library(readxl)
A5 <- read_excel("AI4OPT/R stats/A5.xlsx")
str(A5)
## tibble [12 × 4] (S3: tbl_df/tbl/data.frame)
## $ Time : chr [1:12] "Week 1" "Week 1" "Week 1" "Week 1" ...
## $ Stock1: num [1:12] 15 15 16 16 16 17 17 17 17 15 ...
## $ Stock2: num [1:12] 23 22 23 22 23 23 22 23 23 22 ...
## $ Stock3: num [1:12] 5 5 6 6 7 7 9 9 5 5 ...
A5$Time<-as.factor(A5$Time)
str(A5)
## tibble [12 × 4] (S3: tbl_df/tbl/data.frame)
## $ Time : Factor w/ 3 levels "Week 1","Week 2",..: 1 1 1 1 2 2 2 2 3 3 ...
## $ Stock1: num [1:12] 15 15 16 16 16 17 17 17 17 15 ...
## $ Stock2: num [1:12] 23 22 23 22 23 23 22 23 23 22 ...
## $ Stock3: num [1:12] 5 5 6 6 7 7 9 9 5 5 ...
library(mlbench)
library(plyr)
library(plotfunctions)
## Warning: package 'plotfunctions' was built under R version 4.2.1
# Global Mean and Median of first column Stock1
mean(A5$Stock1)
## [1] 16.25
median(A5$Stock1)
## [1] 16
#Global Mean and Median of 3rd column: Stock3
MeanS3<-mean(A5$Stock3)
MedianS3<-median(A5$Stock3)
# Mean and Median of Stock1 per Time values
aggregate(Stock1~Time, A5, each(mean, median))
## Time Stock1.mean Stock1.median
## 1 Week 1 15.50 15.50
## 2 Week 2 16.75 17.00
## 3 Week 3 16.50 16.00
# Mean and Median of Stock3 per Time values
aggregate(Stock3~Time, A5, each(mean, median))
## Time Stock3.mean Stock3.median
## 1 Week 1 5.5 5.5
## 2 Week 2 8.0 8.0
## 3 Week 3 3.5 3.5
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
A5 %>% select(Stock1, Stock3) %>%
mutate( difference = Stock1-Stock3)
## # A tibble: 12 × 3
## Stock1 Stock3 difference
## <dbl> <dbl> <dbl>
## 1 15 5 10
## 2 15 5 10
## 3 16 6 10
## 4 16 6 10
## 5 16 7 9
## 6 17 7 10
## 7 17 9 8
## 8 17 9 8
## 9 17 5 12
## 10 15 5 10
## 11 15 2 13
## 12 19 2 17