Tibbles are a modern reimagining of R’s traditional data.frame. They are designed to make working with data frames easier and more consistent. Here’s how to create a basic tibble:
Create a tibble with the tibble function. It’s use is very similar to how you would use the data.frame function to create a dataframe (we’re assuming that you’re familiar with creating dataframes in R)
# convert a dataframe to a tibble
df = data.frame(
numbers = 1:3,
letters = c("a", "b", "c")
)
tbl = as_tibble(df)
tbl
# convert a matrix to a tibble
mat = matrix(seq(10,120,by=10), nrow=3,ncol = 4)
tbl = as_tibble(mat)
tblTibbles have several important differences from traditional data.frames:
# Create a wide tibble with many columns 25 rows.
health_data = tibble(
patient_id = sprintf("P%04d", 1:25),
admit_date = as.Date("2024-01-01") + sample(0:30, 25, replace = TRUE),
age = sample(18:95, 25, replace = TRUE),
heart_rate = round(rnorm(25, 75, 10)),
blood_pressure_sys = round(rnorm(25, 120, 15)),
blood_pressure_dia = round(rnorm(25, 80, 10)),
temperature = round(rnorm(25, 98.6, 0.5), 1),
oxygen_saturation = round(rnorm(25, 98, 2)),
cholesterol = round(rnorm(25, 190, 30)),
glucose = round(rnorm(25, 100, 20)),
weight_kg = round(rnorm(25, 70, 15), 1),
height_cm = round(rnorm(25, 170, 10))
)
