Homework3 Reproduce the plot on the NLSY 86 example by completing the R script using the data file in wide format.
# input data
dta <- read.csv("C:/Users/Ching-Fang Wu/Documents/data/nlsy86wide.csv")
# 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
head(dta)
## id sex race grade86 grade88 grade90 grade92 age86year age88year
## 1 23901 Female Majority 0 2 3 5 6 8
## 2 25601 Female Majority 0 1 3 6 6 8
## 3 37401 Female Majority 0 2 5 6 6 8
## 4 40201 Male Majority 0 1 2 5 5 8
## 5 63501 Male Majority 1 3 4 6 7 9
## 6 70301 Male Majority 0 2 3 5 5 8
## age90year age92year age86month age88month age90month age92month math86
## 1 10 12 67 96 119 142 14.285714
## 2 10 12 66 95 119 143 20.238095
## 3 10 12 67 95 122 144 17.857143
## 4 9 12 60 91 112 139 7.142857
## 5 11 13 78 108 132 155 29.761905
## 6 10 12 62 93 117 139 14.285714
## math88 math90 math92 read86 read88 read90 read92
## 1 15.47619 38.09524 41.66667 19.047619 29.76190 28.57143 45.23810
## 2 36.90476 52.38095 58.33333 21.428571 32.14286 45.23810 57.14286
## 3 22.61905 53.57143 58.33333 21.428571 45.23810 69.04762 78.57143
## 4 21.42857 53.57143 51.19048 7.142857 21.42857 50.00000 59.52381
## 5 50.00000 47.61905 71.42857 30.952381 50.00000 63.09524 82.14286
## 6 36.90476 55.95238 63.09524 17.857143 46.42857 64.28571 96.42857
#寬資料轉長資料
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyr)
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 14.285714 19.047619
## 2 25601 Female Majority 6 66 20.238095 21.428571
## 3 37401 Female Majority 6 67 17.857143 21.428571
## 4 40201 Male Majority 5 60 7.142857 7.142857
## 5 63501 Male Majority 7 78 29.761905 30.952381
## 6 70301 Male Majority 5 62 14.285714 17.857143
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --
## √ ggplot2 3.3.2 √ purrr 0.3.4
## √ tibble 3.0.4 √ stringr 1.4.0
## √ readr 1.3.1 √ forcats 0.5.0
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
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()