garrick
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tidyexplain
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Only attach tidyexplain in README 

Calls to tidyr and dplyr are now prefixed with package name. This lets us use the README as a test to make sure all namespace issues are fixed. We can roll this back when the package is more stable.
pull/18/merge
Garrick Aden-Buie 7 年之前
父節點
e4a3de3c06
當前提交
326b48a5dc
共有 2 個檔案被更改，包括 44 行新增 和 49 行删除
分割檢視 顯示文件統計
  1. +22
    -25
      README.Rmd
  2. +22
    -24
      README.md

+ 22
- 25
README.Rmd 查看文件

@@ -16,7 +16,6 @@ knitr::opts_chunk$set(
fig.path = "man/figures/tidyexplain-",
cache = TRUE
)
library(dplyr)
library(tidyexplain)
set_font_size(11, 26)
```
@@ -67,12 +66,12 @@ library(tidyexplain)
## Mutating Joins

```{r intial-dfs}
x <- data_frame(
x <- dplyr::data_frame(
id = 1:3,
x = paste0("x", 1:3)
)

y <- data_frame(
y <- dplyr::data_frame(
id = (1:4)[-3],
y = paste0("y", (1:4)[-3])
)
@@ -96,7 +95,7 @@ animate_inner_join(x, y, by = "id")


```{r}
inner_join(x, y, by = "id")
dplyr::inner_join(x, y, by = "id")
```

### Left Join
@@ -109,7 +108,7 @@ animate_left_join(x, y, by = "id")


```{r}
left_join(x, y, by = "id")
dplyr::left_join(x, y, by = "id")
```

### Left Join (Extra Rows in y)
@@ -117,14 +116,14 @@ left_join(x, y, by = "id")
> ... If there are multiple matches between `x` and `y`, all combinations of the matches are returned.

```{r left-join-extra}
y_extra <- bind_rows(y, data_frame(id = 2, y = "y5"))
y_extra <- dplyr::bind_rows(y, dplyr::data_frame(id = 2, y = "y5"))
y_extra # has multiple rows with the key from `x`

animate_left_join(x, y_extra, by = "id")
```

```{r}
left_join(x, y_extra, by = "id")
dplyr::left_join(x, y_extra, by = "id")
```

### Right Join
@@ -137,7 +136,7 @@ animate_right_join(x, y, by = "id")


```{r}
right_join(x, y, by = "id")
dplyr::right_join(x, y, by = "id")
```

### Full Join
@@ -150,7 +149,7 @@ animate_full_join(x, y, by = "id")


```{r}
full_join(x, y, by = "id")
dplyr::full_join(x, y, by = "id")
```

## Filtering Joins
@@ -165,7 +164,7 @@ animate_semi_join(x, y, by = "id")


```{r}
semi_join(x, y, by = "id")
dplyr::semi_join(x, y, by = "id")
```

### Anti Join
@@ -178,17 +177,17 @@ animate_anti_join(x, y, by = "id")


```{r}
anti_join(x, y, by = "id")
dplyr::anti_join(x, y, by = "id")
```

## Set Operations

```{r intial-dfs-so}
x <- data_frame(
x <- dplyr::data_frame(
x = c(1, 1, 2),
y = c("a", "b", "a")
)
y <- data_frame(
y <- dplyr::data_frame(
x = c(1, 2),
y = c("a", "b")
)
@@ -212,7 +211,7 @@ animate_union(x, y)


```{r}
union(x, y)
dplyr::union(x, y)
```


@@ -220,7 +219,7 @@ union(x, y)
```{r union-y-x}
animate_union(y, x)

union(y, x)
dplyr::union(y, x)
```

### Union All
@@ -234,7 +233,7 @@ animate_union_all(x, y)


```{r}
union_all(x, y)
dplyr::union_all(x, y)
```


@@ -248,7 +247,7 @@ animate_intersect(x, y)


```{r}
intersect(x, y)
dplyr::intersect(x, y)
```

### Set Difference
@@ -261,14 +260,14 @@ animate_setdiff(x, y)


```{r}
setdiff(x, y)
dplyr::setdiff(x, y)
```


```{r setdiff-y-x}
animate_setdiff(y, x)

setdiff(y, x)
dplyr::setdiff(y, x)
```

## Tidy Data and `gather()`, `spread()` functionality
@@ -286,14 +285,12 @@ to be formatted as a tidy dataset and the
you organize your data into tidy data.

