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Prepping Data for EDA

library(readxl)
Conc <- read_excel("~/Desktop/NCEA/NCEA.xlsx")
## New names:
## * `` -> ...7
## * `` -> ...14
## * `` -> ...19
## * `` -> ...23
## * `` -> ...30
#View(Conc)
Conc<-Conc[,-c(7,14,19,23,30)]
str(Conc)
## tibble [123 × 28] (S3: tbl_df/tbl/data.frame)
##  $ Entry #                                : num [1:123] 198 197 196 195 194 193 192 191 190 189 ...
##  $ BMI                                    : num [1:123] 26 20.6 22 20.2 21 ...
##  $ BMI Ordinal classification             : num [1:123] 3 2 2 2 2 2 3 2 2 2 ...
##  $ Concussion Raw                         : chr [1:123] "1" "0" "0" "0" ...
##  $ Concussion Binary                      : num [1:123] 1 2 2 2 2 2 2 1 2 1 ...
##  $ Concussion Ordinal                     : num [1:123] 1 0 0 0 0 0 0 1 0 1 ...
##  $ P! Arth Nk Binary                      : num [1:123] 2 2 2 2 2 1 2 2 2 1 ...
##  $ P! Arth Nk Ordinal                     : num [1:123] 0 0 0 0 0 1 0 0 0 1 ...
##  $ P! Athr TS Binary                      : num [1:123] 1 2 2 2 2 2 2 2 2 2 ...
##  $ P! Athr TS Ordinal                     : num [1:123] 2 0 0 0 0 0 0 0 0 0 ...
##  $ P! Arth LS/Tailbone Binary             : num [1:123] 2 2 2 2 2 2 2 2 2 1 ...
##  $ P! Arth LS/Tailbone Ordinal            : num [1:123] 0 0 0 0 0 0 0 0 0 1 ...
##  $ R Sh RC P! Ordinal                     : num [1:123] 0 0 0 0 0 0 0 0 0 1 ...
##  $ L Sh RC P! Ordinal                     : num [1:123] 0 0 0 0 0 0 0 0 0 1 ...
##  $ R Wrist Fx. Binary                     : num [1:123] 1 0 0 0 0 0 0 0 0 0 ...
##  $ L Wrist Fx. Binary                     : num [1:123] 0 0 0 0 0 0 0 0 0 0 ...
##  $ R. Generalized kn P! Ordinal (non arth): num [1:123] 0 1 0 0 0 0 0 0 0 0 ...
##  $ L. Generalized kn P! Ordinal (non arth): num [1:123] 0 0 0 0 0 0 0 0 0 0 ...
##  $ B. Kn P! Binary                        : num [1:123] 0 0 0 0 0 0 0 0 0 0 ...
##  $ R. Ankle Spr Ordinal                   : num [1:123] 0 0 0 0 0 0 0 0 0 0 ...
##  $ L. Ankle Spr Ordinal:                  : num [1:123] 0 0 0 0 0 0 0 0 0 0 ...
##  $ B. Ankle Sprain Binary                 : num [1:123] 0 0 0 0 0 0 0 0 0 0 ...
##  $ R. Ankle P! Ordinal                    : num [1:123] 0 0 0 0 0 3 0 0 0 0 ...
##  $ L. Ankle P! Ordinal                    : num [1:123] 0 0 0 0 0 0 0 0 0 0 ...
##  $ B. Ankle P! Binary                     : num [1:123] 0 0 0 0 0 0 0 0 0 0 ...
##  $ R. Hp P!/Impingement Ordinal           : num [1:123] 0 0 1 0 0 0 0 0 0 1 ...
##  $ L. Hp P!/Impingement Ordinal           : num [1:123] 0 0 1 0 0 0 0 0 0 1 ...
##  $ B. Hip P! Binary                       : num [1:123] 0 0 1 0 0 0 0 0 0 1 ...
Conc$`Entry #`<-as.character(Conc$`Entry #`)
Conc$`Concussion Raw`<-as.numeric(Conc$`Concussion Raw`)
## Warning: NAs introduced by coercion
Conc$`BMI Ordinal classification`<-as.factor(Conc$`BMI Ordinal classification`)
Conc$`Concussion Binary`<-as.factor(Conc$`Concussion Binary`)
Conc$`Concussion Ordinal`<-as.factor(Conc$`Concussion Ordinal`)
Conc$`P! Arth Nk Ordinal`<-as.factor(Conc$`P! Arth Nk Ordinal`)
str(Conc)
