• is.na(),any(is.na()), all(is.na()),count(), and functions from naniar like gg_miss_var() and miss_var_summary can help determine if we have NA values
• miss_var_which() can help you drop columns that have any missing values.
• filter() automatically removes NA values
• drop_na() can help you remove NA values
• NA values can change your calculation results
• think about what NA values represent - don’t drop them if you shouldn’t
• replace_na() will replace `NA values with a particular value

• case_when() can recode entire values based on conditions
  • remember case_when() needs TRUE ~ varaible to keep values that aren’t specified by conditions, otherwise will be NA
• stringr package has great functions for looking for specific parts of values especially filter() and str_detect() combined
  • also has other useful string manipulation functions like str_replace() and more!
  • separate() can split columns into additional columns
  • unite() can combine columns

📃Cheatsheet

In this module, we will show you how to:

1. Reshape data from wide to long
2. Reshape data from long to wide
3. Merge Data/Joins

Data is stored differently in the tibble.

Wide: has many columns

# A tibble: 1 × 4
  State   June_vacc_rate May_vacc_rate April_vacc_rate
  <chr>            <dbl>         <dbl>           <dbl>
1 Alabama          0.516         0.514           0.511

Long: column names become data

# A tibble: 3 × 3
  State   name            value
  <chr>   <chr>           <dbl>
1 Alabama June_vacc_rate  0.516
2 Alabama May_vacc_rate   0.514
3 Alabama April_vacc_rate 0.511

Wide: multiple columns per individual, values spread across multiple columns

# A tibble: 2 × 4
  State   June_vacc_rate May_vacc_rate April_vacc_rate
  <chr>            <dbl>         <dbl>           <dbl>
1 Alabama          0.516         0.514           0.511
2 Alaska           0.627         0.626           0.623

Long: multiple rows per observation, a single column contains the values

# A tibble: 6 × 3
  State   name            value
  <chr>   <chr>           <dbl>
1 Alabama June_vacc_rate  0.516
2 Alabama May_vacc_rate   0.514
3 Alabama April_vacc_rate 0.511
4 Alaska  June_vacc_rate  0.627
5 Alaska  May_vacc_rate   0.626
6 Alaska  April_vacc_rate 0.623

https://github.com/gadenbuie/tidyexplain/blob/main/images/tidyr-pivoting.gif

Wide: Easier for humans to read

# A tibble: 2 × 4
  State   June_vacc_rate May_vacc_rate April_vacc_rate
  <chr>            <dbl>         <dbl>           <dbl>
1 Alabama          0.516         0.514           0.511
2 Alaska           0.627         0.626           0.623

Long: Easier for R to make plots & do analysis

# A tibble: 6 × 3
  State   name            value
  <chr>   <chr>           <dbl>
1 Alabama June_vacc_rate  0.516
2 Alabama May_vacc_rate   0.514
3 Alabama April_vacc_rate 0.511
4 Alaska  June_vacc_rate  0.627
5 Alaska  May_vacc_rate   0.626
6 Alaska  April_vacc_rate 0.623

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

• pivot_longer - make multiple columns into variables, (wide to long)
• pivot_wider - make a variable into multiple columns, (long to wide)
• separate - string into multiple columns (review)

The reshape command exists. Its arguments are considered more confusing, so we don’t recommend it.

You might see old functions gather and spread when googling. These are older iterations of pivot_longer and pivot_wider, respectively.

wide_vacc <- read_csv(
  file = "https://jhudatascience.org/intro_to_r/data/wide_vacc.csv")

wide_vacc

# A tibble: 1 × 3
  June_vacc_rate May_vacc_rate April_vacc_rate
           <dbl>         <dbl>           <dbl>
1          0.516         0.514           0.511

long_vacc <- wide_vacc %>% pivot_longer(cols = everything())
long_vacc

# A tibble: 3 × 2
  name            value
  <chr>           <dbl>
1 June_vacc_rate  0.516
2 May_vacc_rate   0.514
3 April_vacc_rate 0.511

wide_vacc

# A tibble: 1 × 3
  June_vacc_rate May_vacc_rate April_vacc_rate
           <dbl>         <dbl>           <dbl>
1          0.516         0.514           0.511

long_vacc <- wide_vacc %>% pivot_longer(cols = everything(),
                                        names_to = "Month",
                                        values_to = "Rate")
long_vacc

# A tibble: 3 × 2
  Month            Rate
  <chr>           <dbl>
1 June_vacc_rate  0.516
2 May_vacc_rate   0.514
3 April_vacc_rate 0.511

Newly created column names are enclosed in quotation marks.

http://jhudatascience.org/intro_to_r/data/Charm_City_Circulator_Ridership.csv

library(jhur)
circ <- read_circulator()
head(circ, 5)

# A tibble: 5 × 15
  day       date  orangeBoardings orangeAlightings orangeAverage purpleBoardings
  <chr>     <chr>           <dbl>            <dbl>         <dbl>           <dbl>
1 Monday    01/1…             877             1027          952               NA
2 Tuesday   01/1…             777              815          796               NA
3 Wednesday 01/1…            1203             1220         1212.              NA
4 Thursday  01/1…            1194             1233         1214.              NA
5 Friday    01/1…            1645             1643         1644               NA
# ℹ 9 more variables: purpleAlightings <dbl>, purpleAverage <dbl>,
#   greenBoardings <dbl>, greenAlightings <dbl>, greenAverage <dbl>,
#   bannerBoardings <dbl>, bannerAlightings <dbl>, bannerAverage <dbl>,
#   daily <dbl>

Let’s imagine we want to create a table of average boardings by route/line. Results should look something like:

# A tibble: 4 × 2
  line   avg_boardings
  <chr>  <chr>        
1 orange 600(?)       
2 purple 700(?)       
3 green  500(?)       
4 banner 400(?)       

long <- circ %>% 
  pivot_longer(starts_with(c("orange","purple","green","banner")))
long

# A tibble: 13,752 × 5
   day    date       daily name             value
   <chr>  <chr>      <dbl> <chr>            <dbl>
 1 Monday 01/11/2010   952 orangeBoardings    877
 2 Monday 01/11/2010   952 orangeAlightings  1027
 3 Monday 01/11/2010   952 orangeAverage      952
 4 Monday 01/11/2010   952 purpleBoardings     NA
 5 Monday 01/11/2010   952 purpleAlightings    NA
 6 Monday 01/11/2010   952 purpleAverage       NA
 7 Monday 01/11/2010   952 greenBoardings      NA
 8 Monday 01/11/2010   952 greenAlightings     NA
 9 Monday 01/11/2010   952 greenAverage        NA
10 Monday 01/11/2010   952 bannerBoardings     NA
# ℹ 13,742 more rows

Un-pivoted columns (day, date, daily) are similar

circ %>% select(day, date, daily) %>% head()

# A tibble: 6 × 3
  day       date       daily
  <chr>     <chr>      <dbl>
1 Monday    01/11/2010  952 
2 Tuesday   01/12/2010  796 
3 Wednesday 01/13/2010 1212.
4 Thursday  01/14/2010 1214.
5 Friday    01/15/2010 1644 
6 Saturday  01/16/2010 1490.

long %>% select(day, date, daily) %>% head()

# A tibble: 6 × 3
  day    date       daily
  <chr>  <chr>      <dbl>
1 Monday 01/11/2010   952
2 Monday 01/11/2010   952
3 Monday 01/11/2010   952
4 Monday 01/11/2010   952
5 Monday 01/11/2010   952
6 Monday 01/11/2010   952

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

long <- long %>% mutate(
  name = str_replace(string = name, pattern = "B", replacement = "_B"),
  name = str_replace(string = name, pattern = "A", replacement = "_A")
)
long

