Reshaping Data

In this module, we will show you how to:

  1. Reshaping data from wide (fat) to long (tall)
  2. Reshaping data from long (tall) to wide (fat)
  3. Merging Data
  4. Perform operations by a grouping variable

Setup

What is wide/long data?

What is wide/long data?

More accurately, data is wide or long with respect to certain variables.

Data used: Charm City Circulator

http://johnmuschelli.com/intro_to_r/data/Charm_City_Circulator_Ridership.csv

circ = read_csv(
  paste0("http://johnmuschelli.com/intro_to_r/",
         "data/Charm_City_Circulator_Ridership.csv"))
head(circ, 2)
# A tibble: 2 x 15
  day   date  orangeBoardings orangeAlightings orangeAverage purpleBoardings
  <chr> <chr>           <dbl>            <dbl>         <dbl>           <dbl>
1 Mond… 01/1…             877             1027           952              NA
2 Tues… 01/1…             777              815           796              NA
# … with 9 more variables: purpleAlightings <dbl>, purpleAverage <dbl>,
#   greenBoardings <dbl>, greenAlightings <dbl>, greenAverage <dbl>,
#   bannerBoardings <dbl>, bannerAlightings <dbl>, bannerAverage <dbl>,
#   daily <dbl>
class(circ$date)
[1] "character"

Creating a Date class from a character date

library(lubridate) # great for dates!
sum(is.na(circ$date))
[1] 0
sum( circ$date == "")
[1] 0
circ = mutate(circ, date = mdy(date))
sum( is.na(circ$date) ) # all converted correctly
[1] 0
head(circ$date, 3)
[1] "2010-01-11" "2010-01-12" "2010-01-13"
class(circ$date)
[1] "Date"

Reshaping data from wide (fat) to long (tall): base R

The reshape command exists. It is a confusing function. Don’t use it.

tidyr package

tidyr allows you to “tidy” your data. We will be talking about:

  • gather - make multiple columns into variables, (wide to long)
  • spread - make a variable into multiple columns, (long to wide)
  • separate - string into multiple columns
  • unite - multiple columns into one string

Reshaping data from wide (fat) to long (tall): tidyr

tidyr::gather - puts column data into rows.

We want the column names into “var” variable in the output dataset and the value in “number” variable. We then describe which columns we want to “gather:”

long = gather(circ, key = "var", value = "number", 
              -day, -date, -daily)
head(long, 4)
# A tibble: 4 x 5
  day       date       daily var             number
  <chr>     <date>     <dbl> <chr>            <dbl>
1 Monday    2010-01-11  952  orangeBoardings    877
2 Tuesday   2010-01-12  796  orangeBoardings    777
3 Wednesday 2010-01-13 1212. orangeBoardings   1203
4 Thursday  2010-01-14 1214. orangeBoardings   1194

Reshaping data from wide (fat) to long (tall): tidyr

  • Could be explicit on what we want to gather
long = gather(circ, key = "var", value = "number", 
              starts_with("orange"), starts_with("purple"),
              starts_with("green"), starts_with("banner"))
long
# A tibble: 13,752 x 5
   day       date       daily var             number
   <chr>     <date>     <dbl> <chr>            <dbl>
 1 Monday    2010-01-11  952  orangeBoardings    877
 2 Tuesday   2010-01-12  796  orangeBoardings    777
 3 Wednesday 2010-01-13 1212. orangeBoardings   1203
 4 Thursday  2010-01-14 1214. orangeBoardings   1194
 5 Friday    2010-01-15 1644  orangeBoardings   1645
 6 Saturday  2010-01-16 1490. orangeBoardings   1457
 7 Sunday    2010-01-17  888. orangeBoardings    839
 8 Monday    2010-01-18  999. orangeBoardings    999
 9 Tuesday   2010-01-19 1035  orangeBoardings   1023
10 Wednesday 2010-01-20 1396. orangeBoardings   1375
# … with 13,742 more rows

Reshaping data from wide (fat) to long (tall): tidyr

long %>% count(var)
# A tibble: 12 x 2
   var                  n
   <chr>            <int>
 1 bannerAlightings  1146
 2 bannerAverage     1146
 3 bannerBoardings   1146
 4 greenAlightings   1146
 5 greenAverage      1146
 6 greenBoardings    1146
 7 orangeAlightings  1146
 8 orangeAverage     1146
 9 orangeBoardings   1146
10 purpleAlightings  1146
11 purpleAverage     1146
12 purpleBoardings   1146

