Challenge 2 Instructions

challenge_2

railroads

faostat

hotel_bookings

Author

Sahasra Iyer

Published

August 16, 2022

Code

library(tidyverse)

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

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

# Read in the railroad dataset
clean_county_data = read_csv("_data/railroad_2012_clean_county.csv")

# Displaying the top 10 rows 
head(clean_county_data, n = 10)

# A tibble: 10 × 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

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).

The data looks to be collected from the railroad department of US’s different counties, with a mapping of the state to each county. It contains the counts of the total employees of each county working in the railroad department. The data seems to be gathered as an effort for conducting a census of the railroad employees. The cases are the total employee counts for each county and the state the county belongs to, and the variables are “state”, “county” and “total_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

# Function for computing the mode 
mode <- function(group_num){
  which.max(tabulate(group_num))
}
# Getting mean, median and mode of total_employees
grouped_data_stats <- clean_county_data %>%
  group_by(state) %>%
  summarise(total_emp = sum(total_employees),
            avg_emp = mean(total_employees), 
            median_emp = median(total_employees), 
            mode_emp = mode(total_employees),
            sd_emp = sd(total_employees), 
            quant_0.25_emp = quantile(total_employees, 0.25),
            quant_0.75_emp = quantile(total_employees, 0.75),
            quant_0.5_emp = quantile(total_employees, 0.5),
            min_emp = min(total_employees),
            max_emp = max(total_employees)) %>%
  arrange(desc(sd_emp))

head(grouped_data_stats, n=5)

# A tibble: 5 × 11
  state total_emp avg_emp media…¹ mode_…² sd_emp quant…³ quant…⁴ quant…⁵ min_emp
  <chr>     <dbl>   <dbl>   <dbl>   <int>  <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
1 IL        19131    186.      42      14   829.    14       98       42       1
2 DE         1495    498.     158      62   674.   110      716.     158      62
3 NY        17050    280.      71      11   591.    27      196       71       5
4 CA        13137    239.      61       2   549.    12.5    200.      61       1
5 CT         2592    324      125      26   520.    63.8    231      125      26
# … with 1 more variable: max_emp <dbl>, and abbreviated variable names
#   ¹median_emp, ²mode_emp, ³quant_0.25_emp, ⁴quant_0.75_emp, ⁵quant_0.5_emp

Code

# Finding county with maximum employees
filter(clean_county_data, total_employees==max(total_employees))

# A tibble: 1 × 3
  state county total_employees
  <chr> <chr>            <dbl>
1 IL    COOK              8207

Code

# Construct boxplot of total employees across all counties
boxplot(grouped_data_stats$total_emp)

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.

I chose to group the data by state to get a count of total employees working in the railroad department in each state. From the boxplot, we can observe that the median is somewhere in the range of 3000-500 employees, while the maximum employees are found to be closer to 20000. Further, we have very few outliers (data points that go beyond the interquartile ranges).

--- title: "Challenge 2 Instructions" author: "Sahasra Iyer" desription: "Data wrangling: using group() and summarise()" date: "08/16/2022" format: html: toc: true code-fold: true code-copy: true code-tools: true categories: - challenge_2 - railroads - faostat - hotel_bookings --- ```{r} #| label: setup #| warning: false #| message: false 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 ⭐⭐⭐⭐ ```{r} # Read in the railroad dataset clean_county_data = read_csv("_data/railroad_2012_clean_county.csv") # Displaying the top 10 rows head(clean_county_data, n = 10) ``` 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). ## The data looks to be collected from the railroad department of US's different counties, with a mapping of the state to each county. It contains the counts of the total employees of each county working in the railroad department. The data seems to be gathered as an effort for conducting a census of the railroad employees. The cases are the total employee counts for each county and the state the county belongs to, and the variables are "state", "county" and "total_employees". ```{r} #| label: summary ``` ## 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. ```{r} # Function for computing the mode mode <- function(group_num){ which.max(tabulate(group_num)) } # Getting mean, median and mode of total_employees grouped_data_stats <- clean_county_data %>% group_by(state) %>% summarise(total_emp = sum(total_employees), avg_emp = mean(total_employees), median_emp = median(total_employees), mode_emp = mode(total_employees), sd_emp = sd(total_employees), quant_0.25_emp = quantile(total_employees, 0.25), quant_0.75_emp = quantile(total_employees, 0.75), quant_0.5_emp = quantile(total_employees, 0.5), min_emp = min(total_employees), max_emp = max(total_employees)) %>% arrange(desc(sd_emp)) head(grouped_data_stats, n=5) ``` ```{r} # Finding county with maximum employees filter(clean_county_data, total_employees==max(total_employees)) # Construct boxplot of total employees across all counties boxplot(grouped_data_stats$total_emp) ``` ### 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. ## I chose to group the data by state to get a count of total employees working in the railroad department in each state. From the boxplot, we can observe that the median is somewhere in the range of 3000-500 employees, while the maximum employees are found to be closer to 20000. Further, we have very few outliers (data points that go beyond the interquartile ranges).