1 Subset or Filter Columns and Vectors in R

Using the columns or vectors below:

col_num = c(2, 4, 6, 8, 10, 12, 14, 16, 18, 20)
col_num

 [1]  2  4  6  8 10 12 14 16 18 20

col_chr = c("ht", "tt", "ht", "th", "hh")
col_chr

[1] "ht" "tt" "ht" "th" "hh"

Select column elements by index number:

# Select index 5
col_num[5]

[1] 10

# Select index 3 to 6
col_num[3:6] 

[1]  6  8 10 12

# Select index 3 and 5
col_chr[c(3, 5)] 

[1] "ht" "hh"

Select columns by quality:

Select values greater than 11:

which(col_num > 11)

[1]  6  7  8  9 10

# Select values greater than 11
col_num[which(col_num > 11)] 

[1] 12 14 16 18 20

Select values equal to 12 or “ht”:

which(col_num == 12)

[1] 6

# Select values equal to 12
col_num[which(col_num == 12)] 

[1] 12

which(col_chr == "ht")

[1] 1 3

# Select values equal to "ht
col_chr[which(col_chr == "ht")] 

[1] "ht" "ht"

2 Subset or Filter Dataframes (or Matrices) in R

Using the dataframe below:

dtf = data.frame(Name = c("Randy", "Sam", "Jay", "Reah"),
                 Team = c("A", "B", "B", "C"),
                 Hand = c("Right", "Left", "Right", "Right"),
                 Points = c(8, 9, 7, 9),
                 Position = c(3, 1, 4, 1))
dtf

   Name Team  Hand Points Position
1 Randy    A Right      8        3
2   Sam    B  Left      9        1
3   Jay    B Right      7        4
4  Reah    C Right      9        1

Select first few rows:

# First two row
head(dtf, 2) 

   Name Team  Hand Points Position
1 Randy    A Right      8        3
2   Sam    B  Left      9        1

Select last few rows:

# Last three row
tail(dtf, 3) 

  Name Team  Hand Points Position
2  Sam    B  Left      9        1
3  Jay    B Right      7        4
4 Reah    C Right      9        1

Select a column by name:

subset(dtf, select = c(Position))

  Position
1        3
2        1
3        4
4        1

Or alternatively subset the dataframe column as a vector:

Applies to dataframes only.

dtf$Position

[1] 3 1 4 1

Select some rows or columns by number:

# Select column 4
dtf[, 4] 

[1] 8 9 7 9

# Select rows 2 to 4
dtf[2:4,] 

  Name Team  Hand Points Position
2  Sam    B  Left      9        1
3  Jay    B Right      7        4
4 Reah    C Right      9        1

Select some columns by names, and some rows that are greater than a value:

Applies to dataframes only.

subset(dtf, Points >= 8, select = c(Name, Points))

   Name Points
1 Randy      8
2   Sam      9
4  Reah      9

Or alternatively, select columns by column numbers c(1, 4), and rows by dtf$Points >= 8:

Applies to dataframes only.

dtf[which(dtf$Points >= 8), c(1, 4)]

   Name Points
1 Randy      8
2   Sam      9
4  Reah      9

Exclude some columns by names, and select some rows that are equal to a value:

Applies to dataframes only.

subset(dtf, Hand == "Right", select = - c(Team, Points))

   Name  Hand Position
1 Randy Right        3
3   Jay Right        4
4  Reah Right        1

Or alternatively, exclude columns by column numbers c(2, 4), and select rows by dtf$Hand == “Right”:

Applies to dataframes only.

dtf[which(dtf$Hand == "Right"), - c(2, 4)]

   Name  Hand Position
1 Randy Right        3
3   Jay Right        4
4  Reah Right        1

Filter by two or more columns:

subset(dtf, Hand == "Right" & Points > 7)

   Name Team  Hand Points Position
1 Randy    A Right      8        3
4  Reah    C Right      9        1

Or alternatively:

dtf[which(dtf$Hand == "Right" & dtf$Points > 7), ]

   Name Team  Hand Points Position
1 Randy    A Right      8        3
4  Reah    C Right      9        1

Select a continuous set of columns by names:

subset(dtf, select = Team:Points)

  Team  Hand Points
1    A Right      8
2    B  Left      9
3    B Right      7
4    C Right      9

Or alternatively, select the columns by column numbers (2:4):

dtf[, 2:4]

  Team  Hand Points
1    A Right      8
2    B  Left      9
3    B Right      7
4    C Right      9