Chapter 44 14. Clean environmental exposure data
For Analysis 2, we need to identify whether each postal code is located in a cold climate. A cold climate is defined as:
- average daily high temperature less than 10°C, and
- average daily low temperature less than -3°C.
The required variables are:
| Original variable | New variable | Meaning |
|---|---|---|
POSTALCODE12 |
Postal_code |
Postal code |
WTHNRC12_04 |
max_temp |
Annual average of daily maximum temperature |
WTHNRC12_05 |
min_temp |
Annual average of daily minimum temperature |
env_clean <- env %>%
select(POSTALCODE12, WTHNRC12_04, WTHNRC12_05) %>%
rename(
Postal_code = POSTALCODE12,
max_temp = WTHNRC12_04,
min_temp = WTHNRC12_05
) %>%
mutate(
exposure = if_else(max_temp < 10 & min_temp < -3, 1, 0)
)
env_clean## # A tibble: 116,011 x 4
## Postal_code max_temp min_temp exposure
## <chr> <dbl> <dbl> <dbl>
## 1 V0C1E0 2.91 -8.12 1
## 2 V0C1W0 3.92 -7.65 1
## 3 V0C2X0 4.01 -7.93 1
## 4 V0C2Z0 4.32 -6.89 1
## 5 V0T1W0 4.33 -3.65 1
## 6 V0W1A0 4.51 -4.92 1
## 7 V0C1L0 4.58 -6.14 1
## 8 V0J1K0 4.71 -5.34 1
## 9 V0B1A1 4.91 -4.42 1
## 10 V0B1T6 4.91 -4.42 1
## # i 116,001 more rows## # A tibble: 2 x 2
## exposure n
## <dbl> <int>
## 1 0 114146
## 2 1 1865