Challenge 2 solutions

Code

library(tidyverse)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Challenge Overview

Today’s challenge is to

1. read in a data set, and describe the data using both words and any supporting information (e.g., tables, etc)
2. 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

railroad <- read_csv("_data/railroad_2012_clean_county.csv")
railroad

# 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 rows

Add any comments or documentation as needed. More challenging data may require additional code chunks and documentation.

Describe 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).

Code

#The data provided is the Countys in each state and the ammount of Employees who work for the railroads. Each county has a certain amount of counties and we will be grouping them together to show states and counties together to add up the employees 

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

library(dplyr)
railroad%>%
  group_by(state) %>%
  summarise(n = n())

# A tibble: 53 × 2
   state     n
   <chr> <int>
 1 AE        1
 2 AK        6
 3 AL       67
 4 AP        1
 5 AR       72
 6 AZ       15
 7 CA       55
 8 CO       57
 9 CT        8
10 DC        1
# … with 43 more rows

Code

railroad%>%
  group_by(state)%>%
  summarise(E=  sum(total_employees))

# A tibble: 53 × 2
   state     E
   <chr> <dbl>
 1 AE        2
 2 AK      103
 3 AL     4257
 4 AP        1
 5 AR     3871
 6 AZ     3153
 7 CA    13137
 8 CO     3650
 9 CT     2592
10 DC      279
# … with 43 more rows

Code

railroad%>%
  group_by(county)%>%
  summarise(E=  sum(total_employees))

# A tibble: 1,709 × 2
   county        E
   <chr>     <dbl>
 1 ABBEVILLE   124
 2 ACADIA       13
 3 ACCOMACK      4
 4 ADA          81
 5 ADAIR        29
 6 ADAMS       878
 7 ADDISON       8
 8 AIKEN       193
 9 AITKIN       19
10 ALACHUA      22
# … with 1,699 more rows

Code

library(dplyr)
filter(railroad, 'total_employees' >=100)

# 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 rows

Code

railroad%>%
  filter(total_employees>10)

# A tibble: 1,939 × 3
   state county               total_employees
   <chr> <chr>                          <dbl>
 1 AK    SKAGWAY MUNICIPALITY              88
 2 AL    AUTAUGA                          102
 3 AL    BALDWIN                          143
 4 AL    BIBB                              25
 5 AL    BLOUNT                           154
 6 AL    BULLOCK                           13
 7 AL    BUTLER                            29
 8 AL    CALHOUN                           45
 9 AL    CHAMBERS                          13
10 AL    CHILTON                           72
# … with 1,929 more rows

Explain and Interpret

Be 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.

#explain The data i used above was able to group by different columns # the first code The first code was used to find the amount labeled(N) to see how many times a state was mentioned in the columns

#the second code The second code was using the state and seeing how many employees were in that state

#the third code The third code allowed to go into depth on the employee count and sum the number of employees in each county.

#the Fifth Code The fifth code explains total employees over the number of 10