ANALISIS DATA JUMLAH AHLI GIZI DALAM BOX PLOT DAN DOT PLOT #KELOMPOK 5

PROSES IMPORT DATA

#Import Data

library(readxl)
## Warning: package 'readxl' was built under R version 4.3.3
DATA_KELOMPOK5_KOMSTAT <- read_excel("C:/Users/UseR/Downloads/DATA_KELOMPOK5_KOMSTAT.xls")
DATA_KELOMPOK5_KOMSTAT
## # A tibble: 38 × 3
##       No `Kabupaten/Kota` `Jumlah Ahli Gizi (Nutritionist)`
##    <dbl> <chr>                                        <dbl>
##  1     1 Pacitan                                         69
##  2     2 Ponorogo                                        58
##  3     3 Trenggalek                                      73
##  4     4 Tulungagung                                     79
##  5     5 Blitar                                          73
##  6     6 Kediri                                         114
##  7     7 Malang                                         118
##  8     8 Lumajang                                        85
##  9     9 Jember                                         106
## 10    10 Banyuwangi                                      99
## # ℹ 28 more rows

PENGECEKAN LOKASI SINTAKS

Langkah awal adalah pengecekan di mana lokasi sintaks ini disimpan {r} getwd()

#Mean

Mean<-mean(DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)`)
Mean
## [1] 71.92105

#Median

Median<-median(DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)`)
Median
## [1] 59.5

Range

NilaiMax<-max(DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)`)
NilaiMax
## [1] 310
NilaiMin<-min(DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)`)
NilaiMin
## [1] 24
rangedata<-NilaiMax-NilaiMin
rangedata
## [1] 286

###Kuartil 1

Q1<-quantile(DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)`, probs=(0.25))
Q1
##   25% 
## 48.25

###Kuartil 3

Q3<-quantile(DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)`, probs=(0.75))
Q3
##  75% 
## 77.5

###IQR

IQR <- Q3-Q1
IQR
##   75% 
## 29.25

###STDEV

STDEV<-sd(DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)`)
STDEV
## [1] 49.80469

VARIANS

Varian<-var(DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)`)
Varian
## [1] 2480.507

#Grafik Box-Plot #Hinges

H1<-Q1
H1
##   25% 
## 48.25
H2<-Q3
H2
##  75% 
## 77.5

#Nilai Ekstrem

NilaiEkstrem <- (DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)` > Q3+3*IQR) | 
  (DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)` < Q1-3*IQR)
a<-Q3+3*IQR
a
##    75% 
## 165.25
b<-Q1-3*IQR
b
##   25% 
## -39.5

#Nilai Ekstrem

DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)`[(NilaiEkstrem)]
## [1] 168 310

#Outlier

Outlier <- (DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)` > Q3+1.5*IQR) | 
  (DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)` < Q1-1.5*IQR)
p<-Q3+1.5*IQR
p
##     75% 
## 121.375
q<-Q1-1.5*IQR
q
##   25% 
## 4.375

#Membuat Box Plot

boxplot(DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)`, horizontal =F ,
        main = "Box Plot Jumlah Ahli Gizi (Nutritionist) ", col="#FA8072", 
        border = "#008000",pch=19,
        ylab="Jumlah Ahli Gizi (Nutritionist)")

#Membuat Dot Plot

library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.3.3
ggplot(DATA_KELOMPOK5_KOMSTAT, aes(x = DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)`)) +
  geom_dotplot(fill = "#FA8072") +
  theme_minimal() +                                 
  labs(title = "Dot Plot Jumlah Ahli Gizi (Nutritionist)",
       y = "Proporsi",
       x = "Jumlah Ahli Gizi (Nutritionist)")
## Warning: Use of `` DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)` `` is
## discouraged.
## ℹ Use `Jumlah Ahli Gizi (Nutritionist)` instead.
## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.

# UJI NORMALITAS

library(nortest)
ad.test(DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)`)
## 
##  Anderson-Darling normality test
## 
## data:  DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)`
## A = 2.9409, p-value = 1.541e-07

#Transformasi data

library(rcompanion)
## Warning: package 'rcompanion' was built under R version 4.3.3
trans1<- transformTukey(DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)`)

## 
##     lambda     W Shapiro.p.value
## 388 -0.325 0.976          0.5781
## 
## if (lambda >  0){TRANS = x ^ lambda} 
## if (lambda == 0){TRANS = log(x)} 
## if (lambda <  0){TRANS = -1 * x ^ lambda}

trans1
##  [1] -0.2525655 -0.2672304 -0.2479819 -0.2416970 -0.2479819 -0.2145388
##  [7] -0.2121477 -0.2360146 -0.2196725 -0.2246046 -0.2588162 -0.2628862
## [13] -0.2687452 -0.2513872 -0.2127353 -0.2735093 -0.2325135 -0.2751759
## [19] -0.2822840 -0.3065987 -0.2601443 -0.2549915 -0.2702955 -0.2549915
## [25] -0.2735093 -0.3242099 -0.3015300 -0.2881401 -0.2841820 -0.2672304
## [31] -0.3347501 -0.1891361 -0.3310820 -0.3559847 -0.3275725 -0.2687452
## [37] -0.1549913 -0.3426153

ANDERSON DARLING SETELAH TRANSFORMASI PERTAMA

ad.test(trans1)
## 
##  Anderson-Darling normality test
## 
## data:  trans1
## A = 0.42818, p-value = 0.296

Boxplot Seteleh Transformasi Pertama

boxplot(trans1, horizontal =F ,
        main = "Box Plot Jumlah Ahli Gizi (Nutritionist) ", col="#FA8072", 
        border = "#008000",pch=19,
        ylab="Jumlah Ahli Gizi (Nutritionist)")

#VISUALISASI DATA # BARCHART

# Bar chart
barplot(DATA_KELOMPOK5_KOMSTAT$`Jumlah Ahli Gizi (Nutritionist)`, names.arg = DATA_KELOMPOK5_KOMSTAT$`Kabupaten/Kota`, col = "lightgreen", 
        xlab = "Kabupaten/Kota", ylab = "Jumlah Ahli Gizi", 
        main = "Bar Chart Jumlah Ahli Gizi per Kabupaten/Kota", las=2, cex.names=0.7)

# Menambahkan grid pada sumbu y
grid(nx = NA, ny = NULL, col = "gray", lty = "dotted")