```{r}
library(tidyr)

long <- data_frame(
long <- dplyr::data_frame(
year = c(2010, 2011, 2010, 2011, 2010, 2011),
person = c("Alice", "Alice", "Bob", "Bob", "Charlie", "Charlie"),
sales = c(105, 110, 100, 97, 90, 95)
)
wide <- data_frame(
wide <- dplyr::data_frame(
year = 2010:2011,
Alice = c(105, 110),
Bob = c(100, 97),
@@ -311,7 +308,7 @@ animate_gather(wide, key = "person", value = "sales", -year)
```

```{r}
gather(wide, key = "person", value = "sales", -year)
tidyr::gather(wide, key = "person", value = "sales", -year)
```

### Spread
@@ -323,7 +320,7 @@ animate_spread(long, key = "person", value = "sales")
```

```{r}
spread(long, key = "person", value = "sales")
tidyr::spread(long, key = "person", value = "sales")
```



+ 22
- 24
README.md 查看文件

@@ -56,12 +56,12 @@ library(tidyexplain)
## Mutating Joins

``` r
x <- data_frame(
x <- dplyr::data_frame(
id = 1:3,
x = paste0("x", 1:3)
)

y <- data_frame(
y <- dplyr::data_frame(
id = (1:4)[-3],
y = paste0("y", (1:4)[-3])
)
@@ -100,7 +100,7 @@ animate_inner_join(x, y, by = "id")
![](man/figures/tidyexplain-inner-join-1.gif)<!-- -->

``` r
inner_join(x, y, by = "id")
dplyr::inner_join(x, y, by = "id")
#> # A tibble: 2 x 3
#> id x y
#> <int> <chr> <chr>
@@ -120,7 +120,7 @@ animate_left_join(x, y, by = "id")
![](man/figures/tidyexplain-left-join-1.gif)<!-- -->

``` r
left_join(x, y, by = "id")
dplyr::left_join(x, y, by = "id")
#> # A tibble: 3 x 3
#> id x y
#> <int> <chr> <chr>
@@ -135,7 +135,7 @@ left_join(x, y, by = "id")
> of the matches are returned.

``` r
y_extra <- bind_rows(y, data_frame(id = 2, y = "y5"))
y_extra <- dplyr::bind_rows(y, dplyr::data_frame(id = 2, y = "y5"))
y_extra # has multiple rows with the key from `x`
#> # A tibble: 4 x 2
#> id y
@@ -151,7 +151,7 @@ animate_left_join(x, y_extra, by = "id")
![](man/figures/tidyexplain-left-join-extra-1.gif)<!-- -->

``` r
left_join(x, y_extra, by = "id")
dplyr::left_join(x, y_extra, by = "id")
#> # A tibble: 4 x 3
#> id x y
#> <dbl> <chr> <chr>
@@ -173,7 +173,7 @@ animate_right_join(x, y, by = "id")
![](man/figures/tidyexplain-right-join-1.gif)<!-- -->

``` r
right_join(x, y, by = "id")
dplyr::right_join(x, y, by = "id")
#> # A tibble: 3 x 3
#> id x y
#> <int> <chr> <chr>
@@ -194,7 +194,7 @@ animate_full_join(x, y, by = "id")
![](man/figures/tidyexplain-full-join-1.gif)<!-- -->

``` r
full_join(x, y, by = "id")
dplyr::full_join(x, y, by = "id")
#> # A tibble: 4 x 3
#> id x y
#> <int> <chr> <chr>
@@ -218,7 +218,7 @@ animate_semi_join(x, y, by = "id")
![](man/figures/tidyexplain-semi-join-1.gif)<!-- -->

