Getting-data-into-R.Rmd

---
title: "Getting data into R"
author: "Ben Bond-Lamberty"
date: "`r Sys.Date()`"
output: slidy_presentation
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

library(dplyr)
library(ggplot2)
theme_set(theme_bw())
```

# Outline {.bigger}

How do we get our data _into_ R?

Today's we're talking about specifically with respect to **tabular data**.


# Comma-separated value (CSV) files

These are plain-text files in which columns are separated by _commas_ as delimiters:

```{r, echo=FALSE}
cat(readLines("test-files/basic-file.csv"), sep = "\n")
```


# read.csv

read.csv as a workhorse tool

>This function is the principal means of reading tabular data into R.

>Unless colClasses is specified, all columns are read as character columns and then converted using type.convert to logical, integer, numeric, complex or (depending on as.is) factor as appropriate. Quotes are (by default) interpreted in all fields, so a column of values like "42" will result in an integer column.

>A field or line is ‘blank’ if it contains nothing (except whitespace if no separator is specified) before a comment character or the end of the field or line.

# read.csv

Demonstrate it


# Caution: file paths

**Do not use absolute file paths.**

>If the first line of your R script is
>setwd("C:\Users\jenny\path\that\only\I\have")
>I will come into your office and SET YOUR COMPUTER ON FIRE 🔥.

https://www.tidyverse.org/blog/2017/12/workflow-vs-script/

# Caution: Excel

Microsoft Excel does _not_ handle CSVs well in some circumstances.
It mucks with significant digits, and will mangle ("reformat") dates.

**Use a dedicated CSV editor, not Excel, if at all possible.**


# read.csv

`read.csv` is a front-end for the more general `read.table`:

```
> read.csv
function (file, header = TRUE, sep = ",", quote = "\"", dec = ".", 
    fill = TRUE, comment.char = "", ...) 
read.table(file = file, header = header, sep = sep, quote = quote, 
    dec = dec, fill = fill, comment.char = comment.char, ...)
```

# read.csv: crucial parameters

* `skip`

```{r, echo=FALSE}
cat(readLines("test-files/file-with-header.csv"), sep = "\n")
```

```{r}
read.csv("test-files/file-with-header.csv", skip = 1)
```


# read.csv: crucial parameters

* `comment.char`

```{r}
read.csv("test-files/file-with-header.csv", comment.char = "#")
```


# read.csv: crucial parameters

* `na.strings`

```{r, echo=FALSE}
cat(readLines("test-files/missing-values.csv"), sep = "\n")
```

```{r}
read.csv("test-files/missing-values.csv")
```

```{r}
read.csv("test-files/missing-values.csv", na.strings = "4")
```


# read.csv: crucial parameters

* `check.names`
* `colClasses`
* `fileEncoding`


# readr::read_csv

Some advantages:

* Produces a tibble
* Handles dates and times better?
* No `stringsAsFactors` (but read.csv behavior has changed with R 4)
* Compact `col_types` parameter
* No row names


# Fancier things: reading from online

```{r}
read.csv("https://raw.githubusercontent.com/bpbond/R-workshops/main/test-files/basic-file.csv")
```


# Fancier things: reading from memory

```{r}
my_data <- c("A,B", "1,2", "3,4")
read.csv(text = my_data)
```


# Skipping a units line

This is fairly common and seems like a pain:

```{r, echo=FALSE}
cat(readLines("test-files/units-line.csv"), sep = "\n")
```

This forces every column to be read as `character`; we then need to remove
the row and re-class each column. What a pain!

```{r}
x <- read.csv("test-files/units-line.csv")
glimpse(x)
x <- x[-1,]
x$Y <- as.numeric(x$Y)
x$Z <- as.numeric(x$Z)
glimpse(x)
```

# Skipping a units line

A slicker way is to read the file in as raw text; delete the problematic
line(s), and the `read.csv` directly from memory:

```{r}
x_raw <- readLines("test-files/units-line.csv")
print(x_raw)
read.csv(text = x_raw[-2])
```


# Skipping columns

Set the appropriate `colClasses` to NULL.

`read_csv` is easier here...


# Other delimiters

What if your data are tab-delimited (or something else)?

See `read.delim` and the more general `read.table`