# Tibbles show only the first 10 rows by default
# and only columns that fit on screen
health_data# A tibble: 25 × 12
patient_id admit_date age heart_rate blood_pressure_sys
<chr> <date> <int> <dbl> <dbl>
1 P0001 2024-01-05 69 61 106
2 P0002 2024-01-01 72 69 125
3 P0003 2024-01-10 89 62 132
4 P0004 2024-01-20 75 53 120
5 P0005 2024-01-25 22 79 134
6 P0006 2024-01-12 73 80 132
7 P0007 2024-01-31 39 79 114
8 P0008 2024-01-27 22 75 123
9 P0009 2024-01-11 54 81 124
10 P0010 2024-01-05 52 90 130
# ℹ 15 more rows
# ℹ 7 more variables: blood_pressure_dia <dbl>,
# temperature <dbl>, oxygen_saturation <dbl>,
# cholesterol <dbl>, glucose <dbl>, weight_kg <dbl>,
# height_cm <dbl> patient_id admit_date age heart_rate blood_pressure_sys
1 P0001 2024-01-05 69 61 106
2 P0002 2024-01-01 72 69 125
3 P0003 2024-01-10 89 62 132
4 P0004 2024-01-20 75 53 120
5 P0005 2024-01-25 22 79 134
6 P0006 2024-01-12 73 80 132
7 P0007 2024-01-31 39 79 114
8 P0008 2024-01-27 22 75 123
9 P0009 2024-01-11 54 81 124
10 P0010 2024-01-05 52 90 130
11 P0011 2024-01-04 64 82 124
12 P0012 2024-01-18 93 76 86
13 P0013 2024-01-01 58 77 120
14 P0014 2024-01-12 78 76 126
15 P0015 2024-01-17 23 69 128
16 P0016 2024-01-09 43 69 103
17 P0017 2024-01-17 86 70 114
18 P0018 2024-01-17 62 52 138
19 P0019 2024-01-09 36 79 127
20 P0020 2024-01-14 34 74 149
21 P0021 2024-01-07 79 71 101
22 P0022 2024-01-19 53 81 104
23 P0023 2024-01-03 81 69 100
24 P0024 2024-01-02 30 77 141
25 P0025 2024-01-19 81 65 153
blood_pressure_dia temperature oxygen_saturation
1 70 99.3 98
2 75 98.1 100
3 75 99.0 98
4 71 98.0 97
5 76 98.6 99
6 92 99.5 100
7 71 98.8 101
8 85 99.2 99
9 66 99.6 101
10 80 99.0 98
11 55 97.3 99
12 84 98.6 99
13 84 99.1 102
14 74 98.2 102
15 90 98.8 97
16 78 98.8 100
17 74 98.5 95
18 59 99.1 100
19 83 98.9 93
20 98 98.4 103
21 79 97.5 100
22 73 99.0 96
23 76 98.1 98
24 75 98.8 100
25 71 99.1 97
cholesterol glucose weight_kg height_cm
1 178 109 76.9 168
2 180 79 54.9 182
3 202 113 53.9 189
4 125 79 104.1 190
5 142 122 73.7 163
6 149 80 54.7 157
7 176 100 69.9 156
8 178 77 87.1 158
9 222 83 78.0 162
10 233 107 65.4 178
11 192 101 70.7 158
12 121 113 67.6 140
13 219 128 71.8 163
14 198 104 43.9 193
15 168 91 52.2 164
16 234 97 48.0 176
17 164 110 96.2 167
18 164 106 108.4 171
19 169 70 62.3 175
20 165 138 76.1 168
21 218 111 64.0 169
22 245 83 54.1 173
23 183 131 49.0 168
24 183 68 69.3 163
25 174 119 61.5 162# By default, print() shows 10 rows. Use n= to show more rows
print(health_data, n = 20) # Shows 20 rows# A tibble: 25 × 12
patient_id admit_date age heart_rate blood_pressure_sys
<chr> <date> <int> <dbl> <dbl>
1 P0001 2024-01-05 69 61 106
2 P0002 2024-01-01 72 69 125
3 P0003 2024-01-10 89 62 132
4 P0004 2024-01-20 75 53 120
5 P0005 2024-01-25 22 79 134
6 P0006 2024-01-12 73 80 132
7 P0007 2024-01-31 39 79 114
8 P0008 2024-01-27 22 75 123
9 P0009 2024-01-11 54 81 124
10 P0010 2024-01-05 52 90 130
11 P0011 2024-01-04 64 82 124
12 P0012 2024-01-18 93 76 86
13 P0013 2024-01-01 58 77 120
14 P0014 2024-01-12 78 76 126
15 P0015 2024-01-17 23 69 128
16 P0016 2024-01-09 43 69 103
17 P0017 2024-01-17 86 70 114
18 P0018 2024-01-17 62 52 138
19 P0019 2024-01-09 36 79 127
20 P0020 2024-01-14 34 74 149
# ℹ 5 more rows
# ℹ 7 more variables: blood_pressure_dia <dbl>,
# temperature <dbl>, oxygen_saturation <dbl>,
# cholesterol <dbl>, glucose <dbl>, weight_kg <dbl>,
# height_cm <dbl># A tibble: 25 × 12
patient_id admit_date age heart_rate blood_pressure_sys
<chr> <date> <int> <dbl> <dbl>
1 P0001 2024-01-05 69 61 106
2 P0002 2024-01-01 72 69 125
3 P0003 2024-01-10 89 62 132
4 P0004 2024-01-20 75 53 120
5 P0005 2024-01-25 22 79 134
6 P0006 2024-01-12 73 80 132
7 P0007 2024-01-31 39 79 114
8 P0008 2024-01-27 22 75 123
9 P0009 2024-01-11 54 81 124
10 P0010 2024-01-05 52 90 130
11 P0011 2024-01-04 64 82 124
12 P0012 2024-01-18 93 76 86
13 P0013 2024-01-01 58 77 120
14 P0014 2024-01-12 78 76 126
15 P0015 2024-01-17 23 69 128
16 P0016 2024-01-09 43 69 103
17 P0017 2024-01-17 86 70 114
18 P0018 2024-01-17 62 52 138
19 P0019 2024-01-09 36 79 127
20 P0020 2024-01-14 34 74 149
21 P0021 2024-01-07 79 71 101
