# 81 R based data organization and visualization

Tengteng Tao

library(tidyverse)
library(ggridges)
library(ggplot2)
library(scales)

## 81.1 Introduction

In this cheat sheet, I conclude all the important functions we have used in the past two graded homeworks. The whole cheat sheet has two sections. In the first section, all functions related to plotting will be listed. In the second section, all functions which are helpful in organizing data will be mentioned.

## 81.2 Plots and their corresponding codes

### 81.2.1 Box plots

To create box plots, we use the function geom_boxplot() from the ggplot2. The basic function is

ggplot(dataframe, aes(y = TheColumnForYaxis , x = TheColumnForXaxis ))+ geom_boxplot()

The example used data fastfood from the openintro package. I used the data calories and restaurant to make the box plot. In addition,to make sure the plot is intuitive enough, the plot is ordered by the mean value from high to low.

df <- openintro::fastfood
ggplot(df, aes(x = reorder(restaurant, calories, median), y = calories))+
geom_boxplot()+
coord_flip()+
xlab("")+
theme_grey(14)

### 81.2.2 Histograms

In this section, I will introduce the two ways to make histograms. Examples used data mtl from package openintro The first way is plotting with base R. We can simply use function hist() like

hist(datafram$TheColumnYouWantToPlot) df <- openintro::mtl hist(df$asubic, col = "lightblue",  xlab = "asubic", main = "Histogram of asubic based on R")
The second way is using ggplot2. The thing needed to be noticed is that the bin number is default on 30 for any data set. Acoordingly, for a more intuitive plot, we always need to change the bins’ number. The basic function is:

ggplot(datafram, aes(TheColumnYouWantToPlot))+ geom_hist()

ggplot(df, aes(asubic))+
geom_histogram()+stat_bin(bins = 9)+
ggtitle("Histogram of asubic based on ggplot")

### 81.2.3 Density curves

To create density curves, we use geom_density from ggplot2. Basic function is:

ggplot(dataframe, aes(x=TheColumnYouWantToPlot))+ geom_density()

The data we used for example here is the astralia soybean from the agridat package.

df <- agridat::australia.soybean
ggplot(df, aes(x = yield))+
geom_density(alpha = .2, color = "blue")+
theme_grey(14)

### 81.2.4 Normal curves

To create normal curves, we need use stat_function(fun = dnorm, args = list(mean, sd)). The basic function is:

ggplot(dataframe, aes(x=TheColumnYouWantToPlot))+ stat_function(fun = dnorm, args = list(mean = mean(TheColumnYouWantToPlot), sd = sd(TheColumnYouWantToPlot)))

The data we used for example here is the astralia soybean from the agridat package.

df <- agridat::australia.soybean
ggplot(df, aes(x = yield))+
stat_function(fun = dnorm, args = list(mean = mean(df$yield), sd = sd(df$yield)), color = "red")

### 81.2.5 Ridgeline plot

To create a ridgeline plot, we need to use geom_density_ridges() from the package ggridges. The basic function is:

ggplot(df, aes(y = TheColumnForYaxis , x = TheColumnForXaxis ))+ geom_density_ridges()+

The example used data loans_full_schema from package openintro. In addition,to make sure the plot is intuitive enough, the plot is ordered by the mean value from high to low.

df <- openintro::loans_full_schema
ggplot(df, aes(y = reorder(loan_purpose, loan_amount, median),
x = loan_amount))+
geom_density_ridges(fill = "blue", alpha = .5, scale = 1)+
theme_ridges()+
theme(legend.position = "none")

### 81.2.6 Frequency Bar Chart

To create a frequency bar chart, we need to use geom_bar() from ggplot2. Here is the basic function:

ggplot(dataframe, aes(x = TheColumnYouWantToPlot))+ geom_bar(aes(y = ..count..))

