• 1 Goals
  • 2 Instructions
  • 3 Overview
  • 4 Install R
    • 4.1 Check in
  • 5 Install RStudio
    • 5.1 Check in
  • 6 Install packages
  • 7 Make a name plot
  • 8 Make a new name plot
    • 8.1 Save and share

1 Goals

At the end of this Lab, we will:

  • have installed R and RStudio on our machines
  • understood how to add additional R-packages for specific features and graphic capability
  • run code within RStudio and interpret the results
  • have learnt to look for help within R and RStudio

2 Instructions

  1. Keep this tab open in your browser as you work through it. Read all the way to the end.

3 Overview

This guide will lead you through the steps to install and use R, a free and open-source software environment for statistical computing and graphics.

What is R?

  • R is the name of the programming language itself, based off S from Bell Labs, which users access through a command-line interpreter (>)

What is RStudio?

  • RStudio is a powerful and convenient user interface that allows you to access the R programming language along with a lot of other bells and whistles that enhance functionality (and sanity).

Our end goal is to get you looking at a screen like this:

4 Install R

Install R from CRAN, the Comprehensive R Archive Network. Please choose a precompiled binary distribution for your operating system.

4.1 Check in

Launch R. You should see one console with a command line interpreter (>). Close R.

5 Install RStudio

Install the free, open-source edition of RStudio: http://www.rstudio.com/products/rstudio/download/

RStudio provides a powerful user interface for R, called an integrated development environment. RStudio includes:

  • a console (the standard command line interface: >),
  • a syntax-highlighting editor that supports direct code execution, and
  • tools for plotting, history, debugging and workspace management.

5.1 Check in

Launch RStudio. You should get a window similar to the screenshot you see here, but yours will be empty. Look at the bottom left pane: this is the same console window you saw when you opened R in step 1.15.

  • Place your cursor where you see > and type x <- 2 + 2, hit enter or return, then type x, and hit enter/return again.
  • If [1] 4 prints to the screen, you have successfully installed R and RStudio, and you can move onto installing packages.

6 Install packages

The version of R that you just downloaded is considered base R, which provides you with good but basic statistical computing and graphics powers. For analytical and graphical super-powers, you’ll need to install add-on packages, which are user-written, to extend/expand your R capabilities. Packages can live in one of two places:

  • They may be carefully curated by CRAN (which involves a thorough submission and review process), and thus are easy install using install.packages("name_of_package", dependencies = TRUE) in your CONSOLE.

Place your cursor in the CONSOLE again (where you last typed x and [4] printed on the screen). You can use the first method to install the following packages directly from CRAN, all of which we will use:

  • knitr,

  • dplyr,

  • ggplot2,

  • babynames

  • To install a package, you put the name of the package in quotes as in install.packages("name_of_package"). Mind your use of quotes carefully with packages.

  • To use an already installed package, you must load it first, as in library(name_of_package), leaving the name of the package bare. You only need to do this once per RStudio session.

You can download all of these at once, too:

install.packages(c("knitr", "dplyr", "ggplot2", "babynames"), dependencies = TRUE)

A brief aside: c() is a command in R that allows us to combine things into a vector ( one of the ways data is represented in R)

c("hello", "my", "name", "is", "arvind")
## [1] "hello"  "my"     "name"   "is"     "arvind"
c(1:3, 20, 50)
## [1]  1  2  3 20 50
  • If you want help, no quotes are needed: help(name_of_package) or ?name_of_package.
  • If you want the citation for a package (and you should give credit where credit is due), ask R as in citation("name_of_package").
install.packages("dplyr", dependencies = TRUE)
library(dplyr)
help("dplyr")
citation("ggplot2")
## 
## To cite ggplot2 in publications, please use:
## 
##   H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Book{,
##     author = {Hadley Wickham},
##     title = {ggplot2: Elegant Graphics for Data Analysis},
##     publisher = {Springer-Verlag New York},
##     year = {2016},
##     isbn = {978-3-319-24277-4},
##     url = {https://ggplot2.tidyverse.org},
##   }

7 Make a name plot

The webpage you are looking at is derived from a R Markdown doc that you can download, edit and compute with. We will meet R Markdown in the next class.

Download this .Rmd file using the Code->Download Rmd button at the top right corner.

