Capítulo 8 Soluciones a problemas

Todos los problemas en programación tienen más de una forma de llegar a ellos, es por esto que las soluciones acá mostradas deben tomarse solo como una referencia, y revisar si el resultado final de tu código (aunque sea distinto de este), sea igual al que presentamos.

8.1 Capítulo 1

8.1.1 Ejercicio 1

Algunas posibles soluciones:

storms %>% filter(status == "hurricane") %>% select(year, wind, 
    hu_diameter) %>% group_by(year) %>% summarize_all(mean)

storms %>% filter(status == "hurricane" & !is.na(hu_diameter)) %>% 
    select(year, wind, hu_diameter) %>% group_by(year) %>% summarize_all(mean)

storms %>% filter(status == "hurricane") %>% select(year, wind, 
    hu_diameter) %>% group_by(year) %>% summarize_all(funs(mean), 
    na.rm = TRUE)

8.1.2 Ejercicio 2

Una de las soluciones posibles:

Solution <- mpg %>% filter(year > 2004 & class == "compact") %>% 
    mutate(kpl = (cty * 1.609)/3.78541)

8.2 Capítulo 2

8.2.1 Ejercicio 1

Una posible solución a este problema sería:

`r mean((iris %>% filter(Species == "virginica"))$Petal.Length)`

8.3 Capítulo 3

8.3.1 Ejercicio 1

8.3.1.1 a

Sola <- Huemul %>% dplyr::select(lon, lat, basisOfRecord) %>% 
    filter(!is.na(lat) & !is.na(lon))

lon	lat	basisOfRecord
-72.89979	-50.85621	HUMAN_OBSERVATION
-72.88027	-49.33296	HUMAN_OBSERVATION
-73.00350	-46.69153	HUMAN_OBSERVATION
-72.61874	-46.76851	HUMAN_OBSERVATION
-72.97728	-51.07892	HUMAN_OBSERVATION
-71.87139	-45.94562	HUMAN_OBSERVATION
-71.86151	-46.17067	HUMAN_OBSERVATION
-71.88937	-46.04310	HUMAN_OBSERVATION
-72.87022	-51.12036	HUMAN_OBSERVATION
-72.00898	-46.01153	HUMAN_OBSERVATION
-73.14041	-50.84305	HUMAN_OBSERVATION
-71.85057	-45.99832	HUMAN_OBSERVATION
-72.07593	-46.05139	HUMAN_OBSERVATION
-71.91163	-45.84344	HUMAN_OBSERVATION
-71.91210	-46.12133	HUMAN_OBSERVATION
-73.99358	-48.54769	HUMAN_OBSERVATION
-71.81252	-46.04818	HUMAN_OBSERVATION
-72.54534	-46.65755	HUMAN_OBSERVATION
-72.97587	-51.18353	HUMAN_OBSERVATION
-73.03667	-51.04976	HUMAN_OBSERVATION
-72.33888	-47.07042	HUMAN_OBSERVATION
-72.93940	-49.37483	HUMAN_OBSERVATION
-72.75092	-46.08747	HUMAN_OBSERVATION
-72.17776	-46.88179	HUMAN_OBSERVATION
-72.91720	-49.30175	HUMAN_OBSERVATION
-72.87917	-49.37064	HUMAN_OBSERVATION
-72.97712	-51.01511	HUMAN_OBSERVATION
-71.87026	-46.08686	HUMAN_OBSERVATION
-72.02424	-45.92156	HUMAN_OBSERVATION
-72.13481	-46.11689	HUMAN_OBSERVATION
-72.89062	-46.03708	HUMAN_OBSERVATION
-72.43751	-47.20485	HUMAN_OBSERVATION
-72.45489	-47.00600	HUMAN_OBSERVATION
-73.01456	-51.03635	HUMAN_OBSERVATION
-73.03190	-51.17531	HUMAN_OBSERVATION
-72.72944	-46.25602	HUMAN_OBSERVATION
-71.31538	-41.30110	PRESERVED_SPECIMEN
-71.31538	-41.30110	PRESERVED_SPECIMEN
-71.71667	-44.86667	PRESERVED_SPECIMEN
-71.71667	-44.86667	PRESERVED_SPECIMEN
-71.30989	-40.81978	PRESERVED_SPECIMEN
-71.31538	-41.30110	PRESERVED_SPECIMEN
-73.02467	-50.46476	PRESERVED_SPECIMEN
-71.33186	-41.26523	PRESERVED_SPECIMEN
-73.01764	-50.46747	PRESERVED_SPECIMEN
-71.00000	-30.00000	PRESERVED_SPECIMEN
-71.00000	-30.00000	PRESERVED_SPECIMEN
-71.70000	-45.26667	PRESERVED_SPECIMEN
-71.70000	-45.26667	PRESERVED_SPECIMEN
-71.70000	-45.26667	PRESERVED_SPECIMEN
-72.08000	-47.25000	PRESERVED_SPECIMEN
-71.36714	-41.13574	PRESERVED_SPECIMEN
-71.71094	-42.75692	HUMAN_OBSERVATION
-70.91129	-53.15483	PRESERVED_SPECIMEN
-73.84761	-42.54746	PRESERVED_SPECIMEN
-71.00000	-30.00000	PRESERVED_SPECIMEN
-71.00000	-30.00000	PRESERVED_SPECIMEN
-71.64718	-40.22605	PRESERVED_SPECIMEN
-67.88534	-43.99376	PRESERVED_SPECIMEN

8.3.1.2 b

Solb <- Huemul %>% group_by(basisOfRecord) %>% summarize(N = n())

## `summarise()` ungrouping output (override with `.groups` argument)

basisOfRecord	N
HUMAN_OBSERVATION	132
PRESERVED_SPECIMEN	70