I am trying to create a dashboard and my code is saying the below on the first chunk of the libraries any help on this:
Error in getSymbols(c("MSFT", "HPQ", "INTC"), from = "2014-01-01", auto.assign = TRUE) :
could not find function "getSymbols"
The code is below:
title: "My First Dashboard"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: fill
social: ["twitter", "facebook", "menu"]
---
```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(plotly)
library(dplyr)
library(openintro)
library(highcharter)
library(ggvis)
library(dygraphs)
library(quantmod)
getSymbols(c("MSFT", "HPQ", "INTC"), from = "2014-01-01", auto.assign=TRUE)
```
```{r}
data <- read.csv("C:/Users/fungai/Google Drive/phiri/Sales.csv")
```
```{r}
mycolours<-c("blue", "#FFC125", "darkgreen","darkorange")
```
Interactive Data Visualisation
===============================================
Row
-----------------------------------------------
### Sales Analysis by Region
```{r}
valueBox(paste("Sales"),
color = "warning")
```
### Average Sales Revenue
```{r}
valueBox(mean(data$Total.Revenue),
icon = "fa-user")
```
### **Average Revenue**
```{r}
gauge(round(mean(data$Total.Revenue),
digits = 2),
min = 0,
max = 5000000,
gaugeSectors(success = c(2000000,5000000),
warning = c(500000,2000000),
danger = c(0,500000),
colors = c("green", "yellow","red")))
```
### Asia
```{r}
valueBox(sum(data$Region == "Asia"),
icon = 'fa-building')
```
### Europe
```{r}
valueBox(sum(data$Region == "Europe"),
icon = 'fa-building')
```
### North America
```{r}
valueBox(sum(data$Region == "North America"),
icon = 'fa-building')
```
### Australia and Oceania
```{r}
valueBox(sum(data$Region == "Australia and Oceania"),
icon = 'fa-building')
```
Row
----------------------------------------------------
### Sales Units by Category
```{r}
p1<- data %>%
group_by(Item.Type) %>%
summarise(Rev = sum(Total.Revenue)) %>%
plot_ly(x = ~Item.Type,
y = ~Rev,
type = 'bar') %>%
layout(xaxis = list(title = "Revenue by Region"),
yaxis = list(title = 'Revenue'))
p1
```
### Revenue by Region
```{r}
p2<- data %>%
group_by(Region) %>%
summarise(Rev2 = sum(Total.Revenue)) %>%
plot_ly(labels = ~Region,
values = ~Rev2) %>%
add_pie(hole = 0.4) %>%
layout(xaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F),
yaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F))
p2
```
Data Table
======================================================
```{r}
datatable(data,
caption = "Sales Data",
rownames = T,
filter = "top",
options = list(pagelength = 25))
```
Pivot Table
======================================================
```{r}
rpivotTable(data,
aggregatorName = "Sum",
rows = "Country",
vals = c("Units.Sold", "Total.Revenue"),
rendererName = "Heatmap")
```
Further Analysis - Units
======================================================
Columns
----------------------------------------------------
### Microsoft
```{r}
dygraph(MSFT[,2:4], group = "stocks") %>%
dySeries(c("MSFT.Low", "MSFT.Close", "MSFT.High"), label = "MSFT")
```