From f9e69b7b94d98078bfeb5a9f076bfd57d51463d6 Mon Sep 17 00:00:00 2001 From: rlzijdeman Date: Sun, 5 Jun 2016 09:19:39 +0200 Subject: [PATCH] typo fixes in transform.Rmd --- transform.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/transform.Rmd b/transform.Rmd index d77fbbc..4adc241 100644 --- a/transform.Rmd +++ b/transform.Rmd @@ -630,7 +630,7 @@ flights %>% summarise(mean = mean(dep_delay)) ``` -We get a lot of missing values! That's because aggregation functions obey the usual rule of missing values: if there's any missing value in the input, the output will be a missing value. `x %>% f(y)` turns into `f(x, y)`ou'll learn more about aggregation functions in Section 5.7.4. Fortunately, all aggregation functions have an `na.rm` argument which removes the missing values prior to computation: +We get a lot of missing values! That's because aggregation functions obey the usual rule of missing values: if there's any missing value in the input, the output will be a missing value. `x %>% f(y)` turns into `f(x, y)` you'll learn more about aggregation functions in Section 5.7.4. Fortunately, all aggregation functions have an `na.rm` argument which removes the missing values prior to computation: ```{r} flights %>% @@ -731,7 +731,7 @@ batters %>% arrange(desc(ba)) You can find a good explanation of this problem at and . -### Other summary functions. +### Other summary functions Just using means, counts, and sum can get you a long way, but R provides many other useful summary functions: