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DSMolgenisArmadillo.Rmd
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DSMolgenisArmadillo.Rmd
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---
title: "Analyse your data"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Analyse your data}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
When you start to use Armadillo as a backend for DataSHIELD you can use the `DSMolgenisArmadillo` package for research purposes.
There is a default workflow in DataSHIELD to do analysis. These are the steps that you need to take:
### Authenticate
First obtain a token from the authentication server to authenticate in DataSHIELD.
```r
# Load the necessary packages.
library(dsBaseClient)
library(DSMolgenisArmadillo)
# specify server url
armadillo_url <- "https://armadillo-demo.molgenis.net"
# get token from central authentication server
token <- armadillo.get_token(armadillo_url)
#> [1] "We're opening a browser so you can log in with code 5FW3FV"
```
Then build a login dataframe and perform the login on the Armadillo server.
The important part is to specify the driver. This should be `ArmadilloDriver`.
```r
# build the login dataframe
builder <- DSI::newDSLoginBuilder()
builder$append(
server = "armadillo",
url = armadillo_url,
token = token,
driver = "ArmadilloDriver",
profile = "xenon",
)
# create loginframe
login_data <- builder$build()
# login into server
conns <- DSI::datashield.login(logins = login_data, assign = FALSE)
```
> You can append multiple servers to the login frame to perform an analysis
across multiple cohorts.
### Assign data
To work with DataSHIELD you need to be able to query data.
You can do this by assigning data in the Armadillo service.
#### Assign data using expressions
You can assign values from expressions to symbols.
```r
# assign some data to 'K'
datashield.assign.expr(conns = conns, symbol = "K", "c(10,50,100)")
```
```r
# calculate the mean of 'K' to see that the assignment has worked
ds.mean("K", datasources = conns)
#> $Mean.by.Study
#> EstimatedMean Nmissing Nvalid Ntotal
#> armadillo 53.33333 0 3 3
#>
#> $Nstudies
#> [1] 1
#>
#> $ValidityMessage
#> ValidityMessage
#> armadillo "VALID ANALYSIS"
```
#### Assign data from tables
You can check which tables (`data.frame`'s) are available on the Armadillo.
```r
datashield.tables(conns)
#> $armadillo
#> [1] "study1/2_1-core-1_0/nonrep" "study1/2_1-core-1_0/yearlyrep"
#> [3] "study1/1_1-outcome-1_0/yearlyrep" "gecko/2_1-core-1_0/trimesterrep"
#> [5] "gecko/2_1-core-1_0/nonrep" "gecko/2_1-core-1_0/yearlyrep"
#> [7] "gecko/2_1-core-1_0/monthlyrep" "gecko/1_1-outcome-1_0/nonrep"
#> [9] "gecko/1_1-outcome-1_0/yearlyrep" "test/data/LT-example-dataset_long-format"
#> [11] "test/data/d" "trajectories/data/alspac"
#> [13] "trajectories/data/chs" "trajectories/data/bib"
#> [15] "trajectories/data/bcg" "trajectories/data/d"
#> [17] "trajectories/data/probit" "inma/1_2_urban_ath_1_0/yearly_rep"
#> [19] "inma/1_2_urban_ath_1_0/trimester_rep" "inma/1_2_urban_ath_1_0/non_rep"
#> [21] "inma/1_1_outcome_ath_1_0/trimester_rep" "inma/1_1_outcome_ath_1_0/non_rep"
#> [23] "inma/1_0_outcome_ath_1_0/trimester_rep" "inma/1_0_outcome_ath_1_0/non_rep"
#> [25] "longitools/testparquet/LT_example_data" "longitools/mydata/nonrep"
```
And load data from one of these tables.
