Principles of Data Visualization and Introduction to ggplot2
I have provided you with data about the 5,000 fastest growing companies in the US, as compiled by Inc. magazine. lets read this in:
##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## Warning: package 'e1071' was built under R version 4.0.4##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alphainc <- read.csv("https://raw.githubusercontent.com/charleyferrari/CUNY_DATA_608/master/module1/Data/inc5000_data.csv", header= TRUE)And lets preview this data:
## Rank Name Growth_Rate Revenue
## 1 1 Fuhu 421.48 1.179e+08
## 2 2 FederalConference.com 248.31 4.960e+07
## 3 3 The HCI Group 245.45 2.550e+07
## 4 4 Bridger 233.08 1.900e+09
## 5 5 DataXu 213.37 8.700e+07
## 6 6 MileStone Community Builders 179.38 4.570e+07
## Industry Employees City State
## 1 Consumer Products & Services 104 El Segundo CA
## 2 Government Services 51 Dumfries VA
## 3 Health 132 Jacksonville FL
## 4 Energy 50 Addison TX
## 5 Advertising & Marketing 220 Boston MA
## 6 Real Estate 63 Austin TX## Rank Name Growth_Rate Revenue
## Min. : 1 Length:5001 Min. : 0.340 Min. :2.000e+06
## 1st Qu.:1252 Class :character 1st Qu.: 0.770 1st Qu.:5.100e+06
## Median :2502 Mode :character Median : 1.420 Median :1.090e+07
## Mean :2502 Mean : 4.612 Mean :4.822e+07
## 3rd Qu.:3751 3rd Qu.: 3.290 3rd Qu.:2.860e+07
## Max. :5000 Max. :421.480 Max. :1.010e+10
##
## Industry Employees City State
## Length:5001 Min. : 1.0 Length:5001 Length:5001
## Class :character 1st Qu.: 25.0 Class :character Class :character
## Mode :character Median : 53.0 Mode :character Mode :character
## Mean : 232.7
## 3rd Qu.: 132.0
## Max. :66803.0
## NA's :12Think a bit on what these summaries mean. Use the space below to add some more relevant non-visual exploratory information you think helps you understand this data:
Thoughts-based on describe function used here The median growth rate is around 1.5, with a SD of 14 and mean of 4.61.sensing some serious skew towards lower growth rates, which is corroborated with skewness function in e1071 package.Intuitively, the skewness is a measure of symmetry. As a rule, negative skewness indicates that the mean of the data values is less than the median, and the data distribution is left-skewed. Positive skewness would indicate that the mean of the data values is larger than the median, and the data distribution is right-skewed. City frequency medians/means match up, should probably do a visual exploratory analysis on that. We see the employee range is 66802, with a minimum of 1…This indicates these businesses are not sampled from their respectively tiered size. (We should see quite a bit of variation between businesses)
## vars n mean sd median trimmed
## Rank 1 5001 2501.64 1443.51 2.502e+03 2501.73
## Name* 2 5001 2501.00 1443.81 2.501e+03 2501.00
## Growth_Rate 3 5001 4.61 14.12 1.420e+00 2.14
## Revenue 4 5001 48222535.49 240542281.14 1.090e+07 17334966.26
## Industry* 5 5001 12.10 7.33 1.300e+01 12.05
## Employees 6 4989 232.72 1353.13 5.300e+01 81.78
## City* 7 5001 732.00 441.12 7.610e+02 731.74
## State* 8 5001 24.80 15.64 2.300e+01 24.44
## mad min max range skew kurtosis se
## Rank 1853.25 1.0e+00 5.0000e+03 4.9990e+03 0.00 -1.20 20.41
## Name* 1853.25 1.0e+00 5.0010e+03 5.0000e+03 0.00 -1.20 20.42
## Growth_Rate 1.22 3.4e-01 4.2148e+02 4.2114e+02 12.55 242.34 0.20
## Revenue 10674720.00 2.0e+06 1.0100e+10 1.0098e+10 22.17 722.66 3401441.44
## Industry* 8.90 1.0e+00 2.5000e+01 2.4000e+01 -0.10 -1.18 0.10
## Employees 53.37 1.0e+00 6.6803e+04 6.6802e+04 29.81 1268.67 19.16
## City* 604.90 1.0e+00 1.5190e+03 1.5180e+03 -0.04 -1.26 6.24
## State* 19.27 1.0e+00 5.2000e+01 5.1000e+01 0.12 -1.46 0.22## [1] 12.54951Question 1
Create a graph that shows the distribution of companies in the dataset by State (ie how many are in each state). There are a lot of States, so consider which axis you should use. This visualization is ultimately going to be consumed on a ‘portrait’ oriented screen (ie taller than wide), which should further guide your layout choices.
## `summarise()` ungrouping output (override with `.groups` argument)ggplot(data = state, aes(x = reorder(State, Count), y = Count)) +
geom_bar(stat = "identity") +
coord_flip() +
labs(title = "Number of Companies by State", x = "State", y = "Number of Companies")
Question 2
Lets dig in on the state with the 3rd most companies in the data set. Imagine you work for the state and are interested in how many people are employed by companies in different industries. Create a plot that shows the average and/or median employment by industry for companies in this state (only use cases with full data, use R’s complete.cases() function.) In addition to this, your graph should show how variable the ranges are, and you should deal with outliers.
# Answer Question 2 here
# From the barchart above, we can tell NY has the 3rd most companies among the states
employment <- inc %>%
filter(State == "NY") %>%
filter(complete.cases(.))
ggplot(employment, aes(x = Industry, y = Employees)) +
geom_boxplot() +
coord_flip() +
labs(title = "Distribution of Employments by Industry in NY", x = "Industry", y = "Number of Employees")
# To view the graph without the outliners
ggplot(employment, aes(x = Industry, y = Employees)) +
geom_boxplot(outlier.shape = NA) +
labs(title = "Distribution of Employments by Industry in NY", x = "Industry", y = "Number of Employees") +
coord_cartesian(ylim = c(0, 1500)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
Question 3
Now imagine you work for an investor and want to see which industries generate the most revenue per employee. Create a chart that makes this information clear. Once again, the distribution per industry should be shown.
# Answer Question 3 here
# Answer Question 3 here
options(scipen = 5) # turn off scientific notation
revenue <- inc %>%
group_by(Industry) %>%
summarize(TotalRev = sum(Revenue), TotalEmp = sum(Employees), RevPerEmp = TotalRev/TotalEmp) %>%
arrange(desc(RevPerEmp)) %>%
na.omit()## `summarise()` ungrouping output (override with `.groups` argument)ggplot(data = revenue, aes(x = reorder(Industry, RevPerEmp), y = RevPerEmp)) +
geom_bar(stat = "identity") +
labs(title = "Revenue per Employee by Industry", x = "Industy", y = "Revenue per Employee") +
coord_flip()