In order to use this function, you need to download the .rar file available at this megaupload link.
Mirror: mediafire link.
Then unrar the file, and put the Growth folder in your main directory, as selected in R. You are now able to use the two functions i'm going to illustrate.
growthFun.R
The function
growthFun allows you to draw 8 different growth chart, which are different for Male and Female (sixteen in total).The only parameters you need to input are:
sex = c("m", "f") type = c("wac36", "lac36", "wlc", "hac", "wsc", "wac20", "lac20", "bac")The explanation for the type's parameters code are in the first part of the function code.
Eventually you can modify the
pat variable, if you want to put the Growth folder in another place (not in the main directory of R).I reccomend to use the
pdf() graphic device for best resolution.Hese is an example of the output you can obtain, with the following code:
pdf("hac_example.pdf", paper="a4", width=0, height=0)
growthFun("m", "hac")
dev.off()
MygrowthFun.R
The function
MygrowthFun allows you to personalize the output of the previous function, with specific patient's data.The parameters you can modify are:
sex=c("m", "f")
type=c("wac36", "lac36", "wlc", "hac", "wsc", "wac20", "lac20", "bac", "bmi.adv")
path="./Growth/"
name = NULL
surname = NULL
birth_date = NULL
mydataAA = NULLThe three parameter
sex, type and path are the same of the growthFun function. The three parameters name, surname and birth_date refer to the patient's data; you can add this data in form of character().mydataAA is an optional parameters with the values measured on your patients during the time you follow up him. Generally you need to input this data in form of a data.frame().In the
type parameter there is an additional choice: bmi.adv allows you to obtain three chart (wac20, lac20, bac - see the explanation codes), if your mydataAA dataframe contains data about Stature and Weight during the time of follow up.Details.
Let's see the format of
mydataAA, according to the type of chart you want to graph.type = wac36
mydataAA:
first column = months of measurement, from 0 to 36
second column = weight (in kg)
type = lac36
mydataAA:
first column = months of measurement, from 0 to 36
second column = length (in cm)
type = hac
mydataAA:
first column = months of measurement, from 0 to 36
second column = head circumference (in cm)
type = wac20
mydataAA:
first column = months of measurement, from 24 to 240 (from 2 to 20 years)
second column = weight (in kg)
type = lac20
mydataAA:
first column = months of measurement, from 24 to 240 (from 2 to 20 years)
second column = stature (in cm)
type = bmi.adv
mydataAA:
first column (months) = months of measurement, from 24 to 240 (from 2 to 20 years)
second column (stature) = stature (in cm)
third column (weight)= weight (in kg)
In the last type it's not importat the order of the columns, but here are important their names.
Examples.
Let's see some example. Suppose that you are following the growth of a new born (her name is Alyssa Gigave, born on 07/08/2009), and you collect the following data:
Months Length
0 50
2 55
3 56
5 61
8 71
9 72
12 75
15 75
18 81
21 89
26 91
27 94
30 95
35 98
So you can create your personalized graph in this way:
alyssa_data <- data.frame( months=c(0, 2, 3, 5, 8, 9, 12, 15, 18, 21, 26, 27, 30, 35), length=c(50, 55, 56, 61, 71, 72, 75, 75, 81, 89, 91, 94, 95, 98)) pdf("alyssa_growth_chart.pdf", paper="a4", width=0, height=0) MygrowthFun(sex="f", type="lac36", name="Alyssa", surname="Gigave", birth_date="july 08, 2009", mydataAA=alyssa_data) dev.off()The output is the following pdf file:
Now suppose that you're a pediatric doctor, and that you follow a boy (Tommy Cigalino, born on 07/08/1980). Whenever he has come to you, you collect his weight and stature, and the months from his birth he was. So you have the following data:
months stature weight
25 98 17
31 100 21
34 102 27
35 104 29
58 106 30
60 109 32
70 111 33
85 118 34
88 119 36
89 120 39
91 121 42
102 126 45
107 128 47
108 135 49
120 144 51
134 145 52
154 148 54
166 152 55
169 157 62
170 158 63
178 163 64
179 167 68
181 168 71
219 169 74
234 176 76
So you can create three graphs (
wac20, lac20, bac), using the bmi.adv type:tommy_data <- data.frame( months = c( 25, 31, 34, 35, 58, 60, 70, 85, 88, 89, 91, 102, 107, 108, 120, 134, 154, 166, 169, 170, 178, 179, 181, 219, 234), stature = c( 98, 100, 102, 104, 106, 109, 111, 118, 119, 120, 121, 126, 128, 135, 144, 145, 148, 152, 157, 158, 163, 167, 168, 169, 176), weight = c( 17, 21, 27, 29, 30, 32, 33, 34, 36, 39, 42, 45, 47, 49, 51, 52, 54, 55, 62, 63, 64, 68, 71, 74, 76)) pdf("tommy_growth_chart.pdf", paper="a4", width=0, height=0) MygrowthFun(sex="m", type="bmi.adv", name="Tommy", surname="Cigalino", birth_date="july 08, 1980", mydataAA=tommy_data) dev.off()Tommaso MARTINO, 17/09/2011
REFERENCES
- http://www.cdc.gov/growthcharts/cdc_charts.htm
- http://www.cdc.gov/growthcharts/clinical_charts.htm
- http://www.cdc.gov/growthcharts/percentile_data_files.htm
- Kuczmarski RJ, Ogden CL, Guo, SS, et al. CDC growth charts for the United States: Methods and Development. Vital Health Stat; 11 (246) National Center for Health Statistics. 2002.
