One of the best packages that R language provides for this purpose is googleVis and guess what, we are going to see its amazing features together. In this first part we are going to see basic stuff of three useful types of charts ( Line Chart , Bar Chart and Column Chart ) before “digging deeper” in the next parts.
NOTE : The charts are created locally by your browser. In case they are not displayed at once press F5 to reload the page.
Read the examples below to understand the logic of what we are going to do and then test yous skills with the exercise set we prepared for you. Lets begin!
Answers to the exercises are available here .
Install the Package.
Exercise 1
Install and load the package googleVis.
Create a data frame
First of all let’s create an experimental
data.frame
to use for all our plots. This is an example:
dfr=data.frame(name=c("GRE", "ARG", "BRA"),
val1=c(20,32,19),
val2=c(25,52,12))
####################
# #
# Exercise 1 #
# #
####################
install.packages("googleVis")
library(googleVis)
Exercise 2
Create a data frame named “df”. Give as variables the “Pts” (Points) and “Rbs” (Rebounds) of three NBA players. Names and values are up to you.
Line Chart
To produce a Line Chart you can use:
LineC <- gvisLineChart(df)
plot(LineC)
####################
# #
# Exercise 2 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
Exercise 3
Create a
list
named “LineC” and pass to it the “df” data frame you just created as a line chart.
HINT
: Use
gvisLineChart()
.
####################
# #
# Exercise 3 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
LineC <- gvisLineChart(df)
Exercise 4
Plot the line chart.
HINT
: Use
plot()
.
Line chart with two axis
Below there is an example of this type of Line Chart:
LineC2 <- gvisLineChart(df, "name", c("val1","val2"),
options=list(
series="[{targetAxisIndex: 0},
{targetAxisIndex:1}]",
vAxes="[{title:'val1'}, {title:'val2'}]"
))
plot(LineC2)
As you can see you create a
list
(options) that corresponds values 1 & 2 in the two axes respectively.
####################
# #
# Exercise 4 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
LineC <- gvisLineChart(df)
plot(LineC)
Exercise 5
Create a single axis Line chart that displays only the “Pts” of the “df” data frame.
####################
# #
# Exercise 5 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
LineC2 <- gvisLineChart(df, "name", c("Pts","Rbs"),
)
plot(LineC2)
Exercise 6
Now create a two axis line chart that displays both “Pts” and “Rbs” of the “df” data frame.
HINT
: Use
list()
.
Bar Chart
It is quite simple to create a bar chart with googleVis with:
BarC <- gvisBarChart(df)
plot(BarC)
####################
# #
# Exercise 6 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
LineC2 <- gvisLineChart(df, "name", c("Pts","Rbs"),
options=list(
series="[{targetAxisIndex: 0},
{targetAxisIndex:1}]",
vAxes="[{title:'Pts'}, {title:'Rbs'}]"
))
plot(LineC2)
Exercise 7
Create a
list
named “BarC” and pass to it the “df” data frame you just created as a bar chart.
HINT
: Use
gvisBarChart()
.
####################
# #
# Exercise 7 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
BarC <- gvisBarChart(df)
Exercise 8
Plot the bar chart.
HINT
: Use
plot()
.
Column Chart
Column charts could be considered as vertical bar charts and are quite simple to be created too.
ColumnC <- gvisColumnChart(df)
plot(ColumnC)
####################
# #
# Exercise 8 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
BarC <- gvisBarChart(df)
plot(BarC)
Exercise 9
Create a
list
named “ColumnC” and pass to it the “df” data frame you just created as a column chart.
HINT
: Use
gvisColumnChart()
.
####################
# #
# Exercise 9 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
ColumnC <- gvisColumnChart(df)
Exercise 10
Plot the column chart.
HINT
: Use
plot()
.
####################
# #
# Exercise 10 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
ColumnC <- gvisColumnChart(df)
plot(ColumnC)
In the second part of our series we are going to meet three more googleVis charts. More specifically these charts are Area Chart, Stepped Area Chart and Combo Chart.
Read the examples below to understand the logic of what we are going to do and then test yous skills with the exercise set we prepared for you. Lets begin!
Answers to the exercises are available here.
