We showed different ways to read data into R using:

readr::read_csv()
readr::read_delim()
readxl::read_excel()

In this module, we will show you how select rows and columns of datasets.

We will be using the \(\color{red}{\text{dplyr}}\) package in the tidyverse.

Here are several resources on how to use dplyr:

• https://dplyr.tidyverse.org/
• https://r4ds.had.co.nz/
• https://cran.rstudio.com/web/packages/dplyr/vignettes/dplyr.html
• https://stat545.com/dplyr-intro.html

The dplyr package also interfaces well with tibbles.

We will be using the diamonds dataset in the ggplot2 package as an example (so make sure you initiate the ggplot2 package if you are following along on your own).

head(diamonds)

# A tibble: 6 × 10
  carat cut       color clarity depth table price     x     y     z
  <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48

To grab just the values from a single column, you would use the pull function. The output will be a vector (and not a tibble).

Since this is a long vector we will just show the first 6 values using the head function around the output of the pull function.

head(pull(diamonds,carat))

[1] 0.23 0.21 0.23 0.29 0.31 0.24

That was a lot of typing and nested functions, which can be confusing. Recently, the pipe %>% makes things such as this much more readable. It reads left side “pipes” into right side. RStudio CMD/Ctrl + Shift + M shortcut.

Pipe diamonds into select, then pipe that into pull, and then show the head:

diamonds %>% pull(carat) %>% head()

[1] 0.23 0.21 0.23 0.29 0.31 0.24

The pull function is equivalent to using the $ method (in base R).

Note that base R and tidyverse don’t always play nice together.

head(pull(diamonds, carat))

[1] 0.23 0.21 0.23 0.29 0.31 0.24

head(diamonds$carat)

[1] 0.23 0.21 0.23 0.29 0.31 0.24

Note this does not return a tibble (or data.frame) but rather a vector.

The select function extracts one or more columns from a tibble or data.frame and returns a tibble (not a vector).

select(diamonds, carat)

# A tibble: 53,940 × 1
   carat
   <dbl>
 1  0.23
 2  0.21
 3  0.23
 4  0.29
 5  0.31
 6  0.24
 7  0.24
 8  0.26
 9  0.22
10  0.23
# … with 53,930 more rows

The select command from dplyr is very flexible. You just need to list all columns you want to extract separated by commas. You can use this as a way to just keep the columns you want for example.

select(diamonds, carat, depth)

# A tibble: 53,940 × 2
   carat depth
   <dbl> <dbl>
 1  0.23  61.5
 2  0.21  59.8
 3  0.23  56.9
 4  0.29  62.4
 5  0.31  63.3
 6  0.24  62.8
 7  0.24  62.3
 8  0.26  61.9
 9  0.22  65.1
10  0.23  59.4
# … with 53,930 more rows

Type tidyselect:: to see functions available.

Here are a few:

last_col()
ends_with()
starts_with()
contains() # search for a pattern
everything()

For example, we can take all columns that start with a “c”:

diamonds %>% select(starts_with("c"))

# A tibble: 53,940 × 4
   carat cut       color clarity
   <dbl> <ord>     <ord> <ord>  
 1  0.23 Ideal     E     SI2    
 2  0.21 Premium   E     SI1    
 3  0.23 Good      E     VS1    
 4  0.29 Premium   I     VS2    
 5  0.31 Good      J     SI2    
 6  0.24 Very Good J     VVS2   
 7  0.24 Very Good I     VVS1   
 8  0.26 Very Good H     SI1    
 9  0.22 Fair      E     VS2    
10  0.23 Very Good H     VS1    
# … with 53,930 more rows

Or we can take all columns that end with an “e”:

diamonds %>% select(ends_with("e"))

# A tibble: 53,940 × 2
   table price
   <dbl> <int>
 1    55   326
 2    61   326
 3    65   327
 4    58   334
 5    58   335
 6    57   336
 7    57   336
 8    55   337
 9    61   337
10    61   338
# … with 53,930 more rows

We are going to cover “fancier” ways of matching column names (and strings more generally) in the data cleaning lecture.

The command in dplyr for subsetting rows is filter. Try ?filter.

