jebesh dva
Gasper Spagnolo
parent
02a2f06e03
commit
7d334030e2
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@ -0,0 +1,512 @@
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l <- (TRUE, TRUE, FALSE, F, F, F T)
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l <- (TRUE, TRUE, FALSE, F, F, F, T)
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a <- (TRUE, TRUE, FALSE, F, F, F, T)
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a <- c(TRUE, TRUE, FALSE, F, F, F, T)
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mode(b)
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mode(a)
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install.packages('ggplot2')
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install.packages('dplyr')
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ls
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ls
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setcwd('/home/gasperspagnolo/Documents/faks/3-letnik/1sem/is/vaje/v3')
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setwd('/home/gasperspagnolo/Documents/faks/3-letnik/1sem/is/vaje/v3')
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ls
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ls
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# for example:
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# setwd("c:\\labs\\data\\")
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library(ggplot2)
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library(dplyr)
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# To read data from a text file, use the "read.table" command.
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# The parameter header=TRUE indicates that the file to be read includes a first line with the column names
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md <- read.table(file="movies.txt", sep=",", header=TRUE)
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head(md)
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summary(md)
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summary(md)
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str(md)
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names(md)
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md$Action <- as.factor(md$Action)
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md$Animation <- as.factor(md$Animation)
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str(md)
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# The remaining columns will be transformed using the for loop
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for (i in 20:24)
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md[,i] <- as.factor(md[,i])
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md[30,]
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md[30,3]
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md[30,"length"]
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md[,3]
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md$length
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plot(md$length)
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hist(md$length)
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plot(density(md$length))
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boxplot(md$length)
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barplot(table(md$Drama))
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pie(table(md$mpaa))
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## nicer plots with ggplot2 + dplyr
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md %>% ggplot(aes(length)) + geom_histogram(bins = 40) + ggtitle("A genetic histogram") + xlab("Length"
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## nicer plots with ggplot2 + dplyr
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md %>% ggplot(aes(length)) + geom_histogram(bins = 40) + ggtitle("A genetic histogram") + xlab("Length")
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## nicer plots with ggplot2 + dplyr
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md %>% ggplot(aes(length)) + geom_histogram(bins = 40) + ggtitle("A genetic histogram") + xlab("Length")
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## plotting w.r.t. multiple mpaa categories
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md %>% ggplot(aes(length,fill = mpaa)) + geom_density(alpha = 0.2)
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## What about a nicer boxplot w.r.t mpaa?
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## theme_bw() is more neutral theme
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md %>% ggplot(aes(Drama, rating, color = mpaa)) + geom_boxplot() + theme_bw()
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barplot(table(md$Comedy))
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barplot(table(md$Comedy))
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pie(table(md$Comedy))
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tab <- table(md$Comedy)
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names(tab) <- c("Other genres", "Comedies")
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tab
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barplot(table(md$Comedy))
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pie(table(md$Comedy))
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tab <- table(md$Comedy)
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names(tab) <- c("Other genres", "Comedies")
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tab
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tab <- table(md$Comedy)
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names(tab) <- c("Other genres", "Comedies")
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tab
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pie(tab)
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sum(tab)
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barplot(tab, ylab="Number of titles", main="Proportion of comedies to other genres")
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barplot(tab / sum(tab) * 100, ylab="Percentage of titles", main="The proportion of comedies to other genres")
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pie(tab, main = "Proportion of comedies to other genres")
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# Plot the rating distribution for comedies
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hist(md[md$Comedy == "1", "rating"], xlab="Rating", ylab="Frequency", main="Histogram of ratings for comedies")
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boxplot(md[md$Comedy == "1", "rating"], ylab="Rating", main="Boxplot of ratings for comedies")
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quantile(md$rating[md$Comedy == 1])
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comedy <- md$Comedy == "1"
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# Calculate the mean rating value for comedies and non-comedies
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mean(md[comedy,"rating"])
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mean(md[!comedy,"rating"])
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boxplot(rating ~ Comedy, data=md)
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boxplot(rating ~ Comedy, data=md, names=c("Other genres", "Comedies"), ylab="Rating", main="Comparison of ratings between comedies and non-comedies")
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md %>% group_by(Comedy) %>% select(rating) %>% summarise(mean(rating))
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md %>% group_by(Comedy)
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sel <- md$year >= 1990
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table(md$Comedy[sel], md$year[sel])
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table(md$year[sel])
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tabcomedy <- table(md$Comedy[sel], md$year[sel])
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tabyear <- table(md$year[sel])
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tabcomedy[2,]/tabyear
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ratio <- tabcomedy[2,]/tabyear
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barplot(ratio, xlab="Year", ylab="Relative frequency", main="Proportion of comedies")
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plot(x=names(ratio), y=as.vector(ratio), type="l", xlab="Year", ylab="Relative frequency", main="Proportion of comedies, 1990-2005")
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## Is this dependent on the mpaa?