22 P0022 2024-01-19 53 81 104
23 P0023 2024-01-03 81 69 100
24 P0024 2024-01-02 30 77 141
25 P0025 2024-01-19 81 65 153
# ℹ 7 more variables: blood_pressure_dia <dbl>,
# temperature <dbl>, oxygen_saturation <dbl>,
# cholesterol <dbl>, glucose <dbl>, weight_kg <dbl>,
# height_cm <dbl># width argument to print specifies the number of characters that should
# be printed in the widest row. In effect, this limits the number of columns
# being output to those columns that fit in the specified width.
print(health_data, width = 75)# A tibble: 25 × 12
patient_id admit_date age heart_rate blood_pressure_sys
<chr> <date> <int> <dbl> <dbl>
1 P0001 2024-01-05 69 61 106
2 P0002 2024-01-01 72 69 125
3 P0003 2024-01-10 89 62 132
4 P0004 2024-01-20 75 53 120
5 P0005 2024-01-25 22 79 134
6 P0006 2024-01-12 73 80 132
7 P0007 2024-01-31 39 79 114
8 P0008 2024-01-27 22 75 123
9 P0009 2024-01-11 54 81 124
10 P0010 2024-01-05 52 90 130
# ℹ 15 more rows
# ℹ 7 more variables: blood_pressure_dia <dbl>, temperature <dbl>,
# oxygen_saturation <dbl>, cholesterol <dbl>, glucose <dbl>,
# weight_kg <dbl>, height_cm <dbl># A tibble: 25 × 12
patient_id admit_date age heart_rate blood_pressure_sys blood_pressure_dia temperature oxygen_saturation cholesterol glucose weight_kg height_cm
<chr> <date> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 P0001 2024-01-05 69 61 106 70 99.3 98 178 109 76.9 168
2 P0002 2024-01-01 72 69 125 75 98.1 100 180 79 54.9 182
3 P0003 2024-01-10 89 62 132 75 99 98 202 113 53.9 189
4 P0004 2024-01-20 75 53 120 71 98 97 125 79 104. 190
5 P0005 2024-01-25 22 79 134 76 98.6 99 142 122 73.7 163
6 P0006 2024-01-12 73 80 132 92 99.5 100 149 80 54.7 157
7 P0007 2024-01-31 39 79 114 71 98.8 101 176 100 69.9 156
8 P0008 2024-01-27 22 75 123 85 99.2 99 178 77 87.1 158
9 P0009 2024-01-11 54 81 124 66 99.6 101 222 83 78 162
10 P0010 2024-01-05 52 90 130 80 99 98 233 107 65.4 178
# ℹ 15 more rows# data.frames can have row names
df_rownames = data.frame(
x = 1:3,
y = letters[1:3],
row.names = c("row1", "row2", "row3")
)
df_rownames x y
row1 1 a
row2 2 b
row3 3 c# Tibbles don't support row names
# If you convert a data.frame with row names to a tibble,
# the row names become a regular column called 'rowname'
as_tibble(df_rownames, rownames = "id")# A tibble: 3 × 3
id x y
<chr> <int> <chr>
1 row1 1 a
2 row2 2 b
3 row3 3 c You can create tibbles in several ways:
While reading the raw code for creating a dataframe or a tibble, it can be challenging to visualize what the actual dataframe/tibble will look like. This is because when typing the data into the code, each column is typed horrizontally instead of vertically. For example:
# Using tribble() for transposed input
# Useful for small, manual data entry
stuff = tribble(
col1 = c("a", "b", "c"),
col2 = c( 1, 2, 3)
col3 = c(TRUE, FALSE, TRUE))
# The code above lays out columns horizontally.
# The actual dataframe displays columns vertically.
stuffA “tribble” (i.e. TRansposed tIBBLE) is just a different way of typing the code that becomes a tibble. Each column heading is prefixed with a tilde (~). The columns can be laid out vertically in the code, making the code much more readable. See the example below.
# Using tribble() for transposed input
# Useful for small, manual data entry
stuff = tribble(
~col1, ~col2, ~col3,
"a", 1, TRUE,
"b", 2, FALSE,
"c", 3, TRUE
)
# The following looks much more similar to the code that created it.
stuff# data.frames modify non-syntactic names
df_names = data.frame(
`1` = 1:3,
`2+2` = 4:6,
check.names = TRUE # default behavior
)
names(df_names) # Names are modified[1] "X1" "X2.2"# Tibbles preserve original names
tbl_names = tibble(
`1` = 1:3,
`2+2` = 4:6
)
names(tbl_names) # Original names kept[1] "1" "2+2"# Subsetting differences
# data.frame allows partial matching of variable names
df = data.frame(numbers = 1:3, letters = c("a", "b", "c"))
df$num # Partial matching works[1] 1 2 3# Tibbles require exact matching
tbl = tibble(numbers = 1:3, letters = c("a", "b", "c"))
try(tbl$num) # This will raise an errorWarning: Unknown or uninitialised column: `num`.NULL