Session1_R-intro.Rmd

---
title: "R intro"
output:
  pdf_document: default
  html_notebook: default
---

#Version and directory
```{r}
#display the R version (plus the version of other attached packages)
sessionInfo() 
```

```{r}
# print working directory
getwd()
```


```{r}
# this line itself will not print as it is commented, but the next line will print
print("this will be printed")
```


#Arithmetic operations
```{r}
#Arithmetic: +,-,/, * are called operators
2 + 2
2 - 2
15 / 2
5 * 4
2 + 2 *3 
(2 + 2) * 3
```

#Variables
##A variable is any text (letter/word/alpha-numeric) which stores some value
##We use the assignment operator '<-' or '=' to pass a value to the variable
```{r}
# Variables
x <- 4
some_number <- 20
```


##Add two variables
```{r}
x+some_number
```

##Change the value of a variable
```{r}
x=100
x
x=x+2
x
```



#Vectors
##A vector is a collection of data elements
```{r}
 
some_vector1 <- c(1,2,3,4,5,6) # c is a 'function' that combines its arguments into a vector
some_vector1
some_vector2 <- 1:6 # a shortcut
some_vector2

series1 <- seq(from = 2, to = 20, by = 4) #series1 is a vector created by function seq
series1

```

```{r}
##how to get help on a function in R
?seq
```

```{r}
#we can perform operations on vectors such as addition, multiplication etc
x <- 1:5
x
y= x*2
y
```

```{r}
# add the two vectors
joined_vectors <- some_vector1 + some_vector2 # the vectors will be added element-wise
joined_vectors
```

## indexing and manipulating elements in a vector
## - Square brackets [ ] - used for indexing (position)
## - Parentheses () - used in functions to provide arguements
```{r}

x=seq(1,12,2)
x


```
```{r}
x[3] # 3 here is the index value, i.e 3rd element in x
x[c(3,4)]
## removing an element
x[-3] ## is x changed?
y= x[-4]
y
```

## searching for items in vectors
```{r}
# the which command will return the index (indices) of the value
x=c(1,7,6,5,4,9)
which(x == 7)
which(x > 5)
which(x == 3)
```
## minimum, maximum, mean, median
```{r}
max(x)
min(x)
range(x)
```
```{r}
which.max(x)
which.min(x)
```
```{r}
mean(x)
median(x)
```

## character vectors: Instead of numbers we use strings
```{r}
char_vec1 <- c("Maurice", "Jodi-Ann", "Essiet", "Brhan", "Jheannelle", "Layla")
char_vec1
char_vec1[3]
which(char_vec1=="Layla")
```


#Functions
##Functions are an operation or task performed on input 'arguments', which can be specified as variables
```{r}
## seq function is a build-in creating a series of numbers on the basis of specified parameters. 
x <- seq(from = 2, to = 20, by = 4)
x
length(x) # length is a function
```

## Getting help
```{r}
?seq
```



#Data frames
##In any form of data analysis, it is important to understand the format of dataset(s). 
##Data frames are form of data structures in tabular format. They are the most widely used format for data analysis in R. 
##Data frames consist of:
##- rows : Observations
##- columns : Vectors of particular data types (such as character, integer,logical).
##Different columns can be of different datatype.
##Elements of same column must be same type

##Creating a data frame
```{r}
firstName <- c("Jheannelle","Jodi-Ann","Maurice","Brhan","Layla","Essiet") ## firstName is a Vector
lastName <- c("Johnson", "Richards", "Wade", "Gebremedhin", "Sana", "Ette")
Gender <- c("female", "female", "male", "male", "female","female")
id_no <- c(20,29,32,15,26,19)
Fav_icecream <- c("Vanilla","Strawberry","Pistachio","Chocolate","Vanilla","Chocolate")


ndcf_fellows <-  data.frame(firstName, lastName, FullName=paste(firstName, lastName), Gender, id_no, Fav_icecream) # data.frame is a function to create a new data frame, ndcf_fellows is dataframe
ndcf_fellows
```

##Displaying features or data types within the data frame 
```{r}
#summary(ndcf_fellows)
str(ndcf_fellows)
```
```{r}
class(Fav_icecream)
```

###Accessing columns in a data frame with $ notation
```{r}
ndcf_fellows
ndcf_fellows$FullName
```

