43 lines
1.2 KiB
R
43 lines
1.2 KiB
R
# Projekt 1: Przygotowanie wizualnej analizy danych z wykorzystaniem podstawowej biblioteki graficznej R i/lub biblioteki ggplot2
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# zaladowanie bibliotek
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library(Hmisc)
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library(dplyr)
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library(ggplot2)
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library(RColorBrewer)
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# zaladowanie danych
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german_credit_risk <- read.csv("german_credit_data.csv", header = TRUE)
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# sprawdzenie danych
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head(german_credit_risk)
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tail(german_credit_risk)
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str(german_credit_risk)
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summary(german_credit_risk)
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describe(german_credit_risk)
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# zmiana nazwy pierwszej kolumny
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colnames(german_credit_risk)[1] <- "index"
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min(german_credit_risk$Age)
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na.omit(german_credit_risk)
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# violin plot
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ggplot(german_credit_risk, aes(x=Purpose, y=Age, fill=Sex)) +
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geom_violin(trim=TRUE) +
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stat_summary(fun = mean, geom="point", shape=25, size=2, position=position_dodge(.9)) +
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labs(title="Credit purpose by age", x="Purpose", y = "Age") +
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scale_fill_brewer(palette="Accent") +
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theme_minimal() +
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theme(legend.position="bottom")
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ggplot(german_credit_risk, aes(x=Purpose, y=Credit.amount, fill=Credit.amount)) +
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geom_density_ridges(scale = 4) +
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xlab('Purpose') +
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ylab('Credit amount') +
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labs('?') +
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scale_fill_manual(values = wes_palette("Moonrise3")) +
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theme_minimal()
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