library(tidyverse)

## Warning: package 'tidyverse' was built under R version 4.4.3

## Warning: package 'ggplot2' was built under R version 4.4.3

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(pastecs)

## Warning: package 'pastecs' was built under R version 4.4.3

## 
## Attaching package: 'pastecs'
## 
## The following objects are masked from 'package:dplyr':
## 
##     first, last
## 
## The following object is masked from 'package:tidyr':
## 
##     extract

1. From the data you have chosen, select a variable that you are interested in

pavements<-read.csv("pavements_3192083553624189959.csv")

2. Use pastecs::stat.desc to describe the variable. Include a few sentences about what the variable is and what it’s measuring.

stat.desc(pavements$PCI)

##      nbr.val     nbr.null       nbr.na          min          max        range 
## 9.797200e+04 1.351000e+03 0.000000e+00 0.000000e+00 9.999000e+01 9.999000e+01 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 7.725931e+06 8.700000e+01 7.885856e+01 6.046350e-02 1.185077e-01 3.581694e+02 
##      std.dev     coef.var 
## 1.892536e+01 2.399912e-01

Variable description: Pavement Condition Index (PCI) is the rating assigned to the roadway segment

3. Remove NA’s if needed using dplyr:filter (or anything similar)

pavement<-pavements%>%select(PCI)%>%na.omit(.)
pavement_removed<-pavement%>%filter(PCI>0)

4. Provide a histogram of the variable (as shown in this lesson)

hist(pavement$PCI,breaks=10,probability = T)
lines(density(pavement$PCI),col='red',lwd=2)

5. transform the variable using the log transformation or square root transformation (whatever is more appropriate) using dplyr::mutate or something similar

pavement_log<-pavement_removed %>% mutate(LOG_PCI=log(PCI)) %>% select(PCI,LOG_PCI)

6. provide a histogram of the transformed variable

hist(pavement_log$LOG_PCI,breaks=10,probability = T)
lines(density(pavement_log$LOG_PCI),col='red',lwd=2)

pavement_sqrt<-pavement_removed %>% mutate(PCI_SQRT=sqrt(PCI))

hist(pavement_sqrt$PCI_SQRT,breaks=10,probability = T)
lines(density(pavement_sqrt$PCI_SQRT),col='red',lwd=2)

7. submit via rpubs on CANVAS

This log and sqrt did not make my data look more normal. Should I mutate my data to have less obersations? Wouldn’t this be incorrect since its destorying the data to make it fit what I want to see?