loading all necessary packages: library(readr) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(g...
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Exploratory Data Analysis in R
1 Exploring categorical data In this chapter we will work with the comics dataset library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(readr) comics <- read_csv...
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Sampling in R
1 Sampling and Point Estimates 1.1 Estimating the population of France knitr::include_graphics("./France1.png") knitr::include_graphics("./France2.png") knitr::include_graphics("./France3.png") knitr::include_graphics("./France4.png") 2 Population vs. Sample The population is the complete dataset. It doesn’t have to refer to people. We typ...
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Hypothesis Testing in R
library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(ggplot2) library(tibble) library(fst) install.packages("infer", repos = "http://cran.us.r-project.org") ## ...
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Introduction to Writing Functions in R
1 Writing a basic function: eliminating repetition Writing functions reduces the number of calculation we’d do otherwise. Let’s say we wanted to process different variables of a dataset the same way, like below. knitr::include_graphics("./whyWriteFunctions.png") Notice that we’re doing the same things for each exam score variable. This is re...
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