filter-control-preclass.Rmd

---
title: "Blain Morin | Pre-Class #3"
output: html_document
---




### Question 1:

Using a loop, print the integers from 1 to 50. 
```{r}
for (i in 1:50) {
  print(i)
}

```
### Question 2:

A.  Using a loop, add all the integers between 0 and 1000.
```{r}
y=0
for (i in 0:1000){
  y = y +i
}

y
```

B. Now, add all the EVEN integers between 0 and 1000 (hint: use seq())
```{r}

z = 0

for (i in seq(from = 0, to = 1000, by = 2)) {
  z = z + i
}

z
```
C. Now, repeat A and B WITHOUT using a loop.

```{r}
### A:
sum(1:1000)

### B:
sum(seq(0,1000, by = 2))

```

### Question 3:

Here is a dataframe of survey data containing 5 questions :

```{r}
survey <- data.frame(
                     "participant" = c(1, 2, 3, 4, 5, 6),
                     "q1" = c(5, 3, 2, 7, 11, 0),
                     "q2" = c(4, 2, 2, 5, -10, 99),
                     "q3" = c(-4, -3, 4, 2, 9, 10),
                     "q4" = c(-30, 5, 2, 23, 4, 2),
                     "q5" = c(88, 4, -20, 2, 4, 2)
                     )
```
The response to each question should be an integer between 1 and 5. Obviously, we have some bad values in the dataframe. The goal of this problem is to fix them.

A. Using a loop, create a new dataframe called survey.clean where all the invalid values (those that are not integers between 1 and 5) are set to NA.

```{r}

survey.clean = survey

for (i in 2:dim(survey.clean)[1]) {
  vector = survey.clean [, i]
  for (j in 1:length(vector)){
    if (vector[j] > 5 | vector[j]< 1){
      vector[j] = NA
    } else {
      vector[j] = vector[j]
    }
  }
  survey.clean[, i] = vector
}

survey.clean
```
B. Now, again using a loop, add a new column to the dataframe called “invalid.answers” that indicates, for each participant, how many bad answers they gave.
```{r}
survey.clean2 = survey.clean

survey.clean2$invalid.answers = 0

for (i in 1:dim(survey.clean2[1])){
  vector = survey.clean[i, ]
  survey.clean2$invalid.answers[i] = sum(is.na(vector))
}

survey.clean2
```