Lab Part 1

Making a separator

We will use str_replace from stringr to put _ in the names

long = long %>% mutate(
  var = var %>% 
    str_replace("Board", "_Board") %>% 
    str_replace("Alight", "_Alight") %>% 
    str_replace("Average", "_Average") 
)
long %>% count(var)
# A tibble: 12 x 2
   var                   n
   <chr>             <int>
 1 banner_Alightings  1146
 2 banner_Average     1146
 3 banner_Boardings   1146
 4 green_Alightings   1146
 5 green_Average      1146
 6 green_Boardings    1146
 7 orange_Alightings  1146
 8 orange_Average     1146
 9 orange_Boardings   1146
10 purple_Alightings  1146
11 purple_Average     1146
12 purple_Boardings   1146

Reshaping data from wide (fat) to long (tall): tidyr

Now each var is boardings, averages, or alightings. We want to separate these so we can have these by line. Remember “.” is special character:

long = separate(long, var, into = c("line", "type"), sep = "_")
head(long, 2)
# A tibble: 2 x 6
  day     date       daily line   type      number
  <chr>   <date>     <dbl> <chr>  <chr>      <dbl>
1 Monday  2010-01-11   952 orange Boardings    877
2 Tuesday 2010-01-12   796 orange Boardings    777
unique(long$line)
[1] "orange" "purple" "green"  "banner"
unique(long$type)
[1] "Boardings"  "Alightings" "Average"   

Re-uniting all the lines

If we had the opposite problem, we could use the unite function:

reunited = long %>% 
  unite(col = var, line, type, sep = "_")  
reunited %>% select(day, var) %>% head(3) %>% print
# A tibble: 3 x 2
  day       var             
  <chr>     <chr>           
1 Monday    orange_Boardings
2 Tuesday   orange_Boardings
3 Wednesday orange_Boardings

We could also use paste/paste0.

Lab Part 2

Reshaping data from long (tall) to wide (fat): tidyr

In tidyr, the spread function spreads rows into columns. Now we have a long data set, but we want to separate the Average, Alightings and Boardings into different columns:

# have to remove missing days
wide = long %>% filter(!is.na(date))
wide = wide %>% spread(type, number)
head(wide)
# A tibble: 6 x 7
  day    date       daily line   Alightings Average Boardings
  <chr>  <date>     <dbl> <chr>       <dbl>   <dbl>     <dbl>
1 Friday 2010-01-15 1644  banner         NA      NA        NA
2 Friday 2010-01-15 1644  green          NA      NA        NA
3 Friday 2010-01-15 1644  orange       1643    1644      1645
4 Friday 2010-01-15 1644  purple         NA      NA        NA
5 Friday 2010-01-22 1394. banner         NA      NA        NA
6 Friday 2010-01-22 1394. green          NA      NA        NA

Lab Part 3

Merging: Simple Data

base has baseline data for ids 1 to 10 and Age

base <- tibble(id = 1:10, Age = seq(55,60, length=10))
head(base, 2)
# A tibble: 2 x 2
     id   Age
  <int> <dbl>
1     1  55  
2     2  55.6

visits has ids 1 to 8, then 11 (new id), and 3 visits and outcome

visits <- tibble(id = c(rep(1:8, 3), 11), visit= c(rep(1:3, 8), 3),
                    Outcome = seq(10,50, length=25))
tail(visits, 2)
# A tibble: 2 x 3
     id visit Outcome
  <dbl> <dbl>   <dbl>
1     8     3    48.3
2    11     3    50  

Joining in dplyr

  • Merging/joining data sets together - usually on key variables, usually “id”
  • ?join - see different types of joining for dplyr
  • Let’s look at https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf
  • inner_join(x, y) - only rows that match for x and y are kept
  • full_join(x, y) - all rows of x and y are kept
  • left_join(x, y) - all rows of x are kept even if not merged with y
  • right_join(x, y) - all rows of y are kept even if not merged with x
  • anti_join(x, y) - all rows from x not in y keeping just columns from x.