```r
# assign table data to a symbol
datashield.assign.table(
conns = conns,
table = "gecko/2_1-core-1_0/nonrep",
symbol = "core_nonrep"
)
```
```r
# check the columns in the non-repeated data
ds.colnames("core_nonrep", datasources = conns)
#> $armadillo
#> [1] "row_id" "child_id" "mother_id" "cohort_id" "preg_no"
#> [6] "child_no" "coh_country" "recruit_age" "cob_m" "ethn1_m"
#> [11] "ethn2_m" "ethn3_m" "agebirth_m_y" "agebirth_m_d" "death_m"
#> [16] "death_m_age" "prepreg_weight" "prepreg_weight_mes" "prepreg_weight_ga" "latepreg_weight"
#> [21] "latepreg_weight_mes" "latepreg_weight_ga" "preg_gain" "preg_gain_mes" "height_m"
#> [26] "height_mes_m" "prepreg_dia" "preg_dia" "preg_thyroid" "preg_fever"
#> [31] "preeclam" "preg_ht" "asthma_m" "prepreg_psych" "preg_psych"
#> [36] "ppd" "prepreg_smk" "prepreg_cig" "preg_smk" "preg_cig"
#> [41] "prepreg_alc" "prepreg_alc_unit" "preg_alc" "preg_alc_unit" "folic_prepreg"
#> [46] "folic_preg12" "folic_post12" "parity_m" "preg_plan" "mar"
#> [51] "ivf" "outcome" "mode_delivery" "plac_abrup" "cob_p"
#> [56] "cob_p_fath" "ethn1_p" "ethn2_p" "ethn3_p" "ethn_p_fath"
#> [61] "agebirth_p_y" "agebirth_p_d" "agebirth_p_fath" "death_p" "death_p_age"
#> [66] "death_p_fath" "weight_f1" "weight_mes_f1" "weight_f1_fath" "height_f1"
#> [71] "height_mes_f1" "height_f1_fath" "dia_bf" "asthma_bf" "psych_bf"
#> [76] "smk_p" "smk_cig_p" "smk_fath" "birth_month" "birth_year"
#> [81] "apgar" "neo_unit" "sex" "plurality" "ga_lmp"
#> [86] "ga_us" "ga_mr" "ga_bj" "birth_weight" "birth_length"
#> [91] "birth_head_circum" "weight_who_ga" "plac_weight" "con_anomalies" "major_con_anomalies"
#> [96] "cer_palsy" "sibling_pos" "death_child" "death_child_age" "breastfed_excl"
#> [101] "breastfed_any" "breastfed_ever" "solid_food" "childcare_intro" "cats_preg"
#> [106] "dogs_preg" "cats_quant_preg" "dogs_quant_preg"
```
#### Assign data at login time
You can also specify a table in the login frame and assign the data when you
login.
```r
# build the login dataframe
builder <- DSI::newDSLoginBuilder()
builder$append(
server = "armadillo",
url = armadillo_url,
token = token,
driver = "ArmadilloDriver",
table = "gecko/2_1-core-1_0/nonrep",
profile = "xenon",
)
# create loginframe
login_data <- builder$build()
# login into server
conns <- DSI::datashield.login(logins = login_data, assign = TRUE, symbol="core_nonrep")
```
### Subsetting data
Before you are working with the data you can subset to a specific range of
variables you want to use in the set.
```r
# assign the repeated data to reshape
datashield.assign.table(
conns = conns,
table = "gecko/2_1-core-1_0/yearlyrep",
symbol = "core_yearlyrep"
)
# check dimensions of repeatead measures
ds.dim("core_yearlyrep", datasources = conns)
#> $`dimensions of core_yearlyrep in armadillo`
#> [1] 19000 34
#>
#> $`dimensions of core_yearlyrep in combined studies`
#> [1] 19000 34
# subset the data to the first 2 years
ds.dataFrameSubset(
df.name = "core_yearlyrep",
newobj = "core_yearlyrep_1_3",
V1.name = "core_yearlyrep$age_years",
V2.name = "2",
Boolean.operator = "<="
)
#> $is.object.created
#> [1] "A data object <core_yearlyrep_1_3> has been created in all specified data sources"
#>
#> $validity.check
#> [1] "<core_yearlyrep_1_3> appears valid in all sources"
# check the columns
ds.colnames("core_yearlyrep_1_3", datasources = conns)
#> $armadillo
#> [1] "row_id" "child_id" "age_years" "cohab_" "occup_m_"
#> [6] "occupcode_m_" "edu_m_" "occup_f1_" "occup_f1_fath" "occup_f2_"
#> [11] "occup_f2_fath" "occupcode_f1_" "occupcode_f1_fath" "occupcode_f2_" "occupcode_f2_fath"
#> [16] "edu_f1_" "edu_f1_fath" "edu_f2_" "edu_f2_fath" "childcare_"
#> [21] "childcarerel_" "childcareprof_" "childcarecentre_" "smk_exp" "pets_"
#> [26] "cats_" "cats_quant_" "dogs_" "dogs_quant_" "mental_exp"
#> [31] "hhincome_" "fam_splitup" "famsize_child" "famsize_adult"
# check dimensions again
ds.dim("core_yearlyrep_1_3", datasources = conns)
#> $`dimensions of core_yearlyrep_1_3 in armadillo`
#> [1] 3000 34
#>
#> $`dimensions of core_yearlyrep_1_3 in combined studies`
#> [1] 3000 34
```
```r
# strip the redundant columns
ds.dataFrame(
x = c("core_yearlyrep_1_3$child_id",
"core_yearlyrep_1_3$age_years",
"core_yearlyrep_1_3$dogs_",
"core_yearlyrep_1_3$cats_",
"core_yearlyrep_1_3$pets_"),
completeCases = TRUE,
newobj = "core_yearlyrep_1_3_stripped",
datasources = conns
)
#> $is.object.created
#> [1] "A data object <core_yearlyrep_1_3_stripped> has been created in all specified data sources"
#>
#> $validity.check
#> [1] "<core_yearlyrep_1_3_stripped> appears valid in all sources"
```
### Transform data
In general you need 2 methods to work with data that is stored in long format,
the `reshape` and `merge` functions in DataSHIELD. You can reshape data with
the Armadillo to transform data from [wide-format to long-format](https://www.theanalysisfactor.com/wide-and-long-data/) and
vice versa.