``` r
semi_join(x, y, by = "id")
dplyr::semi_join(x, y, by = "id")
#> # A tibble: 2 x 2
#> id x
#> <int> <chr>
@@ -238,7 +238,7 @@ animate_anti_join(x, y, by = "id")
![](man/figures/tidyexplain-anti-join-1.gif)<!-- -->

``` r
anti_join(x, y, by = "id")
dplyr::anti_join(x, y, by = "id")
#> # A tibble: 1 x 2
#> id x
#> <int> <chr>
@@ -248,11 +248,11 @@ anti_join(x, y, by = "id")
## Set Operations

``` r
x <- data_frame(
x <- dplyr::data_frame(
x = c(1, 1, 2),
y = c("a", "b", "a")
)
y <- data_frame(
y <- dplyr::data_frame(
x = c(1, 2),
y = c("a", "b")
)
@@ -289,7 +289,7 @@ animate_union(x, y)
![](man/figures/tidyexplain-union-1.gif)<!-- -->

``` r
union(x, y)
dplyr::union(x, y)
#> # A tibble: 4 x 2
#> x y
#> <dbl> <chr>
@@ -307,7 +307,7 @@ animate_union(y, x)

``` r

union(y, x)
dplyr::union(y, x)
#> # A tibble: 4 x 2
#> x y
#> <dbl> <chr>
@@ -328,7 +328,7 @@ animate_union_all(x, y)
![](man/figures/tidyexplain-union-all-1.gif)<!-- -->

``` r
union_all(x, y)
dplyr::union_all(x, y)
#> # A tibble: 5 x 2
#> x y
#> <dbl> <chr>
@@ -350,7 +350,7 @@ animate_intersect(x, y)
![](man/figures/tidyexplain-intersect-1.gif)<!-- -->

``` r
intersect(x, y)
dplyr::intersect(x, y)
#> # A tibble: 1 x 2
#> x y
#> <dbl> <chr>
@@ -369,7 +369,7 @@ animate_setdiff(x, y)
![](man/figures/tidyexplain-setdiff-1.gif)<!-- -->

``` r
setdiff(x, y)
dplyr::setdiff(x, y)
#> # A tibble: 2 x 2
#> x y
#> <dbl> <chr>
@@ -385,7 +385,7 @@ animate_setdiff(y, x)

``` r

setdiff(y, x)
dplyr::setdiff(y, x)
#> # A tibble: 1 x 2
#> x y
#> <dbl> <chr>
@@ -407,14 +407,12 @@ to be formatted as a tidy dataset and the
you organize your data into tidy data.

``` r
library(tidyr)

long <- data_frame(
long <- dplyr::data_frame(
year = c(2010, 2011, 2010, 2011, 2010, 2011),
person = c("Alice", "Alice", "Bob", "Bob", "Charlie", "Charlie"),
sales = c(105, 110, 100, 97, 90, 95)
)
wide <- data_frame(
wide <- dplyr::data_frame(
year = 2010:2011,
Alice = c(105, 110),
Bob = c(100, 97),
@@ -437,7 +435,7 @@ animate_gather(wide, key = "person", value = "sales", -year)
![](man/figures/tidyexplain-gather-1.gif)<!-- -->

``` r
gather(wide, key = "person", value = "sales", -year)
tidyr::gather(wide, key = "person", value = "sales", -year)
#> # A tibble: 6 x 3
#> year person sales
#> <int> <chr> <dbl>
@@ -461,7 +459,7 @@ animate_spread(long, key = "person", value = "sales")
![](man/figures/tidyexplain-spread-1.gif)<!-- -->

``` r
spread(long, key = "person", value = "sales")
tidyr::spread(long, key = "person", value = "sales")
#> # A tibble: 2 x 4
#> year Alice Bob Charlie
#> <dbl> <dbl> <dbl> <dbl>

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