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submit assignment 5 #210

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19 changes: 14 additions & 5 deletions assignment5.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ The data you will be using comes from the Assistments online intelligent tutorin

## Start by uploading the data
```{r}
D1 <-
D1 <- read.csv("Assistments-confidence.csv", header = TRUE)

```

Expand All @@ -38,7 +38,9 @@ ggcorr(D1[,-1], method = c("everything", "pearson")) #ggcorr() doesn't have an e
## Create a new data frame with the mean_correct variable removed, we want to keep that variable intact. The other variables will be included in our PCA.

```{r}
D2 <-
library(tidyr)
library(dplyr)
D2 <- select(D1, id, prior_prob_count, prior_percent_correct, problems_attempted, mean_hint, mean_attempt, mean_confidence)

```

Expand Down Expand Up @@ -73,15 +75,22 @@ plot(pca, type = "lines")
```{r}
#Now, create a data frame of the transformed data from your pca.

D3 <-
pca$x
D3 <- pca$x

#Attach the variable "mean_correct" from your original data frame to D3.


D3 <- data.frame(D3, D1$mean_correct)

#Now re-run your correlation plots between the transformed data and mean_correct. If you had dropped some components would you have lost important infomation about mean_correct?


plot(D3$PC1, D3$D1.mean_correct)
plot(D3$PC2, D3$D1.mean_correct)
plot(D3$PC3, D3$D1.mean_correct)
plot(D3$PC4, D3$D1.mean_correct)
plot(D3$PC5, D3$D1.mean_correct)
plot(D3$PC6, D3$D1.mean_correct)
plot(D3$PC7, D3$D1.mean_correct)

```
## Now print out the loadings for the components you generated:
Expand Down
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