Import and Tidy Data
MATH-7360 Data Analysis
Dr. Xiang Ji @ Tulane University
Sep 14, 2020
rm(list = ls()) # clean-up workspaceWe follow R for Data Science by Garrett Grolemund and Hadley Wickham for the next couple of lectures.
Acknowledgement
Dr. Hua Zhou’s slides
Workflow
A typical data science project:
Data wrangling.
The art of getting your data into R in a useful form for visualisation and modelling.
Data import
Data transformation.
select important variables
filter out key observations
create new variables
compute summaries
Visualisation.
- Make elegant and informative plots that help you understand data.
Data wrangling
There are three main parts to data wrangling:
We will proceed with:
tibbles | r4ds chapter 10
A data frame variant
how to construct them
data import | r4ds chapter 11
- how to get data from disk into R
tidy data | r4ds chapter 12
- a consistent way of storing your data that makes everything easier (transformation, visualisation, and modelling)
Tidyverse
tidyverseis a collection of R packages that make data wrangling easy.Install
tidyversefrom RStudio menuTools -> Install Packages...orinstall.packages("tidyverse")After installation, load
tidyversebylibrary("tidyverse")## ── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4 ## ✓ tibble 3.0.3 ✓ dplyr 1.0.2 ## ✓ tidyr 1.1.1 ✓ stringr 1.4.0 ## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ── ## x dplyr::filter() masks stats::filter() ## x dplyr::lag() masks stats::lag()environment(filter)## <environment: namespace:dplyr>environment(stats::filter)## <environment: namespace:stats>
Tibbles
Tibbles are one of the unifying features of the tidyverse.
create tibbles
coerce a data frame to a tibble
as_tibble()Most other R packages use regular data frames.
irisis a data frame available in base R:
# a regular data frame iris## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5.0 3.6 1.4 0.2 setosa ## 6 5.4 3.9 1.7 0.4 setosa ## 7 4.6 3.4 1.4 0.3 setosa ## 8 5.0 3.4 1.5 0.2 setosa ## 9 4.4 2.9 1.4 0.2 setosa ## 10 4.9 3.1 1.5 0.1 setosa ## 11 5.4 3.7 1.5 0.2 setosa ## 12 4.8 3.4 1.6 0.2 setosa ## 13 4.8 3.0 1.4 0.1 setosa ## 14 4.3 3.0 1.1 0.1 setosa ## 15 5.8 4.0 1.2 0.2 setosa ## 16 5.7 4.4 1.5 0.4 setosa ## 17 5.4 3.9 1.3 0.4 setosa ## 18 5.1 3.5 1.4 0.3 setosa ## 19 5.7 3.8 1.7 0.3 setosa ## 20 5.1 3.8 1.5 0.3 setosa ## 21 5.4 3.4 1.7 0.2 setosa ## 22 5.1 3.7 1.5 0.4 setosa ## 23 4.6 3.6 1.0 0.2 setosa ## 24 5.1 3.3 1.7 0.5 setosa ## 25 4.8 3.4 1.9 0.2 setosa ## 26 5.0 3.0 1.6 0.2 setosa ## 27 5.0 3.4 1.6 0.4 setosa ## 28 5.2 3.5 