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begginers_workshop_rladies.R

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+#This is a comment, what you write here it is not going to print
+#for funning any line, press ctrl+enter
+getwd() # get current directory
+setwd("any directory you like") # set a new working directory
+dir() #getting contents in current directory
+1
+
+1+2 #1-2,1*2
+3^3
+
+#operations
+sum(1,2,4,6,89)
+min(1,6,9)
+max(1,6,9)
+sqrt(16)
+
+
+# define variables 
+x = 4
+y = 5
+x <- 3# = & <- are same , you can use both of them for assigning the variables
+
+y <- 12
+z <- x+y
+z
+
+print("Hello!")
+txt <- "wow"
+print(txt) # it is same as next command
+txt #it is same as previous command 
+?print # get help documentation for print function
+
+y = 10 #R is case sensitive, so y & Y are different variables
+Y # you will get error here
+y
+
+1:30 #create values from 1 to 30
+rep(2,100) #repeat values
+########################################################
+#creating a single dimension vector
+vec1 <- c(1:5,10, 20,30) #numeric vector
+vec2 <- c(1,5,10,c(2,6,8))
+length(vec2) #get length of vector
+class(vec1) #get type of vector
+
+#create foor loop
+for (i in 1:5)
+{
+  print(i)
+}
+
+#condition
+if(y>10)
+{
+  x=6
+}
+
+
+str1<-c("this","is","a","great","day") #character vector
+class(str1)
+length(str1)
+
+log1 <- c(TRUE, FALSE, TRUE) # create logical vector
+log1
+
+#create named vectors
+c(vegies="tomato","celery",fruits="apple","kiwi") 
+ex1 <- c("tomato","celery","apple","kiwi")
+names(ex1) <- c("vegies","","fruits","")
+ex1[1:4] # get values of vector from first till forth index
+ex1[1] #get first element from vector
+ex1[c(1,4)] #get first & fourth element from vector
+#create a variable from 100 to 200 
+#and select the first 10
+ex1 = c(1,2)
+ex1[1:10] # for not avaiable indexes the value will be null
+
+########################################################
+#two dimensional arrays or matrices
+mat = matrix(1:20, nrow = 5, ncol = 4)
+mat
+mat = matrix(1:20, nrow = 5, ncol = 4, byrow = TRUE)
+mat
+mat[1,] #get first row values
+mat[,1] # get first column values
+mat[1:2,]# get first & second row values
+mat[,3:1] # get 3,2 and 1 column value
+
+#a) create matrix with values as 1 to 100,10*10, by row & column, b) select the second column
+x1 = matrix(1:100,10,10, byrow = TRUE)
+x1[,2]
+
+#data frames, which you can integrate variosu type of variables together
+a<-c(1:4)
+b<-rep(c(TRUE,FALSE),2)
+c<-c("hello","to","the","Rladies")
+myData <- data.frame(a,b,c)
+dim(myData) # get dimention of data frame
+
+rownames(myData) # get row's name
+colnames(myData) # get column's name
+
+colnames(myData)<- c("num","log","msg") # assign column's name
+rownames(myData)<- c("a","b","c","d") # assign row's name
+rownames(myData)
+colnames(myData)
+myData
+dim(myData)
+summary(myData)
+cbind(myData,c(100,3333,222,777)) # bind other data with your data frame
+
+#applying condition and data sub selection
+myData[myData$num==4,] # get values from data fram that in the num column there is only value 4
+myData[myData$log==TRUE,] # get values from data fram that in the log column there is TRUE value  
+
+duplicated(c(c(1:5),5,6,7)) # find dulicated values in data frame
+#########################################################
+library(utils) #install.packages("utils")
+# get data from imdb dataset, which includes around 5000 data from films in the imdb site
+dataImdb = read.csv("imdb.csv", sep = ";", stringsAsFactors = TRUE) # read csv file from same directory as your code is located 
+dim(dataImdb)
+head(dataImdb) # get the first few rows from data frame
+tail(dataImdb) # get the last rows from DF
+dataImdb[1:10,] 
+colnames(dataImdb)
+summary(dataImdb) # some summary of data frame
+str(dataImdb) # get summary of value types from data frame
+
+dataImdb$color <- as.factor(dataImdb$color) # convert variable to a categorial variable 
+dataImdb$language<- as.factor(dataImdb$language)
+levels(dataImdb$color) # get levels of categoy variable
+levels(dataImdb$language)
+str(dataImdb) # check now the columns type have changed 
+unique(dataImdb$title_year) # get unique values from title_year column
+table(dataImdb$color) # get number of element in each category in the color column
+table(dataImdb$director_name)
+table(dataImdb$color,dataImdb$language)
+
+
+is.na(dataImdb$duration) # find if there is a null value in the duration column
+dataImdb$duration[is.na(dataImdb$duration)] # find the null values 
