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11.1.1: Empirical Frequency (Section 10.2.2)

  • Page ID
    8781
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    Let’s walk through how we computed empirical frequency of rain in San Francisco.

    First we load the data:

    # we will remove the STATION and NAME variables 
    # since they are identical for all rows
    SFrain <- read_csv("data/SanFranciscoRain/1329219.csv") %>% 
      dplyr::select(-STATION, -NAME)
      
    glimpse(SFrain)
    ## Observations: 365
    ## Variables: 2
    ## $ DATE <date> 2017-01-01, 2017-01-02, 2017-01-03, 2017-01…
    ## $ PRCP <dbl> 0.05, 0.10, 0.40, 0.89, 0.01, 0.00, 0.82, 1.…

    We see that the data frame contains a variable called PRCP which denotes the amount of rain each day. Let’s create a new variable called rainToday that denotes whether the amount of precipitation was above zero:

    SFrain <- 
      SFrain %>%
      mutate(rainToday = as.integer(PRCP > 0))
    
    glimpse(SFrain)
    ## Observations: 365
    ## Variables: 3
    ## $ DATE      <date> 2017-01-01, 2017-01-02, 2017-01-03, 20…
    ## $ PRCP      <dbl> 0.05, 0.10, 0.40, 0.89, 0.01, 0.00, 0.8…
    ## $ rainToday <int> 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, …

    Now we will summarize the data to compute the probability of rain:

    pRainInSF <- 
      SFrain %>%
      summarize(
        pRainInSF = mean(rainToday)
      ) %>%
      pull()
    
    pRainInSF
    ## [1] 0.2

    This page titled 11.1.1: Empirical Frequency (Section 10.2.2) is shared under a not declared license and was authored, remixed, and/or curated by Russell A. Poldrack via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.