The easiest way to filter is by testing whether numeric observations are greater than or less than some cutoff:

filter(diamonds, depth > 60)

# A tibble: 48,315 × 10
   carat cut       color clarity depth table price     x     y     z
   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
 1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
 2  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
 3  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
 4  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
 5  0.24 Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47
 6  0.26 Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
 7  0.22 Fair      E     VS2      65.1    61   337  3.87  3.78  2.49
 8  0.3  Good      J     SI1      64      55   339  4.25  4.28  2.73
 9  0.23 Ideal     J     VS1      62.8    56   340  3.93  3.9   2.46
10  0.22 Premium   F     SI1      60.4    61   342  3.88  3.84  2.33
# … with 48,305 more rows

Note, no subsetting is necessary. R “knows” depth refers to a column of diamonds.

You can also using piping here:

diamonds %>% filter(depth > 60)

# A tibble: 48,315 × 10
   carat cut       color clarity depth table price     x     y     z
   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
 1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
 2  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
 3  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
 4  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
 5  0.24 Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47
 6  0.26 Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
 7  0.22 Fair      E     VS2      65.1    61   337  3.87  3.78  2.49
 8  0.3  Good      J     SI1      64      55   339  4.25  4.28  2.73
 9  0.23 Ideal     J     VS1      62.8    56   340  3.93  3.9   2.46
10  0.22 Premium   F     SI1      60.4    61   342  3.88  3.84  2.33
# … with 48,305 more rows

You can combine filtering on multiple columns by separating the filter arguments with commas:

diamonds %>% filter(depth > 60, table > 60, price > 2775)

# A tibble: 1,704 × 10
   carat cut       color clarity depth table price     x     y     z
   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
 1  0.72 Premium   F     SI1      61.8    61  2777  5.82  5.71  3.56
 2  0.72 Very Good H     VS1      60.6    63  2782  5.83  5.76  3.51
 3  0.81 Good      G     SI2      61      61  2789  5.94  5.99  3.64
 4  0.71 Premium   F     VS1      60.1    62  2790  5.77  5.74  3.46
 5  0.71 Premium   G     VS1      62.4    61  2803  5.7   5.65  3.54
 6  0.74 Fair      F     VS2      61.1    68  2805  5.82  5.75  3.53
 7  0.7  Good      F     VS1      62.8    61  2810  5.57  5.61  3.51
 8  0.7  Very Good F     VS2      60.9    61  2812  5.66  5.71  3.46
 9  0.71 Good      E     SI1      62.8    64  2817  5.6   5.54  3.5 
10  0.7  Premium   E     VS2      62.4    61  2818  5.66  5.63  3.52
# … with 1,694 more rows

You can also filter character strings by a single value or category. Here we need quotes around character strings.

diamonds %>% filter(color == "I", 
            clarity == "SI2", cut == "Premium")

# A tibble: 312 × 10
   carat cut     color clarity depth table price     x     y     z
   <dbl> <ord>   <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
 1  0.42 Premium I     SI2      61.5    59   552  4.78  4.84  2.96
 2  1    Premium I     SI2      58.2    60  2795  6.61  6.55  3.83
 3  0.9  Premium I     SI2      62.2    59  2826  6.11  6.07  3.79
 4  1.05 Premium I     SI2      58.3    57  2911  6.72  6.67  3.9 
 5  0.91 Premium I     SI2      62      59  2913  6.18  6.23  3.85
 6  0.9  Premium I     SI2      62.5    58  2948  6.15  6.1   3.83
 7  0.9  Premium I     SI2      60.6    60  2948  6.28  6.23  3.79
 8  1.06 Premium I     SI2      61.5    57  2968  6.57  6.49  4.02
 9  0.91 Premium I     SI2      60.2    59  2981  6.29  6.24  3.77
10  0.9  Premium I     SI2      60.6    60  3001  6.23  6.28  3.79
# … with 302 more rows

Sometimes you want to be able to filter on matching several values or categories. The %in% operator is useful here:

diamonds %>% filter(clarity %in% c("SI1", "SI2"))

# A tibble: 22,259 × 10
   carat cut       color clarity depth table price     x     y     z
   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
 1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
 2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
 3  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
 4  0.26 Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
 5  0.3  Good      J     SI1      64      55   339  4.25  4.28  2.73
 6  0.22 Premium   F     SI1      60.4    61   342  3.88  3.84  2.33
 7  0.31 Ideal     J     SI2      62.2    54   344  4.35  4.37  2.71
 8  0.2  Premium   E     SI2      60.2    62   345  3.79  3.75  2.27
 9  0.3  Ideal     I     SI2      62      54   348  4.31  4.34  2.68
10  0.3  Good      J     SI1      63.4    54   351  4.23  4.29  2.7 
# … with 22,249 more rows

You can mix and match filtering on numeric and categorical/character columns in the same filter() command:

diamonds %>% filter(clarity %in% c("SI1", "SI2"),
                    cut == "Premium", price > 3000)

# A tibble: 3,976 × 10
   carat cut     color clarity depth table price     x     y     z
   <dbl> <ord>   <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
 1  0.9  Premium I     SI2      60.6    60  3001  6.23  6.28  3.79
 2  0.81 Premium F     SI1      61.9    58  3004  5.99  5.96  3.7 
 3  0.92 Premium D     SI2      60.2    61  3004  6.32  6.27  3.79
 4  0.9  Premium D     SI1      62.2    60  3013  6.08  6.05  3.77
 5  0.96 Premium E     SI2      62.8    60  3016  6.3   6.24  3.94
 6  0.93 Premium G     SI2      61.4    56  3019  6.27  6.23  3.84
 7  0.78 Premium D     SI1      60.4    57  3019  6.02  5.97  3.62
 8  0.75 Premium E     SI1      61.7    60  3024  5.84  5.8   3.59
 9  0.75 Premium D     SI1      59.2    58  3024  5.96  5.93  3.52
10  1.02 Premium G     SI2      61.7    58  3027  6.46  6.41  3.97
# … with 3,966 more rows

R will interpret quotes around numbers as the characters themselves and not their numeric meaning. Thus it’s generally best to avoid quotes around numeric unless it is not being treated as a numeric value - for example levels or grades.

diamonds %>% filter(price > 3001) #This works
diamonds %>% filter(price > "3001") # This does not

diamonds %>% filter(price == 3001) # This works
diamonds %>% filter(price == "3001") # this works 

Other useful logical tests:

& : AND

| : OR

<= : less than or equals

>= : greater than or equals

!= : not equals

The AND operator (&) is the what is being performed “behind the scenes” when chaining together filter statements with commas:

diamonds %>% filter(depth > 60 & table > 60 & price > 2775)

# A tibble: 1,704 × 10
   carat cut       color clarity depth table price     x     y     z
   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
 1  0.72 Premium   F     SI1      61.8    61  2777  5.82  5.71  3.56
 2  0.72 Very Good H     VS1      60.6    63  2782  5.83  5.76  3.51
 3  0.81 Good      G     SI2      61      61  2789  5.94  5.99  3.64
 4  0.71 Premium   F     VS1      60.1    62  2790  5.77  5.74  3.46
 5  0.71 Premium   G     VS1      62.4    61  2803  5.7   5.65  3.54
 6  0.74 Fair      F     VS2      61.1    68  2805  5.82  5.75  3.53
 7  0.7  Good      F     VS1      62.8    61  2810  5.57  5.61  3.51
 8  0.7  Very Good F     VS2      60.9    61  2812  5.66  5.71  3.46
 9  0.71 Good      E     SI1      62.8    64  2817  5.6   5.54  3.5 
10  0.7  Premium   E     VS2      62.4    61  2818  5.66  5.63  3.52
# … with 1,694 more rows

You can use either syntax.

The OR operator (|) is more permissive than the AND operator:

diamonds %>% filter(depth > 60 | table > 60 | price > 2775)

# A tibble: 52,198 × 10
   carat cut       color clarity depth table price     x     y     z
   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
 1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
 2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
 3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
 4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
 5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
 6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
 7  0.24 Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47
 8  0.26 Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
 9  0.22 Fair      E     VS2      65.1    61   337  3.87  3.78  2.49
10  0.23 Very Good H     VS1      59.4    61   338  4     4.05  2.39
# … with 52,188 more rows

The OR operator (|) can be a substitute for %in% (although it might take more typing):

diamonds %>% filter(clarity =="SI1" | clarity == "SI2") %>% head(2)

