From d2c97eb8ff1f884f4594647e13ba8147aaf0ed0e Mon Sep 17 00:00:00 2001 From: spcanelon Date: Wed, 23 Sep 2020 11:12:43 -0400 Subject: [PATCH] New example for #2 --- 04-dplyr.Rmd | 82 +++++----- 2020-rladies-chi-tidyverse.html | 152 +++++++----------- .../figure-html/unnamed-chunk-53-1.png | Bin 71389 -> 71439 bytes .../figure-html/unnamed-chunk-56-1.png | Bin 71439 -> 50950 bytes .../figure-html/unnamed-chunk-57-1.png | Bin 71439 -> 50928 bytes .../figure-html/unnamed-chunk-58-1.png | Bin 50928 -> 50950 bytes .../figure-html/unnamed-chunk-60-1.png | Bin 0 -> 50950 bytes 7 files changed, 91 insertions(+), 143 deletions(-) create mode 100644 2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-60-1.png diff --git a/04-dplyr.Rmd b/04-dplyr.Rmd index 6a29d69..7c14648 100644 --- a/04-dplyr.Rmd +++ b/04-dplyr.Rmd @@ -111,47 +111,60 @@ penguins %>% ] ] + .panel[.panel-name[Group By & Summarize] .pull-left[ -We can summarize the data using `group_by()` and `summarize()` to obtain counts by **species** and **sex** -```{r} -penguins %>% - group_by(species, sex) %>% #<< - summarize(n = n()) #<< -``` +.middle[We can use `group_by()` to group our data by **species** and **sex**, and `summarize()` to calculate the average **body_mass_g** for each grouping.] ] .pull-right[ -And because we're just _counting_, we also have the option to use `count()` which simplifies our code! - -.small-text[Example kindly [contributed by Alison Hill (@apreshill)](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)] - ```{r} penguins %>% - count(species, sex) #<< + select(species, sex, body_mass_g) %>% + group_by(species, sex) %>% #<< + summarize(mean = mean(body_mass_g)) #<< ``` ] ] -.panel[.panel-name[Mutate: Ex. 1] + +.panel[.panel-name[Counting 1] +If we're just interested in _counting_ the observations in each grouping, we can group and summarize with special functions `count()` and `add_count()`. + +---- .pull-left[ -We can use `mutate()` to create a new variable **n_species** that adds up all observations per **species** +Counting can be done with `group_by()` and `summarize()`, but it's a little cumbersome. + +It involves... +1. using `mutate()` to create an intermediate variable **n_species** that adds up all observations per **species**, and +2. an `ungroup()`-ing step +] + +.pull-right[ ```{r} penguins %>% group_by(species) %>% - mutate(n_species = n()) %>% #<< - ungroup() %>% + mutate(n_species = n()) %>% #<< + ungroup() %>% #<< group_by(species, sex, n_species) %>% - summarize(n = n()) + summarize(n = n()) ``` ] +] -.pull-right[ -**OR** we can use `count()`'s friend `add_count()` to create **n_species**, again because we're just _counting_ +.panel[.panel-name[Counting 2] +If we're just interested in _counting_ the observations in each grouping, we can group and summarize with special functions `count()` and `add_count()`. + +---- + +.pull-left[ +In contrast, `count()` and `add_count()` offer a simplified approach .small-text[Example kindly [contributed by Alison Hill (@apreshill)](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)] +] +.pull-right[ ```{r} penguins %>% count(species, sex) %>% @@ -161,52 +174,29 @@ penguins %>% ] ] -.panel[.panel-name[Mutate: Ex. 2] - -With either approach, we can use `mutate()` to create a new variable **prop**, which represents the proportion of penguins of each **sex**, grouped by **species** +.panel[.panel-name[Mutate] .pull-left[ -```{r} -penguins %>% - group_by(species) %>% - mutate(n_species = n()) %>% - ungroup() %>% - group_by(species, sex, n_species) %>% - summarize(n = n()) %>% - mutate(prop = n/n_species*100) #<< -``` +We can add to our counting example by using `mutate()` to create a new variable **prop**, which represents the proportion of penguins of each **sex**, grouped by **species** +.small-text[Example kindly [contributed by Alison Hill (@apreshill)](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)] ] .pull-right[ -.small-text[Example kindly [contributed by Alison Hill (@apreshill)](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)] - ```{r} penguins %>% count(species, sex) %>% add_count(species, wt = n, name = "n_species") %>% - mutate(prop = n/n_species*100) #<< + mutate(prop = n/n_species*100) #<< ``` ] ] .panel[.panel-name[Filter] -Finally, we can filter rows to only show us **Chinstrap** penguin summaries by adding `filter()` to our pipeline - .pull-left[ -```{r} -penguins %>% - group_by(species) %>% - mutate(n_species = n()) %>% - ungroup() %>% - group_by(species, sex, n_species) %>% - summarize(n = n()) %>% - mutate(prop = n/n_species*100) %>% - filter(species == "Chinstrap") #<< -``` +Finally, we can filter rows to only show us **Chinstrap** penguin summaries by adding `filter()` to our pipeline] -] .pull-right[ ```{r} penguins %>% diff --git a/2020-rladies-chi-tidyverse.html b/2020-rladies-chi-tidyverse.html index 314db49..be692ae 100644 --- a/2020-rladies-chi-tidyverse.html +++ b/2020-rladies-chi-tidyverse.html @@ -1190,12 +1190,12 @@ ### You might see .pull-left[ -- Gentoo penguins have higher body mass than Adelie and Chinstrap penguins +- Gentoo penguins have higher body mass than Adélie and Chinstrap penguins - Higher body mass among male Gentoo penguins compared to female penguins -- Pattern not as discernable when comparing Adelie and Chinstrap penguins -- No `NA`s among Chinstrap penguin data points! `sex` was available for each observation +- Pattern not as discernible when comparing Adélie and Chinstrap penguins +- No _NA_s among Chinstrap penguin data points! **sex** was available for each observation -I wonder what percentage of observations are `NA` for each species? Let's get the tidyverse to help us with this! +I wonder what percentage of observations are _NA_ for each species? Let's get the tidyverse to help us with this! Next stop, `dplyr`! ] @@ -1368,66 +1368,59 @@ ] ] + .panel[.panel-name[Group By & Summarize] .pull-left[ -We can summarize the data using `group_by()` and `summarize()` to obtain counts by **species** and **sex** - -```r -penguins %>% -* group_by(species, sex) %>% -* summarize(n = n()) -## # A tibble: 8 x 3 -## # Groups: species [3] -## species sex n -## <fct> <fct> <int> -## 1 Adelie female 73 -## 2 Adelie male 73 -## 3 Adelie <NA> 6 -## 4 Chinstrap female 34 -## 5 Chinstrap male 34 -## 6 Gentoo female 58 -## 7 Gentoo male 61 -## 8 Gentoo <NA> 5 -``` +.middle[We can use `group_by()` to group our data by **species** and **sex**, and `summarize()` to calculate the average **body_mass_g** for each grouping.] ] .pull-right[ -And because we're just _counting_, we also have the option to use `count()` which simplifies our code! - -.small-text[Example kindly [contributed by Alison Hill (@apreshill)](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)] - ```r penguins %>% -* count(species, sex) + select(species, sex, body_mass_g) %>% +* group_by(species, sex) %>% +* summarize(mean = mean(body_mass_g)) ## # A tibble: 8 x 3 -## species sex n -## <fct> <fct> <int> -## 1 Adelie female 73 -## 2 Adelie male 73 -## 3 Adelie <NA> 6 -## 4 Chinstrap female 34 -## 5 Chinstrap male 34 -## 6 Gentoo female 58 -## 7 Gentoo male 61 -## 8 Gentoo <NA> 5 +## # Groups: species [3] +## species sex mean +## <fct> <fct> <dbl> +## 1 Adelie female 3369. +## 2 Adelie male 4043. +## 3 Adelie <NA> NA +## 4 Chinstrap female 3527. +## 5 Chinstrap male 3939. +## 6 Gentoo female 4680. +## 7 Gentoo male 5485. +## 8 Gentoo <NA> NA ``` ] ] -.panel[.panel-name[Mutate: Ex. 1] + +.panel[.panel-name[Counting 1] +If we're just interested in _counting_ the observations in each grouping, we can group and summarize with special functions `count()` and `add_count()`. + +---- .pull-left[ -We can use `mutate()` to create a new variable **n_species** that adds up all observations per **species** +Counting can be done with `group_by()` and `summarize()`, but it's a little cumbersome. + +It involves... +1. using `mutate()` to create an intermediate variable **n_species** that adds up all observations per **species**, and +2. an `ungroup()`-ing step +] + +.pull-right[ ```r penguins %>% group_by(species) %>% * mutate(n_species = n()) %>% - ungroup() %>% +* ungroup() %>% group_by(species, sex, n_species) %>% - summarize(n = n()) + summarize(n = n()) ## # A tibble: 8 x 4 ## # Groups: species, sex [8] ## species sex n_species n @@ -1442,11 +1435,19 @@ ## 8 Gentoo <NA> 124 5 ``` ] +] -.pull-right[ -**OR** we can use `count()`'s