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Working with Data in R

Dr. Mine Dogucu

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Goals

  • Data Frames (and Tibbles)
  • Vectors (and lists)
  • The pipe operator
  • Changing variable names & types
  • Summarizing variables
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Review

3 / 77

Data Frames

A typical data frame has

columns that each represents a variable.
rows that each represents an observation.

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Functions for Data Frames

head(mtcars)
                   mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
5 / 77

Functions for Data Frames

tail(mtcars)
                mpg cyl  disp  hp drat    wt qsec vs am gear carb
Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.7  0  1    5    2
Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.9  1  1    5    2
Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.5  0  1    5    4
Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.5  0  1    5    6
Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.6  0  1    5    8
Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.6  1  1    4    2
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Functions for Data Frames

glimpse(mtcars)
Rows: 32
Columns: 11
$ mpg  <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8,~
$ cyl  <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8,~
$ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 16~
$ hp   <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180~
$ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,~
$ wt   <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.~
$ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18~
$ vs   <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,~
$ am   <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,~
$ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3,~
$ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2,~
7 / 77

Functions for Data Frames

Note that summary() function is useful beyond data frames.

summary(mtcars)
      mpg             cyl             disp             hp       
 Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
 1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
 Median :19.20   Median :6.000   Median :196.3   Median :123.0  
 Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
 3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
 Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
      drat             wt             qsec             vs        
 Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
 1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
 Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
 Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
 3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
 Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
       am              gear            carb      
 Min.   :0.0000   Min.   :3.000   Min.   :1.000  
 1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
 Median :0.0000   Median :4.000   Median :2.000  
 Mean   :0.4062   Mean   :3.688   Mean   :2.812  
 3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
 Max.   :1.0000   Max.   :5.000   Max.   :8.000
8 / 77

Data Frames vs. Tibbles

                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
9 / 77

Data Frames vs. Tibbles

# A tibble: 32 x 11
    mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1  21       6   160   110  3.9   2.62  16.5     0     1     4     4
2  21       6   160   110  3.9   2.88  17.0     0     1     4     4
3  22.8     4   108    93  3.85  2.32  18.6     1     1     4     1
4  21.4     6   258   110  3.08  3.22  19.4     1     0     3     1
5  18.7     8   360   175  3.15  3.44  17.0     0     0     3     2
6  18.1     6   225   105  2.76  3.46  20.2     1     0     3     1
# ... with 26 more rows
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Vectors in R

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Numeric Vectors (integer and double)

SibSp Number of Siblings/Spouses Aboard on Titanic

# A tibble: 891 x 2
  SibSp  Fare
  <int> <dbl>
1     1  7.25
2     1 71.3 
3     0  7.92
4     1 53.1 
5     0  8.05
6     0  8.46
# ... with 885 more rows
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Character Vectors

SibSp Number of Siblings/Spouses Aboard on Titanic

# A tibble: 891 x 2
  Name                                                Ticket          
  <chr>                                               <chr>           
1 Braund, Mr. Owen Harris                             A/5 21171       
2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) PC 17599        
3 Heikkinen, Miss. Laina                              STON/O2. 3101282
4 Futrelle, Mrs. Jacques Heath (Lily May Peel)        113803          
5 Allen, Mr. William Henry                            373450          
6 Moran, Mr. James                                    330877          
# ... with 885 more rows
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Logical

Do you have any children under eighteen?

# A tibble: 1,040 x 1
  children_under_18
  <lgl>            
1 NA               
2 TRUE             
3 FALSE            
4 FALSE            
5 FALSE            
6 TRUE             
# ... with 1,034 more rows
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Factor

a.k.a. categorical variable in statistics

# A tibble: 891 x 1
  Embarked   
  <chr>      
1 Southampton
2 Cherbourg  
3 Southampton
4 Southampton
5 Southampton
6 Queenstown 
# ... with 885 more rows
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Ordered factor

Is it rude to recline your seat on a plane?

# A tibble: 1,040 x 1
  recline_rude
  <ord>       
1 <NA>        
2 Somewhat    
3 No          
4 No          
5 No          
6 No          
# ... with 1,034 more rows
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Vector Types in R

Missing values are represented with NA in R. NULL represents anything that is undefined. Absence of a vector is often represented by NULL.

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Augmented Vectors

Augmented vectors are atomic vectors that have additional metadata.

factor is an integer vector with levels.

ordered factor is an integer vector with ordered levels.

date is a numeric vector.

date-time is numeric vector.

