title: "EDA" output: github_document --- ```{r setup, include=FALSE} library(tidyverse) library(p8105.datasets) knitr::opts_chunk$set( fig.width = 6, fig.asp = .6, out.width = "90%" ) theme_set(theme_minimal() + theme(legend.position = "bottom")) options( ggplot2.continuous.colour = "viridis", ggplot2.continuous.fill = "viridis" ) scale_colour_discrete = scale_colour_viridis_d scale_fill_discrete = scale_fill_viridis_d ``` Import the weather data ```{r} data("weather_df") weather_df = weather_df |> mutate(month = floor_date(date, unit = "month")) ``` Make plots ```{r} weather_df |> ggplot(aes(x = prcp)) + geom_histogram() ``` Check on extreme values ```{r} weather_df |> filter(prcp > 1000) ``` Look at data again. ```{r} weather_df |> filter(tmax >= 20, tmax <= 30) |> ggplot(aes(x = tmin, y = tmax, color = name, shape = name)) + geom_point() ``` ## Add groups ```{r} weather_df |> group_by(name, month) ``` Group and count things ```{r} weather_df |> group_by(name) |> summarize( n = n() ) ``` ```{r} weather_df |> group_by(month) |> summarize( n = n_distinct(date) ) ``` You can count directly ```{r} weather_df |> count(name) ```