1.5 0.2 setosa ## 29 5.2 3.4 1.4 0.2 setosa ## 30 4.7 3.2 1.6 0.2 setosa ## 31 4.8 3.1 1.6 0.2 setosa ## 32 5.4 3.4 1.5 0.4 setosa ## 33 5.2 4.1 1.5 0.1 setosa ## 34 5.5 4.2 1.4 0.2 setosa ## 35 4.9 3.1 1.5 0.2 setosa ## 36 5.0 3.2 1.2 0.2 setosa ## 37 5.5 3.5 1.3 0.2 setosa ## 38 4.9 3.6 1.4 0.1 setosa ## 39 4.4 3.0 1.3 0.2 setosa ## 40 5.1 3.4 1.5 0.2 setosa ## 41 5.0 3.5 1.3 0.3 setosa ## 42 4.5 2.3 1.3 0.3 setosa ## 43 4.4 3.2 1.3 0.2 setosa ## 44 5.0 3.5 1.6 0.6 setosa ## 45 5.1 3.8 1.9 0.4 setosa ## 46 4.8 3.0 1.4 0.3 setosa ## 47 5.1 3.8 1.6 0.2 setosa ## 48 4.6 3.2 1.4 0.2 setosa ## 49 5.3 3.7 1.5 0.2 setosa ## 50 5.0 3.3 1.4 0.2 setosa ## 51 7.0 3.2 4.7 1.4 versicolor ## 52 6.4 3.2 4.5 1.5 versicolor ## 53 6.9 3.1 4.9 1.5 versicolor ## 54 5.5 2.3 4.0 1.3 versicolor ## 55 6.5 2.8 4.6 1.5 versicolor ## 56 5.7 2.8 4.5 1.3 versicolor ## 57 6.3 3.3 4.7 1.6 versicolor ## 58 4.9 2.4 3.3 1.0 versicolor ## 59 6.6 2.9 4.6 1.3 versicolor ## 60 5.2 2.7 3.9 1.4 versicolor ## 61 5.0 2.0 3.5 1.0 versicolor ## 62 5.9 3.0 4.2 1.5 versicolor ## 63 6.0 2.2 4.0 1.0 versicolor ## 64 6.1 2.9 4.7 1.4 versicolor ## 65 5.6 2.9 3.6 1.3 versicolor ## 66 6.7 3.1 4.4 1.4 versicolor ## 67 5.6 3.0 4.5 1.5 versicolor ## 68 5.8 2.7 4.1 1.0 versicolor ## 69 6.2 2.2 4.5 1.5 versicolor ## 70 5.6 2.5 3.9 1.1 versicolor ## 71 5.9 3.2 4.8 1.8 versicolor ## 72 6.1 2.8 4.0 1.3 versicolor ## 73 6.3 2.5 4.9 1.5 versicolor ## 74 6.1 2.8 4.7 1.2 versicolor ## 75 6.4 2.9 4.3 1.3 versicolor ## 76 6.6 3.0 4.4 1.4 versicolor ## 77 6.8 2.8 4.8 1.4 versicolor ## 78 6.7 3.0 5.0 1.7 versicolor ## 79 6.0 2.9 4.5 1.5 versicolor ## 80 5.7 2.6 3.5 1.0 versicolor ## 81 5.5 2.4 3.8 1.1 versicolor ## 82 5.5 2.4 3.7 1.0 versicolor ## 83 5.8 2.7 3.9 1.2 versicolor ## 84 6.0 2.7 5.1 1.6 versicolor ## 85 5.4 3.0 4.5 1.5 versicolor ## 86 6.0 3.4 4.5 1.6 versicolor ## 87 6.7 3.1 4.7 1.5 versicolor ## 88 6.3 2.3 4.4 1.3 versicolor ## 89 5.6 3.0 4.1 1.3 versicolor ## 90 5.5 2.5 4.0 1.3 versicolor ## 91 5.5 2.6 4.4 1.2 versicolor ## 92 6.1 3.0 4.6 1.4 versicolor ## 93 5.8 2.6 4.0 1.2 versicolor ## 94 5.0 2.3 3.3 1.0 versicolor ## 95 5.6 2.7 4.2 1.3 versicolor ## 96 5.7 3.0 4.2 1.2 versicolor ## 97 5.7 2.9 4.2 1.3 versicolor ## 98 6.2 2.9 4.3 1.3 versicolor ## 99 5.1 2.5 3.0 1.1 versicolor ## 100 5.7 2.8 4.1 1.3 versicolor ## 101 6.3 3.3 6.0 2.5 virginica ## 102 5.8 2.7 5.1 1.9 virginica ## 103 7.1 3.0 5.9 2.1 virginica ## 104 6.3 2.9 5.6 1.8 virginica ## 105 6.5 3.0 5.8 2.2 virginica ## 106 7.6 3.0 6.6 2.1 virginica ## 107 4.9 2.5 4.5 1.7 virginica ## 108 7.3 2.9 