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#install.packages(c("tidyverse","multcompView","dplyr","tidyr","ggplot2","readxl"))
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.3     v dplyr   1.0.7
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   2.0.1     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(multcompView)
library(dplyr)
library(tidyr)
library(ggplot2)
library(readxl)
library(datasets)
#cars1<-utils::read.csv("https://github.com/tidyverse/readr/raw/master/inst/extdata/mtcars.csv")
#system.time(accident<-read_csv("https://vincentarelbundock.github.io/Rdatasets/csv/DAAG/nassCDS.csv"))
data("PlantGrowth")
PlantGrowth <- tibble::as_tibble(PlantGrowth)
print(paste('Overall Mean Weighted = ',mean(PlantGrowth$weight)))
## [1] "Overall Mean Weighted =  5.073"
print(paste('Overall Mean Weighted = ',PlantGrowth$weight %>% mean()))
## [1] "Overall Mean Weighted =  5.073"
PlantGrowth %>% group_by(group) %>% summarise(mean=mean(weight), .groups = 'drop')
## # A tibble: 3 x 2
##   group  mean
##   <fct> <dbl>
## 1 ctrl   5.03
## 2 trt1   4.66
## 3 trt2   5.53
PlantGrowth %>% arrange(group,weight)
## # A tibble: 30 x 2
##    weight group
##     <dbl> <fct>
##  1   4.17 ctrl 
##  2   4.5  ctrl 
##  3   4.53 ctrl 
##  4   4.61 ctrl 
##  5   5.14 ctrl 
##  6   5.17 ctrl 
##  7   5.18 ctrl 
##  8   5.33 ctrl 
##  9   5.58 ctrl 
## 10   6.11 ctrl 
## # ... with 20 more rows
PlantGrowth %>% arrange(desc(group,weight))
## # A tibble: 30 x 2
##    weight group
##     <dbl> <fct>
##  1   6.31 trt2 
##  2   5.12 trt2 
##  3   5.54 trt2 
##  4   5.5  trt2 
##  5   5.37 trt2 
##  6   5.29 trt2 
##  7   4.92 trt2 
##  8   6.15 trt2 
##  9   5.8  trt2 
## 10   5.26 trt2 
## # ... with 20 more rows
PlantGrowth %>% filter(group=='ctrl')
## # A tibble: 10 x 2
##    weight group
##     <dbl> <fct>
##  1   4.17 ctrl 
##  2   5.58 ctrl 
##  3   5.18 ctrl 
##  4   6.11 ctrl 
##  5   4.5  ctrl 
##  6   4.61 ctrl 
##  7   5.17 ctrl 
##  8   4.53 ctrl 
##  9   5.33 ctrl 
## 10   5.14 ctrl
PlantGrowth %>% select(-weight)
## # A tibble: 30 x 1
##    group
##    <fct>
##  1 ctrl 
##  2 ctrl 
##  3 ctrl 
##  4 ctrl 
##  5 ctrl 
##  6 ctrl 
##  7 ctrl 
##  8 ctrl 
##  9 ctrl 
## 10 ctrl 
## # ... with 20 more rows
new_variable = PlantGrowth$weight %>% exp() %>% log() %>% round(1)
PlantGrowth %>% mutate(log_weighted = new_variable)
## # A tibble: 30 x 3
##    weight group log_weighted
##     <dbl> <fct>        <dbl>
##  1   4.17 ctrl           4.2
##  2   5.58 ctrl           5.6
##  3   5.18 ctrl           5.2
##  4   6.11 ctrl           6.1
##  5   4.5  ctrl           4.5
##  6   4.61 ctrl           4.6
##  7   5.17 ctrl           5.2
##  8   4.53 ctrl           4.5
##  9   5.33 ctrl           5.3
## 10   5.14 ctrl           5.1
## # ... with 20 more rows