pacman:: p_load(plotly , DT , patchwork , ggstatsplot, tidyverse)In-Class Exercise 4
exam_data = read_csv("data/Exam_data.csv")Rows: 322 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): ID, CLASS, GENDER, RACE
dbl (3): ENGLISH, MATHS, SCIENCE
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.plot_ly(data = exam_data,
x = ~ENGLISH,
y = ~MATHS,
color = ~RACE)No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plotly.com/r/reference/#scatterNo scatter mode specifed:
Setting the mode to markers
Read more about this attribute -> https://plotly.com/r/reference/#scatter-modep = ggplot(data=exam_data,
aes(x = MATHS,
y = ENGLISH)) +
geom_point(dotsize = 1) +
coord_cartesian(xlim=c(-100,100),
ylim=c(-100,100))Warning in geom_point(dotsize = 1): Ignoring unknown parameters: `dotsize`ggplotly(p)ggbetweenstats(
data = exam_data,
x = GENDER,
y = MATHS,
type = "p",
messages = FALSE
)
ggscatterstats(
data = exam_data,
x = MATHS,
y = ENGLISH,
marginal = TRUE
)Registered S3 method overwritten by 'ggside':
method from
+.gg ggplot2`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
pacman::p_load(readxl, performance, parameters, see)car_resale <- read_xls("data/ToyotaCorolla.xls", "data")
car_resale# A tibble: 1,436 × 38
Id Model Price Age_0…¹ Mfg_M…² Mfg_Y…³ KM Quart…⁴ Weight Guara…⁵
<dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 81 TOYOTA Cor… 18950 25 8 2002 20019 100 1180 3
2 1 TOYOTA Cor… 13500 23 10 2002 46986 210 1165 3
3 2 TOYOTA Cor… 13750 23 10 2002 72937 210 1165 3
4 3 TOYOTA Co… 13950 24 9 2002 41711 210 1165 3
5 4 TOYOTA Cor… 14950 26 7 2002 48000 210 1165 3
6 5 TOYOTA Cor… 13750 30 3 2002 38500 210 1170 3
7 6 TOYOTA Cor… 12950 32 1 2002 61000 210 1170 3
8 7 TOYOTA Co… 16900 27 6 2002 94612 210 1245 3
9 8 TOYOTA Cor… 18600 30 3 2002 75889 210 1245 3
10 44 TOYOTA Cor… 16950 27 6 2002 110404 234 1255 3
# … with 1,426 more rows, 28 more variables: HP_Bin <chr>, CC_bin <chr>,
# Doors <dbl>, Gears <dbl>, Cylinders <dbl>, Fuel_Type <chr>, Color <chr>,
# Met_Color <dbl>, Automatic <dbl>, Mfr_Guarantee <dbl>,
# BOVAG_Guarantee <dbl>, ABS <dbl>, Airbag_1 <dbl>, Airbag_2 <dbl>,
# Airco <dbl>, Automatic_airco <dbl>, Boardcomputer <dbl>, CD_Player <dbl>,
# Central_Lock <dbl>, Powered_Windows <dbl>, Power_Steering <dbl>,
# Radio <dbl>, Mistlamps <dbl>, Sport_Model <dbl>, Backseat_Divider <dbl>, …model <- lm(Price ~ Age_08_04 + Mfg_Year + KM +
Weight + Guarantee_Period, data = car_resale)Comment : can use gtsummary::tbl_regression to capture and the model and translate it into data table format. # {r} # table1 = tbl_regression(model1, intercept = TRUE) #
Comment: Check for multicollinearity by diagnostic test and visualize the results using check_collinearity() of the performance package. using VIF ( variance inflation factor)
It will tell you give 2 predictors that have low correlation and 2 predictors that have high correlation. after that we can even use plot to check the collinearity. Above 10 >= high correlated, below 3 >= no sign of multi collinearity.
check_collinearity(model)# Check for Multicollinearity
Low Correlation
Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
Guarantee_Period 1.04 [1.01, 1.17] 1.02 0.97 [0.86, 0.99]
Age_08_04 31.07 [28.08, 34.38] 5.57 0.03 [0.03, 0.04]
Mfg_Year 31.16 [28.16, 34.48] 5.58 0.03 [0.03, 0.04]
High Correlation
Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
KM 1.46 [1.37, 1.57] 1.21 0.68 [0.64, 0.73]
Weight 1.41 [1.32, 1.51] 1.19 0.71 [0.66, 0.76]check_c <- check_collinearity(model)
plot(check_c)Variable `Component` is not in your data frame :/
Comment: Checking Normality of the model
model1 <- lm(Price ~ Age_08_04 + KM +
Weight + Guarantee_Period, data = car_resale)check_n <- check_normality(model1)
plot(check_n)
check_model(model1)Variable `Component` is not in your data frame :/
plot(parameters(model1))
ggcoefstats(model1,
output = "plot")
my_sum <- exam_data %>%
group_by(RACE) %>%
summarise(
n=n(),
mean=mean(MATHS),
sd=sd(MATHS)
) %>%
mutate(se=sd/sqrt(n-1))my_sum# A tibble: 4 × 5
RACE n mean sd se
<chr> <int> <dbl> <dbl> <dbl>
1 Chinese 193 76.5 15.7 1.13
2 Indian 12 60.7 23.4 7.04
3 Malay 108 57.4 21.1 2.04
4 Others 9 69.7 10.7 3.79ggplot(my_sum) +
geom_errorbar(
aes(x=RACE,
ymin=mean-se,
ymax=mean+se),
width=0.2,
colour="black",
alpha=0.9,
size=0.5) +
geom_point(aes
(x=RACE,
y=mean),
stat="identity",
color="red",
size = 1.5,
alpha=1) +
ggtitle("Standard error of mean
maths score by rac")Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.