# A tibble: 13,752 × 5
   day    date       daily name              value
   <chr>  <chr>      <dbl> <chr>             <dbl>
 1 Monday 01/11/2010   952 orange_Boardings    877
 2 Monday 01/11/2010   952 orange_Alightings  1027
 3 Monday 01/11/2010   952 orange_Average      952
 4 Monday 01/11/2010   952 purple_Boardings     NA
 5 Monday 01/11/2010   952 purple_Alightings    NA
 6 Monday 01/11/2010   952 purple_Average       NA
 7 Monday 01/11/2010   952 green_Boardings      NA
 8 Monday 01/11/2010   952 green_Alightings     NA
 9 Monday 01/11/2010   952 green_Average        NA
10 Monday 01/11/2010   952 banner_Boardings     NA
# ℹ 13,742 more rows

• first argument - which column should be split up?
• “into =” gives names to the new columns
• “sep =” to show where the separation should happen.

long <- long %>% 
  separate(name, into = c("line", "type"), sep = "_")
long

# A tibble: 13,752 × 6
   day    date       daily line   type       value
   <chr>  <chr>      <dbl> <chr>  <chr>      <dbl>
 1 Monday 01/11/2010   952 orange Boardings    877
 2 Monday 01/11/2010   952 orange Alightings  1027
 3 Monday 01/11/2010   952 orange Average      952
 4 Monday 01/11/2010   952 purple Boardings     NA
 5 Monday 01/11/2010   952 purple Alightings    NA
 6 Monday 01/11/2010   952 purple Average       NA
 7 Monday 01/11/2010   952 green  Boardings     NA
 8 Monday 01/11/2010   952 green  Alightings    NA
 9 Monday 01/11/2010   952 green  Average       NA
10 Monday 01/11/2010   952 banner Boardings     NA
# ℹ 13,742 more rows

Filter by Boardings only..

long <- long %>% 
  filter(type == "Boardings")
long

# A tibble: 4,584 × 6
   day       date       daily line   type      value
   <chr>     <chr>      <dbl> <chr>  <chr>     <dbl>
 1 Monday    01/11/2010  952  orange Boardings   877
 2 Monday    01/11/2010  952  purple Boardings    NA
 3 Monday    01/11/2010  952  green  Boardings    NA
 4 Monday    01/11/2010  952  banner Boardings    NA
 5 Tuesday   01/12/2010  796  orange Boardings   777
 6 Tuesday   01/12/2010  796  purple Boardings    NA
 7 Tuesday   01/12/2010  796  green  Boardings    NA
 8 Tuesday   01/12/2010  796  banner Boardings    NA
 9 Wednesday 01/13/2010 1212. orange Boardings  1203
10 Wednesday 01/13/2010 1212. purple Boardings    NA
# ℹ 4,574 more rows

Now our data is more tidy, and we can take the averages easily!

long %>% 
  group_by(line) %>% 
  summarize("avg_boardings" = mean(value, na.rm = TRUE))

# A tibble: 4 × 2
  line   avg_boardings
  <chr>          <dbl>
1 banner          830.
2 green          1929.
3 orange         3031.
4 purple         4127.

There are many ways to select the columns we want. Check out https://dplyr.tidyverse.org/reference/dplyr_tidy_select.html to look at more column selection options.

circ %>% 
  pivot_longer( !c(day, date, daily))

# A tibble: 13,752 × 5
   day    date       daily name             value
   <chr>  <chr>      <dbl> <chr>            <dbl>
 1 Monday 01/11/2010   952 orangeBoardings    877
 2 Monday 01/11/2010   952 orangeAlightings  1027
 3 Monday 01/11/2010   952 orangeAverage      952
 4 Monday 01/11/2010   952 purpleBoardings     NA
 5 Monday 01/11/2010   952 purpleAlightings    NA
 6 Monday 01/11/2010   952 purpleAverage       NA
 7 Monday 01/11/2010   952 greenBoardings      NA
 8 Monday 01/11/2010   952 greenAlightings     NA
 9 Monday 01/11/2010   952 greenAverage        NA
10 Monday 01/11/2010   952 bannerBoardings     NA
# ℹ 13,742 more rows

pivot_wider() - spreads row data into columns (tidyr package)

• names_from = the old column whose contents will be spread into multiple new column names.
• values_from = the old column whose contents will fill in the values of those new columns.

