R Notebook
attach(Exercise)The following objects are masked from Exercise (pos = 6):
age, Analyse_result, birth_date, br_cr, br_dy, breed,
Breed_Type, Gender, herdsize, ht, ID, kkrpct, krypct, mlkpct,
n_cattle_1km, n_cattle_5km, n_cattle1km, n_cattle5km,
n_goat_1km, n_goat_5km, n_goat1km, n_goat5km, n_herd_1km,
n_herd_5km, n_movement, n_sheep_1km, n_sheep_5km, n_sheep1km,
n_sheep5km, n_total1km, n_total5km, newres, prodtype, res,
sex, slaughter_date### Descriptive Analysis
summary(Exercise) #Basic summary ID Analyse_result birth_date
Min. :1.010e+09 Min. : -2.990 Min. :1994-11-20 00:00:00
1st Qu.:3.105e+09 1st Qu.: 2.045 1st Qu.:2010-03-04 18:00:00
Median :4.840e+09 Median : 4.660 Median :2011-04-02 12:00:00
Mean :4.989e+09 Mean : 12.083 Mean :2010-03-04 20:36:36
3rd Qu.:6.443e+09 3rd Qu.: 8.693 3rd Qu.:2011-07-09 06:00:00
Max. :1.173e+10 Max. :402.000 Max. :2012-01-25 00:00:00
Gender slaughter_date n_movement
Length:800 Min. :2012-07-02 00:00:00 Min. : 2.000
Class :character 1st Qu.:2012-08-01 00:00:00 1st Qu.: 2.000
Mode :character Median :2012-08-21 00:00:00 Median : 2.000
Mean :2012-08-24 03:41:24 Mean : 2.839
3rd Qu.:2012-09-19 00:00:00 3rd Qu.: 3.000
Max. :2012-10-16 00:00:00 Max. :11.000
herdsize mlkpct krypct kkrpct
Min. : 1.0 Min. : 0.000 Min. : 0.000 Min. : 0.00
1st Qu.: 23.0 1st Qu.: 0.000 1st Qu.: 1.912 1st Qu.: 0.00
Median : 56.0 Median : 7.572 Median : 15.305 Median : 20.80
Mean : 119.3 Mean : 34.314 Mean : 27.599 Mean : 38.09
3rd Qu.: 133.0 3rd Qu.: 79.663 3rd Qu.: 46.733 3rd Qu.: 82.11
Max. :1792.0 Max. :100.000 Max. :100.000 Max. :100.00
NA's :3
prodtype n_herd_1km n_cattle_1km n_sheep_1km
Length:800 Min. : 0.000 Min. :0.000 Min. :0.0000
Class :character 1st Qu.: 1.000 1st Qu.:1.000 1st Qu.:0.0000
Mode :character Median : 2.000 Median :2.000 Median :0.0000
Mean : 2.612 Mean :1.933 Mean :0.6388
3rd Qu.: 4.000 3rd Qu.:3.000 3rd Qu.:1.0000
Max. :12.000 Max. :8.000 Max. :5.0000
n_goat_1km n_herd_5km n_cattle_5km n_sheep_5km
Min. :0.000 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.:0.000 1st Qu.: 41.00 1st Qu.:28.00 1st Qu.: 8.00
Median :0.000 Median : 53.00 Median :39.00 Median :12.00
Mean :0.335 Mean : 52.94 Mean :39.06 Mean :13.23
3rd Qu.:1.000 3rd Qu.: 65.00 3rd Qu.:49.00 3rd Qu.:17.00
Max. :5.000 Max. :101.00 Max. :76.00 Max. :47.00
n_goat_5km age res Breed_Type
Min. : 0.000 Min. : 0.611 Length:800 Length:800
1st Qu.: 4.000 1st Qu.: 1.110 Class :character Class :character
Median : 5.000 Median : 1.390 Mode :character Mode :character
Mean : 6.051 Mean : 2.475
3rd Qu.: 8.000 3rd Qu.: 2.489
Max. :22.000 Max. :17.805
newres breed sex br_dy
Min. :1.000 Length:800 Min. :1.000 Min. :1.000
1st Qu.:1.000 Class :character 1st Qu.:1.000 1st Qu.:1.000
Median :1.000 Mode :character Median :1.000 Median :1.000
Mean :1.055 Mean :1.384 Mean :1.331
3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:2.000
Max. :2.000 Max. :2.000 Max. :2.000
br_cr n_cattle1km n_sheep1km n_goat1km
Min. :1.000 Min. : 0.0 Min. : 0.00 Min. : 0.000
1st Qu.:1.000 1st Qu.: 4.0 1st Qu.: 0.00 1st Qu.: 0.000
