dplyr and tidy data

class: center, middle, inverse, title-slide

.title[
# dplyr and tidy data
]
.subtitle[
## STA35B: Statistical Data Science 2
]
.author[
### Spencer Frei
]

---

## Reminder about packages

Packages are things we load using `library()`.

We'll primarily use the `tidyverse` library, but occasionally we'll use other packages.

If you haven't installed a package `PACKAGENAME` before, you'll get an error.

To install a package, `install.packages(PACKAGENAME)`.

---

## dataframes/tibbles 
The main objects we'll care about in R are dataframes and tibbles - you should be familiar with these.

```r
library(nycflights13) # loads the `flights` tibble into environment
flights
```

```
# A tibble: 336,776 × 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
# ℹ 336,766 more rows
# ℹ 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>
```

---

## dataframes/tibbles
Useful functions for inspecting dataframes: `head()` and `str()`

```r
head(flights)
```

```
# A tibble: 6 × 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
# ℹ 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>
```

---

## dataframes/tibbles
Useful functions for inspecting dataframes: `head()` and `str()`

```r
str(flights)
```

```
tibble [336,776 × 19] (S3: tbl_df/tbl/data.frame)
 $ year          : int [1:336776] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 ...
 $ month         : int [1:336776] 1 1 1 1 1 1 1 1 1 1 ...
 $ day           : int [1:336776] 1 1 1 1 1 1 1 1 1 1 ...
 $ dep_time      : int [1:336776] 517 533 542 544 554 554 555 557 557 558 ...
 $ sched_dep_time: int [1:336776] 515 529 540 545 600 558 600 600 600 600 ...
 $ dep_delay     : num [1:336776] 2 4 2 -1 -6 -4 -5 -3 -3 -2 ...
 $ arr_time      : int [1:336776] 830 850 923 1004 812 740 913 709 838 753 ...
...
```

---

## dataframes/tibbles
To return the data from a column in a dataframe/tibble, use `df$COLUMN` (returns vector)

```r
flights$distance
```

```
    [1] 1400 1416 1089 1576  762  719 1065  229  944  733 1028 1005 2475 2565
   [15] 1389  187 2227 1076  762 1023 1020  502 1085  760 1085  719 2586 1074
   [29] 1598  746 2133 1096 1416 1008 1020 2153 1598 2454  733  185  529  212
   [43] 1389  950  187 1023  529 1065  301 1620 2434 2248 1182 1035 1990 2586
   [57] 1096  733  944 1147  997 1076  762 2475 1096 1069  502 1068 1608 2475
...
```
`df[,"COLUMN"]` (returns tibble)

```r
flights[,"distance"]
```

```
# A tibble: 336,776 × 1
 distance
 <dbl>
 1 1400
 2 1416
...
```

---
## Piping: `%>%` and `|>`
The operators `%>%` (and equivalently, `|>`) allow for you to write operations sequentially.

```r
mean(flights$distance)
```

```
[1] 1039.913
```
Same as:

```r
flights$distance %>%
  mean
```

```
[1] 1039.913
```
We will see more complex examples of this shortly.

---
### The `select()` function

Returns a tibble with the columns specified, in order specified. 
Do not need quotation marks.

```r
(colnames(flights))
```

```
 [1] "year"           "month"          "day"            "dep_time"      
 [5] "sched_dep_time" "dep_delay"      "arr_time"       "sched_arr_time"
 [9] "arr_delay"      "carrier"        "flight"         "tailnum"       
[13] "origin"         "dest"           "air_time"       "distance"      
[17] "hour"           "minute"         "time_hour"     
```

```r
after_select <- flights %>%
 select(time_hour, origin, dest, air_time, distance)
(colnames(after_select))
```

```
[1] "time_hour" "origin"    "dest"      "air_time"  "distance" 
```

Helper functions for `select()`:

*    `starts_with("abc")`: matches names that begin with “abc”.
*    `ends_with("xyz")`: matches names that end with “xyz”.
*    `contains("ijk")`: matches names that contain “ijk”.
*    `num_range("x", 1:3)`: matches x1, x2 and x3.