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## or with dplyr + ggplot + adding votes
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md %>% select(budget, rating, votes, mpaa) %>%
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na.omit() %>%
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ggplot(aes(budget, rating, color = votes, fill = mpaa)) + geom_point() + geom_smooth(method = "lm", formula = y ~ x) + theme_bw()
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## or with dplyr directly
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md %>% filter(year >= 1990) %>%
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group_by(year, Comedy) %>%
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summarise(n = n()) %>% mutate(freq = n / sum(n)) %>%
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filter(Comedy == 1) %>% select(year, freq) %>%
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ggplot(aes(year, freq)) + geom_point() + ggtitle("Frequency of comedies") + ylab("Frequency") + xlab("Year")
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md %>% filter(year >= 1990)
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summary(md$budget)
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is.na(md$budget)
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table(is.na(md$budget))
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which(is.na(md$budget))
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# select complete observations only
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sel <- is.na(md$budget)
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mdsub <- md[!sel,]
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nrow(mdsub)
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summary(mdsub$budget)
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plot(mdsub$budget, mdsub$rating, xlab="Budget in $", ylab="Rating", main="Movie rating vs budget")
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# Plotted points are mostly located in the upper left part of the diagram,
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# which means that a higher budget usually leads to a higher rating
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# Utilization of the budget in terms of rating
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ratio <- mdsub$budget/mdsub$rating
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hist(ratio)
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# Which movie has the worst budget utilization?
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mdsub[which.max(ratio),]
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# Let's discretize these budgets to:
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# low (less than 1M), mid (between 1M and 50M) and big (more than 50M)
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disbudget <- cut(mdsub$budget, c(0, 1000000, 50000000, 500000000), labels=c("low", "mid", "big"))
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barplot(table(disbudget)/length(disbudget), xlab="Budget", ylab="Relative frequency", main="Proportion of movies vs budget")
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# Side-by-side boxplots of ratings grouped by budget values
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boxplot(mdsub$rating ~ disbudget, xlab="Budget", ylab="Rating", main="Boxplot of movie rating vs budget")
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## Is this dependent
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## or with dplyr + ggplot + adding votes
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md %>% select(budget, rating, votes, mpaa) %>%
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na.omit() %>%
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ggplot(aes(budget, rating, color = votes, fill = mpaa)) + geom_point() + geom_smooth(method = "lm", formula = y ~ x) + theme_bw()
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## or with dplyr
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md %>% select(budget, year) %>% na.omit() %>%
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group_by(year) %>% summarise(budget2 = sum(as.numeric(budget))) %>%
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arrange(year) %>% mutate(csum = cumsum(budget2)) %>%
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ggplot(aes(year, csum)) + geom_bar(stat = "identity") + theme_bw()
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## Is this dependent on the mpaa?
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## or with dplyr + ggplot + adding votes
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md %>% select(budget, rating, votes, mpaa) %>%
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na.omit() %>%
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ggplot(aes(budget, rating, color = votes, fill = mpaa)) + geom_point() + geom_smooth(method = "lm", formula = y ~ x) + theme_bw()
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#######################################
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md <- read.table("movies.txt", sep=",", header=TRUE)
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# - Are there more movies shorter than 100 min or longer than (or equal to) 100 minutes?
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# (show your answer numerically and graphically)
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md
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md <- read.table("movies.txt", sep=",", header=TRUE)
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# - Are there more movies shorter than 100 min or longer than (or equal to) 100 minutes?
