02-R_basics.Rmd

# R basics

How to manipulate data in R ? How to install and load a package ? Let's see..

## Getting some help

## Where am I ?

To get the current directory, use `getwd()` : 

```{r getwd}
getwd()
```

If you need to change the directory, there is `setwd()`

```{r setwd, eval=FALSE}
setwd("path/to/my/directory")
```


If you use Rstudio, I can only recommand to work with a [project workflow](https://www.tidyverse.org/articles/2017/12/workflow-vs-script/) to avoid path issues on another computer.

## Make calculations

```{r calculations}
1+1

3 * 4

7/3

7%%3 # rest of the division

```
## Arthmetic functions
R provides a lot of arithmetic functions by default :

```{r arithmetic_functions}
sqrt(4.0)
abs(-625)
log10(12900)
```

## Assign values to a variable

```{r fruits}
fruits <- c("apples", "pears", "lemons")
fruits
```


```{r quantities}
quantities <- c(3, 2, 1)
print(quantities)
```

```{block2, type='rmdcaution'}
Indices in R start at 1 !
```

```{r show_indices}
print(fruits[1])
print(fruits[0]) # returns nothing
```

## For loop and print
### Simple for loop
```{r print}
for (fruit in fruits) {
  print(fruit)
}
```

### For loop with indices

```{r loop_indices}
for (x in seq(length(fruits))) {
  print(paste0("I have ", quantities[x]," ", fruits[x],"."))
}
```

```{block2, type='rmdwarning'}
For loops in R are possible but not memory efficient.
So if you need to walk through a large amount of data, please consider using functions instead.
```
## Data types

### Vectors
`fruits` and `quantities` are character and numeric vectors.

```{r, vectors}
class(fruits)
class(quantities)
```

Vectors are the most basic R data object. There is six types of atomic vectors: logical, integer, double, complex, character and raw. You can't mix types in vectors.

### Dataframes

Another frequently encountered data type is the **dataframe**. It is a collection data organized by rows and columns. Columns that can be of different types. Rows don't have to unique but having [tidy data](https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html) is known as a good pratice :


> 1. Each variable forms a column.
  1. Each observation forms a row.
  1. Each type of observational unit forms a table.

Good thing is, in GIS, we tend to have tidy data, right ?

How to create a data frame from our vectors ?

#### With `cbind.data.frame()`


```{r build_dataframe}
df1 <- cbind.data.frame(fruits, quantities) # column binding
print(df1)
class(df1)
```


```{r build_dataframe2}
df2 <- as_data_frame(fruits) # column binding
colnames(df2) <- "fruits" # change column name
print(df2)
class(df2)
```
```{block2, type='rmdnote'}
Tibbles (`tbl` // `tbl_df`) are dataframes on steroids from the tidyverse.
```

### Add columns to a dataframe
```{r, add_column_to_df}

df3 <- cbind(df2, # entry dataframe
             quantities, # column with quantities
             price = c(4,7,9) # new colum with price
             )
df3
```



### Other datatypes

* Matrices
* Lists (`list()`) : collection of objects of different kind

```{block2, type='rmdcaution'}
List in R are not like lists in Python.
```

## Filtering / Subsetting

In R, you can subset your data by value or variable. There is several way to do it, here is some of them.


### Select variables

```{r, get_colnames}
names(df3)
```

```{r select_indices}
df3[, 2:3]
df3[, c("fruits","price")]
df3 %>% # pipe symbol
  select(fruits, quantities) # select from dplyr
```

### Filter values

```{r filtering_data}
df3[df3["price"] > 5,] # don't forget the column comma

df3 %>%
  filter(quantities >= 2)
```
### Mixing selection and filtering

```{r mix_select_filter}

df3[df3["price"] > 5, 1] # select the prices > 5

df3 %>% 
  filter(price > 5) %>%  # filter first
  select(fruits)         # select second
```


## Joins

Let's create a new dataframe to join

```{r df4}
df4 <- cbind.data.frame(fruits = fruits, buyer = c("Sophie", "Marc", "Nathan"))
df4
```

### Merge

```{r merger_left}
merged_df <- merge(x = df3, y = df4, by = "fruits", all = TRUE) # OUTER JOIN
merged_df
```


See that [answer on StackOverflow](https://stackoverflow.com/questions/1299871/how-to-join-merge-data-frames-inner-outer-left-right) for more details on left, right, inner and outer joins with `merge()`.

### Dplyr

```{r dplyr_join}
merged_df <- df3 %>%
  full_join(df4) ## or full_join(df4, by = "fruits")

merged_df
```

See the [documentation of {dplyr}](https://dplyr.tidyverse.org/reference/join.html) for more information on joins.

## Going further 

If you want to go further in the learning of the R language and the Tidyverse tools, there is a lot of resources online. You might want to start by those :

* Base R : [R manuals](https://colinfay.me/r-manuals/)
* Tidyverse : [R for Data Science (free ebook)](https://r4ds.had.co.nz)