Use tibble instead of data.frame
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@ -32,7 +32,7 @@ library(purrr)
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Imagine we have this simple data frame:
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Imagine we have this simple data frame:
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```{r}
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```{r}
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df <- data.frame(
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df <- tibble::tibble(
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a = rnorm(10),
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a = rnorm(10),
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b = rnorm(10),
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b = rnorm(10),
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c = rnorm(10),
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c = rnorm(10),
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@ -170,7 +170,7 @@ There are four variations on the basic theme of the for loop:
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Sometimes you want to use a for loop to modify an existing object. For example, remember our challenge from [functions]. We wanted to rescale every column in a data frame:
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Sometimes you want to use a for loop to modify an existing object. For example, remember our challenge from [functions]. We wanted to rescale every column in a data frame:
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```{r}
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```{r}
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df <- data.frame(
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df <- tibble::tibble(
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a = rnorm(10),
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a = rnorm(10),
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b = rnorm(10),
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b = rnorm(10),
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c = rnorm(10),
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c = rnorm(10),
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@ -369,7 +369,7 @@ For loops are not as important in R as they are in other languages because R is
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To see why this is important, consider (again) this simple data frame:
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To see why this is important, consider (again) this simple data frame:
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```{r}
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```{r}
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df <- data.frame(
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df <- tibble::tibble(
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a = rnorm(10),
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a = rnorm(10),
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b = rnorm(10),
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b = rnorm(10),
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c = rnorm(10),
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c = rnorm(10),
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@ -941,7 +941,11 @@ x %>% accumulate(`+`)
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But it has a number of bugs as illustrated with the following inputs:
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But it has a number of bugs as illustrated with the following inputs:
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```{r, eval = FALSE}
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```{r, eval = FALSE}
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df <- data.frame(z = c("a", "b", "c"), x = 1:3, y = 3:1)
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df <- tibble::tibble(
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x = 1:3,
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y = 3:1,
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z = c("a", "b", "c")
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)
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# OK
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# OK
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col_sum3(df, mean)
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col_sum3(df, mean)
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# Has problems: don't always return numeric vector
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# Has problems: don't always return numeric vector
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