Hands-on_Ex04

pacman::p_load(ggstatsplot,tidyverse)

exam <- 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.

#set.seed(1234)

gghistostats(
  data = exam,
  x = ENGLISH,
  type = "bayes",
  test.value = 60,
  xlab = "English scores"
)

ggbetweenstats(
  data = exam,
  x = GENDER, 
  y = MATHS,
  type = "np",
  messages = FALSE
)

ggbetweenstats(
  data = exam,
  x = RACE, 
  y = ENGLISH,
  type = "p",
  mean.ci = TRUE, 
  pairwise.comparisons = TRUE, 
  pairwise.display = "all",
  p.adjust.method = "fdr",
  messages = FALSE
)

Visualising models

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_08_04 Mfg_Month Mfg_Year     KM Quarterly_Tax Weight
   <dbl> <chr>    <dbl>     <dbl>     <dbl>    <dbl>  <dbl>         <dbl>  <dbl>
 1    81 TOYOTA … 18950        25         8     2002  20019           100   1180
 2     1 TOYOTA … 13500        23        10     2002  46986           210   1165
 3     2 TOYOTA … 13750        23        10     2002  72937           210   1165
 4     3  TOYOTA… 13950        24         9     2002  41711           210   1165
 5     4 TOYOTA … 14950        26         7     2002  48000           210   1165
 6     5 TOYOTA … 13750        30         3     2002  38500           210   1170
 7     6 TOYOTA … 12950        32         1     2002  61000           210   1170
 8     7  TOYOTA… 16900        27         6     2002  94612           210   1245
 9     8 TOYOTA … 18600        30         3     2002  75889           210   1245
10    44 TOYOTA … 16950        27         6     2002 110404           234   1255
# ℹ 1,426 more rows
# ℹ 29 more variables: Guarantee_Period <dbl>, 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>, …

model <- lm(Price ~ Age_08_04 + Mfg_Year + KM + 
              Weight + Guarantee_Period, data = car_resale)
model

Call:
lm(formula = Price ~ Age_08_04 + Mfg_Year + KM + Weight + Guarantee_Period, 
    data = car_resale)

Coefficients:
     (Intercept)         Age_08_04          Mfg_Year                KM  
      -2.637e+06        -1.409e+01         1.315e+03        -2.323e-02  
          Weight  Guarantee_Period  
       1.903e+01         2.770e+01