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Creating Vectors with Multiple Elements

78:83
[1] 78 79 80 81 82 83
3.4:8.5
[1] 3.4 4.4 5.4 6.4 7.4 8.4
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Creating Vectors with Multiple Elements

# A numeric vector
c(5, 7, 8)
[1] 5 7 8
# A character vector
c("Hello", "World", "today")
[1] "Hello" "World" "today"
# A character vector
c("Hello", "World", 5)
[1] "Hello" "World" "5"

Note that even if we use numeric and character values within a (atomic) vector, a (atomic) vector has only one type.

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Creating Vectors with Multiple Elements

seq(from = 2, to = 4, by = 0.3)
[1] 2.0 2.3 2.6 2.9 3.2 3.5 3.8
rep(1, times = 20)
 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Note that in the R output the vector elements are enumerated at the beginning of each line.

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Selecting elements of a vector

names <- c("Menglin", "James", "Gloria")
names[2]
[1] "James"
names[2:3]
[1] "James"  "Gloria"
names[-2]
[1] "Menglin" "Gloria"
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Selecting Vectors from a Data Frame or Tibble

mtcars$mpg
 [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
[16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
[31] 15.0 21.4
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Selecting Vectors from a Data Frame or Tibble

mtcars[1,] # selects first row
          mpg cyl disp  hp drat   wt  qsec vs am gear carb
Mazda RX4  21   6  160 110  3.9 2.62 16.46  0  1    4    4
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Selecting Vectors from a Data Frame or Tibble

mtcars[1:3,] # selects first through third row
               mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4     21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710    22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
25 / 77

Selecting Vectors from a Data Frame or Tibble

mtcars[,5] # selects fifth column
 [1] 3.90 3.90 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 3.92 3.07 3.07 3.07 2.93
[16] 3.00 3.23 4.08 4.93 4.22 3.70 2.76 3.15 3.73 3.08 4.08 4.43 3.77 4.22 3.62
[31] 3.54 4.11
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Selecting Vectors from a Data Frame or Tibble

mtcars[1:2,5] # selects fifth column of first and second row
[1] 3.9 3.9
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Lists

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my_list <- list("Gloria",
                7, 
                c(8,15.56,15, 16), 
                c(TRUE, FALSE), 
                mtcars)
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my_list
[[1]]
[1] "Gloria"
[[2]]
[1] 7
[[3]]
[1]  8.00 15.56 15.00 16.00
[[4]]
[1]  TRUE FALSE
[[5]]
                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
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Selecting elements from a list

my_list[[5]]
                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
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Selecting elements from a list

my_list[[5]][2]
                    cyl
Mazda RX4             6
Mazda RX4 Wag         6
Datsun 710            4
Hornet 4 Drive        6
Hornet Sportabout     8
Valiant               6
Duster 360            8
Merc 240D             4
Merc 230              4
Merc 280              6
Merc 280C             6
Merc 450SE            8
Merc 450SL            8
Merc 450SLC           8
Cadillac Fleetwood    8
Lincoln Continental   8
Chrysler Imperial     8
Fiat 128              4
Honda Civic           4
Toyota Corolla        4
Toyota Corona         4
Dodge Challenger      8
AMC Javelin           8
Camaro Z28            8
Pontiac Firebird      8
Fiat X1-9             4
Porsche 914-2         4
Lotus Europa          4
Ford Pantera L        8
Ferrari Dino          6
Maserati Bora         8
Volvo 142E            4
32 / 77

Selecting elements from a list

my_list[[3]][3]
[1] 15
33 / 77

small_list <- list(3, 
                 c(5, 2, 5.6))
long_list <- list("STATS 295",
     small_list)
long_list
[[1]]
[1] "STATS 295"
[[2]]
[[2]][[1]]
[1] 3
[[2]][[2]]
[1] 5.0 2.0 5.6
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Checking structure of R objects

str(my_list)
List of 5
 $ : chr "Gloria"
 $ : num 7
 $ : num [1:4] 8 15.6 15 16
 $ : logi [1:2] TRUE FALSE
 $ :'data.frame':    32 obs. of  11 variables:
  ..$ mpg : num [1:32] 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
  ..$ cyl : num [1:32] 6 6 4 6 8 6 8 4 4 6 ...
  ..$ disp: num [1:32] 160 160 108 258 360 ...
  ..$ hp  : num [1:32] 110 110 93 110 175 105 245 62 95 123 ...
  ..$ drat: num [1:32] 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
  ..$ wt  : num [1:32] 2.62 2.88 2.32 3.21 3.44 ...
  ..$ qsec: num [1:32] 16.5 17 18.6 19.4 17 ...
  ..$ vs  : num [1:32] 0 0 1 1 0 1 0 1 1 1 ...
  ..$ am  : num [1:32] 1 1 1 0 0 0 0 0 0 0 ...
  ..$ gear: num [1:32] 4 4 4 3 3 3 3 4 4 4 ...
  ..$ carb: num [1:32] 4 4 1 1 2 1 4 2 2 4 ...
35 / 77

Checking structure of R objects

str(long_list)
List of 2
 $ : chr "STATS 295"
 $ :List of 2
  ..$ : num 3
  ..$ : num [1:3] 5 2 5.6
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The pipe operator

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Three solutions to a single problem

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What is the average of 4, 8, 16 approximately?

