Week 3 Homework

Synopsis

This is my homework report for week 3. In this week, our main focus is to investigate good practices for when we get a new data set. Understanding the basic characteristics of our data is the first step towards a meaningful analysis. We are going to focus on three objectives that we should have when we first open up a new data set: 1. Review the codebook 2. Learn about the data 3. Visualize the data

library(knitr)
read_chunk("week-3.R")

Packages Required

library(knitr)
library(tidyverse)

Source Code

The data is scraped from here This archive contains files of average daily temperatures for 157 U.S. and 167 international cities. The files are updated on a regular basis and contain data from January 1, 1995 to present. Source data for these files are from the Global Summary of the Day (GSOD) database archived by the National Climatic Data Center (NCDC). The average daily temperatures posted on this site are computed from 24 hourly temperature readings in the Global Summary of the Day (GSOD) data.

Details about the Data: 1. The data fields in each file posted on this site are: month, day, year, average daily temperature (F).
2. We use “-99” as a no-data flag when data are not available.

Scraping the Data :

url <- "http://academic.udayton.edu/kissock/http/Weather/gsod95-current/OHCINCIN.txt"
weather_data <- read.table(url, header=FALSE, col.names = c("Month","Day","Year","Temperature"))
kable(head(weather_data))

Month	Day	Year	Temperature
1	1	1995	41.1
1	2	1995	22.2
1	3	1995	22.8
1	4	1995	14.9
1	5	1995	9.5
1	6	1995	23.8

Data Description

Number of Variables

ncol(weather_data)

## [1] 4

Names of the Variables

names(weather_data)

## [1] "Month"       "Day"         "Year"        "Temperature"

Number of Rows

nrow(weather_data)

## [1] 7963

Number of Rows and Variables

dim(weather_data)

## [1] 7963    4

Structure of the Data

kable(str(weather_data))

## 'data.frame':    7963 obs. of  4 variables:
##  $ Month      : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ Day        : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Year       : int  1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 ...
##  $ Temperature: num  41.1 22.2 22.8 14.9 9.5 23.8 31.1 26.9 31.3 31.5 ...

A Few observations

kable(head(weather_data))

Month	Day	Year	Temperature
1	1	1995	41.1
1	2	1995	22.2
1	3	1995	22.8
1	4	1995	14.9
1	5	1995	9.5
1	6	1995	23.8

kable(tail(weather_data))

	Month	Day	Year	Temperature
7958	10	14	2016	54.4
7959	10	15	2016	63.2
7960	10	16	2016	68.7
7961	10	17	2016	71.1
7962	10	18	2016	74.4
7963	10	19	2016	75.3

Number of Missing Values

sum((weather_data==-99))

## [1] 14

Removing the missing values

weather_data[weather_data==-99] <- NA
weather_data <- na.omit(weather_data)

Summary of the Data

summary(weather_data)

##      Month             Day             Year       Temperature   
##  Min.   : 1.000   Min.   : 1.00   Min.   :1995   Min.   :-2.20  
##  1st Qu.: 4.000   1st Qu.: 8.00   1st Qu.:2000   1st Qu.:40.20  
##  Median : 6.000   Median :16.00   Median :2005   Median :57.10  
##  Mean   : 6.477   Mean   :15.71   Mean   :2005   Mean   :54.73  
##  3rd Qu.: 9.000   3rd Qu.:23.00   3rd Qu.:2011   3rd Qu.:70.70  
##  Max.   :12.000   Max.   :31.00   Max.   :2016   Max.   :89.20

Data Visualization

A basic ggplot of year vs temperature colored by month
A smooth ggplot of year vs temperature
A boxplot for each month showing temperature values

ggplot(data = weather_data) + 
  geom_point(mapping = aes(x = Year, y = Temperature, color=Month))

ggplot(data = weather_data) + 
  geom_smooth(mapping = aes(x = Year, y = Temperature))