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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang="" xml:lang="">
<head>
<title>mini wrangle</title>
<meta charset="utf-8" />
<meta name="author" content="Gina Reynolds, January 2020" />
<link href="libs/remark-css-0.0.1/default.css" rel="stylesheet" />
<link href="libs/remark-css-0.0.1/hygge.css" rel="stylesheet" />
<link href="libs/remark-css-0.0.1/ninjutsu.css" rel="stylesheet" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
# mini wrangle
## made with flipbookr and xaringan
### Gina Reynolds, January 2020
---
```r
library(gapminder)
library(tidyverse)
```
```
## ── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──
```
```
## ✓ ggplot2 3.3.0.9000 ✓ purrr 0.3.3
## ✓ tibble 2.1.3 ✓ dplyr 0.8.4
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.4.0
```
```
## ── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
```
```r
knitr::opts_chunk$set(cache = F, comment = "")
```
---
class: split-40
count: false
.column[.content[
```r
*gapminder
```
]]
.column[.content[
```
# A tibble: 1,704 x 6
country continent year lifeExp pop gdpPercap
<fct> <fct> <int> <dbl> <int> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779.
2 Afghanistan Asia 1957 30.3 9240934 821.
3 Afghanistan Asia 1962 32.0 10267083 853.
4 Afghanistan Asia 1967 34.0 11537966 836.
5 Afghanistan Asia 1972 36.1 13079460 740.
6 Afghanistan Asia 1977 38.4 14880372 786.
7 Afghanistan Asia 1982 39.9 12881816 978.
8 Afghanistan Asia 1987 40.8 13867957 852.
9 Afghanistan Asia 1992 41.7 16317921 649.
10 Afghanistan Asia 1997 41.8 22227415 635.
# … with 1,694 more rows
```
]]
---
class: split-40
count: false
.column[.content[
```r
gapminder %>%
* distinct(country, continent)
```
]]
.column[.content[
```
# A tibble: 142 x 2
country continent
<fct> <fct>
1 Afghanistan Asia
2 Albania Europe
3 Algeria Africa
4 Angola Africa
5 Argentina Americas
6 Australia Oceania
7 Austria Europe
8 Bahrain Asia
9 Bangladesh Asia
10 Belgium Europe
# … with 132 more rows
```
]]
---
class: split-40
count: false
.column[.content[
```r
gapminder %>%
distinct(country, continent) %>%
* # tally and count are the same
* count(continent)
```
]]
.column[.content[
```
# A tibble: 5 x 2
continent n
<fct> <int>
1 Africa 52
2 Americas 25
3 Asia 33
4 Europe 30
5 Oceania 2
```
]]
---
class: split-40
count: false
.column[.content[
```r
gapminder %>%
distinct(country, continent) %>%
# tally and count are the same
count(continent) %>%
* rename(count = n)
```
]]
.column[.content[
```
# A tibble: 5 x 2
continent count
<fct> <int>
1 Africa 52
2 Americas 25
3 Asia 33
4 Europe 30
5 Oceania 2
```
]]
---
class: split-40
count: false
.column[.content[
```r
gapminder %>%
distinct(country, continent) %>%
# tally and count are the same
count(continent) %>%
rename(count = n) ->
*count_in_continents
```
]]
.column[.content[
]]
---
class: split-40
count: false
.column[.content[
```r
gapminder %>%
distinct(country, continent) %>%
# tally and count are the same
count(continent) %>%
rename(count = n) ->
count_in_continents
* # another pipeline
*gapminder
```
]]
.column[.content[
```
# A tibble: 1,704 x 6
country continent year lifeExp pop gdpPercap
<fct> <fct> <int> <dbl> <int> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779.