6.3 1.8 virginica ## 109 6.7 2.5 5.8 1.8 virginica ## 110 7.2 3.6 6.1 2.5 virginica ## 111 6.5 3.2 5.1 2.0 virginica ## 112 6.4 2.7 5.3 1.9 virginica ## 113 6.8 3.0 5.5 2.1 virginica ## 114 5.7 2.5 5.0 2.0 virginica ## 115 5.8 2.8 5.1 2.4 virginica ## 116 6.4 3.2 5.3 2.3 virginica ## 117 6.5 3.0 5.5 1.8 virginica ## 118 7.7 3.8 6.7 2.2 virginica ## 119 7.7 2.6 6.9 2.3 virginica ## 120 6.0 2.2 5.0 1.5 virginica ## 121 6.9 3.2 5.7 2.3 virginica ## 122 5.6 2.8 4.9 2.0 virginica ## 123 7.7 2.8 6.7 2.0 virginica ## 124 6.3 2.7 4.9 1.8 virginica ## 125 6.7 3.3 5.7 2.1 virginica ## 126 7.2 3.2 6.0 1.8 virginica ## 127 6.2 2.8 4.8 1.8 virginica ## 128 6.1 3.0 4.9 1.8 virginica ## 129 6.4 2.8 5.6 2.1 virginica ## 130 7.2 3.0 5.8 1.6 virginica ## 131 7.4 2.8 6.1 1.9 virginica ## 132 7.9 3.8 6.4 2.0 virginica ## 133 6.4 2.8 5.6 2.2 virginica ## 134 6.3 2.8 5.1 1.5 virginica ## 135 6.1 2.6 5.6 1.4 virginica ## 136 7.7 3.0 6.1 2.3 virginica ## 137 6.3 3.4 5.6 2.4 virginica ## 138 6.4 3.1 5.5 1.8 virginica ## 139 6.0 3.0 4.8 1.8 virginica ## 140 6.9 3.1 5.4 2.1 virginica ## 141 6.7 3.1 5.6 2.4 virginica ## 142 6.9 3.1 5.1 2.3 virginica ## 143 5.8 2.7 5.1 1.9 virginica ## 144 6.8 3.2 5.9 2.3 virginica ## 145 6.7 3.3 5.7 2.5 virginica ## 146 6.7 3.0 5.2 2.3 virginica ## 147 6.3 2.5 5.0 1.9 virginica ## 148 6.5 3.0 5.2 2.0 virginica ## 149 6.2 3.4 5.4 2.3 virginica ## 150 5.9 3.0 5.1 1.8 virginica- Convert a regular data frame to tibble:
as_tibble(iris)## # A tibble: 150 x 5 ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## <dbl> <dbl> <dbl> <dbl> <fct> ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5 3.6 1.4 0.2 setosa ## 6 5.4 3.9 1.7 0.4 setosa ## 7 4.6 3.4 1.4 0.3 setosa ## 8 5 3.4 1.5 0.2 setosa ## 9 4.4 2.9 1.4 0.2 setosa ## 10 4.9 3.1 1.5 0.1 setosa ## # … with 140 more rows- Convert a tibble to data frame:
as.data.frame(tb)
Create tibble from individual vectors.
tibble( x = 1:5, y = 1, z = x ^ 2 + y )## # A tibble: 5 x 3 ## x y z ## <int> <dbl> <dbl> ## 1 1 1 2 ## 2 2 1 5 ## 3 3 1 10 ## 4 4 1 17 ## 5 5 1 26Note values for y are recycled
Only length one vectors are recycled
tibble()does less thandata.frame():never changes the type of the inputs (e.g. it never converts strings to factors)
never changes the names of variables
never creates row names
Transposed tibbles:
tribble( ~x, ~y, ~z, #--|--|---- "a", 2, 3.6, "b", 1, 8.5 )## # A tibble: 2 x 3 ## x y z ## <chr> <dbl> <dbl> ## 1 a 2 3.6 ## 2 b 1 8.5
Printing of a tibble
By default, tibble prints the first 10 rows and all columns that fit on screen.