Package & Data frame
As you already know, the first thing you have to do is install and load the googleVis package with:
install.packages("googleVis")
library(googleVis)
Secondly we will create an experimental data frame which will be used for our charts’ plotting. You can create it with:
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
NOTE: The charts are created locally by your browser. In case they are not displayed at once press F5 to reload the page.
Area chart
It is quite simple to create an area chart with googleVis with:
AreaC <- gvisBarChart(df)
plot(AreaC)
Exercise 1
Create a list named “AreaC” and pass to it the “df” data frame you just created as an area chart. HINT: Use gvisAreaChart().
####################
# #
# Exercise 1 #
# #
####################
library(googleVis)
df=data.frame(name=c("quot;James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
AreaC <- gvisAreaChart(df)
Exercise 2
Plot the area chart. HINT: Use plot().
Stepped Area chart
Creating a stepped area chart is a little different than the area chart. You have to set the X and Y variables and also make it stacked in order to display the values correctly. Here is an example:
SteppedAreaC <- gvisSteppedAreaChart(df, xvar="name",
yvar=c("val1", "val2"),
options=list(isStacked=TRUE))
plot(SteppedAreaC)
####################
# #
# Exercise 2 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
AreaC <- gvisAreaChart(df)
plot(AreaC)
Exercise 3
Create a list named “SteppedAreaC” and pass to it the “df” data frame you just created as a stepped area chart. You should also set the X variable as the players’ names and Y variable as their values. HINT: Use gvisSteppedAreaChart(), xvar and yvar.
####################
# #
# Exercise 3 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
SteppedAreaC <- gvisSteppedAreaChart(df, xvar="name",
yvar=c("Pts", "Rbs"))
Exercise 4
Plot the stepped area chart. HINT: Use plot().
####################
# #
# Exercise 4 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
SteppedAreaC <- gvisSteppedAreaChart(df, xvar="name",
yvar=c("Pts", "Rbs"))
plot(SteppedAreaC)
Exercise 5
Now transform your stepped area chart to stacked to correct it and plot it.
Combo chart
The next chart we are going to meet is the combination of lines and bars chart known as combo chart. You can produce it like this:
ComboC <- gvisComboChart(df, xvar="country",
yvar=c("val1", "val2"),
options=list(seriesType="bars",
series='{1: {type:"line"}}'))
plot(ComboC)
####################
# #
# Exercise 5 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
SteppedAreaC <- gvisSteppedAreaChart(df, xvar="name",
yvar=c("Pts", "Rbs"),
options=list(isStacked=TRUE))
plot(SteppedAreaC)
Exercise 6
Create a list named “ComboC” and pass to it the “df” data frame you just created as a combo chart. You should also set the X variable as the players’ names and Y variable as their values. HINT: Use gvisComboChart(), xvar and yvar.
####################
# #
# Exercise 6 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
ComboC <- gvisComboChart(df, xvar="name",
yvar=c("Pts", "Rbs")
)
Exercise 7
Plot the chart. What kind of chart do you see? HINT: Use plot().
In order to add the bars we have to set it as the example below.
options=list(seriesType="bars",
series='{1: {type:"line"}}')
####################
# #
# Exercise 7 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
ComboC <- gvisComboChart(df, xvar="name",
yvar=c("Pts", "Rbs")
)
plot(ComboC)
Exercise 8
Transform the chart you just created into a combo chart with bars and lines and plot it. HINT: Use list().
####################
# #
# Exercise 8 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
ComboC <- gvisComboChart(df, xvar="name",
yvar=c("Pts", "Rbs"),
options=list(seriesType="bars",
series='{1: {type:"line"}}'))
plot(ComboC)
Exercise 9
In the previous exercise “Pts” are represented by the bars and “Rbs” by the lines. Try to reverse them.
You can easily transform your combo chart into a column chart or a line chart just be setting series='{1: {type:"line"}}' to 2
####################
# #
# Exercise 9 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
ComboC <- gvisComboChart(df, xvar="name",
yvar=c("Pts", "Rbs"),
options=list(seriesType="line",
series='{1: {type:"bars"}}'))
plot(ComboC)
Exercise 10
Transform the combo chart into a column chart and then into a line chart.
