using

Author

kirit ved

ggplot2

setting R environment

 [1] "kbv"       "janitor"   "lubridate" "forcats"   "stringr"   "dplyr"    
 [7] "purrr"     "readr"     "tidyr"     "tibble"    "ggplot2"   "tidyverse"
[13] "pacman"

loading iris dataset & viewing it

Code
d=iris |> janitor::clean_names()
head(d)
  sepal_length sepal_width petal_length petal_width species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa
Code
tail(d)
    sepal_length sepal_width petal_length petal_width   species
145          6.7         3.3          5.7         2.5 virginica
146          6.7         3.0          5.2         2.3 virginica
147          6.3         2.5          5.0         1.9 virginica
148          6.5         3.0          5.2         2.0 virginica
149          6.2         3.4          5.4         2.3 virginica
150          5.9         3.0          5.1         1.8 virginica
Code
str(d)
'data.frame':   150 obs. of  5 variables:
 $ sepal_length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ sepal_width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ petal_length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ petal_width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
Code 
  sepal_length    sepal_width     petal_length    petal_width   
 Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
 Median :5.800   Median :3.000   Median :4.350   Median :1.300  
 Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
 Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
       species  
 setosa    :50  
 versicolor:50  
 virginica :50

viewing histogram

viewing density plot

viewing scattered plots

viewing box plots

viewing bar plots

Code
d1=d|>
  group_by(species)|>
  summarise(cnt=n(),m=mean(sepal_length),s=sd(sepal_length))|>mutate(cnt=round(cnt,2),m=round(m,2),s=round(s,2))
d1
# A tibble: 3 × 4
  species      cnt     m     s
  <fct>      <dbl> <dbl> <dbl>
1 setosa        50  5.01  0.35
2 versicolor    50  5.94  0.52
3 virginica     50  6.59  0.64
Code
d1|>
  ggplot(aes(species,cnt,fill=species,label=cnt))+
  geom_col()+kbv_tmpx()+
  geom_text(aes(label=cnt),vjust=5)

Code
d1|>
  ggplot(aes(species,m,fill=species,label=cnt))+
  geom_col()+kbv_tmpx()+
  geom_text(aes(label=m),vjust=5)

Code
d1|>
  ggplot(aes(species,s,fill=species,label=cnt))+
  geom_col()+kbv_tmpx()+
  geom_text(aes(label=s),vjust=5)