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# (show your answer numerically and graphically)
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movies_shorter_or_equal_100_min <- md$length >= 100
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movies_shorter_or_equal_100_min
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barplot(movies, xlab="Year", ylab="Relative frequency", main="Proportion of comedies")
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barplot(movies_shorter_or_equal_100_min, xlab="Year", ylab="Relative frequency", main="Proportion of comedies")
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md[30,]
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md[30,3]
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md[30,"length"]
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md[,3]
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md$length
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# Useful data visualization functions
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plot(md$length)
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hist(md$length)
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plot(density(md$length))
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boxplot(md$length)
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barplot(table(md$Drama))
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pie(table(md$mpaa))
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## nicer plots with
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## nicer plots with ggplot2 + dplyr
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md %>% ggplot(aes(length)) + geom_histogram(bins = 40) + ggtitle("A genetic histogram") + xlab("Length")
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tab <- table(md$Comedy)
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names(tab) <- c("Other genres", "Comedies")
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tab
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pie(tab)
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tab
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tab <- table(md$Comedy)
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tab
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hist(md[md$Comedy == "1", "rating"], xlab="Rating", ylab="Frequency", main="Histogram of ratings for comedies")
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pie(table(md$Comedy))
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md$Comedy
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md$length
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names(tab) <- c("Other genres", "Comedies")
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tab
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pie(tab)
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barplot(table(md$length))
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movies_shorter_or_equal_100_min <- md$length >= 100
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table(movies_shorter_or_equal_100_min)
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movies_shorter_or_equal_100_min <- md$length >= 100
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tab <- table(movies_shorter_or_equal_100_min)
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names(tab) <- c("Less than 100", "More or equal to 100")
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pie(tab)
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df
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md
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md$Comedy
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md$Comedy
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md <- read.table("movies.txt", sep=",", header=TRUE)
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# We will transform binary attributes into nominal variables with a fixed number of possible values (factors)
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md$Action <- as.factor(md$Action)
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md$Animation <- as.factor(md$Animation)
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# The remaining columns will be transformed using the for loop
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for (i in 20:24)
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md[,i] <- as.factor(md[,i])
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movies_shorter_or_equal_100_min <- md$length >= 100
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tab <- table(movies_shorter_or_equal_100_min)
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names(tab) <- c("Less than 100", "More or equal to 100")
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pie(tab)
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<- md$Romance
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romantic_comedies
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romantic_comedies <- md$Romance
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romantic_comedies
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romantic_comedies <- sum(md$Romance == 1)
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romantic_comedies
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## or with dplyr directly
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md %>% filter(year >= 1990) %>%
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group_by(year, Comedy) %>%
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summarise(n = n()) %>% mutate(freq = n / sum(n)) %>%
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filter(Comedy == 1) %>% select(year, freq) %>%
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ggplot(aes(year, freq)) + geom_point() + ggtitle("Frequency of comedies") + ylab("Frequency") + xlab("Year") + geom_line() + theme_bw()
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######################################################
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# - Are there more action comedies or romantic comedies?
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md %>% group_by(Comedy, Action)
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# - Are there more action comedies or romantic comedies?
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md %>% group_by(Comedy, Action) %>% summarise(n = n())
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# - Are there more action comedies or romantic comedies?
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md %>% group_by(Action, Comedy) %>% summarise(n = n())
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# - Are there more action comedies or romantic comedies?
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md %>% group_by(Action == 1, Comedy) %>% summarise(n = n())
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# - Are there more action comedies or romantic comedies?
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md %>% group_by(Action == 1, Comedy == 1) %>% summarise(n = n())
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# - Are there more action comedies or romantic comedies?
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md %>% filter(Action == 1, Comedy == 1) %>% summarise(n = n())
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md %>% filter(Action == 1, Comedy == 1) %>% summarise(n = n())
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md %>% filter(Action == 1, Comedy == 1) %>% summarise(n = n())
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md %>% filter(Romantic == 1, Comedy == 1) %>% summarise(n = n())
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# - Are there more action comedies or romantic comedies?