####################
# #
# Exercise 10 #
# #
####################
#column chart
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
ComboC <- gvisComboChart(df, xvar="name",
yvar=c("Pts", "Rbs"),
options=list(seriesType="bars",
series='{2: {type:"line"}}'))
plot(ComboC)
#line chart
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
ComboC <- gvisComboChart(df, xvar="name",
yvar=c("Pts", "Rbs"),
options=list(seriesType="line",
series='{2: {type:"bars"}}'))
plot(ComboC)
Scatter & Bubble chart
This is the third part of our data visualization series and at this part we will explore the features of two more of the charts that googleVis provides.
Read the examples below to understand the logic of what we are going to do and then test yous skills with the exercise set we prepared for you. Lets begin!
Answers to the exercises are available here.
Package Installation
As you already know, the first thing you have to do is install and load the googleVis package with:
install.packages("googleVis")
library(googleVis)
NOTE: The charts are created locally by your browser. In case they are not displayed at once press F5 to reload the page.
Scatter chart
It is quite simple to create a scatter chart with googleVis. We will use the cars dataset. Look at the example below:
ScatterC <- gvisScatterChart(cars)
plot(ScatterC)
Exercise 1
Create a list named “ScatterC” and pass to it the cars dataset as a scatter chart. HINT: Use gvisScatterChart().
#################### # # # Exercise 1 # # # #################### library(googleVis) ScatterC <- gvisScatterChart(cars)
Exercise 2
Plot the the scatter chart. HINT: Use plot().
Titles
It is time to learn how to enhance the appearance of our googleVis charts. We shall give a title to the chart and also name hAxis and vAxis. Look at the example:
options=list(title="Cars", vAxis="{title:'speed'}",
hAxis="{title:'dist'}" )
#################### # # # Exercise 2 # # # #################### library(googleVis) ScatterC <- gvisScatterChart(cars) plot(ScatterC)
Exercise 3
Name your chart “Cars”, your chart’s vAxis “speed”, your chart’s hAxis “dist” and plot the chart. HINT: Use list().
Size
You can adjust the size with width and height.
####################
# #
# Exercise 3 #
# #
####################
library(googleVis)
ScatterC <- gvisScatterChart(cars,
options=list(
title="Cars", vAxis="{title:'speed'}",
hAxis="{title:'dist'}" ))
plot(ScatterC)
Exercise 4
Set your chart’s width to 600 and height to 300.
Legend
You can deactivate your chart’s legend if you set it to “none”.
####################
# #
# Exercise 4 #
# #
####################
library(googleVis)
ScatterC <- gvisScatterChart(cars,
options=list(
title="Cars", vAxis="{title:'speed'}",
hAxis="{title:'dist'}",
width=600, height=300))
plot(ScatterC)
Exercise 5
Deactivate your chart’s legend.
Point size & Line width
You can determine the size of the chart’s points with pointsize and also choose to unite them with line with linewidth. For example:
pointSize=4,linewidth=3
####################
# #
# Exercise 5 #
# #
####################
library(googleVis)
ScatterC <- gvisScatterChart(cars,
options=list(
legend="none",
title="Cars", vAxis="{title:'speed'}",
hAxis="{title:'dist'}",
width=600, height=300))
plot(ScatterC)
Exercise 6
Set point size to 3 and line width to 2.
Bubble Chart
Another amazing type of chart that googleVis provides is the bubble chart. You can create a simple Bubble Chart of the Fruits dataset like this:
BubbleC <- gvisBubbleChart(Fruits)
plot(BubbleC)
####################
# #
# Exercise 6 #
# #
####################
library(googleVis)
ScatterC <- gvisScatterChart(cars,
options=list(
legend="none",
pointSize=3,lineWidth=2,
title="Cars", vAxis="{title:'speed'}",
hAxis="{title:'dist'}",
width=600, height=300))
plot(ScatterC)
Exercise 7
Create a list named “BubbleC” and pass to it the Fruits dataset as a bubble chart. HINT: Use gvisBubbleChart().
#################### # # # Exercise 7 # # # #################### library(googleVis) BubbleC <- gvisBubbleChart(Fruits)
Exercise 8
Plot the chart. HINT: Use plot().