You can do this using the `ds.reshape` function:
```r
# reshape the data for the wide-format variables (yearlyrep)
ds.reShape(
data.name = "core_yearlyrep_1_3_stripped",
timevar.name = "age_years",
idvar.name = "child_id",
v.names = c("pets_", "cats_", "dogs_"),
direction = "wide",
newobj = "core_yearlyrep_1_3_wide",
datasources = conns
)
#> $is.object.created
#> [1] "A data object <core_yearlyrep_1_3_wide> has been created in all specified data sources"
#>
#> $validity.check
#> [1] "<core_yearlyrep_1_3_wide> appears valid in all sources"
```
```r
# show the reshaped columns of the new data frame
ds.colnames("core_yearlyrep_1_3_wide", datasources = conns)
#> $armadillo
#> [1] "child_id" "pets_.0" "cats_.0" "dogs_.0" "pets_.1" "cats_.1" "dogs_.1" "pets_.2" "cats_.2" "dogs_.2"
```
When you reshaped and subsetted the data you often need to merge your dataframe
with others to get your analysis dataframe. You can do this using the `ds.merge`
function:
```r
# merge non-repeated table with wide-format repeated table
# make sure the disclosure measure regarding stringshort is set to '100'
ds.merge(
x.name = "core_nonrep",
y.name = "core_yearlyrep_1_3_wide",
by.x.names = "child_id",
by.y.names = "child_id",
newobj = "analysis_df",
datasources = conns
)
#> $is.object.created
#> [1] "A data object <analysis_df> has been created in all specified data sources"
#>
#> $validity.check
#> [1] "<analysis_df> appears valid in all sources"
```
```r
ds.colnames("analysis_df", datasources = conns)
#> $armadillo
#> [1] "child_id" "row_id" "mother_id" "cohort_id" "preg_no"
#> [6] "child_no" "coh_country" "recruit_age" "cob_m" "ethn1_m"
#> [11] "ethn2_m" "ethn3_m" "agebirth_m_y" "agebirth_m_d" "death_m"
#> [16] "death_m_age" "prepreg_weight" "prepreg_weight_mes" "prepreg_weight_ga" "latepreg_weight"
#> [21] "latepreg_weight_mes" "latepreg_weight_ga" "preg_gain" "preg_gain_mes" "height_m"
#> [26] "height_mes_m" "prepreg_dia" "preg_dia" "preg_thyroid" "preg_fever"
#> [31] "preeclam" "preg_ht" "asthma_m" "prepreg_psych" "preg_psych"
#> [36] "ppd" "prepreg_smk" "prepreg_cig" "preg_smk" "preg_cig"
#> [41] "prepreg_alc" "prepreg_alc_unit" "preg_alc" "preg_alc_unit" "folic_prepreg"
#> [46] "folic_preg12" "folic_post12" "parity_m" "preg_plan" "mar"
#> [51] "ivf" "outcome" "mode_delivery" "plac_abrup" "cob_p"
#> [56] "cob_p_fath" "ethn1_p" "ethn2_p" "ethn3_p" "ethn_p_fath"
#> [61] "agebirth_p_y" "agebirth_p_d" "agebirth_p_fath" "death_p" "death_p_age"
#> [66] "death_p_fath" "weight_f1" "weight_mes_f1" "weight_f1_fath" "height_f1"
#> [71] "height_mes_f1" "height_f1_fath" "dia_bf" "asthma_bf" "psych_bf"
#> [76] "smk_p" "smk_cig_p" "smk_fath" "birth_month" "birth_year"
#> [81] "apgar" "neo_unit" "sex" "plurality" "ga_lmp"
#> [86] "ga_us" "ga_mr" "ga_bj" "birth_weight" "birth_length"
#> [91] "birth_head_circum" "weight_who_ga" "plac_weight" "con_anomalies" "major_con_anomalies"
#> [96] "cer_palsy" "sibling_pos" "death_child" "death_child_age" "breastfed_excl"
#> [101] "breastfed_any" "breastfed_ever" "solid_food" "childcare_intro" "cats_preg"
#> [106] "dogs_preg" "cats_quant_preg" "dogs_quant_preg" "pets_.0" "cats_.0"
#> [111] "dogs_.0" "pets_.1" "cats_.1" "dogs_.1" "pets_.2"
#> [116] "cats_.2" "dogs_.2"
```
### Save your work
When you finished building your analysis frame you can save it using
[workspaces](workspaces.html).