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md %>% filter(Comedy == 1) %>% summarise(n = n())
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md %>% filter(Comedy == 1) %>% mutate(number_of_actions = sum(Action == 1)) %>%
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mutate(number_of_actions = sum(Action == 1)) %>%
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mutate(number_of_actions = sum(Romance == 1))
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# - Are there more action comedies or romantic comedies?
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md %>% filter(Comedy == 1) %>% mutate(number_of_actions = sum(Action == 1)) %>%
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mutate(number_of_action_movies = sum(Action == 1)) %>%
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mutate(number_of_Romances = sum(Romance == 1)) %>%
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select(number_of_action_movies, number_of_Romances)
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# - Are there more action comedies or romantic comedies?
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md %>% filter(Comedy == 1) %>% mutate(number_of_actions = sum(Action == 1)) %>%
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mutate(number_of_action_movies = sum(Action == 1)) %>%
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mutate(number_of_Romances = sum(Romance == 1)) %>%
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select(number_of_action_movies, number_of_Romances)
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md %>% filter(Comedy == 1) %>% mutate(number_of_actions = sum(Action == 1)) %>%
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mutate(number_of_action_movies = sum(Action == 1)) %>%
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mutate(number_of_Romances = sum(Romance == 1)) %>%
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select(number_of_action_movies, number_of_Romances) >%>%
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unique()
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md %>% filter(Comedy == 1) %>% mutate(number_of_actions = sum(Action == 1)) %>%
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mutate(number_of_action_movies = sum(Action == 1)) %>%
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mutate(number_of_Romances = sum(Romance == 1)) %>%
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select(number_of_action_movies, number_of_Romances) %>%
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unique()
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md %>% filter(Comedy == 1) %>% mutate(number_of_actions = sum(Action == 1)) %>%
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mutate(number_of_action_movies = sum(Action == 1)) %>%
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mutate(number_of_Romances = sum(Romance == 1)) %>%
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select(number_of_action_movies, number_of_Romances) %>%
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unique() %>% pie()
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# - Are there more action comedies or romantic comedies?
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md %>% filter(Comedy == 1) %>% mutate(number_of_actions = sum(Action == 1)) %>%
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mutate(number_of_action_movies = sum(Action == 1)) %>%
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mutate(number_of_Romances = sum(Romance == 1)) %>%
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select(number_of_action_movies, number_of_Romances) %>%
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unique()
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# - Are there more action comedies or romantic comedies?
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md %>% filter(Comedy == 1) %>% mutate(number_of_actions = sum(Action == 1)) %>%
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mutate(number_of_action_movies = sum(Action == 1)) %>%
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mutate(number_of_Romances = sum(Romance == 1)) %>%
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select(number_of_action_movies, number_of_Romances) %>%
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unique()
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# - Are there more action comedies or romantic comedies?
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md %>% filter(Comedy == 1) %>% mutate(number_of_actions = sum(Action == 1)) %>%
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mutate(number_of_action_movies = sum(Action == 1)) %>%
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mutate(number_of_Romances = sum(Romance == 1)) %>%
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unique() %>%
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select(number_of_action_movies, number_of_Romances)
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md %>% filter(Comedy == 1) %>% mutate(number_of_actions = sum(Action == 1)) %>%
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mutate(number_of_action_movies = sum(Action == 1)) %>%
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mutate(number_of_Romances = sum(Romance == 1)) %>%
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unique() %>%
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select(number_of_action_movies, number_of_Romances)
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md %>% filter(Comedy == 1) %>% mutate(number_of_actions = sum(Action == 1)) %>%
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mutate(number_of_action_movies = sum(Action == 1)) %>%
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mutate(number_of_Romances = sum(Romance == 1)) %>%
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select(number_of_action_movies, number_of_Romances) %>%
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unique()
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# - Plot a histogram of the ratings for drama movies.
