Generated by summarytools 1.0.1 (R version 4.2.1) 2022-12-23
Tidying snl_actors (2306 rows, 3 columns):
No missing data.
We can leave “aid” as a character column and convert type and gender to factors.
Tidying snl_casts (614 rows, 8 columns):
Missing data in first_epid and last_epid - this is fine because we used complete() to make the NAs explicit.
“aid” is also present in this dataset, which hopefully makes it intuitive to put the 2 datasets together (no renaming needed). There are much fewer values for Actor ID, so this might just be a subset of the other dataset.
Generated by summarytools 1.0.1 (R version 4.2.1) 2022-12-23
After joining, we have 10 columns. We have a lot more missing values, which was also expected - the snl_casts dataset, which has more columns, has much fewer rows. There are 156 overlaps in both datasets - let’s try to verify this.
Now I map over the values from snl_mega just for these 156 actors, so as to minimize the number of NA values. I would usually do an index/match on Excel for this - how would I do this in R?
Join Data2
add 1 column from snl_mega to snl_seasons (another dataset) –> number of actors per season.
# create subset.sub <- snl_mega %>%select(aid, sid) %>%filter(!is.na(aid)) %>%group_by(sid) %>%unique()# get number of actors per season.sub_final <- sub %>%group_by(sid) %>%summarise("actors_per_season"=n())# sanity check.sum(sub_final$actors_per_season)
Generated by summarytools 1.0.1 (R version 4.2.1) 2022-12-23
Ok - from the univariate graph produced by summarytools, it looks like the seasons in the middle had the most actors. We do need to qualify this statement, because we filtered out NA values when calculating actors per season.
Source Code
---title: "Challenge_8_Jyoti"author: "Jyoti Rani"description: "Joining Data"date: "2022-08-22"format: html: df-print: paged toc: true code-copy: true code-tools: true css: "styles.css"categories: - challenge_8 - summarytools---## Including libraries```{r}library(tidyverse)library(summarytools)knitr::opts_chunk$set(echo =TRUE, warning=FALSE, message=FALSE)```## Read in dataI read 3 SNL datasets, together```{r}snl_actors <- (read_csv("_data/snl_actors.csv", show_col_types =FALSE) [,-2])snl_actors <-complete(snl_actors)snl_casts <-read_csv("_data/snl_casts.csv", show_col_types =FALSE)snl_casts <-complete(snl_casts)snl_seasons <-read_csv("_data/snl_seasons.csv", show_col_types =FALSE)snl_seasons <-complete(snl_seasons)```### Briefly describe and tidy data```{r}#| label: summaryprint(dfSummary(snl_actors, varnumbers =FALSE, plain.ascii =FALSE, graph.magnif =0.30, style ="grid", valid.col =FALSE), method ='render', table.classes ='table-condensed')print(dfSummary(snl_casts, varnumbers =FALSE, plain.ascii =FALSE, graph.magnif =0.30, style ="grid", valid.col =FALSE), method ='render', table.classes ='table-condensed')```- Tidying snl_actors (2306 rows, 3 columns): - No missing data. - We can leave "aid" as a character column and convert type and gender to factors.- Tidying snl_casts (614 rows, 8 columns): - Missing data in first_epid and last_epid - this is fine because we used complete() to make the NAs explicit. - "aid" is also present in this dataset, which hopefully makes it intuitive to put the 2 datasets together (no renaming needed). There are much fewer values for Actor ID, so this might just be a subset of the other dataset.- Tidying snl_seasons (46 rows, 5 columns):```{r}#| label: tidy# mutation.snl_actors <- snl_actors %>%mutate(type =as.factor(type), gender =as.factor(gender))# sanity check.head(snl_actors)```## Join Data 1```{r}#| label: joinsnl_mega <- snl_actors %>%left_join(snl_casts, by ="aid")print(dfSummary(snl_mega, varnumbers =FALSE, plain.ascii =FALSE, graph.magnif =0.30, style ="grid", valid.col =FALSE), method ='render', table.classes ='table-condensed')```After joining, we have 10 columns. We have a lot more missing values, which was also expected - the snl_casts dataset, which has more columns, has much fewer rows. There are 156 overlaps in both datasets - let's try to verify this.```{r}#| label: backtracesnl_casts %>%select(aid) %>%filter(snl_casts$aid %in% snl_actors$aid) %>%unique()```Now I map over the values from snl_mega just for these 156 actors, so as to minimize the number of NA values. I would usually do an index/match on Excel for this - **how would I do this in R?**## Join Data2add 1 column from snl_mega to snl_seasons (another dataset) --> number of actors per season.```{r}#| label: create subset with number of actors per season# create subset.sub <- snl_mega %>%select(aid, sid) %>%filter(!is.na(aid)) %>%group_by(sid) %>%unique()# get number of actors per season.sub_final <- sub %>%group_by(sid) %>%summarise("actors_per_season"=n())# sanity check.sum(sub_final$actors_per_season)```Now I join them.```{r}#| label: join 2snl_szns <- snl_seasons %>%right_join(sub_final, by ="sid")print(dfSummary(snl_szns, varnumbers =FALSE, plain.ascii =FALSE, graph.magnif =0.30, style ="grid", valid.col =FALSE), method ='render', table.classes ='table-condensed')```Ok - from the univariate graph produced by summarytools, it looks like the seasons in the middle had the most actors. We do need to qualify this statement, because we filtered out NA values when calculating actors per season.