+dataImdb$duration[is.na(dataImdb$duration)] <- 0 # assign 0 to null values in duration column
+dataImdb$budget[is.na(dataImdb$budget)] <- 0  # assign 0 to null values in budget column
+dataImdb$title_year[is.na(dataImdb$title_year)] <- 0  # assign 0 to null values in year column
+dim(dataImdb)
+dataImdb <- dataImdb[!duplicated(dataImdb$movie_title),] # remove duplicated values in the data frame
+dim(dataImdb) # how many values are removed from data frame
+
+summary(dataImdb) # now take in to consideration Null values (NA) from other columns too
+max(dataImdb$imdb_score)  # what is the maximum imdb score 
+goodMovies = dataImdb[dataImdb$imdb_score>8,] #how many good movies are present in data frame?  (assupmtion: good movies above score 8)
+dim(goodMovies)
+badMovies = dataImdb[dataImdb$imdb_score<6,]#how many bad movies are present in data frame?  (assupmtion: bad movies below score 6)
+dim(badMovies)
+
+min(unique(goodMovies$title_year)) # when is the earliest year for good moviess
+max(unique(goodMovies$title_year)) # when is the latest year for good moviess
+
+goodMovies = dataImdb[dataImdb$imdb_score>8 & dataImdb$title_year>=2000,] # how many recent good movies r present in data 
+dim(goodMovies)
+goodMovies[1:10,]
+goodMovies[order(goodMovies$imdb_score, decreasing = TRUE),][1:5,] # order the good movies and get the top recent ones 
+goodMovies[order(goodMovies$imdb_score, decreasing = TRUE),][1:5,"movie_title"] # which movie are they?
+dim(subset(dataImdb,dataImdb$imdb_score>8 & dataImdb$title_year>=2000)) # this is equivalent command for getting recent good movies using subset function
+
+
+dataImdb[order(dataImdb$budget, decreasing = TRUE),c("budget","movie_title")][1:10,]# find the top film with highest budget
+###################################################
+
+
+plot(dataImdb$duration) #plot all values in duration column
+plot(dataImdb$title_year) # how about year?
+hist(dataImdb$imdb_score) # see distribution of imdb score
+mean(dataImdb$imdb_score) # get average of imdb score
+
+library (ggplot2) # for visualization we use ggokit library, install the package by : install.packages("ggplot2")
+qplot(data=dataImdb,title_year,color) # visualization within 2 variables
+qplot(data=dataImdb,title_year,imdb_score, color = country) # visulization within 3 variables , can u find ur country? :)
+
+ggplot(data=dataImdb, aes(x=title_year,y = imdb_score))+geom_point() #ggplot is giving u better visualization functionalities
+newData <- dataImdb[which(dataImdb$title_year >0),] # get just data that year is present for them
+dim(newData) # dimention of data
+# more neat & organized plot
+ggplot(data=newData, aes(x=title_year,y = imdb_score))+geom_point()+ xlab("Year") + ylab("IMDB score") + ggtitle("Yar vs score")
+
+ggplot(data=newData, aes(x=newData$duration))+ geom_histogram() # plot distribution of duration of movies
+ggplot(data=newData, aes(x=newData$duration))+ geom_histogram( binwidth = 2)
+
+ggplot(data=newData, aes(x=newData$duration, fill = newData$color ))+ geom_histogram() # you can comapre the color and duration of movies
+ggplot(data=newData, aes(x=newData$imdb_score ))+ geom_histogram( ) # how about imdb score
+ggplot(data=newData, aes(x=newData$imdb_score ))+ geom_histogram( fill = "green3") # make ur plots colorful
+#####################################################################
+newData$scoreCat <- 0 # crate new colum in ur data
+plot(newData$scoreCat)
+length(which(newData$imdb_score>7)) # how many high score movies do we have?
+newData[which(newData$imdb_score>=7),"scoreCat"] <- "HighRank" # label them as high rank
+newData[which(newData$imdb_score<7 & newData$imdb_score>= 5 ),"scoreCat"] <- "MidRank" # label mid rank as movies with score within 7 & 5
+newData[which(newData$imdb_score<5 ),"scoreCat"] <- "LowRank" #label the rest as low ranks
+table(newData$scoreCat)# how many low, mid and high rank movies do u have?
+ggplot(data=newData, aes(x=newData$imdb_score ,fill = newData$scoreCat))+ geom_histogram()
+
+
+ggplot(data = newData,aes(x = newData$color)) + geom_bar() # barplots
+
+ggplot(data = newData,aes(x = newData$language)) + geom_bar() # barplot
+subData <- newData[newData$language!="English",] # movies which are not english
+ggplot(data = subData,aes(x = subData$language)) + geom_bar() + 
+  theme(axis.text.x = element_text(angle = 90))
+# find average number of movies for each language
+ggplot(data = subData,aes(x = subData$language, fill =subData$language )) + geom_bar(stat="count",aes(y  = ..count../sum(..count..)))