Inner Join

ij = inner_join(base, visits)
Joining, by = "id"
dim(ij)
[1] 24  4
tail(ij)
# A tibble: 6 x 4
     id   Age visit Outcome
  <dbl> <dbl> <dbl>   <dbl>
1     7  58.3     1    20  
2     7  58.3     3    33.3
3     7  58.3     2    46.7
4     8  58.9     2    21.7
5     8  58.9     1    35  
6     8  58.9     3    48.3

Left Join

lj = left_join(base, visits)
Joining, by = "id"
dim(lj)
[1] 26  4
tail(lj)
# A tibble: 6 x 4
     id   Age visit Outcome
  <dbl> <dbl> <dbl>   <dbl>
1     7  58.3     2    46.7
2     8  58.9     2    21.7
3     8  58.9     1    35  
4     8  58.9     3    48.3
5     9  59.4    NA    NA  
6    10  60      NA    NA  

Logging the joins

The tidylog package can show you log outputs from dplyr (newly added). You will need to install to use.

library(tidylog)
left_join(base, visits)
Joining, by = "id"left_join: added 2 columns (visit, Outcome)
           > rows only in x    2
           > rows only in y  ( 1)
           > matched rows     24    (includes duplicates)
           >                 ====
           > rows total       26
# A tibble: 26 x 4
      id   Age visit Outcome
   <dbl> <dbl> <dbl>   <dbl>
 1     1  55       1    10  
 2     1  55       3    23.3
 3     1  55       2    36.7
 4     2  55.6     2    11.7
 5     2  55.6     1    25  
 6     2  55.6     3    38.3
 7     3  56.1     3    13.3
 8     3  56.1     2    26.7
 9     3  56.1     1    40  
10     4  56.7     1    15  
# … with 16 more rows

Right Join

rj = right_join(base, visits)
Joining, by = "id"right_join: added 2 columns (visit, Outcome)
            > rows only in x  ( 2)
            > rows only in y    1
            > matched rows     24
            >                 ====
            > rows total       25
tail(rj, 3)
# A tibble: 3 x 4
     id   Age visit Outcome
  <dbl> <dbl> <dbl>   <dbl>
1     7  58.3     2    46.7
2     8  58.9     3    48.3
3    11  NA       3    50  

Right Join: Switching arguments

rj2 = right_join(visits, base)
Joining, by = "id"right_join: added one column (Age)
            > rows only in x  ( 1)
            > rows only in y    2
            > matched rows     24    (includes duplicates)
            >                 ====
            > rows total       26
tail(rj2, 3)
# A tibble: 3 x 4
     id visit Outcome   Age
  <dbl> <dbl>   <dbl> <dbl>
1     8     3    48.3  58.9
2     9    NA    NA    59.4
3    10    NA    NA    60  
identical(rj2, lj) ## after some rearranging
[1] TRUE

Full Join

fj = full_join(base, visits)
Joining, by = "id"full_join: added 2 columns (visit, Outcome)
           > rows only in x    2
           > rows only in y    1
           > matched rows     24    (includes duplicates)
           >                 ====
           > rows total       27
tail(fj, 3)
# A tibble: 3 x 4
     id   Age visit Outcome
  <dbl> <dbl> <dbl>   <dbl>
1     9  59.4    NA      NA
2    10  60      NA      NA
3    11  NA       3      50

Duplicated

  • The duplicated command can give you indications if there are duplications in a vector:
duplicated(1:5)
[1] FALSE FALSE FALSE FALSE FALSE
duplicated(c(1:5, 1))
[1] FALSE FALSE FALSE FALSE FALSE  TRUE
fj %>% mutate(dup_id = duplicated(id))
mutate: new variable 'dup_id' with 2 unique values and 0% NA
# A tibble: 27 x 5
      id   Age visit Outcome dup_id
   <dbl> <dbl> <dbl>   <dbl> <lgl> 
 1     1  55       1    10   FALSE 
 2     1  55       3    23.3 TRUE  
 3     1  55       2    36.7 TRUE  
 4     2  55.6     2    11.7 FALSE 
 5     2  55.6     1    25   TRUE  
 6     2  55.6     3    38.3 TRUE  
 7     3  56.1     3    13.3 FALSE 
 8     3  56.1     2    26.7 TRUE  
 9     3  56.1     1    40   TRUE  
10     4  56.7     1    15   FALSE 
# … with 17 more rows