Bubble Chart’s Features
As you can see, you created a bubble chart but it seems to be useless. In order to make it useful you should pass to it some of your dataset’s variables as features. It depends on what you want to be displayed and how. If you type head(Fruits) you can easily recognize the numeric variables of your dataset. Then you can use them like this:
BubbleC <- gvisBubbleChart(Fruits,idvar="VAR1",
xvar="VAR2", yvar="VAR3",
colorvar="VAR4", sizevar="VAR5")
#################### # # # Exercise 8 # # # #################### library(googleVis) BubbleC <- gvisBubbleChart(Fruits) plot(BubbleC)
Exercise 9
Find the numeric variables of Fruits, then set “Fruit” as idvar, “Sales” as xvar, “Expenses” as yvar, “Year” as colorvar and “Profit” as sizevar and plot your chart. HINT: Use head().
Data range
You can also adjust the minimum and maximum number of hAxis and vAxis that you want to be displayed. Look at the example below:
options=list(
hAxis='{minValue:50, maxValue:150}')
####################
# #
# Exercise 9 #
# #
####################
library(googleVis)
head(Fruits)
BubbleC <- gvisBubbleChart(Fruits,idvar="Fruit",
xvar="Sales", yvar="Expenses",
colorvar="Year", sizevar="Profit")
plot(BubbleC)
Exercise 10
Set your hAxis range from 70 to 130 and your vAxis range from 50 to 100.
####################
# #
# Exercise 10 #
# #
####################
library(googleVis)
BubbleC <- gvisBubbleChart(Fruits,idvar="Fruit",
xvar="Sales", yvar="Expenses",
colorvar="Year", sizevar="Profit",
options=list(
hAxis='{minValue:70, maxValue:130}',
vAxis='{minValue:50, maxValue:100}'))
plot(BubbleC)
Scatter & Bubble chart
This is the third part of our data visualization series and at this part we will explore the features of two more of the charts that googleVis provides.
Read the examples below to understand the logic of what we are going to do and then test yous skills with the exercise set we prepared for you. Lets begin!
Answers to the exercises are available here.
Package Installation
As you already know, the first thing you have to do is install and load the googleVis package with:
install.packages("googleVis")
library(googleVis)
NOTE: The charts are created locally by your browser. In case they are not displayed at once press F5 to reload the page.
Scatter chart
It is quite simple to create a scatter chart with googleVis. We will use the cars dataset. Look at the example below:
ScatterC <- gvisScatterChart(cars)
plot(ScatterC)
Exercise 1
Create a list named “ScatterC” and pass to it the cars dataset as a scatter chart. HINT: Use gvisScatterChart().
#################### # # # Exercise 1 # # # #################### library(googleVis) ScatterC <- gvisScatterChart(cars)
Exercise 2
Plot the the scatter chart. HINT: Use plot().
Titles
It is time to learn how to enhance the appearance of our googleVis charts. We shall give a title to the chart and also name hAxis and vAxis. Look at the example:
options=list(title="Cars", vAxis="{title:'speed'}",
hAxis="{title:'dist'}" )
#################### # # # Exercise 2 # # # #################### library(googleVis) ScatterC <- gvisScatterChart(cars) plot(ScatterC)
Exercise 3
Name your chart “Cars”, your chart’s vAxis “speed”, your chart’s hAxis “dist” and plot the chart. HINT: Use list().
Size
You can adjust the size with width and height.
####################
# #
# Exercise 3 #
# #
####################
library(googleVis)
ScatterC <- gvisScatterChart(cars,
options=list(
title="Cars", vAxis="{title:'speed'}",
hAxis="{title:'dist'}" ))
plot(ScatterC)
Exercise 4
Set your chart’s width to 600 and height to 300.
Legend
You can deactivate your chart’s legend if you set it to “none”.
####################
# #
# Exercise 4 #
# #
####################
library(googleVis)
ScatterC <- gvisScatterChart(cars,
options=list(
title="Cars", vAxis="{title:'speed'}",
hAxis="{title:'dist'}",
width=600, height=300))
plot(ScatterC)
Exercise 5
Deactivate your chart’s legend.
Point size & Line width
You can determine the size of the chart’s points with pointsize and also choose to unite them with line with linewidth. For example:
pointSize=4,linewidth=3
####################
# #
# Exercise 5 #
# #
####################
library(googleVis)
ScatterC <- gvisScatterChart(cars,
options=list(
legend="none",
title="Cars", vAxis="{title:'speed'}",
hAxis="{title:'dist'}",
width=600, height=300))
plot(ScatterC)
Exercise 6
Set point size to 3 and line width to 2.
