Problem statement

Reproduce the plot on the NLSY 86 example by completing the R script using the data file in wide format.

Introduction

The data are drawn from the National Longitudinal Survey of Youth (NLSY). The sample observations are from the 1986, 1988, 1990, and 1992 assessment periods. Children were selected to be in kindergarten, first, and second grade and to be of age 5, 6, or 7 at the first assessment (1986). Both reading and mathematical achievement scores are recorded. The former is a recognition subscore of the Peabody Individual Achievement Test (PIAT). This was scaled as the percentage of 84 items that were answered correctly. The same 84 items were administered at all four time points, providing a consistent scale over time. The data set is a subsample of 166 subjects with complete observations.

Source: Bollen, K.A. & Curran, P.J. (2006). Latent curve models. A structural equation perspective. p.59.

Column 1: Student ID Column 2: Gender, male or female Column 3: Race, minority or majority Column 4: Measurement occasions Column 5: Grade at which measurements were made, Kindergarten = 0, First grade = 1, Second grade = 2 Column 6: Age in years Column 7: Age in months Column 8: Math score Column 9: Reading score

Data management

# input data
dta <- read.csv("C:/Users/Ching-Fang Wu/Documents/lmm/nlsy86wide.csv", h=T)
# inspect data structure
str(dta)
'data.frame':   166 obs. of  23 variables:
 $ id        : int  23901 25601 37401 40201 63501 70301 72001 76101 76801 77001 ...
 $ sex       : chr  "Female" "Female" "Female" "Male" ...
 $ race      : chr  "Majority" "Majority" "Majority" "Majority" ...
 $ grade86   : int  0 0 0 0 1 0 0 0 0 0 ...
 $ grade88   : int  2 1 2 1 3 2 1 3 2 2 ...
 $ grade90   : int  3 3 5 2 4 3 3 4 5 4 ...
 $ grade92   : int  5 6 6 5 6 5 5 6 6 5 ...
 $ age86year : int  6 6 6 5 7 5 6 7 6 6 ...
 $ age88year : int  8 8 8 8 9 8 8 9 9 8 ...
 $ age90year : int  10 10 10 9 11 10 10 11 11 10 ...
 $ age92year : int  12 12 12 12 13 12 12 13 13 12 ...
 $ age86month: int  67 66 67 60 78 62 66 79 76 67 ...
 $ age88month: int  96 95 95 91 108 93 94 109 104 94 ...
 $ age90month: int  119 119 122 112 132 117 118 131 128 117 ...
 $ age92month: int  142 143 144 139 155 139 140 154 151 139 ...
 $ math86    : num  14.29 20.24 17.86 7.14 29.76 ...
 $ math88    : num  15.5 36.9 22.6 21.4 50 ...
 $ math90    : num  38.1 52.4 53.6 53.6 47.6 ...
 $ math92    : num  41.7 58.3 58.3 51.2 71.4 ...
 $ read86    : num  19.05 21.43 21.43 7.14 30.95 ...
 $ read88    : num  29.8 32.1 45.2 21.4 50 ...
 $ read90    : num  28.6 45.2 69 50 63.1 ...
 $ read92    : num  45.2 57.1 78.6 59.5 82.1 ...
# examine first 6 lines
knitr::kable(head(dta))
id sex race grade86 grade88 grade90 grade92 age86year age88year age90year age92year age86month age88month age90month age92month math86 math88 math90 math92 read86 read88 read90 read92
23901 Female Majority 0 2 3 5 6 8 10 12 67 96 119 142 14.2857 15.476 38.095 41.667 19.0476 29.762 28.571 45.238
25601 Female Majority 0 1 3 6 6 8 10 12 66 95 119 143 20.2381 36.905 52.381 58.333 21.4286 32.143 45.238 57.143
37401 Female Majority 0 2 5 6 6 8 10 12 67 95 122 144 17.8571 22.619 53.571 58.333 21.4286 45.238 69.048 78.571
40201 Male Majority 0 1 2 5 5 8 9 12 60 91 112 139 7.1429 21.429 53.571 51.190 7.1429 21.429 50.000 59.524
63501 Male Majority 1 3 4 6 7 9 11 13 78 108 132 155 29.7619 50.000 47.619 71.429 30.9524 50.000 63.095 82.143
70301 Male Majority 0 2 3 5 5 8 10 12 62 93 117 139 14.2857 36.905 55.952 63.095 17.8571 46.429 64.286 96.429 
library(dplyr)
#install.packages("tidyr")
library(tidyr)
# 寬資料轉換成長資料
long_grade <- dta %>% gather (key = grade_y, value = grade, grade86:grade92)
longgrade <- long_grade[,c("id","sex","race","grade_y","grade")]

