8 More on data frame

(AST230) R for Data Science

Author

Md Rasel Biswas

Data frame

  • A two-dimensional array with two or more atomic vectors of the same length is known as a data frame

  • Most useful storage structure for data analysis

  • Columns are variables, and rows are observations

  • R’s equivalent to spreadsheet

  • R function data.frame() is used to create a new data frame, where atomic vectors can be used as inputs

# Creating two atomic vectors
cage <- c(11, 9, 8, 10, 5)
cgender <- c("boy", "boy", "girl", "boy", "girl")
# Creating data frame
df <- data.frame(age = cage, gender = cgender)
# Print df
df
  age gender
1  11    boy
2   9    boy
3   8   girl
4  10    boy
5   5   girl

Some useful functions

# Variable names of the data frame
names(df)
[1] "age"    "gender"
# Dimension of the data frame
dim(df)
[1] 5 2
# Details of a df
str(df)
'data.frame':   5 obs. of  2 variables:
 $ age   : num  11 9 8 10 5
 $ gender: chr  "boy" "boy" "girl" "boy" ...
is.data.frame(df)
[1] TRUE
# Summary of the data frame
summary(df)
      age          gender         
 Min.   : 5.0   Length:5          
 1st Qu.: 8.0   Class :character  
 Median : 9.0   Mode  :character  
 Mean   : 8.6                     
 3rd Qu.:10.0                     
 Max.   :11.0                     
# Summary of a specific variable
summary(df$age)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    5.0     8.0     9.0     8.6    10.0    11.0 
# Frequency table of a variable
table(df$gender)

 boy girl 
   3    2

Ordering data frames

We want to reorder the observations of the data df by the variable age.

Recall: order() is used to order an atomic vector by its value. Remember the following example?

age <- c(11, 9, 8, 10, 5)
sort(age)
[1]  5  8  9 10 11
order(age)
[1] 5 3 2 4 1
age[order(age)] #equivalent to sort()
[1]  5  8  9 10 11
# Original data
df
  age gender
1  11    boy
2   9    boy
3   8   girl
4  10    boy
5   5   girl
# Ordering the data by `age`
df[order(df$age), ]
  age gender
5   5   girl
3   8   girl
2   9    boy
4  10    boy
1  11    boy

Handling missing data

  • The NA (Not Applicable) character is used as a placeholder of missing observation in R

  • Most of the R functions have an argument na.rm, which takes a logical value to exclude the missing value from the calculation

mean(c(1:10, NA, 14:16), 
     na.rm = TRUE)
[1] 7.692308
  • na.omit() is used to exclude all rows of a data frame that include a missing observation
xmd <- data.frame(
  x = c(NA, 11:14),
  y = c(rep("boy", 4), NA))
xmd # Data with missing values
   x    y
1 NA  boy
2 11  boy
3 12  boy
4 13  boy
5 14 <NA>
# Data after omitting missing values
na.omit(xmd)
   x   y
2 11 boy
3 12 boy
4 13 boy

Adding new column or rows

Adding a new variable using $

df$loc <- c("UK", "BN", "CZ", "CZ", "UK")
df
  age gender loc
1  11    boy  UK
2   9    boy  BN
3   8   girl  CZ
4  10    boy  CZ
5   5   girl  UK
# convert `gender` to a factor 
df$gender_fac <- factor(df$gender)
df
  age gender loc gender_fac
1  11    boy  UK        boy
2   9    boy  BN        boy
3   8   girl  CZ       girl
4  10    boy  CZ        boy
5   5   girl  UK       girl
# rbind for rows
df1 <- data.frame(id = 1:4, height = c(120, 150, 132, 122),
                        weight = c(44, 56, 49, 45))
df1
  id height weight
1  1    120     44
2  2    150     56
3  3    132     49
4  4    122     45
df2 <- data.frame(id = 5:6, height = c(119, 110),
                        weight = c(39, 35))
df2
  id height weight
1  5    119     39
2  6    110     35
rbind(df1, df2)
  id height weight
1  1    120     44
2  2    150     56
3  3    132     49
4  4    122     45
5  5    119     39
6  6    110     35
# cbind for columns
df1
  id height weight
1  1    120     44
2  2    150     56
3  3    132     49
4  4    122     45
df3 <- data.frame(location = c("UK", "CZ", "CZ", "UK"))
df3
  location
1       UK
2       CZ
3       CZ
4       UK
cbind(df1, df3)
  id height weight location
1  1    120     44       UK
2  2    150     56       CZ
3  3    132     49       CZ
4  4    122     45       UK

Analyse a subset of data

# Full data
df
  age gender loc gender_fac
1  11    boy  UK        boy
2   9    boy  BN        boy
3   8   girl  CZ       girl
4  10    boy  CZ        boy
5   5   girl  UK       girl
# A subset of boy's data
df_boy <- df[df$gender == "boy", ]
df_boy
  age gender loc gender_fac
1  11    boy  UK        boy
2   9    boy  BN        boy
4  10    boy  CZ        boy
# Mean age of boys
mean(df_boy$age)
[1] 10

Exercise 8

  • Load the mtcars data, which is available in R
  • Obtain the variable list of the data frame mtcars
  • How many observations and variables do the mtcars data have?
  • Check the types of the variables of mtcars
  • Extract the first and the last variables from the mtcars data set
  • Order the dataset in ascending order of the variable mpg (miles per gallon)
  • Convert the variable cyl (number of cylinders) to factor variable
  • Obtain a subset of mtcars for which mpg is less than 30 and save the dataset naming mtcars30
  • Find the mean, median, mode, standard deviation, and IQR of mpg variable of the mtcars30 dataset
  • Find the frequency table of cyl of mtcars data