Bubble Chart
Another amazing type of chart that googleVis provides is the bubble chart. You can create a simple Bubble Chart of the Fruits dataset like this:
BubbleC <- gvisBubbleChart(Fruits)
plot(BubbleC)
####################
# #
# Exercise 6 #
# #
####################
library(googleVis)
ScatterC <- gvisScatterChart(cars,
options=list(
legend="none",
pointSize=3,lineWidth=2,
title="Cars", vAxis="{title:'speed'}",
hAxis="{title:'dist'}",
width=600, height=300))
plot(ScatterC)
Exercise 7
Create a list named “BubbleC” and pass to it the Fruits dataset as a bubble chart. HINT: Use gvisBubbleChart().
#################### # # # Exercise 7 # # # #################### library(googleVis) BubbleC <- gvisBubbleChart(Fruits)
Exercise 8
Plot the chart. HINT: Use plot().
Bubble Chart’s Features
As you can see, you created a bubble chart but it seems to be useless. In order to make it useful you should pass to it some of your dataset’s variables as features. It depends on what you want to be displayed and how. If you type head(Fruits) you can easily recognize the numeric variables of your dataset. Then you can use them like this:
BubbleC <- gvisBubbleChart(Fruits,idvar="VAR1",
xvar="VAR2", yvar="VAR3",
colorvar="VAR4", sizevar="VAR5")
#################### # # # Exercise 8 # # # #################### library(googleVis) BubbleC <- gvisBubbleChart(Fruits) plot(BubbleC)
Exercise 9
Find the numeric variables of Fruits, then set “Fruit” as idvar, “Sales” as xvar, “Expenses” as yvar, “Year” as colorvar and “Profit” as sizevar and plot your chart. HINT: Use head().
Data range
You can also adjust the minimum and maximum number of hAxis and vAxis that you want to be displayed. Look at the example below:
options=list(
hAxis='{minValue:50, maxValue:150}')
####################
# #
# Exercise 9 #
# #
####################
library(googleVis)
head(Fruits)
BubbleC <- gvisBubbleChart(Fruits,idvar="Fruit",
xvar="Sales", yvar="Expenses",
colorvar="Year", sizevar="Profit")
plot(BubbleC)
Exercise 10
Set your hAxis range from 70 to 130 and your vAxis range from 50 to 100.
####################
# #
# Exercise 10 #
# #
####################
library(googleVis)
BubbleC <- gvisBubbleChart(Fruits,idvar="Fruit",
xvar="Sales", yvar="Expenses",
colorvar="Year", sizevar="Profit",
options=list(
hAxis='{minValue:70, maxValue:130}',
vAxis='{minValue:50, maxValue:100}'))
plot(BubbleC)
Adding Features to your Charts
We saw in the previous charts some basic and well-known types of charts that googleVis offers to users. Before continuing with other, more sophisticated charts in the next parts we are going to “dig a little deeper” and see some interesting features of those we already know.
Read the examples below to understand the logic of what we are going to do and then test yous skills with the exercise set we prepared for you. Lets begin!
Answers to the exercises are available here.
Package & Data frame
As you already know, the first thing you have to do is install and load the googleVis package with:
install.packages("googleVis")
library(googleVis)
Secondly we will create an experimental data frame which will be used for our charts’ plotting. You can create it with:
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
NOTE: The charts are created locally by your browser. In case they are not displayed at once press F5 to reload the page.
Customizing Chart
We are going to use the two-axis Line Chart we created in part 1. This is the code we used, in case you forgot it:
LineC2 <- gvisLineChart(df, "name", c("Pts","Rbs"),
options=list(
series="[{targetAxisIndex: 0},
{targetAxisIndex:1}]",
vAxes="[{title:'Pts'}, {title:'Rbs'}]"
))
plot(LineC2)
Colours
To set the color of every line we can use:
series="[{color:'green', targetAxisIndex: 0,
Exercise 1
Change the colours of your line chart to green and yellow respectively and plot the chart.