- you don’t accidentally overwhelm your console when printing large data frames
nycflights13::flights## # A tibble: 336,776 x 19 ## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time ## <int> <int> <int> <int> <int> <dbl> <int> <int> ## 1 2013 1 1 517 515 2 830 819 ## 2 2013 1 1 533 529 4 850 830 ## 3 2013 1 1 542 540 2 923 850 ## 4 2013 1 1 544 545 -1 1004 1022 ## 5 2013 1 1 554 600 -6 812 837 ## 6 2013 1 1 554 558 -4 740 728 ## 7 2013 1 1 555 600 -5 913 854 ## 8 2013 1 1 557 600 -3 709 723 ## 9 2013 1 1 557 600 -3 838 846 ## 10 2013 1 1 558 600 -2 753 745 ## # … with 336,766 more rows, and 11 more variables: arr_delay <dbl>, ## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>, ## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>To change number of rows and columns to display:
nycflights13::flights %>% print(n = 10, width = Inf)## # A tibble: 336,776 x 19 ## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time ## <int> <int> <int> <int> <int> <dbl> <int> <int> ## 1 2013 1 1 517 515 2 830 819 ## 2 2013 1 1 533 529 4 850 830 ## 3 2013 1 1 542 540 2 923 850 ## 4 2013 1 1 544 545 -1 1004 1022 ## 5 2013 1 1 554 600 -6 812 837 ## 6 2013 1 1 554 558 -4 740 728 ## 7 2013 1 1 555 600 -5 913 854 ## 8 2013 1 1 557 600 -3 709 723 ## 9 2013 1 1 557 600 -3 838 846 ## 10 2013 1 1 558 600 -2 753 745 ## arr_delay carrier flight tailnum origin dest air_time distance hour minute ## <dbl> <chr> <int> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> ## 1 11 UA 1545 N14228 EWR IAH 227 1400 5 15 ## 2 20 UA 1714 N24211 LGA IAH 227 1416 5 29 ## 3 33 AA 1141 N619AA JFK MIA 160 1089 5 40 ## 4 -18 B6 725 N804JB JFK BQN 183 1576 5 45 ## 5 -25 DL 461 N668DN LGA ATL 116 762 6 0 ## 6 12 UA 1696 N39463 EWR ORD 150 719 5 58 ## 7 19 B6 507 N516JB EWR FLL 158 1065 6 0 ## 8 -14 EV 5708 N829AS LGA IAD 53 229 6 0 ## 9 -8 B6 79 N593JB JFK MCO 140 944 6 0 ## 10 8 AA 301 N3ALAA LGA ORD 138 733 6 0 ## time_hour ## <dttm> ## 1 2013-01-01 05:00:00 ## 2 2013-01-01 05:00:00 ## 3 2013-01-01 05:00:00 ## 4 2013-01-01 05:00:00 ## 5 2013-01-01 06:00:00 ## 6 2013-01-01 05:00:00 ## 7 2013-01-01 06:00:00 ## 8 2013-01-01 06:00:00 ## 9 2013-01-01 06:00:00 ## 10 2013-01-01 06:00:00 ## # … with 336,766 more rowsHere we see the pipe operator
%>%pipes the output from previous command to the (first) argument of the next command.To change the default print setting:
options(tibble.print_max = n, tibble.print_min = m): if more thannrows, print onlymrows.options(tibble.print_min = Inf): print all roww.options(tibble.width = Inf): print all columns.
Subsetting
df <- tibble( x = runif(5), y = rnorm(5) ) df## # A tibble: 5 x 2 ## x y ## <dbl> <dbl> ## 1 0.382 0.410 ## 2 0.343 -0.102 ## 3 0.197 1.65 ## 4 0.0799 -0.351 ## 5 0.241 1.73Extract by name:
df$x## [1] 0.38245371 0.34339284 0.19746657 0.07986239 0.24125372df[["x"]]## [1] 0.38245371 0.34339284 0.19746657 0.07986239 0.24125372Extract by position：
df[[1]]## [1] 0.38245371 0.34339284 0.19746657 0.07986239 0.24125372Pipe:
df %>% .$x## [1] 0.38245371 0.34339284 0.19746657 0.07986239 0.24125372df %>% .[["x"]]## [1] 0.38245371 0.34339284 0.19746657 0.07986239 0.24125372.is a special placeholder.