## tibble [123 × 28] (S3: tbl_df/tbl/data.frame)
##  $ Entry #                                : chr [1:123] "198" "197" "196" "195" ...
##  $ BMI                                    : num [1:123] 26 20.6 22 20.2 21 ...
##  $ BMI Ordinal classification             : Factor w/ 4 levels "1","2","3","4": 3 2 2 2 2 2 3 2 2 2 ...
##  $ Concussion Raw                         : num [1:123] 1 0 0 0 0 0 0 1 0 1 ...
##  $ Concussion Binary                      : Factor w/ 2 levels "1","2": 1 2 2 2 2 2 2 1 2 1 ...
##  $ Concussion Ordinal                     : Factor w/ 4 levels "0","1","2","3": 2 1 1 1 1 1 1 2 1 2 ...
##  $ P! Arth Nk Binary                      : num [1:123] 2 2 2 2 2 1 2 2 2 1 ...
##  $ P! Arth Nk Ordinal                     : Factor w/ 4 levels "0","1","2","3": 1 1 1 1 1 2 1 1 1 2 ...
##  $ P! Athr TS Binary                      : num [1:123] 1 2 2 2 2 2 2 2 2 2 ...
##  $ P! Athr TS Ordinal                     : num [1:123] 2 0 0 0 0 0 0 0 0 0 ...
##  $ P! Arth LS/Tailbone Binary             : num [1:123] 2 2 2 2 2 2 2 2 2 1 ...
##  $ P! Arth LS/Tailbone Ordinal            : num [1:123] 0 0 0 0 0 0 0 0 0 1 ...
##  $ R Sh RC P! Ordinal                     : num [1:123] 0 0 0 0 0 0 0 0 0 1 ...
##  $ L Sh RC P! Ordinal                     : num [1:123] 0 0 0 0 0 0 0 0 0 1 ...
##  $ R Wrist Fx. Binary                     : num [1:123] 1 0 0 0 0 0 0 0 0 0 ...
##  $ L Wrist Fx. Binary                     : num [1:123] 0 0 0 0 0 0 0 0 0 0 ...
##  $ R. Generalized kn P! Ordinal (non arth): num [1:123] 0 1 0 0 0 0 0 0 0 0 ...
##  $ L. Generalized kn P! Ordinal (non arth): num [1:123] 0 0 0 0 0 0 0 0 0 0 ...
##  $ B. Kn P! Binary                        : num [1:123] 0 0 0 0 0 0 0 0 0 0 ...
##  $ R. Ankle Spr Ordinal                   : num [1:123] 0 0 0 0 0 0 0 0 0 0 ...
##  $ L. Ankle Spr Ordinal:                  : num [1:123] 0 0 0 0 0 0 0 0 0 0 ...
##  $ B. Ankle Sprain Binary                 : num [1:123] 0 0 0 0 0 0 0 0 0 0 ...
##  $ R. Ankle P! Ordinal                    : num [1:123] 0 0 0 0 0 3 0 0 0 0 ...
##  $ L. Ankle P! Ordinal                    : num [1:123] 0 0 0 0 0 0 0 0 0 0 ...
##  $ B. Ankle P! Binary                     : num [1:123] 0 0 0 0 0 0 0 0 0 0 ...
##  $ R. Hp P!/Impingement Ordinal           : num [1:123] 0 0 1 0 0 0 0 0 0 1 ...
##  $ L. Hp P!/Impingement Ordinal           : num [1:123] 0 0 1 0 0 0 0 0 0 1 ...
##  $ B. Hip P! Binary                       : num [1:123] 0 0 1 0 0 0 0 0 0 1 ...
#remove rows with NA value in any column data frame
Conc<- Conc[complete.cases(Conc), ]
#Make output more readable
options(digits = 3)
#Compute summary statistics
mean(~ BMI, data = Conc,na.rm = TRUE)
## [1] 22.5
#Other summary statistics work similarly: Table of  summary statistics
favstats(~ BMI, data = Conc,na.rm = TRUE)
##  min   Q1 median   Q3  max mean   sd   n missing
##   18 21.1   22.2 23.8 33.5 22.5 2.47 115       0
#Summary statistics by group
favstats(BMI~`Concussion Ordinal`, data = Conc,na.rm = TRUE)
##   Concussion Ordinal  min   Q1 median   Q3  max mean   sd  n missing
## 1                  0 18.0 21.0   22.0 23.4 30.1 22.4 2.35 62       0
## 2                  1 18.1 21.1   22.5 23.8 33.5 22.6 2.62 46       0
## 3                  2 20.4 21.8   23.8 25.4 27.5 23.7 2.58  7       0
## 4                  3   NA   NA     NA   NA   NA  NaN   NA  0       0
#Quantiles
quantile(~BMI, data = Conc,prob = c(0.25, 0.5, 0.8),na.rm = TRUE)
##  25%  50%  80% 
## 21.1 22.2 24.3
#Histogram
gf_histogram(~BMI, data = Conc, binwidth = 5, center = 2.5)

