Example: Cat lovers

A survey asked respondents their name and number of cats. The instructions said to enter the number of cats as a numerical value.

cat_lovers <- read_csv("data/cat-lovers.csv")

## # A tibble: 60 x 3
##   name           number_of_cats handedness
##   <chr>          <chr>          <chr>     
## 1 Bernice Warren 0              left      
## 2 Woodrow Stone  0              left      
## 3 Willie Bass    1              left      
## 4 Tyrone Estrada 3              left      
## 5 Alex Daniels   3              left      
## 6 Jane Bates     2              left      
## # ... with 54 more rows

Oh why won't you work?!

cat_lovers %>%
  summarise(mean_cats = mean(number_of_cats))

## Warning: There was 1 warning in `summarise()`.
## i In argument: `mean_cats = mean(number_of_cats)`.
## Caused by warning in `mean.default()`:
## ! argument is not numeric or logical: returning NA

## # A tibble: 1 x 1
##   mean_cats
##       <dbl>
## 1        NA

?mean

Oh why won't you still work??!!

cat_lovers %>%
  summarise(mean_cats = mean(number_of_cats, na.rm = TRUE))

## Warning: There was 1 warning in `summarise()`.
## i In argument: `mean_cats = mean(number_of_cats, na.rm = TRUE)`.
## Caused by warning in `mean.default()`:
## ! argument is not numeric or logical: returning NA

## # A tibble: 1 x 1
##   mean_cats
##       <dbl>
## 1        NA

Take a breath and look at your data

glimpse(cat_lovers)

## Rows: 60
## Columns: 3
## $ name           <chr> "Bernice Warren", "Woodrow Stone", "Will~
## $ number_of_cats <chr> "0", "0", "1", "3", "3", "2", "1", "1", ~
## $ handedness     <chr> "left", "left", "left", "left", "left", ~

Let's take another look

Check out the responses from "Ginger Clark" and "Doug Bass"

Sometimes you might need to babysit your respondents

cat_lovers %>%
  mutate(number_of_cats = case_when(
    name == "Ginger Clark" ~ 2,
    name == "Doug Bass"    ~ 3,
    TRUE                   ~ as.numeric(number_of_cats)
    )) %>%
  summarise(mean_cats = mean(number_of_cats))

## Warning: There was 1 warning in `mutate()`.
## i In argument: `number_of_cats = case_when(...)`.
## Caused by warning:
## ! NAs introduced by coercion

## # A tibble: 1 x 1
##   mean_cats
##       <dbl>
## 1     0.833

You always need to respect data types

cat_lovers %>%
  mutate(
    number_of_cats = case_when(
      name == "Ginger Clark" ~ "2",
      name == "Doug Bass"    ~ "3",
      TRUE                   ~ number_of_cats
      ),
    number_of_cats = as.numeric(number_of_cats)
    ) %>%
  summarise(mean_cats = mean(number_of_cats))

## # A tibble: 1 x 1
##   mean_cats
##       <dbl>
## 1     0.833

Now that we know what we're doing...

cat_lovers <- cat_lovers %>%
  mutate(
    number_of_cats = case_when(
      name == "Ginger Clark" ~ "2",
      name == "Doug Bass"    ~ "3",
      TRUE                   ~ number_of_cats
      ),
    number_of_cats = as.numeric(number_of_cats)
    )

Moral of the story

• If your data does not behave how you expect it to, type coercion upon reading in the data might be the reason.
• Go in and investigate your data, apply the fix, save your data, live happily ever after.

Data types in R

• logical
• double
• integer
• character
• and some more, but we won't be focusing on those

Logical & character

typeof(TRUE)

## [1] "logical"

typeof("hello")

## [1] "character"

Double & integer

typeof(1.335)

## [1] "double"

typeof(7)

## [1] "double"

typeof(7L)

## [1] "integer"

typeof(1:3)

## [1] "integer"

Concatenation

Vectors can be constructed using the c() function.

c(1, 2, 3)

## [1] 1 2 3

c("Hello", "World!")

## [1] "Hello"  "World!"

c(c("hi", "hello"), c("bye", "jello"))

## [1] "hi"    "hello" "bye"   "jello"

Converting between types

with intention...

x <- 1:3
x

## [1] 1 2 3

typeof(x)

## [1] "integer"

y <- as.character(x)
y

## [1] "1" "2" "3"

typeof(y)

## [1] "character"

Converting between types

with intention...

x <- c(TRUE, FALSE)
x

## [1]  TRUE FALSE

typeof(x)

## [1] "logical"

y <- as.numeric(x)
y

## [1] 1 0

typeof(y)

## [1] "double"

Converting between types

without intention...

R will happily convert between various types without complaint when different types of data are concatenated in a vector, and that's not always a great thing!

c(1, "Hello")

## [1] "1"     "Hello"

c(FALSE, 3L)

## [1] 0 3

c(1.2, 3L)

## [1] 1.2 3.0

c(2L, "two")

## [1] "2"   "two"

Explicit vs. implicit coercion

Let's give formal names to what we've seen so far:

• Explicit coercion is when you call a function like as.logical(), as.numeric(), as.integer(), as.double(), or as.character()

• Implicit coercion happens when you use a vector in a specific context that expects a certain type of vector

Special values

• NA: Not available
• NaN: Not a number
• Inf: Positive infinity
• -Inf: Negative infinity

pi / 0

## [1] Inf

0 / 0

## [1] NaN

1/0 - 1/0

## [1] NaN

1/0 + 1/0

## [1] Inf