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1.What is the average of 4, 8, 16 approximately?

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2.What is the average of 4, 8, 16 approximately?

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3.What is the average of 4, 8, 16 approximately?

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Solution 1: Functions within Functions

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c(4, 8, 16)
[1]  4  8 16
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c(4, 8, 16)
[1]  4  8 16
mean(c(4, 8, 16))
[1] 9.333333
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c(4, 8, 16)
[1]  4  8 16
mean(c(4, 8, 16))
[1] 9.333333
round(mean(c(4, 8, 16)))
[1] 9
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Problem with writing functions within functions

Things will get messy and more difficult to read and debug as we deal with more complex operations on data.

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Solution 2: Creating Objects

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numbers <- c(4, 8, 16)
numbers
[1]  4  8 16
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numbers <- c(4, 8, 16)
numbers
[1]  4  8 16
avg_number <- mean(numbers)
avg_number
[1] 9.333333
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numbers <- c(4, 8, 16)
numbers
[1]  4  8 16
avg_number <- mean(numbers)
avg_number
[1] 9.333333
round(avg_number)
[1] 9
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Problem with creating many objects

We will end up with too many objects in Environment.

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Solution 3: The (forward) Pipe Operator %>%

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Shortcut:
Ctrl (Command) + Shift + M

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c(4, 8, 16) %>% 
  mean() %>% 
  round()
[1] 9


Combine 4, 8, and 16 and then
Take the mean and then
Round the output

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c(4, 8, 16) %>% 
  mean() %>% 
  round()
[1] 9


Combine 4, 8, and 16 and then
Take the mean and then
Round the output

The output of a function becomes the first argument of the next function.

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Recall composite functions such as f∘g(x)?

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Recall composite functions such as f∘g(x)?

Now we have f∘g∘h(x) or round(mean(c(4, 8, 16)))

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Recall composite functions such as f∘g(x)?

Now we have f∘g∘h(x) or round(mean(c(4, 8, 16)))

h(x) %>% 
  g() %>% 
  f()
c(4, 8, 16) %>% 
  mean() %>% 
  round()
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Fun fact

library(magrittr)

Treachery of Images by René Magritte

Image for Treachery of Images is from University of Alabama website and used under fair use for educational purposes.

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Changing Variable Names and Types

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glimpse(lapd)
Rows: 14,824
Columns: 3
$ `Department Title` <chr> "Police (LAPD)", "Police (LAPD)", "Police (LAPD)", ~
$ `Base Pay`         <dbl> 119321.60, 113270.70, 148116.00, 78676.87, 109373.6~
$ `Employment Type`  <chr> "Full Time", "Full Time", "Full Time", "Full Time",~
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Cleaning variable names

clean_names() changes variable names consistent with the tidyverse style.

clean_names(lapd)
# A tibble: 14,824 x 3
  department_title base_pay employment_type
  <chr>               <dbl> <chr>          
1 Police (LAPD)     119322. Full Time      
2 Police (LAPD)     113271. Full Time      
3 Police (LAPD)     148116  Full Time      
4 Police (LAPD)      78677. Full Time      
5 Police (LAPD)     109374. Full Time      
6 Police (LAPD)      95002. Full Time      
# ... with 14,818 more rows
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Renaming variables

clean_names(lapd) %>% 
  rename(dept_title = department_title)
# A tibble: 14,824 x 3
  dept_title    base_pay employment_type
  <chr>            <dbl> <chr>          
1 Police (LAPD)  119322. Full Time      
2 Police (LAPD)  113271. Full Time      
3 Police (LAPD)  148116  Full Time      
4 Police (LAPD)   78677. Full Time      
5 Police (LAPD)  109374. Full Time      
6 Police (LAPD)   95002. Full Time      
# ... with 14,818 more rows
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Renaming variables

More than one variable within a single rename() function can be renamed.