2 Afghanistan Asia 1957 30.3 9240934 821.
3 Afghanistan Asia 1962 32.0 10267083 853.
4 Afghanistan Asia 1967 34.0 11537966 836.
5 Afghanistan Asia 1972 36.1 13079460 740.
6 Afghanistan Asia 1977 38.4 14880372 786.
7 Afghanistan Asia 1982 39.9 12881816 978.
8 Afghanistan Asia 1987 40.8 13867957 852.
9 Afghanistan Asia 1992 41.7 16317921 649.
10 Afghanistan Asia 1997 41.8 22227415 635.
# … with 1,694 more rows
```
]]
---
class: split-40
count: false
.column[.content[
```r
gapminder %>%
distinct(country, continent) %>%
# tally and count are the same
count(continent) %>%
rename(count = n) ->
count_in_continents
# another pipeline
gapminder %>%
* summarize(mean_life_exp = mean(lifeExp),
* median_life_exp = median(lifeExp))
```
]]
.column[.content[
```
# A tibble: 1 x 2
mean_life_exp median_life_exp
<dbl> <dbl>
1 59.5 60.7
```
]]
---
class: split-40
count: false
.column[.content[
```r
gapminder %>%
distinct(country, continent) %>%
# tally and count are the same
count(continent) %>%
rename(count = n) ->
count_in_continents
# another pipeline
gapminder %>%
summarize(mean_life_exp = mean(lifeExp),
median_life_exp = median(lifeExp)) ->
*overall_summaries
```
]]
.column[.content[
]]
---
class: split-40
count: false
.column[.content[
```r
gapminder %>%
distinct(country, continent) %>%
# tally and count are the same
count(continent) %>%
rename(count = n) ->
count_in_continents
# another pipeline
gapminder %>%
summarize(mean_life_exp = mean(lifeExp),
median_life_exp = median(lifeExp)) ->
overall_summaries
* # another
*gapminder
```
]]
.column[.content[
```
# A tibble: 1,704 x 6
country continent year lifeExp pop gdpPercap
<fct> <fct> <int> <dbl> <int> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779.
2 Afghanistan Asia 1957 30.3 9240934 821.
3 Afghanistan Asia 1962 32.0 10267083 853.
4 Afghanistan Asia 1967 34.0 11537966 836.
5 Afghanistan Asia 1972 36.1 13079460 740.
6 Afghanistan Asia 1977 38.4 14880372 786.
7 Afghanistan Asia 1982 39.9 12881816 978.
8 Afghanistan Asia 1987 40.8 13867957 852.
9 Afghanistan Asia 1992 41.7 16317921 649.
10 Afghanistan Asia 1997 41.8 22227415 635.
# … with 1,694 more rows
```
]]
---
class: split-40
count: false
.column[.content[
```r
gapminder %>%
distinct(country, continent) %>%
# tally and count are the same
count(continent) %>%
rename(count = n) ->
count_in_continents
# another pipeline
gapminder %>%
summarize(mean_life_exp = mean(lifeExp),
median_life_exp = median(lifeExp)) ->
overall_summaries
# another
gapminder %>%
* group_by(continent)
```
]]
.column[.content[
```
# A tibble: 1,704 x 6
# Groups: continent [5]
country continent year lifeExp pop gdpPercap
<fct> <fct> <int> <dbl> <int> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779.
2 Afghanistan Asia 1957 30.3 9240934 821.
3 Afghanistan Asia 1962 32.0 10267083 853.
4 Afghanistan Asia 1967 34.0 11537966 836.
5 Afghanistan Asia 1972 36.1 13079460 740.
6 Afghanistan Asia 1977 38.4 14880372 786.
7 Afghanistan Asia 1982 39.9 12881816 978.
8 Afghanistan Asia 1987 40.8 13867957 852.
9 Afghanistan Asia 1992 41.7 16317921 649.
10 Afghanistan Asia 1997 41.8 22227415 635.
# … with 1,694 more rows
```
]]
---
class: split-40
count: false
.column[.content[
```r
gapminder %>%
distinct(country, continent) %>%
# tally and count are the same
count(continent) %>%
rename(count = n) ->
count_in_continents
# another pipeline
gapminder %>%
summarize(mean_life_exp = mean(lifeExp),
median_life_exp = median(lifeExp)) ->
overall_summaries
# another
gapminder %>%
group_by(continent) %>%
* summarize(mean_life_exp = mean(lifeExp),
* median_life_exp = median(lifeExp))
```
]]
.column[.content[
```
# A tibble: 5 x 3
continent mean_life_exp median_life_exp
<fct> <dbl> <dbl>
1 Africa 48.9 47.8
2 Americas 64.7 67.0
3 Asia 60.1 61.8
4 Europe 71.9 72.2
5 Oceania 74.3 73.7
```
]]
---
class: split-40
count: false
.column[.content[
```r
gapminder %>%
distinct(country, continent) %>%
# tally and count are the same
count(continent) %>%
rename(count = n) ->
count_in_continents
# another pipeline
gapminder %>%
summarize(mean_life_exp = mean(lifeExp),
median_life_exp = median(lifeExp)) ->
overall_summaries
# another
gapminder %>%
group_by(continent) %>%
summarize(mean_life_exp = mean(lifeExp),
median_life_exp = median(lifeExp)) ->
*summaries_by_continent
```
]]
.column[.content[
]]
<style type="text/css">
.remark-code{line-height: 1.5; font-size: 70%}
</style>
</textarea>
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