Challenge 2

challenge_2

railroads

faostat

hotel_bookings

Data wrangling: using group() and summarise()

Author

Gabrielle Roman

Published

June 15, 2023

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

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

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 contains 2,930 rows and 3 columns. The data is a count of number of employees in different counties and their respective states.

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

railroad_data%>%
  group_by(state)%>%
  summarise('average total employees' = mean(total_employees))

# A tibble: 53 × 2
   state `average 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  
# ℹ 43 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.

I grouped by state in order to collect an average number of employees per state. The result returned 53 rows, indicating that some territories are not states but districts, etc.

--- title: "Challenge 2" author: Gabrielle Roman description: "Data wrangling: using group() and summarise()" date: "6/15/2023" 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} railroad_data <- read_csv("_data/railroad_2012_clean_county.csv") ``` 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 contains 2,930 rows and 3 columns. The data is a count of number of employees in different counties and their respective states. ## 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} railroad_data%>% group_by(state)%>% summarise('average total employees' = mean(total_employees)) ``` ### 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 grouped by state in order to collect an average number of employees per state. The result returned 53 rows, indicating that some territories are not states but districts, etc.