##Tabulating entries in the data frame
```{r}
#ndcf_fellows$firstName
table(ndcf_fellows$Gender)

table(ndcf_fellows$Gender, ndcf_fellows$Fav_icecream)
```

##Writing the table to a csv file
```{r}
#save this table
write.csv(ndcf_fellows,"ndcf_fellows.csv")
```

##Reading a csv file into a data frame
```{r}
# open and read a csv file
ndcf_fellows2 <- read.csv("ndcf_fellows.csv", row.names = 1)
ndcf_fellows2
```

##Exploring data frames
```{r}

#Size
dim(ndcf_fellows) #returns number of rows and the number of columns

nrow(ndcf_fellows) # number of rows

ncol(ndcf_fellows) # number of columns

```

##Viewing parts of the data frame
```{r}
#Content
head(ndcf_fellows)
tail(ndcf_fellows)
colnames(ndcf_fellows)
rownames(ndcf_fellows)
#rownames(ndcf_fellows) <- ndcf_fellows$firstName
#rownames(ndcf_fellows)

```

##Summarize features in a data frame
```{r}
summary(ndcf_fellows)
```


##Indexing and subsetting data frames
##use [] bracketcs, [row position,column position] 
```{r}
ndcf_fellows
```

```{r}
ndcf_fellows[2,]#extract entire row, which is a dataframe with single observation
```
```{r}
ndcf_fellows[2,3] # extract a particular element at row2 column3
```

##Column notation
```{r}
#Extract entire column, which is a vector
#ndcf_fellows$firstName
#ndcf_fellows[["firstName"]]
#ndcf_fellows[,"firstName"]
#ndcf_fellows[,"Gender"]
```

```{r}
#Extract particular cell
ndcf_fellows[4,6]
```
```{r}
#extract cell using column name
ndcf_fellows[4,"firstName"]
```

##Subset rows and columns
```{r}
# subsetting
#extract n number of rows
ndcf_fellows[c(1,2,4,6),]
```

```{r}
##extract some rows and columns
new_set<- ndcf_fellows[1:5,c("firstName","id_no", "Gender")]
new_set
```

```{r}
#extract rows based on some condition
ndcf_fellows[which(ndcf_fellows$Gender=="female"),]
```


```{r}
##make a new table with only female members
ndcf_fellows[which(ndcf_fellows$Gender=="female"),]
female_members <- ndcf_fellows[which(ndcf_fellows$Gender=="female"),]
female_members

```

##Exclusion notation
```{r}
#show everything except particular column
ndcf_fellows[,-1]
```

```{r}
#show everything except particular rows
ndcf_fellows[-c(1,3),]
```

##Text searching with grep
```{r}
###using Grep
## grep index values i.e.tell me the name of row numbers where the first name starts with 'J'
grep("^J", ndcf_fellows$firstName) # ^ is a regular expression also called regex in coding

```


```{r}
####subsetting with grep
firstnameL <- ndcf_fellows[grep("^J",ndcf_fellows$firstName),]
firstnameL

```

##change the values in data frame
```{r}
ndcf_fellows[4,5] <- "50"
ndcf_fellows
```

```{r}
# gsub function
ndcf_fellows$Fav_icecream <- gsub("Vanilla","GreenTea",ndcf_fellows$Fav_icecream )
ndcf_fellows
```


#Conditional statements and loops
```{r}
# open saved file
ndcf_fellows2 <- read.csv("ndcf_fellows.csv", row.names = 1)
ndcf_fellows2
```

##If statements
##These statements allow certain actions to be performed only if a statement is true
```{r}
if (nrow(ndcf_fellows2) > 10) {
  print("There are more than 10 Fellows")
} else {
  print("There are 10 or fewer Fellows")
}
```

```{r}
if (ndcf_fellows2$firstName[1]=="Layla") {
  print("Layla is listed first in the table")
} else {
  print("Someone other than Layla is listed first in the table")
}
```

##For loops
##These loops will run a block of code a certain number of times
```{r}
for (loopvariable in 1:5) {
  print(loopvariable)
}
```

##More complex code in loop
```{r}
ndcf_fellows2$id_no=as.numeric(ndcf_fellows2$id_no)
ndcf_fellows2
for (var1 in 1:nrow(ndcf_fellows2)) {
  ndcf_fellows2$id_no[var1]=2*ndcf_fellows2$id_no[var1]
}
ndcf_fellows2
```