---

## The `select()` function

```r
colnames(flights)
```

```r
after_select <- flights %>%
 select(time_hour, origin, dest, contains("sched"))
colnames(after_select)
```

```
[1] "time_hour"      "origin"         "dest"           "sched_dep_time"
[5] "sched_arr_time"
```

---
## The `filter()` function

```r
# data = data to filter
# expr<#> = expression used to filter data, typically using ==, >, etc
filter(<data>, <expr1>)
filter(<data>, <expr1>, <expr2>, <expr3>)
```

Example: flights on February 15

```r
filter(flights, month == 2, day == 15)
```

```
# A tibble: 954 × 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 2 15 3 2358 5 503 438
 2 2013 2 15 6 2115 171 48 2214
 3 2013 2 15 6 2250 76 117 5
 4 2013 2 15 22 2230 112 510 312
 5 2013 2 15 36 2352 44 607 437
...
```

---
## The `filter()` function

```r
# data = data to filter
# expr = expression used to filter data, typically using ==, >, etc
filter(<data>, <expr>)
```

Expressions can be built up using comparison operators and logical operators.
* `&` is "and"
* `|` is "or"
* `!` is "not"
* `%in%` is a membership checker (is an object inside of a vector)
* `>`, `<` mean greater than, less than
* `>=`, `<=` mean greater than or equal to, less than or requal to
* `==` means "is equal"
* `!=` means "is not equal"

---

## Examples of `filter()`

```r
flights %>%
 select(time_hour, origin, dest, air_time, distance) %>%
 filter(distance < 120)
```

```
# A tibble: 2,076 × 5
 time_hour origin dest air_time distance
 <dttm> <chr> <chr> <dbl> <dbl>
 1 2013-01-01 09:00:00 LGA PHL 32 96
 2 2013-01-01 13:00:00 EWR BDL 25 116
 3 2013-01-01 16:00:00 JFK PHL 35 94
...
```

```r
flights %>%
 select(time_hour, origin, dest, air_time, distance) %>%
 filter(distance < 120, origin == 'EWR')
```

```
# A tibble: 493 × 5
 time_hour origin dest air_time distance
 <dttm> <chr> <chr> <dbl> <dbl>
 1 2013-01-01 13:00:00 EWR BDL 25 116
 2 2013-01-01 22:00:00 EWR BDL 24 116
...
```

---
## Spot the error?

```r
flights %>%
  select(time_hour, origin, dest, air_time, distance) %>%
  filter(origin == SFO)
```

---

## Spot the error?

```r
flights %>%
  select(time_hour, origin, dest, air_time, distance) %>%
  filter(origin == 'SFO')
```

---
## Equivalent ways to filter on multiple conditions

```r
flights %>%
 select(time_hour, origin, dest, air_time, distance) %>%
 filter(distance < 120, origin == "EWR")
```

```r
flights %>%
 select(time_hour, origin, dest, air_time, distance) %>%
 filter(distance < 120 & origin == "EWR")
```

```r
flights %>%
 select(time_hour, origin, dest, air_time, distance) %>%
 filter(distance < 120) %>%
 filter(origin == "EWR")
```

---

## `count()`

.pull-left[ 
If you want to count occurrences of different pairs/"tuples" of values across columns:

```r
dataframe %>%
  count(column1, column2)
```
e.g. for flights, want to count how many flights by origin:

```r
flights %>%
  count(origin)
```

```
# A tibble: 3 × 2
 origin n
 <chr> <int>
1 EWR 120835
2 JFK 111279
3 LGA 104662
```

]

.pull-right[
Or how many origin-destination pairs:

```r
flights %>%
  count(origin, dest, sort = TRUE)
```

```
# A tibble: 224 × 3
 origin dest n
 <chr> <chr> <int>
 1 JFK LAX 11262
 2 LGA ATL 10263
 3 LGA ORD 8857
 4 JFK SFO 8204
 5 LGA CLT 6168
 6 EWR ORD 6100
...
```
]

