R Programming Practice codes

# For loop in R

directory <- "specdata" 

file_list <- list.files(directory)
file_list
da = read.csv(file_list[1])
#comp_file <- data.frame()
comp_file <- rbind(comp_file, read.csv(file_list[1]))
id = 1:332

for (i in 1:332) {
  comp_file <- rbind(comp_file, read.csv(file_list[i]))
  comp_file
}

# Statistical Inference module - 1 using Swirl R package

install.packages("swirl")
packageVersion("swirl")
library(swirl)
install_from_swirl("Statistical Inference")
swirl()
install.packages(file.choose(), repos = NULL)
?qunif()

qunif(0.75, min = 0, max = 3, lower.tail = TRUE, log.p = FALSE)

x <- 1:4; x
p <- x/sum(x); p
temp <- rbind(x, p); temp
rownames(temp) <- c("X", "Prob")
temp; temp
mean = sum(x*p); mean

(0.3*0.75)/( (0.3*0.75) + (0.7*0.25))

##########################################################################################################################

# Just for practice code


x <- 4
class(x)
x <- c(4, "a", TRUE)
class(x)

x <- 1:4 
y <- 2:3
x+y
class(x + y)

x <- c(1,3, 5) 
y <- c(3, 2, 10)
rbind(x,y)

x <- list(2, "a", "b", TRUE)
class(x[[1]])

x = 1:4
y = 2:3
class(x+y)

x <- c(3, 5, 1, 10, 12, 6)
x[x < 6] <- 0
x
x <- c(3, 5, 1, 10, 12, 6)
x[x < 6] == 0
x

# Suppose I have a vector x <- c(17, 14, 4, 5, 13, 12, 10) and I want to set all elements of this vector that are greater than 10 to be equal to 4. What R code achieves this?
# x[x < 10] <- 4
# x[x >= 11] <- 4
# x[x > 4] <- 10
# x[x == 10] <- 4
x <- c(17, 14, 4, 5, 13, 12, 10)
x[x >= 11] <- 4
x
# To create a varible named "mydata", which holds excel data given in "hw1_data R programming" excel file
mydata <- read.xls("hw1_data R programming.xlsx")
# this data is loaded in enviornment.

# To print mydata.
mydata
# ALternate way of printing mydata is print (mydata)
print (mydata)

#read.csv("hw1_data R programming.csv")  
# read.csv ("" ) is alternate way for reading excel files which are saved in csv format
# read.table ("") is alternate way of for reading excel files ...

# To view structure of the data frame named "mydata"
str(mydata)

mydata[152, ]
mydata[153, ]

nrows(mydata)

# To print only the column named Ozone 
mydata$Ozone
#To check which values of this column are NA values and print them
NA_Ozone = is.na(mydata$Ozone)
NA_Ozone

To subset dataframe named "mydata" for multiple conditions on different columns
subset1 <- subset(mydata, Ozone > 31 & Temp > 90)
class(subset1)

mean(subset1$Solar.R)

!is.na(subset1)

subset2 = subset(mydata, Month = 6)
mean(subset2$Temp)

subset3 = subset(mydata, Month = 5)
na.omit(subset3$Ozone)
max(na.omit(subset3$Ozone))
max(!is.na(subset3$Ozone))
mean(!is.na(subset(mydata, Month = 6)))

mean(as.numeric(!is.na(subset1)))
numric_subset1 = as.numeric(subset1)
mean(numric_subset1)



#Suppose I define the following function in R

cube <- function(x, n) {
  x^3
}
#What is the result of running
cube(3)

# The following code will produce a warning in R.Why?
x <- 1:10
if(x > 5) {
  x <- 0
}
x

# The options are:  There are no elements in 'x' that are greater than 5
#The expression uses curly braces.
#You cannot set 'x' to be 0 because 'x' is a vector and 0 is a scalar.
#The syntax of this R expression is incorrect.
#'x' is a vector of length 10 and 'if' can only test a single logical statement.