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head(md)
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ratio <- mdsub$budget/mdsub$rating
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hist(ratio)
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plot(mdsub$budget, mdsub$rating, xlab="Budget in $", ylab="Rating", main="Movie rating vs budget")
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ratio <- mdsub$budget/mdsub$rating
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hist(ratio)
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hist(md$mpaa)
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hist(md$mpaa)
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hist(md$r1)
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(md$r1)
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hist(md$r1)
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md %>% filter(Drama == 1) %>% hist()
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md %>% filter(Drama == 1
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md %>% filter(Drama == 1)
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md %>% filter(Drama == 1)
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md %>% filter(Drama == 1) %>% select(raiting)
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md %>% filter(Drama == 1) %>% select(rating)
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md %>% filter(Drama == 1) %>% select(rating) %>% hist()
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md %>% filter(Drama == 1) %>% select(rating) %>% class()
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md %>% filter(Drama == 1) %>% select(rating)
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md %>% filter(Drama == 1) %>% select(rating) %>% hist(rating)
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md %>% filter(Drama == 1) %>% select(rating) %>% hist(.$rating)
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md %>% filter(Drama == 1) %>% tidyr::gather(rating, value) %>% ggplot(aes(value)
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md %>% filter(Drama == 1) %>% tidyr::gather(rating, value) %>% ggplot(aes(value))
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md %>% filter(Drama == 1) %>% tidyr::gather(rating, value) %>% ggplot(aes(value))
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md %>% filter(Drama == 1) %>% ggplot(aes(rating))
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md %>% filter(Drama == 1) %>% ggplot(rating)
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md %>% filter(Drama == 1) %>% ggplot(rating)
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aes(
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md %>% filter(Drama == 1) %>% ggplot(aes(rating)) geom_histogram(bins = 10))
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md %>% filter(Drama == 1) %>% ggplot(aes(rating)) geom_histogram(bins = 10))
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md %>% filter(Drama == 1) %>% ggplot(aes(rating)) + eom_histogram(bins = 10))
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md %>% filter(Drama == 1) %>% ggplot(aes(rating)) + geom_histogram(bins = 10))
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md %>% filter(Drama == 1) %>% ggplot(aes(rating)) + geom_histogram(bins = 10)
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md %>% filter(Drama == 1) %>% select(rating) %>% mutate(higher_than_avg_rating = rating > mean(rating))
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md %>% filter(Drama == 1) %>% select(rating) %>% mutate(higher_than_avg_rating = rating > mean(rating))%>%
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pie(table(aes(sum(rating == TRUE), aes(sum(rating == FALSE))
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md %>% filter(Drama == 1) %>% select(rating) %>% mutate(higher_than_avg_rating = rating > mean(rating))%>%
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pie(table(aes(sum(rating == TRUE)), aes(sum(rating == FALSE)))
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md %>% filter(Drama == 1) %>% select(rating) %>% mutate(higher_than_avg_rating = rating > mean(rating))%>%
