data <- read.csv("../00_data/myData.csv")
SMTDAYS_by_TOTDAYS <- data %>%
group_by(TOTDAYS) %>%
summarise(
avg_SMTDAYS = mean(TOTDAYS + SMTDAYS, na.rm = TRUE)
)
SMTDAYS_by_TOTDAYS
## # A tibble: 57 × 2
## TOTDAYS avg_SMTDAYS
## <int> <dbl>
## 1 0 1.75
## 2 1 1
## 3 2 4
## 4 3 5
## 5 4 7.25
## 6 5 7.5
## 7 6 10
## 8 7 12.2
## 9 8 13.2
## 10 9 15.5
## # ℹ 47 more rows
SMTDAYS_by_TOTDAYS %>%
ggplot(aes(x = avg_SMTDAYS, y = TOTDAYS)) +
geom_point()
data %>%
ggplot(aes(x = HOST_FACTOR, y = HOST)) +
geom_point()
fct_recode
data %>% distinct(HOST_FACTOR)
## HOST_FACTOR
## 1 China
## 2 Nepal
## 3 India
data %>%
mutate(HOST_FACTOR_rev = fct_recode(HOST_FACTOR, "Nepal" = "EVER")) %>%
select(HOST_FACTOR, HOST_FACTOR_rev) %>%
filter(HOST_FACTOR == "EVER")
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `HOST_FACTOR_rev = fct_recode(HOST_FACTOR, Nepal = "EVER")`.
## Caused by warning:
## ! Unknown levels in `f`: EVER
## [1] HOST_FACTOR HOST_FACTOR_rev
## <0 rows> (or 0-length row.names)
fct_collapse
data %>%
mutate(HOST_FACTOR_col = fct_collapse(HOST_FACTOR,
"Nepal" = c("EVER"))) %>%
select(HOST_FACTOR, HOST_FACTOR_col) %>%
filter(HOST_FACTOR == "EVER")
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `HOST_FACTOR_col = fct_collapse(HOST_FACTOR, Nepal =
## c("EVER"))`.
## Caused by warning:
## ! Unknown levels in `f`: EVER
## [1] HOST_FACTOR HOST_FACTOR_col
## <0 rows> (or 0-length row.names)
fct_lump
data %>% count(HOST)
## HOST n
## 1 1 855
## 2 2 25
## 3 3 2
data %>% mutate(HOST_lump = fct_lump(f = "HOST")) %>% distinct(HOST_lump)
## HOST_lump
## 1 HOST
No need to do anything here.