# For the function
f <- function(x) {
  g <- function(y) {
    y + z
  }
  z <- 4
  x + g(x)
}
#If I then run in R
z <- 10
f(3) # What value is returned?

x <- 5
y <- if(x < 3) {
  NA
} else {
  10
}
y

#Which symbol in the following function is a free variable? Answer is 'f'
h <- function(x, y = NULL, d = 3L) {
  z <- cbind(x, d)
  if(!is.null(y))
    z <- z + y
  else
    z <- z + f
  g <- x + y / z
  if(d == 3L)
    return(g)
  g <- g + 10
  g
}

#lexical scoping
y <- 10
f = function(x){
  y <- 2
  y^2 + g(x)
}
g = function(x){
  x*y
}
f(3)



library(datasets)
data(iris)
?iris
str(iris)
sepal_length = iris$Sepal.Length
mean(sepal_length)
subset2 = subset(iris, Species == virginica, select = Sepal.Length )
# subset3 = iris[Species == virginica, ]
subset2
sepal_length_virginica

ls


debug(ls)
ls

apply(iris[, 1:4], 2, mean)
apply(iris, 2, mean)
apply(iris[, 1:4], 1, mean)


library(datasets)
data(mtcars)
?mtcars

lapply(mtcars, mean)
with(mtcars, tapply(mpg, cyl, mean))
sapply(mtcars, cyl, mean)
tapply(mtcars$cyl, mtcars$mpg, mean)






## Practice codes for matrices, data frame, subsetting loops, functions etc.

# https://github.com/rdpeng/practice_assignment/blob/master/practice_assignment.rmd

dataset_url <- "http://s3.amazonaws.com/practice_assignment/diet_data.zip"
download.file(dataset_url, "diet_data.zip")
unzip("diet_data.zip", exdir = "diet_data")

andy <- read.csv("diet_data/Andy.csv")
head(andy) #will give you the 6 first rows
tail(andy) #will give you the 6 last rows
str(andy) # will give you structure of the df andy
dim(andy) # will give you dimension of the df andy
summary(andy) # will give you summary of the df andy
names(andy) # will give you the names in the data df named andy
length(andy$Day)
length(andy$Age)
nrow(andy) #will give you the numbers of rows in the df named andy
n <- nrow(andy)
andy[(n - 3 + 1):n, ]  # will give you the last 3 rows


# Specifically, the first row of the 'Weight' column:

andy[1, "Weight"]

#We can do the same thing to find his final weight on Day 30:
andy[30, "Weight"]

# Alternatively, you could create a subset of the 'Weight' column where the data where 'Day' is equal to 30.

andy[which(andy$Day == 30), "Weight"]
andy[which(andy[,"Day"] == 30), "Weight"]

#Or, we could use the subset() function to do the same thing:
subset(andy$Weight, andy$Day==30)

# Let's assign Andy's starting and ending weight to vectors:

andy_start <- andy[1, "Weight"]
andy_end <- andy[30, "Weight"]
#We can find out how much weight he lost by subtracting the vectors:
andy_loss <- andy_start - andy_end
andy_loss
?list.files
list.files()
files = list.files("diet_data") # file is now a list of contents of diet_data in alphabetical order
files   # No matter in what order files are saved in your diet_data folder in current working directory.
class(files)

files[1]
files[2]
files[3:5]

head(read.csv(files[3]))

# To append the path of the file name
files_full <- list.files("diet_data", full.names=TRUE)
files_full
head(read.csv(files_full[3]))

# To create a dataframe
andy_david <- cbind(andy, read.csv(files_full[2]))
#This line of code took our existing data frame, Andy, and added the rows from David.csv to the end of it. We can check this with:

head(andy_david)
tail(andy_david)

for (i in 1:5) {
  dat <- rbind(dat, read.csv(files_full[i]))
}
#Whoops. Object 'dat' not found. This is because you can't rbind something into a file that doesn't exist yet. So let's create an empty data frame called 'dat' before running the loop.

dat <- data.frame()
for (i in 1:5) {
  dat <- rbind(dat, read.csv(files_full[i]))
}
str(dat)



dat_30 <- dat[which(dat[, "Day"] == 30),]
dat_30
median(dat_30$Weight)
tail(andy_david)