Median :1.000 Median : 42.0 Median : 0.00 Median : 0.000
Mean :1.284 Mean : 178.2 Mean : 11.09 Mean : 2.039
3rd Qu.:2.000 3rd Qu.: 258.0 3rd Qu.: 6.25 3rd Qu.: 0.000
Max. :2.000 Max. :1399.0 Max. :800.00 Max. :586.000
n_cattle5km n_sheep5km n_goat5km n_total1km
Min. : 0 Min. : 0.0 Min. : 0.00 Min. : 0.00
1st Qu.: 1441 1st Qu.: 87.0 1st Qu.: 11.00 1st Qu.: 10.75
Median : 3218 Median : 158.5 Median : 21.00 Median : 57.50
Mean : 3851 Mean : 289.7 Mean : 42.11 Mean : 191.37
3rd Qu.: 5689 3rd Qu.: 311.0 3rd Qu.: 37.00 3rd Qu.: 277.75
Max. :12292 Max. :3623.0 Max. :2005.00 Max. :1422.00
n_total5km ht
Min. : 0 Length:800
1st Qu.: 1764 Class :character
Median : 3481 Mode :character
Mean : 4183
3rd Qu.: 5959
Max. :13034
library(psych)
describe(Exercise[ , c(6:10, 16:21, 24, 28:35)]) #Accessing only numerical variables
#Numerical data visualization - Histogram. Box plot,Violin Plot, Scatter Plot, Line Plot,Heat map
library(ggplot2)
ggplot(data = Exercise, aes(x=herdsize))+
geom_histogram(binwidth = , color = "black", fill = "lightblue", alpha =0.5)+
labs(title = "Histogram of Herd Size", x = "Herd Size", y = "No of Animal")+
theme_minimal()
#Box plot (Single axis)
ggplot(data = Exercise, aes(y= age))+
geom_boxplot()+
labs(title = "Box Plot of Age" )
#Box plot (X-axis = cat, Y-axis = num)
ggplot(data = Exercise, aes(x=prodtype, y=mlkpct, fill = prodtype ))+
geom_boxplot(color = "black")+
scale_fill_manual(values = c("k"= "purple", "m"= "yellow", "x"="red"))
# Box plot (as.factor issue)
ggplot(data = Exercise, aes(x=as.factor(sex), y=age, fill = as.factor(sex) ))+
geom_boxplot(color = "black")+
scale_fill_manual(values = c("1"= "purple", "2"= "yellow"))+
labs(title = "Box Plot of Age", x = "Sex of Cattle", y= "age" )
#Violin plot (X-axis = cat, Y-axis = num)
ggplot(data = Exercise, aes(x=prodtype, y=mlkpct, fill = prodtype ))+
geom_violin(color = "black")+
scale_fill_manual(values = c("k"= "purple", "m"= "yellow", "x"="red")) 
# Violin plot (as.factor issue)
ggplot(data = Exercise, aes(x=as.factor(sex), y=age, fill = as.factor(sex) ))+
geom_violin(color = "black")+
scale_fill_manual(values = c("1"= "purple", "2"= "yellow"))+
labs(title = "Box Plot of Age", x = "Sex of Cattle", y= "age")
#Scatter Plot
ggplot(data = Exercise, aes(x=prodtype, y=mlkpct, color = prodtype ))+
geom_point()+
scale_color_manual(values = c("k"= "purple", "m"= "yellow", "x"="red")) 
#Scatter Plot
ggplot(data = Exercise, aes(x=mlkpct, y=kkrpct, color = mlkpct ))+
geom_point()
#scale_color_manual(values = c("k"= "purple", "m"= "yellow", "x"="red"))
#Heat Map
age_kkrpct= cor(Exercise[3:10,6:10])
age_kkrpct n_movement herdsize mlkpct krypct kkrpct
n_movement 1.00000000 0.6604868 0.3771081 -0.3082160 0.05298212
herdsize 0.66048685 1.0000000 0.5968764 -0.1641058 -0.31157441
mlkpct 0.37710813 0.5968764 1.0000000 -0.5859320 -0.14213575
krypct -0.30821598 -0.1641058 -0.5859320 1.0000000 -0.71885087
kkrpct 0.05298212 -0.3115744 -0.1421357 -0.7188509 1.00000000ggcorrplot(age_kkrpct)
detach(Exercise)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