Example

mydata <- data.frame("ID" = c(1, 2, 3), "Age" = c(22, 25, 27), "Gender" = c(1, 2, 2))

print(mydata) # Showing the object mydata

##   ID Age Gender
## 1  1  22      1
## 2  2  25      2
## 3  3  27      2

mydata[2, 2] = 24
mydata[2, 2] <- 24

print(mydata)

##   ID Age Gender
## 1  1  22      1
## 2  2  24      2
## 3  3  27      2

mydata1  <- mydata[ , -3] # We removed 3rd variable

mydata3 <- mydata[1  ,  ] #We selected only the first unit/observation

mydata3 <- mydata[ c(-2, -3)  ,  ] #We removed 2nd and 3rd observation

In the existing table mydata include new variable Height with values 160, 177, 168.

mydata$Height <- c(160, 177, 168)

Create a new variable, called Height1, where everyone grows by 5 cm.

mydata$Height1 <- mydata$Height + 5

summary(mydata[  ,  c(-1, -3) ])

##       Age            Height         Height1     
##  Min.   :22.00   Min.   :160.0   Min.   :165.0  
##  1st Qu.:23.00   1st Qu.:164.0   1st Qu.:169.0  
##  Median :24.00   Median :168.0   Median :173.0  
##  Mean   :24.33   Mean   :168.3   Mean   :173.3  
##  3rd Qu.:25.50   3rd Qu.:172.5   3rd Qu.:177.5  
##  Max.   :27.00   Max.   :177.0   Max.   :182.0

summary(mydata[c(-1, -3) ])

##       Age            Height         Height1     
##  Min.   :22.00   Min.   :160.0   Min.   :165.0  
##  1st Qu.:23.00   1st Qu.:164.0   1st Qu.:169.0  
##  Median :24.00   Median :168.0   Median :173.0  
##  Mean   :24.33   Mean   :168.3   Mean   :173.3  
##  3rd Qu.:25.50   3rd Qu.:172.5   3rd Qu.:177.5  
##  Max.   :27.00   Max.   :177.0   Max.   :182.0

mean(mydata$Height1)

## [1] 173.3333

sd(mydata$Height1)

## [1] 8.504901

sapply( mydata[c(-1, -3) ], FUN = var  )

##       Age    Height   Height1 
##  6.333333 72.333333 72.333333

#install.packages("psych")

library(psych)
describe(mydata)

##         vars n   mean   sd median trimmed   mad min max range  skew kurtosis
## ID         1 3   2.00 1.00      2    2.00  1.48   1   3     2  0.00    -2.33
## Age        2 3  24.33 2.52     24   24.33  2.97  22  27     5  0.13    -2.33
## Gender     3 3   1.67 0.58      2    1.67  0.00   1   2     1 -0.38    -2.33
## Height     4 3 168.33 8.50    168  168.33 11.86 160 177    17  0.04    -2.33
## Height1    5 3 173.33 8.50    173  173.33 11.86 165 182    17  0.04    -2.33
##           se
## ID      0.58
## Age     1.45
## Gender  0.33
## Height  4.91
## Height1 4.91

Describe your data using function stat.desc

#install.packages("pastecs")
library(pastecs)

round(stat.desc(mydata[c(-1, -3)]), 2)

##                Age Height Height1
## nbr.val       3.00   3.00    3.00
## nbr.null      0.00   0.00    0.00
## nbr.na        0.00   0.00    0.00
## min          22.00 160.00  165.00
## max          27.00 177.00  182.00
## range         5.00  17.00   17.00
## sum          73.00 505.00  520.00
## median       24.00 168.00  173.00
## mean         24.33 168.33  173.33
## SE.mean       1.45   4.91    4.91
## CI.mean.0.95  6.25  21.13   21.13
## var           6.33  72.33   72.33
## std.dev       2.52   8.50    8.50
## coef.var      0.10   0.05    0.05