From a .csv file

First, upload your document in the lower right-hand panel of your console by pressing the upload button in the Files tab. The document will appear in the list of documents that you can use for your project.

Second, you need to load your data into your environment (top right-hand panel). You need to create a new variable that will hold all information from your .csv file. This way, you will be able to interact with your data. For a .csv file, you need to write the following command inside a R chunk:

# Loading the dataset into a variable named variableName
variableName <- read.csv("rNanocourse2DataForGraph.csv", header = TRUE, sep = ";")

With:

• variableName the name of your new variable that will hold all information from a dataset;
• "rNanocourse2DataForGraph.csv" corresponding to the name of the files you previously imported;
• header = TRUE if the first line of your dataset contains the names of all columns, header = FALSE otherwise;
• sep = ";" if the separation between columns is set to be a semicolon (could be "," depending on your dataset then sep = ",").

Now, you can interact with your dataset by using the name of your variable. We will go in details during the third laboratory dedicated to data manipulation. For example:

# Show first 6 lines of the dataset
head(variableName)

# Summary of the dataset
summary(variableName)

##  date.country.variable.value
##  Length:18                  
##  Class :character           
##  Mode  :character

From a Google sheet

A second way to import data is to go through Google Drive, by using Google Sheets. RStudio is able to connect to any spreadsheet in a Google Drive account, which helps any work involving multiple people. A third and optimal option will be presented in the next session, using the gsheet package.

You need to create a new Google Sheets document. After naming your spreadsheet, you need to configure the sharing options. In order to do so, click on the Share button in the corner of your spreadsheet. A panel will appear and you need to select the following setting:

• Anyone with the link can edit

Copy the link you will obtain after this step. The link will be somewhat like this:

https://docs.google.com/spreadsheets/d/1DNvPgCJvKRzUwVigrj7NLgbvJ00isL4j4zYMYuI0Ya0/edit?usp=sharing

First, you need to load a package in your document, the gsheet package and then use the gsheet2tbl() function. You have to copy the link between parenthesis and quotation marks as following:

# Loading the gsheet package
library(gsheet)

# Read data from the URL
data2 <- gsheet2tbl("https://docs.google.com/spreadsheets/d/1DNvPgCJvKRzUwVigrj7NLgbvJ00isL4j4zYMYuI0Ya0/edit?usp=sharing")

# Delete the line with a missing value
data2 <- na.omit(data2)

# Show first 6 lines of the dataset
head(data2)

# Summary of the dataset
summary(data2)

##      RD2014          rdGrowth       RDGrowth3yr       RDintensity   
##  Min.   :  28.8   Min.   :-44.70   Min.   :-53.500   Min.   : 0.20  
##  1st Qu.:  36.4   1st Qu.: -2.55   1st Qu.: -4.750   1st Qu.: 3.80  
##  Median :  63.1   Median :  8.90   Median :  3.300   Median : 9.10  
##  Mean   : 201.6   Mean   : 23.79   Mean   :  5.463   Mean   :11.86  
##  3rd Qu.: 182.2   3rd Qu.: 40.05   3rd Qu.: 18.800   3rd Qu.:14.20  
##  Max.   :1508.1   Max.   :208.90   Max.   : 48.600   Max.   :58.70

Now you should be able to fullfil the first task of this laboratory, i.e. loading data from a .csv file or from a Google Sheets document.

Bar charts

First, you need to load the different packages used for your graphics.

# Loading the ggplot2 and the ggthemes packages
library(ggplot2)
library(ggthemes)

After that, we will load a dataset that will be used for the three different graphics.

# Loading data
dataGraph <- read.csv("rNanocourse2DataForGraph.csv", header = TRUE, sep = ",")

# Reading the first 10 lines of the dataset
head(dataGraph, n = 10)

# Bar chart elements
ggplot(data = dataGraph, aes(x = date, y = value, fill = country)) + 
  geom_bar(stat = "identity", width = 0.5, position = "dodge")  +  
  ylab("")  +
  xlab("") +
  guides(col = guide_legend(row = 1)) +
  theme_hc() +
  scale_fill_brewer(direction = -1)

Lines

# Loading the ggplot2 and the ggthemes packages
library(ggplot2)
library(ggthemes)

# Line chart elements
ggplot(data = dataGraph, aes(x = date, y = value, color = country)) +
  geom_line()  + 
  ylab("")  +
  xlab("") +
  geom_smooth(span = 0.8) +
  ggtitle("") +
  theme_hc() +
  scale_color_brewer(direction = -1) +
  guides(fill=FALSE) +
  geom_point(colour = "blue", size = 2,shape = 22)

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Bubble chart

# Loading the ggplot2 and the ggthemes packages
library(ggplot2)
library(ggthemes)

# Bubble chart elements
ggplot(data = dataGraph, aes(x = date, y = value, color = country)) +
  geom_point(aes(size = value)) +
  scale_size_continuous(range = c(2,15)) +
  ggtitle("") +
  theme_hc() +
  scale_color_brewer(direction = -1) +
  theme(axis.title.x = element_blank()) +
  guides(fill=FALSE)

Now, you are able to:

1. load any data from a .csv file and a Google Sheets document;
2. visualize your data in a simple and efficient manner.

Resources

For more on ggplot2, please refer to:

• https://www.rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf
• http://docs.ggplot2.org/current/

For some tips on bar and line graphs using ggplot2:

• http://www.cookbook-r.com/Graphs/Bar_and_line_graphs_(ggplot2)/

Packages

• ggplot2: H. Wickham & W. Chang
• XML: D. Temple Lang et al.
• ggthemes: J. Arnold et al.