# A tibble: 2 × 10
  carat cut     color clarity depth table price     x     y     z
  <dbl> <ord>   <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1  0.23 Ideal   E     SI2      61.5    55   326  3.95  3.98  2.43
2  0.21 Premium E     SI1      59.8    61   326  3.89  3.84  2.31

diamonds %>% filter(clarity %in% c("SI1", "SI2")) %>% head(2)

# A tibble: 2 × 10
  carat cut     color clarity depth table price     x     y     z
  <dbl> <ord>   <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1  0.23 Ideal   E     SI2      61.5    55   326  3.95  3.98  2.43
2  0.21 Premium E     SI1      59.8    61   326  3.89  3.84  2.31

You can combine filter and select to subset the rows and columns, respectively, of a data.frame:

diamonds %>% 
  filter(clarity == "SI2") %>% 
  select(starts_with("c"))

# A tibble: 9,194 × 4
   carat cut       color clarity
   <dbl> <ord>     <ord> <ord>  
 1  0.23 Ideal     E     SI2    
 2  0.31 Good      J     SI2    
 3  0.31 Ideal     J     SI2    
 4  0.2  Premium   E     SI2    
 5  0.3  Ideal     I     SI2    
 6  0.3  Good      I     SI2    
 7  0.33 Ideal     I     SI2    
 8  0.33 Ideal     I     SI2    
 9  0.32 Good      H     SI2    
10  0.32 Very Good H     SI2    
# … with 9,184 more rows

The order of these functions matters though, since you can remove columns that you might want to filter on.

diamonds %>% 
  select(starts_with("c")) %>%
  filter(table > 60))

This will result in an error because the table column is now gone after the select() function!

head(diamonds, 2)

# A tibble: 2 × 10
  carat cut     color clarity depth table price     x     y     z
  <dbl> <ord>   <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1  0.23 Ideal   E     SI2      61.5    55   326  3.95  3.98  2.43
2  0.21 Premium E     SI1      59.8    61   326  3.89  3.84  2.31

diamonds %>% select(price, starts_with("c"))

# A tibble: 53,940 × 5
   price carat cut       color clarity
   <int> <dbl> <ord>     <ord> <ord>  
 1   326  0.23 Ideal     E     SI2    
 2   326  0.21 Premium   E     SI1    
 3   327  0.23 Good      E     VS1    
 4   334  0.29 Premium   I     VS2    
 5   335  0.31 Good      J     SI2    
 6   336  0.24 Very Good J     VVS2   
 7   336  0.24 Very Good I     VVS1   
 8   337  0.26 Very Good H     SI1    
 9   337  0.22 Fair      E     VS2    
10   338  0.23 Very Good H     VS1    
# … with 53,930 more rows

Follows OR logic.

diamonds %>% select(starts_with("c"), ends_with("e"))

# A tibble: 53,940 × 6
   carat cut       color clarity table price
   <dbl> <ord>     <ord> <ord>   <dbl> <int>
 1  0.23 Ideal     E     SI2        55   326
 2  0.21 Premium   E     SI1        61   326
 3  0.23 Good      E     VS1        65   327
 4  0.29 Premium   I     VS2        58   334
 5  0.31 Good      J     SI2        58   335
 6  0.24 Very Good J     VVS2       57   336
 7  0.24 Very Good I     VVS1       57   336
 8  0.26 Very Good H     SI1        55   337
 9  0.22 Fair      E     VS2        61   337
10  0.23 Very Good H     VS1        61   338
# … with 53,930 more rows

Need to combine the patterns with the c() function.

diamonds %>% select(starts_with(c("c", "p")))

# A tibble: 53,940 × 5
   carat cut       color clarity price
   <dbl> <ord>     <ord> <ord>   <int>
 1  0.23 Ideal     E     SI2       326
 2  0.21 Premium   E     SI1       326
 3  0.23 Good      E     VS1       327
 4  0.29 Premium   I     VS2       334
 5  0.31 Good      J     SI2       335
 6  0.24 Very Good J     VVS2      336
 7  0.24 Very Good I     VVS1      336
 8  0.26 Very Good H     SI1       337
 9  0.22 Fair      E     VS2       337
10  0.23 Very Good H     VS1       338
# … with 53,930 more rows

If you try to filter or select for a column that does not exist it will not work:

• misspelled column name
• column that was already removed

Did the filter work the way you expected? Did the dimensions change?

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