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Data wrangling: tidy data

INFO 5940
Cornell University

1 / 27

Importing data in R

2 / 27

readr vs. base R

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readr vs. base R

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Alternative file formats

  • CSV
  • RDS
  • Feather
  • Excel
  • SPSS/Stata
5 / 27

challenge

## # A tibble: 2,000 × 2
##        x y     
##    <dbl> <date>
##  1   404 NA    
##  2  4172 NA    
##  3  3004 NA    
##  4   787 NA    
##  5    37 NA    
##  6  2332 NA    
##  7  2489 NA    
##  8  1449 NA    
##  9  3665 NA    
## 10  3863 NA    
## # … with 1,990 more rows
6 / 27

RDS

# compare file size
file.info(here("static", "data", "challenge.rds"))$size %>%
  utils:::format.object_size("auto")
## [1] "11.6 Kb"
file.info(here("static", "data", "challenge.csv"))$size %>%
  utils:::format.object_size("auto")
## [1] "37.1 Kb"
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RDS

8 / 27

feather

9 / 27

readxl

library(readxl)
xlsx_example <- readxl_example(path = "datasets.xlsx")
read_excel(path = xlsx_example)
## # A tibble: 150 × 5
##    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
##           <dbl>       <dbl>        <dbl>       <dbl> <chr>  
##  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
10 / 27

readxl

excel_sheets(path = xlsx_example)
## [1] "iris"     "mtcars"   "chickwts" "quakes"
read_excel(path = xlsx_example, sheet = "chickwts")
## # A tibble: 71 × 2
##    weight feed     
##     <dbl> <chr>    
##  1    179 horsebean
##  2    160 horsebean
##  3    136 horsebean
##  4    227 horsebean
##  5    217 horsebean
##  6    168 horsebean
##  7    108 horsebean
##  8    124 horsebean
##  9    143 horsebean
## 10    140 horsebean
## # … with 61 more rows
11 / 27

haven and SAS

library(haven)
read_sas(data_file = system.file("examples", "iris.sas7bdat",
  package = "haven"
))
## # A tibble: 150 × 5
##    Sepal_Length Sepal_Width Petal_Length Petal_Width Species
##           <dbl>       <dbl>        <dbl>       <dbl> <chr>  
##  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
12 / 27

haven and SPSS

read_sav(file = system.file("examples", "iris.sav",
  package = "haven"
))
## # A tibble: 150 × 5
##    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
##           <dbl>       <dbl>        <dbl>       <dbl>  <dbl+lbl>
##  1          5.1         3.5          1.4         0.2 1 [setosa]
##  2          4.9         3            1.4         0.2 1 [setosa]
##  3          4.7         3.2          1.3         0.2 1 [setosa]
##  4          4.6         3.1          1.5         0.2 1 [setosa]
##  5          5           3.6          1.4         0.2 1 [setosa]
##  6          5.4         3.9          1.7         0.4 1 [setosa]
##  7          4.6         3.4          1.4         0.3 1 [setosa]
##  8          5           3.4          1.5         0.2 1 [setosa]
##  9          4.4         2.9          1.4         0.2 1 [setosa]
## 10          4.9         3.1          1.5         0.1 1 [setosa]
## # … with 140 more rows
13 / 27

haven and Stata

read_dta(file = system.file("examples", "iris.dta",
  package = "haven"
))
## # A tibble: 150 × 5
##    sepallength sepalwidth petallength petalwidth species
##          <dbl>      <dbl>       <dbl>      <dbl> <chr>  
##  1        5.10       3.5         1.40      0.200 setosa 
##  2        4.90       3           1.40      0.200 setosa 
##  3        4.70       3.20        1.30      0.200 setosa 
##  4        4.60       3.10        1.5       0.200 setosa 
##  5        5          3.60        1.40      0.200 setosa 
##  6        5.40       3.90        1.70      0.400 setosa 
##  7        4.60       3.40        1.40      0.300 setosa 
##  8        5          3.40        1.5       0.200 setosa 
##  9        4.40       2.90        1.40      0.200 setosa 
## 10        4.90       3.10        1.5       0.100 setosa 
## # … with 140 more rows
14 / 27

Tidy data

15 / 27

Tidy data

Stylized text providing an overview of Tidy Data. The top reads 'Tidy data is a standard way of mapping the meaning of a dataset to its structure. - Hadley Wickham.' On the left reads 'In tidy data: each variable forms a column; each observation forms a row; each cell is a single measurement.' There is an example table on the lower right with columns ‘id’, ‘name’ and ‘color’ with observations for different cats, illustrating tidy data structure.

