Fran Trejo PAD 6833 Homework 4

1. From the data you have chosen, select a variable that you are interested in

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(pastecs)

## 
## Attaching package: 'pastecs'
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## The following objects are masked from 'package:dplyr':
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##     first, last
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## The following object is masked from 'package:tidyr':
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##     extract

teacher_data <- read_csv("Teacher_Hiring_Certification_Turnover.csv")

## Rows: 33 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (5): REGION, distname, geotype_new, region_lea, Year
## dbl (20): district, schyr, intern, other_temp, oos_std, lag_starter, no_cert...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

summary(teacher_data$no_cert)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00    5.00   25.00   44.73   48.00  395.00

2. Use pastecs::stat.desc to describe the variable. Include a few sentences about what the variable is and what it’s measuring.

pastecs::stat.desc(teacher_data$no_cert)

##      nbr.val     nbr.null       nbr.na          min          max        range 
##    33.000000     2.000000     0.000000     0.000000   395.000000   395.000000 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##  1476.000000    25.000000    44.727273    13.831800    28.174455  6313.517045 
##      std.dev     coef.var 
##    79.457643     1.776492

The no_cert variable represents newly hired teachers who lack Texas certification each year from 2012-2023 from three Houston-area school districts. This variable is used to measure the prevalence of uncertified teachers entering the Texas education system and may correlate with various factors like teacher retention and student outcomes.

3. Remove NA’s if needed using dplyr:filter (or anything similar)

teacher_data <- teacher_data %>% filter(!is.na(no_cert))

There are no NA’s that need to be removed.

4. Provide a histogram of the variable (as shown in this lesson)

hist(teacher_data$no_cert, main="Histogram of Noncertified Teachers", xlab="No Certification", breaks=50)

5. transform the variable using the log transformation or square root transformation (whatever is more appropriate) using dplyr::mutate or something similar)

td_log <- teacher_data%>%mutate(no_cert_log = log(no_cert))
head(td_log$no_cert_log)

## [1] 3.091042 3.912023 3.637586 4.007333 4.304065 4.007333

summary(td_log$no_cert_log)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    -Inf   1.609   3.219    -Inf   3.871   5.979

There are some zeros in the data, which is why the min and mean come out as -Inf because log(0) does not exist.

td_sqrt <- teacher_data%>%mutate(no_cert_sqrt = sqrt(no_cert))
head(td_sqrt$no_cert_sqrt)

## [1] 4.690416 7.071068 6.164414 7.416198 8.602325 7.416198

summary(td_sqrt$no_cert_sqrt)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   2.236   5.000   5.300   6.928  19.875

Here, it makes more sense to use the square root transformation than the log transformation.

6. provide a histogram of the transformed variable

hist(td_sqrt$no_cert_sqrt, main="Histogram of Noncertified Teachers (sqrt)", xlab="No Certification", breaks=50)

7. submit via rpubs on CANVAS