# Book Recomendations

Hello there!, this is just a random (maybe not?) list of books that I like and maybe you can find useful!.

Physician-Epidemiologist | Health Data Scientist | Health Policy | Global Health Equity

Here is just some random code for future use.

```
#https://seer.cancer.gov/stdpopulations/stdpopdic.html
standard_pop <- read.fwf("https://seer.cancer.gov/stdpopulations/stdpop.18ages.txt", widths = c(3,3,8),
header = 0, col.names = c('Standard_ID','Age','Standard_Population')) %>%
filter(Standard_ID==009 ) %>% ## 009 = World (WHO 2000-2025) Std Million (18 age groups)
mutate(AGE = (Age-1)*5, ## as the numbers are 1 to 18, the formula X-1 * 5 will give us the value.
Proportion = Standard_Population/sum(Standard_Population)) %>%
select(AGE, Standard_Population, Proportion) %>%
mutate(AGE=ifelse(AGE>=80,80, AGE)) %>%
group_by(AGE) %>%
summarise(Standard_Population=sum(Standard_Population), Proportion=sum(Proportion)) %>%
ungroup()
standard_pop %>%
#filter(AGE>5) %>%
mutate(prop_test = Standard_Population / sum(Standard_Population)) %>%
mutate(sum(Proportion), sum(prop_test))
```

Data Sample

location | AGE | counts | population |
---|---|---|---|

Guatemala | 0 | 35 | 10250 |

Guatemala | 5 | 25 | 12859 |

Panama | 5 | 50 | 80253 |

Panama | 5 | 38 | 21224 |

Costa Rica | 20 | 25 | 15351 |

You must have the same number of age groups as your `standard_pop`

to be abble to merge, and remember to replace all the missings with `0`

in the `counts`

column.

```
data %>%
mutate(rate=counts/population) %>%
left_join(standard_pop, by = "AGE") %>%
group_by(location) %>%
summarise(adjusted1= weighted.mean(rate, Standard_Population),
adjusted2= sum(rate*Proportion))
```

In the standard_pop you can have proportions or total numbers to adjust your rates. Both work fine, if you choice to work with total numbers you must use `weighted.mean()`

,
if you choice to use proportions you can use `sum(rate*Proportion)`

Well, lets start over. This is my X^10 intent to start a blog. But now, as simple as possible.