As we learned in our tutorial, more advanced tools for indexing and slicing data frames are available through the dplyr package. In this exercise, we will apply the select, filter, and arrange tools to manipulate and organize our Covid$-19$ data frame. In this exercise, we will focus on the Covid$-19$ hospitalization data from countries in Europe. Therefore, we first want to subset our data frame to include only the columns with keywords icu and hosp, along with the metadata on continent, location, and date.

Q$1.$ Using the dplyr package, generate a new dataframe called hosp$\_$covid$\_$df using the select function that contains only the metadata $($continent$,$ location, and date$)$ and the nine columns that contain keywords icu and hosp from the dataframe primary$\_$covid$\_$df$.$

Note, there may be many rows or cells with NA$,$ leave them be for now.As we learned in our tutorial, more advanced tools for indexing and slicing data frames are available through the dplyr package. In this exercise, we will apply the select, filter, and arrange tools to manipulate and organize our Covid$-19$ data frame. In this exercise, we will focus on the Covid$-19$ hospitalization data from countries in Europe. Therefore, we first want to subset our data frame to include only the columns with keywords icu and hosp, along with the metadata on continent, location, and date.

Q$1.$ Using the dplyr package, generate a new dataframe called hosp$\_$covid$\_$df using the select function that contains only the metadata $($continent$,$ location, and date$)$ and the nine columns that contain keywords icu and hosp from the dataframe primary$\_$covid$\_$df$.$

Note, there may be many rows or cells with NA$,$ leave them be for now.