{wide_data} <- {long_data} %>% 
  pivot_wider(names_from = {Old column name: contains new column names},
              values_from = {Old column name: contains new cell values})

long_vacc

# A tibble: 3 × 2
  Month            Rate
  <chr>           <dbl>
1 June_vacc_rate  0.516
2 May_vacc_rate   0.514
3 April_vacc_rate 0.511

wide_vacc <- long_vacc %>% pivot_wider(names_from = "Month", 
                                       values_from = "Rate") 
wide_vacc

# A tibble: 1 × 3
  June_vacc_rate May_vacc_rate April_vacc_rate
           <dbl>         <dbl>           <dbl>
1          0.516         0.514           0.511

long

# A tibble: 4,584 × 6
   day       date       daily line   type      value
   <chr>     <chr>      <dbl> <chr>  <chr>     <dbl>
 1 Monday    01/11/2010  952  orange Boardings   877
 2 Monday    01/11/2010  952  purple Boardings    NA
 3 Monday    01/11/2010  952  green  Boardings    NA
 4 Monday    01/11/2010  952  banner Boardings    NA
 5 Tuesday   01/12/2010  796  orange Boardings   777
 6 Tuesday   01/12/2010  796  purple Boardings    NA
 7 Tuesday   01/12/2010  796  green  Boardings    NA
 8 Tuesday   01/12/2010  796  banner Boardings    NA
 9 Wednesday 01/13/2010 1212. orange Boardings  1203
10 Wednesday 01/13/2010 1212. purple Boardings    NA
# ℹ 4,574 more rows

wide <- long %>% pivot_wider(names_from = "line", 
                             values_from = "value") 
wide

# A tibble: 1,146 × 8
   day       date       daily type      orange purple green banner
   <chr>     <chr>      <dbl> <chr>      <dbl>  <dbl> <dbl>  <dbl>
 1 Monday    01/11/2010  952  Boardings    877     NA    NA     NA
 2 Tuesday   01/12/2010  796  Boardings    777     NA    NA     NA
 3 Wednesday 01/13/2010 1212. Boardings   1203     NA    NA     NA
 4 Thursday  01/14/2010 1214. Boardings   1194     NA    NA     NA
 5 Friday    01/15/2010 1644  Boardings   1645     NA    NA     NA
 6 Saturday  01/16/2010 1490. Boardings   1457     NA    NA     NA
 7 Sunday    01/17/2010  888. Boardings    839     NA    NA     NA
 8 Monday    01/18/2010 1000. Boardings    999     NA    NA     NA
 9 Tuesday   01/19/2010 1035  Boardings   1023     NA    NA     NA
10 Wednesday 01/20/2010 1396. Boardings   1375     NA    NA     NA
# ℹ 1,136 more rows

• tidyr package helps us convert between wide and long data
• pivot_longer() goes from wide -> long
  • Specify columns you want to pivot
  • Specify names_to = and values_to = for custom naming
• pivot_wider() goes from long -> wide
  • Specify names_from = and values_from =

🏠 Class Website

💻 Lab

“Combining datasets”

• Merging/joining data sets together - usually on key variables, usually “id”
• ?join - see different types of joining for dplyr
• 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.

data_As <- read_csv(
  file = "https://jhudatascience.org/intro_to_r/data/data_As_1.csv")
data_cold <- read_csv(
  file = "https://jhudatascience.org/intro_to_r/data/data_cold_1.csv")

data_As

# A tibble: 2 × 3
  State   June_vacc_rate May_vacc_rate
  <chr>            <dbl>         <dbl>
1 Alabama          0.516         0.514
2 Alaska           0.627         0.626

data_cold

# A tibble: 2 × 2
  State  April_vacc_rate
  <chr>            <dbl>
1 Maine            0.795
2 Alaska           0.623

https://github.com/gadenbuie/tidyexplain/blob/main/images/inner-join.gif

ij <- inner_join(data_As, data_cold)

Joining with `by = join_by(State)`

ij

# A tibble: 1 × 4
  State  June_vacc_rate May_vacc_rate April_vacc_rate
  <chr>           <dbl>         <dbl>           <dbl>
1 Alaska          0.627         0.626           0.623

https://raw.githubusercontent.com/gadenbuie/tidyexplain/main/images/left-join.gif

