Update datasets (addresses #600)

modules/Statistics/Statistics.Rmd

@@ -110,6 +110,8 @@ cor.test(x, y = NULL, alternative(c("two.sided", "less", "greater")),
 
 ## Correlation {.small}
 
+Using the Charm City Circulator data
+
 ```{r cor1, comment="", message = FALSE}
 circ <- read_csv("https://jhudatascience.org/intro_to_r/data/Charm_City_Circulator_Ridership.csv")
 head(circ)

modules/Statistics/lab/Statistics_Lab.Rmd

@@ -13,7 +13,7 @@ knitr::opts_chunk$set(echo = TRUE)
 
 ### 1.1
 
-Load the packages needed in this lab. Then, read in the following child mortality data. Assign it to the "mort" variable. Change its first column name from `...1` into `country`. You can find the data here: https://jhudatascience.org/intro_to_r/data/mortality.csv
+Load the packages needed in this lab. Then, read in the following child mortality data by country. Assign it to the "mort" variable. Change its first column name from `...1` into `country`. You can find the data here: https://jhudatascience.org/intro_to_r/data/mortality.csv.
 
 Note that the data has lots of `NA` values - don't worry if you see that.
 
@@ -66,7 +66,7 @@ Perform a t-test to determine if there is evidence of a difference between child
 
 ### 2.1
 
-Read in the Kaggle cars auction dataset.  Assign it to the "cars" variable. You can find the data here:  http://jhudatascience.org/intro_to_r/data/kaggleCarAuction.csv.
+Read in the Kaggle used car auction dataset (https://www.kaggle.com/datasets/tunguz/used-car-auction-prices).  Assign it to the "cars" variable. You can find the data here:  http://jhudatascience.org/intro_to_r/data/kaggleCarAuction.csv.
 
 ```{r 2.1response}
 

modules/Statistics/lab/Statistics_Lab_Key.Rmd

@@ -13,7 +13,7 @@ knitr::opts_chunk$set(echo = TRUE)
 
 ### 1.1
 
-Load the packages needed in this lab. Then, read in the following child mortality data. Assign it to the "mort" variable. Change its first column name from `...1` into `country`. You can find the data here: https://jhudatascience.org/intro_to_r/data/mortality.csv
+Load the packages needed in this lab. Then, read in the following child mortality data by country. Assign it to the "mort" variable. Change its first column name from `...1` into `country`. You can find the data here: https://jhudatascience.org/intro_to_r/data/mortality.csv.
 
 Note that the data has lots of `NA` values - don't worry if you see that.
 
@@ -85,7 +85,7 @@ tidy(t.test(x, y))
 
 ### 2.1
 
-Read in the Kaggle cars auction dataset.  Assign it to the "cars" variable. You can find the data here:  http://jhudatascience.org/intro_to_r/data/kaggleCarAuction.csv.
+Read in the Kaggle used car auction dataset (https://www.kaggle.com/datasets/tunguz/used-car-auction-prices).  Assign it to the "cars" variable. You can find the data here:  http://jhudatascience.org/intro_to_r/data/kaggleCarAuction.csv.
 
 ```{r 2.1response}
 cars <- read_csv("http://jhudatascience.org/intro_to_r/data/kaggleCarAuction.csv")