Data used here is Roof.Style from ames in the package openintro. In addition,to make sure the plot is intuitive enough, the plot is ordered in ascending order.

df <- openintro::ames

ggplot(df,  aes(x = fct_rev(fct_infreq(Roof.Style)))) +
geom_bar( aes(y = ..count..), color = "blue", fill = "lightblue") +
xlab("") +
ylab("Frequency") +
ggtitle("Frequency bar chart for the roof styles of the properties")

### 81.2.7 Cleveland dot plots

To create a cleveland dot plots, we need to geom_point from ggplot2. The basic function is

ggplot(dataframe, aes(y =reorder(TheColumnForYaxis, TheColumnForXaxis) , x = TheColumnForXaxis))+ geom_point()

Data used here is seattlepets from package openintro. I plotted the most popular 30 names.

df <- openintro::seattlepets %>% dplyr::count(animal_name, sort = TRUE) %>% drop_na()

ggplot(df[1:30,], aes(x = n, y = reorder(animal_name, n)))+
geom_point()

### 81.2.8 Scatter plot

To create a scatter plot, we also use geom_point(). The basic function is

ggplot(dataframe, aes(y = TheColumnForYaxis , x = TheColumnForXaxis))+ geom_point()

Data used here is ames from the package openintro

df <- openintro::ames
ggplot(df, aes(area, price))+
geom_point( alpha = .15, stroke = 0,size = 1.5)+
ggtitle("Scatter plot of price vs. area")

### 81.2.9 Density contour lines

To create density contour lines, we need to use geom_density_2d(). Here is the basic function:

ggplot(dataframe, aes(y = TheColumnForYaxis , x = TheColumnForXaxis))+ geom_density_2d()

Data used here is ames from the package openintro.

ggplot(df,aes(area, price)) +
geom_density_2d(aes(colour=..level..)) +
ggtitle("Density contour lines of price vs. area")

### 81.2.10 Hexagonal heatmap

To create hexagonal heatmap, we need to use geom_hex(). Here is the basic function:

ggplot(dataframe, aes(y = TheColumnForYaxis , x = TheColumnForXaxis))+ geom_hex()

Data used here is ames from the package openintro.

ggplot(df,aes(area, price)) +
geom_hex(bins = 30) +
scale_fill_gradient(low = "#F2F0F7", high = "#08519C" ) +
theme_bw() +
ggtitle("Hexagonal heatmap of price vs. area")

### 81.2.11 Square heatmap

To create hexagonal heatmap, we need to use geom_bin_2d(). Here is the basic function:

ggplot(dataframe, aes(y = TheColumnForYaxis , x = TheColumnForXaxis))+ geom_bin_2d()

Data used here is ames from the package openintro.

ggplot(df, aes(area, price)) +
geom_bin_2d(bins = 20) +
scale_fill_gradient(low = "#F2F0F7", high = "#08519C" ) +
theme_bw() +
ggtitle("Square heatmap of price vs. area") 

## 81.3 Data organization functions

### 81.3.1 Pipe(%>%) Operator

Pipe operator can be used to simplified your code. It can be simple interpreted as “and then” For example: filter(data, variable == numeric_value) and data %>% filter(variable == numeric_value) will yield same result.

By using this operator properly, we can make our code clean and brief. ### facet_warp() facet_warp() allow us to combine multiple plots, which can give us a more directly view of comparison between those type of plots.

The data we used for example here is the astralia soybean from the agridat package.

df <- agridat::australia.soybean
ggplot(df, aes(x = yield, color = loc, fill = loc))+
geom_histogram(aes(y = ..density..),  fill = NA) +
facet_wrap(~loc, nrow = 2, strip.position = "right")+
theme_grey(14)

### 81.3.2 filter()

filter() function allows us to select those variable with specific values. For example, in the data seattlepets from the package openintro, we can use filter to find all dogs’ name:

df <- openintro::seattlepets %>% filter(species == "Dog")
df
## # A tibble: 35,181 × 7
##    <date>             <chr>          <chr>       <chr>   <chr>   <chr>   <chr>
##  1 2018-11-16         8002756        Wall-E      Dog     Mixed … Mix     98108
##  2 2018-11-11         S124529        Andre       Dog     Terrie… Dachsh… 98117
##  3 2018-11-21         903793         Mac         Dog     Retrie… <NA>    98136
##  4 2018-12-16         S138529        Cody        Dog     Retrie… <NA>    98103
##  5 2017-10-04         580652         Millie      Dog     Terrie… <NA>    98115
##  6 2018-12-23         961052         Sabre       Dog     Terrier <NA>    98126
##  7 2018-12-07         S125461        Thomas      Dog     Chihua… Mix     98177
##  8 2018-11-07         8002543        Lulu        Dog     Vizsla… Mix     98105
##  9 2018-12-15         S138838        Milo        Dog     Boxer   Retrie… 98109
## 10 2018-11-27         S123980        Anubis      Dog     Poodle… <NA>    98112
## # … with 35,171 more rows, and abbreviated variable names ¹​primary_breed,
## #   ²​secondary_breed, ³​zip_code

### 81.3.3 group_by()

group_by()function allow us to group the data by some variables.

