Challenge 2 solution

challenge_2

railroads

faostat

hotel_bookings

Author

Xiaoyan Hu

Published

September 27, 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

# setwd("/Users/cassie199/Desktop/22fall/DACSS601/601_Fall_2022/posts/_data")
data1 <- read.csv("birds.csv")

Error in file(file, "rt"): cannot open the connection

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

# there are 30977 observations and 14 variables in this data set
# varibles including domains, area, elements, item, value, flag and so on.
dim(data1)

Error in eval(expr, envir, enclos): object 'data1' not found

Code

colnames(data1)

Error in is.data.frame(x): object 'data1' not found

Code

head(data1)

Error in head(data1): object 'data1' not found

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

# In this trunk, I chose group the data by the area and calculted their mean if they have any avaiable numerical data. 
data1%>%
  group_by(Area) %>%
  summarise_all(mean, na.rm=TRUE)

Error in group_by(., Area): object 'data1' not found

Code

# In this trunk I chose the first ten columns to simplify the data, and then use filter to take out the years from 1990 to latest. 
data1%>%
  select(1:10)%>%
  head() %>%
  filter(Year>=1990)

Error in select(., 1:10): object 'data1' not found

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.

--- title: "Challenge 2 solution" author: "Xiaoyan Hu" desription: "Data wrangling: using group() and summarise()" date: "09/27/2022" 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} # setwd("/Users/cassie199/Desktop/22fall/DACSS601/601_Fall_2022/posts/_data") data1 <- read.csv("birds.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). ```{r} #| label: summary # there are 30977 observations and 14 variables in this data set # varibles including domains, area, elements, item, value, flag and so on. dim(data1) colnames(data1) head(data1) ``` ## 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} # In this trunk, I chose group the data by the area and calculted their mean if they have any avaiable numerical data. data1%>% group_by(Area) %>% summarise_all(mean, na.rm=TRUE) ``` ```{r} # In this trunk I chose the first ten columns to simplify the data, and then use filter to take out the years from 1990 to latest. data1%>% select(1:10)%>% head() %>% filter(Year>=1990) ``` ### 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.