Change the author name to your own!

Hit the green “play” button to run this “load_packages” chunk to include in your R session all the installed packages you need:

Let us greet our data first !!

glimpse(babynames) # dplyr
Rows: 1,924,665
Columns: 5
$ year <dbl> 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1…
$ sex  <chr> "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", …
$ name <chr> "Mary", "Anna", "Emma", "Elizabeth", "Minnie", "Margaret", "Ida", "Alice", "Bertha", "Sarah", "Annie", "Clara", "El…
$ n    <int> 7065, 2604, 2003, 1939, 1746, 1578, 1472, 1414, 1320, 1288, 1258, 1226, 1156, 1063, 1045, 1040, 1012, 995, 982, 949…
$ prop <dbl> 0.07238359, 0.02667896, 0.02052149, 0.01986579, 0.01788843, 0.01616720, 0.01508119, 0.01448696, 0.01352390, 0.01319…
head(babynames) # base R
# A tibble: 6 × 5
   year sex   name          n   prop
  <dbl> <chr> <chr>     <int>  <dbl>
1  1880 F     Mary       7065 0.0724
2  1880 F     Anna       2604 0.0267
3  1880 F     Emma       2003 0.0205
4  1880 F     Elizabeth  1939 0.0199
5  1880 F     Minnie     1746 0.0179
6  1880 F     Margaret   1578 0.0162
tail(babynames) # same
# A tibble: 6 × 5
   year sex   name       n       prop
  <dbl> <chr> <chr>  <int>      <dbl>
1  2017 M     Zyhier     5 0.00000255
2  2017 M     Zykai      5 0.00000255
3  2017 M     Zykeem     5 0.00000255
4  2017 M     Zylin      5 0.00000255
5  2017 M     Zylis      5 0.00000255
6  2017 M     Zyrie      5 0.00000255
names(babynames) # same
[1] "year" "sex"  "name" "n"    "prop"

If you have done the above and produced sane-looking output, you are ready for the next bit. Use the code below to create a new data frame called arvind.

my_name_data <- babynames %>%
  filter(name == "Arvind" | name == "Aravind") %>% 
  filter(sex == "M")

  • The first bit makes a new dataset called my_name_data that is a copy of the babynames dataset- the %>% tells you we are doing some other stuff to it later.

  • The second bit filters our babynames to only keep rows where the name is either Arvind or Aravind (read | as “or”.)

  • The third bit applies another filter to keep only those where sex is male.

Let’s check out the data.

my_name_data
# A tibble: 61 × 5
    year sex   name       n       prop
   <dbl> <chr> <chr>  <int>      <dbl>
 1  1970 M     Arvind     5 0.00000262
 2  1972 M     Arvind     8 0.00000478
 3  1975 M     Arvind     7 0.00000431
 4  1976 M     Arvind     5 0.00000306
 5  1977 M     Arvind     9 0.00000526
 6  1978 M     Arvind     6 0.00000351
 7  1979 M     Arvind     7 0.00000391
 8  1980 M     Arvind     6 0.00000323
 9  1981 M     Arvind     8 0.0000043 
10  1982 M     Arvind     6 0.00000318
# … with 51 more rows
glimpse(my_name_data)
Rows: 61
Columns: 5
$ year <dbl> 1970, 1972, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1…
$ sex  <chr> "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", …
$ name <chr> "Arvind", "Arvind", "Arvind", "Arvind", "Arvind", "Arvind", "Arvind", "Arvind", "Arvind", "Arvind", "Arvind", "Arvi…
$ n    <int> 5, 8, 7, 5, 9, 6, 7, 6, 8, 6, 7, 7, 7, 13, 8, 11, 6, 8, 12, 10, 17, 6, 14, 21, 21, 6, 20, 5, 24, 10, 25, 8, 26, 15,…
$ prop <dbl> 2.620e-06, 4.780e-06, 4.310e-06, 3.060e-06, 5.260e-06, 3.510e-06, 3.910e-06, 3.230e-06, 4.300e-06, 3.180e-06, 3.760…

Again, if you have sane-looking output here, move along to plotting the data!

plot <- ggplot(my_name_data, aes(x = year, 
                           y = prop,  
                           group = name, 
                           color = name)) + 
  geom_line()

Now if you did this right, you will not see your plot!

