UntitledRMD.knit

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The easiest way to get ggplot2 is to install the whole tidyverse:

install.packages(“tidyverse”) WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/ Installing package into ‘C:/Users/USER/AppData/Local/R/win-library/4.4’ (as ‘lib’ is unspecified) trying URL ‘https://cran.rstudio.com/bin/windows/contrib/4.4/tidyverse_2.0.0.zip’ Content type ‘application/zip’ length 431553 bytes (421 KB) downloaded 421 KB

package ‘tidyverse’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in C:_packages > > # Alternatively, install just ggplot2: > install.packages(“ggplot2”) WARNING: Rtools is required to build R packages but is not currently installed. Please download and install the appropriate version of Rtools before proceeding:

https://cran.rstudio.com/bin/windows/Rtools/ Installing package into ‘C:/Users/USER/AppData/Local/R/win-library/4.4’ (as ‘lib’ is unspecified) trying URL ‘https://cran.rstudio.com/bin/windows/contrib/4.4/ggplot2_3.5.1.zip’ Content type ‘application/zip’ length 5015279 bytes (4.8 MB) downloaded 4.8 MB

package ‘ggplot2’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in C:_packages > > # Or the development version from GitHub: > # install.packages(“pak”) > pak::pak(“tidyverse/ggplot2”) Error in loadNamespace(x) : there is no package called ‘pak’ > Statistic <-c(68,85,74,88,63,78,90,80,58,63) > Math <-c(85,91,74,100,82,84,78,100,51,70) > plot(Statistic,Math, + pch=17, + col=“red”, + xlab=“Statistic”, + ylab=“Math”, + main=“統計成績與數學成績”) > abc<-c(68,85,74,88,63,78,90,80,58,63) > hist(abc, + col= “orange”, + main =“統計成績”, + xlab =“Statistic”, + ylab =““) > library(”ggplot2”) > data <- data.frame( + name=c(“娛樂休閒”,“知識閱讀”,“體育競技”,“科學創新”,“公益活動”) ,
+ 次數=c(185,82,36,28,25) + ) > ggplot(data, aes(x=name, y=次數)) + + geom_bar(stat = “identity”, width=0.2, fill=“yellow”) > labels<- c(“娛樂休閒”,“知識閱讀”,“體育競技”,“科學創新”,“公益活動”)
> data<- c(185,82,36,28,25) > pie(data,labels,main =“大學生最喜歡參加的社團”, col=terrain.colors(length(data))) > Japanese<-c(84,63,61,49,89,51,59,53,79,91) > > stem(Japanese)

The decimal point is 1 digit(s) to the right of the |

4 | 9 5 | 139 6 | 13 7 | 9 8 | 49 9 | 1

Japanese<-c(84,63,61,49,89,51,59,53,79,91)

mean(Japanese) [1] 67.9 median(Japanese) [1] 62 as.numeric(names(table(Japanese)))[which.max(table(Japanese))] [1] 49 sd(Japanese) [1] 16.25115 var(Japanese) [1] 264.1 Q1 <- quantile(Japanese,1/4) print(Q1) 25% 54.5 Q3 <- quantile(Japanese,3/4) print(Q3) 75% 82.75