8 More on data frame

(AST230) R for Data Science

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

We have already discussed subsetting data frames

# 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