---

## `mutate()`
The function `mutate()` adds new columns to the data frame using functions of extant columns.

Example:

```r
small_flights <- flights %>%
 select(origin, dest, air_time, distance, air_time)
small_flights %>%
 mutate(hours = air_time / 60,
 mi_per_hour = distance / hours, 
 km_per_hour = 1.61 * mi_per_hour)
```

```
# A tibble: 336,776 × 7
 origin dest air_time distance hours mi_per_hour km_per_hour
 <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
 1 EWR IAH 227 1400 3.78 370. 596.
 2 LGA IAH 227 1416 3.78 374. 603.
 3 JFK MIA 160 1089 2.67 408. 657.
 4 JFK BQN 183 1576 3.05 517. 832.
...
```

Notice how `mi_per_hour` uses `hours`, which was created within the same `mutate()` call.

---

## Summary statistics and missing data
Common summary statistics of interest in data:
* Mean (`mean()`)
* Min/max (`min()`, `max()`)
* Median (`median()`)
* Standard deviation / variance (`sd()`, `var()`)

R denotes missing data using `NA`.  Typically, if you compute a function of a vector with `NA`s, it will return `NA`, unless you put `na.rm=TRUE`.

```r
x <- c(-1, 0, 1, NA)
(mean(x))
```

```
[1] NA
```

```r
(mean(x, na.rm=TRUE))
```

```
[1] 0
```

---
## `summarise()`
If you want to compute summary statistics of dataframe, use `summarise()`.

```r
# data = data you want to create a new variable from
# new var name = the name your new variable will be
# calc exp = the calculation you want to perform
summarise(<data>, <new var name> = <calc exp>)
```

```r
flights %>%
  select(origin, dest, air_time, distance) %>%
  mutate(hours = air_time / 60,
         mi_per_hour = distance / hours) %>%
  summarise(flight_time = mean(mi_per_hour))
```

```
# A tibble: 1 × 1
 flight_time
 <dbl>
1 NA
```

---
## `summarize()`
If you want to compute summary statistics of dataframe, use `summarise()`.

```r
# data = data you want to create a new variable from
# new var name = the name your new variable will be
# calc exp = the calculation you want to perform
summarize(<data>, <new var name> = <calc exp>)
```

```r
flights %>%
  select(origin, dest, air_time, distance) %>%
  mutate(hours = air_time / 60,
         mi_per_hour = distance / hours) %>%
  summarize(flight_time = mean(mi_per_hour, na.rm=TRUE))
```

```
# A tibble: 1 × 1
 flight_time
 <dbl>
1 394.
```

---
## `group_by()` and `summarise()`
If you would like to compute summary statistics per category, use `group_by()` first.

```r
flights %>%
  select(origin, dest, air_time, distance) %>%
  group_by(origin) %>%
  summarize(median_distance = median(distance, na.rm=TRUE),
            max_air_time = max(air_time, na.rm=TRUE))
```

```
# A tibble: 3 × 3
 origin median_distance max_air_time
 <chr> <dbl> <dbl>
1 EWR 872 695
2 JFK 1069 691
3 LGA 762 331
```

---
## Complex calculations

.delay[

Average flight time, in hours, per origin airport for flights > 500 miles long?

]
.delay[

```r
flights %>%
  select(origin, dest, air_time, distance) %>%
  mutate(air_time_hrs = air_time / 60) %>%
  filter(distance > 500) %>%
  group_by(origin) %>%
  summarize(avg_flight_time_hrs = mean(air_time_hrs, na.rm=TRUE))
```

```
# A tibble: 3 × 2
 origin avg_flight_time_hrs
 <chr> <dbl>
1 EWR 2.99
2 JFK 3.76
3 LGA 2.27
```

]

---
## Complex calculations
Average flight time, in minutes, per origin airport, when flight is > 500 miles long vs. <= 500 miles long?

We need to `group_by` two variables: origin airport, and whether or not flight is > 500 or <= 500 miles.

