Code
library(tidyverse)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 2 Instructions
challenge_2
railroads
faostat
hotel_bookings
Challenge Overview
Today’s challenge is to
- read in a data set, and describe the data using both words and any supporting information (e.g., tables, etc)
- provide summary statistics for different interesting groups within the data, and interpret those statistics
Read in the Data
Read in one (or more) of the following data sets, available in the posts/_data folder, using the correct R package and command.
- railroad*.csv or StateCounty2012.xls ⭐
- FAOstat*.csv or birds.csv ⭐⭐⭐
- hotel_bookings.csv ⭐⭐⭐⭐
Code
data <- read_csv('_data/railroad_2012_clean_county.csv')
data# A tibble: 2,930 × 3
state county total_employees
<chr> <chr> <dbl>
1 AE APO 2
2 AK ANCHORAGE 7
3 AK FAIRBANKS NORTH STAR 2
4 AK JUNEAU 3
5 AK MATANUSKA-SUSITNA 2
6 AK SITKA 1
7 AK SKAGWAY MUNICIPALITY 88
8 AL AUTAUGA 102
9 AL BALDWIN 143
10 AL BARBOUR 1
# … with 2,920 more rowsAdd any comments or documentation as needed. More challenging data may require additional code chunks and documentation.
The dataset chosen by me is Railroad, it describes the information about total number of employees for a given combination of state and county. It has been collected for n number of state and county within them.
Code
data %>% group_by(county,state) %>%
summarize_at(c('total_employees'),mean)# A tibble: 2,930 × 3
# Groups: county [1,709]
county state total_employees
<chr> <chr> <dbl>
1 ABBEVILLE SC 124
2 ACADIA LA 13
3 ACCOMACK VA 4
4 ADA ID 81
5 ADAIR IA 5
6 ADAIR KY 1
7 ADAIR MO 21
8 ADAIR OK 2
9 ADAMS CO 553
10 ADAMS IA 7
# … with 2,920 more rowsDescribe the data
Using a combination of words and results of R commands, can you provide a high level description of the data? Describe as efficiently as possible where/how the data was (likely) gathered, indicate the cases and variables (both the interpretation and any details you deem useful to the reader to fully understand your chosen data).
Provide Grouped Summary Statistics
Conduct some exploratory data analysis, using dplyr commands such as group_by(), select(), filter(), and summarise(). Find the central tendency (mean, median, mode) and dispersion (standard deviation, mix/max/quantile) for different subgroups within the data set.
Code
data %>% group_by(state) %>%
summarize_at(c('total_employees'),mean)# A tibble: 53 × 2
state total_employees
<chr> <dbl>
1 AE 2
2 AK 17.2
3 AL 63.5
4 AP 1
5 AR 53.8
6 AZ 210.
7 CA 239.
8 CO 64.0
9 CT 324
10 DC 279
# … with 43 more rowsCode
data %>% group_by(county) %>%
summarize_at(c('total_employees'),mean)# A tibble: 1,709 × 2
county total_employees
<chr> <dbl>
1 ABBEVILLE 124
2 ACADIA 13
3 ACCOMACK 4
4 ADA 81
5 ADAIR 7.25
6 ADAMS 73.2
7 ADDISON 8
8 AIKEN 193
9 AITKIN 19
10 ALACHUA 22
# … with 1,699 more rowsCode
data %>% group_by(county) %>%
summarize_at(c('total_employees'),median)# A tibble: 1,709 × 2
county total_employees
<chr> <dbl>
1 ABBEVILLE 124
2 ACADIA 13
3 ACCOMACK 4
4 ADA 81
5 ADAIR 3.5
6 ADAMS 19.5
7 ADDISON 8
8 AIKEN 193
9 AITKIN 19
10 ALACHUA 22
# … with 1,699 more rowsCode
data %>% group_by(state) %>%
summarize_at(c('total_employees'),median)# A tibble: 53 × 2
state total_employees
<chr> <dbl>
1 AE 2
2 AK 2.5
3 AL 26
4 AP 1
5 AR 16.5
6 AZ 94
7 CA 61
8 CO 10
9 CT 125
10 DC 279
# … with 43 more rowsCode
library(dplyr)
target <- c("AR")
ar <- filter(data, state %in% target)
ar %>% summarize_at(c('total_employees'),mean)# A tibble: 1 × 1
total_employees
<dbl>
1 53.8Explain and Interpret
Code
library(dplyr)
target <- c("AK")
ak <- filter(data, state %in% target)
ak %>% summarize_at(c('total_employees'),mean)# A tibble: 1 × 1
total_employees
<dbl>
1 17.2Code
data %>% group_by(state) %>%
summarize_at(c('total_employees'),sd)# A tibble: 53 × 2
state total_employees
<chr> <dbl>
1 AE NA
2 AK 34.8
3 AL 130.
4 AP NA
5 AR 131.
6 AZ 228.
7 CA 549.
8 CO 128.
9 CT 520.
10 DC NA
# … with 43 more rowsCode
data %>% group_by(state) %>%
summarize_at(c('total_employees'),sd)# A tibble: 53 × 2
state total_employees
<chr> <dbl>
1 AE NA
2 AK 34.8
3 AL 130.
4 AP NA
5 AR 131.
6 AZ 228.
7 CA 549.
8 CO 128.
9 CT 520.
10 DC NA
# … with 43 more rowsCode
data %>% group_by(county) %>%
summarize_at(c('total_employees'),sd)# A tibble: 1,709 × 2
county total_employees
<chr> <dbl>
1 ABBEVILLE NA
2 ACADIA NA
3 ACCOMACK NA
4 ADA NA
5 ADAIR 9.32
6 ADAMS 155.
7 ADDISON NA
8 AIKEN NA
9 AITKIN NA
10 ALACHUA NA
# … with 1,699 more rowsCode
library(dplyr)
target <- c("CA")
ak <- filter(data, state %in% target)
ak %>% summarize_at(c('total_employees'),mean)# A tibble: 1 × 1
total_employees
<dbl>
1 239.Code
library(dplyr)
target <- c("CT")
ak <- filter(data, state %in% target)
ak %>% summarize_at(c('total_employees'),mean)# A tibble: 1 × 1
total_employees
<dbl>
1 324Be sure to explain why you choose a specific group. Comment on the interpretation of any interesting differences between groups that you uncover. This section can be integrated with the exploratory data analysis, just be sure it is included.
I looked into different central tendencies for the dataset - mean, median and also looked into standard deviation for the states. We can see that for AE, DC and AP the standard deviation is NA stating that the dispersion is 0 and it has similar values for all counties. The two groups i.e. states AK and AR where compared to see the difference in mean of the total_employees present. There is a difference of almost 35 employees, as AR has a mean of 53 while AK stands at 17. It is interesting to see that employees vary a lot per state. I also looked into the employees from the states with highest SD, to check what mean value stands at. According to the dispersion criteria, CA and CT had the most SD, but while checking means CT has a mean of 324 while CA stands at 238. We can see that the difference between them is as high as 100.