Because we saved the ggplot with a name (plot), R just saved the object for you. But check out the top right pane in RStudio again: under the Environment pane you should see plot, so it is there, you just have to ask for it. Here’s how:

plot

8 Make a new name plot

Edit my code above to create a new dataset. Pick 2 names to compare how popular they each are (these could be different spellings of your own name, like I did, but you can choose any 2 names that are present in the dataset). Make the new plot, changing the name of the first argument arvind in ggplot() to the name of your new dataset.

8.1 Save and share

Save your work so you can share your favorite plot with us. You will not like the looks of your plot if you mouse over to Export and save it. Instead, use ggplot2’s command for saving a plot with sensible defaults:

help(ggsave)
ggsave("file_name_here.pdf", plot) # please make the filename unique!

Upload this exported plot to Teams -> Assignments.

Before you do that, check that you can submit stuff/assignments on Teams by uploading a cat picture first, especially if you are an ailurophile like me. (Acceptable Breeds: Scottish Folds or Maine Coons)

---
title: 'Lab 00 - Introduce Yourself'
author: "Arvind Venkatadri"
date: 06/July/2021
output:
  html_document:
    theme: flatly
    toc: TRUE
    toc_float: TRUE
    toc_depth: 2
    number_sections: TRUE
    code_folding: hide
    code_download: TRUE

abstract: Part of the `R for Artists and Designers` course at the School of Foundation Studies, Srishti Manipal Institute of Art, Design, and Technology, Bangalore.
---

# Goals

At the end of this Lab, we will:

- have installed R and RStudio on our machines
- understood how to add additional R-packages for specific features and graphic capability
- run code within RStudio and interpret the results
- have learnt to look for help within R and RStudio

# Instructions

1. Keep this tab open in your browser as you work through it. Read all the way to the end.


# Overview

This guide will lead you through the steps to install and use [R, a free and open-source software environment for statistical computing and graphics.](https://www.r-project.org) 


What is R?

* **R** is the name of the programming language itself, based off S from Bell Labs, which users access through a command-line interpreter (`>`)

What is RStudio?

* **RStudio** is a powerful and convenient user interface that allows you to access the R programming language along with a lot of other bells and whistles that enhance functionality (and sanity). 

Our end goal is to get you looking at a screen like this:

![](./images/00_final-screenshot.png)

# Install R

Install R from [CRAN, the Comprehensive R Archive Network](https://cran.rstudio.com). Please choose a **precompiled binary distribution** for your operating system.

## Check in

Launch R. You should see one console with a command line interpreter (`>`). Close R.

# Install RStudio 

Install the free, open-source edition of RStudio: http://www.rstudio.com/products/rstudio/download/

RStudio provides a powerful user interface for R, called an *integrated development environment*. RStudio includes:

* a console (the standard command line interface: `>`), 
* a syntax-highlighting editor that supports direct code execution, and 
* tools for plotting, history, debugging and workspace management.


## Check in

Launch RStudio. You should get a window similar to the screenshot you see [here](https://www.rstudio.com/wp-content/uploads/2014/04/rstudio-workbench.png), but yours will be empty. Look at the bottom left pane: this is the same console window you saw when you opened R in step 1.15. 

* Place your cursor where you see `>` and type `x <- 2 + 2`, hit enter or return, then type `x`, and hit enter/return again. 
* If `[1] 4` prints to the screen, you have successfully installed R and RStudio, and you can move onto installing packages.

# Install packages

The version of R that you just downloaded is considered base R, which provides you with good but basic statistical computing and graphics powers. For analytical and graphical super-powers, you'll need to install add-on packages, which are user-written, to extend/expand your R capabilities. Packages can live in one of two places:

* They may be carefully curated by CRAN (which involves a thorough submission and review process), and thus are easy install using `install.packages("name_of_package", dependencies = TRUE)` in your CONSOLE. 

Place your cursor in the CONSOLE again (where you last typed `x` and `[4]` printed on the screen). You can use the first method to install the following packages directly from CRAN, all of which we will use:

  * [`knitr`,](https://yihui.org/knitr/)
  * [`dplyr`,](http://dplyr.tidyverse.org)
  * [`ggplot2`,](http://ggplot2.tidyverse.org)
  * [`babynames`](https://github.com/hadley/babynames)

* To *install* a package, you put the name of the package **in quotes** as in `install.packages("name_of_package")`. 
Mind your use of quotes carefully with packages.

