Project_I.Rmd

---
title: "Project I: Data Cleaning"
author: "Patricia Kiambo"
date: "6/18/2019"
output: html_document
---

## Malaria Cases by Counties in Kenya - Year: 2006 & 2013

```{r setup}
library(dplyr)
```

## Data Import
  Data was sourced from    https://data.humdata.org/dataset/bed-nets-and-illness-by-county-kenya
  
```{r}
malaria_ke <- read.csv("malaria_explore.csv")
```
## Dimension of the Dataset

```{r}
dim(malaria_ke)
```

## Class of the Dataset
```{r}
class(malaria_ke)
```

## Structure of the Dataset
```{r}
str(malaria_ke)
```

## Head and Tail of the Dataset prior to Data Cleaning
```{r}
head(malaria_ke)
tail(malaria_ke)
```

## Remane of the columns of the Dataset
```{r}
names(malaria_ke)[2]<-"Malaria_2006"
names(malaria_ke)[3]<-"Malaria_2013"
names(malaria_ke)[4]<-"Poverty_Rate_2006"
names(malaria_ke)[5]<-"Physician_Density_2013"
names(malaria_ke)[6]<-"Under_BedNet_2006"
names(malaria_ke)[7]<-"HealthSpending_2006"
names(malaria_ke)[8]<-"Health_Facilities"
head(malaria_ke,3)
```

## Remove of the Missing values from dataset
A set of dummy rows were added.

```{r}
tail(malaria_ke)
dim(malaria_ke)
malaria_ke_complete <- malaria_ke[complete.cases(malaria_ke),]
malaria_complete <- na.omit(malaria_ke)
dim(malaria_ke_complete)
tail(malaria_complete)
```

## Summary of the Dataset
```{r}
summary(malaria_ke_complete)
```

## Subsetting of the Dataset
Which countries had an increase and decrease in Malaria cases from 2006 to 2013?
The resulting dataset removes the columns: Malaria_2006 & Malaria_2013

```{r}
increased_malaria <- subset(malaria_complete, Malaria_2006 < Malaria_2013 & Health_Facilities >100)
increased_malaria <-(select(increased_malaria, -(Malaria_2006), -(Malaria_2013)))
increased_malaria

decreased_malaria <- subset(malaria_complete, Malaria_2006 > Malaria_2013 & Health_Facilities <100)
decreased_malaria <-(select(decreased_malaria, -(Malaria_2006), -(Malaria_2013)))
decreased_malaria

```

## Reordering the subset Dataset
The subset dataset is reodered, ascending, in relation to the number of Health Facilities

```{r}
increased_malaria <- increased_malaria[order(increased_malaria$Health_Facilities) , ]
increased_malaria

decreased_malaria <- decreased_malaria[order(decreased_malaria$Health_Facilities) , ]
decreased_malaria

```

## Mutate the Dataset
The ration of Facilities to Physician Density (per 100K people)
```{r}
malaria_complete <- mutate(malaria_complete, Facilities2Physician= Health_Facilities/Physician_Density_2013, na.rm = TRUE) 
head(select(malaria_complete,County, Facilities2Physician),20)

```