For example, we want to group the data that crimes in 2020 by county and region, we can do this

df <- read.csv("https://data.ny.gov/api/views/ca8h-8gjq/rows.csv")
df %>% filter(Year == 2020) %>% group_by(County, Region)
## # A tibble: 628 × 15
## # Groups:   County, Region [62]
##    County Agency       Year Month…¹ Index…² Viole…³ Murder  Rape Robbery Aggra…⁴
##    <chr>  <chr>       <int>   <int>   <int>   <int>  <int> <int>   <int>   <int>
##  1 Albany Albany Cit…  2020      12    3571     881     18    61     166     636
##  2 Albany Albany Cou…  2020      12       2       0      0     0       0       0
##  3 Albany Albany Cou…  2020      12     131      13      0     4       0       9
##  4 Albany Albany Cou…  2020      12     103      26      0    18       2       6
##  5 Albany Altamont V…  2020      12       6       2      0     0       0       2
##  6 Albany Bethlehem …  2020      12     407      25      1     7       3      14
##  7 Albany Coeymans T…  2020      12      49       8      0     0       1       7
##  8 Albany Cohoes Cit…  2020      12     138      26      0     3       4      19
##  9 Albany Colonie To…  2020      12    1936      83      0     4      28      51
## 10 Albany County Tot…  2020      NA    7450    1127     19   109     215     784
## # … with 618 more rows, 5 more variables: Property.Total <int>, Burglary <int>,
## #   Larceny <int>, Motor.Vehicle.Theft <int>, Region <chr>, and abbreviated
## #   variable names ¹​Months.Reported, ²​Index.Total, ³​Violent.Total,
## #   ⁴​Aggravated.Assault

### 81.3.4 summarise()

We can use summarise() to measure some value for a certain group. For example, in data set ames from openintro, we want to know the average price and area for each Neighborhood, we can do this:

df <-openintro::ames %>%
group_by(Neighborhood) %>%
summarise(price = mean(price), area = mean(area))

df
## # A tibble: 28 × 3
##    Neighborhood   price  area
##    <fct>          <dbl> <dbl>
##  1 Blmngtn      196662. 1405.
##  2 Blueste      143590  1160.
##  3 BrDale       105608. 1115.
##  4 BrkSide      124756. 1235.
##  5 ClearCr      208662. 1744.
##  6 CollgCr      201803. 1496.
##  7 Crawfor      207551. 1723.
##  8 Edwards      130843. 1338.
##  9 Gilbert      190647. 1621.
## 10 Greens       193531. 1157.
## # … with 18 more rows

### 81.3.5 summarise_at()

For some specific situation, we need to summaries many variables. Writing them one by one will be time comsuming and we can do this by using summarise_at().

For Example, we want to know for year 2020, the total number of each type of crime happened in every county. We can write something like this:

df2020 <- read.csv("https://data.ny.gov/api/views/ca8h-8gjq/rows.csv") %>%
filter(Year == 2020)
df2020\$Property.Total = NULL
df2020 %>%
group_by(County) %>%
summarise_at(.vars = names(.)[7:13], .funs = c(sum = "sum"))
## # A tibble: 62 × 8
##    County      Murder_sum Rape_sum Robbery_sum Aggrava…¹ Burgl…² Larce…³ Motor…⁴
##    <chr>            <int>    <int>       <int>     <int>   <int>   <int>   <int>
##  1 Albany              38      218         430      1568    1446   10338     862
##  2 Allegany             6       76           6        58     172     430      56
##  3 Bronx              111      523        3519      8976    2230   18728    2130
##  4 Broome              10      252         156       902    1338    7268     434
##  5 Cattaraugus          2       76          12       162     274    1112     112
##  6 Cayuga               4      136          36       250     334    1706     124
##  7 Chautauqua           2      166          72       556    1084    3838     154
##  8 Chemung              4       64          56       220     262    2266     134
##  9 Chenango             2      104          14       106     292    1002      50
## 10 Clinton              2      118           6       132     246    1724      52
## # … with 52 more rows, and abbreviated variable names ¹​Aggravated.Assault_sum,
## #   ²​Burglary_sum, ³​Larceny_sum, ⁴​Motor.Vehicle.Theft_sum