“Everything to the left of the comma”

lj <- left_join(data_As, data_cold)

Joining with `by = join_by(State)`

lj

# A tibble: 2 × 4
  State   June_vacc_rate May_vacc_rate April_vacc_rate
  <chr>            <dbl>         <dbl>           <dbl>
1 Alabama          0.516         0.514          NA    
2 Alaska           0.627         0.626           0.623

# install.packages("tidylog")
library(tidylog)
left_join(data_As, data_cold)

Joining with `by = join_by(State)`
left_join: added one column (April_vacc_rate)
> rows only in x 1
> rows only in y (1)
> matched rows 1
> ===
> rows total 2

# A tibble: 2 × 4
  State   June_vacc_rate May_vacc_rate April_vacc_rate
  <chr>            <dbl>         <dbl>           <dbl>
1 Alabama          0.516         0.514          NA    
2 Alaska           0.627         0.626           0.623

https://raw.githubusercontent.com/gadenbuie/tidyexplain/main/images/right-join.gif

“Everything to the right of the comma”

rj <- right_join(data_As, data_cold)

Joining with `by = join_by(State)`
right_join: added one column (April_vacc_rate)
> rows only in x (1)
> rows only in y 1
> matched rows 1
> ===
> rows total 2

rj

# A tibble: 2 × 4
  State  June_vacc_rate May_vacc_rate April_vacc_rate
  <chr>           <dbl>         <dbl>           <dbl>
1 Alaska          0.627         0.626           0.623
2 Maine          NA            NA               0.795

lj2 <- left_join(data_cold, data_As)

Joining with `by = join_by(State)`
left_join: added 2 columns (June_vacc_rate, May_vacc_rate)
> rows only in x 1
> rows only in y (1)
> matched rows 1
> ===
> rows total 2

lj2

# A tibble: 2 × 4
  State  April_vacc_rate June_vacc_rate May_vacc_rate
  <chr>            <dbl>          <dbl>         <dbl>
1 Maine            0.795         NA            NA    
2 Alaska           0.623          0.627         0.626

https://raw.githubusercontent.com/gadenbuie/tidyexplain/main/images/full-join.gif

fj <- full_join(data_As, data_cold)

Joining with `by = join_by(State)`
full_join: added one column (April_vacc_rate)
> rows only in x 1
> rows only in y 1
> matched rows 1
> ===
> rows total 3

fj

# A tibble: 3 × 4
  State   June_vacc_rate May_vacc_rate April_vacc_rate
  <chr>            <dbl>         <dbl>           <dbl>
1 Alabama          0.516         0.514          NA    
2 Alaska           0.627         0.626           0.623
3 Maine           NA            NA               0.795

data_As <- read_csv(
  file = "https://jhudatascience.org/intro_to_r/data/data_As_2.csv")
data_cold <- read_csv(
  file = "https://jhudatascience.org/intro_to_r/data/data_cold_2.csv")

data_As

# A tibble: 2 × 2
  State   state_bird      
  <chr>   <chr>           
1 Alabama wild turkey     
2 Alaska  willow ptarmigan

data_cold

# A tibble: 3 × 3
  State  vacc_rate month
  <chr>      <dbl> <chr>
1 Maine      0.795 April
2 Alaska     0.623 April
3 Alaska     0.626 May  

lj <- left_join(data_As, data_cold)

Joining with `by = join_by(State)`
left_join: added 2 columns (vacc_rate, month)
> rows only in x 1
> rows only in y (1)
> matched rows 2 (includes duplicates)
> ===
> rows total 3

Data including the joining column (“State”) has been duplicated.

lj

# A tibble: 3 × 4
  State   state_bird       vacc_rate month
  <chr>   <chr>                <dbl> <chr>
1 Alabama wild turkey         NA     <NA> 
2 Alaska  willow ptarmigan     0.623 April
3 Alaska  willow ptarmigan     0.626 May  

Note that “Alaska willow ptarmigan” appears twice.

https://github.com/gadenbuie/tidyexplain/blob/main/images/left-join-extra.gif

unloadNamespace() does the opposite of library().

unloadNamespace("tidylog")

By default joins use the intersection of column names. If by is specified, it uses that.

full_join(data_As, data_cold, by = "State")

# A tibble: 4 × 4
  State   state_bird       vacc_rate month
  <chr>   <chr>                <dbl> <chr>
1 Alabama wild turkey         NA     <NA> 
2 Alaska  willow ptarmigan     0.623 April
3 Alaska  willow ptarmigan     0.626 May  
4 Maine   <NA>                 0.795 April

You can join based on multiple columns by using something like by = c(col1, col2).