### Performing analysis
There are a variety of analysis you can perform in DataSHIELD. You can perform
basic methods such as summary statistics and more advanced methods such as GLM.
#### Simple statistical methods
You execute a summary on the a variable within you analysis frame. It will
return summary statistics.
```r
ds.summary("analysis_df$pets_.1", datasources = conns)
#> $armadillo
#> $armadillo$class
#> [1] "numeric"
#>
#> $armadillo$length
#> [1] 1000
#>
#> $armadillo$`quantiles & mean`
#> 5% 10% 25% 50% 75% 90% 95% Mean
#> 8.000 15.000 32.750 61.000 90.000 108.000 113.000 60.954
```
#### Advanced statistical methods
When you finished the analysis dataframe, you can perform the actual analysis.
You can use a wide variety of functions. The example below is showing the `glm`.
```r
datashield.assign.table(
conns = conns,
table = "gecko/1_1-outcome-1_0/nonrep",
symbol = "outcome_nonrep"
)
armadillo_glm <- ds.glm(
formula = "asthma_ever_CHICOS~pets_preg",
data = "outcome_nonrep",
family = "binomial",
datasources = conns
)
```
Do the meta analysis and install prerequisites.
```r
if (!require('metafor')) install.packages('metafor')
```
```r
yi <- c(armadillo_glm$coefficients["pets_preg", "Estimate"])
sei <- c(armadillo_glm$coefficients["pets_preg", "Std. Error"])
res <- metafor::rma(yi, sei = sei)
res
#>
#> Random-Effects Model (k = 1; tau^2 estimator: REML)
#>
#> tau^2 (estimated amount of total heterogeneity): 0
#> tau (square root of estimated tau^2 value): 0
#> I^2 (total heterogeneity / total variability): 0.00%
#> H^2 (total variability / sampling variability): 1.00
#>
#> Test for Heterogeneity:
#> Q(df = 0) = 0.0000, p-val = 1.0000
#>
#> Model Results:
#>
#> estimate se zval pval ci.lb ci.ub
#> -0.1310 0.1267 -1.0343 0.3010 -0.3793 0.1173
#>
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
metafor::forest(res, xlab = "OR", transf = exp, refline = 1, slab = c("armadillo_glm"))
```
<div class="figure">
<img src="DSMolgenisArmadillo-meta-analysis-1.png" alt="plot of chunk meta-analysis" width="100%" />
<p class="caption">plot of chunk meta-analysis</p>
</div>
#### Creating figures
You can directly create figures with the DataSHIELD methods. For example:
```r
# create histogram
ds.histogram(x = "core_nonrep$coh_country", datasources = conns)
```
<div class="figure">
<img src="DSMolgenisArmadillo-create-a-histogram-1.png" alt="plot of chunk create-a-histogram" width="100%" />
<p class="caption">plot of chunk create-a-histogram</p>
</div>
```
#> $breaks
#> [1] 35.31138 116.38319 197.45500 278.52680 359.59861 440.67042 521.74222 602.81403 683.88584 764.95764 846.02945
#>
#> $counts
#> [1] 106 101 92 103 106 104 105 101 113 69
#>
#> $density
#> [1] 0.0013074829 0.0012458092 0.0011347965 0.0012704787 0.0013074829 0.0012828134 0.0012951481 0.0012458092 0.0013938261
#> [10] 0.0008510974
#>
#> $mids
#> [1] 75.84729 156.91909 237.99090 319.06271 400.13451 481.20632 562.27813 643.34993 724.42174 805.49355
#>
#> $xname
#> [1] "xvect"
#>
#> $equidist
#> [1] TRUE
#>
#> attr(,"class")
#> [1] "histogram"
```
```r
# create a heatmap
ds.heatmapPlot(x = "analysis_df$pets_.1", y = "analysis_df$dogs_.1", datasources = conns)
```
<div class="figure">
<img src="DSMolgenisArmadillo-create-a-heatmap-1.png" alt="plot of chunk create-a-heatmap" width="100%" />
<p class="caption">plot of chunk create-a-heatmap</p>
</div>
```r
# logout
datashield.logout(conns)
```