library(tidyverse)
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## ✔ lubridate 1.9.5     ✔ tidyr     1.3.2
## ✔ purrr     1.2.2     
## ── 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(readxl)
library(pastecs)
## 
## Attaching package: 'pastecs'
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## The following objects are masked from 'package:dplyr':
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##     first, last
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## The following object is masked from 'package:tidyr':
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##     extract
library(dplyr)

data1<-(X2020Election[-1,])

Elections <- read_xlsx("2024Elections.xlsx")
## New names:
## • `` -> `...4`
Elections2024<- (Elections[-1,])
#removes United States total row 
Elections2024$INELIGIBLE_FELONS_TOTAL<- parse_number(Elections2024$INELIGIBLE_FELONS_TOTAL)

#Elections2024$INELIGIBLE_FELONS_TOTAL <- as.numeric(Elections2024$INELIGIBLE_FELONS_TOTAL)
#Elections2024$INELIGIBLE_FELONS_TOTAL <- as.numeric(Elections2024$INELIGIBLE_FELONS_TOTAL)
#MutantElections2024<- Elections2024 %>% mutate(INELIGIBLE_FELONS_TOTAL) = as.numeric(INELIGIBLE_FELONS_TOTAL)

HOMEWORK

  1. From the data you have chosen, select a variable that you are interested in
  2. Use pastecs::stat.desc to describe the variable. Include a few sentences about what the variable is and what it’s measuring. Remember to load pastecs “library(pastecs)”
  3. Remove NA’s if needed using dplyr:filter (or anything similar)
  4. Provide a histogram of the variable (as shown in this lesson)
  5. transform the variable using the log transformation or square root transformation (whatever is more appropriate) using dplyr::mutate or something similar
  6. provide a histogram of the transformed variable
  7. submit via rpubs on CANVAS
pastecs::stat.desc(Elections2024$INELIGIBLE_FELONS_TOTAL)
##      nbr.val     nbr.null       nbr.na          min          max        range 
## 5.100000e+01 3.000000e+00 0.000000e+00 0.000000e+00 4.857810e+05 4.857810e+05 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 2.373735e+06 1.641200e+04 4.654382e+04 1.110335e+04 2.230174e+04 6.287504e+09 
##      std.dev     coef.var 
## 7.929378e+04 1.703637e+00
CleanElections<- Elections2024 |>drop_na(INELIGIBLE_FELONS_TOTAL) %>%filter(INELIGIBLE_FELONS_TOTAL>0)
#district_clean<- district |>drop_na(DPSTURNR) %>%filter(DPSTURNR>0)
hist(CleanElections$INELIGIBLE_FELONS_TOTAL)

CleanElections<- CleanElections %>% mutate(IFT_SQRT=sqrt(INELIGIBLE_FELONS_TOTAL))
hist(CleanElections$IFT_SQRT)