Exploratory Analysis - Call Volume.Rmd

---
title: "Exploratory Analysis - Call Volume"
author: "Barry Davis"
date: "July 13, 2017"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
```

# Staging
## Environment
```{r}
library(lubridate)
library(data.table)
library(tidyverse)
library(plotly)
```

## Load Data
```{r}
apps <- fread("aggint_application_refined.csv", header=TRUE, stringsAsFactors = TRUE)
skills <- fread("agg_int_skillset_refined.csv")
```

## Filter Desired Data Sets
```{r}
primaryApps <- apps[ApplicationID %in% c(10025, 10021, 10139, 10120, 10037, 10115)]
primarySkills <- skills[SkillsetID %in% c(10058,10037, 10055,10029,10168,10180)]
```

# Call Volume
In this document we will perform exploratory analysis on Calls Offered, Calls Answered, and Calls Abandoned. 

# Monthly Call Volume
We're going to be looking at specific applications to determine call volume. The applications are directly accessed after selecting a menu option when the client's phone number is dialed.

```{r}
p <- ggplot(primaryApps, aes(x = month(statTimestamp, label = TRUE, abbr = TRUE), y = CallsOffered, fill = ApplicationName)) + 
  geom_bar(stat="identity") +
  xlab("Month") + ylab("Total Calls") +
  ggtitle("Monthly Total Call Volume")
ggplotly(p)
ggplot(primaryApps, aes(x = month(statTimestamp), y = CallsAbandoned, fill = ApplicationName)) +
  geom_bar(stat="identity")
ggplot(primarySkills, aes(x = month(statTimestamp), y = CallsAnswered, fill = SkillsetName)) + 
  geom_bar(stat="identity")
```


## Including Plots

You can also embed plots, for example:

```{r pressure, echo=FALSE}
plot(pressure)
```

Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.