library(tidyverse)
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## x dplyr::filter() masks stats::filter()
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library(p8105.datasets)
library(plotly)
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## Attaching package: 'plotly'
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## last_plot
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## layout
We’re going to look at the Instacart dataset and graph a scatterplot, line graph and bar graph. Plotly was used to produce the graphs below as well as the ones produced in the dashboard.
instacart %>%
count(aisle) %>%
plot_ly(x = ~aisle, y = ~n, color = ~aisle, type = "scatter")
## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
instacart %>%
sample_n(10) %>%
group_by(product_name, order_dow) %>%
summarize(mean = mean(order_hour_of_day)) %>%
plot_ly(y = ~mean, x = ~product_name, type = "scatter", mode = "line")
## `summarise()` has grouped output by 'product_name'. You can override using the `.groups` argument.
instacart %>%
count(department) %>%
mutate(department = fct_reorder(department, n)) %>%
plot_ly(x = ~department, y = ~n, color = ~department, type = "bar", colors = "viridis")