```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` ### Usage and Adaptation of Data Carpentry Materials: Most material found in this document has been adapted from [Data Carpentry][https://datacarpentry.org/r-socialsci/] materials, under the [creative commons attribution license][https://creativecommons.org/licenses/by/4.0/]. Minor amendments have been made to allow for compatability in order. ### Exercise 0 Having installed tidyverse and here packages earlier, we must still load in the packages for our system to use it. Go ahead and do this. ```{r Tidyverse and Here Loading} library(tidyverse) library(here) ``` ------------- For this workshop, we will need to import a tidy data set. We will choose one from the data carpentry. ```{r DataImport} interviews <- read_csv("https://raw.githubusercontent.com/datacarpentry/r-socialsci/main/episodes/data/SAFI_clean.csv") ``` ### Exercise 1 Now you have the data imported, try some of the data summary functions that we discussed on this data. Once you've done that write a short paragraph detailing how many entries there are, how many vairables and what their types are. ```{r Here Install and Loading} str(interviews) head(interviews) glimpse(interviews) ``` ------------- ### Exercise 2 Some of the variables in the dataframe are encoded as strings, when in fact they are categories. Go ahead and change one of the variables that is like this into a factor. ```{r Factor Change} memb_assoc <- interviews$memb_assoc memb_assoc <- as.factor(memb_assoc) ``` ------------- ### Exercise 3 In the tutorial we saw we could dissect the variable 'interview_date' into each of the year, month and date components. Go ahead and replicate this saving each variable under a different name. ```{r Interview Date Dissection} interviews$day <- day(dates) interviews$month <- month(dates) interviews$year <- year(dates) interviews ```