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md %>% filter(Drama == 1) %>% select(rating) %>%
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mutate(higher_than_avg_rating = rating > mean(rating))
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md %>% filter(Drama == 1) %>% select(rating) %>%
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mutate(higher_than_avg_rating = rating > mean(rating)) %>%
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select(higher_than_avg_rating)
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md %>% filter(Drama == 1) %>% select(rating) %>%
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mutate(higher_than_avg_rating = rating > mean(rating)) %>%
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select(higher_than_avg_rating) %>%
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summarise(counts = n())
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md %>% filter(Drama == 1) %>% select(rating) %>%
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mutate(higher_than_avg_rating = rating > mean(rating)) %>%
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select(higher_than_avg_rating) %>%
|
||||
summarise(is_higher = sum(higher_than_avg_rating == TRUE), is_lower = sum(higher_than_avg_rating == FALSE))
|
||||
mutate(higher_than_avg_rating = rating > mean(rating)) %>%
|
||||
select(higher_than_avg_rating) %>%
|
||||
summarise(is_higher = sum(higher_than_avg_rating == TRUE), is_lower = sum(higher_than_avg_rating == FALSE)) %>%
|
||||
pie()
|
||||
md %>% filter(Drama == 1) %>% select(rating) %>%
|
||||
mutate(higher_than_avg_rating = rating > mean(rating)) %>%
|
||||
select(higher_than_avg_rating) %>%
|
||||
summarise(is_higher = sum(higher_than_avg_rating == TRUE), is_lower = sum(higher_than_avg_rating == FALSE)) %>%
|
||||
pie()
|
||||
ggplot(aes(.))) +
|
||||
geom_bar(stat="identity", width=1) +
|
||||
coord_polar("y", start=0)
|
||||
md %>% filter(Drama == 1) %>% select(rating) %>%
|
||||
mutate(higher_than_avg_rating = rating > mean(rating)) %>%
|
||||
select(higher_than_avg_rating) %>%
|
||||
summarise(is_higher = sum(higher_than_avg_rating == TRUE), is_lower = sum(higher_than_avg_rating == FALSE)) %>%
|
||||
ggplot(aes(.)) +
|
||||
geom_bar(stat="identity", width=1) +
|
||||
coord_polar("y", start=0)
|
||||
md %>% filter(Drama == 1) %>% select(rating) %>%
|
||||
mutate(higher_than_avg_rating = rating > mean(rating)) %>%
|
||||
select(higher_than_avg_rating) %>%
|
||||
summarise(is_higher = sum(higher_than_avg_rating == TRUE), is_lower = sum(higher_than_avg_rating == FALSE)) %>%
|
||||
ggplot(aes(.)) +
|
||||
coord_polar("y", start=0)
|
||||
md %>% filter(Drama == 1) %>% select(rating) %>%
|
||||
mutate(higher_than_avg_rating = rating > mean(rating)) %>%
|
||||
select(higher_than_avg_rating) %>%
|
||||
summarise(is_higher = sum(higher_than_avg_rating == TRUE), is_lower = sum(higher_than_avg_rating == FALSE)) %>%
|
||||
ggplot(aes(.)) +
|
||||
coord_polar("y", start=0)
|
||||
summary <- md %>% filter(Drama == 1) %>% select(rating) %>%
|
||||
mutate(higher_than_avg_rating = rating > mean(rating)) %>%
|
||||
select(higher_than_avg_rating) %>%
|
||||
summarise(is_higher = sum(higher_than_avg_rating == TRUE), is_lower = sum(higher_than_avg_rating == FALSE)) %>%
|
||||
summary
|
||||
summary <- md %>% filter(Drama == 1) %>% select(rating) %>%
|
||||
mutate(higher_than_avg_rating = rating > mean(rating)) %>%
|
||||
select(higher_than_avg_rating) %>%
|
||||
summarise(is_higher = sum(higher_than_avg_rating == TRUE), is_lower = sum(higher_than_avg_rating == FALSE)) %>%
|
||||
summary
|
||||
summary
|
||||
h <- md %>% filter(Drama == 1) %>% select(rating) %>%
|
||||
mutate(higher_than_avg_rating = rating > mean(rating)) %>%
|
||||
select(higher_than_avg_rating) %>%
|
||||
summarise(is_higher = sum(higher_than_avg_rating == TRUE), is_lower = sum(higher_than_avg_rating == FALSE)) %>%
|
||||
h
|
||||
pie(h)
|
||||
h <- md %>% filter(Drama == 1) %>% select(rating) %>%
|
||||
mutate(higher_than_avg_rating = rating > mean(rating)) %>%
|
||||
select(higher_than_avg_rating) %>%
|
||||
