Tidyverse Extend
Cassandra Coste
4/28/2021
Tage N Singh TIDYVERSE Create
This vignette will provide a brief exploration of the “tidyverse” package using its built-in libraries
The tidyverse is a powerful collection of R packages that are actually data tools All packages of the tidyverse share an underlying common APIs as listed below.
Lists
- ggplot2, which implements the grammar of graphics. You can use it to visualize your data.
- dplyr is a grammar of data manipulation. You can use it to solve the most common data manipulation challenges.
- tidyr helps you to create tidy data or data where each variable is in a column, each observation is a row end each value is a cell.
- readr is a fast and friendly way to read rectangular data.
- purrr enhances R’s functional programming (FP) toolkit by providing a complete and consistent set of tools for working with functions and vectors.
- tibble is a modern re-imaginging of the data frame.
- stringr provides a cohesive set of functions designed to make working with strings as easy as posssible
- forcats provide a suite of useful tools that solve common problems with factors.
Now for the package
#Loading just the "tidyverse" library !
library(tidyverse)# For this exercise we are using a dataset from kaggle.com which contain
#information about avacado sales in various cities in the USA
# ---------- Using the "rear" function "read_csv" -------
avacado <- data.frame(read_csv(file = "https://raw.githubusercontent.com/tagensingh/SPS-DATA607-TIDYVERSE/main/avocado.csv"))## Warning: Missing column names filled in: 'X1' [1]##
## -- Column specification --------------------------------------------------------
## cols(
## X1 = col_double(),
## Date = col_date(format = ""),
## AveragePrice = col_double(),
## `Total Volume` = col_double(),
## `4046` = col_double(),
## `4225` = col_double(),
## `4770` = col_double(),
## `Total Bags` = col_double(),
## `Small Bags` = col_double(),
## `Large Bags` = col_double(),
## `XLarge Bags` = col_double(),
## type = col_character(),
## year = col_double(),
## region = col_character()
## )# ---------- Using the "tibble" function to look at a snapshot of the dataframe -------
tibble(avacado)## # A tibble: 18,249 x 14
## X1 Date AveragePrice Total.Volume X4046 X4225 X4770 Total.Bags
## <dbl> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 2015-12-27 1.33 64237. 1037. 54455. 48.2 8697.
## 2 1 2015-12-20 1.35 54877. 674. 44639. 58.3 9506.
## 3 2 2015-12-13 0.93 118220. 795. 109150. 130. 8145.
## 4 3 2015-12-06 1.08 78992. 1132 71976. 72.6 5811.
## 5 4 2015-11-29 1.28 51040. 941. 43838. 75.8 6184.
## 6 5 2015-11-22 1.26 55980. 1184. 48068. 43.6 6684.
## 7 6 2015-11-15 0.99 83454. 1369. 73673. 93.3 8319.
## 8 7 2015-11-08 0.98 109428. 704. 101815. 80 6829.
## 9 8 2015-11-01 1.02 99811. 1022. 87316. 85.3 11388.
## 10 9 2015-10-25 1.07 74339. 842. 64757. 113 8626.
## # ... with 18,239 more rows, and 6 more variables: Small.Bags <dbl>,
## # Large.Bags <dbl>, XLarge.Bags <dbl>, type <chr>, year <dbl>, region <chr># ---------- Using the "arrange" function from "dplyr"to sort the dataframe by date-------
avacado_date <- arrange(avacado,Date)
tibble(avacado_date)## # A tibble: 18,249 x 14
## X1 Date AveragePrice Total.Volume X4046 X4225 X4770 Total.Bags
## <dbl> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 51 2015-01-04 1.22 40873. 2.82e3 2.83e4 4.99e1 9716.
## 2 51 2015-01-04 1 435021. 3.64e5 2.38e4 8.22e1 46816.
## 3 51 2015-01-04 1.08 788025. 5.40e4 5.53e5 4.00e4 141137.
## 4 51 2015-01-04 1.01 80034. 4.46e4 2.50e4 2.75e3 7756.
## 5 51 2015-01-04 1.02 491738 7.19e3 3.97e5 1.29e2 87663.
## 6 51 2015-01-04 1.4 116253. 3.27e3 5.57e4 1.10e2 57183.
## 7 51 2015-01-04 0.93 5777335. 2.84e6 2.27e6 1.37e5 528452.
## 8 51 2015-01-04 1.19 166006. 2.94e4 4.72e4 3.86e4 50798.
## 9 51 2015-01-04 1.11 783068. 3.03e4 5.51e5 1.25e5 77539.
## 10 51 2015-01-04 0.88 228570. 3.27e3 1.69e5 1.45e3 55083.
## # ... with 18,239 more rows, and 6 more variables: Small.Bags <dbl>,
## # Large.Bags <dbl>, XLarge.Bags <dbl>, type <chr>, year <dbl>, region <chr># ---------- Using the "ggplot" function from "ggplot2"to chart the pricing density of avacados-------
# Histogram overlaid with kernel density curve
ggplot(avacado, aes(x=AveragePrice)) +
geom_histogram(aes(y=..density..), # Histogram with density instead of count on y-axis
binwidth=.1,
colour="black", fill="white") +
geom_density(alpha=.1, fill="#FF6666")+# Overlay with transparent density plot
ggtitle("Avacados Pricing Density")
As is shown above, the tidyverse packages is one of the most versatile packages in the R sphere….
Cassandra Coste TIDYVERSE Extend
I am going to extend on the work of Tage by exploring the group by and summarize function within dplyr with the data and visualize the results.
avocado_region <- avacado %>% group_by(region)You might notice that the group_by function won’t make the data set look any different, but when we run calculations on the data, the group_by function will change the way the data is processed.
Now, when we run the summarise function to calculate the mean it will do the calculation by region. This is a helpful function since you often want to compare groups to infer meaning from datasets.
avocado_region <- avocado_region %>% summarise(regional_avg = mean(AveragePrice))Finally, using ggplot we can compare regionally, where avocado prices are on average.
ggplot(avocado_region, aes(x = reorder(region,-regional_avg), y = regional_avg, fill = "red")) + geom_bar(stat="identity") +
coord_flip() + ggtitle("Regional Average of Avocado Prices") + ylab("Average Price (USD)") + xlab("Region") + scale_y_continuous(labels = scales::dollar_format(), limits=c(0,2)) + theme(legend.position = "none") 
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