Analyse your data

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.

# Load the necessary packages.
library(dsBaseClient)
library(DSMolgenisArmadillo)

# specify server url
armadillo_url <- "https://armadillo.dev.molgenis.org"

# 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 CG3FHG"

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.

# build the login dataframe
builder <- DSI::newDSLoginBuilder()
builder$append(server = "armadillo",
               url = armadillo_url,
               token = token,
               driver = "ArmadilloDriver")

# create loginframe
logindata <- builder$build()

# login into server
conns <- datashield.login(logins = logindata, 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.

# assign some data to 'K'
datashield.assign.expr(conns = conns, symbol = "K", "c(10,50,100)")

# 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.

datashield.tables(conns)
#> $armadillo
#> [1] "chicago/crime/crimes"            "gecko/1_1-outcome-1_0/nonrep"    "gecko/1_1-outcome-1_0/yearlyrep"
#> [4] "gecko/2_1-core-1_0/monthlyrep"   "gecko/2_1-core-1_0/nonrep"       "gecko/2_1-core-1_0/trimesterrep"
#> [7] "gecko/2_1-core-1_0/yearlyrep"    "load-test/data/patient"

And load data from one of these tables.

# assign table data to a symbol
datashield.assign.table(
  conns = conns,
  table = "gecko/2_1-core-1_0/nonrep",
  symbol = "core_nonrep"
)

# check the columns in the non-repeated data
ds.colnames("core_nonrep", datasources = conns)
#> $armadillo
#>   [1] "row_id"              "child_id"            "mother_id"           "cohort_id"          
#>   [5] "preg_no"             "child_no"            "coh_country"         "recruit_age"        
#>   [9] "cob_m"               "ethn1_m"             "ethn2_m"             "ethn3_m"            
#>  [13] "agebirth_m_y"        "agebirth_m_d"        "death_m"             "death_m_age"        
#>  [17] "prepreg_weight"      "prepreg_weight_mes"  "prepreg_weight_ga"   "latepreg_weight"    
#>  [21] "latepreg_weight_mes" "latepreg_weight_ga"  "preg_gain"           "preg_gain_mes"      
#>  [25] "height_m"            "height_mes_m"        "prepreg_dia"         "preg_dia"           
#>  [29] "preg_thyroid"        "preg_fever"          "preeclam"            "preg_ht"            
#>  [33] "asthma_m"            "prepreg_psych"       "preg_psych"          "ppd"                
#>  [37] "prepreg_smk"         "prepreg_cig"         "preg_smk"            "preg_cig"           
#>  [41] "prepreg_alc"         "prepreg_alc_unit"    "preg_alc"            "preg_alc_unit"      
#>  [45] "folic_prepreg"       "folic_preg12"        "folic_post12"        "parity_m"           
#>  [49] "preg_plan"           "mar"                 "ivf"                 "outcome"            
#>  [53] "mode_delivery"       "plac_abrup"          "cob_p"               "cob_p_fath"         
#>  [57] "ethn1_p"             "ethn2_p"             "ethn3_p"             "ethn_p_fath"        
#>  [61] "agebirth_p_y"        "agebirth_p_d"        "agebirth_p_fath"     "death_p"            
#>  [65] "death_p_age"         "death_p_fath"        "weight_f1"           "weight_mes_f1"      
#>  [69] "weight_f1_fath"      "height_f1"           "height_mes_f1"       "height_f1_fath"     
#>  [73] "dia_bf"              "asthma_bf"           "psych_bf"            "smk_p"              
#>  [77] "smk_cig_p"           "smk_fath"            "birth_month"         "birth_year"         
#>  [81] "apgar"               "neo_unit"            "sex"                 "plurality"          
#>  [85] "ga_lmp"              "ga_us"               "ga_mr"               "ga_bj"              
#>  [89] "birth_weight"        "birth_length"        "birth_head_circum"   "weight_who_ga"      
#>  [93] "plac_weight"         "con_anomalies"       "major_con_anomalies" "cer_palsy"          
#>  [97] "sibling_pos"         "death_child"         "death_child_age"     "breastfed_excl"     
#> [101] "breastfed_any"       "breastfed_ever"      "solid_food"          "childcare_intro"    
#> [105] "cats_preg"           "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.

# 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")

# create loginframe
logindata <- builder$build()

# login into server
conns <- datashield.login(
  logins = logindata,
  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.

