Please download all exercise files for this exercise at http://github.com/summerofgeorge/rtraining.

It's a good idea to call the libraries you're going to use in your script at the beginning of the program.

We will be using two libraries,tidyverse and readxl. Load them now:

library(tidyverse)
library(readxl)

If you see an error message, check your packages are installed: install.packages().

While R has its own file extension for storing data, it's more common to reading and writing to outside file types. We will cover how to read and write to .csv, .txt and .xlsx files.

we will be importing in the famous iris dataset which is popular in data science training.

The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres.

An active R session always has a working directory associated with it. This is where, by default, all files are read and write to.

Check the location of your current directory with the getwd function:

getwd()

You can set a working directory with setwd().

File paths in R are always enclosed in double quotation marks, and R uses forward slashes, not backslashes, when specifying folder locations. This will take some getting used to, especially for Windows users!

setwd("C:/RFiles/")

There are two ways to refer to outside files in R: using relative or absolute file paths.

Which to use? It all depends on whether you have set a working directory with setwd() and whether the file you want to read/write to is in that folder.

For example, let's say I have the iris dataset saved in the folder C:/RFiles/ and I have set the working directory to the same folder.

In this case I will use the relative file path…

setwd("C:/RFiles")
iris<-read_csv("iris.csv")

Let's say that instead I had set my working directory to another folder, "C:/Iris".

In this case a relative reference will not work.

setwd("C:/Iris")
iris<-read_csv("iris.csv")

Instead, we need to use the absolute file path because the file is not in the working directory.

For the remainder of this unit, I will save all files to C:/RFiles which will also be my working directory:

setwd("C:/RFiles")

To read files we will use read_csv()

Make sure to assign this function to an object! If not, you will simply have read the file in R without any way to call it!

To write files we will use (you guessed it!) write_csv().

This function will take one extra argument in that we first type the object where the dataset is pointed to and then write to the file we want.

However, we will not need to assign it to an object, since we are exporting it anyway.

#write the iris dataframe to iriswrite.csv
write_csv(iris,"iriswrite.csv")

This file will now appear in our working directory (C:/RFiles).

This will be very similar to the read_csv and write_csv functions: We use read_tsv and write_tsv.

#assign the iris.txt dataset to iris variable
iris<-read_tsv("iris.txt")

#write the iris dataset to Iriswrite.txt
iris<-write_tsv("iriswrite.txt")

We will now read in the Iris.xlsx file using the read_excel function:

#read file to excel
iris<-read_excel("iris.xlsx")

To write files to Excel, we will… write to csv.

Unfortunately, the readxl package only reads files from Excel, as the name suggests. There are packages to write to Excel, but I find that writing to CSV and then opening in Excel is the easiest way.

#write to csv and then open in Excel
write_csv(iris,"C:/RFiles/iriswrite2.csv")

If you are used to exploring your data visually, as you might when scrolling down a spreadsheet, you can use view().

#open up spreadsheet-type environment
view(iris)

Because you're often dealing with such huge datasets, it may not be practical to try viewing the entire table.

Instead, use head and tail to print the first and last X rows, respectively.

#view first 3 observations
head(iris,3)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa

#view last 3 observations
tail(iris,3)
##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 148          6.5         3.0          5.2         2.0 virginica
## 149          6.2         3.4          5.4         2.3 virginica
## 150          5.9         3.0          5.1         1.8 virginica

You may have noticed your output for the previous functions began with: A tibble: 3 x 5.

What exactly is a tibble?

Put simply, it is a 'modern' data frame used by the libraries of the tidyverse. They are nearly identical in concept - tibbles are just programmed to work better for tidyverse libraries.

Back to our scheduled programming (heh, heh)…

The str() function gives us an idea of the structure and type of the object.

str(iris)
## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

If you just want a list of the names in the dataset, you can run the names() function.

names(iris)
## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width" 
## [5] "Species"

Finally, you can get summary statistics of your variables with the summary() function:

summary(iris)
##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
##