Illustrations from the Openscapes blog Tidy Data for reproducibility, efficiency, and collaboration by Julia Lowndes and Allison Horst)

16 / 27

Tidy data

There are two sets of anthropomorphized data tables. The top group of three tables are all rectangular and smiling, with a shared speech bubble reading 'our columns are variables and our rows are observations!'. Text to the left of that group reads 'The standard structure of tidy data means that 'tidy datasets are all alike…' The lower group of four tables are all different shapes, look ragged and concerned, and have different speech bubbles reading (from left to right) 'my column are values and my rows are variables', 'I have variables in columns AND in rows', 'I have multiple variables in a single column', and 'I don’t even KNOW what my deal is.' Next to the frazzled data tables is text '...but every messy dataset is messy in its own way. -Hadley Wickham.'

Illustrations from the Openscapes blog Tidy Data for reproducibility, efficiency, and collaboration by Julia Lowndes and Allison Horst)

17 / 27

Tidy data

On the left is a happy cute fuzzy monster holding a rectangular data frame with a tool that fits the data frame shape. On the workbench behind the monster are other data frames of similar rectangular shape, and neatly arranged tools that also look like they would fit those data frames. The workbench looks uncluttered and tidy. The text above the tidy workbench reads 'When working with tidy data, we can use the same tools in similar ways for different datasets…' On the right is a cute monster looking very frustrated, using duct tape and other tools to haphazardly tie data tables together, each in a different way. The monster is in front of a messy, cluttered workbench. The text above the frustrated monster reads '...but working with untidy data often means reinventing the wheel with one-time approaches that are hard to iterate or reuse.'

Illustrations from the Openscapes blog Tidy Data for reproducibility, efficiency, and collaboration by Julia Lowndes and Allison Horst)

18 / 27

Tidy data

Digital illustration of a cute fuzzy monster holding a brief case that says 'tidy data,' standing beside a friendly looking data table character, being welcomed with cheers by many other data tables and another cute monster jumping with joy.

Illustrations from the Openscapes blog Tidy Data for reproducibility, efficiency, and collaboration by Julia Lowndes and Allison Horst)

19 / 27

Tidy data

Digital illustration of two cute fuzzy monsters sitting on a park bench with a smiling data table between them, all eating ice cream together. In text above the illustration are the hand drawn words 'make friends with tidy data.'

Illustrations from the Openscapes blog Tidy Data for reproducibility, efficiency, and collaboration by Julia Lowndes and Allison Horst)

20 / 27

Common tidying tasks

  • Pivoting
    • Longer
    • Wider
  • Separating
  • Uniting
21 / 27

Pivot longer

table4a
## # A tibble: 3 × 3
##   country     `1999` `2000`
## * <chr>        <int>  <int>
## 1 Afghanistan    745   2666
## 2 Brazil       37737  80488
## 3 China       212258 213766
22 / 27

Pivot longer

table4a
## # A tibble: 3 × 3
##   country     `1999` `2000`
## * <chr>        <int>  <int>
## 1 Afghanistan    745   2666
## 2 Brazil       37737  80488
## 3 China       212258 213766
pivot_longer(
  data = table4a,
  cols = c(`1999`, `2000`),
  names_to = "year",
  values_to = "cases"
)
## # A tibble: 6 × 3
##   country     year   cases
##   <chr>       <chr>  <int>
## 1 Afghanistan 1999     745
## 2 Afghanistan 2000    2666
## 3 Brazil      1999   37737
## 4 Brazil      2000   80488
## 5 China       1999  212258
## 6 China       2000  213766
22 / 27

Pivot wider

table2
## # A tibble: 12 × 4
##    country      year type            count
##    <chr>       <int> <chr>           <int>
##  1 Afghanistan  1999 cases             745
##  2 Afghanistan  1999 population   19987071
##  3 Afghanistan  2000 cases            2666
##  4 Afghanistan  2000 population   20595360
##  5 Brazil       1999 cases           37737
##  6 Brazil       1999 population  172006362
##  7 Brazil       2000 cases           80488
##  8 Brazil       2000 population  174504898
##  9 China        1999 cases          212258
## 10 China        1999 population 1272915272
## 11 China        2000 cases          213766
## 12 China        2000 population 1280428583
23 / 27