# 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

# 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 and vice versa.

You can do this using the ds.reshape function:

# 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"

# 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" 
#> [10] "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:

# 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"

ds.colnames("analysis_df", datasources = conns)
#> $armadillo
#>   [1] "child_id"            "row_id"              "mother_id"           "cohort_id"          
#>   [5] "preg_no"             "child_no"            "coh_country"         "recruit_age"        
#>   [9] "cob_m"               "ethn1_m"             "ethn2_m"             "ethn3_m"            
#>  [13] "agebirth_m_y"        "agebirth_m_d"        "death_m"             "death_m_age"        
#>  [17] "prepreg_weight"      "prepreg_weight_mes"  "prepreg_weight_ga"   "latepreg_weight"    
#>  [21] "latepreg_weight_mes" "latepreg_weight_ga"  "preg_gain"           "preg_gain_mes"      
#>  [25] "height_m"            "height_mes_m"        "prepreg_dia"         "preg_dia"           
#>  [29] "preg_thyroid"        "preg_fever"          "preeclam"            "preg_ht"            
#>  [33] "asthma_m"            "prepreg_psych"       "preg_psych"          "ppd"                
#>  [37] "prepreg_smk"         "prepreg_cig"         "preg_smk"            "preg_cig"           
#>  [41] "prepreg_alc"         "prepreg_alc_unit"    "preg_alc"            "preg_alc_unit"      
#>  [45] "folic_prepreg"       "folic_preg12"        "folic_post12"        "parity_m"           
#>  [49] "preg_plan"           "mar"                 "ivf"                 "outcome"            
#>  [53] "mode_delivery"       "plac_abrup"          "cob_p"               "cob_p_fath"         
#>  [57] "ethn1_p"             "ethn2_p"             "ethn3_p"             "ethn_p_fath"        
#>  [61] "agebirth_p_y"        "agebirth_p_d"        "agebirth_p_fath"     "death_p"            
#>  [65] "death_p_age"         "death_p_fath"        "weight_f1"           "weight_mes_f1"      
#>  [69] "weight_f1_fath"      "height_f1"           "height_mes_f1"       "height_f1_fath"     
#>  [73] "dia_bf"              "asthma_bf"           "psych_bf"            "smk_p"              
#>  [77] "smk_cig_p"           "smk_fath"            "birth_month"         "birth_year"         
#>  [81] "apgar"               "neo_unit"            "sex"                 "plurality"          
#>  [85] "ga_lmp"              "ga_us"               "ga_mr"               "ga_bj"              
#>  [89] "birth_weight"        "birth_length"        "birth_head_circum"   "weight_who_ga"      
#>  [93] "plac_weight"         "con_anomalies"       "major_con_anomalies" "cer_palsy"          
#>  [97] "sibling_pos"         "death_child"         "death_child_age"     "breastfed_excl"     
#> [101] "breastfed_any"       "breastfed_ever"      "solid_food"          "childcare_intro"    
#> [105] "cats_preg"           "dogs_preg"           "cats_quant_preg"     "dogs_quant_preg"    
#> [109] "pets_.0"             "cats_.0"             "dogs_.0"             "pets_.1"            
#> [113] "cats_.1"             "dogs_.1"             "pets_.2"             "cats_.2"            
#> [117] "dogs_.2"

Save your work

When you finished building your analysis frame you can save it using workspaces.

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 susch as GLM.

Simple statistical methods

You execute a summary on the a variable within you analysis frame. It will return summary statistics.

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.

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.

if (!require('metafor')) install.packages('metafor')

yi <- c(armadillo_glm$coefficients["pets_preg", "Estimate"])
sei <- c(armadillo_glm$coefficients["pets_preg", "Std. Error"])

res <- metafor::rma(yi, sei = sei)
res
#> 
#> Fixed-Effects Model (k = 1)
#> 
#> 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"))

plot of chunk meta-analysis

Creating figures

You can directly create figures with the DataSHIELD methods. For example:

# create histogram
ds.histogram(x = "core_nonrep$coh_country", datasources = conns)

plot of chunk create-a-histogram

#> $breaks
#>  [1]  34.40373 115.17616 195.94859 276.72101 357.49344 438.26587 519.03830 599.81072 680.58315 761.35558
#> [11] 842.12800
#> 
#> $counts
#>  [1] 103 103  91 102 108 103 103  98 117  72
#> 
#> $density
#>  [1] 0.0012751876 0.0012751876 0.0011266221 0.0012628072 0.0013370899 0.0012751876 0.0012751876 0.0012132853
#>  [9] 0.0014485141 0.0008913933
#> 
#> $mids
#>  [1]  74.78995 155.56237 236.33480 317.10723 397.87965 478.65208 559.42451 640.19694 720.96936 801.74179
#> 
#> $xname
#> [1] "xvect"
#> 
#> $equidist
#> [1] TRUE
#> 
#> attr(,"class")
#> [1] "histogram"

# create a heatmap
ds.heatmapPlot(x = "analysis_df$pets_.1", y = "analysis_df$dogs_.1", datasources = conns)

plot of chunk create-a-heatmap

# logout
datashield.logout(conns)