Lab Part 4

Finding the First (or Last) record

Website

Reshaping data from long (tall) to wide (fat): tidyr

We can use rowSums to see if any values in the row is NA and keep if the row, which is a combination of date and line type has any non-missing data.

head(wide, 3)
# A tibble: 3 x 7
  day    date       daily line   Alightings Average Boardings
  <chr>  <date>     <dbl> <chr>       <dbl>   <dbl>     <dbl>
1 Friday 2010-01-15  1644 banner         NA      NA        NA
2 Friday 2010-01-15  1644 green          NA      NA        NA
3 Friday 2010-01-15  1644 orange       1643    1644      1645
not_namat = wide %>% select(Alightings, Average, Boardings)
select: dropped 4 variables (day, date, daily, line)
not_namat = !is.na(not_namat)
head(not_namat, 2)
     Alightings Average Boardings
[1,]      FALSE   FALSE     FALSE
[2,]      FALSE   FALSE     FALSE
wide$good = rowSums(not_namat) > 0

Reshaping data from long (tall) to wide (fat): tidyr

Now we can filter only the good rows and delete the good column.

wide = wide %>% filter(good) %>% select(-good)
filter: removed 1,700 rows (37%), 2,884 rows remaining
select: dropped one variable (good)
head(wide)
# A tibble: 6 x 7
  day    date       daily line   Alightings Average Boardings
  <chr>  <date>     <dbl> <chr>       <dbl>   <dbl>     <dbl>
1 Friday 2010-01-15 1644  orange       1643   1644       1645
2 Friday 2010-01-22 1394. orange       1388   1394.      1401
3 Friday 2010-01-29 1332  orange       1322   1332       1342
4 Friday 2010-02-05 1218. orange       1204   1218.      1231
5 Friday 2010-02-12  671  orange        678    671        664
6 Friday 2010-02-19 1642  orange       1647   1642       1637

Finding the First (or Last) record

  • slice allows you to select records (compared to first/last on a vector)
long = long %>% filter(!is.na(number) & number > 0)
filter: removed 5,364 rows (39%), 8,388 rows remaining
first_and_last = long %>% arrange(date) %>% # arrange by date
  filter(type == "Boardings") %>% # keep boardings only
  group_by(line) %>% # group by line
  slice( c(1, n())) # select ("slice") first and last (n() command) lines
filter: removed 5,630 rows (67%), 2,758 rows remaining
group_by: one grouping variable (line)
slice (grouped): removed 2,750 rows (>99%), 8 rows remaining
first_and_last %>%  head(4)
# A tibble: 4 x 6
# Groups:   line [2]
  day     date        daily line   type      number
  <chr>   <date>      <dbl> <chr>  <chr>      <dbl>
1 Monday  2012-06-04 13342. banner Boardings    520
2 Friday  2013-03-01    NA  banner Boardings    817
3 Tuesday 2011-11-01  8873  green  Boardings    887
4 Friday  2013-03-01    NA  green  Boardings   2592

Merging in base R (not covered)

Data Merging/Append in Base R

  • merge() is the most common way to do this with data sets
    • we will use the “join” functions from dplyr
  • rbind/cbind - row/column bind, respectively
    • rbind is the equivalent of “appending” in Stata or “setting” in SAS
    • cbind allows you to add columns in addition to the previous ways
  • t() can transpose data but doesn’t make it a data.frame

Merging

merged.data <- merge(base, visits, by = "id")
head(merged.data, 5)
  id      Age visit  Outcome
1  1 55.00000     1 10.00000
2  1 55.00000     3 23.33333
3  1 55.00000     2 36.66667
4  2 55.55556     2 11.66667
5  2 55.55556     1 25.00000
dim(merged.data)
[1] 24  4

Merging

all.data <- merge(base, visits, by = "id", all = TRUE)
tail(all.data)
   id      Age visit  Outcome
22  8 58.88889     2 21.66667
23  8 58.88889     1 35.00000
24  8 58.88889     3 48.33333
25  9 59.44444    NA       NA
26 10 60.00000    NA       NA
27 11       NA     3 50.00000
dim(all.data)
[1] 27  4