summarise(is_higher = sum(higher_than_avg_rating == TRUE), is_lower = sum(higher_than_avg_rating == FALSE)) %>%
|
||||
h <- md %>% filter(Drama == 1) %>% select(rating) %>%
|
||||
mutate(higher_than_avg_rating = rating > mean(rating)) %>%
|
||||
select(higher_than_avg_rating) %>%
|
||||
summarise(is_higher = sum(higher_than_avg_rating == TRUE), is_lower = sum(higher_than_avg_rating == FALSE))
|
||||
md %>% filter(Drama == 1) %>% select(rating) %>%
|
||||
mutate(higher_than_avg_rating = rating > mean(rating)) %>%
|
||||
select(higher_than_avg_rating) %>%
|
||||
summarise(is_higher = sum(higher_than_avg_rating == TRUE), is_lower = sum(higher_than_avg_rating == FALSE))
|
||||
lol <- md %>% filter(Drama == 1) %>% select(rating) %>%
|
||||
mutate(higher_than_avg_rating = rating > mean(rating)) %>%
|
||||
select(higher_than_avg_rating) %>%
|
||||
summarise(is_higher = sum(higher_than_avg_rating == TRUE), is_lower = sum(higher_than_avg_rating == FALSE))
|
||||
lol
|
||||
pie(lol)
|
||||
lol <- md %>% filter(Drama == 1) %>% select(rating) %>%
|
||||
mutate(higher_than_avg_rating = rating > mean(rating)) %>%
|
||||
select(higher_than_avg_rating)# %>%
|
||||
# summarise(is_higher = sum(higher_than_avg_rating == TRUE), is_lower = sum(higher_than_avg_rating == FALSE))
|
||||
pie(lol)
|
||||
# summarise(is_higher = sum(higher_than_avg_rating == TRUE), is_lower = sum(higher_than_avg_rating == FALSE))
|
||||
lol
|
||||
pie(table(lol))
|
||||
lol <- md %>% filter(Drama == 1) %>% select(rating) %>%
|
||||
mutate(higher_than_avg_rating = rating > mean(rating)) %>%
|
||||
select(higher_than_avg_rating)# %>%
|
||||
# summarise(is_higher = sum(higher_than_avg_rating == TRUE), is_lower = sum(higher_than_avg_rating == FALSE))
|
||||
pie(table(lol))
|
||||
pie(table(lol))
|
||||
sel <- md$year >= 1990
|
||||
# the table() command can be used to get a two-way contigency table
|
||||
table(md$Comedy[sel], md$year[sel])
|
||||
sel <- md$year >= 1999 && md$year <= 2005
|
||||
table(md$Animation[sel])
|
||||
sel <- md$year >= 1999 && md$year <= 2005
|
||||
table(md$Animation[sel], md$year[sel])
|
||||
table(md$Animation[sel] == 1, md$year[sel])
|
||||
sel <- md$year >= 1999 && md$year <= 2005
|
||||
table(md$Animation[sel], md$year[sel])
|
||||
tabcomedy <- table(md$Comedy[sel], md$year[sel])
|
||||
tabyear <- table(md$year[sel])
|
||||
tabcomedy[2,]/tabyear
|
||||
tabyear <- table(md$year[sel])
|
||||
tabyear
|
||||
tabcomedy <- table(md$Comedy[sel], md$year[sel])
|
||||
tabcomedy <- table(md$Comedy[sel], md$year[sel])
|
||||
tabcomedy
|
||||
table(md$year[sel])
|
||||
table(md$Comedy[sel], md$year[sel])
|
||||
table(md$year[sel])
|
||||
tabcomedy <- table(md$Comedy[sel], md$year[sel])
|
||||
table(md$Comedy[sel], md$year[sel])
|
||||
tabcomedy <- table(md$Comedy[sel], md$year[sel])
|
||||
tabcomedy
|
||||
tabyear <- table(md$year[sel])
|
||||
tabyear
|
||||
ratio <- tabcomedy[2,]/tabyear
|
||||
## or with dplyr directly
|
||||
md %>% filter(year >= 1990) %>%
|
||||
group_by(year, Comedy) %>%
|
||||
summarise(n = n()) %>% mutate(freq = n / sum(n)) %>%
|
||||
filter(Comedy == 1) %>% select(year, freq) %>%
|
||||
ggplot(aes(year, freq)) + geom_point() + ggtitle("Frequency of comedies") + ylab("Frequency") + xlab("Year") + geom_line() + theme_bw()
|
||||
ratio <- tabcomedy[2,]/tabyear
|
||||
barplot(ratio, xlab="Year", ylab="Relative frequency", main="Proportion of comedies")
|
||||
ratio <- tabcomedy[2,]/tabyear
|
||||
sel <- md$year >= 1999 && md$year <= 2005
|
||||
table(md$Animation[sel], md$year[sel])
|
||||
tt[,2]
|
||||
tt <- table(md$Animation[sel], md$year[sel])
|
||||
tt[,2]
|
||||
tt <- table(md$Animation[sel], md$year[sel])
|
||||
tt[2,]
|
||||
sel <- md$year >= 1999 && md$year <= 2005
|
||||
tt <- table(md$Animation[sel], md$year[sel])
|
||||
sel <- md$year >= 1999 && md$year <= 2005
|
||||
tt <- table(md$Animation[sel], md$year[sel])
|
||||
tt[2,]
|
||||
sel <- md$year >= 1999 && md$year <= 2005
|
||||
sel
|
||||
tt <- table(md$Animation[sel], md$year[sel])
|
||||
tt[2,]
|
||||
sel <- md$year >= 1999 && md$year <= 2005
|
||||
sel
|
||||
tt <- table(md$Animation[sel], md$year[sel])
|
||||
tt <- table(md$Animation[sel], md$year[sel])
|
||||
tt[2,]