long_ageyear <- dta %>% gather (key = age_y, value = ageyear, age86year:age92year)
longageyear <- long_ageyear[,c("id","sex","race","age_y","ageyear")]

long_agemonth <- dta %>% gather (key = age_m, value = agemonth, age86month:age92month)
longagemonth <- long_agemonth[,c("id","sex","race","age_m","agemonth")]

long_math <- dta %>% gather (key = math_y, value = math, math86:math92)
longmath <- long_math[,c("id","sex","race","math_y","math")]

long_read <- dta %>% gather (key = read_y, value = read, read86:read92)
longread <- long_read[,c("id","sex","race","read_y","read")]

longdta <- cbind ((longgrade[,c("id","sex","race","grade_y","grade")]),
                      (longageyear[,c("id","age_y","ageyear")]),
                      (longagemonth[,c("id","age_m","agemonth")]), 
                      (longmath[,c("id","math_y","math")]), 
                      (longread[,c("id","read_y","read")]),by= "id")
                  
longdtafinal<-longdta[,c("id","sex","race","ageyear","agemonth","math","read")]

head(longdtafinal)
     id    sex     race ageyear agemonth    math    read
1 23901 Female Majority       6       67 14.2857 19.0476
2 25601 Female Majority       6       66 20.2381 21.4286
3 37401 Female Majority       6       67 17.8571 21.4286
4 40201   Male Majority       5       60  7.1429  7.1429
5 63501   Male Majority       7       78 29.7619 30.9524
6 70301   Male Majority       5       62 14.2857 17.8571

Visualization

library(tidyverse)
# plot
ggplot(data=longdtafinal, aes(x=agemonth, y=read, group=id)) +
 geom_point(size=rel(.5)) +
 stat_smooth(mapping = NULL,
             data = NULL,
             geom = "smooth",
             position = "identity",
             method ="lm", 
             formula= y ~ x,
             se=F, 
             fullrange = FALSE,
             level = 0.95,
             color="skyblue2", 
             linetype=1, 
             size=rel(.1)) +
  facet_grid(rows = vars(race),
             cols = vars(sex),
             scales = "free",
             space = "free",
             shrink = T,
             labeller = "label_value",
             as.table = T,
             switch = NULL,
             drop = T,
             margins = T, #F就變成原本的四格圖
             facets = NULL)  +
  labs(x="Month", y="Reading score") +
  theme_bw()

ggplot(data=longdtafinal, aes(x=agemonth, y=read, group=id)) +
 geom_point(size=rel(.5)) +
 stat_smooth(mapping = NULL,
             data = NULL,
             geom = "smooth",
             position = "identity",
             method ="lm", 
             formula= y ~ x,
             se=F, 
             fullrange = FALSE,
             level = 0.95,
             color="skyblue2", 
             linetype=1, 
             size=rel(.1)) +
  facet_grid(rows = vars(race),
             cols = vars(sex),
             scales = "free",
             space = "free",
             shrink = T,
             labeller = "label_value",
             as.table = T,
             switch = NULL,
             drop = T,
             margins = F, 
             facets = NULL)  +
  labs(x="Month", y="Reading score") +
  theme_bw()

The End