Basics&dplyr.Rmd

---
title: "R Basics"
output: html_notebook
---
## Adding useful links for tutorials about dplyr
https://appsilon.com/r-dplyr-tutorial/
https://www.listendata.com/2016/08/dplyr-tutorial.html

## Markdown basics
-bullets
**bold**
*italics*
[links](www.github.com)
run in line 'r code' 

Ctrl + alt+ C = add new code chunk

```{r}
#installa r markdown
#install.packages("rmarkdown")
```


### Data types
```{r}

```

```{r}
#character
character<- "Valeria"
class(character)
#numeric
number <- 3
class(number)
#logical
binary <- TRUE
class(binary)
#NA
class(NA)
```


### Vectors
```{r}
#Vectors are a list-like object in R
months <- c("January", "February", "March")
#Check data type
class(months)
#check data type
typeof(months)

```

To access individual elements of a vector, we use []
```{r}
months[2]
```

Another vector example with numbers
```{r}
phone <- c(55,555,5679)
phone
```


```{r}
#Look for a specific value within phone, remember that we need to know the number elements within the vector
#check length of a vector or object
length(phone)
```

```{r}
phone[3]
```

### Conditionals
Syntax
```{r}
if (TRUE){
  print('a new message')
} else {
    print('message two')
  }
```

Using the conditional, example.
```{r}
message <- 'I change based on a condition.'
if (TRUE) {
   message <- 'I execute this when true!'
} else {
   message <- 'I execute this when false!'
}
message
```

```{r}
message1 <- 
if (TRUE) {
   message1 <- 'I execute this when true!'
} else {
   message1 <- 'I execute this when false!'
}
message1
```

### Operators
Less than: <
Greater than: >
Less than or equal to: <=
Greater than or equal to: >=
Is equal to: ==
Is NOT equal to: !=

```{r}
#Check if 126 is greater than 2450
126>2450
#Check if 126 is not equal to 2450
126!=126
#Check if its equal
126==126
```

#### Loogical operators
the and operator (&)
the or operator (|)
the not operator, otherwise known as the bang operator (!)

```{r}
message <- 'Should I pack an umbrella?'
weather <- 'cloudy'
high_chance_of_rain <- TRUE


if (weather == 'cloudy' & high_chance_of_rain ) {
  message <- "Pack umbrella!"
} else {
  message <- "No need for umbrella!"
}

message
```


### Some basic functions
c() combine
```{r}
#order elements
sort(c(1,200,87,43,9541))
# total count of elements
length(c(1,200,87,43,9541))
#sum of numbers
sum(c(1,200,87,43,9541))
# remove duplicates show the unique elements of a vector
unique(c(1,2,4,5,6,77,88,1,2))
#floor round down to the next integer
round_down <- floor(3.14)
#ceiling round up to the next integer
round_up <- ceiling(3.14)
round_down
round_up
```

### Data Frames
```{r}
library(dplyr)
```

Some functions
cols()check the name of the columns

```{r}
#create a data frame from scratch
df <- data.frame("id" = 1:2, "Age" = c(21,15), "Name" = c("John", "Dora"), stringsAsFactors = FALSE)
str(df)
df
#name of columns
colnames(df)
names(df)
#number of cols
ncol(df)
#number of rows
nrow(df)
#length of the list same as ncol
length(df)
```

#### Accessing data within the dataframe
We can use:
[] this search returns a data frame
[[ this double bracket returns a vector
$ returns a vector

```{r}
#look for id columns and show it as data frame
df['id']
```

```{r}
#while $ or [[]]  show as vector
df$id
df[['id']]
```

Another example
```{r}
#shows the list of available test datasets
library(help = "datasets")

str(trees)
```

```{r}
#select rows and not columns
trees[1:3, ]
```
```{r}
#select from trees those species with height larger than 82
trees[trees$Height>82,]
```

```{r}
#select rows 10 to 12 and show only column 2 display result as data frame with drop false
trees[10:12, 2, drop=FALSE]
```

#### Modify a dataframe
Use base R to modify a single value from a dataframe
```{r}
df
```


```{r}
#from dataframe row 1 column Age modify value to 29
df[1,"Age"]<- 29
df
```

#### Adding columns to a dataframe
```{r}
#Cbind adds a new column but save it as a new dataframe to see the new column
cbind(df, Last_name=c("NY", "FL"))
```

```{r}
#or use an easier method
df$State <- c("NY", "FL"); df
```

### Dplyr for dataframe editing
More functions with dplyr like piping %>%. This is useful to nest of link multiple functions together.
```{r}
trees %>%
  head()
```

select() function
Use to select the columns that you need using %>%