|
||||
# - Plot the number of animated movies being produced every year for the period 1995-2005.
|
||||
sel <- md$year >= 1999 && md$year <= 2005
|
||||
tt <- table(md$Animation[sel], md$year[sel])
|
||||
tt[2,]
|
||||
tt <- table(md$Animation[sel], md$year[sel])
|
||||
tt[3,]
|
||||
sel <- md$year >= 1999 && md$year <= 2005
|
||||
tt <- table(md$Animation[sel], md$year[sel])
|
||||
tt[1,]
|
||||
sel <- md$year >= 1999 && md$year <= 2005
|
||||
tt <- table(md$Animation[sel], md$year[sel])
|
||||
tt[2,]
|
||||
#####################
|
||||
barplot(tt[2,], xlab="Year", ylab="Relative frequency", main="Proportion of comedies")
|
||||
sel <- md$year >= 1999 and md$year <= 2005
|
||||
sel <- md[md$year >= 1999 && md$year <= 2005]
|
||||
sel <- md[md$year >= 1999 & md$year <= 2005]
|
||||
tt <- table(md$Animation[sel], md$year[sel])
|
||||
sel <- md[md$year >= 1999 & md$year <= 2005]
|
||||
sel <- md$year >= 1999 & md$year <= 2005
|
||||
sel
|
||||
tt <- table(md$Animation[sel], md$year[sel])
|
||||
barplot(tt[2,], xlab="Year", ylab="Relative frequency", main="Proportion of animated movies")
|
||||
sel <- md$year >= 1999 & md$year <= 2005
|
||||
tt <- table(md$Animation[sel], md$year[sel])
|
||||
barplot(tt[2,], xlab="Year", ylab="Relative frequency", main="Proportion of animated movies")
|
||||
#######################
|
||||
|
|
@ -70,7 +70,7 @@ pie(table(lol))
|
|||
# - Plot the number of animated movies being produced every year for the period 1995-2005.
|
||||
sel <- md$year >= 1999 & md$year <= 2005
|
||||
tt <- table(md$Animation[sel], md$year[sel])
|
||||
barplot(tt[2,], xlab="Year", ylab="Relative frequency", main="Proportion of animated movies")
|
||||
wbarplot(tt[2,], xlab="Year", ylab="Relative frequency", main="Proportion of animated movies")
|
||||
|
||||
# - Is there a clear boundary between short and feature movies (according to their length)?
|
||||
|
||||
|
|
@ -93,3 +93,15 @@ barplot(tt[2,], xlab="Year", ylab="Relative frequency", main="Proportion of anim
|
|||
#
|
||||
#######################################################################################################################
|
||||
|
||||
players <- read.table("players.txt", sep=",", header = T)
|
||||
|
||||
# - Plot the proportion of players according to playing positions.
|
||||
position_proportion <- players %>% group_by(position) %>% select(position) %>% table()
|
||||
pie(position_proportion)
|
||||
|
||||
# - Compare career rebounds (the "reb" attribute) with respect to playing position
|
||||
boxplot(reb ~ position, data=players, ylab="Carrer rebounds", main="Comparison of carrer rebounds with respect to playing position")
|
||||
|
||||
# - Show the distribution of free throw percentages.
|
||||
# The percentage is determined by dividing the number of shots made ("ftm") by the total number of shots attempted ("fta").
|
||||
ftp <- players %>% mutate(free_throw_percentage = ftm/fta *100) %>% select(free_throw_percentage)
|
||||
|
|
|
|||
Loading…
Reference in New Issue