We need to create the latter variable.

```r
flights %>%
  select(origin, dest, air_time, distance) %>%
  mutate(air_time_hrs = air_time / 60,
         distance_greater_500mi = distance > 500)
```

```
# A tibble: 336,776 × 6
 origin dest air_time distance air_time_hrs distance_greater_500mi
 <chr> <chr> <dbl> <dbl> <dbl> <lgl> 
 1 EWR IAH 227 1400 3.78 TRUE 
 2 LGA IAH 227 1416 3.78 TRUE 
 3 JFK MIA 160 1089 2.67 TRUE 
 4 JFK BQN 183 1576 3.05 TRUE 
...
```

---
## Complex calculations
Average flight time, in minutes, per origin airport, when flight is > 500 miles long vs. <= 500 miles long?

```r
flights %>%
  select(origin, dest, air_time, distance) %>%
  mutate(air_time_hrs = air_time / 60,
         distance_greater_500mi = distance > 500) %>%
  group_by(origin, distance_greater_500mi) %>%
  summarize(avg_flight_time_hrs = mean(air_time_hrs, na.rm=TRUE))
```

```
# A tibble: 6 × 3
# Groups: origin [3]
 origin distance_greater_500mi avg_flight_time_hrs
 <chr> <lgl> <dbl>
1 EWR FALSE 0.900
2 EWR TRUE 2.99 
3 JFK FALSE 0.849
4 JFK TRUE 3.76 
5 LGA FALSE 0.916
6 LGA TRUE 2.27 
```

---

### Tidy data

Multiple equivalent ways of organizing data into a dataframe.

```r
table1
#> # A tibble: 6 × 4
#> country year cases population
#> <chr> <dbl> <dbl> <dbl>
#> 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

table2
#> # A tibble: 12 × 4
#> country year type count
#> <chr> <dbl> <chr> <dbl>
#> 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
#> # ℹ 6 more rows
```
table1 is **tidy** - easier to work with using `tidyverse`.

---
### Tidy data

*    Each variable is a column; each column is a variable.
*    Each observation is a row; each row is an observation.
*    Each value is a cell; each cell is a single value.

![Tidy data](tidy.png)

Why tidy data?  
* Consistency - uniform format makes collaboration easier
* Vectorization - R commands often operate on vectors of data, best for each column to be a vector of data

---
### **Tidying** data

Unfortunately, most real-world datasets you encounter are NOT tidy.

In fact, a significant portion of "data scientist work" consists in tidying data ("cleaning data").

The next two weeks will primarily be about how to tidy/rearrange data so that you can do data analysis and visualization properly.

```r
library(tidyverse) # loads in tidyr::billboard into namespace 
```

```r
billboard
```

```
# A tibble: 317 × 79
 artist track date.entered wk1 wk2 wk3 wk4 wk5 wk6 wk7 wk8
 <chr> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1 2 Pac Baby… 2000-02-26 87 82 72 77 87 94 99 NA
 2 2Ge+her The … 2000-09-02 91 87 92 NA NA NA NA NA
 3 3 Doors D… Kryp… 2000-04-08 81 70 68 67 66 57 54 53
 4 3 Doors D… Loser 2000-10-21 76 76 72 69 67 65 55 59
 5 504 Boyz Wobb… 2000-04-15 57 34 25 17 17 31 36 49
 6 98^0 Give… 2000-08-19 51 39 34 26 26 19 2 2
 7 A*Teens Danc… 2000-07-08 97 97 96 95 100 NA NA NA
...
```

---
### Lengthening data with `pivot_longer()`

```r
billboard
```

Billboard dataset: 
* Each observation is a song
* First three columns are variable describing the song: `artist`, `track`, `date.entered`
* Then 76 columns (`wk1`, `wk2`, ..., `wk76`) saying rank of the song in each week.
* The column names (`week`) are actually a *variable*, while the cell values are another variable (the `rank`)
* To make it tidy, each observation (`artist-track-date.entered-week-rank`) should be a row, so need to be longer dataframe to make tidy.