If the datasets have two different names for the same data, use:

full_join(x, y, by = c("a" = "b"))

Entries in data_As but not in data_cold

anti_join(data_As, data_cold, by = "State")

# A tibble: 1 × 2
  State   state_bird 
  <chr>   <chr>      
1 Alabama wild turkey

Entries in data_cold but not in data_As

anti_join(data_cold, data_As, by = "State") # order switched

# A tibble: 1 × 3
  State vacc_rate month
  <chr>     <dbl> <chr>
1 Maine     0.795 April

• Merging/joining data sets together - assumes all column names that overlap
  • use the by = c("a" = "b") if they differ
• 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
• Use the tidylog package for a detailed summary
• antijoin(x, y) shows what is only in x (missing from y)

🏠 Class Website

💻 Lab

Image by Gerd Altmann from Pixabay

We might want to determine what indexes ARE in the first dataset that AREN’T in the second.

For this to work, the datasets need the same columns.

We’ll just select the index using select().

A_states <- data_As %>% select(State)
cold_states <- data_cold %>% select(State)

States in A_states but not in cold_states

dplyr::setdiff(A_states, cold_states)

# A tibble: 1 × 1
  State  
  <chr>  
1 Alabama

States in cold_states but not in A_states

dplyr::setdiff(cold_states, A_states)

# A tibble: 1 × 1
  State
  <chr>
1 Maine

Why did we use dplyr::setdiff?

There is a base R function, also called setdiff that requires vectors.

In other words, we use dplyr:: to be specific about the package we want to use.

More set operations can be found here: https://dplyr.tidyverse.org/reference/setops.html

Notice “daily” column has different values

long2

# A tibble: 4,584 × 6
   day       date       daily line   type      value
   <chr>     <chr>      <dbl> <chr>  <chr>     <dbl>
 1 Monday    01/11/2010  952  orange Boardings   877
 2 MONDAY    01/11/2010  952  purple Boardings    NA
 3 Monday    01/11/2010  952  green  Boardings    NA
 4 Monday    01/11/2010  952  banner Boardings    NA
 5 Tuesday   01/12/2010  796  orange Boardings   777
 6 Tuesday   01/12/2010  796  purple Boardings    NA
 7 Tuesday   01/12/2010  796  green  Boardings    NA
 8 Tuesday   01/12/2010  796  banner Boardings    NA
 9 Wednesday 01/13/2010 1212. orange Boardings  1203
10 Wednesday 01/13/2010 1212. purple Boardings    NA
# ℹ 4,574 more rows

R won’t drop data while pivoting.

wide2 <- long2 %>% pivot_wider(names_from = "type", 
                             values_from = "value") 
wide2

# A tibble: 4,584 × 5
   day       date       daily line   Boardings
   <chr>     <chr>      <dbl> <chr>      <dbl>
 1 Monday    01/11/2010  952  orange       877
 2 MONDAY    01/11/2010  952  purple        NA
 3 Monday    01/11/2010  952  green         NA
 4 Monday    01/11/2010  952  banner        NA
 5 Tuesday   01/12/2010  796  orange       777
 6 Tuesday   01/12/2010  796  purple        NA
 7 Tuesday   01/12/2010  796  green         NA
 8 Tuesday   01/12/2010  796  banner        NA
 9 Wednesday 01/13/2010 1212. orange      1203
10 Wednesday 01/13/2010 1212. purple        NA
# ℹ 4,574 more rows

https://sebkrantz.github.io/collapse/

Might be helpful if your data is very large. However, dplyr and tidyr functions are great for most applications.