* To *use* an already installed package, you must load it first, as in `library(name_of_package)`, leaving the name of the package bare. You only need to do this **once** per RStudio session.
  
You can download all of these at once, too:

```{r c_install, eval = FALSE}
install.packages(c("knitr", "dplyr", "ggplot2", "babynames"), dependencies = TRUE)
```

A brief aside: 
`c()` is a command in R that allows us to combine things into a `vector` ( one of the ways data is represented in R)

```{r c_demo}
c("hello", "my", "name", "is", "arvind")
c(1:3, 20, 50)

```


* If you want *help*, no quotes are needed: `help(name_of_package)` or `?name_of_package`.
* If you want the *citation* for a package (and you should give credit where credit is due), ask R as in `citation("name_of_package")`.

```{r show_dplyr, eval = F}
install.packages("dplyr", dependencies = TRUE)
library(dplyr)
help("dplyr")
```

```{r help_dplyr}
citation("ggplot2")
```

# Make a name plot

The webpage you are looking at is derived from a `R Markdown` doc that you can download, edit and compute with. We will meet `R Markdown` in the next class. 

Download this .Rmd file using the `Code->Download Rmd` button at the top right corner.

Change the author name to your own!

Hit the green "play" button to run this "load_packages" *chunk* to include in your R session all the installed packages you need: 

```{r load_packages, include = FALSE, cache = FALSE}

knitr::opts_chunk$set(error = TRUE, comment = NA, warnings = FALSE, errors = FALSE, messages = FALSE, tidy = FALSE)
library(knitr) # to use this....document! More later!!
library(dplyr) # for manipulating data
library(ggplot2) # for plotting data
library(babynames) # A package containing, yes, Baby Names

```

Let us greet our data first !!

```{r hlo_dataset}
glimpse(babynames) # dplyr
head(babynames) # base R
tail(babynames) # same
names(babynames) # same
```


If you have done the above and produced sane-looking output, you are ready for the next bit. Use the code below to create a new data frame called `arvind`. 

```{r manipulate_name_data}
my_name_data <- babynames %>%
  filter(name == "Arvind" | name == "Aravind") %>% 
  filter(sex == "M") 
```

* The first bit makes a new dataset called `my_name_data` that is a copy of the `babynames` dataset- the `%>%` tells you we are doing some other stuff to it later.

* The second bit `filters` our `babynames` to only keep rows where the `name` is either Arvind or Aravind (read `|` as _"or"_.) 

* The third bit applies another `filter` to keep only those where sex is male.

Let's check out the data.

```{r}
my_name_data
glimpse(my_name_data)
```

Again, if you have sane-looking output here, move along to plotting the data!

```{r plot_name_data}

plot <- ggplot(my_name_data, aes(x = year, 
                           y = prop,  
                           group = name, 
                           color = name)) + 
  geom_line()

```

Now if you did this right, you will **not see** your plot!

Because we saved the `ggplot` with a name (`plot`), R just saved the object for you. But check out the top right pane in RStudio again: under the `Environment` pane you should see `plot`, so it is there, you just have to ask for it. Here's how:

```{r}

plot 

```

# Make a new name plot

Edit my code above to create a new dataset. Pick 2 names to compare how popular they each are (these could be different spellings of your own name, like I did, but you can choose any 2 names that are present in the dataset). Make the new plot, changing the name of the first argument `arvind` in `ggplot()` to the name of your new dataset.


## Save and share 

Save your work so you can share your favorite plot with us. You will not like the looks of your plot if you mouse over to `Export` and save it. Instead, use `ggplot2`'s command for saving a plot with sensible defaults:

```{r eval = FALSE}

help(ggsave)

```

```{r eval = F}

ggsave("file_name_here.pdf", plot) # please make the filename unique!

```

Upload this exported plot to Teams -> Assignments. 

**Before you do that**, check that you can submit stuff/assignments on Teams by uploading a cat picture first, especially if you are an **ailurophile** like me. (Acceptable Breeds: Scottish Folds or Maine Coons)