friend `add_count()` to create **n_species**, again because we're just _counting_ +.panel[.panel-name[Counting 2] +If we're just interested in _counting_ the observations in each grouping, we can group and summarize with special functions `count()` and `add_count()`. + +---- + +.pull-left[ +In contrast, `count()` and `add_count()` offer a simplified approach .small-text[Example kindly [contributed by Alison Hill (@apreshill)](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)] +] +.pull-right[ ```r penguins %>% @@ -1468,38 +1469,14 @@ ] ] -.panel[.panel-name[Mutate: Ex. 2] - -With either approach, we can use `mutate()` to create a new variable **prop**, which represents the proportion of penguins of each **sex**, grouped by **species** +.panel[.panel-name[Mutate] .pull-left[ +We can add to our counting example by using `mutate()` to create a new variable **prop**, which represents the proportion of penguins of each **sex**, grouped by **species** -```r -penguins %>% - group_by(species) %>% - mutate(n_species = n()) %>% - ungroup() %>% - group_by(species, sex, n_species) %>% - summarize(count = n()) %>% -* mutate(prop = count/n_species*100) -## # A tibble: 8 x 5 -## # Groups: species, sex [8] -## species sex n_species count prop -## <fct> <fct> <int> <int> <dbl> -## 1 Adelie female 152 73 48.0 -## 2 Adelie male 152 73 48.0 -## 3 Adelie <NA> 152 6 3.95 -## 4 Chinstrap female 68 34 50 -## 5 Chinstrap male 68 34 50 -## 6 Gentoo female 124 58 46.8 -## 7 Gentoo male 124 61 49.2 -## 8 Gentoo <NA> 124 5 4.03 -``` - +.small-text[Example kindly [contributed by Alison Hill (@apreshill)](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)] ] .pull-right[ -.small-text[Example kindly [contributed by Alison Hill (@apreshill)](https://github.com/spcanelon/2020-rladies-chi-tidyverse/issues/2)] - ```r penguins %>% @@ -1524,28 +1501,9 @@ .panel[.panel-name[Filter] -Finally, we can filter rows to only show us **Chinstrap** penguin summaries by adding `filter()` to our pipeline - .pull-left[ +Finally, we can filter rows to only show us **Chinstrap** penguin summaries by adding `filter()` to our pipeline] -```r -penguins %>% - group_by(species) %>% - mutate(n_species = n()) %>% - ungroup() %>% - group_by(species, sex, n_species) %>% - summarize(count = n()) %>% - mutate(prop = count/n_species*100) %>% -* filter(species == "Chinstrap") -## # A tibble: 2 x 5 -## # Groups: species, sex [2] -## species sex n_species count prop -## <fct> <fct> <int> <int> <dbl> -## 1 Chinstrap female 68 34 50 -## 2 Chinstrap male 68 34 50 -``` - -] .pull-right[ ```r @@ -1901,7 +1859,7 @@ ### Now let's make it tidy again! -#### We'll use the help of `pivot_longer()` +We'll use the help of `pivot_longer()` ```r @@ -2009,12 +1967,12 @@ .pull-left[ ### Let's turn this plot -<img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-53-1.png" width="504" /> +<img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-50-1.png" width="504" /> ] .pull-right[ ### Into this one! -<img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-54-1.png" width="504" /> +<img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-51-1.png" width="504" /> .panel[.panel-name[Option 1] @@ -2041,7 +1999,7 @@ ] .pull-right[ -<img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-57-1.png" width="504" /> +<img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-54-1.png" width="504" /> ] ] @@ -2058,7 +2016,7 @@ * scale_fill_manual(values = nord::nord_palettes$frost) ``` -<img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-58-1.png" width="360" /> +<img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-55-1.png" width="360" /> ] .pull-right[ @@ -2071,7 +2029,7 @@ * nord::scale_fill_nord(palette = "frost") ``` -<img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-59-1.png" width="360" /> +<img src="2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-56-1.png" width="360" /> ] ] diff --git a/2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-53-1.png b/2020-rladies-chi-tidyverse_files/figure-html/unnamed-chunk-53-1.png index cd21350a31f5f9a85607416b589de3484df23e7d..abedc12655487a6689403f85c9905efc93789577 100644 GIT binary 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