---

### Lengthening data with `pivot_longer()`

```r
billboard %>%
  pivot_longer(
    cols = starts_with("wk"),
    names_to = "week",
    values_to = "rank"
  )
```

```
# A tibble: 24,092 × 5
 artist track date.entered week rank
 <chr> <chr> <date> <chr> <dbl>
 1 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk1 87
 2 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk2 82
 3 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk3 72
 4 2 Pac Baby Don't Cry (Keep... 2000-02-26 wk4 77
...
```
* `cols` specifies which columns need to be pivoted (= are NOT variables)
* `names_to` names the variable stored in the column names ("week")
* `values_to` names the variable stored in cell values ("rank")
* "week" and "rank" do not appear as column names in billboard, so need quotes

---

### Lengthening data with `pivot_longer()`

```r
billboard %>%
  pivot_longer(
    cols = starts_with("wk"),
    names_to = "week",
    values_to = "rank"
  )
```

* `week` should ideally be a number, not a character
* We can use `readr::parse_numbers()` that extracts first number from string to fix.

---

### Lengthening data with `pivot_longer()`

```r
billboard %>%
  pivot_longer(
    cols = starts_with("wk"),
    names_to = "week",
    values_to = "rank"
  ) %>%
  mutate(week = parse_number(week))
```

```
# A tibble: 24,092 × 5
 artist track date.entered week rank
 <chr> <chr> <date> <dbl> <dbl>
 1 2 Pac Baby Don't Cry (Keep... 2000-02-26 1 87
 2 2 Pac Baby Don't Cry (Keep... 2000-02-26 2 82
 3 2 Pac Baby Don't Cry (Keep... 2000-02-26 3 72
 4 2 Pac Baby Don't Cry (Keep... 2000-02-26 4 77
...
```

---

### Understanding pivoting

.pull-left[

Let's consider toy dataset: three people (A,B,C) with two blood pressure (BP) measurements each.

```r
df
```

```
# A tibble: 3 × 3
 id bp1 bp2
 <chr> <dbl> <dbl>
1 A 100 120
2 B 140 115
3 C 120 125
```

]

--
.pull-right[

For new dataset, want each observation (id, bp) to have its own row:

```r
df %>%
  pivot_longer(
    cols = c(bp1, bp2),
    names_to = "measurement",
    values_to = "value"
  )
```

```
# A tibble: 6 × 3
 id measurement value
 <chr> <chr> <dbl>
1 A bp1 100
2 A bp2 120
3 B bp1 140
4 B bp2 115
5 C bp1 120
6 C bp2 125
```

]

---

.pull-left[

```r
df %>%
  pivot_longer(
    cols = c(bp1, bp2),
    names_to = "measurement",
    values_to = "value"
  )
```
* `cols`: which columns to be pivoted
* `names_to`: names var stored in the col names 
* `values_to` names the var stored in cell values

![Columns that are already vars need to be repeated](variables.png)
If col is already a var, needs to repeat for each pivoted col.

]

.pull-right[

![Col names of pivoted columns become values in a new column](column-names.png)
Pivoted column names become values in a new variable, with name given by `names_to`.
They need to be repeated for each row in the original dataset.

![Cell values become new variable, with name given by values_to](cell-values.png)
Cell values are values in a new variable, with name `values_to`, unwound row by row.
]

---

### Widening data
We'll now use `pivot_wider()` to widen data which is (too) long.

```r
(cms_patient_experience <- tidyr::cms_patient_experience %>% select(-measure_title))
```

```
# A tibble: 500 × 4
 org_pac_id org_nm measure_cd prf_rate
 <chr> <chr> <chr> <dbl>
 1 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP_1 63
 2 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP_2 87
 3 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP_3 86
 4 0446157747 USC CARE MEDICAL GROUP INC CAHPS_GRP_5 57
...
```
The basic unit studied is an organization, but it's spread across six rows for different measurements.