#pie chart

# 3D Exploded Pie Chart
require(kableExtra)
t<-tally(~`Concussion Ordinal`, data = Conc, margins = TRUE)
as.table(t) %>%
  kbl() %>%
  kable_styling()
Concussion.Ordinal Freq
0 62
1 46
2 7
3 0
Total 115 
###########pie chart############
library(plotrix)
## 
## Attaching package: 'plotrix'
## The following object is masked from 'package:mosaic':
## 
##     rescale
slices <- c(62, 46, 7) 
lbls <- c("0", "1", "2")
pie3D(slices,labels=lbls,explode=0.1,
   main="Pie Chart of Concussions ")

require(kableExtra)
require(mosaic)
#Contingency table with margins
t1<-tally(~`BMI Ordinal classification`+`Concussion Ordinal`, data = Conc, margins = TRUE)
kbl(as.table(t1))
0 1 2 3 Total
1 1 2 0 0 3
2 51 39 5 0 95
3 9 4 2 0 15
4 1 1 0 0 2
Total 62 46 7 0 115 
#Percentages by column
t2<-tally(~ `BMI Ordinal classification` | `Concussion Ordinal`, data = Conc,format = "percent")
kbl(t2)
0 1 2 3
1.61 4.35 0.0 NaN
82.26 84.78 71.4 NaN
14.52 8.70 28.6 NaN
1.61 2.17 0.0 NaN 
# Mosaic plot
my_tbl <- tally(`BMI Ordinal classification` ~ `Concussion Ordinal`, data = Conc)
mosaicplot(my_tbl, color = TRUE)

library(ggplot2)

t3<-tally(`Concussion Ordinal`~`P! Arth Nk Ordinal`, data=Conc)
t3 %>%
  kbl() %>%
  kable_classic_2(full_width = F)
0 1 2 3
0 55 4 3 0
1 32 8 3 3
2 6 0 1 0
3 0 0 0 0 
# grouped bar plot
ggplot(Conc, 
       aes(x =`Concussion Ordinal` , 
           fill = `P! Arth Nk Ordinal`)) + 
  geom_bar(position = "dodge")

# Binary

t4<-tally(`Concussion Binary`~`P! Arth Nk Binary`, data=Conc)
t4 %>%
  kbl() %>%
  kable_classic_2(full_width = F)
1 2
15 38
7 55 
#plot
ggplot(Conc, 
       aes(x =`Concussion Binary` , 
           fill = `P! Arth Nk Ordinal`)) + 
  geom_bar(position = "dodge")