Line Width
You can determine the line width of every line with:
series="[{color:'green',targetAxisIndex: 0, lineWidth: 3},
####################
# #
# Exercise 1 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
LineCD2 <- gvisLineChart(df, "name", c("Pts","Rbs"),
options=list(
series="[{color:'green',targetAxisIndex: 0},
{color:'yellow',targetAxisIndex:1}]",
vAxes="[{title:'Pts'}, {title:'Rbs'}]"
))
plot(LineCD2)
Exercise 2
Change the line width of your lines to 3 and 6 respectively and plot the chart.
Dashed lines
You can tranform your lines to dashed with:
series="[{color:'green', targetAxisIndex: 0,
lineWidth: 1, lineDashStyle: [2, 2, 20, 2, 20, 2]},
There are many styles and colours available and you can find them here.
####################
# #
# Exercise 2 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
LineCD2 <- gvisLineChart(df, "name", c("Pts","Rbs"),
options=list(
series="[{color:'green',targetAxisIndex: 0, lineWidth: 3},
{color:'yellow',targetAxisIndex:1,lineWidth: 6}]",
vAxes="[{title:'Pts'}, {title:'Rbs'}]"
))
plot(LineCD2)
Exercise 3
Choose two different styles of dashed lines for every line of your chart from the link above and plot your chart.
Point Shape
With the pointShape option you can choose from a variety of shapes for your points.
We will use the scatter chart we built in part 3 to see how it works. Here is the code:
ScatterCD <- gvisScatterChart(cars,
options=list(
legend="none",
pointSize=3,lineWidth=2,
title="Cars", vAxis="{title:'speed'}",
hAxis="{title:'dist'}",
width=600, height=300))
plot(ScatterCD)
####################
# #
# Exercise 3 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
LineCD2 <- gvisLineChart(df, "name", c("Pts","Rbs"),
options=list(
series="[{color:'green',targetAxisIndex: 0, lineWidth: 3,
lineDashStyle: [14, 2, 2, 7]},
{color:'yellow',targetAxisIndex:1,lineWidth: 6,
lineDashStyle: [10, 2]}]",
vAxes="[{title:'Pts'}, {title:'Rbs'}]"
))
plot(LineCD2)
Exercise 4
Change the shape of your scatter chart’s points to ‘square’ and plot it. HINT: Use pointShape.
####################
# #
# Exercise 4 #
# #
####################
library(googleVis)
ScatterCD <- gvisScatterChart(cars,
options=list(
legend="none",
pointSize=3,lineWidth=2,pointShape='square',
title="Cars", vAxis="{title:'speed'}",
hAxis="{title:'dist'}",
width=600, height=300))
plot(ScatterCD)
Exercise 5
Change the shape of your scatter chart’s points to ‘triangle’, their point size to 7 and plot it.
Edit Button
A really useful and easy feature that googleVis provides is the edit button which gives the user the ability to customize the chart in an automated way.
options=list(gvis.editor="Edit!"))
####################
# #
# Exercise 5 #
# #
####################
library(googleVis)
ScatterCD <- gvisScatterChart(cars,
options=list(
legend="none",
pointSize=7,lineWidth=2,pointShape='triangle',
title="Cars", vAxis="{title:'speed'}",
hAxis="{title:'dist'}",
width=600, height=300))
plot(ScatterCD)
Exercise 6
Add an edit button in the scatter chart you just created. HINT: Use gvis.editor.
Chart with more options
Now let’s see how we can create a chart with many features that can enhance its appearance. We will use again the 2-axis line that we used before.
LineCD2 <- gvisLineChart(df, "name", c("Pts","Rbs"),
options=list(
series="[{color:'green',targetAxisIndex: 0, lineWidth: 3,
lineDashStyle: [14, 2, 2, 7]},
{color:'yellow',targetAxisIndex:1,lineWidth: 6,
lineDashStyle: [10, 2]}]",
vAxes="[{title:'Pts'}, {title:'Rbs'}]"
))
plot(LineCD2)
Background color
You can decide the background color of your chart with:
backgroundColor="red",
####################
# #
# Exercise 6 #
# #
####################
library(googleVis)
ScatterCD <- gvisScatterChart(cars,
options=list(gvis.editor="Edit",
legend="none",
pointSize=7,lineWidth=2,pointShape='triangle',
title="Cars", vAxis="{title:'speed'}",
hAxis="{title:'dist'}",
width=600, height=300))
plot(ScatterCD)
Exercise 7
Set the background color of your line chart to “lightblue” and plot it. HINT: Use backgroundColor.