Pivot wider

table2
## # A tibble: 12 × 4
##    country      year type            count
##    <chr>       <int> <chr>           <int>
##  1 Afghanistan  1999 cases             745
##  2 Afghanistan  1999 population   19987071
##  3 Afghanistan  2000 cases            2666
##  4 Afghanistan  2000 population   20595360
##  5 Brazil       1999 cases           37737
##  6 Brazil       1999 population  172006362
##  7 Brazil       2000 cases           80488
##  8 Brazil       2000 population  174504898
##  9 China        1999 cases          212258
## 10 China        1999 population 1272915272
## 11 China        2000 cases          213766
## 12 China        2000 population 1280428583
pivot_wider(
  data = table2,
  names_from = type,
  values_from = count
)
## # A tibble: 6 × 4
##   country      year  cases population
##   <chr>       <int>  <int>      <int>
## 1 Afghanistan  1999    745   19987071
## 2 Afghanistan  2000   2666   20595360
## 3 Brazil       1999  37737  172006362
## 4 Brazil       2000  80488  174504898
## 5 China        1999 212258 1272915272
## 6 China        2000 213766 1280428583
23 / 27

Separating

table3
## # A tibble: 6 × 3
##   country      year rate             
## * <chr>       <int> <chr>            
## 1 Afghanistan  1999 745/19987071     
## 2 Afghanistan  2000 2666/20595360    
## 3 Brazil       1999 37737/172006362  
## 4 Brazil       2000 80488/174504898  
## 5 China        1999 212258/1272915272
## 6 China        2000 213766/1280428583
24 / 27

Separating

table3
## # A tibble: 6 × 3
##   country      year rate             
## * <chr>       <int> <chr>            
## 1 Afghanistan  1999 745/19987071     
## 2 Afghanistan  2000 2666/20595360    
## 3 Brazil       1999 37737/172006362  
## 4 Brazil       2000 80488/174504898  
## 5 China        1999 212258/1272915272
## 6 China        2000 213766/1280428583
separate(
  data = table3,
  col = rate,
  into = c(
    "cases",
    "population"
  ),
  convert = TRUE
)
## # A tibble: 6 × 4
##   country      year  cases population
##   <chr>       <int>  <int>      <int>
## 1 Afghanistan  1999    745   19987071
## 2 Afghanistan  2000   2666   20595360
## 3 Brazil       1999  37737  172006362
## 4 Brazil       2000  80488  174504898
## 5 China        1999 212258 1272915272
## 6 China        2000 213766 1280428583
24 / 27

Uniting

table5
## # A tibble: 6 × 4
##   country     century year  rate             
## * <chr>       <chr>   <chr> <chr>            
## 1 Afghanistan 19      99    745/19987071     
## 2 Afghanistan 20      00    2666/20595360    
## 3 Brazil      19      99    37737/172006362  
## 4 Brazil      20      00    80488/174504898  
## 5 China       19      99    212258/1272915272
## 6 China       20      00    213766/1280428583
25 / 27

Uniting

table5
## # A tibble: 6 × 4
##   country     century year  rate             
## * <chr>       <chr>   <chr> <chr>            
## 1 Afghanistan 19      99    745/19987071     
## 2 Afghanistan 20      00    2666/20595360    
## 3 Brazil      19      99    37737/172006362  
## 4 Brazil      20      00    80488/174504898  
## 5 China       19      99    212258/1272915272
## 6 China       20      00    213766/1280428583
unite(
  data = table5,
  col = "year",
  century, year
)
## # A tibble: 6 × 3
##   country     year  rate             
##   <chr>       <chr> <chr>            
## 1 Afghanistan 19_99 745/19987071     
## 2 Afghanistan 20_00 2666/20595360    
## 3 Brazil      19_99 37737/172006362  
## 4 Brazil      20_00 80488/174504898  
## 5 China       19_99 212258/1272915272
## 6 China       20_00 213766/1280428583
25 / 27

Uniting

table5
## # A tibble: 6 × 4
##   country     century year  rate             
## * <chr>       <chr>   <chr> <chr>            
## 1 Afghanistan 19      99    745/19987071     
## 2 Afghanistan 20      00    2666/20595360    
## 3 Brazil      19      99    37737/172006362  
## 4 Brazil      20      00    80488/174504898  
## 5 China       19      99    212258/1272915272
## 6 China       20      00    213766/1280428583
# remove underscore
unite(
  data = table5,
  col = "year",
  century, year,
  sep = ""
) %>%
  mutate(year = parse_number(year))
## # A tibble: 6 × 3
##   country      year rate             
##   <chr>       <dbl> <chr>            
## 1 Afghanistan  1999 745/19987071     
## 2 Afghanistan  2000 2666/20595360    
## 3 Brazil       1999 37737/172006362  
## 4 Brazil       2000 80488/174504898  
## 5 China        1999 212258/1272915272
## 6 China        2000 213766/1280428583
26 / 27

Let's get messy!

27 / 27

Importing data in R

2 / 27
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