• 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 ~ variable 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

📃Day 5 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.

Here’s a small dataset looking at vaccination rates over three months in Alabama.

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)

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.

ex_wide

# 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

ex_long <- ex_wide %>% pivot_longer(cols = ends_with("rate"))
ex_long

# 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

What does pivot_longer() do?

A. Summarize data

B. Import data

C. Reshape data

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

Filter by Boardings only..

long <- long %>% filter(str_detect(name, "Boardings"))
long

# A tibble: 4,584 × 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  purpleBoardings    NA
 3 Monday    01/11/2010  952  greenBoardings     NA
 4 Monday    01/11/2010  952  bannerBoardings    NA
 5 Tuesday   01/12/2010  796  orangeBoardings   777
 6 Tuesday   01/12/2010  796  purpleBoardings    NA
 7 Tuesday   01/12/2010  796  greenBoardings     NA
 8 Tuesday   01/12/2010  796  bannerBoardings    NA
 9 Wednesday 01/13/2010 1212. orangeBoardings  1203
10 Wednesday 01/13/2010 1212. purpleBoardings    NA
# ℹ 4,574 more rows

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

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

# A tibble: 4 × 2
  name            avg_boardings
  <chr>                   <dbl>
1 bannerBoardings          830.
2 greenBoardings          1929.
3 orangeBoardings         3031.
4 purpleBoardings         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})

We can use pivot_wider to convert long data to wide format. Let’s try it with the vaccine data from earlier.

ex_long

# A tibble: 6 × 3
  State   Month            Rate
  <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

We can use pivot_wider to convert long data to wide format. Let’s try it with the vaccine data from earlier.

ex_wide2 <- ex_long %>% pivot_wider(names_from = "Month", 
                                       values_from = "Rate") 
ex_wide2

# 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

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

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

Artwork by @allison_horst. https://allisonhorst.com/

• tidyr package (part of tidyverse) 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 data_As 1
> rows only in data_cold (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 data_As (1)
> rows only in data_cold 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 data_cold 1
> rows only in data_As (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 data_As 1
> rows only in data_cold 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 data_As 1
> rows only in data_cold (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

Why use join functions?

A. Combine different data sources

B. Connect Rmd to other files

C. Using one data source is too easy and we want our analysis ~ fancy ~

• 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
• anti_join(x, y) shows what is only in x (missing from y)

🏠 Class Website

💻 Lab

~

📃 Day 6 Cheatsheet

📃 Posit’s tidyr Cheatsheet

📃 Posit’s dplyr Cheatsheet

🔎️ Joining Open Case Study

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

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

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