library(readxl)
test <- read_excel("D:/1.xlsx")

str(test)
## tibble [410 × 2] (S3: tbl_df/tbl/data.frame)
##  $ Gender: chr [1:410] "男" "男" "男" "男" ...
##  $ Height: num [1:410] 160 172 170 168 171 ...
summary(test)
##     Gender              Height     
##  Length:410         Min.   :145.9  
##  Class :character   1st Qu.:159.9  
##  Mode  :character   Median :165.7  
##                     Mean   :165.9  
##                     3rd Qu.:171.6  
##                     Max.   :208.2
head(test)
## # A tibble: 6 × 2
##   Gender Height
##   <chr>   <dbl>
## 1 男       160.
## 2 男       172.
## 3 男       170.
## 4 男       168.
## 5 男       171.
## 6 男       174.
print(test)
## # A tibble: 410 × 2
##    Gender Height
##    <chr>   <dbl>
##  1 男       160.
##  2 男       172.
##  3 男       170.
##  4 男       168.
##  5 男       171.
##  6 男       174.
##  7 男       167.
##  8 男       179.
##  9 男       167.
## 10 男       175.
## # ℹ 400 more rows
library(moments)

skewness(test$Height)
## [1] 0.4101161
kurtosis(test$Height)
## [1] 4.013246
hist(test$Height)

quantile(test$Height)
##      0%     25%     50%     75%    100% 
## 145.900 159.925 165.700 171.600 208.200
boxplot(test$Height)

knitr::kable(test,caption = "An expample table caption.")
An expample table caption.
Gender Height
159.6
171.8
170.4
167.7
171.4
174.5
167.3
179.4
166.9
174.9
170.6
183.8
170.0
182.1
170.0
165.0
171.2
172.5
176.3
168.4
164.3
171.9
175.1
174.3
166.8
170.3
173.7
176.4
160.9
176.5
174.5
171.5
174.5
164.4
164.4
167.3
169.3
167.0
173.1
172.5
177.2
172.9
163.4
173.3
171.6
169.8
208.2
175.0
174.8
171.5
176.5
167.2
160.5
159.7
177.1
168.2
166.2
175.4
171.9
181.0
164.7
172.6
184.9
171.2
172.1
168.9
181.9
166.9
164.7
172.8
190.1
169.0
166.4
173.8
168.0
166.8
168.9
173.1
164.9
163.8
166.6
171.4
166.9
177.9
171.4
174.1
167.3
182.3
179.6
169.2
165.9
170.3
172.1
180.9
171.6
175.7
175.5
166.5
172.6
175.5
164.3
170.9
161.3
169.7
172.2
164.2
172.9
171.2
167.7
170.7
171.8
171.4
169.7
177.5
174.9
164.6
180.8
168.7
174.0
168.2
164.4
172.6
171.0
161.8
180.0
159.3
163.7
179.4
164.8
168.3
177.1
182.6
164.0
178.8
175.4
169.4
176.0
173.2
166.8
173.4
166.8
164.3
174.1
174.0
167.1
170.7
173.7
167.8
172.3
168.3
171.9
182.6
175.8
170.3
176.0
170.3
172.3
167.9
167.8
181.1
176.8
173.7
169.4
171.6
170.1
173.3
174.7
170.5
168.4
169.9
175.0
170.3
173.1
169.7
168.9
179.4
167.3
171.6
174.8
165.0
175.2
171.4
173.0
172.7
172.4
168.0
161.5
170.3
181.7
173.2
178.6
170.5
173.5
177.2
162.5
169.3
175.4
174.0
181.9
167.4
177.5
162.0
169.0
182.5
169.7
170.9
173.3
161.9
169.7
177.4
154.3
162.0
158.3
160.7
162.0
171.0
156.2
160.1
154.0
160.0
163.8
163.6
165.8
176.3
163.0
163.7
155.2
154.2
159.9
160.1
151.6
156.2
156.3
166.9
160.0
158.8
150.0
159.6
162.8
161.9
155.3
166.6
158.8
153.5
161.8
152.3
157.8
159.2
160.8
164.2
166.2
159.0
169.6
154.2
165.9
160.4
160.9
158.1
157.9
163.0
162.2
159.8
160.3
157.8
156.1
161.9
152.5
153.2
145.9
161.9
155.4
156.8
167.6
162.9
149.7
168.0
159.8
158.1
155.6
159.1
156.3
155.8
158.0
163.0
160.3
156.5
167.2
156.3
157.4
166.8
150.8
159.6
148.9
159.8
158.9
173.5
162.1
160.1
160.8
159.2
170.4
158.6
169.4
161.4
163.4
153.8
159.9
163.9
155.9
150.6
164.5
153.9
161.2
166.2
153.6
154.1
165.1
165.5
162.6
158.1
158.6
163.6
150.3
155.3
152.1
158.5
160.5
161.5
154.9
168.0
165.6
159.1
161.6
162.7
150.6
156.0
158.1
161.5
164.6
162.6
161.1
156.2
157.0
154.6
160.0
160.7
157.4
164.5
165.3
156.4
158.8
162.5
164.4
162.7
160.9
155.7
165.1
160.4
156.2
153.3
164.4
165.8
158.3
160.1
153.6
152.6
164.3
153.6
168.9
157.7
160.2
157.1
166.9
160.7
154.6
162.1
157.4
158.3
160.9
160.3
168.9
153.5
163.2
160.2
151.4
151.8
159.1
158.2
166.8
164.6
157.3
164.6
156.2
163.3
168.3
162.0
152.7
159.9
156.6
164.1
157.6
163.6
161.3
161.5
171.1
154.6
159.8
157.7
165.3
149.0