clean_names(lapd) %>% 
  rename(dept_title = department_title,
         emp_type = employment_type)
# A tibble: 14,824 x 3
  dept_title    base_pay emp_type 
  <chr>            <dbl> <chr>    
1 Police (LAPD)  119322. Full Time
2 Police (LAPD)  113271. Full Time
3 Police (LAPD)  148116  Full Time
4 Police (LAPD)   78677. Full Time
5 Police (LAPD)  109374. Full Time
6 Police (LAPD)   95002. Full Time
# ... with 14,818 more rows
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Making changes to variable types

mutate() function helps make create new variables or make changes to existing ones.

clean_names(lapd) %>% 
  rename(dept_title = department_title,
         emp_type = employment_type) %>% 
  mutate(emp_type2 = as.factor(emp_type))
# A tibble: 14,824 x 4
  dept_title    base_pay emp_type  emp_type2
  <chr>            <dbl> <chr>     <fct>    
1 Police (LAPD)  119322. Full Time Full Time
2 Police (LAPD)  113271. Full Time Full Time
3 Police (LAPD)  148116  Full Time Full Time
4 Police (LAPD)   78677. Full Time Full Time
5 Police (LAPD)  109374. Full Time Full Time
6 Police (LAPD)   95002. Full Time Full Time
# ... with 14,818 more rows
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Making changes to variable types

We normally would not call the new variable as emp_type2 instead we would call it emp_type to override the older version.

clean_names(lapd) %>% 
  rename(dept_title = department_title,
         emp_type = employment_type) %>% 
  mutate(emp_type = as.factor(emp_type))
# A tibble: 14,824 x 3
  dept_title    base_pay emp_type 
  <chr>            <dbl> <fct>    
1 Police (LAPD)  119322. Full Time
2 Police (LAPD)  113271. Full Time
3 Police (LAPD)  148116  Full Time
4 Police (LAPD)   78677. Full Time
5 Police (LAPD)  109374. Full Time
6 Police (LAPD)   95002. Full Time
# ... with 14,818 more rows
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Making changes to variable types

Changes to other vector types are also possible with the following functions

as.numeric()
as.double()
as.integer()
as.character()
as.logical()

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Why does lapd object does not reflect any of the data cleaning that we have accomplished? 

lapd
# A tibble: 14,824 x 3
  `Department Title` `Base Pay` `Employment Type`
  <chr>                   <dbl> <chr>            
1 Police (LAPD)         119322. Full Time        
2 Police (LAPD)         113271. Full Time        
3 Police (LAPD)         148116  Full Time        
4 Police (LAPD)          78677. Full Time        
5 Police (LAPD)         109374. Full Time        
6 Police (LAPD)          95002. Full Time        
# ... with 14,818 more rows
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We can overwrite the old lapd object by assigning the cleaner version of lapd

lapd <- clean_names(lapd) %>% 
  rename(dept_title = department_title,
         emp_type = employment_type) %>% 
  mutate(emp_type = as.factor(emp_type))
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lapd
# A tibble: 14,824 x 3
  dept_title    base_pay emp_type 
  <chr>            <dbl> <fct>    
1 Police (LAPD)  119322. Full Time
2 Police (LAPD)  113271. Full Time
3 Police (LAPD)  148116  Full Time
4 Police (LAPD)   78677. Full Time
5 Police (LAPD)  109374. Full Time
6 Police (LAPD)   95002. Full Time
# ... with 14,818 more rows
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Summarizing Numeric Variables

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Numerical Descriptive Functions

mean()
median()
sd()
var()
min()
max()
quantile()

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Mean

summarize(lapd, 
          mean(base_pay))
# A tibble: 1 x 1
  `mean(base_pay)`
             <dbl>
1           85149.
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Mean

summarize(lapd, 
          mean(base_pay))
# A tibble: 1 x 1
  `mean(base_pay)`
             <dbl>
1           85149.
mean(lapd$base_pay)
[1] 85149.05
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We can get multiple summaries with one summarize() function.

summarize(lapd,
          mean(base_pay),
          median(base_pay))
# A tibble: 1 x 2
  `mean(base_pay)` `median(base_pay)`
             <dbl>              <dbl>
1           85149.             97601.

Note how the variables names in this table is not easy to read.

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In order to display the variable names more legibly in the output, we can assign variable names to numerical summaries (e.g. mean_base_pay).

summarize(lapd,
          mean_base_pay = mean(base_pay),
          med_base_pay = median(base_pay))
# A tibble: 1 x 2
  mean_base_pay med_base_pay
          <dbl>        <dbl>
1        85149.       97601.
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Goals

  • Data Frames (and Tibbles)
  • Vectors (and lists)
  • The pipe operator
  • Changing variable names & types
  • Summarizing variables
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