---

### Widening data

```r
cms_patient_experience %>%
  pivot_wider(
    id_cols = starts_with("org"), # org_pac_id and org_nm are identifiers
    names_from = measure_cd,
    values_from = prf_rate
  )
```

```
# A tibble: 95 × 8
 org_pac_id org_nm CAHPS_GRP_1 CAHPS_GRP_2 CAHPS_GRP_3 CAHPS_GRP_5 CAHPS_GRP_8
 <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
 1 0446157747 USC C… 63 87 86 57 85
 2 0446162697 ASSOC… 59 85 83 63 88
 3 0547164295 BEAVE… 49 NA 75 44 73
 4 0749333730 CAPE … 67 84 85 65 82
...
```

If you don't supply `id_cols`, R assumes that all columns EXCEPT for `names_from` and `values_from` are id_cols. 
---

### Understanding `pivot_wider()`

.pull-left[
Dataset where two patients (A, B), with between 2 and 3 BP measurements.

```r
df
```

```
# A tibble: 5 × 3
 id measurement value
 <chr> <chr> <dbl>
1 A bp1 100
2 B bp1 140
3 B bp2 115
4 A bp2 120
5 A bp3 105
```

]

.pull-right[

```r
df %>%
  pivot_wider(
    names_from = measurement,
    values_from = value
  )
```

```
# A tibble: 2 × 4
 id bp1 bp2 bp3
 <chr> <dbl> <dbl> <dbl>
1 A 100 120 105
2 B 140 115 NA
```

]

Since there is no measurement for bp3 for B, R puts in `NA`.

Since `id_cols` is empty, R assumes that all columns EXCEPT for `names_from` and `values_from` are id_cols.

---

### Going from long to wide and back again

.pull-left[

```r
df
```

```r
(wide_df <- df %>%
 pivot_wider(
 names_from = measurement,
 values_from = value
 ) )
```

```
# A tibble: 2 × 4
 id bp1 bp2 bp3
 <chr> <dbl> <dbl> <dbl>
1 A 100 120 105
2 B 140 115 NA
```

]

.pull-right[

```r
(long_df <- wide_df %>%
 pivot_longer(
 cols = c(bp1, bp2, bp3),
 names_to = "measurement",
 values_to = "value"))
```

```
# A tibble: 6 × 3
 id measurement value
 <chr> <chr> <dbl>
1 A bp1 100
2 A bp2 120
3 A bp3 105
4 B bp1 140
5 B bp2 115
6 B bp3 NA
```
Note that we now have an additional row to account for the `NA`. 
]

---

### Tidy check

.pull-left[
Make the table wider.

```r
df
```

```
# A tibble: 8 × 4
 City Date Measurement Value
 <chr> <chr> <chr> <dbl>
1 CityA 2024-01-01 Temperature 20
2 CityA 2024-01-01 Humidity 80
3 CityA 2024-01-02 Temperature 22
4 CityA 2024-01-02 Humidity 82
5 CityB 2024-01-01 Temperature 18
6 CityB 2024-01-01 Humidity 85
7 CityB 2024-01-02 Temperature 19
8 CityB 2024-01-02 Humidity 88
```
Not tidy, since each variable is not a column, e.g. temperature/humidity.

"Tidyness" can be a bit ambiguous, but if a column has different units (e.g., degrees vs. % humidity), likely not tidy.

]

.pull-right[

```r
df %>%
  pivot_wider(
    names_from = Measurement,
    values_from = Value
  )
```

```
# A tibble: 4 × 4
 City Date Temperature Humidity
 <chr> <chr> <dbl> <dbl>
1 CityA 2024-01-01 20 80
2 CityA 2024-01-02 22 82
3 CityB 2024-01-01 18 85
4 CityB 2024-01-02 19 88
```

]

---

.pull-left[
Make the table longer by having a column called Subject, values Math/Science.

```r
scores
```

```
# A tibble: 3 × 3
 Student Math Science
 <chr> <dbl> <dbl>
1 Alice 85 90
2 Bob 92 78
3 Charlie 88 95
```

Is the following code correct?

```r
scores %>%
  pivot_longer(
    cols = c(Math, Science),
    names_to = Subject,
    values_to = Score
  )
```
]

.pull-right[

No, since `scores` tries to refer to `Subject` and `Score` which are not columns.

```r
scores %>%
  pivot_longer(
    cols = c(Math, Science),
    names_to = "Subject",
    values_to = "Score"
  )
```

```
# A tibble: 6 × 3
 Student Subject Score
 <chr> <chr> <dbl>
1 Alice Math 85
2 Alice Science 90
3 Bob Math 92
4 Bob Science 78
5 Charlie Math 88
6 Charlie Science 95
```

]