Title
To give a title and decide its features you can use:
title="Title",
titleTextStyle="{color:'orange',
fontName:'Courier',
fontSize:14}",
####################
# #
# Exercise 7 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
LineCD2 <- gvisLineChart(df, "name", c("Pts","Rbs"),
options=list(backgroundColor="lightblue",
series="[{color:'green',targetAxisIndex: 0, lineWidth: 3,
lineDashStyle: [14, 2, 2, 7]},
{color:'yellow',targetAxisIndex:1,lineWidth: 6,
lineDashStyle: [10, 2]}]",
vAxes="[{title:'Pts'}, {title:'Rbs'}]"
))
plot(LineCD2)
Exercise 8
Give a title of your choise to the line chart and set its font to blue, Courier of size 16. HINT: Use titleTextStyle.
Curve Type & Legend
Another nice-looking choise that googleVis gives you is to display the lines like curves with:
curveType="function"
You can also move the legend of your chart to the bottom with:
legend="bottom"
####################
# #
# Exercise 8 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
LineCD2 <- gvisLineChart(df, "name", c("Pts","Rbs"),
options=list(backgroundColor="lightblue",
title="Line Chart",
titleTextStyle="{color:'blue',
fontName:'Courier',
fontSize:16}",
series="[{color:'green',targetAxisIndex: 0, lineWidth: 3,
lineDashStyle: [14, 2, 2, 7]},
{color:'yellow',targetAxisIndex:1,lineWidth: 6,
lineDashStyle: [10, 2]}]",
vAxes="[{title:'Pts'}, {title:'Rbs'}]"
))
plot(LineCD2)
Exercise 9
Smooth the lines of your line chart by setting the curveType option to function and move the legend to the bottom. HINT: Use curveType and legend.
Axes features
Finally you can “play” with your axes. This is an example:
vAxis="{gridlines:{color:'green', count:4}}",
hAxis="{title:'City', titleTextStyle:{color:'red'}}",
series="[{color:'yellow', targetAxisIndex: 0},
{color: 'brown',targetAxisIndex:1}]",
vAxes="[{title:'val1'}, {title:'val2'}]",
####################
# #
# Exercise 9 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
LineCD2 <- gvisLineChart(df, "name", c("Pts","Rbs"),
options=list(backgroundColor="lightblue",
title="Line Chart",
titleTextStyle="{color:'blue',
fontName:'Courier',
fontSize:16}",
curveType="function",
series="[{color:'green',targetAxisIndex: 0, lineWidth: 3,
lineDashStyle: [14, 2, 2, 7]},
{color:'yellow',targetAxisIndex:1,lineWidth: 6,
lineDashStyle: [10, 2]}]",
vAxes="[{title:'Pts'}, {title:'Rbs'}]"
))
plot(LineCD2)
Exercise 10
Give the title “Name” to your hAxis and color it orange. Separate your vAxis with 3 red gridlines. HINT: Use titleTextStyle and gridlines
####################
# #
# Exercise 10 #
# #
####################
library(googleVis)
df=data.frame(name=c("James", "Curry", "Harden"),
Pts=c(20,23,34),
Rbs=c(13,7,9))
LineCD2 <- gvisLineChart(df, "name", c("Pts","Rbs"),
options=list(backgroundColor="lightblue",
title="Line Chart",
titleTextStyle="{color:'blue',
fontName:'Courier',
fontSize:16}",
curveType="function",
legend="bottom",
vAxis="{gridlines:{color:'red', count:4}}",
hAxis="{title:'Name', titleTextStyle:{color:'orange'}}",
series="[{color:'green',targetAxisIndex: 0, lineWidth: 3,
lineDashStyle: [14, 2, 2, 7]},
{color:'yellow',targetAxisIndex:1,lineWidth: 6,
lineDashStyle: [10, 2]}]",
vAxes="[{title:'Pts'}, {title:'Rbs'}]"
))
plot(LineCD2)